Dr. Golden will discuss the findings in his new book, Dynamic Sustainability which he uses to define the unique juncture in the history of our epic sustainability transition. At one end of the spectrum government policies and technology advancements are inching us closer to achieving the long sought-after goal of transitioning from a fossil fuel-dependent economy to a renewable green economy. This transition requires a new generation of sustainable technologies which is accelerating at an unprecedented pace. In many cases these policies are meant not to simply ensure a healthier environment but are also intended to achieve near-term and longer-term economic gains.

However, on the other end of the spectrum society is challenged by unprecedented competition and pressures for critical natural resources necessary to power the transition that in part are further driving increased nationalistic policies impacting global trade and raising national security concerns. In the United States, domestic pressures are rising from both the rural-urban divide as well as well the blue-red divide in national and local politics. Concerns over environmental justice and social equity further complicate the sustainability transition and as presented in this book raise the real possibilities for unintended consequences and risks to our environment, our society and the global economy.

The transition to sustainable agriculture will rely not only on developing sustainable management practices, but also on creating policies and market mechanisms to support their implementation. In the midwestern United States, developing markets for cover crops – including animal feed and bioenergy markets – could incentivize farmers to plant winter cover crops in corn-soybean rotations, which could have positive environmental benefits like increasing soil carbon sequestration and decreasing nitrogen leaching. However, complex interactions between management and environmental dynamics cause variable benefits across different locations. This variability must be considered when developing policies to promote cover crops and when considering the potential impacts of cover crop products, but it has not yet been thoroughly explored through a life cycle lens. We use modeled corn, soybean, and cover crop data and a life cycle assessment framework to answer: What is the potential per kg GHG and N leaching impacts of harvested winter rye? What variation can be expected in these impacts in the central Midwest? What are the main drivers of the impacts and of their variation?

Using the biogeochemical model ecosys and estimates of upstream impacts from GREET, we create spatially explicit life cycle inventories of key impacts of corn-soybean rotations under multiple scenarios, including business-as-usual (no cover crop), and a winter rye cover crop under multiple management practices, including varying planting dates, harvesting dates, fertilization rates, and harvest rates. Dynamic modeling of these scenarios allows for investigation of the relative roles of management practices, environmental characteristics, and upstream impacts in determining the total impacts of the cover crop. For example, preliminary results suggest fertilization of winter rye results in upstream GHG emissions that overshadow soil carbon benefits in most locations in high fertilization scenarios. Our results will also include the impacts of cover crop fertilization and harvest on soil carbon changes and nitrogen leaching and how much these impacts vary across weather and soil conditions. Combining life cycle inventory results across scenarios will produce a life cycle assessment that identifies the largest sources of impacts and also of uncertainty for a winter rye cover crop.

This analysis furthers discussion on the role of biogeochemical modeling in life cycle assessment and produces results that are important for those who wish to promote cover crop planting through marketing cover crop products and for those who wish to market cover crop products by promoting their environmental benefits.

Beef has a large environmental impact and is widely consumed in the United States. The United States federal government aims to find food protein production methods with GHG emissions 50% or less of current methods by 2028. One potential pathway to reduce the impact of the protein provided by beef is to instead consume meat alternatives. Heme proteins are a nascent additive to meat alternatives and are used to emulate gastronomic characteristics of beef. However the impact of heme alone as part of meat alternatives has not been explored in depth. Previous studies have compared the impact of a heme containing meat alternative with ground beef, but have not quantified the proportion of impact of heme. To study the environmental impact of heme protein production and its contribution to the impact of a meat alternative, two cradle-to-gate LCAs were conducted using information provided by Motif Foodworks. The LCA of meat alternative production compared its results with one kilogram of ground beef. The functional units used in this study were one kilogram of meat alternative and one kilogram of heme additive.

Heme protein had a carbon footprint of 117 kg CO2 eq. per kilogram of heme, while the meat alternative had an impact of 3.45 kg CO2 eq. per kilogram meat alternative, 90% lower than the global warming potential of the same quantity of ground beef. Furthermore, the meat alternative was 93% and 99% lower than ground beef in water consumption and land use respectively, yet had a 9% greater freshwater ecotoxicity impact. Impacts in each category were broken down and geospatial electricity mix and ingredient production analyses showed sensitivity to renewables content and growing locations. The key drivers of GHG emissions per functional unit of meat alternative production were identified using sensitivity analysis. Heme production rate resulted in the largest range of impact in global warming potential. Electricity, ingredient procurement and heme production parameters all have considerable impacts on product environmental performance and are potential areas to further improve the environmental sustainability of the meat alternative. This study demonstrates that despite the high impact of heme as a key ingredient in the meat alternative, the meat alternative has a lower environmental footprint than ground beef in most environmental impact categories, though technical parameters in its manufacturing can decrease relative performance. These results can be used to advance system sustainability through targeted improvements and inform the public of the environmental impact of meat alternative and plant-based protein production.

Global food systems are characterized by complex, dynamic, and predominantly linear supply chains. The need to transition towards a circular value proposition within the food supply chain (FSC) has led to different research efforts quantifying food loss and by-products from on-farm, post-harvest, and processing stages. These stages are estimated to account for 20-30% of total food loss and waste. They also generate a number of by-products (e.g. peels, seeds, pomace, etc.) that represent both a significant environmental burden and a largely untapped potential for valorization and profit generation. Despite recent efforts to characterize and map food loss and waste flows, there is still a lack of information and harmonization across the FSC to allow for a comprehensive quantification of food loss and by-products generated.

The processed tomato supply chain is one of the major FSCs in California, which supplies approximately one third of the world’s processed tomato products. This FSC epitomizes some of the challenges associated with implementing sustainability principles across the value chain. Despite the demonstrated potential of tomato processing by-products as a source of valuable molecules for high-value industries (e.g. pharmaceutics, cosmetics), the scalability has been limited. This limitation is partly due to the lack of quantification mentioned above, as well as the fact that the concentration and nature of the extractable chemical compounds depend on the variety, growing conditions, transportation, and processing conditions.

To fill this knowledge and data gap, we present a comprehensive model that quantifies the tomato by-products across California, identifies their associated chemical compounds, and links them to their commercial value. The model is based on a mass balance that considers the 24 counties with tomato industry operations in California, the tomato variety grown and intended for tomato processing within each county, the growing conditions (organic vs inorganic), and the processing conditions of each of the 17 processing facilities that manufacture tomato paste, peeled tomatoes, and/or tomato-based foods. The data were extracted from federal and national government agencies, seed suppliers, tomato processing companies’ websites, and scientific literature. The collected data were then cross-validated by expert opinion, harmonized, aggregated, and analyzed to quantify detailed material flows for the final mass balance.

Using California’s processing tomato industry as a case study, this research aims to explore the opportunity to extract high-value compounds by quantifying and characterizing what was previously considered low-value waste streams. The high granularity of our mass balance enables us to consider the influence of distinct processing conditions on the generation of food losses and by-products as well as determine the final chemical composition of a given stream. While the model was developed originally to analyze mass flows in the tomato industry, it can be adapted and applied to assess valorization opportunities for a range of food products and FSCs, both in California and across wider geographic.

Historical intensification of the food system has contributed to a number of environmental challenges, including air pollution, which is additionally damaging to human health. Exposure to ambient particulate matter less than 2.5µm in diameter (PM2.5) is an environmental health risk factor for chronic obstructive pulmonary disease (COPD), lung cancer, ischemic heart disease, stroke, among other causes of death. Of all of the anthropogenic air quality-related health damages in the United States (100,000 deaths), approximately one-fifth are from food and agricultural activities. In previous work, we showed that of the 17,900 annual air quality-related deaths from food and agricultural activities in the United States, 12,400 are from activities emitting ammonia, a precursor to secondary PM2.5. Manure management and synthetic fertilizer are responsible for nearly all of these ammonia-related deaths.

In this study, we focus on manure management activities contributing to PM2.5-related health damages in the United States for three livestock categories: poultry, cattle, and swine. We evaluate the direct air quality-related impact of the livestock production and benefits of implementing intervention strategies to reduce PM2.5-related health damages through ammonia abatement. We use updated primary PM2.5 and secondary PM2.5 precursor emissions inventories from the 2020 EPA National Emissions Inventory (NEI) with three reduced complexity air quality models (InMAP, EASIUR, AP3) to attribute health damages to specific commodities using updated activity data. Interventions to reduce PM2.5-related health damages include dietary shifts and changes to manure management practices (storage and application techniques) and proximity to dense populations.

Changes from the livestock related ammonia emissions inventory from the 2014 NEI to the 2020 NEI include updates in activity data resulting in higher reported ammonia emissions from livestock. Preliminary data suggests poultry, swine, and cattle livestock waste, not including land application, account for 8,700 out of 11,600 annual deaths from manure management in the updated model. Implementing ammonia emissions intervention strategies would reduce air quality-related health damages across the United States, especially in areas of intense agricultural activity.

Agricultural activity has been the main driver of nitrogen pollution in the Chesapeake Bay Watershed (CBW). Best management practices (BMPs) have been used to reduce nitrogen loading to the streams. Introducing perennial grasses and winter biomass crops into agricultural landscapes are two BMPs that can help with nutrient pollution while offering opportunities to produce renewable energy, through anaerobic digestion, and/or animal feed. In this study, we updated and used a nitrogen accounting model, the Commodity Specific Net Anthropogenic Nitrogen Inputs model (CSNANI), to represent the change in nitrogen inputs due to the introduction of grasses and winter biomass crops to act as BMPs, helping to reduce nitrogen loads to the streams, and providing new economic opportunities through producing biogas within the CBW. Besides that, we estimated the nitrogen demand necessary for common agricultural commodities in the region and biogas produced while adopting different BMP scenarios. CSNANI estimates the N flows associated with 20 crop commodities and 19 livestock commodities and internally relates crops required for livestock diets, thus enabling N inventory development for a study region through a nutrient flow analysis perspective. We developed scenarios to approximate land use changes corresponding to changes to the crops produced, we then used CSNANI to estimate changes in nitrogen flows in the CBW and the demand for nitrogen per commodity. In this analysis, cropland available for cultivation is not allowed to expand beyond current cropland use and grasses will be assumed to replace land currently used for corn-soybean production. In contrast, winter biomass crops were restricted to previously cultivated lands. Given that our scenarios reduce overall feed availability, we considered scenarios that held livestock production constant and those that reduced livestock production when grass displaced feed crops. This allows us to identify the implications in the regional nitrogen balance and consequences for the livestock sector for producing biogas from grasses and winter biomass crops as another source of animal feed. Our results show that there will be a reduction in the main components of the nitrogen input when implementing grasses in the landscape. The N input that comes from fertilizer can be reduced by a maximum of 8%, while N fixation can be reduced by a maximum of 14%. The reduction of N and the production of biogas come with the expense of producing feed in the region. Regarding nitrogen input to commodity production, our preliminary results indicate that for the baseline scenario, the animal commodities that consumed more kg N per kg of protein produced were beef meat (3.3), pork meat (2.0), and dairy milk (1.2); and the plant commodities that consumed more N per protein produced were oats (0.24), corn grain (0.23), and alfalfa (0.21). Ultimately, this assessment helps to evaluate the overall change in the nitrogen load to the region associated with the commodities’ production, the nutrient demand to produced agricultural commodities in the region, and draws attention to the benefits of introducing grasses and winter biomass crops as a strategy to improve the nutrient management of the CBW.

The Global Change Analysis Model (GCAM) is an open source, publicly available market equilibrium integrated assessment model, which integrates human and natural earth systems science. GCAM examines the multisector dynamics between the socioeconomic, energy, water, land, climate, and emissions systems at 5-year time steps through 2100 and operates with a spatial resolution of 32 economic regions, 283 land regions, and 233 water basins. The model has been developed at Pacific Northwest National Laboratory’s Joint Global Change Research Institute (PNNL JGCRI) for over 30 years and has been used to explore questions related to energy transition, decarbonization and carbon mitigation, technology development and policies, air pollution, energy/water/food nexus, climate impacts, and many others. The GCAM community also continues to develop versions of GCAM with state-level detail in larger countries such as GCAM-USA, GCAM-China, GCAM-Canada, and others. This presentation will give an overview of GCAM, the range of analyses that have been conducted using the model, and how GCAM can supplement traditional ISSST analyses.

Wildfires are a significant threat in California, burning more than 1.7 million acres per year and costing the state billions of dollars. In addition to directly damaging homes and infrastructure, fires can destabilize soil, leading to debris flows when significant precipitation events occur in recently burned areas. Electric utilities own and operate infrastructure located in areas that are vulnerable to both wildfires and post-fire debris flows. While in recent years there has been a focus on understanding the risks of wildfire ignition caused by power infrastructure and identifying the responsible electric utilities to mitigate these risks, there has been little work to understand the vulnerability of utilities themselves to wildfires and post-fire debris flows. This is partly due to the complex nature of these hazards, defined by multiple disciplines and dynamics. In this work we assess the vulnerability of transmission lines, electric substations, and power generation facilities in California to wildfires and post-fire debris flows. We assess wildfire risk by overlaying geospatial power infrastructure data with wildfire probability from Cal Adapt and post-fire debris flow threat levels from recent modeling efforts. We assess risk in today’s climate and in the future. To understand uncertainty in future climate impacts, we use two climate models: the Canadian Earth Systems Model, which produces an average prediction of future climate and the Hadley Centre Global Environment Model, which produces a warmer, drier estimate. In conjunction with both climate models, we use two representative concentration pathways (RCPs): RCP 4.5, representing a future in which greenhouse gas emissions begin to decrease in the mid-21st century, and RCP 8.5, in which emissions increase through the end of the century. We assess risks at the state level and identify vulnerable electric utility companies. We find that under current conditions, electric utility assets in Northern California are most vulnerable, being located in areas with up to 40% fire probability compared to the state average of roughly 10%, and high risk for post-fire debris flow. Under future conditions, we find that fire risk to assets may increase substantially in the Sierra Nevada and Northern Coast regions, and post-fire debris flow risk may increase substantially in the coastal ranges and North Central California. However, there is large uncertainty in future risks across climate scenarios. While many electric utility companies have primarily low-risk power infrastructure assets, we find that some smaller utilities may be particularly vulnerable due to the majority of their transmission lines and substations being located in high risk areas. Power generation is also vulnerable to wildfire and post-fire debris flow, with geothermal, hydro, and nuclear power plants in the state facing the highest risks in current and future climate scenarios. These results provide a basis for decision-making around the allocation of resources for infrastructure resilience to wildfire impacts.

Decarbonizing the U.S. electric power sector will require massive deployment of clean energy infrastructure, including utility-scale solar photovoltaics (solar PV) and other renewables. This deployment, though, must comply with local zoning ordinances, which impose a nationwide patchwork of restrictions on where deployment can actually occur. While zoning restrictions on deployment may be developed for legitimate purposes to protect public health and safety, they could impede or increase the costs of decarbonization of the electric power sector, but no research in this area exists. We quantify the role of utility-scale solar zoning ordinances on power sector decarbonization across the Great Lakes region (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin) by integrating a first-of-a-kind database of 6,300 rural community zoning ordinances into a power system planning model. Our results indicate zoning ordinances can play a pivotal role in shaping sub-regional and regional decarbonization outcomes. Relative to no ordinances, solar zoning ordinances reduce total available capacity of solar PV by 52% (or 1.6 TW) across our region. Currently, however, the biggest zoning barrier to deployment is zoning ordinances which are silent on utility-scale solar, often interpreted as a de facto ban. This absence of guidelines decreases available capacity by 31% across the region and up to 59% at the state level. Outright bans—by explicitly disallowing solar—contributes another 6% reduction across the region and up to 13% reduction at the state level. Deployment restrictions translate to up to 4 GW greater investment needs and 5.6% greater PV investment costs to achieve a 10% PV generation target. Starker shifts occur at the state level, e.g. Wisconsin sees a 40% reduction in PV investments due to zoning restrictions; these investments shift to other states with laxer ordinances, e.g. Illinois. Our results underscore the need for planning that aligns local zoning laws with state and regional decarbonization objectives.

Thailand has established a target of carbon neutrality by 2050. Reaching this goal will require coordinated efforts across the energy system at both the national and subnational levels. Robust decarbonization scenarios incorporating current plans and targets, additional measures needed, and trade-offs between strategies can help stakeholders make informed decisions in the face of uncertainty. Through iterative engagement with decision makers, we develop and analyze carbon neutral scenarios for Thailand that incorporate Bangkok’s role using a global integrated assessment model. We find that Thailand can reach carbon neutrality through power sector decarbonization, energy efficiency and widespread electrification in the buildings, industry, and transportation sectors, and advanced technologies including carbon capture and storage. Negative emissions technologies will also be needed to offset Thailand and Bangkok’s hardest-to-abate CO2 emissions. Bangkok, as a major population and economic center, contributes significantly to Thailand’s energy demand and emissions and can therefore play an important role in climate change mitigation. Accordingly, our results underscore the importance of subnational climate action in meeting Thailand’s carbon neutral goal. These insights can help energy system stakeholders identify priorities, consider tradeoffs, and make decisions that will impact Bangkok and Thailand’s long-term climate change mitigation potential. We also note that Thailand's carbon neutral 2050 pathway is based on a targeted emissions reduction of 40% by 2030 as stated in Thailand’s NDC. However, Thailand’s unconditional NDC is only 30% in 2030. Thus, Thailand may need international support to achieve carbon neutrality by 2050 through the pathways investigated here.

Electric vehicles (EVs) have significant sustainability impacts, not only in terms of shifting their primary energy source from fossil fuels to electricity, but also due to differences in their material supply chains and manufacturing processes.

This study investigates the effect of the 2022 Inflation Reduction Act (IRA) incentives on U.S. electric vehicle battery industry in terms of supply chain decisions and technology choices, specifically examining the dynamics of different chemistry choices and production geographies from the perspective of cost minimization. Using the BatPaC model, we explore a number of scenarios based on potential future market developments to analyze the effect of the various subsidies.

We find that the total value of all IRA incentives exceeds the total production cost of EV batteries in the United States. Though the total possible amount of incentives in pure dollar quantities is slightly larger for batteries with the Nickel Manganese Cobalt Oxide (NMC) chemistry, the Lithium Iron Phosphate (LFP) chemistry continues to dominate on a dollar per kWh basis due to its markedly lower manufacturing cost, as well as the lower number of critical minerals needed to meet the critical mineral requirement. Furthermore, these incentives can render U.S. batteries competitive even without meeting critical mineral requirements: the $45 per kilowatt-hour (kWh) incentive to produce battery cells, modules, and packs domestically is sufficient to be competitive with current modeled production in China. However, direct credits for critical minerals extraction and processing have very limited relative effect on the cost of battery manufacturing, rendering them less important in terms of shifting their geography of production from the perspective of an automaker trying to claim vehicle-based tax credits based on their supply chain.

Our analysis underscores the impact (or lack thereof) of upstream credits for critical minerals and components, prompting a re-evaluation of the feasibility of relocating challenging segments within the supply chain and their subsequent environmental, security, and economic implications. This research provides crucial insights for policymakers and industry stakeholders navigating the transformed EV supply chain landscape post-IRA implementation.

A core drawback of conventional life cycle assessment (LCA) is its failure to account for the temporal dynamics of the technological and environmental background in which a technology is assumed to operate. The temporal implications of the background system can be particularly relevant when evaluating systems in an early stage of development as they usually require time-intensive research and development effort. The use of static life cycle inventory databases can also result in the misrepresentation of the environmental impacts of technologies with operational lifetimes spanning decades since temporal changes in the background system, such as the decarbonization of the electrical grid, are not commonly accounted for. In a context where achieving near-term climate targets depends heavily on advancing technologies currently at a low technology readiness level, capturing the systematic changes in background dynamics and supply chains is critical for the accurate assessment of technology potential and effective decision-making.

This study explores the application of prospective LCA (pLCA) to evaluate the environmental impacts of sustainable aviation fuel pathways. Specifically, the research focuses on comparing two pathways: one involving the conversion of corn grain-derived ethanol to a jet fuel blendstock and the other converting algal oil to jet fuel through hydrotreating esters and fatty acids. The pLCA model leverages data on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) to transform life cycle inventories of key sectors, such as power, cement, steel, and transportation fuels, across different futuristic scenarios. Data for life cycle inventory transformation includes outputs from a “middle of the road” SSP, which aligns with the objectives of the Paris Agreement, and a RCP assuming a global mean surface temperature of 1.6°C (RCP 2.6) by 2100. The transformed pLCA database is then coupled to engineering process models of the SAF systems to simulate the well-to-wake life cycle greenhouse gas (GHGs) emissions over their lifetime. Moreover, potential tax credits derived from reducing GHGs over time will be evaluated through techno-economic analysis.

The preliminary pLCA underscores the significance of incorporating supply chain dynamics into LCA. Results reveal that conventional (static) LCA methods may provide a distorted view of environmental impacts compared to dynamic LCA. For instance, results challenge the notion that algal-based jet fuel has a higher global warming potential (58 g CO2E MJ-1) than the ethanol-to-jet pathway (53 g CO2E MJ-1), if static LCA methods are used. Results from the pLCA demonstrate that algal jet fuel eventually achieves a lower life cycle carbon intensity than corn-derived jet fuel, with estimated GHG emissions of 27 g CO2E MJ-1 ¬¬¬and 39 g CO2E MJ-1 by 2050 respectively. The change in emissions observed in the algal pathway is primarily due to reductions in the carbon intensity of the electrical grid. Based on reductions in GHG emissions, both pathways are potentially eligible for tax credits under current policies. In conclusion, preliminary work emphasizes the importance of considering background system dynamics when evaluating energy-intensive technologies such as SAF pathways and underscores the importance of comprehensive and dynamic LCA methodologies in shaping informed decision-making and policy development.

Limiting global warming to the greatest extent possible is critical for reducing the adverse effects of climate change on humanity and the planet. To achieve long-term temperature mitigation targets, such as 1.5℃ or 2℃, it is crucial to reckon not only with the total emissions reductions required but also with the anticipated "committed" greenhouse gas (GHG) emissions from existing infrastructure. While data on the former is more accessible, information on the latter is often confined to snapshot publications with limited transparency on geographic or technological scales.

In this study, we introduce a comprehensive bottom-up model of "committed" energy supplies for the United States, aimed at informing climate policy decision-making. This work addresses critical questions about the existing energy system, allowing us to estimate the size of the remaining gap between locked-in US emissions from existing infrastructure and modeled emissions trajectories leading to 1.5℃ and 2℃ targets. The model incorporates facility-level lifecycle emissions for both fossil and non-fossil sources, utilizing well-level data for natural gas and oil, and power plant data for coal, renewables, and biomass. Designed for regular updates to accommodate additional energy projects or early retirements, the model ensures relevance and accuracy over time.

Utilizing this model, we analyze the GHG emissions gap across various project-relevant timescales, comparing locked-in US emissions with pathways to 1.5℃ and 2℃ targets based on the latest models from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Our findings reveal that, while some space exists between the modeled scenario pathway and already locked-in emissions over certain lifetime scales, a minimal number of new fossil energy projects could entirely eliminate this window. Additionally, Monte Carlo analysis is employed to illustrate the robustness of our conclusions to uncertainties in model parameters.

This committed energy system model serves as a core input to the recently-published "climate test" decision-support tool. This tool is specifically designed for policymakers tasked with evaluating individual fossil infrastructure projects and their alignment with climate targets under US law. Together, these tools empower government agencies to make scientifically informed and climate-protective decisions for our energy future, facilitating our collective efforts to realize overarching climate goals.

This paper presents a political-industrial ecology analysis of an emerging petrochemical corridor in Appalachia. Within Appalachia, various ethane “cracker” plants are under construction, or are being permitted, to transform ethane by-products from hydraulicly fractured shale gas in the Marcellus and Utica shales into plastics. Political-industrial ecology is a nascent field of geography that embeds resource metabolisms within their broader political economic contexts. I advance the field by presenting a “metabolic tour” of the petrochemical supply chain that analyzes how petrochemicals forge and transform human-environmental relationships along the chain. The political-industrial ecology analysis links these developments in the former steel belt to the growing environmental burdens of plastics, highlighting how record state subsidies are facilitating these linkages. Further, the systems perspective afforded by a political-industrial ecology view reveals three notable findings. First, the footprint of the corridor extends well beyond the Ohio River Valley to Canada, the US Gulf Coast and international markets in Europe and Asia. Second, the corridor is a significant step towards establishing more globally integrated markets for ethane and natural gas. Third, the analysis illustrates the myriad of environmental systems and communities interlinked through the corridor, which can serve as a roadmap for facilitating cumulative impact analysis, a key gap in environmental impact and justice scholarship.

Solar energy is widely viewed as one of our best tools to combat climate change, reduce carbon emissions, and save money -- but solar projects (both thermal and photovoltaic) typically don't fit into the same mold as most energy efficiency projects. This paper will discuss the basics of how solar generation fits into the energy sector, and the successes and challenges of including solar projects in a major utility’s ratepayer funded energy efficiency program.

The paper will start by addressing the need to innovate and work collaboratively with major utilities and utility commissions in order to find pathways to include renewables in energy efficiency programs in ways that will work for all parties. Renewable energy such as solar PV is at a once in a lifetime moment with the passage of the Inflation Reduction Act, and utilities are looking to meet lofty goals for saving energy at a time when the low hanging fruit such as lighting retrofits are beginning to sunset.

Integrating solar PV projects into ratepayer funded programs can be a challenge, but this real world example of how it was done benefitted from close collaboration between the utility, implementer, and 3rd party evaluator. The program was developed to ensure that the program would be cost effective and allayed concerns about the “death spiral” for the utility. The paper will review lessons learned from several years of experience covering numerous projects and adjustments made during evaluation and implementation.

This study explores how cities can reach energy sector net-zero greenhouse gas (GHG) goals from a life cycle perspective. Although life cycle assessment (LCA) is recognized in academia and industry as a holistic means of accounting for emissions along the entire supply chain of a product or process, it has not yet been widely applied to US climate policy. City GHG inventories largely adhere to the Scoping system suggested by the GHG Protocol, which omits upstream and downstream emissions from energy resources. Because the GHG emissions associated with most renewable energy resources are entirely from upstream and/or downstream activities and not direct combustion of fuel, the GHG Protocol method underestimates energy-related emissions and is not conducive to optimizing the energy transition to maximize GHG reductions. Maximizing GHG reductions on a life cycle basis avoids burden shifting from one geographic location or economic sector to another to ensure genuine net benefits.

This analysis focuses on the pledges made in the 2021 Austin Climate Equity Plan to reach net-zero community-wide GHG emissions in Austin, Texas by 2040. The scope of the study covers the energy sector within the geographic bounds of the city, including residential, commercial, and industrial demand for electricity and thermal power, excluding liquid fuels for transportation. We also account for projected fluctuations in energy demand and energy storage requirements due to electrification initiatives, efficiency upgrades, and population increase. Geospatially relevant data from literature, governmental, and NGO sources, along with original research results, are used to estimate life cycle GHG emissions for different energy resources and associated infrastructure using a parametric, bottom-up approach. This is compared against the bottom-up production-based and top-down consumption-based GHG inventories that have been provided by the city of Austin. Modelling accounts for uncertainty and dynamism of inputs with sensitivity analysis of key parameters. Recommendations for specific technologies over alternative options are provided with context for decision-makers to understand tradeoffs. The results demonstrate the value in LCA to aide in developing climate policy and provide a framework for other cities to consider adopting.

The University of Massachusetts Lowell, in collaboration with National Grid and the City of Lowell, has embarked on a pioneering geothermal energy pilot project, emphasizing the use of ground source heat pump (GSHP) systems. This initiative, a part of the state’s goal to achieve net-zero emissions by 2050, focuses on using the Earth's thermal properties to provide efficient heating and cooling. The start of this project provides an excellent opportunity to evaluate the environmental impact of the university's geothermal plant and its contribution to decarbonization efforts at a critical point in global climate change mitigation. Previous research indicates that GSHP systems offer significant environmental advantages over traditional heating and cooling methods, reducing carbon emissions, energy use, and fossil fuel dependence. Their ability to maintain stable ground temperatures leads to consistent energy efficiency and a lower environmental footprint, making them crucial for sustainable development and climate change mitigation.

Our primary objective in this study is to conduct a comprehensive Life Cycle Assessment (LCA) of UMass Lowell's GSHP systems. We are utilizing open-source Brightway2 and Ecoinvent 3.5 datasets for this cradle-to-grave assessment, which involves comparing the GSHP system with traditional air conditioning (AC) systems in terms of carbon intensity and other environmental impacts. The analysis covers various life stages of the GSHP system, from raw material extraction through construction, operation, and eventual decommissioning, with a particular focus on configurations in campus commercial buildings.

Currently, we are in the phase of collecting life cycle inventory data, where we plan to integrate machine learning (ML) tools. This integration aims to improve the accuracy of our environmental impact evaluation by uncovering patterns and insights within the data for a more comprehensive and precise LCA. Moreover, we are employing ML tools for sensitivity analysis to gain deeper insights into how different parameters impact the LCA results, enabling us to identify key factors for effective environmental impact reduction strategies more accurately. Our methodological approach includes a thorough life cycle analysis, encompassing both Midpoint and Endpoint categories in impact assessment. The study combines primary data from the UMass Lowell project with secondary data to provide a holistic evaluation of the GSHP system’s environmental footprint.

The expected outcome of this research is to establish a new standard for future GSHP projects through a detailed LCA. It will quantify the GSHP system's carbon footprint and GHG emissions and suggest ways to reduce them, such as low-carbon drilling and energy-efficient operations. By comparing the environmental impact of the GSHP system with conventional AC systems, we aim to underscore the potential of geothermal energy in reducing carbon emissions and promoting sustainable heating practices. The findings are expected to significantly contribute to UMass Lowell's sustainability initiatives, support National Grid's net-zero emission goals, and aid the global effort to mitigate climate change.

The effects of climate change are impacting all sectors of the economy worldwide. Changes in weather patterns and extreme weather events have resulted in frequent and prolonged power outages, underscoring the vulnerability of centralized power systems. This emphasizes a critical necessity to boost community resilience. Adapting school buildings to climate change can play a pivotal role, given their importance in providing essential social services, especially during and after extreme weather events, such as hurricanes, heat waves, or ice storms.

Here, we show the potential of school buildings as energy resilience hubs for communities and the trade-offs between costs, technical considerations, and environmental performance. Importantly, it highlights the need for customized design strategies across diverse climate zones, as a one-size-fits-all approach proves inadequate.

Although energy resilience in the built environment has received increased attention from researchers, it must be effectively incorporated into building codes and design practices. The energy-resilience-environmental nexus needs to be more studied, with techno-economic and environmental analyses often limited to operational costs and carbon emissions, neglecting embodied carbon and other environmental impacts throughout the system's life cycle.

Examining how passive design strategies impact the energy and environmental performance of school buildings, especially during power outages, sheds light on the resiliency and sustainability of this type of infrastructure. Our approach utilizes reference models of schools for various U.S. climate zones to develop the energy models and estimate the energy demand. Then, a building-integrated photovoltaics (BIPV) plus storage system is incorporated into the design to enhance the system's energy resilience. Finally, the environmental profile of the system is evaluated through a life cycle assessment. As a result, we assess the effectiveness of these strategies in terms of costs, technical performance, and environmental footprint, encompassing not only carbon emissions but also other environmental factors. Indeed, the case study of an elementary school building in Puerto Rico shows that using BIPV leads to a one-third reduction in the carbon footprint and similar improvements in other environmental impact categories like ozone layer depletion and ozone formation. However, some impact categories, including eutrophication, ecotoxicity, and mineral scarcity, show higher values attributed to PV module and battery cell production. The embodied impact burden varies significantly, accounting for 16% of global warming, 33% of freshwater eutrophication, and 88% of mineral resource scarcity. This underscores the importance of adopting a comprehensive life cycle approach, particularly the growing significance of embodied impacts in energy-efficient and resilient buildings.

The implications of this research extend to informing future building codes, design practices, and policy frameworks. By understanding the relationship between passive design strategies, energy resilience, and environmental impact, stakeholders can make informed decisions to enhance the overall resilience of the built environment.

Decarbonization technologies are a solution to reducing greenhouse gas emissions and mitigating climate change. However, the decarbonization transition will undoubtedly be mineral-intensive with critical minerals, like cobalt, lithium, and nickel, serving as the backbone of many decarbonization technologies. Mining is the primary acquisition method for critical minerals but wields many environmental and social effects – both positive (i.e. job opportunities and boosting the local economy1) and negative (i.e. land degradation2, reduced water quality3, and community displacement2). Life cycle analysis (LCA) is a tool that can assess effects and sustainability. However, there are significant gaps and a lack of structure in how LCAs of mineral mining are completed (e.g., inappropriate data sources, inconsistent system boundaries, indicators, and functional units). Overall, there is no standardized framework for critical mineral mining LCAs, which complicates comparisons, decision-making, and policy design.

We propose a critical mineral mining LCA framework using the standard four life-cycle phases.4 We conduct an LCA of a proposed copper-nickel mine in Minnesota, the first non-ferrous mine in the state, to guide development and application of the framework.

Several aspects of this LCA demonstrate the framework. For example, we employ two functional units, one ton of mineral equivalent and one year of mining operation. We define the system boundary to encompass mining operations and reclamation. We used foreground system processing and site information from legal documents as an appropriate, open data source. We use Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) Model5, and literature for background system inventory data.

We calculated energy use, water use, and greenhouse emissions of the proposed mine. Mineral refining processes (beneficiation and hydrometallurgical) and wastewater treatment account for most of the energy and water consumption, representing 81% and 94% of the total energy and water burdens, respectively. However, refining and wastewater treatment processes only comprise 52% of the greenhouse gas (GHG) emissions. Land clearing and blasting contribute 40% of GHG emissions. Land clearing will release 6.7 billion kg of CO2 into the atmosphere, 35% of total GHGs.

Carrying out this LCA using publicly available information identified challenges and gaps in completing mining LCAs that must be addressed using new, local, and timely data sources. For example, water pollutant emissions and biodiversity changes are prominent mining environmental effects. We describe an approach to improving mining LCA coverage of these important effects as part of developing a standard LCA framework for critical minerals mining. The framework will enable better comparisons, decision-making, and policy design. The standardized framework will offer a template for critical mineral mining LCAs conducted by other researchers and allow comparison of mines and LCA results.

References:

1. Hosseinpour, M., et al. Evaluation of positive and negative impacts of mining on sustainable... (2022).

2. Wilson, S. A., et al. Livelihood impacts of iron ore mining-induced... (2022).

3. Uugwanga, M. N. & Kgabi, N. A. Heavy metal pollution index of surface and groundwater… (2021).

4. International Standards Organization. ISO 14040:2006 (2006).

5. Argonne National Laboratory. (2021).

To mitigate climate change and preserve resources for future generations, the economy is investing in a profound industrial transition. These transition efforts can be grouped into two broad categories: efforts to turn away from fossil-based energy sources with greater energy efficiency and efforts to use materials more efficiently. The development of industrial symbioses in which the energy and material waste flows of one industry are managed such that they serve as inputs to complementary industries constitute a promising avenue to potentially combine both energy and material efficiencies measures. But when trade-offs arise between increasing material efficiency or energy efficiency, or between energy and resources recovery, how should these choices be optimized? A case can be made that a single indicator of resource use should guide the design of industrial symbioses. There does exist a single indicator from thermodynamics that can quantify the “quality” of both material and energy resources by linking these to the notion of work: exergy. This approach has not commonly been used for several historical and fundamental reasons. Yet it seems important to find ways of communicating what thermodynamics has to say about the increasing consumption of resources and their dispersions. Is an exergy indicator relevant and practical in guiding the optimisation of an industrial symbiosis? Our study focuses on a tire production company. Through a material and energy flow analysis, the use flows and waste flows are quantified and translated into exergy flows. We represent the loss of exergy throughout the production cycle and end-of-life transformation. A linear optimisation model then selects the technological mixes and operation parameters that minimizes the total exergy losses. We conduct a comparative analysis between optimizing the industrial symbiosis using exergy, relative to using other sustainability and efficiency indicators. In our work, we provide a unified exergy indicator for energetic and material resources as well as an optimisation tool based on constraints which represents the reality of goods-producing companies in their environmental objectives, leading them to a more sustainable ecological transition. Our other aim is to furnish information to guide the target audience on the requirements needed to use the indicator. This method can then be used in any industry that aims to improve efficiency in resource use.

The transition from fossil to renewable energy sources is a key lever to mitigate greenhouse gas (GHG) emissions and restrict global temperature to below 1.5C above pre-industrial levels. Yet growth trajectory and predicted ultimate scale could lead to PV becoming one of the largest global industries with enormous annual GHG emissions from manufacturing, even while lowering carbon emissions compared to the incumbent energy sources they displace. Prior studies have calculated a payback time for those upfront GHG emissions. While simple to calculate and communicate, the usefulness of the payback time and similar metrics are limited because they treat all GHG emissions as equal no matter when emitted, and yet we know there is greater impact on climate from emissions earlier within a given timeframe. Herein we will demonstrate how low carbon manufacturing, even for an already low carbon energy source like PV, can yield significant additional climate benefits, as measured by reduced future temperature change.

Temperature increase resulting from GHG emissions can be quantified by the absolute global warming potential (AGTP) metric. The AGTP metric reveals that the temperature-increase caused by a GHG emission pulse is directly proportional to the lifetime of the GHG in the atmosphere. Consequently, emission of a GHG occurring earlier in a given timeframe will remain longer within that timeframe and, thereby, induce a greater temperature increase than an equal mass of GHG emitted later in that timeframe. If a societal goal is to reduce future temperature increase, earlier GHG emission reductions (e.g., in the manufacturing stage of a technology’s life cycle) are of higher value than later.

We use PV illustratively to exemplify how the AGTP metric can be applied to quantify temperature mitigation potential of low carbon manufacturing of RE systems considering the net impact of when GHGs are emitted and avoided over their life cycle. The methods presented herein can be applied to any product with lower-carbon manufacturing alternatives. Whereas we demonstrate that all PV provides net AGTP benefits over their 30-year lifetime, shifting from high to low-carbon PV manufacturing increases the net AGTP benefit by up to 175% points under US-average grid and solar insolation conditions.

Many plans for reducing greenhouse gas emissions envision future electricity systems that are heavily reliant on energy supplied by wind and solar resources. Recent studies examine how the capacities of energy infrastructure required to reliably meet electricity demand in wind- and solar-heavy energy systems will be determined by fluctuations in these renewable resources. Better understanding of the characteristics of these fluctuations and their effects on potential future energy systems can inform the development and deployment of technologies that reduce the costs of reliably providing electricity. In this work, we examine fluctuations in wind and solar energy resources from 1980–2020 with high temporal and geographical resolution across the coterminous United States. We then evaluate potential approaches for supplementing wind and solar energy to address this variability. We find that while wind and solar resource availabilities vary according to season and geography, the most stressful meteorological patterns for energy system infrastructures have some common characteristics. We characterize how the costs and availabilities of other technologies affect the cost-optimal capacities and operation of physical energy infrastructure in systems with high penetrations of wind and solar energy. Finally, we conclude that while all modeled methods require substantial investment, strategic combinations of geographical resource aggregation, energy storage, supplemental non-wind/solar generation, and demand management can help reduce the overbuilding of physical infrastructure and lower energy costs.

Assessments of building energy consumption en masse at city or regional scale are valuable for such purposes as identifying the most or least energy-intensive commercial activities or contemplating alternate land and building use, energy efficiency modifications, or distributed renewable generation scenarios. Energy utilities possess customer consumption data but they generally guard that information very closely due to privacy and business intelligence concerns. Furthermore, it is not always possible, may be impractical, and risks exposure to criminal trespass charges to canvass a region to collect data from buildings’ electric and gas meters. To get past that limitation, the authors describe and demonstrate a method for estimating the annual energy consumption of commercial buildings by using a combination of GIS data describing buildings within a region, degree day maps of that same region, the US Energy Information Administration’s Commercial Building Energy Consumption Survey (CBECS) dataset, and our original decomposition model of energy consumption. The decomposition model is cast in the form of a linear expression that uses as inputs the variables that the GIS and degree day data can be manipulated to reach equivalency with those of the CBECS observations. For each represented PBA, we perform a regression using the CBECS data for that PBA including the total annual energy consumption field to obtain a set of coefficients for the terms in the model expression. We can subsequently run real-world building data through the expressions using those coefficients to obtain annual consumption estimates and assess the correlation between CBECS total consumption and our method’s estimates. As an example, for one PBA, “Enclosed Mall,” and using the 2016 update of the 2012 CBECS release, all such commercial buildings represented by CBECS have annual consumption estimates that lie within an order of magnitude of the corresponding CBECS observations’ published consumption value and 60 percent lie within half or double that value. For buildings of all PBAs represented in CBECS, 81 percent have annual consumption estimates that lie within an order of magnitude of the corresponding CBECS observations’ consumption value and 45 percent lie within half or double that value. Real-world building data thoroughness and quality is a significant problem encountered in utilizing this method; in attempting to estimate consumption for commercial buildings in Georgia’s Fulton County, we found that GIS data supplied by Fulton County contained largely inaccurate or missing values for the number of stories, which is necessary to establish square footage. We therefore performed a spatial join between that data and proprietary data purchased from CoStar Market Analytics that included more accurate values for the number of stories. Additionally, we lacked building data showing whether heating was supplied by heat pump or direct heating; in the former case a different model for heating energy consumption as a function of physical building parameters would have been used and therefore confidence in the heating energy component of our estimates relies on the general prevalence within each PBA of heat pump versus direct heating that is innately embedded in the CBECS data.

Research Background

As a carbon-free fuel with high energy content, hydrogen plays a crucial role in decarbonizing the industrial sector that requires high energy inputs. To incentivize the early commercial-scale implementation of hydrogen, the U.S. Congress enacted the Inflation Reduction Act in August 2022 to provide Production Tax Credits (PTC) for low-emissions hydrogen and tax credits for CO2 capture under Sections 45V and 45Q, respectively.

However, the infrastructure for pure hydrogen storage and transportation is yet to be constructed to accommodate large-scale hydrogen utilization. Therefore, many pilot projects on hydrogen utilization are developed to blend a small ratio of hydrogen in the natural gas pipelines. To assess whether a hydrogen-natural gas blend is a feasible transition strategy to achieve moderate decarbonization, we assessed the total emissions of powering natural gas-based boilers in the U.S. using pure natural gas versus a 15 vol. % hydrogen and 85 vol. % natural gas blend.

Methods

We started with defining the process flow, which includes upstream natural gas processing and transportation, hydrogen production and compression, and fuel combustion. For hydrogen production, we considered various production routes, including steam methane reforming and electrolysis using either natural gas or water as feedstock, respectively. We also considered incorporating carbon capture and sequestration at steam methane reforming facilities. We compiled information regarding the total capacity of natural gas-powered industrial boilers in the U.S., hydrogen production capacity by state, and the emission factors for different process stages. An overlay of the locations of the boilers, hydrogen production sites, and natural gas pipelines was created to examine the feasibility of hydrogen blending. We then calculated the total national annual emissions of powering natural gas-based boilers with a 15 vol. % /85 vol. % hydrogen/natural gas blend.

Results

Our calculations showed the blending of 15% hydrogen for heating natural gas-based boilers can potentially be an intermittent tactic to reduce GHG emissions when carbon capture and sequestration is implemented for hydrogen production from natural gas. Meanwhile, blending 15% hydrogen produced via water electrolysis with the current grid would drastically increase GHG emissions. The major contributors include emissions from grid electricity generation and upstream natural gas transmission and distribution. Regional differences in emissions were also observed, where the Northeast sees the lowest increase in emissions and could potentially benefit more from hydrogen blending. However, the Northeast is also the region with the greatest hydrogen deficiency and requires a rapid ramp-up in hydrogen production.

(1) Background

Universities have a unique position to lead in the implementation of climate actions. In March 2023, the White House and the University of Washington co-hosted a forum on how universities have the capability to deliver climate change solutions to their communities [1].

Over the past 14 years, the University of Pittsburgh has completed eight greenhouse gas inventories. Consistently, one of the largest contributors to the University’s carbon footprint has been emissions associated with the building sector, specifically for electricity and heating. In fiscal year (FY) 2022, purchased electricity accounted for 37% of emissions at the University, and steam was the second largest contributor at 28%. In 2022, the University published its first Climate Action Plan (PittCAP), a document that strategizes how the University will achieve carbon neutrality by 2037. This presentation will identify the PittCAP goals for building-related emissions, and how our greenhouse gas inventories are used to assess the achievement of that goal at both a campus-wide and individual building scale.

(2) Methods

Historical data on steam, electricity, and natural gas used in the greenhouse gas inventories were analyzed to compare the University’s current and previous energy use to building energy goals defined in by PittCAP. This data was collected for FY 2019, 2020, and 2021. Because FY 2019 is the launchpad for the PittCAP, it was chosen as the starting year for this analysis. Building energy use data was paired with emissions factors from the University of New Hampshire’s Sustainability Indicator Management & Analysis Platform (SIMAP) to calculate greenhouse gas emissions at the individual building level.

Once the energy use and corresponding carbon emissions were determined for a building from FY19 to FY21, the next step was to determine how that building will contribute to the University’s carbon neutrality goal by 2037. After defining the 2037 carbon emissions target for an individual building, an emissions target was created for every year from 2019 to 2037. This yearly target indicates whether we are on track to meet the 2037 goal. The process was repeated for the 95 buildings included in the University’s Pittsburgh campus GHG inventories.

(3) Preliminary Results

The analysis showed that each of the 95 buildings analyzed would need to decrease their energy use approximately 14% by 2037 to meet the PittCAP goal, which is less than 1% per year from 2019 to 2037. In examining overall building energy use emissions, building energy use performance in fiscal years 2020 and 2021 was beating targets. For FY21, of the 95 buildings analyzed, 22 buildings had already reached their 2037 emissions goal, 27 are on track or ahead of schedule, and 46 buildings are not on track. This information helps the University target which buildings are underperforming and informs decisions about where to prioritize energy efficiency upgrades. Results have already been used at the University of Pittsburgh to inform building upgrade prioritization (in conjunction with other considerations, including energy use intensity, building square footage, and estimated reduction potential).

References

[1] Readout of the White House Forum on Campus and Community-Scale Climate Change Solutions | OSTP. (2023, March 15). The White House. https://www.whitehouse.gov/ostp/news-updates/2023/03/15/readout-of-the-white-house-forum-on-campus-and-community-scale-climate-change-solutions/

The APEX (“Advanced Practices for Environmental Excellence in Cities”) Green Cities Program is a International Finance Corporation (IFC) initiative that supports cities in emerging economies to accelerate the implementation of ambitious and transformative policy actions and investments that significantly contribute to transitioning to low-carbon and resource-efficient growth pathways. The program leverages the APEX online software, which helps cities to quickly assess the most cost-effective way to incorporate measures into their investment and policy pipelines, in order to achieve targets related to energy, transportation, waste, water, and GHG emissions. IFC is part of the World Bank Group.

APEX helps quantify the future impacts and costs of investment, planning, and policy solutions—referred to as measures. There are over 100 measures preloaded into APEX, as well as the option to create custom measures. Each measure has an engine that quantifies its impacts and costs based on the specific situation in the city. The methodology behind each measure is based on prevailing engineering calculations, existing studies in the literature, and/or case studies from other cities. APEX also incorporates information from global datasets and proxy calculations to support cities in data-scarce environments.

This presentation will introduce the APEX program, overview the online software, and highlight case study examples of where APEX has supported cities in Africa and Asia.

Waste-to-energy systems can provide a functional demonstration of the economic and environmental benefits of circularity, innovation, and reimagining existing systems. This study offers a robust quantification of the greenhouse gas (GHG) reduction potential of industry-level adoption of anaerobic digestion (AD) technology on large-scale dairy farms in the contiguous United States. National GHG reduction estimates were developed through a robust life cycle modeling framework considering 20 dairy configurations that capture important differences in housing and manure management practices, applicable AD technologies, regional climates, storage cleanout schedules, and methods of land application. Results illustrate the potential for AD adoption to reduce GHG emissions from the dairy industry by 2.90 MMT of CO2-eq per year considering current economic barriers, and as much as 5.17 MMT of CO2-eq per year with economic barriers removed. At the farm level, AD technology may reduce GHG emissions from manure management systems by 55-77% depending on the region. Discussion focuses on regional differences in GHG emissions from manure management strategies and the challenges and opportunities surrounding AD adoption.

The US government’s Sustainable Aviation Fuel (SAF) Grand Challenge strives to increase annual SAF production to 35 billion gallons by 2050 while decreasing aviation emissions by 50%. The SAF Grand Challenge Roadmap notes that the purpose-grown biomass feedstock potential exists to provide at least 30 billion gallons of SAF, but limited research exists on the large-scale, country wide feasibility of these feedstocks in the US. Additionally, the SAF Grand Challenge Roadmap and others note that power-to-liquid (PtL) fuels could provide limitless fuel production if the required renewable energy is available. Therefore, this research compares the county-level production potential, economics, and environmental impact of purpose grown bioenergy feedstocks and PtL technologies for SAF throughout the Contiguous United States (CONUS) using GIS analysis, multi-objective optimization, and agent-based modeling.

In total, eight bioenergy crops (switchgrass, Miscanthus, poplar, willow, sorghum, algae, corn, and soybeans) and eight land types (barren, deciduous forest, evergreen forest, mixed forest, shrubland, grassland, pastureland, and cropland currently used for bioenergy) were considered for SAF production. Geographic information system mapping techniques were used to partition the CONUS into 200 m by 200 m patches to understand bioenergy production feasibility on various land types while excluding land with prohibitively low yield, unfavorable terrain (e.g. steep slopes), or protected status. Additionally, the Fischer-Tropsch PtL technology was also evaluated using green hydrogen production and renewable electricity to provide the minimum emissions possible. County-level results include SAF production potential, minimum fuel selling price, and greenhouse gas emission impacts including direct land use change (dLUC).

GIS results show that Miscanthus is the most promising bioenergy crop by providing both a competitive fuel selling price ($4.97-$5.81/gal) and low emissions (1.1-38.8 g CO2-eq/MJ vs 84.8 g CO2 eq/MJ for traditional jet fuel) when grown on non-forest land. All bioenergy feedstocks grown on forest land were found to have a larger environmental impact than conventional jet fuel due to dLUC impacts. Algae was found to have the highest fuel selling price ($10.25-$14.77/gal) and emissions (65.4-72.7 g CO2 eq/MJ) of the bioenergy feedstocks, but it also has a SAF productivity (fuel per land area) 10X that of the other feedstocks. Comparatively, the Fischer-Tropsch PtL pathway has low emissions (17 g CO2 eq/MJ) but high fuel selling price ($15.54/gal).

Multi-objective optimization results illustrate the tradeoffs between optimizing CONUS SAF production for minimum fuel selling price, emissions, carbon price, or land use. When minimizing for emissions using bioenergy feedstocks, 14.4% of total CONUS land is required to provide 30 billion gallons of SAF with Miscanthus as the primary feedstock. However, when land area is minimized, only 0.6% of CONUS land is required because high productivity algae is used as the feedstock, but this comes at the cost of higher emissions and fuel prices. Multi-objective optimization results for the Fischer-Tropsch PtL pathway show that 1.4% of CONUS land is required while having lower emissions and higher prices compared to traditional jet fuel.

The transition to low-carbon technologies is reshaping the U.S. economy, catalyzed by the goals set by the Biden administration to reduce economy-wide net greenhouse gas emissions (GHG) to half of their 2005 levels by 2030 and to attain net-zero emissions by 2050. To fulfill these objectives, the Inflation Reduction Act (IRA) and the Bipartisan Infrastructure Law invest in the scale up of clean energy technologies. The IRA also provides incentives to producers of clean fuels and hydrogen to increase their production, alongside the already existing incentives provided by the U.S. Environmental Protection Agency’s Renewable Fuel Standard and the California Air Resources Board’s Low Carbon Fuel Standard (LCFS). These policies are integral in promoting low-carbon technologies, with biofuels emerging as a critical complement to other decarbonization strategies such as electrification with grid decarbonization, especially for some specific sectors which cannot be electrified, e.g., aviation and marine sectors. The surging interest in the biofuel industry has spurred a demand for anticipated biofuel supply in the markets.To address the inherent uncertainty in biofuel markets, this study quantifies the GHG reductions achievable through planned biofuel production and capacity expansions and provides quantitative metrics to track the transition of U.S. fuel use across sectors to alternative fuels and the effect on key environmental performance criteria. We quantified the life cycle GHG emissions and carbon intensity (CI) of U.S. fuel use across all sectors of the economy together with a 10-year projection based on the existing and producers’ planned expansions of alternative fuel production capacity and fuel use projections.

Targeted biofuel types include biodiesel, renewable diesel, ethanol, sustainable aviation fuel, and renewable natural gas. Data on biofuel facilities, production capacity (existing and producers’ capacity expansion plans), operational dates, fuel pathways, and CI are compiled from the U.S. Energy Information Administration (EIA) biofuel production report, LCFS database, biofuel producers’ websites/reports, and other sources. We employed a bottom-up analysis based on scenarios that account for the impact of increased electrification on the US economy-wide future fuel use and existing and planned biofuel facility-level data and linked the biofuel production to the corresponding fuel pathway in greenhouse gases, regulated emissions, and energy use in technologies (GREET) model to estimate the potential economy-wide GHG emissions reduction resulting from the replacement of conventional fuels with biofuels from 2020-2035 .

Our findings indicate that biofuels have the potential to replace up to 3.84 exajoules of conventional fuels, resulting in a saving of approximately 266 million metric tonnes of GHG emissions by 2035. This impact is most profound in the transportation and industrial sectors, due to substitution of diesel with biodiesel/renewable diesel and jet fuel with sustainable aviation fuel. In an economy-wide context, biofuels reduced the total U.S. GHG emissions by 14% in 2035 while the combined impact of electrification and biofuel results in a 39% reduction . This study provides valuable insights for bioenergy stakeholders, enabling them to track the contribution of biofuel technologies to the decarbonization of the U.S. economy over time based on producers’ plan.

Agriculture and animal husbandry is responsible for 11% of the total US national greenhouse gas (GHG) emissions. Manure management alone makes up 9% of the total national methane emissions (EPA, 2023). As such changes to manure management within agricultural settings, including diversion of manure from lagoons to anaerobic digesters, could have substantial impacts on national GHG emissions. However, manure management, particularly in the dairy sector (Meyer et al., 2011), is complex and this contributes to the uncertainty surrounding any estimates of emissions reduction potential. There are a wide range of manure management technologies and designs currently in use. Differences in regional climate, pasture management, economics, and local tradition result in varying farm layouts and emissions. Local climate influences farm layout and pasture availability, as well as the chemistry and microbial community’s activity within manure management technologies, and these factors influence the rate of gaseous emissions (IPCC, 2006; Maldaner et al., 2018).

While factors such as local temperatures and herd sizes can be easily modeled via available government databases, data of real-world farm layouts is generally unavailable. As such it is challenging to model manure availability and farm emissions accurately on a large scale. To capture the breadth of potential baseline conditions across the US, this work utilized county-level temperature data (NOAA) and herd size data (NASS) to estimate housing conditions (barn, feed lots, pastures) as well as emissions rates. To capture the impact of different manure management technologies, this study modeled several potential baseline scenarios including no emissions mitigation, manure solids removal, the inclusion of anaerobic digestion, and covering lagoons.

This presentation will summarize the findings of these models to show volumes and location of: 1) Collectable manure per county; 2) Potential biogas production per county based on manure availability; and 3) Potential CH4, N2O, and eCO2 emissions for an array of manure management scenarios. The data presented will indicate which technologies can be used to target reductions in GHG emissions and improve air quality.

The legalization of industrial hemp farming in the United States has created an abundance of residual biomass, and consequently, an untapped potential for biogas production. Cannabidiol (CBD) has emerged as a highly sought after hemp derived product for its medicinal benefits. Although the hemp plant itself may be considered sustainable for its potential to be a carbon sink, the CBD production process currently follows an unsustainable linear process model. About 80% of the hemp feedstock is wasted during CBD production. In-situ anaerobic digestion is proposed to transform the residual hemp into biomethane which can be used to replace or supplement an existing fuel in a heating element in the CBD production process. Thus, the goal of this research is to investigate the utilization of hemp crop-residue as a biogas and explore the potential for creating a circular CBD industry. A process model is created using SuperPro Designer to simulate theoretical biogas yield and is compared with literature values and preliminary results from our experimental anaerobic digester located in North Carolina. The maximum theoretical biomethane yield produced by the process model is 360 mL CH4/g VS which is in line with our expectations since the average literature value for experimental anaerobic digestion of hemp is about 236 mL CH4/g VS. The percent composition of the biogas is also in line with literature values for biogas composition and preliminary results from our experimental digester. Future work will include measuring the biogas yield from the experimental digester to compare with our process model results. A comparative life cycle assessment (LCA) is conducted to compare the traditional end-of-life options for residual hemp (i.e., composting, incineration, and landfill) to the proposed alternative waste to energy scenario. The alternative scenario accounts for replacing the fossil fuel used in traditional CBD production with biomethane from the anaerobic digestion of hemp. Preliminary results show that the hemp waste-to-energy scenario has the lowest global warming impact compared to baseline scenarios which demonstrates that the proposed system has the potential to be environmentally sustainable. To create circularity within the industry, the system must also be economically sustainable for CBD producers. The global CBD market is projected to reach 44 billion USD with a compound annual growth rate of 27.8% for the forecast period of 2022-2029. This calls for a techno-economic evaluation of the existing and alternative biofuel system. Future work includes calculating biogas production potential for the whole state of North Carolina and working with CBD producers to implement this technology.

Limiting global warming to the greatest extent possible is critical for reducing the adverse effects of climate change on humanity and the planet. To achieve long-term temperature mitigation targets, such as 1.5℃ or 2℃, it is crucial to reckon not only with the total emissions reductions required but also with the anticipated "committed" greenhouse gas (GHG) emissions from existing infrastructure. While data on the former is more accessible, information on the latter is often confined to snapshot publications with limited transparency on geographic or technological scales.

In this study, we introduce a comprehensive bottom-up model of "committed" energy supplies for the United States, aimed at informing climate policy decision-making. This work addresses critical questions about the existing energy system, allowing us to estimate the size of the remaining gap between locked-in US emissions from existing infrastructure and modeled emissions trajectories leading to 1.5℃ and 2℃ targets. The model incorporates facility-level lifecycle emissions for both fossil and non-fossil sources, utilizing well-level data for natural gas and oil, and power plant data for coal, renewables, and biomass. Designed for regular updates to accommodate additional energy projects or early retirements, the model ensures relevance and accuracy over time.

Utilizing this model, we analyze the GHG emissions gap across various project-relevant timescales, comparing locked-in US emissions with pathways to 1.5℃ and 2℃ targets based on the latest models from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Our findings reveal that, while some space exists between the modeled scenario pathway and already locked-in emissions over certain lifetime scales, a minimal number of new fossil energy projects could entirely eliminate this window. Additionally, Monte Carlo analysis is employed to illustrate the robustness of our conclusions to uncertainties in model parameters.

This committed energy system model serves as a core input to the recently-published "climate test" decision-support tool. This tool is specifically designed for policymakers tasked with evaluating individual fossil infrastructure projects and their alignment with climate targets under US law. Together, these tools empower government agencies to make scientifically informed and climate-protective decisions for our energy future, facilitating our collective efforts to realize overarching climate goals.

The U.S. government's commitment to achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 aligns seamlessly with the global climate mitigation objectives outlined in the Paris Agreement. The overarching goal is to limit the average temperature increase to 1.5 °C or less by 2100, compared to preindustrial levels. To realize this ambitious domestic mid-century target, there is an imperative need for the rapid adoption of energy-efficient technologies and the decarbonization of pivotal sectors such as power and transportation. This involves embracing electrification, fuel switching, and the expansion of variable renewable energy sources and storage technologies. Furthermore, an increased emphasis on electrification is crucial for both the buildings and industrial sectors.

While the power and transportation sectors, contributing 29% and 25% to total U.S. national GHG emissions, have extensively outlined and modeled their decarbonization strategies, meeting the 2035 and 2050 targets demands a concerted effort due to the substantial scale of the power sector and the heterogeneous nature of the transport sector. The industrial sector, responsible for 23% of total U.S. GHG emissions, poses a unique challenge due to its activities that are challenging to electrify. Addressing these activities requires the exploration of technologies that are either less understood or have not yet been scaled. Finding innovative solutions in this realm is crucial for achieving the broader climate goals.

Emerging technologies call for the application of forward-looking life cycle assessment (LCA) methodologies. These methodologies enable the comprehensive consideration of technology scaling and process enhancements, including learning by doing. Additionally, the future system context in which these technologies are envisioned to operate holds equal significance in many instances. Background scenarios systematically generated by integrated assessment models (IAMs) proficiently incorporate the evolving dynamics of the energy-economy-land-climate system. These IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, enhancing the comparability of prospective LCAs utilizing various IAMs.

In this context, we present an open-source framework for prospective LCA, known as the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON). LiAISON facilitates the examination of non-linear relationships between technology foreground and the future energy system background, encompassing a spectrum of midpoint and resource-use metrics.

The LiAISON framework has been used to assess the environmental impact of various hydrogen production methods in the United States. This involved combining data from two integrated assessment models, IMAGE and GCAM, to enable life cycle assessment (LCA) across different scenarios. The framework is currently applied to a case study involving industrial heat supply in the US. The study not only analyzes LCA results with temporal and geospatial details for two technologies but also aims to create a foundational framework that can be extended to incorporate other scenarios generated by integrated assessment models and U.S. open-source life cycle inventory databases.

The Global Change Analysis Model (GCAM) is an open source, publicly available market equilibrium integrated assessment model, which integrates human and natural earth systems science. GCAM examines the multisector dynamics between the socioeconomic, energy, water, land, climate, and emissions systems at 5-year time steps through 2100 and operates with a spatial resolution of 32 economic regions, 283 land regions, and 233 water basins. The model has been developed at Pacific Northwest National Laboratory’s Joint Global Change Research Institute (PNNL JGCRI) for over 30 years and has been used to explore questions related to energy transition, decarbonization and carbon mitigation, technology development and policies, air pollution, energy/water/food nexus, climate impacts, and many others. The GCAM community also continues to develop versions of GCAM with state-level detail in larger countries such as GCAM-USA, GCAM-China, GCAM-Canada, and others. This presentation will give an overview of GCAM, the range of analyses that have been conducted using the model, and how GCAM can supplement traditional ISSST analyses.

Considering the emission-intensive materials consumption and their associated environmental impacts, the building industry is a priority sector for reducing its environmental impacts and meeting greenhouse gas (GHG) emissions targets globally. The embodied carbon of building structures, substructures, and enclosures is responsible for 11% of global GHG emissions and 28% of global building sector emissions. Thus, various strategies are gradually adopted to reduce the emissions from buildings, including the early adoption of lifecycle assessment (LCA) tool during the design stage (new construction) to select the less emissive materials, sustainable strategies during the retrofitting process, and demolition/ deconstruction process including the sustainable materials management at the end-of-life of buildings. Numerous efforts have been devoted to developing such tools for conducting building LCA. This study aimed to critically analyze the existing tools to understand their implications and adoption by users through pre-defined criteria such as geographic coverage, construction applications, targeted users, distinct features and specificity, adopted databases and methods, etc. Two groups of tools dedicated to whole building LCA (e.g., TallyLCA, Athena, One Click, etc.), and building materials/ elements (e.g., EC3, TallyCAT, WoodWorks Carbon Calculator, BEAM Estimator, EPIC, BEES) were selected, and their adoption in the existing literature was explored. The study found that most of the tools are regional with specific applications though some of them are adopted globally. To enhance the accuracy of the assessment, local/regional tools with databases are preferred in the scientific community. Some of the tools are based on carbon emission only, where the Environmental Product Declaration (EPD) was used as a database. The use of such tools (as standalone) may be inappropriate for whole building analysis due to limited impact indicators and also a lack of transportation of materials modeling. Though some of the tools adopted waste management (and recycling partially), most of them failed to model the end-of-life of buildings comprehensively, particularly recycling and reuse based on the circular economy (CE) principle. Therefore, a methodological framework is proposed to strengthen the existing ones for developing adaptive building LCA tools for promoting CE, particularly for whole building LCA. The outcomes of this study would facilitate the users to select the most suitable tool for their specific applications comprehensively and accurately while promoting CE adoption in the industry.

The development of timber-based structural materials has the potential to reduce the life-cycle greenhouse gas (GHG) footprint of buildings and even store carbon on a multi-decade timeframe. However, new bio-based materials, such as improved structural composite lumber, often have limited data available for conducting robust life cycle assessment (LCAs). This limitation can affect the ability to conduct early stages of evaluating these potential materials and building designs. In this study, a heuristic decision tree was developed with the goal of providing users a means of estimating life cycle inventory (LCI) parameters of their desired timber-based structural material, even if there is a scarcity of data. Three different sources of timber will also be compared in terms of GHG emissions and energy requirements, while highlighting the effect of steps taken in harvesting, sawmill, and final product formation on GHG emissions. The goal of this work is to provide a simplified decision-making tool that can be used to quickly assess the likely GHG footprint of timber-based structural materials in the absence of detailed material- and location-specific data. This in turn can guide users in designing novel biogenic-based structural materials that can minimize GHG emissions.

Decarbonization technologies are a solution to reducing greenhouse gas emissions and mitigating climate change. However, the decarbonization transition will undoubtedly be mineral-intensive with critical minerals, like cobalt, lithium, and nickel, serving as the backbone of many decarbonization technologies. Mining is the primary acquisition method for critical minerals but wields many environmental and social effects – both positive (i.e. job opportunities and boosting the local economy1) and negative (i.e. land degradation2, reduced water quality3, and community displacement2). Life cycle analysis (LCA) is a tool that can assess effects and sustainability. However, there are significant gaps and a lack of structure in how LCAs of mineral mining are completed (e.g., inappropriate data sources, inconsistent system boundaries, indicators, and functional units). Overall, there is no standardized framework for critical mineral mining LCAs, which complicates comparisons, decision-making, and policy design.

We propose a critical mineral mining LCA framework using the standard four life-cycle phases.4 We conduct an LCA of a proposed copper-nickel mine in Minnesota, the first non-ferrous mine in the state, to guide development and application of the framework.

Several aspects of this LCA demonstrate the framework. For example, we employ two functional units, one ton of mineral equivalent and one year of mining operation. We define the system boundary to encompass mining operations and reclamation. We used foreground system processing and site information from legal documents as an appropriate, open data source. We use Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) Model5, and literature for background system inventory data.

We calculated energy use, water use, and greenhouse emissions of the proposed mine. Mineral refining processes (beneficiation and hydrometallurgical) and wastewater treatment account for most of the energy and water consumption, representing 81% and 94% of the total energy and water burdens, respectively. However, refining and wastewater treatment processes only comprise 52% of the greenhouse gas (GHG) emissions. Land clearing and blasting contribute 40% of GHG emissions. Land clearing will release 6.7 billion kg of CO2 into the atmosphere, 35% of total GHGs.

Carrying out this LCA using publicly available information identified challenges and gaps in completing mining LCAs that must be addressed using new, local, and timely data sources. For example, water pollutant emissions and biodiversity changes are prominent mining environmental effects. We describe an approach to improving mining LCA coverage of these important effects as part of developing a standard LCA framework for critical minerals mining. The framework will enable better comparisons, decision-making, and policy design. The standardized framework will offer a template for critical mineral mining LCAs conducted by other researchers and allow comparison of mines and LCA results.

References:

1. Hosseinpour, M., et al. Evaluation of positive and negative impacts of mining on sustainable... (2022).

2. Wilson, S. A., et al. Livelihood impacts of iron ore mining-induced... (2022).

3. Uugwanga, M. N. & Kgabi, N. A. Heavy metal pollution index of surface and groundwater… (2021).

4. International Standards Organization. ISO 14040:2006 (2006).

5. Argonne National Laboratory. (2021).

To alter the way society uses and manufactures polymers, polymer scientists must screen their sustainable polymer design ideas at early stages of research. Taking this step will enable them to evaluate tradeoffs among feedstock choice, reaction engineering strategies, and possible end-of-life fates. However, a comprehensive whole-life cycle screening tool for sustainble polymer design is notably absent from the literature. In response to this challenge, we have developed a tool to support bench scale polymer scientists in the design of polymers with sustainablity advantages.

The framework of our polymer screening tool encompasses feedstock production, solvent production, conversion, and end-of-life polymer recycling. We included 13 types of feedstocks, 23 classical solvents, 15 recycling techniques for the most common polymers, and 22 plastic resins. We extracted open-source data from the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) model, Environmental Footprint (EF) database, journal articles, and other publicly available literature, and assembled them in the tool for ease of use. Users have the freedom to choose their own system boundary and design polymer production pathways using various feedstocks, solvents, yield, conversion processes, and end-of-life recycling methods. They can explore these choices on energy and water consumption and greenhouse gas emissions of their envisioned polymer.

To demonstrate the features and utility of the tool, we have piloted its use for two early-stage polymer research projects. We considered the extent to which bench scale scientists can make design choices using the tool and the level of information they have about their polymer.

By providing an open-source platform that leverages existing data and models, this tool seeks to foster collaboration and innovation, promoting a more sustainable future for the polymer industry.

Anticipatory Life Cycle Assessment (LCA) was developed to evaluate the environmental impacts of emerging technology1. Inclusive of uncertainty, which can be used to assess both reasonable and extreme cases, modeling advances including dynamic and thermodynamic modeling, and stochastic decision support, anticipatory LCA has the potential to inform designers early in the design process while the cost of change is low. Until now, however, the ability to apply anticipatory LCA has been limited to LCA expert practitioners using the most advanced LCA tools.

Several modeling and assessment techniques have been developed over the last 10 years which have the potential to bring much of anticipatory LCA capability to non LCA-expert audiences. Underspecification is a strategy for streamlining the life cycle inventory data gathering process, enabling designers to choose from an array of inventory instead of having to identify an exact proxy. Since it was first explored by MIT researchers over 10 years ago1, it has been applied primarily in the design of buildings, allowing architects to use LCA to guide major design choices.

The significant computational addition to anticipatory LCA enabled by underspecification is further enhanced by the use of SMAA (stochastic multi-attribute analysis)2. SMAA takes a stochastic approach to determining which impacts are the most important and provides the probability that each design has the least life cycle environmental impact. This is an improvement over single score systems, which rely on weights that represent the values of a very small group of stakeholders.

In 2020, EarthShift Global joined sustainable design researcher Tejaswini Chatty (then a PhD student at Dartmouth College) and the design firm Synapse to begin designing an LCA tool which uses underspecification and SMAA to enable anticipatory LCA for non-expert audiences. The tool is designed around the needs and behavioral patterns of product designers. It enables the use of LCA much earlier in the design process and speed up data collection and modeling3.

By combining underspecification, SMAA, and a subjective, percentage-based uncertainty assignment for primary data, this tool enables many of the principles of anticipatory LCA very early in the design cycle, encouraging environmentally-conscious innovation at minimal cost to the manufacturer.

1 Wender et al., “Anticipatory Life-Cycle Assessment for Responsible Research and Innovation.”

2 Prado and Heijungs, “Implementation of Stochastic Multi Attribute Analysis (SMAA) in Comparative Environmental Assessments.”

3 Chatty, “Enabling the Integration of Sustainable Design Methodological Frameworks and Computational Life Cycle Assessment Tools into Product Development Practice.”

The pulp and paper industry (PPI) is an integral part of the manufacturing industry in the United States. Despite the industry’s success in reducing energy use, it remains an energy-intensive industry, accounting for 11% of the manufacturing sector’s energy consumption. There have been many initiatives to decarbonize the PPI and many novel innovations to make it more energy efficient. One of the novel approaches to decarbonizing this industry is through converting existing pulp and paper mills into integrated biorefineries.

Integrated biorefineries have been defined as ‘facilities that integrate biomass extraction and conversion processes and equipment to produce fuels, power, heat, and value-added chemicals’. The PPI can be converted into an integrated biorefinery by adding different routes, such as gasification, anaerobic digestion, pyrolysis, and fermentation, to transform wastes or lower-value by-products into value-added products. Biorefineries can expand the product portfolio, increasing industry resiliency by taking advantage of the already invested capital and established logistics and services. However, a more comprehensive understanding of these diverse biorefineries, including their feasibility and comparativeness in terms of costs, environmental impacts, and energy use, is essential.

In this study, we use life cycle assessment (LCA) and techno economic analysis (TEA) to investigate and compare the costs, resource depletion, carbon, and energy impacts of different biorefinery approaches chosen based on their applicability. This study has two objectives: (a) to identify different biorefinery options for the PPI and show their feasibility through market demand; and (b) to evaluate the embodied energy, carbon footprint, resource depletion, and production costs for different products in the chosen biorefinery using LCA and TEA.

Based on initial findings on applicability from a literature review, we focus on modifying an existing kraft paper mill into an integrated biorefinery that produces (a) carbon fiber, (b) fertilizers, and (c) bioplastics, along with the pulp production. Since there are multiple products with multiple functions, 1 metric ton of pulp was chosen as the functional unit, and the system expansion methodology with substitution was used to account for the avoidance of conventionally produced products.

Our expected LCA and TEA results will identify the most cost-effective biorefinery concept, determine the technologically superior performer, assess their different environmental impacts, evaluate market viability, and identify their comparative ranking. Furthermore, the results will also highlight the carbon/ energy hotspots in the processes that can be improved.

PPI-based biorefineries are industrial symbiosis systems which share materials and energy between the production processes of pulp and value-added products. This results in reduced usage of virgin materials, energy use, emissions, and waste generation. Therefore, targeting specific product markets could lead to net improvements in the total environmental impacts of the system. Lower production costs could also contribute to a more competitive market. The value-added products produced can replace petroleum-based, virgin materials, impacting material and resource consumption of multiple industries. Thus, the results of this study will help decision makers in choosing the best valorization path based on economic and life-cycle impacts, determining the most feasible, environmentally friendly, and profitable alternative.

In an era where environmental sustainability is paramount, the effective management of wood waste emerges as a pivotal factor in ecological innovation. This research delves into the intricate dynamics of wood's lifecycle, aiming to redefine its sustainability impact in emerging forest-based bioeconomy scenarios. By reassessing the fate of wood waste, particularly from sectors like construction, we seek to extend the lifecycle of wood products, thereby significantly impacting our carbon footprint.

Our project adopts a multifaceted approach to transform wood waste management, emphasizing the principles of a circular economy and sustainable resource efficiency. Beginning with a Stock-driven Material Flow Analysis, we meticulously traced the entire lifecycle of wood, from its origin to end-of-life, with a keen focus on its role as a carbon sink. Simultaneously, we incorporate Life Cycle Assessment (LCA) methodologies to identify potential environmental implications associated with different pathways of wood waste management.

The research identifies gaps and inefficiencies in the current wood waste management system, with a commitment to prioritizing recycling and repurposing over conventional disposal methods. Our goal is to revolutionize the approach to wood waste, ensuring that its use as an energy source is considered only after all other options have been exhausted. By maximizing the carbon storage potential of wood, we aim to reduce the pressure on our forests, promoting a sustainable future.

The methodology employed encompasses a comprehensive Material Flow Analysis, LCA, and an in-depth examination of wood decay rates to gauge material longevity. By understanding how long wood can serve as a carbon sink, we contribute valuable insights to the field. This approach is further complemented by an exploration of broader ecological impacts, particularly the influence of changes in land use on emissions.

As the research unfolds, we strive to provide a holistic view of the ecological footprint of wood waste management. The findings will culminate in actionable recommendations aimed at refining the Life Cycle Assessment framework and steering the industry towards a more sustainable, circular approach in managing wood waste. This project transcends the conventional notion of finding new uses for wood waste; it signifies a paradigm shift in our relationship with this critical resource, fostering a future where sustainability and resource efficiency are at the heart of environmental stewardship.

The chemical industry is transitioning from relying on fossil fuels to renewable biomass as feedstocks for chemical production. Compared to traditional petrochemicals, these bio-based chemicals have the potential to reduce greenhouse gas (GHG) emissions and energy consumption throughout manufacturing processes. Life cycle assessment (LCA) has been widely utilized by researchers to assess the potential environmental impacts of biochemicals. However, the diversity in biorefinery features (e.g., the choice of feedstocks, platforms, and conversion processes) and LCA modeling assumptions (e.g., allocation methods, system boundary, and locations) will affect the environmental outcomes of bio-based chemicals, leading to high uncertainty in estimating their true environmental benefits.

Our research provides a comprehensive analysis of Global Warming Potential (GWP) results for the production of 19 priority bio-based chemicals. We employed a system harmonization approach to minimize variations in functional unit and system boundary across 85 LCA case studies, resulting in 160 harmonized data points. The harmonized GWP results for each biochemical were then visualized by violin graphs and compared to their fossil-based counterparts. The results showed that most bio-based chemicals exhibited lower GWP results, mainly attributed to two mechanisms of GHG emissions reduction: (1) carbon sequestration credit through biomass growth, and (2) reduced emissions from the manufacturing processes (e.g., reduced energy consumption or direct GHG emissions).

In addition, a meta-regression analysis (MRA) was conducted to analyze and contextualize the variability in observed environmental impacts of biochemical production. The GWP results for producing 1 kg bio-based chemical (kg CO2-eq/kg) were designated as the dependent variable. The biorefinery characteristics and LCA methodological choice, by contrast, serve as 37 binary or continuous explanatory variables. The results indicated that GHG emissions from biochemical processes (e.g., fermentation) are statistically higher than those from chemical (e.g., hydrolysis, catalysis) and electrochemical conversion pathways. Moreover, the meta-regression model can be utilized to estimate the GHG emissions of each bio-based chemical. We found that predicted GWP results showed a narrower range of variation compared to the original values, thanks to the benefits of standardizing study-level characteristics through the model.

In this research project, we reviewed the biorefinery processes and the environmental impact of 19 selected bio-based chemicals. We compared the GWP results of biochemicals with the fossil-based benchmarks to identify their potential environmental benefits. Finally, we synthesized the LCA studies through meta-regression analysis to predict GWP results for each chemical. Our findings will give recommendations for enhancing current biorefinery processes and provide an LCA dataset for future research.

Background: Lignocellulosic biomass is an abundant and recalcitrant material primarily composed of cellulose, hemicellulose, and lignin. Due to its recalcitrance, biomass pretreatment is becoming a key part of the biorefinery process to enhance sugar production for biofuels and biomaterials. This step determines the conversion efficiency, production cost, and potentially the product's environmental impact.

Methods: Here, we explore the environmental life cycle analysis (LCA) of four pretreatment methods, namely hot water extraction, hot water extraction combined with disc milling, dilute acid, and steam explosion of willow biomass followed by enzymatic hydrolysis. The system boundary includes the activities associated with the willow biomass production system, transportation to the biorefinery, biomass pretreatment, hydrolysis, and sugar concentration. The functional unit is 1 kg of sugar (C5 and C6 combined) produced. The mass and energy balance data are derived from a robust process simulation model developed in SuperPro Designer based on our laboratory experiments and published studies. The life cycle inventory data for background processes (e.g., electricity) are sourced from the DATASMART and the Ecoinvent database. The impact assessment was conducted in SimaPro using the Tool for Reduction and Assessment of Chemicals and other Environmental Impacts (TRACI 2.1).

Results: The preliminary results indicate that hot water extraction pretreatment has the lowest global warming potential (0.0097 kg CO2eq) while steam explosion has the highest impact (0.0438 kg CO2eq). Combining disc milling with hot water extraction led to an increased impact but was lower than the dilute acid pretreatment. Over the next few months, our team will explore the implications for the other impact categories and identify the most influential factors affecting our results.

Conclusions: Lignocellulosic biomass pretreatment to enhance sugar yield from willow biomass affects climate change and other impacts of the produced sugars. This research will help researchers understand the impact of sugars produced from willow biomass for biofuels, biochemicals, and bioproducts production.

Livestock are the greatest contributors to nutrient runoff in the Chesapeake Bay Watershed, with dairy farms being of particular interest. Developing high-efficiency nutrient management systems is imperative for the sustainable growth of the livestock industry. Duckweeds, small, free-floating aquatic plants, possess the ability to hyper-accumulate nutrients from agricultural runoff and nutrient pools. This capability positions duckweed as a promising approach for manure management and the recirculation of nutrients back into the farm system. Using duckweed for nutrient absorption, retention, and reuse could open avenues to a circular nitrogen bioeconomy, though it has not received much attention in the US agricultural system. This study applied the method of life cycle assessment (LCA) to evaluate the environmental impact of a duckweed-based manure treatment system (DMTS) and compares it with the conventional manure management (CMM) system on the farm. The study considered farms with a herd size of 1300 cattle where 85 percent of the manure was treated through an open pond cultivation system. Following treatment, harvested duckweed was pelletized, and an analysis was conducted on its use as a soil amendment. The results of the LCA indicate that DMTS has lower impacts than CMM for climate change, marine eutrophication, and terrestrial acidification, at 63.8%, 84.6%, and 89.5% of CMM's impacts, respectively. In the category of terrestrial ecotoxicity, DMTS shows a 103% reduction in impact relative to CMM. However, for freshwater ecotoxicity and marine ecotoxicity, CMM impacts are lower, at 86.7% and 68.2% of DMTS's impacts, respectively, while water depletion is notably higher at 100%. The observed reductions in terrestrial acidification, global warming potential, and marine eutrophication by DMTS are critically relevant, particularly for addressing the challenge of nutrient runoff and pollution in the Chesapeake Bay.

Bioresource utilization is expected to perform a pivotal role while complementing other renewable energy pathways en route to deep decarbonization. Mitigating carbon using bioresource coupled with carbon capture and storage (CCS) provides additional opportunities for decarbonization. Replacing conventional fossil-based fuels requires technological update and adoption of new energy forms. Though certain liquid biofuels are infrastructure compatible, others may need technology update while keeping fuel form consistent, relative to fossil fuels. For realization of greenhouse gas (GHG) emissions reduction benefits through bioenergy pathways, consideration of production cost and GHG avoidance cost is critical to screen realistic pathways in terms of engineering, economic and environmental efficacy. In this analysis, we present the use of a harmonization framework to assess select biomass-based pathways (with and without CCS) which produces biofuels, bio-electricity, and biohydrogen to assess their relative GHG reduction potential, their minimum fuel selling price, and marginal cost of carbon avoidance. Further, we utilize U.S. biomass feedstock availability projections at various price-points to estimate the scale-up potential of the selected pathways. We also present an assessment of how a decarbonized electric grid may influence the pathways’ performance. In our study, we consider woody biomass and herbaceous biomass types as feedstocks for the conversion pathways. A total of 22 pathways are analyzed, a subset of which include CCS technology. Our analysis shows that the pathways have higher relative carbon reduction potential when CCS is implemented for a marginal increase in cost of production (10-25% depending on CCS technology and CO2 concentration). The marginal cost of avoidance for studied pathways ranges from $32 to $600 per metric ton (MMT) CO2e avoided relative to current incumbents. The preliminary scale-up study shows carbon reduction potential in scale of 10 MMT CO2e for herbaceous biomass and 10-1000 MMT CO2e for woody biomass, for a farm to biorefinery gate feedstock cost of $70/dry ton of biomass (including production, collection, and processing costs). The best use of biomass framework implemented for the analysis was presented at the 2023 ISSST Conference. This year, we elucidate results obtained from the analyses of pathway-level harmonization as well as extension of the framework to perform a scale-up study.

The industrial sector accounts for nearly a quarter of US greenhouse gas emissions, ranking third after transportation and electricity generation [1]. The Energy Information Administration forecasts that by 2024, industrial emissions could overtake electric generation emissions as the electric sector deploys additional renewable generation sources, even though industry will also likely electrify some processes [2]. Specific heavy industries, such as iron and steel production, present significant challenges and are often labeled “difficult to decarbonize” [3]. Iron and steel comprise approximately 95% of US metal production and 10% of US industrial CO2 emissions [1] [3].

This study investigates the role of hydrogen as a decarbonization strategy for the US iron and steel sector in the presence of an economy-wide net zero CO2 emissions target. We use Temoa, an energy system optimization model, to simulate decarbonization pathways for US steel production in the context of economy-wide decarbonization. We simulate several pathways for iron and steel decarbonization, including carbon capture, electrification, and hydrogen. We seek to understand (1) how provisions of the IRA could affect the deployment of iron and steel decarbonization technologies, (2) what iron and steel decarbonization technologies contribute to a least-cost net-zero emission energy system, and (3) how the availability of particular technologies changes the contribution of the iron and steel sector to economy-wide decarbonization.

Our analysis shows that hydrogen-based direct reduced iron (H2DRI) provides a cost-effective decarbonization strategy only under a relatively narrow set of conditions. Using today’s best estimates of the capital and variable costs of alternative decarbonized iron and steelmaking technologies in a US economy-wide simulation framework, we find that carbon capture technologies can achieve comparable decarbonization levels by 2050, with greater cumulative emissions reductions from iron and steel production. Simulations suggest hydrogen contributes to economy-wide decarbonization, but H2DRI is not the preferred use case for hydrogen under most scenarios. The average abatement cost for US iron and steel production could be as low as $70/tonne CO2; the cost with H2DRI rises to over $500/tonne CO2. We also find that IRA tax credits are insufficient to spur hydrogen use in steelmaking and that a green steel production tax credit would need to be as high as $300/tonne to lead to sustained H2DRI use.

[1] EPA, “Inventory of US Greenhouse Gas Emissions and Sinks: 1990-2020,” US Environmental Protection Agency, 430-R-22–003, 2022. [Online]. Available: https://www.epa.gov/system/files/documents/2022-04/us-ghg-inventory-2022-main-text.pdf

[2] EIA, “Annual Energy Outlook 2022 Table 18: Energy-Related Carbon Dioxide Emissions by Sector and Source,” Energy Information Administration, Washington, DC., 2022. [Online]. Available: https://www.eia.gov/outlooks/aeo/data/browser/#/?id=17-AEO2022&region=1-0&cases=ref2022&start=2020&end=2050&f=A&linechart=ref2022-d011222a.6-17-AEO2022.1-0~ref2022-d011222a.13-17-AEO2022.1-0~ref2022-d011222a.20-17-AEO2022.1-0~ref2022-d011222a.26-17-AEO2022.1-0~ref2022-d011222a.33-17-AEO2022.1-0&map=ref2022-d011222a.4-17-AEO2022.1-0&ctype=linechart&sourcekey=0

[3] S. J. Davis et al., “Net-zero emissions energy systems,” Science, vol. 360, no. 6396, p. eaas9793, Jun. 2018, doi: 10.1126/science.aas9793.

Thailand has established a target of carbon neutrality by 2050. Reaching this goal will require coordinated efforts across the energy system at both the national and subnational levels. Robust decarbonization scenarios incorporating current plans and targets, additional measures needed, and trade-offs between strategies can help stakeholders make informed decisions in the face of uncertainty. Through iterative engagement with decision makers, we develop and analyze carbon neutral scenarios for Thailand that incorporate Bangkok’s role using a global integrated assessment model. We find that Thailand can reach carbon neutrality through power sector decarbonization, energy efficiency and widespread electrification in the buildings, industry, and transportation sectors, and advanced technologies including carbon capture and storage. Negative emissions technologies will also be needed to offset Thailand and Bangkok’s hardest-to-abate CO2 emissions. Bangkok, as a major population and economic center, contributes significantly to Thailand’s energy demand and emissions and can therefore play an important role in climate change mitigation. Accordingly, our results underscore the importance of subnational climate action in meeting Thailand’s carbon neutral goal. These insights can help energy system stakeholders identify priorities, consider tradeoffs, and make decisions that will impact Bangkok and Thailand’s long-term climate change mitigation potential. We also note that Thailand's carbon neutral 2050 pathway is based on a targeted emissions reduction of 40% by 2030 as stated in Thailand’s NDC. However, Thailand’s unconditional NDC is only 30% in 2030. Thus, Thailand may need international support to achieve carbon neutrality by 2050 through the pathways investigated here.

Background

The Information and Communication Technology (ICT) industry accounts for about 330M tons Green House Gase (GHG) emissions annually [1]. The industry manages its GHG impact with a move to 100% renewable energy, and aggressive acquisition of Renewable Energy Certificates (RECs) [2] through Power Purchase Agreements (PPAs) [3], [4]. Variability in renewable energy sources and their dislocation from the actual area of operational demand, made possible through the PPAs, make net GHG emissions intractable. This problem has been identified by the ICT industry [1].

Google, Microsoft, and Iron Mountain have announced 2030-2040 targets to source and match zero-carbon electricity on a 24/7 basis within each grid where demand is located [1]. The goal is to bring time-space coherence of energy supply with demand, to make their GHG emissions tractable. This would require tools to track the switching of energy to renewable sources in time, spatial location on the transmission grid, energy storage in the grid, to the net demand delivered by renewable sources in each 24-hour window of operation. However, such tools are not readily available.

Motivation

The ICT industry is subject to Environmental, Social, and corporate Governance (ESG) audits [5]. ESG scores that drive stock valuations of the industry [6] are based on Corporate Sustainability Reports (CSR)s. GHG protocol [7] is the de facto standard for the energy and emission footprint reporting in the annual CSRs. Gaps in emission targets are covered by the acquisition of RECs through PPAs. PPAs push emission commitments upstream to the energy/utility grid suppliers, dislocating the time-space coherence from the point of energy demand. The emissions footprint published in the CSRs therefore do not represent realistic values.

The CO2 Emission Factor (CEF), the total CO2 emissions/total energy [8], is a relatively new emissions metric the industry uses. CEF can be accounted down to ‘0’ with sufficient PPA acquisition. This further distorts the actual emissions footprint in the industry’s operational metrics.

Our Contribution

Open-Source energy Modelling System (OSeMOSYS) [9], [10] together with Climate Land Energy Water Systems (CLEWs) [11] provide a starting framework to model energy supply-demand and understand impacts of land water usage on the climate i.e. GHG emissions. Our contribution will extend the OSeMOSYS-CLEWs framework, develop the plug-ins needed and provide a comprehensive set of tools to track energy supply sources with demand, in time, space, energy buffering or storage in the transmission grid. Our work will provide data on how much of the demand was met from renewable sources in each 24-hour operational window of the ICT infrastructure. The end-to-end integrated datacenter NetZero (dcNZ) emissions tool set we develop will be open sourced for ready availability to the industry.

We will use our work to extend the GHG protocol standards to enable the ICT industry to publish dcNZ measurements of absolute emission, increase/decrease in carbon footprint, effect on climate, land, energy, and water systems in their annual CSRs. The time-space correlated emissions measurements generated by our dcNZ tool set will provide realistic CEF values, reducing distortions in operational metrics.

Energy system optimization models serve as analytical frameworks for strategizing energy infrastructure development, integrating economic, technical, and environmental dimensions. They facilitate optimal resource allocation, technology selection, and policy analysis by simulating the energy system from cradle to grave – from the extraction and processing of raw materials all the way to the technologies meeting energy service demands. These models support robust decision-making, allowing stakeholders to better understand system behaviour, evaluate variable renewable integration, and ensure system reliability under diverse scenarios. Crucial for sustainable development, they provide insights into cost-effective, reliable, and environmentally sound energy pathways.

In a focused extension, power system planning models specifically address the unique challenges in the electricity sector, including maintaining equilibrium between generation and fluctuating demand, integrating variable renewable energy sources, and ensuring grid stability. By delving into the dynamics of hourly generation, grid constraints, and reliability assessments, these models offer an in-depth analysis, bridging the broader implications of energy systems with the specific operational details of the power system.

The Model Intercomparison Project, an ongoing project led by researchers at the Environmental Defense Fund, critically evaluates four leading open-source power system planning models: GenX, Switch, Temoa, and USENSYS. These models differ in various respects, and there is a need to gain insight into how these individual energy system models compare. To fully unlock the decision-making usefulness of these next-generation power system models, a nuanced understanding is needed to determine how the evaluations produced by these models vary and what features of the models explain such variance.

The results from this study will highlight the structural differences between these leading open-source models, providing model users and policymakers with a better understanding of their relative strengths and weaknesses. Additionally, by running each model across a variety of deep-decarbonization scenarios, the ensemble approach will offer policymakers insights into which aspects of the transition are unanimous across models and which aspects show a significant degree of variance. By providing a clear, empirical basis, the project enhances the robustness of decisions in energy policy development and system planning.

This presentation will introduce the audience to energy and power system planning models, provide motivation for the Model Intercomparison Project, highlight the project's key findings, and discuss the policy implications stemming from the study. The goal is to acquaint the audience with the field of energy system modeling and to provide a clear example of how these models are influencing policy implications.

Project Partners by Model:

- USENSYS: Environmental Defense Fund

- TEMOA: Carnegie Melon University; NC State University; Sutubra Research

- GenX: Priceton

- SWITCH: The University of Hawaii; UC San Diego

Canada, like most of the world, must transform its energy system to meet net-zero greenhouse gas (GHG) emission targets. Meeting national goals requires a deep understanding of how future technologies and infrastructure will integrate to meet Canadians' expectations for clean, reliable, and low-cost energy. Despite progress modelling net-zero transitions in electricity systems, gaps remain in analyzing the role of chemical fuels and interactions between sectors. A limited body of work has examined broad pathways to net-zero across the Canadian energy system, but much of this work used proprietary models that are not widely accessible.

In response to this challenge we are developing the Canadian Open Energy (CANOE) model using the established Tools for Energy Model Optimization and Analysis (TEMOA) framework. TEMOA is an open-source, bottom-up energy system optimization model for assessing the energy system’s engineering, economic and environmental components from local to global scales. TEMOA is a linear programming, multi-sectoral, capacity expansion model. It is built in Python using Pyomo, and implements Modelling to Generate Alternatives (MGA) to allow exploration of model uncertainty. Our initial work focuses on building basic representations of all sectors (buildings, transportation, industry, electricity) for Ontario and Alberta, with greater depth in transportation and industry, followed by plans to expand to other provinces and to link with existing versions of TEMOA developed for the U.S. (Open Energy Outlook) and Atlantic Canada (Net Zero Atlantic ACES model).

Building on our team’s expertise in the oil and gas, transportation, and electricity sectors—as well as life-cycle assessment and challenges of the mid-transition—this multi-sectoral modelling project will holistically assess the roles of chemical fuels in Canada’s energy transition. This poster will introduce our project goals, philosophies and early challenges.

As the global focus on sustainable building practices intensifies, architects face the challenge of designing structures that meet aesthetic and functional criteria and minimize energy consumption.

A critical aspect of achieving energy-efficient buildings is the selection of appropriate building materials with optimal thermal properties.

However, the integration of thermal performance analysis into the architectural design process remains a complex and underdeveloped area.

This research aims to address this gap by exploring the use of OpenFOAM to develop a user-friendly tool that empowers architects to make informed decisions about material selection and its impact on energy efficiency.

Civil engineering is one of the oldest engineering professions, where changes to engineering concepts must overcome long tradition. As a result, the inclusion of new design paradigms and decision frameworks into the engineering curriculum is gradual at best. Anthropogenic climate change is contributing to the gap between university engineering education and necessary knowledge of climate-informed engineering concepts and skills. Past surveys have revealed misinformation and indifference to addressing climate change among civil engineering students enrolled in degree programs (Shealy et al 2016, Shealy et al 2021). This project includes the development and validation of an online questionnaire as a tool to measure students’ self-perceived skill and knowledge on discipline-specific concepts of climate change adaptation. The survey consists of multiple items exploring (1) students’ opinions on climate change, (2) familiarity with concepts such as safe-to-fail, performance-based design, nature-based solutions, and adaptive pathways planning, (3) perception of climate change impacts on civil engineering work, and (4) importance of select 21st century skills. The survey also collects potential predictor values such as previous work experience, degree program information, professional licensing progress, or political association. The method includes establishing face validity with an expert panel and collecting pilot data at multiple 4-year institutions. Additionally, we will assess internal consistency of survey items with the Cronbach-alpha test and underlying characteristics with an exploratory factor analysis using the statistical package for social science (SPSS) software. Anticipated outputs of this study include excerpts of the survey tool, results of the expert review process, preliminary results of the pilot data and work in progress of psychometric analysis, as well as discussion questions for potential new concepts to invite dialogue with the audience. The goal of the study is to produce a validated tool, which can be used by engineering educators to assess the success of their curricula when transitioning to climate-informed engineering education.

References:

Shealy, Tripp, Andrew Katz, et al. “Predicting engineering students’ desire to address climate change in their careers: An exploratory study using responses from a U.S. national survey.” Environmental Education Research, vol. 27, no. 7, 2021, pp. 1054–1079, https://doi.org/10.1080/13504622.2021.1921112.

Shealy, Tripp, Rodolfo Valdes-Vasquez, et al. “Half of students interested in civil engineering do not believe in anthropogenic climate change.” Journal of Professional Issues in Engineering Education and Practice, vol. 143, no. 3, 2017, https://doi.org/10.1061/(asce)ei.1943-5541.0000323.

Food waste (FW) is a global issue that occurs in all parts of the food supply chain, including harvesting, processing, distribution, and consumption. In an effort to divert FW from landfills and recover the energy and nutrient value it contains, anaerobic digestion (AD) and composting have become common treatment pathways. One major challenge, however, with valorizing FW is dealing with physical contaminants, like plastic, cardboard, and metals, which are inadvertently introduced to the waste stream during food packaging, distribution, and serving. These contaminants may impede the performance of valorization technologies, and some residual materials may end up in downstream products, potentially compromising the economic and environmental benefits of this FW pathway. A limited body of literature has begun to investigate the nature and impact of FW contaminants, but there is not yet a comprehensive understanding of their source, composition, quantity in common FW streams, or potential impacts to different valorization technologies.

This study aims to characterize the physical and chemical characteristics of contaminants found in common FW streams, as a first step to investigating how these contaminants impact downstream valorization technologies. Institutional food service and household food waste samples are collected, and non-food contaminants are separated, weighed, measured, and characterized based on material type and properties. For example, chemical characteristics of contaminants are assessed via Fourier Transform Infrared Spectroscopy. Sample collection is repeated over multiple months to control for variability over time. While it may be difficult to accurately predict the nature of the contaminants that will come out from each FW stream, the study will provide valuable information on the range and type of contaminants that can be expected for each FW stream. This poster will summarize key results of this contaminant inventory and discuss how the nature of identified contaminants may impact established and emerging FW treatment systems. The nature of the contaminants will also help FW treatment facilities identify what technologies are optimal for the materials being received. Finding the most feasible and effective treatment options for the different FW streams may potentially allow for more FW to be treated and diverted from landfills, reduce greenhouse gas emissions, allow treatment facilities to collect more revenues, and produce a high-quality product with high economic value and low environmental impact.

Biosand filter is being used as a low-cost, point-of-use, sustainable drinking water treatment technique globally. However, due to the limitations of biosand filter in achieving complete decontamination of drinking water, modification of the biosand filters were proposed which have increased the environmental burden of filter production. This study aims to compare the environmental impact, cost, and performance of the biosand filter (BSF), the physically modified biosand filter with biochar addition (MBSF biochar), and the chemically modified biosand filter (MBSF) by coating the gravel with iron oxide (MBSF IOCG).

To achieve this goal, BSF was constructed and modified with biochar and iron oxide coated gravel (IOCG) to collect life cycle inventory. The life cycle assessment was then applied using the SimaPro version 9.1.17 to highlight the most significant impact of this process with ReCiPe 2016 endpoint (E). Sensitivity analysis was also made to assess the robustness of the result by changing some input variables on emission reduction.

It was found that natural aggregate extraction has the most impact on BSF and MBSFs construction. The total environmental impact of filters was; MBSF IOCG (675.8898 pt), > MBSF biochar (674.9818 pt), > BSF (672.5751 pt). Modification of the BSFs increased the cost and environmental burden of MBSF production. When the modification process in the MBSFs was compared, it was found that the production of biochar was less environmentally friendly (3.34 pt) than the Iron oxide coating of Gravel (IOCG) (3.14 pt). The estimated cost of constructing the filters was; MBSF (IOCG) ($ 37.28) > MBSF (Biochar) ($ 20.99) > BSF ($ 18.49). Although modifications increased the cost and environmental impact of filters, the performance of filters slightly increased for the removal of the tested pollutants. For instance, MBSF (IOCG) showed a removal percentage of 99.27% for Cu, 99.1% for Zn, 95.28% for Fe, 98.6% for Ni, and 95.33% for total coliform. On the other hand, MBSF (biochar) showed 99.2% for Cu, 99% for Zn, 78% for Fe, 75.4% for Ni, and 92.8% for Total coliform removals while the BSF gave a removal percentage of 99% for Cu, 90% for Zn, 60.1% for Fe, 73% for Ni and 93.89% for Total coliform.

The result demonstrates the high cost and environmental impact generated when MBSF is modified with iron oxide coated gravel (IOCG), in comparison to biochar, which can be mitigated by the replacement of IOCG with other greener alternatives such as iron rich sand or greener production routes of IOCG. Accordingly, recommendations were proposed for a more sustainable BSF and its modification. And future studies should focus of the development of cost-effective environmentally friendly MBSFs with high treatment efficiency for production of quality drinking water to regions with water issue.

A life cycle analysis (LCA) based on an Aspen Plus model was used to compare emissions and resource-intensive hotspots across two configurations that produced Xylo-Oligo-Saccharide (XOS) and co-products from oat bran from an autohydrolysis process. Oat bran, the world's largest cereal milling byproduct, produces 90 million tons of bran each year. XOS is a prebiotic valued at $3.34 billion in 2016, it competes with well-known prebiotics such as fructooligosaccharides (FOS) and inulin.

In the first configuration, the auto hydrolysis residue, high in glucan and lignin, is used for the combined production of microfiber and lignin oil (CML). In the second configuration, the residue is burned generating steam and electricity via a combined heat and power (CHP) system. In the CHP case, any excess electricity generated is sold back to the grid as a product. In either case, environmental hotspots are identified at the product and system level using system separation. The product line includes XOS with purity levels of 20, 35, 50, 70, and 95, as well as xylose purity 95, xylose syrup, microfibers, lignin oil, and electricity. In Aspen, CML and CHP were simulated with an oat bran feed rate of 89.45 oven-dry metric tons per day. Individual products were evaluated using LCA in both CML and CHP production scenarios, while the daily production rate remained constant.

In the CML configuration, estimated emissions for XOS 20P and 35P were 12,310 and 8360 kgCO2eq/mt, respectively. At higher purity levels, emissions were estimated at 6994, 6060, and 7130 kgCO2eq/mt for XOS 50P, 70P, and 95P, respectively. For this configuration, the system's total daily emissions were estimated at 503,777 kgCO2eq, yielding 66.28 mt of product.

In the CHP configuration, the estimated emissions for XOS 20P and 35P were 8363 and 5535 kgCO2eq/mt, respectively. At higher purity levels, emissions were estimated at 4456, 3704, and 3704 kgCO2eq/mt for XOS 50P, 70P, and 95P, respectively. The CHP system produced 103,264 kWh of surplus electricity, with (0.69) kgCO2eq per kWh credited to the system, which was sold back to the grid. As a result, the system received a net credit for (1366) kg CO2eq per day. This system configuration resulted in a net credit, when surplus electricity credits equal to 71,134 kg CO2eq are subtracted from total emissions, which were estimated at 69,769 kg CO2eq per day.

The study's findings suggest that the CHP scenario is more sustainable as it relies less on fossil-based fuels for steam and power generation, which had notable effect on impact categories. Revenues were calculated for each product based on market prices. For the CHP scenario less revenue was generated, compared to CML. Variable operating expenses, upfront capital, and installment fees have been estimated for CML, but not yet for CHP.

There are around 79.54 million hectares of timberland and more than 32.83% (59.30 million hectares) of Pine species plantation in northern China. In this region, more than 87% of the planted seedlings in Northern China, including Pinus tabuleaformis (Chinese pine), Pinus koraiensis (Korean pine), and Pinus sylvestris var. Mongolica (Mongolian Scotch pine), are dominant species. Our research aims to illuminate the complex responses of these ecosystems to future climatic changes.

The 3-PG model (Physiological Process Predicting Growth) has been globally applied to assess the diverse impacts of management, climate variation and site characteristics on stand-level attributes such as stem volume growth, biomass dynamics, water use efficiency, carbon balance, It has previously demonstrated success in parameterization for loblolly and slash pine stands. Validation against diverse studies and operational plots across the natural distribution range of these species indicates the model's reliability.

This study focuses on the 3-PG model to project the potential influence of climate change scenarios (RCP 4.5 and 8.5) on Chinese pine biomass dynamics in Northern China. By adopting a rigorous scientific approach, this investigation contributes valuable understanding essential for evidence-based forest management strategies, ensuring the sustainability and resilience of Chinese pine ecosystems under climate change.

Keywords: Climate change, Chinese pines, Northern China, Physiological process-based model (3-PG), Biomass dynamics.

The wide-scale deployment of variable renewable energy technologies (VREs) offers a pathway to decarbonizing the electric grid. One challenge to reliably operating the grid is ensuring sufficient generating capacity to meet demand at all hours. The capacity benefit of variable renewables – namely wind and solar – can be characterized using the capacity credit metric. Here, we explore how various assumptions for capacity credit can ultimately impact broader resource deployment for an entire electric grid. To do this, we use the Temoa capacity expansion model. Using context-specific capacity credit values can improve long-term decision-making in generation capacity expansion, cultivating more economical long-term resource planning for deep decarbonization.

We find significant trends of note when focusing on two major capacity credit allocation scenarios to explore the impact of various photovoltaic solar and wind. Both of these scenarios are run in Temoa in perfect foresight. In the Scenario 1 category, capacity credit values for wind and solar resources are set to zero. This aims to establish a baseline for how Temoa builds out a grid resource mix when wind and solar are found to provide no benefit to the grid during peak demand. In the Scenario 2 category, capacity credit values are set to static values of various combinations. These values range between 0-0.5 based on effective load carrying capability (ELCC) resource estimates from utility authorities such as the Pennsylvania-New Jersey-Maryland Interconnection (PJM), the Midcontinent Independent System Operator (MISO), and the California Independent System Operator (CAISO).

We find substantial differences in the least-cost deployment of solar and wind power based on the capacity credit assumptions. When we assume non-zero values for capacity credit for both solar and wind, we observe a relative drop in solar deployment, with an average of approximately 60 GW less solar and exceeding 100 GW in some cases. Conversely, there is a general increase in wind deployment, averaging approximately 30 GW with a high of almost 75 GW. These major changes in deployment imply that wind outcompetes solar relative to the neutral case in which neither resource receives capacity credit.

Future investigation will work to address additional scenarios that account for resource effective forced outage rate (EFORd) values, existing resource grid penetration levels, and temperature dependent forced outage rate (TDFOR) values. Additionally, increasing the number of representative days and capturing a high geographical resolution will be investigated.

Ultimately, we seek to prove that capacity credit allocation for the renewables of focus does in fact have an impact on their deployment into the future. Utilizing the appropriate capacity credit values will yield scenarios with a satisfactory level of certainty, and thus yield more effective power system planning.

Globally, key organizations in the road infrastructure sector, such as the World Road Association (PIARC) and the International Road Federation (IRF), are actively advocating for sustainable solutions to environmental challenges. This push for sustainability is particularly critical in the context of tire waste management, a significant environmental concern. According to the Environmental Protection Agency (EPA), approximately 242 million tires are discarded annually in the US, underscoring the urgent need for effective recycling strategies. One such innovative approach is the utilization of rubberized asphalt, which incorporates recycled tires into asphalt pavement. This study aims to comprehensively assess the environmental impact of rubberized pavement, addressing the urgent need for sustainable road construction practices. By focusing on the entire life cycle, from raw material extraction to end-of-life disposal, this research contributes to a deeper understanding of the ecological footprint of rubberized pavement.

The methodology of this study is grounded in a holistic Life Cycle Assessment (LCA), examining each stage of the rubberized pavement's life cycle, including raw material sourcing, production, transportation, usage, maintenance, and end-of-life scenarios. The study conducts a comparative analysis with conventional asphalt, aiming to delineate the environmental benefits and possible trade-offs of using rubberized pavement. A sensitivity analysis further enriches the study by evaluating the influence of key variables, such as the lifespan of the pavement and recycling rates, on various environmental impact indicators. The TRACI impact assessment methodology is used.

Preliminary findings indicate that rubberized pavement can significantly alleviate environmental burdens in terms of waste management, resource efficiency, and greenhouse gas emissions. The analysis of its use phase suggests that rubberized pavement surpasses traditional asphalt in terms of durability and maintenance needs. However, it also brings to light potential environmental trade-offs, notably the energy-intensive nature of its production process and the complexities involved in handling rubber materials. The anticipated results aim to not only offer a comprehensive understanding of rubberized pavement's environmental aspects but also to serve as a catalyst for advancing sustainable practices in road construction. Moreover, these findings are expected to inspire further innovation in recycling and sustainable material usage, potentially shaping future standards in road infrastructure development.

Urban waterlogging poses a severe threat to the sustainable development of cities and human survival. Green infrastructure can effectively reduce surface runoff, thereby alleviating urban waterlogging. The impact of various landscape elements on urban waterlogging has been widely documented, but the influence of spatial configuration of green infrastructure on urban waterlogging has not been thoroughly studied. In the context of rapid urbanization, the area of urban green space is further compressed, making it worthwhile to study how to use limited green infrastructure to mitigate urban waterlogging to the greatest extent. This paper focuses on the Jialu River Basin in Zhengzhou City, examining the current impact of green infrastructure on urban surface runoff and the effects of different spatial configurations on surface runoff. The results show that the impact of green infrastructure on urban surface runoff is mainly influenced by factors such as the proportion of green infrastructure area, fragmentation, aggregation index, and patch density. Therefore, under limited urban green space planning, optimizing the configuration of green infrastructure to maximize its ecological benefits becomes even more important. Given the growing concern over urban waterlogging and continuous rapid urbanization, these findings provide a more reliable reference for urban planners and local authorities in urban green space planning.

Josephine Adu-Gyamfi1,2*, Reed Miller1,2*

*Corresponding authors: josephine.adugyamfi@maine.edu, reed.miller@maine.edu

1Department of Civil and Environmental Engineering, University of Maine, Orono – Maine, USA

2Advanced Structures and Composites Center (ASCC), University of Maine, Orono – Maine, USA

Abstract

Large-format additive manufacturing (LFAM) has emerged as a transformative technology in modern manufacturing processes. As industries increasingly adopt LFAM for the production of customized large-scale components, there is a growing need to assess and compare the environmental impacts associated with different materials used in this innovative manufacturing approach.

This study aims to evaluate the life cycle environmental impacts of two compound materials used for LFAM: 1) carbon fiber with ABS plastic and 2) wood flour with bioplastic. These materials are commonly used in the production of 3D-printed objects, and assessing their environmental impacts throughout their life cycle is of importance for driving the future of sustainable manufacturing practices. At University of Maine Advanced Structures and Composites Center, these materials have been used to 3D print large scale items such as the structure of a home, a speed boat, culverts, and precast concrete formwork.

Data is collected and analyzed for each life cycle stage including raw material extraction, manufacturing processes, transportation, product use, and end-of-life scenarios. This study will take a detailed look at the production of these materials, and include considerations about durability, degradation, and recyclability. Carbon fiber is generally known to be energy-intensive to produce, but there are few publicly available LCA studies. Wood flour, which can be thought of as a fine sawdust, is considered by some to be a waste product and therefore carry little to no embodied burden, but that assumption needs to be carefully explored. Best available data will be derived from industry partners, as well as governmental sources, ecoinvent, and GaBi. Sensitivity analyses will be performed on uncertain parameters. Impact categories explored include climate change, human health, and ecosystem toxicity.

The outcomes of this research contribute to the ongoing discourse on environmentally conscious and sustainable manufacturing practices and provide a foundation for further optimization of large-format additive manufacturing processes.

Keywords: large-format additive manufacturing; life cycle assessment; environmental impacts; carbon fiber + ABS plastic; woof flour + bioplastic

The rapid increase in waste generation due to globalization and rapid industrialization has caused significant environmental issues. Maghmoumi et al. (2020) reported that in Tehran, the capital city of Iran, residents generate more than 3 million tons of MSW annually, and a vast fraction of collected waste is disposed of in open dump sites. As a result, sustainable waste management systems are needed to cope; life cycle assessment (LCA) is a powerful tool which can assess the possible environmental impacts and benefits of such systems.

The objective of this study is to assess the economic and environmental effects of greenhouse gases emitted by current Isfahan’s Municipal Solid Waste Management System (MSWMS) in comparison to a proposed alternative scenario. An LCA-based model, Environmental Assessment of Environmental Technologies (EASTECH), was applied to assess the two scenarios and impacts were assessed with the ReCiPe 2016 methodology. The baseline scenario consists of waste generation, collection, transport, Material Recovery Facility (MRF), recycling, Mechanical Biological Treatment (MBT), composting, and landfilling. In the proposed alternative scenario, Anaerobic Digestion (AD) replaces composting.

Life Cycle Cost Analysis (LCCA) was used to evaluate the scenarios in terms of budget cost and externality cost. Budget costs are considered direct expenses for waste management systems and were obtained from the municipality. EASTECH was implemented to calculate the externality cost, which applies a separate social cost for each GHG emitted.

Findings illustrated that the first scenario emitted 1396 kg CO2e/t alongside a budget cost of $24/t, resulting in an external cost of $50/t. By contrast, the second scenario released 1354 kg CO2e/t, at a budget cost of $25/t, with an externality cost amounting to $33/t. It is notable that despite the marginal difference between the two scenarios' GHGs, the alternative scenario performed a 34% improvement in terms of externality cost. This is more likely since more than 90% of the city fraction is diverted to MBT for pre-treatment for organic treatment such as AD systems. On average 75% of Isfahan waste fraction is organic waste which can be used as a food stock in AD. The dominant GHG emission related to AD is CH4 which can be captured and converted to electricity or heat; whereas open windrow composting mainly releases CO2 and N2O to the atmosphere. Furthermore, the AD process performed 114% improvements in terms of GHG reduction compared to composting.

Maghmoumi, A., Marashi, F., & Houshfar, E. (2020). Environmental and economic assessment of sustainable municipal solid waste management strategies in Iran. Sustainable Cities and Society, 59. https://doi.org/10.1016/j.scs.2020.102161

The total global CO2 emissions from the cement sector today are in excess of 2.5Gt. For large CO2 producers such as cement, widespread deployment of capture facilities across the U.S. will be needed to achieve decarbonization targets. Identification of suitable storage sites and/or utilization opportunities is a key first step in understanding deployment strategies for each industry. Since most facilities are not co-located with suitable deep geologic storage, an extensive buildout of a CO2 pipeline transport system will be required to support carbon capture and storage (CCS) at scale. To inform strategies for the cement industry, cost estimates for capture technology, pipeline deployment, and storage or utilization are needed under different adoption and policy scenarios. This study uses spatial-economic analysis to determine the length, size, and cost of the pipeline network and capture system needed to abate different fractions of the cement industry. Scenario analysis is performed to identify potential opportunities to reduce cost barriers such as the formation of Carbon Hubs with other industrial sources that are close to cement sources. Preliminary findings show that the cost of capture is the highest system cost for the cement industry and can vary from ~$68/t CO2 to $193/t CO2.

Transportation is the largest source of greenhouse gas emissions in the United States. For light duty transportation, battery electric vehicles are widely seen as the most viable alternative to those with internal combustion engines powered by fossil fuels. However, there are still questions about the best solutions for the larger vehicles which haul freight across longer distances. Direct electrification is the most energy efficient where possible, but the energy requirements for heavy trucks pose challenges for battery technology. Hydrogen fuel cell trucks are another option, but they have their own set of challenges related to production, distribution, and storage of the hydrogen fuel. Electric rail systems are the only mature technology for moving freight without emissions, but they require a high capital expenditure to build the necessary infrastructure. And biofuels have challenges related to land use.

For this reason, the upcoming study will conduct a life cycle assessment of the freight movement by trucks and trains powered by electricity, hydrogen, biofuels, and compare them with their diesel-powered counterparts. It will consider the life cycle greenhouse gas emissions and water consumption caused the by the manufacture and operation of the vehicles. It will use the ton-mile of freight as a functional unit. It will consider the payload penalty associated with the weight of the batteries or the hydrogen tank, and it will also consider the life cycle of the electric or hydrogen infrastructure, along with the supply chain for the electricity or hydrogen fuel, including the water required for electrolysis. It will also consider the cost operating and maintaining the oil supply chain which is necessary to operate diesel vehicles.

The study will consider three reference corridors in Southern California to simulate the use of freight movement on urban, medium-haul, and regional trucking services to move freight from the Port of Los Angeles, in order to assess the optimal technology for each use case and duty cycle. The reference corridors for road transport will be from the Port of Los Angeles to the Southern California cities of Commerce (23 miles), Ontario (55 miles), and Barstow (132 miles), which are all freight distribution hubs. It will also consider two distinct reference corridors to assess the movement of goods by rail. Rail freight service is used for longer-distance transportation, and so for that reason, the corridor to Barstow, which is the longest reference corridor for road, is the shortest reference corridor for rail transport. The other reference corridor for rail would be the Southern Transcon, which serves as the main railroad line connecting Los Angeles to Chicago and has a length of 2200 miles. The varied topography of these routes will enable the study to find the most optimal low-carbon transportation method for each use case and duty cycle.

By June, this author expects to have preliminary results on the greenhouse gas emissions and water consumption of the electric, hydrogen, and diesel-powered trucks and trains, along with the capital and operating expenses associated with the operation of each.

In 2022, Massachusetts became the first state to issue a Textile Disposal Ban. The new prohibition on disposal includes textiles (e.g., bedding, clothing, curtains, fabric, footwear, and towels) and mattresses. This ban was prompted by decades of our unwanted clothes ending up in landfills at home and overseas, where it destroys their environment and economy. Fast fashion has exacerbated the problem, which is characterized by its cheap labor and materials, unprecedented rates of production, ability to keep up with rapidly evolving trends, and low-price tags. However, most Massachusetts consumers remain largely unaware of the disposal ban, despite its potential to significantly reduce harmful environmental and social practices. Our current study seeks to investigate the effectiveness of different types of messaging that could promote the textile disposal ban. We first sought to assess our current greenhouse gas (GHG) emissions by utilizing data that we collected at UMass Lowell’s Thrift Day, where students were able to thrift free donated clothing. We conducted a life cycle assessment (LCA) that revealed a total avoidance of 507 kg CO2-equivalent, which demonstrated the possibility of larger collections yielding even larger reductions. Thus, we developed a survey that was informed by three different narratives of citizenship, fact-based, engaged, or duty-based citizen, to appeal to different types of consumers. Currently, we are deploying the survey to students, faculty, and staff of UMass Lowell. The results of the survey will allow us to present our findings to the University and suggest changes to current practices and how to increase the positive impact on climate. This will serve as a framework for our future research on the broader Lowell community to assist in developing messaging and policy recommendations that will lower the carbon footprint of textiles in our community. In the near future, we hope that these findings will eventually lay the groundwork for more universities, municipalities, and states to adopt environmentally friendly textile recycling practices.

In response to the escalating global climate crisis, educational institutions are increasingly recognizing the pivotal role they play in fostering sustainable practices and ensuring the surrounding community is considered and prioritized. This study presents findings from a campus-wide survey designed to comprehensively assess the state of sustainability, climate and environmental justice awareness, and interest in community engagement within our academic community.

Our research methodology incorporates a robust survey instrument distributed to diverse campus stakeholders, including students, faculty, and staff. The survey will be created using Qualtrics and administered to the entire campus community via e-mail. The survey will be used to guide the development of the University of California (UC), Merced’s decarbonization plan, and explore how to provide an equitable and just transition away from fossil fuels. This survey study aims to provide results that will be comparable to other UCs/universities and provide a survey template for other campuses to identify climate awareness, interest in potential programs, and interest in community engagement.

The survey delves into key dimensions of sustainability, ranging from awareness and knowledge of climate issues to current sustainable practices across various campus sectors. Additionally, our investigation explores the interest and extent of community engagement initiatives and their impact on both the campus and the broader local community. Expected outcomes include potential discrepancies between different members of the campus community on climate literacy and environmental justice principles. The demographics of UC Merced and California’s Central Valley will also be interesting to note with concerns on the environment, support for expanding campus involvement with the community, and past and future activist participation. Climate awareness will also be studied to compare the willingness to participate in future initiatives.

Key themes addressed in our study include the identification of sustainability educational gaps, the effectiveness of existing awareness programs, and the potential for enhancing community partnerships. There are several different sections in the survey, including Familiarity with Climate Change and Decarbonization Issues, Concerns about Climate Change, Preferences for Transitioning to a Carbon Free Economy in the Central Valley, Civic Engagement and Climate Change, and more. The data collected will not only provide a snapshot of the current state of sustainability on our campus but also serve as a foundation for developing targeted strategies to advance environmental justice and engagement with local communities.

Motivation: Synthetic biology, which harnesses and reapplies the broad set of biological tools that nature provides, uses several strategies for reengineering bacteria and other host organisms to produce compounds of interest. There are several known organisms capable of producing chemicals with industrial relevance, an attractive alternative to fossil-based chemical manufacturing. However, which synthetic biological systems are best suited to displace petrochemical-based precursors and commodity chemicals remains unclear. Different engineered organisms have different upper limits, both theoretical and practically achievable, on product yield, which will determine how well a system can meet product demand. Furthermore, many organisms have multiple sets of feedstocks with which they can generate specific products. The supply chains of these feedstocks strongly influence the environmental effects of the overall product life cycle. Thus, both the selection of host system and the feedstocks will determine how well bio-based chemical alternatives are able to compete with their fossil-based counterparts, in terms of both scalability and sustainability.

Method: We propose a life cycle assessment framework for assessing which combinations of feedstocks and organisms that maximizes the sustainable impact of industrial chemicals produced using synthetic biology strategies. The first step of this framework is similar to a materials flow analysis (MFA), where we illustrate the flows of feedstock and final product under these different combinations. In this work, we highlight bacterially produced 1,3-propanediol and 2,3-butanediol as case examples of bio-based commodity chemicals, due to their industrial relevance and wide representation in synthetic biology literature. Our goal with the MFA is to determine to what extent each biological system could satisfy the demand for its respective -diol product. The results of this analysis will allow us to identify the highest impact areas within the bio-based supply chain, which will subsequently be the subject of life cycle assessment.

Anticipated Results and Impact: We show initial results from our materials flow analysis in this work, including how much of the existing demand for our two products of interest can be fulfilled by the top-producing bacterial strains and how much feedstock is required to meet these targets. Additionally, we determine how closely each strain is operating to its theoretical limit, and, therefore, which strains’ production could be enhanced through further engineering. These results will be used in our proposed life cycle assessment framework. Specifically, we will expand the scope and resolution of the supply chain to include the different biomass sources from which the relevant feedstocks are derived. This work brings a higher level of detail to the common bio- vs. fossil-based distinction in manufacturing strategies by elucidating the variability within the bio-based category. We intend for the results of this work to benefit the synthetic biology research community by offering guidance on which organisms to target for further iteration to maximize the contributions to sustainability endeavors from so-called cell factories. We also discuss how we will use these case examples in our proposed framework to assess the potential environmental impacts of introducing circular and/or regenerative feedstock supply chains into biochemical manufacturing.

Life Cycle Assessment (LCA) is used to quantify inputs and outputs along product supply chains in terms of resources and emissions and to identify potential environmental impacts caused by a product/system throughout its life cycle. According to ISO 14040/14044 guidelines, there are four phases in LCA modelling. The Life Cycle Inventory (LCI) phase involves the compilation of representative data to characterize all of the inputs and outputs of the studied system within the defined system boundary. To achieve a robust and reliable LCI, primary (i.e., operational data collected directly from a facility of interest) is the most reliable and desirable to use but accessing such data is often hindered by confidentiality concerns of potential participants, in particular with respect to unique proprietary processes. Developing a consistent LCI hence often requires substantial effort, expertise, time, and resources in order to compile accurate and dependable data. Challenges in this process include the identification of and access to specific individuals or organizations within an industry, selecting a representative sample, managing confidentiality concerns, dealing with incomplete information, and navigating the dynamic technological nature of industries. These obstacles often prompt the utilization of secondary data from literature, industry reports, or other sources, necessitating validation due to the potential for poor and inconsistent data that may lead to unrealistic findings and conclusions. One of the main problems with using secondary data is dealing with missing/incomplete data. However, this challenge can potentially be resolved via computer-based process simulation based on mass and energy balances. Process simulation is a bottom-up approach to generating LCI by replicating real-world industrial processes in computer-based models to allow virtual experimentation and diverse scenario analyses. In case of missing primary data from industries, process simulation offers an alternative and structured approach for generating LCI and facilitating LCA. Though process simulation-based LCI modelling is not a new research area, there is a gap with respect to common methodological choices for integrating process simulation with LCA. Hence, this study was conducted to systematically review relevant research articles in order to identify common practices in simulating LCI data using process simulation. The reasons for using process simulation, approaches for simulating LCI (mass balance, energy balance), software used, validation processes, and processes to calculate and report uncertainty in the reviewed articles were considered. Lack of industrial data is the prominent reason for using process simulation, and verification of process models and validation of simulated data are often lacking in those studies. While building process simulation models, engagement with industry should be ensured as much as possible to facilitate verification of process models and validation of simulated data with industrial data. The review findings provided the basis for outlining a methodological framework that clarifies the integration of process simulation-based LCI with traditional LCA, with a specific focus on industrial processes.

Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) are widely used tools to respectively quantify the environmental impacts and assess the technical and economic feasibility of a product system. Using them separately is common, but does not support holistic sustainability evaluation. Examples of integrating these approaches may be found for different sectors, but a gap remains with respect to integrated studies in the context of agri-food processing, especially pulse protein extraction. The market for plant-based protein is rapidly growing. It has been suggested that the development of improved pulse-processing technologies should be prioritized to further enhance the acceptability and sustainability of pulse-based foods. Methods to improve the quality of the extracted protein and make the processing systems more sustainable with respect to yields and energy efficiency continue to evolve, but it is unclear which among them result in the best sustainability outcomes, and more information regarding priority intervention points is required to support continued evolution in the sector. To support more holistic evaluation of pulse protein extraction technologies, this study proposed an integrated framework of LCA, TEA, process simulation, multi-objective optimization, and multi-criteria decision-making approaches. A systematic review of published articles helped to identify the key characteristics of the sector-specific integrated frameworks and to formulate a comparable framework appropriate for pulse processing pathways. A common practice is to consider different system boundaries and functional units for LCA (cradle-to-gate) and TEA/process simulation (gate-to-gate), but this is not supported by the current review. Different system boundaries may work for LCA and process-simulation-based TEA, as process-simulation-based TEA is mainly concerned with industry gate-to-gate systems. The same functional units (both mass and functionality-based) based on output material should be considered to make the results of LCA and TEA comparable. Although many TEA studies considered input material or processing capacity as the functional units, it is instead proposed that the focus should be on optimizing output material and/or enhancing the sustainability of the output to support product comparisons and for sustainability communication and marketing. Apart from following ISO 14040/14044 for LCA and standard TEA methodologies, the proposed framework also suggests integrating process simulation, genetic algorithm-based multi-objective optimization, GIS models for spatially explicit LCA of pulse processing scenarios, and analytical hierarchy process to facilitate multi-criteria decision making. A decision tree has been developed to illustrate how multi-objective optimization and multi-criteria decision-making approaches can be integrated, including consideration of uncertainty. Based on the environmental, technical, and economic performance results obtained from LCA and TEA, multi-objective optimization can be carried out to generate a solution set. Finally, the inclusion of an appropriate multi-criteria decision-making approach (both weighting and ranking methods) will enable elucidating the most sustainable and optimized pulse protein extraction pathways.

Food waste ranks high amongst the pressing concerns of the 21st century due to its multifaceted socio-economic and sustainability related impacts. From a sustainability perspective, the issue is particularly important since a huge amount of food waste is landfilled in developed countries every year. Food waste in landfills generates methane, significantly contributing to climate change among other environmental problems. The direct valorization of food waste to livestock feed can be a promising solution with the potential to solve multiple issues, from reducing global climate change burdens to improving the circularity in the food system. A valorized food waste feed may also significantly reduce the environmental footprint of livestock farming since feed production accounts for most of the environmental impacts of livestock production. Furthermore, the direct valorization of food waste to feed avoids certain challenges associated with valorization via intermediaries such as black soldier flies (BSF) or mealworms, including but not limited to the loss of efficiency resulting from an added trophic level. Considering the multifaceted potential sustainability benefits of this pathway, it has seen increased research interest in recent years. However, the pool of literature for direct food waste to feed valorization is still small considering the emerging nature of the field, and there exists a lack of common understanding regarding aspects such as suitable substrates, geographic distribution, and technological readiness of such pathways. Various countries have differing socio-cultural and legal contexts that govern such valorization pathways, and it is hence important to know in which places such pathways are flourishing and why. The current review aims to contribute towards filling these knowledge gaps through a systematic review of relevant literature conducted using the PRISMA method. From our study, it was found that most of the food waste to feed valorization pathways described in the literature focus on mixed food wastes. Most of the studied pathways also exhibited low technological readiness, particularly in the context of western countries, and were either pilot scale or assumption-based theoretical models of such systems. Asian countries were found to lead in commercial scale implementation of such pathways due to a conducive socio-legal environment. Based on the insights obtained, several suggestions were made to encourage the global acceptability of such pathways, including feed production for certain species, supply chain modifications, addition of treatment technologies, and policy and regulatory changes, among others.

Establishing and maintaining an effective Energy Management System (EnMS) in accordance with ISO 50001 involves a systematic approach that integrates key components and principles. The initial phase centers on crafting a robust energy policy that aligns with organizational objectives and identifies Significant Energy Uses (SEUs) for targeted improvement. Strategic planning becomes integral, with a clear roadmap designed to enhance performance and efficiency.

The "Plan-Do-Check-Act" cycle serves as the foundation for continuous improvement. Top management plays a pivotal role in this process, providing crucial commitment through authorizing the EnMS and overseeing its ongoing suitability and effectiveness. Effective communication, rooted in the energy policy, establishes a shared understanding and support throughout the organization.

Meticulous consideration of resources, competence requirements, and operational controls ensures the efficient management of energy. Procurement activities and the exploration of improved technologies contribute to the Energy Management System's long-term performance. Ensuring a high-quality energy supply further solidifies the system's effectiveness.

Monitoring, measurement, analysis, and evaluation form a comprehensive framework that goes beyond tracking energy performance. These processes provide valuable insights into the Energy Management System's effectiveness and guide informed decision-making. Legal and other requirements, internal audits, and annual management reviews reinforce compliance and identify areas for improvement.

Addressing nonconformities is a structured process involving correction and corrective action, with designated personnel ensuring a systematic approach. Training and ongoing communication are emphasized, fostering effective corrective actions and overall Energy Management System's performance.

In summary, the successful implementation of an Energy Management System not only enhances energy efficiency but also aligns with broader sustainability goals, regulatory compliance, and environmental responsibility. This approach fosters continual improvement, driving progress toward energy targets and contributing to the organization's overall success and responsibility in the global landscape.

The transition from fossil to renewable energy sources is a key lever to mitigate greenhouse gas (GHG) emissions and restrict global temperature to below 1.5C above pre-industrial levels. Yet growth trajectory and predicted ultimate scale could lead to PV becoming one of the largest global industries with enormous annual GHG emissions from manufacturing, even while lowering carbon emissions compared to the incumbent energy sources they displace. Prior studies have calculated a payback time for those upfront GHG emissions. While simple to calculate and communicate, the usefulness of the payback time and similar metrics are limited because they treat all GHG emissions as equal no matter when emitted, and yet we know there is greater impact on climate from emissions earlier within a given timeframe. Herein we will demonstrate how low carbon manufacturing, even for an already low carbon energy source like PV, can yield significant additional climate benefits, as measured by reduced future temperature change.

Temperature increase resulting from GHG emissions can be quantified by the absolute global warming potential (AGTP) metric. The AGTP metric reveals that the temperature-increase caused by a GHG emission pulse is directly proportional to the lifetime of the GHG in the atmosphere. Consequently, emission of a GHG occurring earlier in a given timeframe will remain longer within that timeframe and, thereby, induce a greater temperature increase than an equal mass of GHG emitted later in that timeframe. If a societal goal is to reduce future temperature increase, earlier GHG emission reductions (e.g., in the manufacturing stage of a technology’s life cycle) are of higher value than later.

We use PV illustratively to exemplify how the AGTP metric can be applied to quantify temperature mitigation potential of low carbon manufacturing of RE systems considering the net impact of when GHGs are emitted and avoided over their life cycle. The methods presented herein can be applied to any product with lower-carbon manufacturing alternatives. Whereas we demonstrate that all PV provides net AGTP benefits over their 30-year lifetime, shifting from high to low-carbon PV manufacturing increases the net AGTP benefit by up to 175% points under US-average grid and solar insolation conditions.

Life cycle assessment (LCA) has been widely used as a crucial tool to determine the environmental benefits of biorefineries. However, the environmental impact of facility construction has not been well understood and quantified in the field of biorefinery LCA. The Environmental Protection Agency’s (EPA) guideline for biorefinery LCA for the fuel production and distribution stage under the Renewable Fuel Standard (RFS) also neglects the effect of facility construction in their assessment, while no quantitative studies have been done to verify or support such assumption. The goal of this study is to address this gap by quantifying the Greenhouse gas (GHG) emissions associated with the construction of equipment used in biorefineries. This study investigates two bioprocess pathways based on technical reports from the National Renewable Energy Laboratory (NREL) and examines the potential emissions from biorefinery infrastructure. The equipment sizing for pressure vessels and storage tanks is performed following the ASME Boiler and Pressure Vessel Code and the API 650, respectively. GHG emissions associated with the production of pressure vessels and storage tanks are estimated based on equipment weight and emission factors of construction materials from the Greenhouse gases, Regulated Emissions, and Energy use in Technologies (GREET) model. Emissions related to pump and conveyor belt production are determined on OpenLCA with the Ecoinvent database version 3.8.1. Four different methods are used to determine the weight and material consumption of pressure vessel manufacturing, thus further characterizing the GHG emissions from biorefinery equipment construction. We also conduct a scenario analysis on emissions calculated with three different head types for pressure vessels used in a biorefinery plant. Preliminary results indicate equipment construction contributes a minimal fraction to the baseline biorefinery GHG emissions. This fraction is higher as more environmentally friendly approaches are utilized to produce biofuel, but it is still relatively low. Furthermore, the GHG emissions from equipment construction vary depending on the method used to calculate pressure vessel weight. The variation primarily stems from the undefined design temperature, design pressure, and dimensions of the pressure vessels and the heuristics each method employs to find these unspecified parameters. Also, there is a significant deviation in GHG emissions from equipment construction between using the Flanged and Dished head and the other two head types (Hemispherical and 2:1 Semi-Elliptical) for all the pressure vessels in the plant. This is because the Flanged and Dished wall thickness is higher than the thickness of the other two head types which leads to higher head weight. The results of our study suggest the EPA’s assumption to exclude facility construction from its biorefinery LCA requirement under the RFS is justifiable. However, this study only looks into two biorefinery processes and mainly focuses on the impact of equipment construction. Future studies should address these limitations by expanding the scope of the LCA and incorporating a landscape of biorefinery design scenarios to have a holistic understanding of the environmental impact of biorefinery facility construction.

Electric vehicles (EVs) are considered an effective solution for reducing greenhouse gas emissions from transportation. However, the carbon footprint of EVs depends on the source of electricity, and it varies across different regions and time periods. Therefore, our research aims to develop a comprehensive analysis of the carbon footprint of EVs in California across different time periods, locations, and adoption scenarios.

To ensure a comprehensive understanding of the factors affecting energy usage and emissions, we consider various scenarios that reflect the potential range of consumer behavior, including driving patterns, charging habits, and location-based activities. Considerations are also given to California's distinctive geography and energy infrastructure, which significantly impacts the environmental consequences of EV adoption. We also quantify the proportion of energy demand for EV charging from renewable and non-renewable energy sources. This allows for a better understanding of how the environmental impact of EV charging can vary based on time, location, and future EV adoption rates.

We utilize data from CAISO and the Energy Information Administration (EIA) to assess hourly electricity generation and GHG emissions for California's electricity grid from 2018 to 2023. Additionally, the U.S. Department of Energy Alternative Fuels Data Center provides hourly estimates of charging demand profiles for counties in California based on various inputs for user behavior and EV adoption.

Based on the data collected between 2018 and 2023, it was observed that the total amount of CO2 emissions decreases during summer months. This indicates that there is a higher proportion of renewable energy sources, such as solar power, actively contributing to the reduction of overall CO2 emissions. This shift towards cleaner energy generation highlights current environmental progress and presents an opportunity for further enhancing sustainability through the expansion of renewable energy infrastructure and widespread EV adoption.

Amongst the various scenarios observed, it was discovered that the average number of miles traveled and temperature patterns in each county played a significant role in charging demand. This, in turn, led to higher emissions. The study showed that there was a three-fold difference in energy demand and emissions across the different scenarios. These variations highlight the importance of considering local factors when developing sustainable transportation solutions. Furthermore, it highlights the necessity for policies and interventions that target the distinct challenges faced by various regions.

The findings of this study can provide us with a better understanding of how the widespread use of electric vehicles (EVs) can impact the environment. Moreover, it can provide valuable insights into how these impacts change over time and vary across different regions. By examining various scenarios and acknowledging regional disparities in EV adoption, this study can offer practical guidance for policymakers, researchers, and consumers interested in sustainable transportation. As a result, we can develop proactive policies to address emerging environmental opportunities and concerns due to changes in EV adoption and usage patterns.

Many plans for reducing greenhouse gas emissions envision future electricity systems that are heavily reliant on energy supplied by wind and solar resources. Recent studies examine how the capacities of energy infrastructure required to reliably meet electricity demand in wind- and solar-heavy energy systems will be determined by fluctuations in these renewable resources. Better understanding of the characteristics of these fluctuations and their effects on potential future energy systems can inform the development and deployment of technologies that reduce the costs of reliably providing electricity. In this work, we examine fluctuations in wind and solar energy resources from 1980–2020 with high temporal and geographical resolution across the coterminous United States. We then evaluate potential approaches for supplementing wind and solar energy to address this variability. We find that while wind and solar resource availabilities vary according to season and geography, the most stressful meteorological patterns for energy system infrastructures have some common characteristics. We characterize how the costs and availabilities of other technologies affect the cost-optimal capacities and operation of physical energy infrastructure in systems with high penetrations of wind and solar energy. Finally, we conclude that while all modeled methods require substantial investment, strategic combinations of geographical resource aggregation, energy storage, supplemental non-wind/solar generation, and demand management can help reduce the overbuilding of physical infrastructure and lower energy costs.

The effects of climate change are impacting all sectors of the economy worldwide. Changes in weather patterns and extreme weather events have resulted in frequent and prolonged power outages, underscoring the vulnerability of centralized power systems. This emphasizes a critical necessity to boost community resilience. Adapting school buildings to climate change can play a pivotal role, given their importance in providing essential social services, especially during and after extreme weather events, such as hurricanes, heat waves, or ice storms.

Here, we show the potential of school buildings as energy resilience hubs for communities and the trade-offs between costs, technical considerations, and environmental performance. Importantly, it highlights the need for customized design strategies across diverse climate zones, as a one-size-fits-all approach proves inadequate.

Although energy resilience in the built environment has received increased attention from researchers, it must be effectively incorporated into building codes and design practices. The energy-resilience-environmental nexus needs to be more studied, with techno-economic and environmental analyses often limited to operational costs and carbon emissions, neglecting embodied carbon and other environmental impacts throughout the system's life cycle.

Examining how passive design strategies impact the energy and environmental performance of school buildings, especially during power outages, sheds light on the resiliency and sustainability of this type of infrastructure. Our approach utilizes reference models of schools for various U.S. climate zones to develop the energy models and estimate the energy demand. Then, a building-integrated photovoltaics (BIPV) plus storage system is incorporated into the design to enhance the system's energy resilience. Finally, the environmental profile of the system is evaluated through a life cycle assessment. As a result, we assess the effectiveness of these strategies in terms of costs, technical performance, and environmental footprint, encompassing not only carbon emissions but also other environmental factors. Indeed, the case study of an elementary school building in Puerto Rico shows that using BIPV leads to a one-third reduction in the carbon footprint and similar improvements in other environmental impact categories like ozone layer depletion and ozone formation. However, some impact categories, including eutrophication, ecotoxicity, and mineral scarcity, show higher values attributed to PV module and battery cell production. The embodied impact burden varies significantly, accounting for 16% of global warming, 33% of freshwater eutrophication, and 88% of mineral resource scarcity. This underscores the importance of adopting a comprehensive life cycle approach, particularly the growing significance of embodied impacts in energy-efficient and resilient buildings.

The implications of this research extend to informing future building codes, design practices, and policy frameworks. By understanding the relationship between passive design strategies, energy resilience, and environmental impact, stakeholders can make informed decisions to enhance the overall resilience of the built environment.

The University of Massachusetts Lowell, in collaboration with National Grid and the City of Lowell, has embarked on a pioneering geothermal energy pilot project, emphasizing the use of ground source heat pump (GSHP) systems. This initiative, a part of the state’s goal to achieve net-zero emissions by 2050, focuses on using the Earth's thermal properties to provide efficient heating and cooling. The start of this project provides an excellent opportunity to evaluate the environmental impact of the university's geothermal plant and its contribution to decarbonization efforts at a critical point in global climate change mitigation. Previous research indicates that GSHP systems offer significant environmental advantages over traditional heating and cooling methods, reducing carbon emissions, energy use, and fossil fuel dependence. Their ability to maintain stable ground temperatures leads to consistent energy efficiency and a lower environmental footprint, making them crucial for sustainable development and climate change mitigation.

Our primary objective in this study is to conduct a comprehensive Life Cycle Assessment (LCA) of UMass Lowell's GSHP systems. We are utilizing open-source Brightway2 and Ecoinvent 3.5 datasets for this cradle-to-grave assessment, which involves comparing the GSHP system with traditional air conditioning (AC) systems in terms of carbon intensity and other environmental impacts. The analysis covers various life stages of the GSHP system, from raw material extraction through construction, operation, and eventual decommissioning, with a particular focus on configurations in campus commercial buildings.

Currently, we are in the phase of collecting life cycle inventory data, where we plan to integrate machine learning (ML) tools. This integration aims to improve the accuracy of our environmental impact evaluation by uncovering patterns and insights within the data for a more comprehensive and precise LCA. Moreover, we are employing ML tools for sensitivity analysis to gain deeper insights into how different parameters impact the LCA results, enabling us to identify key factors for effective environmental impact reduction strategies more accurately. Our methodological approach includes a thorough life cycle analysis, encompassing both Midpoint and Endpoint categories in impact assessment. The study combines primary data from the UMass Lowell project with secondary data to provide a holistic evaluation of the GSHP system’s environmental footprint.

The expected outcome of this research is to establish a new standard for future GSHP projects through a detailed LCA. It will quantify the GSHP system's carbon footprint and GHG emissions and suggest ways to reduce them, such as low-carbon drilling and energy-efficient operations. By comparing the environmental impact of the GSHP system with conventional AC systems, we aim to underscore the potential of geothermal energy in reducing carbon emissions and promoting sustainable heating practices. The findings are expected to significantly contribute to UMass Lowell's sustainability initiatives, support National Grid's net-zero emission goals, and aid the global effort to mitigate climate change.

This study explores how cities can reach energy sector net-zero greenhouse gas (GHG) goals from a life cycle perspective. Although life cycle assessment (LCA) is recognized in academia and industry as a holistic means of accounting for emissions along the entire supply chain of a product or process, it has not yet been widely applied to US climate policy. City GHG inventories largely adhere to the Scoping system suggested by the GHG Protocol, which omits upstream and downstream emissions from energy resources. Because the GHG emissions associated with most renewable energy resources are entirely from upstream and/or downstream activities and not direct combustion of fuel, the GHG Protocol method underestimates energy-related emissions and is not conducive to optimizing the energy transition to maximize GHG reductions. Maximizing GHG reductions on a life cycle basis avoids burden shifting from one geographic location or economic sector to another to ensure genuine net benefits.

This analysis focuses on the pledges made in the 2021 Austin Climate Equity Plan to reach net-zero community-wide GHG emissions in Austin, Texas by 2040. The scope of the study covers the energy sector within the geographic bounds of the city, including residential, commercial, and industrial demand for electricity and thermal power, excluding liquid fuels for transportation. We also account for projected fluctuations in energy demand and energy storage requirements due to electrification initiatives, efficiency upgrades, and population increase. Geospatially relevant data from literature, governmental, and NGO sources, along with original research results, are used to estimate life cycle GHG emissions for different energy resources and associated infrastructure using a parametric, bottom-up approach. This is compared against the bottom-up production-based and top-down consumption-based GHG inventories that have been provided by the city of Austin. Modelling accounts for uncertainty and dynamism of inputs with sensitivity analysis of key parameters. Recommendations for specific technologies over alternative options are provided with context for decision-makers to understand tradeoffs. The results demonstrate the value in LCA to aide in developing climate policy and provide a framework for other cities to consider adopting.

Solar energy is widely viewed as one of our best tools to combat climate change, reduce carbon emissions, and save money -- but solar projects (both thermal and photovoltaic) typically don't fit into the same mold as most energy efficiency projects. This paper will discuss the basics of how solar generation fits into the energy sector, and the successes and challenges of including solar projects in a major utility’s ratepayer funded energy efficiency program.

The paper will start by addressing the need to innovate and work collaboratively with major utilities and utility commissions in order to find pathways to include renewables in energy efficiency programs in ways that will work for all parties. Renewable energy such as solar PV is at a once in a lifetime moment with the passage of the Inflation Reduction Act, and utilities are looking to meet lofty goals for saving energy at a time when the low hanging fruit such as lighting retrofits are beginning to sunset.

Integrating solar PV projects into ratepayer funded programs can be a challenge, but this real world example of how it was done benefitted from close collaboration between the utility, implementer, and 3rd party evaluator. The program was developed to ensure that the program would be cost effective and allayed concerns about the “death spiral” for the utility. The paper will review lessons learned from several years of experience covering numerous projects and adjustments made during evaluation and implementation.

Electrification of buildings has become a topic of debate for utilities and their customers concerning whether it is cost effective to construct and operate them with electricity only compared with using natural gas fuels.

The objective of this study is to demonstrate the cost effectiveness and positive potential of developing all-electric new construction commercial buildings. With the simulated analysis provided, the goal is to demonstrate that not only are the initial construction costs for all electric buildings competitive with gas space heated and hot water heated buildings, in many cases they are lower than the gas equivalent.

To demonstrate the first cost benefits of designing and building all-electric commercial buildings, eight gas heated and water heated building types were compared with all-electric versions of the buildings. On average, the all-electric buildings yielded an initial cost savings of $1.91 per square foot. In addition to first cost savings, many of the all-electric buildings were able to show annual utility cost savings over their gas heated counterparts. The average buildings served by HVAC heat pumps saved an average about $7,500 when compared to the gas configurations.

Additionally, the study looked to determine whether there was a positive overall impact that electrification has on CO2 produced from operating all-electric buildings as well as utility cost savings. This paper will explore the “tipping point” at which all-electric buildings generate less CO2 than an identical building using natural gas as heating and hot water fuel.

The rise of electric vehicles and energy storage will be the main contributors to elevated amounts of Lithium-ion battery (LIB) waste. With more than twenty years of mass improvement in LIB technology capability, durability, and safety we live in the dawn of possible energy grid and transportation decarbonization. LIB waste is not currently handled accordingly to its toxic risk towards humans and the environment. LIB waste poses risk of explosion, poisonous gases, and leachate requiring designated waste sites instead of being included in the municipal solid waste (MSW) management. Proposing designated LIB waste disposal sites drawn from current information about storing toxic waste is one step towards adaptation and mitigation climate solutions as consumption of LIB’s increase. We characterize regional and local deformation at the sites of decommissioned and active nuclear waste disposals to set upper and lower boundaries on future LIB waste management locations. We process Global Navigation Satellite System (GNSS) station time series to interpolate regional strain fields around the nuclear waste sites to set upper bounds on strain rate limits that a nuclear site can withstand. The strain rate boundaries serve as a template for targeting locations for future LIB waste and recycling sites. We not only want to investigate the strain rate a site can withstand, but will analyze the amount of deformation a subsurface waste site causes using a discretized, numerical 3D heat and pressure halfspace model. We aim to create a 3D halfspace thermal and pressure model for tunnels with short borehole networks and deep boreholes. The thermal and pressure models will show the effect inside the tunnel and boreholes as well as in the local subsurface region. This will determine the sensitivity of temperature of battery waste, size of the waste unit, and depth at which the waste is placed. These nuclear waste examples will serve as a baseline for future LIB burial methods. More importantly, with spatial and temporal resolution we will track the change in thermal expansion and pressure over time and convert expansion to surface and subsurface strain rate. GAGE Multi-Monument sites will be implemented to validate the thermal expansion measurements from our 3D halfspace model. These braced monuments drilled at depth, produce short baseline rate estimates of monument stability which exhibit thermal expansion signals in their time series. We convert thermal expansion signals to strain rates to compare to our halfspace model. Overall, Using our strain rate maps and halfspace modeling, the resulting strain rate boundaries a future site must withstand and the estimated deformation it causes will be input into science policy applications that contribute towards adapting to the rise in LIBs.

A reliable water system that consistently distributes safe water is crucial for public health and economic stability. Thus, water treatment facilities are critical infrastructure that must be able to accommodate periodic and dynamic demand patterns as a result of parameters such as daily residential irrigation or unpredictable demand spikes from natural disaster response. To meet these requirements, it is necessary to have water treatment control strategies that are insensitive to disturbances, thereby guaranteeing that demand will be met. With the reality of climate change and need for greenhouse gas (GHG) reduction strategies across society as a whole, it is also desirable to minimize greenhouse gas emissions (GHG) produced by our treatment processes while meeting these goals. One viable and widely-applicable strategy for optimizing energy use on the supply side of water treatment is model predictive control (MPC). MPC is a class of optimal control that simulates the performance of a system model over a finite prediction horizon to determine appropriate control inputs for a physical plant. The selected control inputs are those that minimize an objective function, which can be designed to consider multiple system goals such as set-point stabilization or energy consumption.

In this work, we have developed an initial model for water storage infrastructure that exists around a facility’s water treatment train and equations to represent the interconnected relationships between these variables and the intrinsic system parameters. Current modeling techniques such as EPANET and Simulink packages in Matlab do not allow for dynamic state inputs and are challenging to work with when testing different control strategies. Therefore, we have started the development of a control-oriented model, informed by the system dynamics that are available in EPANET, to model the impact of controller strategies on the system performance. The initial results of this work indicate that MPC methods can reduce the GHG emissions from pumping at a water treatment plant by 10% in comparison to baseline heuristic system controls.

To mitigate climate change and preserve resources for future generations, the economy is investing in a profound industrial transition. These transition efforts can be grouped into two broad categories: efforts to turn away from fossil-based energy sources with greater energy efficiency and efforts to use materials more efficiently. The development of industrial symbioses in which the energy and material waste flows of one industry are managed such that they serve as inputs to complementary industries constitute a promising avenue to potentially combine both energy and material efficiencies measures. But when trade-offs arise between increasing material efficiency or energy efficiency, or between energy and resources recovery, how should these choices be optimized? A case can be made that a single indicator of resource use should guide the design of industrial symbioses. There does exist a single indicator from thermodynamics that can quantify the “quality” of both material and energy resources by linking these to the notion of work: exergy. This approach has not commonly been used for several historical and fundamental reasons. Yet it seems important to find ways of communicating what thermodynamics has to say about the increasing consumption of resources and their dispersions. Is an exergy indicator relevant and practical in guiding the optimisation of an industrial symbiosis? Our study focuses on a tire production company. Through a material and energy flow analysis, the use flows and waste flows are quantified and translated into exergy flows. We represent the loss of exergy throughout the production cycle and end-of-life transformation. A linear optimisation model then selects the technological mixes and operation parameters that minimizes the total exergy losses. We conduct a comparative analysis between optimizing the industrial symbiosis using exergy, relative to using other sustainability and efficiency indicators. In our work, we provide a unified exergy indicator for energetic and material resources as well as an optimisation tool based on constraints which represents the reality of goods-producing companies in their environmental objectives, leading them to a more sustainable ecological transition. Our other aim is to furnish information to guide the target audience on the requirements needed to use the indicator. This method can then be used in any industry that aims to improve efficiency in resource use.

In the pursuit of a zero waste future, this presentation showcases a transformative and community-centric approach to implementing a Reusable Foodware Program in California. Inspired by the insights shared during the National Zero Waste Conference, our project places a strong emphasis on inclusivity, breaking down barriers, and fostering partnerships with diverse stakeholders.

Addressing the challenges faced by historically marginalized businesses in complying with recent legislation such as AB 1276, our initiative creatively identifies and engages businesses with food service through targeted data collection. We delve into the significance of technical assistance, utilizing door-to-door education, management plan development, and tailored assessments to support businesses in transitioning to reusable options.

Drawing from the experiences of our collaboration with the municipality, APTIM, and local sustainability organizations, we highlight the importance of consistent outreach, surveys, and multiple connections to capture community buy-in. By centering on Clean Up Green Up communities, we contribute to an equitable distribution of resources and funding, addressing the challenges faced by these neighborhoods.

Furthermore, our project aligns with the global perspectives presented during the National Zero Waste Conference, emphasizing the role of grassroots and community-centered initiatives in achieving zero waste equity. We explore how community groups not only contribute to waste diversion but also address broader community challenges, fostering long-term relationships and understanding the interconnected issues of food apartheid, housing, and environmental justice.

As we navigate the complexities of waste reduction strategies and legislation, our presentation seeks to inspire a creative paradigm shift in sustainability. By learning from the expertise of informal waste workers and grassroots organizations, we aim to contribute to the dialogue on international laws, policies, and collaborative efforts that promote inclusivity, and pave the way for a more sustainable and dignified future.

Rapidly increasing renewable energy technologies demonstrate a notable demand for copper. Finding an environmental and economic optimal of life cycle copper production becomes crucial to industry decision-makers. This research designed a material flow analysis (MFA) based optimization model to choose the optimal production methods and recycling rate. We analyzed the copper flow in the U.S. from 2020 to 2050. Results show copper recycling is the dominant solution among all production methods, but the high recycling cost ($7.7/kg copper) will be the major obstacle to increasing the recycling rate. If we maintain the current combination of hydro and pyro metallurgy production, the Greenhouse gas emissions will increase by 10% if the demand surges by 50%. Combining 20% of hydrometallurgy, 17% of pyrometallurgy, 33% of bioleaching and 30% of recycling will be the optimal copper production to reduce overall environmental impacts for the next 30 years. Reducing the primary production emissions and enhancing the recycling rate are recommended in the future copper industry.

Predicting and managing demand are crucial tasks for addressing climate change and other environmental impacts of energy use. The mainstream models of energy demand are reductionist, dividing demand into separate categories such as residential, commercial, and transportation, and analyzing each separately. However, large-scale behavioral and structural changes affect multiple demand sectors, but there is limited focus on high-level models that are able to accurately document how broad social and behavioral shifts can have cascading effects on different energy sectors. Thus we are developing a holistic model of energy demand that comprehends how consumer actions affect multiple sectors at the same time. This knowledge would be critical for energy policy that is often implemented sector-wise without examining the interactive effects on the other sectors of energy demand.

This paper focus on the sub-model that links office building space with employees’ use of them. Teleworking presumably reduces the need for office buildings, which in the US currently accounts for 17% of the commercial building floor space. Given the continued popularity of teleworking, one expects it will drive reduced demand for office building space in years to come. By quantifying the relationship between telework and office building space we can anticipate changes in future energy demand and inform urban planning.

We built a regression model to explain historical office building space based on the number of office workers and teleworkers. Historical data on office building space is available from the CoStar database. However, since there is no direct data source describing the number of office workers for the US, we construct a dataset by first estimating the proportion of office workers per industry sector based on O*NET data and the Occupational Employment and Wage Statistics data. We then apply this estimate of percent teleworking by industry sector to the employment per sector provided by The American Time Use Survey (ATUS) to determine the number of office workers. The number of (full-time) teleworkers is computed by identifying the respondents in ATUS who work from home and then deleting the hybrid teleworkers who work both from home and at an office. Owing to the fact that the average office lease spans about 3 to 5 years as well as other time-lagging effects, transitioning an office worker to a teleworker may not have an immediate reduction in office building space. To account for this delay, the regression model includes the time lag ranging from 0 to 6 years.

The regression results show that the average office building space required by office workers and teleworkers is 32 and 18 m2, respectively. This suggests that shifting an office worker to a teleworker result in an average 44% reduction in office building space (14 m2). This model will be integrated into the holistic model of energy demand, which helps us understand the broader effects of demand interventions.

In many countries around the world, vehicle electrification has become a cornerstone strategy for decarbonizing the light duty vehicle (LDV) fleet. This will place an increasing strain on associated supply chains [1,2]. Recent studies have raised concerns over the feasibility of scaling the supply of battery capacity to keep pace with global demand [3] and evaluated material-focused battery production strategies to meet the increasing demand for battery capacity (e.g. production of different types of batteries, recycling, among others) [4]. Nevertheless, supply chain bottlenecks are likely to remain, and so it will also be necessary to evaluate priorities for where electric vehicle deployment can have the greatest environmental benefits.

This study determines strategies for deployment of the world’s limited battery capacity in the LDV sector to afford the largest reduction in lifecycle greenhouse gas (GHG) emissions and other environmental impacts from 2020 until 2050. The study evaluates the prospective life cycle environmental impacts (GHG emissions as well as particulate matter, toxicity, among others) of the LDV fleet using a bottom-up approach, accounting for often-neglected characteristics such as vehicle classes, trip types, drive cycles and background modified inventories impacting the production characteristics of various relevant sectors such as electricity, fuel and steel. The study tailored to the U.S. context focuses on four electric powertrain technologies (hybrid, plug-in hybrid, fuel cell and battery electric vehicles), five vehicle classes (from compact car up to pickup truck) and two drive cycles (urban and rural). The study integrates the latest U.S. vehicle characteristics as obtained from the 2023 vehicle technology assessment by Argonne National Laboratory as well as other relevant attributes (drive cycle, electricity grid) in Carculator [5]. At a vehicle level, the modeling leverages the Premise background modified inventories to evaluate the impacts per vehicle type under different policy scenarios (business-as-usual, 66% chance of remaining within 2oC and 1.5oC above pre-industrial levels)[6]. The results at a vehicle level are then scaled to a fleet level under defined foreground scenarios modeling possible changes in the vehicle stock, market shares of powertrain technologies, batteries and vehicle classes in the U.S. through 2050.

Results show the mitigated lifecycle emissions per kWh of battery capacity resulting from the shift from one vehicle type to the other, by vehicle size, year of production and policy scenario. Based on the incremental effectiveness of mitigated emissions for every kWh of battery capacity added as well as considerations of cost, range, size and trip type, strategies for the allocation of the limited supply of battery capacity will be proposed, and their impacts at the U.S. fleet level will be assessed. The mitigation gap from a net zero target will then be estimated. Last, the environmental impacts of these strategies going beyond the lifecycle GHG emissions will be evaluated to determine any potential trade-offs from fleet electrification. The results may guide policymakers in navigating supply chain bottlenecks while remaining on course towards achieving net zero emissions in the LDV sector.

References

1. Olivetti, E. A., Ceder, G., Gaustad, G. G. & Fu, X. Lithium-Ion Battery Supply Chain Considerations: Analysis of Potential Bottlenecks in Critical Metals. Joule 1, 229–243 (2017).

2. Xu, C. et al. Future material demand for automotive lithium-based batteries. Commun. Mater. 1, 1–10 (2020).

3. Tarabay, B. et al. New cathodes now, recycling later: Dynamic scenarios to reduce battery material use and greenhouse gas emissions from U.S. light-duty electric vehicle fleet. Resour. Conserv. Recycl. 196, 107028 (2023).

4. Rossi, F. et al. Environmental optimization model for the European batteries industry based on prospective Life Cycle Assessment and Material Flow Analysis.

5. Sacchi, R. & Mutel, C. Carculator: prospective environmental and economic life cycle assessment of vehicles. (2019) doi:10.5281/ZENODO.3778259.

6. User guide — premise 1.7.2 documentation. https://premise.readthedocs.io/en/latest/index.html.

Circular economies are often framed as addressing a trio of problems: environmental degradation, economic stagnation, and social ills, broadly defined. Our study centers on this last claim – that circular economies promise social benefits. There is a dearth of literature focused on the social dimensions of circular economies, and even less attention to the meaning of social justice in the context of circular economies, let alone how it might be enacted in policy and practice. Through this research, we aim to identify the ways in which social justice is defined and discussed – or not – by the actors who seem to be most actively pushing for a circular economy (CE).

Drawing on data generated from focus groups with 15 CE experts and a content analysis of 23 US-based governmental, NGO, and business reports on circular economies, we explore whether and how justice emerges in the CE discourse. We examine the frequency and character of justice claims in the emerging US-based CE discourse, explore the narratives that these actors use to describe justice, and the barriers they see in achieving just and inclusive circular economies.

Our findings suggest that CE discussions in the United States have, to date, largely ignored explicit discussions of justice. Rather than an explicit focus on justice, reports include implicit assumptions of justice, which require close reading and discussion. Discourse that does include justice leans heavily toward neoliberal forms of justice, emphasizing the free pursuit of mutual self-interest, the protection of private property, and freedom of choice. We argue that this is insufficient to ensure a just CE for all. Indeed, there is ample evidence that without deep consideration of justice as a multifaceted concept (something that is achieved in process, in outcome, and by looking backward in time), movement toward more circular economies is likely to reproduce existing inequalities rather than to solve them.

We have proposed definitions of justice in the context of the CE in an effort to aid practitioners, policymakers, and scholars in demonstrating what we mean when we talk about justice in CE. Our analysis suggests how different dimensions of justice might be conceptualized within the context of circular economies. This work addresses the need to articulate clearly what it is we mean by social justice in relation to the CE, and fills a critical gap in the emergent literature on CE in the United States. For if the CE is to contribute to sustainable social transformations, justice must be more than a buzzword – the CE must be just by design.

Social capital has been consistently identified as a key component of community resilience. Social capital is the networks, norms, and trust that facilitate action and cooperation within communities for mutual benefit. The two main dimensions of social capital are bonding and bridging. Bonding refers to strong connections between similar individuals that use to belong to the same groups, and bridging refers to weak ties between heterogeneous people, but that are able to connect different groups. As online communities have developed, the concept of social capital has been split into online and offline social capital. Online social capital is based on bonding and bridging which happen in online communities, while offline social capital is more heavily based on in-person interactions.

Quantifying offline social capital at a population level is an area of ongoing work. Existing approaches to estimating offline social capital either utilize household surveys or aggregate indicators comprised of census data. Household surveys are time consuming and expensive, while population level indicators can aggregate over entire communities. By comparison, online social capital is very easy to measure, with numerous algorithms proposed which take in social media data and estimate bonding and bridging based on social networks and content. However, the question as to whether online and offline social capital are the same remains open.

In this ongoing research, we compare existing offline and online social capital indicators geographically across the US. We build a statistical model which predicts the difference between online and offline social capital using demographic, social, and economic covariates about each county in the US. We utilize interpretable machine learning techniques to assess model quality and how the covariates are associated with differences in online or offline social capital.

Our results indicate that measures of online and offline social capital differ from one another in a systematic way nationwide. Social vulnerability is one key driver of these differences for both bonding and bridging. In areas with high social vulnerability, online bonding is higher than offline bonding, but online bridging is lower than offline bridging. Additionally, homophily –the tendency for people to seek out those who are similar in socio-demographics – is associated with a difference between online and offline social capital. We find places with a higher level of homophily have lower measured online bonding social capital compared to offline.

Our preliminary conclusions from these results are that current measures of online and offline social capital are generally dissimilar from one another, raising questions about the efficacy of these indicators. By understanding the features driving these differences, we hope to improve future measures of quantifying social capital.

Interest in the circular economy in the context of the built environment continues to increase, however, systems-based approaches that support the integration of complex environmental, social, and economic aspects at the local scale are lacking. At the frontlines of waste management, communities are especially positioned to apply circular economy strategies, but are challenged with navigating prescriptive solutions that can be misaligned with their context-specific settings such as geography, economy and financial characteristics, existing infrastructure and related processes, and social and cultural qualities. To bridge the gap between top-down circular economy strategies and local operations, we are leveraging an integrated mixed methods approach called the Circularity Assessment Protocol (CAP), which synthesizes local, community-level data to inform actionable circular strategies guidance related to seven main spokes: input, community, material and product design, use, collection, end of cycle, and leakage, while also accounting for local policy, economic and governing foundations, and input from local stakeholders. Having previously been implemented in over 53 cities and 14 countries to investigate community-based plastic waste management, we aim to adapt the CAP framework to converge circularity across additional waste categories including the built environment. Here, we share our work-in-progress adaptation of the CAP for the construction and demolition (C&D) context and preliminary findings from a pilot application in Pittsburgh, Pennsylvania. The framework encompasses data collection including literature search, field surveys, and qualitative approaches across various aspects of a community’s C&D material management infrastructure and processes ranging from geography, population, construction rates, C&D waste facilities, pollution and relevant policy initiatives. Outcomes from the pilot study will include a holistic evaluation and outlook for Pittsburgh’s C&D waste management strategy including identification of local producers and manufacturers of commonly used construction materials, rate of new construction relative to population growth, prevalence of green building design, thematic trends from stakeholder input, C&D waste generation rates, composition, and treatment outlets, demolition and deconstruction practices, and locations and profiles of C&D materials lost to the local environment via litter, illegal dumping, and abandoned materials or properties. Taken together we identify interconnections throughout the C&D material lifecycle in Pittsburgh and identify opportunities for local intervention strategies. Building on our experience applying the C&D CAP to Pittsburgh, we aim to illuminate local intervention strategies and further iterate on the CAP framework for expansion to other cities in the region and beyond for a collective widespread transition to the circular economy.

This paper presents a political-industrial ecology analysis of an emerging petrochemical corridor in Appalachia. Within Appalachia, various ethane “cracker” plants are under construction, or are being permitted, to transform ethane by-products from hydraulicly fractured shale gas in the Marcellus and Utica shales into plastics. Political-industrial ecology is a nascent field of geography that embeds resource metabolisms within their broader political economic contexts. I advance the field by presenting a “metabolic tour” of the petrochemical supply chain that analyzes how petrochemicals forge and transform human-environmental relationships along the chain. The political-industrial ecology analysis links these developments in the former steel belt to the growing environmental burdens of plastics, highlighting how record state subsidies are facilitating these linkages. Further, the systems perspective afforded by a political-industrial ecology view reveals three notable findings. First, the footprint of the corridor extends well beyond the Ohio River Valley to Canada, the US Gulf Coast and international markets in Europe and Asia. Second, the corridor is a significant step towards establishing more globally integrated markets for ethane and natural gas. Third, the analysis illustrates the myriad of environmental systems and communities interlinked through the corridor, which can serve as a roadmap for facilitating cumulative impact analysis, a key gap in environmental impact and justice scholarship.

Rising global temperatures and climate change are posing a threat to our food systems and impacting the resilience of both soils and communities, including in California’s Central Valley. In light of this, state and federal policies are pushing for increased diversion of food and organic waste from landfills to reduce greenhouse gas (GHG) emissions, mitigate climate change and return valuable nutrients and carbon back to agricultural soils. These include both California Senate Bill 1383 and the US Food Loss and Waste Reduction Goal of 50% by 2030. As more organic waste is diverted from landfills and treated via anaerobic digestion (AD), a waste management strategy that produces both biogas (a source of renewable energy) and digestate (a nutrient-rich organic fertilizer), the production of food waste derived digestate (FWDD) will increase. The high volume of FWDD represents an opportunity to provide an affordable and sustainable alternative to fossil-derived fertilizer in urban agriculture, especially within historically underserved communities, which are disproportionately affected by a warming climate.

This research, led by the Latino Equity Advocacy and Policy Institute (LEAP) and the University of California, Davis, aims to: (1) demonstrate the applicability and social acceptability of FWDD as alternative liquid fertilizer in community learning gardens, (2) promote the resilience of healthy, carbon-rich soils as climate mitigation strategy in an urban agricultural context; and (3) build social resilience through nutritious, affordable, accessible and culturally relevant food produce in historically underserved communities. To achieve the aims mentioned above, this research sets out to grow major California crops in community learning gardens, using FWDD as alternative fertilizer to conventional synthetic fertilizers and fresh-market tomatoes as pilot crop. The sites are located within four communities in California’s San Joaquin Valley: Avenal, Huron, Kettleman City and Westside.

In the pilot stage, an optimal blend of FWDD and compost to be used as growing medium was developed, and phytotoxicity tests were conducted on tomato varietal to assess its viability. Following germination, tomato seedlings were transplanted into one of the four soil treatments: a) standard soil medium (e.g. vermiculite) and filtered water (negative control); b) standard soil medium, filtered water and conventional fertilizer (positive control); c) standard soil medium, filtered water and compost (compost-only treatment); and d) standard soil medium, compost and FWDD (compost-FWDD blend). Preliminary results based on plant growth metrics (plant height, aboveground biomass) and soil performance metrics show that FWDD nutrients are plant-available, and that there is no phytotoxicity at this application rate.

This research ultimately aims to deliver social, environmental and economic benefits to the communities listed above. By demonstrating the use of FWDD as organic fertilizer, it will enable community members to incorporate more sustainable, accessible and co-created growing practices, creating awareness of the benefits of closed-loop urban nutrient cycling and increasing food resilience in urban agriculture.

Critical mineral acquisition has been key to decarbonization technologies. Transition to decarbonization technologies like electric vehicles requires large quantities of critical minerals including nickel, lithium, cobalt, copper, and aluminum.1 Market price and supply of such critical minerals are closely connected to policy constraints and social resistance from the supplying countries and will result in significant environmental justice impacts. However, the interconnections between policies, society, environment, and the economic market remained unexplored. Consequential life cycle analysis (CLCA) takes account of direct and indirect consequences of changes in the product to evaluate its environmental impacts.2 In this work, we propose a toy model to carry out CLCA on critical mineral mining to predict how major supplying countries will respond to mineral price fluctuations based on their policy, social, and environmental constraints. The proposed model consists of three parts: market supply and demand forecasting, policy and social constraint multiplier, and environmental impact evaluation. Mineral price forecasting has been challenging due to its highly non-linear nature. This model employs Artificial Neural Networks (ANN) as a machine learning algorithm that takes in historical critical mineral prices from major supplying countries as inputs and then formulate a complex underlying relationship to predict the future market price fluctuation. Market supply and demand change of critical minerals is then calculated as a response to price fluctuation and price elasticity. In addition to pure market force, the critical mineral market is also highly sensitive to regional policy change and social resistance. Therefore, an elasticity multiplier that quantifies the extent of policy constraints and social resistance of a given region is added to market elasticity to model a more comprehensive response. Aside from the economic, political, and social consequences of mining, the proposed model also assesses the environmental impacts of mining based on different geological locations and mining types, such as underground mining and open pit mining. This model also includes environmental parameters such as GHG emissions and carbon loss in vegetation due to land clearing to compare the significance of environmental consequences between mining in different countries. As more intensive mining on critical minerals is taking place to achieve global decarbonization, this toy model offers a perspective on the potential subsequent changes from mining.

References

1. IRENA (2023), Geopolitics of the energy transition: Critical materials, International Renewable Energy Agency, Abu Dhabi.

2. Renewable Fuel Standard Program (RFS2) Regulatory Impact Assessment. (2010).

Transitioning to green energy technologies requires more sustainable and secure rare earth elements (REE) production. The current production of rare earth oxides (REOs) is completed by an energy and chemically intensive process (beneficiation, leaching, separation by solvent extraction, and refinement) from the mining of REE ores. Investigations into a more sustainable supply of REEs from secondary sources, such as toxic phosphogypsum (PG) waste, is vital to securing the REE supply chain. PG is a waste byproduct of fertilizer production produced at hundreds of millions of tonnes per year. This PG is stored indefinitely in ‘stacks’ which are vulnerable to release of toxic and radioactive waste into the environment. The extraction of REEs from this waste may make remediation feasible. However, it is unclear how to recover the dilute REEs from PG waste (conventional solvent extraction is inefficient and has high environmental impact). In this work, we propose a route for the recovery of REEs from PG using a bio-inspired adsorptive separation, and we assess its financial viability and life cycle environmental impacts via life cycle assessment (LCA), techno-economic analysis (TEA), and identify targeted improvement opportunities through global uncertainty/sensitivity analysis and scenario analysis. We determined that this system can be profitable (net present value of $200 million, internal rate of return of 17%, and minimum selling price (MSP) of $48/kg REO) and shows reduced environmental impact in several ReCiPe 2016 Midpoint impact categories (land occupation and transformation, eutrophication, particulate matter formation, terrestrial ecotoxicity, and ionizing radiation) when compared to conventional REO production and PG stacking. However, the REEPS system underperforms in other impact categories (global warming, freshwater and marine ecotoxicity, human toxicity, terrestrial acidification, and metal and fossil depletion). The life cycle environmental impacts and financial viability are primarily driven by chemical consumption in the leaching, concentration, and wastewater treatment process sections ($15/kg REO). In addition to high chemical costs, the large capital cost of the selective adsorption resin ($18/kg REO) limits profitability. Scenario analysis shows that the system is profitable at capacities larger than 100,000 kg/hr PG with a PG REE content above 0.5 wt%. The most dilute PG sources (0.02-0.1 wt% REE) are inaccessible using the current process scheme (limited by the cost of acid and subsequent neutralization) requiring further examination of new process schemes and improvements in technological performance. Overall, this study evaluates the sustainability of a first-of-its-kind REE recovery process from PG and uses these results to provide clear direction for advancing sustainable REE recovery from secondary sources.

The geothermal brines in the Salton Sea Known Geothermal Resource Area (SS-KGRA) in California are unique in that they are highly saline brines that contain high concentrations of lithium. These brines are being brought to the surface, used to produce geothermal energy, and then they are reinjected back into the subsurface to maintain pressure in the geothermal reservoir. Before reinjection, there is opportunity to extract the lithium, which is a promising development in securing a domestic supply chain of lithium. In our previous work (LBNL-2001557), the environmental impacts of the lithium extraction process and the associated geothermal expansion that would be needed to support this, were evaluated using publicly available data. Based on expected water cuts in the region, caused by declining water levels in the Colorado River, combined with the expanded geothermal/lithium industry has the potential to impact allocations to agricultural water in the region by around 50%. This is a worst-case scenario estimate and the allocation is largely driven by the water cuts, but there is very limited information on availability of actual water use needed for the direct lithium extraction (DLE) process.

The work herein builds upon the existing water analysis to develop models that better characterize uncertainties around water allocation scenarios and the unit processes involved in DLE. To do this, I built a model of DLE unit process trains starting from a recent review paper from Vera et al. 2023, which identified papers that describe DLE processes, and their associated water use. I then incorporated industry and patent data for DLE processes. With this information I develop a python-based model with interchangeable unit processes, where appropriate, and ranges of water use for each of these processes. This allowed us to evaluate uncertainties in water usage from DLE given different extraction process scenarios. I also expanded the water allocation scenarios to represent a range of water cuts and not just a low (10%) and high (40%) estimate as was assumed previously. I will present the DLE models that were developed and an assessment of potential water usage and its impact on water allocations in the region.

As global demand for clean energy grows, wind energy stands out as a sustainable option with substantial potential for expansion. In Canada, supportive policies drive the growth of wind energy projects. However, challenges such as supply chain constraints for critical materials like Rare Earth Elements (REEs) and the aging of the first generation of wind turbines necessitate improved waste management. This study aims to develop the first open-source dynamic material flow analysis (dMFA) model within the wind energy system and conduct Canada’s first comprehensive analysis of material demand and end-of-life management within the wind energy sector. Additionally, this study evaluates carbon emissions and energy consumption associated with wind turbine material production.

This project employs a comprehensive modeling approach, encompassing four sub-models, two energy capacity scenarios, three technological development scenarios, two end-of-life management scenarios, seven wind turbine sub-technologies, and ten types of material. The results indicate that the Global Net Zero scenario emphasizes a more substantial increase in onshore wind energy compared to the GCAM scenario. By 2050, the cumulative material demand in the Global Net Zero scenario is 1.8 times that of GCAM. Despite the predicted increase in average capacity per wind turbine from 2020 to 2050, the material intensity will rise by 26% due to larger turbine sizes. Closed-loop recycling is expected to reduce the demand for virgin metal materials by 20-30% over the next 30 years, but it will only decrease the demand for REEs by less than 2%. The substantial increases in material requirements will not be significantly offset by domestic closed-loop recycling within the first half of the century. By 2050, the demand for Nd in Canadian wind turbine construction will be 7.96 times the current levels, and for Dy, it will be 6.71 times, representing 64% and 30% of global production in 2022, respectively. The supply of REEs will likely become a significant constraint on the future development of wind energy in Canada. From a material availability perspective, smaller wind turbines equipped with doubly fed induction or squirrel cage induction generators are a preferable option for Canada. Moreover, technological advancements are crucial in minimizing environmental impacts, with advanced technology scenarios outpacing optimistic recycling projections in reducing environmental impacts from 2030 to 2050.

Overall, this research is poised to significantly influence both policy-making and practical applications, contributing to broader sustainability goals in the wind energy sector.

Electric vehicles (EVs) have significant sustainability impacts, not only in terms of shifting their primary energy source from fossil fuels to electricity, but also due to differences in their material supply chains and manufacturing processes.

This study investigates the effect of the 2022 Inflation Reduction Act (IRA) incentives on U.S. electric vehicle battery industry in terms of supply chain decisions and technology choices, specifically examining the dynamics of different chemistry choices and production geographies from the perspective of cost minimization. Using the BatPaC model, we explore a number of scenarios based on potential future market developments to analyze the effect of the various subsidies.

We find that the total value of all IRA incentives exceeds the total production cost of EV batteries in the United States. Though the total possible amount of incentives in pure dollar quantities is slightly larger for batteries with the Nickel Manganese Cobalt Oxide (NMC) chemistry, the Lithium Iron Phosphate (LFP) chemistry continues to dominate on a dollar per kWh basis due to its markedly lower manufacturing cost, as well as the lower number of critical minerals needed to meet the critical mineral requirement. Furthermore, these incentives can render U.S. batteries competitive even without meeting critical mineral requirements: the $45 per kilowatt-hour (kWh) incentive to produce battery cells, modules, and packs domestically is sufficient to be competitive with current modeled production in China. However, direct credits for critical minerals extraction and processing have very limited relative effect on the cost of battery manufacturing, rendering them less important in terms of shifting their geography of production from the perspective of an automaker trying to claim vehicle-based tax credits based on their supply chain.

Our analysis underscores the impact (or lack thereof) of upstream credits for critical minerals and components, prompting a re-evaluation of the feasibility of relocating challenging segments within the supply chain and their subsequent environmental, security, and economic implications. This research provides crucial insights for policymakers and industry stakeholders navigating the transformed EV supply chain landscape post-IRA implementation.

When wells reach the end of their productive life, the plugging and abandonment process becomes crucial. Orphaned wells, lacking an accountable owner, pose a variety of environmental and health hazards, threatening water sources and emitting air pollutants [1]. The conventional plugging approach involves cementing and sealing to create an impermeable barrier. However, Portland cement, a primary component, contributes significantly to carbon dioxide emissions and resource consumption [2]. This study explores the potential of carbon sequestration in well plugging operations, specifically through the integration of biochar derived from abundant beetle-killed pine trees in Colorado.

The life cycle analysis assesses key stages, including transporting beetle-killed pine wood to the biochar facility, chipping, pyrolysis, and subsequent transportation of the formed biochar to a cement facility. The cement and biochar are then transported to the orphan well site for plugging, where biochar is integrated into spacer fluids, balance plugs, and well-squeezing operations. Comparative analyses reveal that incorporating biochar into standard well plugging operations results in an 11% reduction in greenhouse gas emissions per well plugged, highlighting biochar's role in mitigating the environmental impact traditionally associated with Portland cement. The findings contribute to ongoing discussions on sustainable practices within the oil and gas industry, positioning biochar as a promising avenue for reducing greenhouse gas emissions and fostering a more sustainable future.

[1] Raimi, D., Krupnick, A. J., Shah, J. S., & Thompson, A. (2021). Decommissioning Orphaned and Abandoned Oil and Gas Wells: New Estimates and Cost Drivers. In Environmental Science and Technology (Vol. 55, Issue 15, pp. 10224–10230). American Chemical Society. https://doi.org/10.1021/acs.est.1c02234

[2] Suarez-Riera, D., Restuccia, L., & Ferro, G. A. (2020). The use of Biochar to reduce the carbon footprint of cement-based materials. Procedia Structural Integrity, 26, 199–210. https://doi.org/10.1016/J.PROSTR.2020.06.023

Abstract: To meet the global greenhouse gas (GHG) emissions reduction goal, a priority concern is given to the building sector as the industry typically uses emission-intensive materials. Thus, various strategies are gradually adopted to reduce the embodied GHG emissions from buildings such as the adoption of design for manufacturing and assembly, increasing reuse and recycling of materials based on the circular economy (CE) principle, renovation of existing buildings, and increasing use of regenerative resources (e.g., bio-based materials). Using regenerative resources can substantially aid in reducing non-renewable resource consumption and subsequent GHG emissions. In addition, the use of wood-based resources can significantly promote the adoption of CE considering its adaptive mode and function. This study scrutinizes the recent trends, adoption, and functions of using wood-based resources in buildings based on the comprehensive literature review. Over a thousand relevant articles including scientific papers, conference papers, book chapters, reports, and thesis were preliminary analyzed based on the Scopus search, and then 180 articles were finalized according to the pre-defined criteria such as wood-based construction, buildings, lifecycle assessment, geographic coverage, applications, distinct features and specificity, evaluation methods and indicators, adopted databases and tools, etc. After synthesizing the selected studies, the key findings were summarized and discussed critically. Though recent studies showed substantial embodied carbon emissions reduction from buildings using bio-based materials compared to conventional materials [1-3], several aspects such as biogenic carbon accounting, multiple impact categories, the use of consistent methodology and databases, and CE adoption and its influence on environmental impacts of buildings need further attention. The existing gaps along with future research direction on how bio-based materials can substantially enhance CE adoption and the sustainability performance of buildings, are highlighted.

Keywords: Building, Sustainability, Wood-based resources, Circular economy, GHG emissions.

References

[1] Andersen C.E., Hoxha E., Rasmussen F.N., Sørensen C.G., Birgisdóttir H. (2024). Evaluating the environmental performance of 45 real-life wooden buildings: A comprehensive analysis of low-impact construction practices. Build. Environ. doi: https://doi.org/10.1016/j.buildenv.2024.111201.

[2] Churkina G., Organschi A., Reyer C.P.O, Ruff A., Vinke K., Liu Z., Reck B.K., Graedel T.E., Schellnhuber H.J (2020). Buildings as a global carbon sink. Nat. Sustain. 3, 269–276.

[3] Andersen C.E., Rasmussen F.N., Habert G., Birgisdóttir H. (2021). Embodied GHG emissions of wooden buildings—challenges of biogenic carbon accounting in current LCA methods. Front. Built Environ. 7:729096.

The southwestern states of California, New Mexico, and Utah, share many commonalities: a semiarid climate, water management challenges, changing hydroclimatic conditions, a growing competition for scant water supplies, and the production of alfalfa hay. Alfalfa greatly contributes to the economies of these states and the United States (US) overall each year. In 2022, the tristate areas collectively harvested 1.45 million acres to produce seven million tons of alfalfa hay valued at $2.1 billion. To cultivate alfalfa on this scale, large amounts of inputs (e.g., water, fertilizers, fuel, and pesticides) are applied, resulting in season-long high forage yields and profits but also risking water security, degrading water quality, impacting soil health, and emitting greenhouse gases (GHGs). This study aims to quantify the water use and other environmental impacts of alfalfa production systems, per cutting and life cycle stage, in the tristate area using a life cycle assessment (LCA). This study is the first LCA of alfalfa in the southwestern US. The cradle-to-farm gate system boundary includes stand establishment, forage production, and forage harvest. The functional unit is one ton of alfalfa hay corrected to 90% dry matter (DM). The primary data sources include the US Department of Agriculture’s National Agricultural Statistics Service (NASS), the University of California extension Cost & Return Studies, the New Mexico State University extension publications, the Utah State University extension publications, and state and county agriculture reports. Data analysis and modeling will be conducted using the LCA software SimaPro 9.2. Preliminary analyses indicate that water use in California, per cutting, is lower than water use in New Mexico and Utah. Results from this study can enable growers to assess tradeoffs between interconnected farm-management decisions using relevant data, charting a course toward a more resilient agricultural future. The findings of this study are scalable to other forage crops and regions with similar climate and water challenges and are especially important as the world moves toward achieving sustainable water use in a changing climate.

Achieving global climate goals is a copper-intensive endeavor. The rise of electrification trends and the widespread deployment of clean energy contribute to a growing demand for copper and the looming threat of a copper supply shortage. With annual copper demand expected to grow by 50% and reach 35 Mt by 2035, the world will need new sources of copper supply. While the development of new mining projects could increase copper production, declining ore grades leads to greater energy and land use for copper production, conflicting with global decarbonization goals and often environmental protection measures. Recycled copper, which has a significantly smaller carbon impact, can play a vital role in meeting present and future demand. Furthermore, enhanced secondary copper systems contributes to a circular economy while mitigating social and environmental risks. In this paper, we analyze opportunity scenarios to meet growing copper demand via increased end-of-life scrap collection and improved sorting efficiencies across six major waste streams: construction and demolition, municipal solid waste, waste electronic and electrical equipment, end of life vehicles, industrial electrical waste, and industrial non-electrical waste. We use an economic model of the copper system to quantify supply evolution while incorporating price feedback between demand and supply. The model quantifies the impact of the increased scrap collection on the displacement of mining production and demonstrates how increasing recycling can reduce supply risks, copper prices, and CO2-equivalent (CO2e) emissions. We benchmarked our findings against existing literature on future copper flows and found that there is an opportunity to increase scrap supply in 2035 by over 50% compared to the current baseline. From a regional analysis, optimized recycling and collection rates lead to an 110% increase in China’s copper supply alone. Building upon these collated results, we suggest strategies through policy and economic initiatives to help seize this end-of-life scrap opportunity.

Population growth in the United States has caused a massive uptick in building construction, both residential and commercial. Given the threat of climate change, it is necessary to understand the environmental impacts of these buildings to encourage sustainable practices. This study aims to quantify the impacts of the Curtis Media Center on UNC-Chapel Hill’s campus through a life cycle assessment (LCA). This study is cradle-to-grave and analyzes utility usage, construction processes, building materials, and transport. Two end-of-life scenarios are considered to highlight the importance of sustainable demolition. University utility data and construction documents, as well as the architect’s materials list, are used to determine quantities of each material used in the construction of the Curtis Center. Life cycle impact assessment (LCIA) values have been obtained from ecoinvent and aggregated to find the total environmental and human health impacts of the building.

Building LCAs are common, yet not much research has been done regarding buildings on college campuses. The Curtis Media Center has rooftop solar panels and is mostly accessible on foot, both of which help to decrease the building’s environmental footprint. This LCA uses the TRACI method to determine human health impacts of each life cycle stage. These impacts are discussed in the study and provide occupational health information that is often omitted in environmental LCAs. This study compares a best-case and worst-case scenario for the building’s end-of-life, which can be used to guide sustainable demolition of other buildings. The Curtis Center’s Energy Star profile is calculated and its implications are discussed in this study. Finally, life cycle impacts of the Curtis Center are compared to other similarly-used buildings to determine the relative “greenness” of the building. This LCA of the Curtis Media Center can be used to guide sustainable building development on other college campuses. More comprehensive statistical analysis must be done to understand the effects of random variables and uncertainty on the results of this study.

Access to good employment opportunities is an important component of a just decarbonization transition. To date, most research on employment impacts has focused on the new jobs that might be created to produce clean energy, not how existing jobs in manufacturing and other industries may change in response to both manufacturing process transformation and transitions to using more zero-carbon energy. At the same time, many industries already face challenges attracting an appropriate workforce, and the demands for decarbonized technology production may only exacerbate existing workforce challenges. Addressing the ability to attract and retain workers, and accurately representing the costs associated with these efforts, in balance with other production costs like capital equipment and energy, is important for identifying sociotechnical solutions that balance the needs of workers with the goal of reducing the costs of producing technologies needed for the decarbonization transition. Using preliminary interviews with workers in the steel industry, where there is already significant electrified production, we identify key tradeoffs workers in this industry are making between monetary rewards and other job attributes. We use these interviews to inform the design of choice-based conjoint surveys to inform discrete choice models of worker preferences for different job attributes. The results of the survey are used in multinomial logit models, with transformations in the willingness to pay space to assess how workers value wages relative to other job attributes. We use the preference space logit model to reduce the computational time of solving the non-convex transformation to the willingness to pay space, while accurately estimating the variance of the model coefficients. We anticipate that workers make distinct tradeoffs between job attributes like wages, hours of overtime worked, and shift schedule, perhaps in response to other responsibilities outside of work, like caregiving. The outputs of these models can inform process-based models to estimate how different combinations of equipment, labor, and low-carbon energy can come together to support decarbonized industries as our energy system continues to evolve.

The lack of affordable, reliable, and resilient energy services is still plaguing U.S. households. Existing energy data are limited in spatial and temporal resolution, thus hindering the discussion on energy inequity from a climate perspective. By introducing multi-source geospatial data, including land surface temperature and nighttime light imagery, this study aims to upsample aggregated annual county-level energy data to monthly tract-level energy data using machine learning and remote sensing techniques in the conterminous U.S. over the past decade. Then, semiparametric models are employed to shape the potentially nonlinear relationship between climate variations and energy equity based on improved high-resolution energy data, with a particular focus on identifying the most vulnerable groups across geographic, demographic, and socioeconomic factors. By contributing to more accurate and precise energy data with a higher space-time resolution, this study is able to capture the most sensitive temperature response functions as the cornerstone of data-driven energy policies. The results show significant spatial and seasonal disparities in U.S. household energy equity. Increasing winter heating demand exacerbates energy inequity more than those associated with summer cooling demand. In addition, the disproportionate burden on energy-vulnerable communities places a higher demand on more equitable and inclusive energy policies.

Responding to the escalating significance of climate change, cities globally are formulating Climate Action Plans (CAPs), recognizing their substantial role in worldwide emission reductions. The surge in climate change awareness, coupled with global agreements such as the Paris Climate Accords and reports like the IPCC, has institutionalized climate action as a pivotal aspect of the broader climate movement.

The surge in climate action planning across the United States demands a critical examination of the integration of equity principles. While the landscape has seen substantial growth, disparities persist in acknowledging and addressing vulnerable communities. This study focuses on the multifaceted nature of equity, encompassing procedural (the equitable involvement in planning procedures), distributive (the equitable distribution of resources), and recognition justice (the acknowledgment of historical injustices) within climate action planning. This study aims to analyze actions and indicators with an equity-centered approach in CAPs to better understand the current state of these considerations and provide recommendations for future climate planning.

This study analyzes equity-focused CAPs across the United States by aligning them with planning guides such as the American Planning Association’s equity policy guidelines, and others. Associations between these guidelines and CAP actions were made through categorization techniques. By comparing CAP's equity actions and indicators to those outlined by equity guides, we were able to extract best practices and identify gaps within national climate planning. Case study cities, selected from various databases such as C40, and other searches provided diverse perspectives on equity-focused planning.

Our preliminary findings highlight the need for a paradigm shift of viewing climate mitigation and adaptation as intertwined with equity, accompanied by increased accountability within climate action plans. Identified best practices can provide city planners with examples for future considerations to prioritize equity and justice targets. For example, the city of Oakland’s Equitable Climate Action Plan (ECAP) prioritizes low-income communities by providing tailored actions that reflect housing instability and transportation access. In addition, Oakland’s ECAP includes metrics for most actions to ensure progress toward established goals. Example metrics include the number of frontline communities engaged in ECAP activities and total mobility infrastructure investment in frontline communities. Integrating underutilized indicators and metrics, especially those related to procedural justice, is important for creating accountability and tracking progress. Policy recommendations aim to bridge gaps and enhance the effectiveness of equity-focused planning.

The study contributes to advancing equitable climate action planning by shedding light on existing gaps and emphasizing the need for a more integrated approach. Education, accountability, and recognition of the interdependence of climate and human health are essential to achieving sustainable and equitable cities. This research forms a foundation for future studies, fostering a deeper understanding of equity-focused planning and providing ongoing recommendations for comprehensive climate action.

Over 11,000 power plants are connected to the electric power grid in the United States, and many more will be required in the coming years to support wide-scale electrification to achieve decarbonization targets. Living near power plants comes with risks. Fossil fuel plants emit toxic air pollutants that cause health damages nearby and in downwind locations. Nuclear generators are generally safe but present a small risk of exposure to radiation. Wind plants generate noise pollution and visual disturbance. Other types of plants present their own risks. Power plants are distributed heterogeneously throughout the country, and many people live within close proximity of electricity generating units. Research suggests that people of color are more likely to live close to fossil fuel plants and other industrial facilities. In this work we assess residential proximity to power plants by fuel type and identify race- and income-related disparities. We overlay block group-level population data with geospatial power plant data and estimate key metrics including the population-weighted mean distance to the nearest power plant, cumulative population within specified distances of each plant type, and population-weighted generation and capacity within 1km of residential block groups. We find that, on average, Asian Americans live closest to power plants, followed by Pacific Islanders, Latino, and Black Americans. People generally live closest to natural gas, biomass and solar generators, and proximity to generator type varies by state. We find that racial disparities greatly exceed income-related disparities. We also identify power plants with the largest amount of generation in close proximity to the most people, finding that the most impactful plants are located in and around New York City. These results suggest that groups who have historically been excluded from decision-making related to power plant siting are the same groups who now live closest to power plants. We suggest that these groups should be included and prioritized in decision-making about plant retirements and new plant development during the energy transition. We hope this work will inform conversations about these decisions.

Since the introduction of electric vehicles (EVs) in the US market, there have been no adjustments to evacuation planning to address their unique charging needs. EV drivers may face range anxiety, and long recharging times while navigating sparse public charging networks, which challenge both preemptive and short-notice evacuations. This research proposes a multi-criteria vulnerability assessment of coupled EV driver and charging station networks in various evacuation scenarios. We study flooding evacuations in Chicago, Illinois, and hurricane evacuations in the Southeast Florida road networks. Our findings show that vehicle and infrastructure characteristics (i.e., charging stations network, driving range, and vehicle type heterogeneity) and evacuation characteristics (i.e., network scale and topology, hazard type, and warning system type) impact evacuation feasibility and performance and increase drivers' vulnerability. Furthermore, the initial state of charge is critical in determining EV drivers’ capability to initiate an evacuation. Last, we discuss potential infrastructure and preparedness measures that can curb vulnerability and better accommodate EV evacuations.

Vehicle electrification is a key component of sustainable development goals yet the mass adoption of electric vehicles can lead to unintended consequences on community mobility during natural hazards. Electric vehicles pose a challenge to owners during natural hazards which lead to power outages, as a lack of home charging limits the mobility of EV owners. Due to the direct relation between driving distance to essential services and vehicle battery consumption, changes in mobility will be impacted by geographic and technological factors. Geography determines the driving distance to essential services which will be translated to electricity consumption devoted to transportation and technology determines the size of electric vehicle batteries and vehicle efficiency. The linkage between mobility, electric power availability and quality-of-life has broad implications for community resilience as equitable access to essential services has been identified as the most important aspect of community resilience. In this work we develop a computational modeling framework to quantify the impact of vehicle electrification on limited mobility and access to essential services in urban areas during prolonged blackouts. We define measures of access risk and evaluate how risk changes across large urban centers in the U.S. Our results indicate inequalities in access to essential services will be exacerbated by vehicle electrification during blackouts. We also find that urban areas with high population density are associated with lower levels of access risk whereas high car ownership rates correlate with higher access risk. Moreover, we test different electric vehicle technologies and find that increased battery size lowers access risk by increasing potential driving distance, however the impact of battery size is highly dependent on the geography of each city. Finally, we test how vehicle to grid (V2G) technology creates a trade-off between access to services and use of household amenities and find that V2G disproportionately benefits access-rich households.

Transportation Network Companies (TNCs) like Uber and Lyft have been rapidly growing. This growth could increase greenhouse gas (GHG) emissions and health risk by air pollutant emissions, as well as congestion, noise, and crashes. In this study, we identify 1 million trips from real-world travel data recorded by TNC services in Chicago. We then assess the social cost of these trips–including GHG emissions, health damages from other air pollution (PM2.5, NOx, SO2, VOC), congestion, and crashes. We repeat the analysis for two cases: one in which we assume that the trips are performed using gasoline vehicles, and other in which we assume that they are performed using electric vehicles. The data record how much the customer paid for the trip and its approximate duration. For each trip, we then use the Google Maps API to identify a method by which the trip could have occurred using only transit. For this transit alternative, we calculate monetary costs, travel time, as well as the same set of social costs we estimate for the TNC trips. We then compare the cost of performing the trip using a TNC ride and using transit. The analysis allows us to characterize the trade-offs between the different types of private and public costs incurred by each mode. By combining trip data with historical weather data, we are also able to quantitatively describe how this trade-off varies with weather conditions (e.g., if, in extreme cold, people use TNCs for trips where the time savings do not compensate for the extra cost). Preliminary results indicate that the total social cost of a TNC trip is $1.5 for electric vehicles and $1.7 for gasoline vehicles. These are significantly higher than the social costs of a transit trip, which range between $0.1 and $0.3. However, these dynamics shift when considering private costs, including both monetary fares and time value. The fare alone of a TNC trip is on average $20, six times that of a transit trip. But when factoring in the monetized value of additional time needed for transit, the private costs would be approximately equal between TNCs and transit. Further analysis revealed that 60% of the monetized additional time cost in transit trips was allocated to walking and transferring, and 40% to waiting time. These primary findings indicate that riders are acting rationally in choosing TNCs over transit, and given the relatively small magnitude of external vs private costs, would continue to make the same choice if external costs were internalized. A policy to improve transit frequency and location of stops could potentially enhance the appeal of transit and increase the social benefits. With more in-depth analysis of these cost trade-offs under different weather conditions and times of day, we discuss further potential policy interventions.

In order to combat the increasing impacts of climate change, countries around the world are pursuing a transition away from fossil fuels and towards electricity and renewable energies. In pursuit of this, California has set goals to have Zero-emission Vehicles (ZEV) account for 100% of new vehicle sales by 2035 (California Air Resources Board, 2022). This goal was enacted to address the ongoing climate crisis, as vehicles with internal combustion engines (ICE) rely on highly emitting fuel sources. ZEVs are instead powered by hydrogen or electricity which makes improving the rate of ZEV adoption key to the sustainable energy transition. However, to meet these goals, California will need to accelerate ZEV sales, support, and infrastructure. In order to understand the existing efforts toward increasing ZEV adoption in California, we evaluated county- and city-level plans for ZEV adoption, their likelihood for success, and the constraints on their success. We implemented a detailed and thorough review of existing ZEV adoption plans and their current results. We will continue to focus on California ZEV adoption blueprints and plans to investigate the current state of planning and knowledge, as well as evaluate case studies from other locations to aid our conclusions. Through our completed and future assessments, we will determine the opportunities for adoption growth, build a solid understanding of existing barriers to ZEV adoption, and format a process for reducing barriers and improving adoption. Our current findings suggest that the greatest barriers to adoption are split between social, political, technical, and economic factors. Many existing plans reference infrastructure needs as well as strategies for reducing the economic burden for consumers. However, very few plans delve into the complex social and political issues associated with ZEV technology and widespread adoption. These issues include lack of access, negative public perception of ZEVs, cost of personal infrastructure, and concerns related to infrastructure and hardware for rural communities. This is the area that presents the most complex barriers with very few proven solutions. As we continue this work, I expect to find further barriers in the social sphere. To complement this work, we are exploring data to identify common socio-economic and ZEV adoption correlations. Once these correlations are clearly identified, we will utilize the results to identify outlier communities. We will find where communities have higher or lower adoption rates than other similar communities. Identifying these communities will open new areas for research and determine what steps can be taken in similar communities to boost adoption. The focus area for this research will be California, however, our findings, particularly concerning outliers and how to improve ZEV adoption, will be widely applicable and important as the world works to prioritize de-carbonizing the transportation sector. The big question moving forward is how our knowledge of existing barriers can inform the solutions and strategies that are implemented to improve ZEV adoption.

While the US rapidly increases electric vehicle (EV) sales to meet decarbonization targets for the transportation sector, its second-hand (SH) EVs have entered international used vehicle markets. Introducing a radically new technology such as EVs without responsive measures in SH market regions may lead to an unintended transfer of economic and environmental burdens to lower and middle-income countries (LMICs) if they are unprepared to manage EVs, especially at the end of life (EOL).

It is unclear if SH exports provide receiving countries environmental benefits, particularly considering the battery state of health, the extended use, the electricity grids used to charge them, local maintenance and repair practices, and available EOL management systems for EV batteries. Additionally, exported SH vehicles could reduce the potential circularity of domestic EV battery production.

Previous EV lifecycle assessments (LCA) have failed to account for exports to international SH markets, assuming EVs operate only in the country where they are sold. Such studies have predominantly focused on developed nations with well-established waste management systems and robust regulation and enforcement systems, underestimating their full lifecycle impact in LMICs.

This research focuses on US-Mexico SH vehicle trade. According to National Customs Agency of Mexico (ANAM) data, Mexico is the largest export market for US SH vehicles, with over 9 million imports between 2005 and 2023, representing over 30% of registrations in Mexico during the period, with recent estimates suggesting the share may be higher due to illegally introduced SH vehicles.

We use LCA modeling to analyze the expanded lifecycle environmental impacts of US-exported EVs reaching EOL in Mexico.

However, Mexico is notorious for the informality of its waste management system, lacking dedicated EOL vehicle management regulation and capacity to enforce applicable health, safety, and environmental regulations. Thus, collection, dismantling, recycling, and data recording are primarily market-driven and carried out informally, posing a major barrier to obtaining comprehensive information on Mexico's EOL vehicle management system and its strategies to accommodate the increasing number of EVs.

To address this gap, we conducted qualitative research through semi-structured interviews with industry and public sector stakeholders. These interviews and an extensive literature review informed our LCA modeling.

Results offer insights into the extended lifecycle environmental impacts of US-exported EVs reaching EOL in Mexico, potentially informing regional policies to prevent the transfer of environmental burdens and maximize economic benefits to the region.

Key interview findings include: (1)Overwhelmingly, EVs reaching EOL in Mexico are SH imports from the US. (2)SH hybrid-electric vehicles are widespread, while SH battery-electric vehicles remain uncommon. (3)The most common chemistry of the spent SH vehicle batteries is nickel-metal hydride, but lithium-ion is increasing. (4) No official regulations exist for EOL EV batteries. (5)Recyclers do not accept spent EV batteries, leading to stockpiling and landfill disposal.

While our study focuses on the US-Mexico trade, our modeling approach and findings offer insights into other LMIC SH vehicle trade relationships, contributing to a deeper understanding of the nascent global SH EV trade implications.

One of the leading topics in the field of life cycle assessment (LCA) is the development, adaptation, and application of techniques to assess emerging technologies early in their development cycle. This interactive special session will include a presentation on several controversial topics in LCA of emerging technologies, followed by several rounds of discussion to solicit feedback and further ideas from the audience.

The ACLCA working group on LCA of Emerging Technologies was founded in 2021 to: 1) Serve as a central body within the LCA community for the identification, resolution, and communication of technical and methodological issues related to emerging technology LCA ; and 2) Facilitate, coordinate and provide guidance for the development, implementation and communication of LCA and its use. Over the past several years, the working group has met regularly online and at the ACLCA and ISSST conferences to craft guidance documents and debate key issues associated with the group’s mandate. Through this process, a key roadblock has arisen: we cannot produce clear guidance when the community is divided about what that guidance should say! We find that these divisions can highlight important areas for research. At the same time, they do not imply a correct answer, but rather may point to the need for special care or even pointing out the limitations of our methodology.

In this special session, we will highlight controversial topics such as: 1) appropriate use of LCA, especially for technologies at low TRL (technology readiness level); 2) whether/how to scale-up development-, from lab-, bench- or pilot-scale data; 3) comparison of emerging technologies with incumbent technologies; 4) managing uncertainty and whether sophisticated probabilistic/stochastic methods are appropriate for emerging technologies; 5) the degree to which stakeholder engagement is required/desirable for LCA of early-stage technology; 6) the degree to which LCA of emerging technologies can/should be standardized (beyond the level of existing ISO 14040/14044 frameworks) or left flexible.

Rather than trying to come to a consensus, the session will present the key arguments that have so far been discussed, and then solicit audience feedback through breakout groups or room-wide discussion. These arguments are the basis for a draft manuscript (currently in preparation for submission to peer-review) that will lay out different perspectives on each topic and explain where the community does/doesn’t agree. Key points from the ISSST discussion may be incorporated into that manuscript, or else may seed next steps and future work by the Working Group. The resulting paper(s) or future report(s) are intended as: an entry-point for junior practitioners who wish to learn about the range of expert perspectives in the LCA community; a guide for LCA stakeholders (e.g., technology developers, funding agencies) to help set expectations or interpret results with more methodological context; as well as a review and pulse-check on the status of LCA of emerging technologies for more seasoned analysts.

The circular economy (CE) and its emergent application to the built environment has been progressively discussed in industry, academia, and government spheres. However, the intrinsic complexity of CE practices and the distinct characteristics of the construction industry pose considerable challenges to CE implementation. Digital tools are increasingly recognized as a viable solution to overcome these challenges, but there remains a notable gap in understanding the extent to which digital tools can facilitate the transition to CE in the built environment, especially within the context of broader decision-making. This study investigates how current research elucidates the role of digital tools in facilitating CE practices, and in what specific ways these tools can effectively enable circularity in the built environment. We provide a comprehensive overview of state-of-the-art approaches for enabling circularity in the built environment with digital tools, and identify the challenges, gaps, potentials, and future themes that are occurring in the current literature. Based on the literature review, we developed a framework that identifies and maps ten prominent digital tools: BIM, GIS, AI & ML, IoT, blockchain, digital twin, AR & VR, digital platform/marketplace, and material bank/database. The proposed framework integrates life cycle assessment stages for buildings and building products with key CE principles and the role of each digital tool in enabling CE in the built environment. Our findings indicate that, despite the considerable focus on Building Information Modeling (BIM) and Geographic Information Systems (GIS), technologies such as the Internet of Things (IoT), big data, and blockchain, while promising, have not seen widespread adoption by industry practitioners. Key challenges identified include the need for continuous updates to GIS and BIM models, uncertainties due to unreliable data, difficulties in data management and accessibility, and limited integration of digital tools in construction. To address these challenges, our framework proposes six strategies: (1) Tracking: Systematic tracing of material flow throughout the life cycle, (2) Reducing: Minimizing resource use and environmental impact through optimized production and design, (3) Collecting: Gathering and storing data from existing building stocks in a digital format, (4) Circulating: Effective end-of-life management through reuse, recycling, and waste management, (5) Maintaining: Regular product maintenance and data updating, and (6) Improving: Promoting biodiversity, environmental conditions, and societal well-being. This study contributes to the body of knowledge by highlighting the use of digital tools in advancing CE practices, and offering valuable insights for researchers, practitioners, and policymakers committed to sustainable development across industries.

Macro energy system optimization models are important tools for understanding the possible outcomes of deep decarbonization pathways and can inform the impacts of policy mechanisms that aim to reach net-zero greenhouse gas emissions by mid-century. Given that current U.S. federal climate policy is insufficient to achieve carbon neutrality, state-level decarbonization efforts are of increasing importance. In this research, we use a comprehensive energy systems model (Temoa) to explore the implications of achieving decarbonization goals through either federal or state action. We consider two carbon budget policy scenarios: (1) 23 climate-friendly states set net-zero carbon dioxide emissions targets by 2050; (2) a federal policy that matches the greenhouse gas emissions levels of the state-policy, but with the added flexibility of this being a national constraint. Both scenarios achieve a carbon dioxide emissions reduction of approximately 40% by 2050, but the spatial distribution of emissions limits varies by scenario. Because our approach endogenizes end use technology choice, through this investigation, we demonstrate that state-driven net-zero carbon pathways select a substantially different least-cost set of technologies relative to the federal policy. Specifically, through state-level climate policy, we see a roughly 17% increase in electricity generation by 2050 as compared to a federally-led scenario, indicating an expansion of the electric sector’s role in decentralized decarbonizations. Of this electric sector expansion, the Northeast accounts for nearly 62% of generation increases by 2050. Additional increased technology deployments within the state-led scenario include direct air capture systems in the California and Northeastern regions while, conversely, the federally-led decarbonization scenario relies heavily on the utilization of bioenergy with carbon capture and storage (BECCS) systems in the Southeast. Further, traditional fuel use increased within the federal scenario’s transport sector. This study offers new understanding of the risks associated with technology lock-in under different decarbonization pathways, highlighting the importance of early planning for long-term action.

Life cycle assessment is a valuable tool used to assess the environmental impacts of a given product or process. Standard attributional life cycle assessment methodology focuses on a set of direct environmental impacts resulting from a given production process or system and often focuses on the global warming potential quantified by the release of greenhouse gasses from the system. While the standard approach can provide a valuable means of comparing a variety of environmental impacts from individual technologies or entire systems, standard practices focus on emissions to air, water, and soil. Studies following this framework fail to quantify stresses to the global supply of food, water, and energy imposed by arable land use, freshwater use, energy consumption, and changes to human well-being, biodiversity, and the global climate system. Ignoring these indirect impacts can lead to incomplete conclusions from life cycle assessment studies by missing disproportionate shifts in the water-energy-food nexus which carry significant economic, social, and environmental implications. This work presents a conceptual framework for quantifying stresses induced by engineered climate solutions on local and global suppliers of food, water, and energy. This framework presents a new results metric, the water-energy-food index (WEF index), to evaluate the sustainability of climate solutions while considering the synergies, trade-offs, and conflicts of these important resource sectors.

(1) Background

The United States healthcare industry is the second largest industry contributor to landfill waste worldwide [1]. The healthcare sector is also responsible for as much as 4.6% of greenhouse gas (GHG) emissions worldwide, with emissions continuing to rise [2 ]. These emissions not only contribute to climate change and negative environmental impacts, but also threaten human health. Communities are directly impacted by the health concerns associated with waste processing, harmful emissions, and pollutants associated with the production of healthcare products. Reducing the amount of healthcare products that end up in landfills could greatly reduce these negative impacts.

Moving from single use to reusable medical devices has been identified as a priority and high-impact intervention in healthcare [3]. Thus, we conducted a comparative life cycle assessment (LCA) study of disposable versus reusable stethoscopes. This research was conducted at UPMC’s Children’s Hospital of Pittsburgh (CHP). Disposable stethoscopes are sometimes used instead of reusable stethoscopes to prevent cross-contamination between patients. A research team at CHP was exploring the option of reducing disposable stethoscopes, and the results from this LCA will be used to aid in decision-making at CHP and other hospitals and physicians.

(2) Methods

The cradle-to-gate system boundary included raw material extraction and the manufacturing phase. The functional unit includes one year at CHP’s emergency department and the equivalent disposable stethoscopes (20,000) and reusable stethoscopes (1/7th the lifespan). Foreground data for the disposable stethoscopes was collected by disassembling, weighing, and inventorying the materials of in the disposable stethoscope used at CHP. For the reusable stethoscope, the material composition and weights were derived from prior literature [4]. We used Ecoinvent version 3.8 and TRACI 2.1 V1.05. [5]. These impacts were then scaled to the functional unit of 1 year of use. The impact categories included: ozone depletion, global warming, smog, acidification, eutrophication, carcinogenics, non carcinogenics, respiratory effects, ecotoxicity, and fossil fuel depletion.

(3) Preliminary Results

For every category, the impacts of the reusable stethoscope are less than 0.005% of the impacts of the disposable stethoscopes over 1 year of use. The most significant material contributors were brass in the disposable stethoscope and steel in the reusable stethoscope. These materials also were the largest contributors to the weight of their respective stethoscopes. Switching from disposable to reusable stethoscopes at CHP for 1 year would save 5,783 kg CO2-equivalent.

References:

[1] American Medical Association. (2022, October 11). U.S. health system must come to terms with its environmental impact. American Medical Association. https://www.ama-assn.org/delivering-care/public-health/us-health-system-must-come-terms-its-environmental-impact

[2] Eckelman, M. J., Huang, K., Lagasse, R., Senay, E., Dubrow, R., & Sherman, J. D. (2020). Health Care Pollution And Public Health Damage In The United States: An Update. Health Affairs, 39(12), 2071–2079. https://doi.org/10.1377/hlthaff.2020.01247

[3] Keil, M., Viere, T., Helms, K., & Rogowski, W. (2023). The impact of switching from single-use to reusable healthcare products: a transparency checklist and systematic review of life-cycle assessments. European journal of public health, 33(1), 56–63. https://doi.org/10.1093/eurpub/ckac174.

[4] Chow, K., Divone, J., Leung, K., & Sunday, C. (2012, February 25). Stethoscope—DDL Wiki. Stethoscope. https://wiki.ece.cmu.edu/ddl/index.php/stethoscope

[5] Bare, J. (2011). TRACI 2.0: The tool for the reduction and assessment of chemical and other environmental impacts 2.0. Clean Technologies and Environmental Policy, 13(5), 687–696. https://doi.org/10.1007/s10098-010-0338-9

Background/Objectives:

To fully grasp the environmental impacts and tradeoffs in different power system scenarios, it's essential to consider both direct and embedded emissions throughout the entire lifecycle, from creation to disposal. This entails examining emissions at each stage, including resource acquisition, construction, manufacturing, transportation, operation, and decommissioning in the power sector. Analyzing the spatial and temporal nuances in environmental impact and resource utilization metrics can uncover potential tradeoffs over time, in different locations, and across sustainability dimensions. Notably, the National Renewable Energy Laboratory's (NREL) power system studies have traditionally concentrated on system costs and direct CO2 emissions. Expanding the range of impact metrics poses challenges and requires significant time and effort, especially in major studies like 'Standard Scenarios.' Explicitly considering Scope 2 emissions on a regional and temporal basis will be crucial for studying future decarbonization scenarios and assessing the benefits of emerging low-carbon technologies.

Approach/Activities:

This study aims to enhance the efficiency of assessing environmental impacts and incorporating new metrics by leveraging the existing NREL code-based platform, Harmonized Impacts of Products, Scenarios, and Technologies across Environmental and Resource use metrics (HIPSTER). Unlike the current fragmented approach relying on disparate assumptions and inputs, HIPSTER is designed to harmonize variables in life cycle assessment (LCA) from cradle to grave. HIPSTER can assess Regional Energy Deployment System (ReEDS) scenarios, providing impacts per kilowatt-hour (kWh) by considering both direct and indirect emissions from resource extraction to electricity distribution. It has the capacity to distinguish among major power generation technologies in ReEDS across 134 spatial US regions, allowing for assessments at various scales, such as state, eGRID, NERC, or national levels. This flexibility facilitates the creation of time-step life cycle inventories for grid mixes at different spatial resolutions.

Results/Lessons Learned:

We have already performed environmental impact analysis of 134 regions from the ReEDS model across 70 different standard scenarios for 2020 through 2050. These updated life cycle inventory databases are available for use by LCA practitioners. Analyzing these standard scenarios across different environmental metrics provides a holistic understanding of the pathways the electricity generation sector in the US can evolve into the future.

Conversion of captured carbon dioxide (CO2) into valuable products for various applications can reduce greenhouse gas (GHG) emissions and support the achievement of net zero carbon emission goals. Photocatalytic conversion of CO2 is a promising carbon conversion pathway that utilizes solar irradiation and can be carried out at low temperatures and pressure and without high energy demand to convert CO2 into fuels or chemicals via photocatalytic reduction of CO2. However, the photocatalytic CO2 reduction process is still at a low technology readiness level, mostly at the lab/pilot scale. Life cycle analysis/assessment (LCA) can help to determine the potential environmental impacts and net life cycle GHG reductions of conversion technologies from a consistent and unbiased viewpoint, which is crucial for the commercialization and acceptance of CO2-derived products in the marketplace. Current literature mostly focuses on the development of photocatalysts and photocatalytic CO2 reduction processes to enhance CO2 conversion efficiency. However, only a few consider the LCA of photocatalytic carbon conversion. This study demonstrates an LCA of the photocatalytic CO2 conversion process to produce methanol using a TiO2 - based photocatalyst to evaluate the potential environmental impacts and capabilities of CO2 utilization. The LCA model considers solar irradiation as a parameter to explore changes in LCA results due to different solar irradiation values and different methods for separation of produced methanol from unreacted CO2 and H2O. It also includes the recycling of CO2 and H2O and photocatalyst degradation in the LCA models. This study uses openLCA 2.0 to develop the LCA model and utilizes the NETL-modified TRACI 2.1 impact assessment method to calculate the potential impacts in different categories. The potential impact of photocatalytic conversion of CO2 to methanol is compared to conventional methanol production via a steam methane reforming process. The study also includes scenario analysis to compare LCA results considering different technology scales, changes in key parameters, and electricity mix. This presentation describes the modeling approach, scenario analysis, key results, and uncertainty associated with this LCA.

A core drawback of conventional life cycle assessment (LCA) is its failure to account for the temporal dynamics of the technological and environmental background in which a technology is assumed to operate. The temporal implications of the background system can be particularly relevant when evaluating systems in an early stage of development as they usually require time-intensive research and development effort. The use of static life cycle inventory databases can also result in the misrepresentation of the environmental impacts of technologies with operational lifetimes spanning decades since temporal changes in the background system, such as the decarbonization of the electrical grid, are not commonly accounted for. In a context where achieving near-term climate targets depends heavily on advancing technologies currently at a low technology readiness level, capturing the systematic changes in background dynamics and supply chains is critical for the accurate assessment of technology potential and effective decision-making.

This study explores the application of prospective LCA (pLCA) to evaluate the environmental impacts of sustainable aviation fuel pathways. Specifically, the research focuses on comparing two pathways: one involving the conversion of corn grain-derived ethanol to a jet fuel blendstock and the other converting algal oil to jet fuel through hydrotreating esters and fatty acids. The pLCA model leverages data on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) to transform life cycle inventories of key sectors, such as power, cement, steel, and transportation fuels, across different futuristic scenarios. Data for life cycle inventory transformation includes outputs from a “middle of the road” SSP, which aligns with the objectives of the Paris Agreement, and a RCP assuming a global mean surface temperature of 1.6°C (RCP 2.6) by 2100. The transformed pLCA database is then coupled to engineering process models of the SAF systems to simulate the well-to-wake life cycle greenhouse gas (GHGs) emissions over their lifetime. Moreover, potential tax credits derived from reducing GHGs over time will be evaluated through techno-economic analysis.

The preliminary pLCA underscores the significance of incorporating supply chain dynamics into LCA. Results reveal that conventional (static) LCA methods may provide a distorted view of environmental impacts compared to dynamic LCA. For instance, results challenge the notion that algal-based jet fuel has a higher global warming potential (58 g CO2E MJ-1) than the ethanol-to-jet pathway (53 g CO2E MJ-1), if static LCA methods are used. Results from the pLCA demonstrate that algal jet fuel eventually achieves a lower life cycle carbon intensity than corn-derived jet fuel, with estimated GHG emissions of 27 g CO2E MJ-1 ¬¬¬and 39 g CO2E MJ-1 by 2050 respectively. The change in emissions observed in the algal pathway is primarily due to reductions in the carbon intensity of the electrical grid. Based on reductions in GHG emissions, both pathways are potentially eligible for tax credits under current policies. In conclusion, preliminary work emphasizes the importance of considering background system dynamics when evaluating energy-intensive technologies such as SAF pathways and underscores the importance of comprehensive and dynamic LCA methodologies in shaping informed decision-making and policy development.

Packaging significantly benefits society by helping to enable modern life through the transportation and storing of goods, playing an essential role in product protection and preservation, and influencing consumer buying decisions. Materials, including plastics, metals, glass, paper, and combinations thereof, play important roles in packaging design for industrial, medical, agricultural, food, and non-food packaging applications. However, the increasing consumption of packaging materials that can be associated with economic growth and prosperity have raised interest in assessing the sustainability of these materials and mitigating their potential environmental impacts. This study presents a comprehensive life cycle assessment comparing the potential environmental impacts of polyethylene-based (PE) packaging and alternative materials such as metals (steel and aluminum), glass, paper, and combinations. Nineteen packaged products were assessed across five prevalent PE packaging applications (collation shrink film, stretch film, heavy-duty sacks, non-food bottles, and flexible food pouches) based on five environmental footprint indicators, Global Warming Potential with and without biogenic carbon, Fossil Resource Use, Water Scarcity, and Mineral Resource Use. Data was gathered from physical samples and supplier specifications. Life cycle inventory models were developed in EcoImpact-COMPASS software, which integrates ecoinvent as its primary background data source. The results show that for the five potential impact categories and 19 alternative solutions considered, PE-based packaging had a lower potential environmental impact in 77 of 95 (81%) packaged product comparisons. The study highlights the material efficiency of PE-based packaging as a significant factor in its reduced environmental impact and suggests that the environmental impacts are a function of packaging weight, design, and the inclusion of non-paper components in paper-based alternatives. This panel reviewed ISO 14040/44 complaint LCA study provides valuable insights for stakeholders and decision-makers in the packaging industry and beyond, contributing to a greater understanding of the potential environmental impacts of various packaging materials.

Critical Infrastructure Systems (CISs) are interdependent to maintain normal operations of the nation-wide economy and social well-being. Due to climate change and the growing population, the occurrence of natural hazards has increased. Recent worldwide events have highlighted that interdependencies within CISs increase the potential for cascading failures and amplify the impact of both large and small-scale initial failures into events of catastrophic proportions. Such interdependencies may cause additional vulnerability and cascading failures in a disaster event. Therefore, identifying and classifying interdependencies and assessing their impact is crucial to prevent or mitigate such unfavorable consequences and enhance resilience in the long term. To better understand CISs for planning, maintenance, and emergency decision-making, modeling and simulation of interdependencies across CISs has recently become an essential field of study. However, more technical guidance and consistent terminology are needed to ensure practical applications of interdependency models. In this study, we compiled and identified classifications of interdependencies and described a new comprehensive classification. Expanding our understanding of how critical infrastructure systems operate in concert is essential to anticipate potential disruptions, manage the impacts, and develop adaptation measures for future conditions.

Critical infrastructure interdependencies are fundamental when assessing the resilience of infrastructure, systems, and communities they serve. Due to the importance of their secure and reliable operations, understanding the behavior of CISs – particularly when stressed or under attack – is essential. Models and simulations have the potential to provide substantial insight into the complex nature of their behaviors and operational characteristics. Several modeling and simulation approaches under development today directly address interdependencies and offer considerable insight into critical infrastructures' operational and behavioral characteristics. We reviewed the existing literature and catalogued existing modeling and simulation approaches. For each type of interdependency model, fundamental assumptions and detailed implementation methods are discussed, explaining precise application areas for stakeholders, advantages, and limitations/opportunities. This study helps decision-makers and stakeholders plan, design, and operate more effectively and efficiently and act promptly and effectively to protect infrastructure systems from cascading failures. The findings of this study will help identify and prioritize potential resilience strategies in the broader perspective of long-term adaptation planning and sustainable development.

The 2022 Southwest Airlines Scheduling Crisis, resulting in approximately 15,000 flight cancellations, demonstrates the challenges of structuring infrastructure systems and their knowledge-making processes for increasingly disruptive conditions. While the point-to-point configuration was the focus of immediate assessments of the failure, it became rapidly evident that the crew-assignment software was unable to operate effectively due to the scale of disruption. The airline failed to recognize environmental shifts associated with internal and external complexity, leaving operations vulnerable to a known potential risk: computer and telecommunications failures due to an extreme weather event resulting in knowledge systems failures. The cascading failures of the crisis emphasize the necessity to invest in adaptive capacity prior to catastrophic events and provide a lesson to other infrastructure managers pursuing resilience in the face of increasingly uncertain environments.

The presentation will also cover some material about de/centralization from 'Centralization and decentralization for resilient infrastructure and complexity (DOI: 10/1088/2634-4505/ac0a4f)' which serves as a foundation of the perspective case study explored above. The abstract of that foundational work is listed below for reference:

The capacities of our infrastructure systems to respond to volatile, uncertain, and increasingly complex environments are increasingly recognized as vital for resilience. Pervasive across infrastructure literature and discourse are the concepts of centralized, decentralized, and distributed systems, and there appears to be growing interest in how these configurations support or hinder adaptive and transformative capacities towards resilience. There does not appear to be a concerted effort to align how these concepts are used, and what different configurations mean for infrastructure systems. This is problematic because how infrastructure are structured and governed directly affects their capabilities to respond to increasing complexity. We review framings of centralization, decentralization, and distributed (referred to collectively as de/centralization) across infrastructure sectors, revealing incommensurate usage leading to polysemous framings. De/centralized networks are often characterized by proximity to resources, capacity of distribution, volume of product, and number of connections. De/centralization of governance within infrastructure sectors is characterized by the number of actors who hold decision-making power. Notably, governance structures are often overlooked in infrastructure de/centralization literature. Next, we describe how de/centralization concepts are applied to emerging resilient infrastructure theory, identifying conditions under which they support resilience principles. While centralized systems are dominant in practice and decentralized systems are promoted in resilience literature, all three configurations—centralized, decentralized, and distributed—were found to align with resilience capacities in various contexts of stability and instability. Going forward, we recommend a multi-dimensional framing of de/centralization through a network-governance perspective where capabilities to shift between stability and instability are paramount and information is a critical mediator.

Fueled by climate change and policies that value property over livelihoods, there is an emergent and compounding problem regarding natural disaster losses in the US. It is indisputable that the costs and burdens of climate change impacts will not be borne equally by individuals in society. Losses due to disasters are only increasing and society will be faced with how to pay for the damages. Once a disaster surpasses the capacity of state and local governments to respond adequately, the federal government, through the Federal Emergency Management Agency (FEMA), provides disaster management and aid. While capacity of counties is known to be quite heterogeneous, the federal government treats local capacity as being equal. This research aims to try to identify patterns of aid allocation with respect to various economic and disaster parameters.

This research adds to the literature through the development of two new disaster resilience metrics, average Burden, and average Donor-Donee Ratio, each defined at the county level over the years 2010-2019 for the contiguous United States. Average Burden factors in individual county GDP and information tabulated from FEMA’s National Risk Index. This metric can be utilized to understand how the proportion of expected disaster losses compares to its overall economic viability, another way of viewing coping capacity. Average Donor-Donee ratio factors in a county's grants from FEMA’s Public Assistance (PA), Individual Assistance (IA), and Hazard Mitigation Grant Programs. This metric provides a benchmark comparison for countys’ aggregate disaster funding to the federal income taxes paid to the government, capturing financial redistribution of federal dollars. This research finds that there are in fact spatial patterns in both metrics across the US.

Furthermore, machine learning provides a descriptive analysis of relative coping capacities and federal aid redistribution through the ten-year snapshot this analysis provides. A partitioning clustering algorithm, k-medoids was applied to the tabulated average Donor-Donee Ratio and average Burden metrics with the addition of a population control. A high-level overview of clustering results shows that most counties are classified by moderately reduced coping capacity (Burden) and are associated with higher-than-average disaster aid redistribution (Donor-Donee Ratio). These results indicate that the federal disaster aid system is working as intended. Although, this research opens the conversation into the relationship between beneficiaries of federal disaster funding and the inherent coping capacity of counties. Sensitivity analyses including the addition of disaster frequency to the model yield additional insights into funding allocation with respect to this dimension. This research provides a starting point for future studies that are conducted at a finer granularity may begin to understand why these spatial patterns emerged in the first place.

Our urban systems and their underlying sub-systems are designed to deliver only a narrow set of human-centered services, with little or no accounting or understanding of how actions undercut the resilience of social-ecological-technological systems (SETS). Embracing a SETS resilience perspective creates opportunities for novel approaches to adaptation and transformation in complex environments. We: i) frame urban systems through a perspective shift from control to entanglement, ii) position SETS thinking as novel sensemaking to create repertoires of responses commensurate with environmental complexity (i.e., requisite complexity), and iii) describe modes of SETS sensemaking for urban system structures and functions as basic tenets to build requisite complexity. SETS sensemaking is an undertaking to reflexively bring sustained adaptation, anticipatory futures, loose-fit design, and co- governance into organizational decision-making and to help reimagine institutional structures and processes as entangled SETS.

Centralized health networks in the United States were designed to a provide high-quality, consistent standard of care. But as technology improves and inter-network communication becomes easier and easier, we must look at new ways to provide equitable, high-quality healthcare to as much of the population as possible. As we consider how to do this, we must also minimize the environmental impact of providing these services to prevent exacerbation of climate change and the associated health impacts. One potential way to minimize the impact of improved health services is to consider what technologies are available as point-of-care platforms. Specifically, point-of-care biomedical imaging technologies are rapidly evolving to not only image but use machine learning to extract medical metrics. Therefore, one way to reduce the greenhouse gas (GHG) impact of centralized health networks is to offload some of the routine imaging capabilities to decentralized networks through these technologies.

In this work, we selected one technology and one demographic region as a starting point for assessing the potential for decentralization of echocardiograms (ECHO), with the goal that this can serve as a framework for assessing a larger scope of healthcare decentralization. We identified Milwaukee, WI as the location for this study because of the available health and transportation data provided by the local health networks and state agencies. ECHO scans were selected as the target scan to assess because they are conducted with ultrasound technology, which is available at many point-of-use scales. The ButterflyIQ system, which is an ultrasound device that can be plugged in to an iPhone, was selected for this analysis because of its market-readiness and push towards incorporating diagnostics. The decentralized network was identified through current out-patient imaging facilities that do not currently conduct ECHOs in the specified area. Our results are expected to compare the GHG emissions associated with business-as-usual travel for ECHO scans in Milwaukee, WI to the GHG emissions associated with our established decentralized network. Future work will assess how the life-cycle impact of the technologies will impact these results.

Climate change exacerbates power outage events that pose significant risks to local economies and can even endanger citizens' lives. An electric vehicle (EV) equipped with bidirectional energy exchange capability and vehicle-to-home (V2H) technology, can serve as a battery resource and provide backup power to meet residential energy needs during a power outage, aiming to curb health hazards caused by overheating or cold temperatures. We simulate the electric V2H system in nine US climate regions, over four seasons, and during short-term, long-term, and extremely severe power outage events. We propose new resilience metrics to evaluate EV-enabled household energy resilience that measures the time duration that EVs can serve household energy needs during power outages, and mobility resilience, which represents the remaining driving range of EVs after the outage. Our findings indicate that contemporary EV models, even when their state of charge is 50%, can meet residential energy needs during a 12-hour outage in mild seasons (i.e., spring and autumn), except for communities in the Central, West North Central, and East North Central regions due to lower temperatures and higher household energy needs. The resilience of the household energy system during long-term outages is affected by the start time of the outage, heating and cooling power requirements, and daily travel needs. During extremely severe power outages, EVs can protect residents from cold stress over multiple days. Our methods and results inform research and practice on smart management of the integrated residential and EV energy system and propose measures for mitigating the impacts of extreme weather events.

As the climate crisis intensifies, switching to renewable energy remains a critical piece of the solution to ensure rapid decarbonization. However, renewable energy generation is highly reliant on the ambient environmental conditions, making it difficult to predict output in the short-term, as well as determine the optimal locations of sites in the long-term, given climate change is likely going to impact local environmental conditions. Accounting for the impact of climate change is particularly difficult, as there remains uncertainty related to the magnitude of climate change within the mid- and long-term in addition to the relatively unknown impacts of climate change on generation capacity of renewable energy technologies, particularly solar. In this work, we aim to fill this gap by leveraging machine learning to investigate the impact of climate change and associated uncertainty on solar power generation. Using data from the National Renewable Energy Laboratory (NREL), we investigate the solar power output and the predictability of that output under climate change across 10 different solar panel technologies. Our goal is to answer the questions: (a) How will climate change impact solar power generation; and (b) Do those impacts differ across panel technology? To do this, we will leverage a complex neural network to predict the optimized output of various solar panel technologies, given the climatic conditions in the surrounding area. We expect that the optimal panel for total power output will change over different climate scenarios. Further, this panel might not be the most predictable (e.g., have the least error in our model), which would create issues of uncertainty for developers. To test these hypotheses, we will present results from a study focused on Puerto Rico. Puerto Rico represents a smaller test site, yet the island has experienced a multitude of climate-related disasters recently that have devastated their energy system. As the island looks to rebuild, decentralized renewable energy has been investigated as possible option for a more resilient system under climate change. This presents an opportunity to investigate how solar power output could evolve over time on the island and which panels might be optimal, given their unique climate conditions. It will also provide a baseline for extending the modeling framework to other regions in the US. Ultimately, the results will provide critical insights into the sustainability of solar power and the viability of certain technologies over the long-term.

Wildfires are a significant threat in California, burning more than 1.7 million acres per year and costing the state billions of dollars. In addition to directly damaging homes and infrastructure, fires can destabilize soil, leading to debris flows when significant precipitation events occur in recently burned areas. Electric utilities own and operate infrastructure located in areas that are vulnerable to both wildfires and post-fire debris flows. While in recent years there has been a focus on understanding the risks of wildfire ignition caused by power infrastructure and identifying the responsible electric utilities to mitigate these risks, there has been little work to understand the vulnerability of utilities themselves to wildfires and post-fire debris flows. This is partly due to the complex nature of these hazards, defined by multiple disciplines and dynamics. In this work we assess the vulnerability of transmission lines, electric substations, and power generation facilities in California to wildfires and post-fire debris flows. We assess wildfire risk by overlaying geospatial power infrastructure data with wildfire probability from Cal Adapt and post-fire debris flow threat levels from recent modeling efforts. We assess risk in today’s climate and in the future. To understand uncertainty in future climate impacts, we use two climate models: the Canadian Earth Systems Model, which produces an average prediction of future climate and the Hadley Centre Global Environment Model, which produces a warmer, drier estimate. In conjunction with both climate models, we use two representative concentration pathways (RCPs): RCP 4.5, representing a future in which greenhouse gas emissions begin to decrease in the mid-21st century, and RCP 8.5, in which emissions increase through the end of the century. We assess risks at the state level and identify vulnerable electric utility companies. We find that under current conditions, electric utility assets in Northern California are most vulnerable, being located in areas with up to 40% fire probability compared to the state average of roughly 10%, and high risk for post-fire debris flow. Under future conditions, we find that fire risk to assets may increase substantially in the Sierra Nevada and Northern Coast regions, and post-fire debris flow risk may increase substantially in the coastal ranges and North Central California. However, there is large uncertainty in future risks across climate scenarios. While many electric utility companies have primarily low-risk power infrastructure assets, we find that some smaller utilities may be particularly vulnerable due to the majority of their transmission lines and substations being located in high risk areas. Power generation is also vulnerable to wildfire and post-fire debris flow, with geothermal, hydro, and nuclear power plants in the state facing the highest risks in current and future climate scenarios. These results provide a basis for decision-making around the allocation of resources for infrastructure resilience to wildfire impacts.

Despite a rapid push to digitize infrastructure data, there remains a dearth of readily available water distribution network (WDN) data to assess resilience challenges such as vulnerability assessment, proactive maintenance, and network reconfiguration. Synthetic models –realistic network models that imitate real-world networks’ appearance and behavior – have been rigorously developed and tested for infrastructures – namely electricity transmission and distribution – but have not yet become pervasive for water systems. We describe the challenges of developing synthetic WDNs using the case studies including Puerto Rico, New York City, Phoenix, Kentucky, and Atlanta. The synthetic WDN models are able to estimate network topography, pipe diameter, pump location and performance, and with EPANET hydraulic performance (see synthetic.resilientinfrastructure.org for additional background). The accuracy of synthetic WDN models is estimated using a locations where true data are available. While realistic networks and hydraulic performance can be synthesized with minimal data (i.e., roadway networks and locations of WTPs), the provision of basic data (i.e., pipe diameters, pump locations and performance, and tank locations) can significantly improve accuracy. The opportunities for synthetic WDNs are described both in characterizing vulnerable assets and network configurations to disrepair and hazards (e.g., climate extreme events) as well as power system outages that cascade to the WDNs.

Access to clean water is critical for drinking, hygiene, and to ensure a stable food supply, yet water insecurity continues to plague billions. Climate change is only expected to exacerbate water stress, leading to less freshwater in the cryosphere, increases in drought, and general water cycle instability. With conventional water supplies at risk, there is a need for innovative technologies capable of generating usable water from atypical sources. Desalination is one such technology already in use in many areas around the world. Seawater desalination via reverse osmosis is a leading desalination technique, but generally recovers up to only half of the inlet water. The rest forms a concentrated brine that requires disposal, most often via discharge into nearby water bodies. Increasingly, there is a push for the adoption of zero/minimal liquid discharge (ZLD/MLD) technologies that recover additional water from this brine while reducing the liquid volumes requiring disposal.

Understanding the implications of ZLD/MLD treatment trains requires a systems approach. Specifically, the analysis presented here consists of technoeconomic and life cycle analysis results of seven overarching treatment trains. These treatment trains consist of different combinations of pre-treatment, concentrator, crystallizer, and disposal technologies, resulting in 75 treatment train configurations. The levelized cost of water (LCOW) and specific energy consumption (SEC) are calculated across a range of potential water recoveries, with life cycle analysis of the ZLD/MLD processes informing emissions and water consumption estimates as well. Combined, the interplay of these metrics provides insight into the tradeoffs at the center of ZLD/MLD processes. The energy-water nexus in particular plays a critical role, with increasing water recovery coinciding with increasing energy demands and increasing energy demands resulting in increasing water consumption. Ultimately, ZLD/MLD treatment trains increase water recovery from desalination brines, but tighten the connections between energy and water systems. The feasibility of such systems will depend greatly on the severity of water insecurity in a location, access to infrastructure, and access to disposal options.

Climate change and sea level rise (SLR) are increasing the risk of tidal and storm flooding in coastal, urban communities. By 2100, sea level is expected to increase at least another three feet on the East Coast of the United States, particularly in Norfolk, VA. Developed watersheds face special challenges in resilience and habitat restoration, requiring deeper involvement with surrounding communities. With a focus on the Southside of Norfolk, this project will study Campostella and Campostella Heights as underserved communities (addressing to UN SDG 16: Peace, Justice, and Strong Institutions) that are vulnerable to rising flood risk. As a community-based research project, the objectives are: (1) To investigate the flood damage, vulnerability, and risk perception in the Southside Norfolk area, (2) To analyze the effect of compound flooding on the Campostella and Campostella Heights neighborhoods at both buildings and urban levels (addressing UN SDG 11: Sustainable Cities and Communities), (3) To propose nature-based solutions (at multiple scales) to improve resilience as well as wildlife habitats in the coastal community case studies (addressing UN SDG 14: Life Below Water)., and (4) To enhance public awareness by full community involvement in design process with a focus on adaptation before significant storm and flooding damage occurs. As for research methodology, a field investigation (observation, interview, and questionnaire) will be performed to collect data related to flooding in the Campostella and Campostella Heights neighborhoods. As a community-based project, civic league members and the residents of the community will be directly involved in data collection, identification of hot spots for recurrent nuisance flooding, evaluation project alternatives, and getting feedback about design solutions to reduce the exposure of the community to existing and future coastal hazards. In conclusion, all survey-based data will be summarized and integrated to develop community-scale adaptive design strategies, utilizing green infrastructure and nature-based solutions, to mitigate flooding and enhance urban resilience in this vulnerable area. Also, we will train the community members on potential interventions that may help in alleviating their flooding problems, while engaging them in planning, data collection related to flooding, and disseminating project results to the larger community to develop community support for the implementation of coastal resilience solutions.

Urban flood is a significant threat to cities worldwide. The rapid nature of flood water accumulation allows little evacuation time causing significant loss of life and damage to property. Issues like global warming, deforestation has been the reason for changing rainfall patterns questioning the resilience of existing flood infrastructure. Most of the existing urban flood models do not capture real time rainfall intensity, flood extents and depth of submergence making the of the model invalidate. Social media platforms (i.e. twitter) generate vast amount of data during a crisis or disaster situation. People tend to share information about on-ground-situation such as affected areas, water levels, experiences, inconveniences, or even immediate community responses. This rich yet underutilized resource has the potential to be used as a real time- user generated data source. The study presents an approach to hybridize social media data particularly tweets with existing flood mitigation measures for improved resilience and capacity building. Firstly, we collected tweets using particular keywords such as ‘flood’, “flooding’, ‘inundation’. The tweets were analyzed to focus on geo-tagged parts that provide specific location-based information. The data is then correlated to existing flood indices such as intense rainfall or response mechanisms like water level sensors and emergency response strategies for validation and acceptability. The study also evaluates the infrastructure in current urban setting to find areas that can be benefitted from the hybridization of social media data. Overall, the model offers a dynamic and more adaptive measure for disasters in terms of improved preparedness, response, and recovery. The framework is expected to enhance the decision-making process including emergency responses to people affected with flooding events. There might be some limitations such as processing the tweets even with sophisticated technologies may not ensure enough accuracy specially when the user profile location and tweet location does not agree. Nevertheless, the research holds significance for urban planners, environmental scientists, hydrologists, policymakers etc. offering a novel approach to integrate digital social media data for an improved model that can be adopted in cities worldwide.

Urbanization and socioeconomic development are occurring rapidly across Sub-Saharan Africa, where the impacts of rapid urbanization compound more frequent and intense extreme weather events driven by climate change. Hazards such as urban flooding are affected by where urban development occurs, increased impervious surface cover, and the construction of inadequate stormwater infrastructure to handle present and future precipitation events. The compound hazards of rapid urbanization and flooding are particularly evident in cities like Kigali, Rwanda, where highly urbanized sub-catchments increase runoff, resulting in frequent and destructive floods. Improving urban resilience and sustainability requires policymakers to develop climate-resilient stormwater systems to better manage urban floods, protect infrastructure, and preserve water resources. However, urban planning and flood management in rapidly growing urban areas face challenges due to a scarcity of data and limited institutional capacity to establish and maintain monitoring networks and integrate data into the decision-making process. Building footprints and data on historic formal and informal urban development are critical to urban stormwater planning, capturing rapid population changes, and describing who is at risk from extreme precipitation and flooding events. Furthermore, building footprints describe high-resolution changes in impervious surface area, which alters runoff and urban flood extents.

Our work aims to develop local-level time-series building footprint datasets for major urban areas in East Africa from 2005-present by leveraging historical high-resolution satellite imagery, which provides access to timely, consistent, multi-decadal data on urban areas globally. Machine learning models like deep convolutional neural networks can extract building footprints from this high-resolution satellite imagery. However, existing building footprint extraction models are predominately trained and validated on ground truth data from high-income countries, where urban areas look and evolve differently than in low-middle income countries. Therefore, these models systematically misclassify and undercount building footprints in dense, often informal, urban settlements whose populations are vulnerable to urban flood hazards. Our work aims to improve the quality of multi-temporal building footprint datasets in developing urban areas without additional, costly ground truth data by pairing existing neural network models for building footprint extraction with a Hidden Markov Model (HMM). A HMM enforces physically consistent probabilistic time-series relationships between extracted building footprints through transition probabilities (how likely a building will be constructed, torn down, or remain the same each period) and emission probabilities (how accurately the neural network model extracts building footprints from a satellite image). The combined model harnesses every year of satellite imagery to improve the consistency and accuracy of past and future building footprints. We present a set of experiments and initial results demonstrating the benefits of the paired method. Furthermore, we compare footprint corrections from HMMs calibrated with modeled data to HMMs calibrated with ground-truth data, highlighting both the potential and associated uncertainty for improving time-series building footprint data without additional ground-truth data collection. We will present the initial results of a comparative study of the spatiotemporal urban development and implications for climate change risk and adaptation opportunities of urban areas in East Africa.

The engineering profession has a responsibility for practicing in a way that cares for the environment and human life – in short, to be thoughtful practitioners capable of integrating different dimensions of sustainability in their work. This study develops and evaluates a procedure for assessing the sustainability content in engineering curriculum, and then applies this workflow to the Faculty of Engineering at the University of Toronto. The study achieves this goal by first developing a framework to define sustainability for engineering and then using that framework to analyse curriculum respectively via course descriptions, course syllabi, and instructor surveys to triangulate results across modes. The University of Toronto is chosen as it is regularly ranked as (or among) the top Engineering programs in Canada and is among the largest undergraduate engineering student enrollments across the country.

The meaning of sustainability continues to evolve and as such, it is challenging to define in general and even more so within engineering. A commonly cited definition for sustainability is from the 1987 Brundtland Report: “meeting the needs of the present without compromising the ability of future generations to meet their own needs”. To help operationalize this definition, the United Nations outlined 17 Sustainable Development Goals (SDGs) in 2015. While these SDGs are guiding sustainability efforts, they are not sufficiently specific to engineering. Several prior studies analyze sustainability within the curriculum both in general and specifically for engineering. However, there is no single framework that captured all engineering concepts for sustainability, and minimal prior comparison of different approaches (i.e., source documents) for analyzing curriculum.

This study developed a new framework and codebook to define sustainability, starting with the three pillars of sustainability: environmental, economic and social. Then, more specific sustainability themes were collected from the SDGs, the Principles of Sustainable Engineering (PSE), the Sustainability Tool for Assessing Universities’ Curricula Holistically (STAUNCH), themes found in the literature that focused on sustainability in engineering and other local/national frameworks such as the Canadian Engineering Grand Challenges (CEGC) and Canadian studies on engineering and SDGs. These themes were classified into the pillars of sustainability, with a detailed list of topics being provided for each theme. A fourth pillar of professional responsibility was added and include additional themes that did not map onto the three pillars. The framework was reviewed by experts in the field of sustainability before being finalized with 20 themes under the four pillars of sustainability.

The framework was used to qualitatively analyse the undergraduate engineering curriculum in stages, going progressively deeper, by starting with the course descriptions, then syllabi and finally faculty perspective. The results indicate that the environmental pillar is most prevalent in the curriculum, followed by economic and social, with increasing sustainability content being identified as the study progressed from descriptions to syllabi to faculty surveys. The civil engineering discipline had the highest sustainability content while electrical and computer engineering had the lowest. The results also indicate that sustainability tend to be taught by pillar rather than connecting the pillars to one another in a holistic manner. The presentation will conclude with a discussion of the benefits and drawbacks from using different modes (descriptions, syllabi surveys) for assessing curriculum, and advice for conducting similar studies at other universities.

Engineering education for sustainability requires pedagogical approaches that help resolve complex global problems. A possible pedagogical approach is transformational learning. Transformational learning can include many aspects such as holistic approaches, specific skills, competencies, attitudes and transdisciplinary work. A common thread among these aspects is reflective practice and critical thinking working together. Reflective practice encourages deeper thinking, considers personal/professional experiences, assesses biases, and questions the decision-making process. Reflective practice in education is common in many fields like medicine and management but is less common in engineering. This study explored whether reflective practice changed sustainability perceptions in engineering students after exposure to a sustainability course focusing on life cycle assessment (LCA). This study is a pre-post qualitative analysis covering engineering, sustainability and reflective practice, which does seem not to be previously done.

In this study, undergraduate engineering students taking the LCA course received an assignment, as part of the course, where they were asked to reflect on sustainable development, environmental stewardship, and engagement, at the beginning of the course and then again at the end of the course. The students were asked to participate in this study by giving permission to use their statements. A total of 86 statements, 43 pairs, representing ~22% of the class, were analysed qualitatively, both deductively and inductively. The deductive coding was completed using a sustainability in engineering framework, followed by inductive coding to glean emerging themes. The coding was then compared between the beginning and end of the course.

For the deductive coding, the sustainability in engineering framework comprised four pillars: environment, social, economic and professional responsibility. Themes in the environment pillar were the most prevalent compared to the other three pillars and overall, themes increased from the beginning of the course to the end of the course in all four pillars, with professional responsibility showing the largest increase. For the inductive coding, there were similar themes emerging like prioritizing cost, advocacy, greenwashing. However, some themes that emerged from the statements at the end of the course included the limitations of the LCA process itself, nature of the industry and difficulty in creating change, lack of confidence by students regarding their ability to advance sustainability change as new engineers, and internal conflicts about needing to earn money to live versus practicing engineering in sustainability fields where opportunities are considered less lucrative. These are promising results for integrating reflective practice within engineering education for sustainability due to the increasing depth and nuance of reflection and advancing critical thinking about sustainability from the beginning to the end of course.

Canadians have one of the highest annual per capita emissions compared to other regions around the world at ~15 tCO2e. This carbon footprint is due in part to activities like using private fossil fuel cars, taking long haul flights, personal diets that include substantial meat and dairy products consumption and personal residence heating/cooling. Global average annual per capita greenhouse gas (GHG) emissions need to be reduced to ~2.8 tCO2e by 2030 to help maintain global warming below 1.5°C pre-industrial levels (Ivanova et al. 2020). While technological advancement is important in reducing the per capita emissions, humans could also take responsibility in reducing their personal carbon footprint, especially in countries like Canada where the per capita emissions are larger than the global target. Prior work has shown that there are misconceptions about the personal actions that contribute significantly to GHG emissions. Uncertainty also exists about whether knowledge (literacy) about these high impact actions affects human pro-environmental behaviour.

This present study is investigating the relationship between carbon literacy (knowledge) and pro-environmental behaviour (operationalized through a carbon footprint estimation) among students at the University of Toronto (UofT). This study builds on a past study that measured this relationship among undergraduate engineering students at the UofT in 2021. The choice of students in Toronto seems apt given that Toronto is the most populous city in Canada and reducing per capita emission in Toronto could have an overall reduction in Canada’s emissions.

In the original study and this current study, the students’ carbon literacy and carbon footprints data were estimated using an instrument designed to collect information about their personal actions (high, moderate or low impact) and their view on how impactful these actions are. Carbon literacy was calculated based on the participants’ accuracy in identifying the high, moderate and low impact actions. The students’ carbon footprint was estimated using a life cycle assessment approach (LCA) with emissions being augmented by upstream factors and manufacturing. The instrument from the original study was enhanced to include additional items like expenditure and readministered to all UofT students.

In the original study, the average carbon footprint (among the survey items included) was ~4.8 tCO2 which was lower than average for Toronto residents, Ontario and Canada overall but still higher than the target of ~2.8 tCO2. Carbon literacy was generally more accurate for higher impact actions, with more misconceptions appearing in relation to the moderate and low impact actions. The overall relationship between pro-environmental action and carbon literacy was weak. The relationship between pro-environmental action and carbon literacy showed that for high impact actions, there was a slight positive correlation in carbon literacy and pro-environmental actions whereas for moderate and low impact actions, there was a negative correlation. This study will illuminate what differences exist (if any) among the students in different departments at UofT or between graduate and undergraduate students and can recommend future actions to reduce per capita emissions.

Sustainable Project Management (SPM) has been defined (Silvius and Schipper, 2014, p.79) and is recognized as an emerging school of thought in the field of project management. Despite recognition of the importance of integrating sustainability and project management, there remains a lack of understanding on how to operationalize sustainability in practice and apply it to specific projects. The goals of this study are to explore 1) the role of project managers in promoting and implementing sustainability initiatives within their organizations, 2) how project managers learn about sustainability and its practical application to project management, 3) the barriers project managers experience in the implementation of SPM, and 4) the tool(s) project managers need to effectively implement sustainable practices into the projects that they manage. This presentation provides an analysis of survey and focus group data from project managers from a local PMI Chapter and an opportunity to engage and discuss tools, techniques, and methodologies for sustainable project management. The results of the analysis suggest four main themes: 1) There appears to be a drive to implement sustainability into practice by project managers, but the knowledge, training, and tools do not seem readily available. There is a need to incorporate sustainability competencies, tools, and methodologies into project management educational processes through curriculum development in university project management courses, accreditation training, and training workshops offered by the PMO or wider organization. 2) Most participants indicated that they practice sustainable project management and view themselves as sustainability change agents however there also seems to be limiting constraints such as a knowledge gap, formal training, and the availability of sustainability tools as well as the constraints of organizational culture and leadership. How then are the project managers engaging with sustainability within their projects? 3) Working within an organization with a sustainability focus appears to result in a higher percentage of project managers implementing SPM, however, does not completely guarantee SPM. Additionally, one can work at an organization with no public facing information about sustainability and still practice SPM. What are the other constraining factors involved? 4) An obstacle to implementing SPM may lie within the ambiguity of the term ‘sustainability’ for each individual project manager. Additionally, each industry may define, practice, and assess sustainability in different ways, suggesting the development of a toolkit resembling a resources guide that instructs best practices for the adoption of SPM to be much needed in practice.

The word sustainability includes many aspects related to human wellbeing, status of ecosystem health, policy and economics. Preference for one aspect over another often stems from personal ethical perspectives due to our experiences. Researchers and students in science, technology, engineering and mathematical (STEM) programs rarely have open discussion about what sustainability means to them nor ethical perspectives related to sustainability. Addressing this gap is critical for all sustainability researchers, but agricultural and food systems raise even more complexity about what ought to be done. To fill this gap, I created a 15-hour graduate seminar titled ‘Representing Sustainability in Agroecology and Industrial Ecology Research’ and shared a 90-minute conference workshop at ISSST 2023, both attended mostly by STEM sustainability researchers. The content of these offerings centered on environmental ethics and readings from agroecology and industrial ecology that touched on the ethical perspectives documented in these research fields. Both offerings began with an exercise on defining our positionality and articulating origin stories of interest in sustainability research, followed by a review of environmental ethics and group discussions about what aspect of sustainability our research addresses and from what ethical argument. Participants expressed little to no prior exposure to environmental ethics and that it helped them to consider the unspoken underpinnings of what defines and justifies their pursuit sustainability. Participants indicated that these activities helped to facilitate conversation among small groups about their commonalities and differences about what sustainability ought to achieve. There was also agreement that these discussions would help teams communicate better toward common goals.

Cities are and will continue to be affected by a set of problems considered wicked by the literature (Rittel & Webber, 1973) and the perception of these problems generates pressure on managers to create solutions that meet social demands (Botton, Pinheiro, Oliveira & Jesus-Lopes, 2021), as well as shows the need for innovative solutions and transformations (Veeckman & Temmerman, 2021). The adoption of Living Labs (LL) or Urban Living Labs (ULL) are considered tools for achieving innovation (Compagnucci, Spigarelli, Coelho & Duarte, 2021; Hossain, Leminen & Westerlund, 2019), which can help achieve more sustainable development (Evans & Karvonen, 2012), at different scales and themes. It can also contribute to the change of citizens through co-creation (Sauer, 2012), and is also considered a quadruple helix platform, which involves private companies, government, higher education and society as actors (Compagnucci et al., 2021). However, studies on this topic are concentrated in Europe, presenting a gap in studies that consider the characteristics and approaches to these laboratories in developing countries, such as Brazil (Amorim, Menezes & Fernandes, 2022; McCrory, Schäpke, Holmén & Holmberg 2020). This study, in progress, aims to point out the approach to sustainability found in Brazilian Living Labs or Urban Living Labs. This is a descriptive and exploratory research, with a mixed approach, carried out in two methodological stages for data collection and analysis. The first stage aimed to identify the Brazilian Living Labs and Urban Living Labs, initially carrying out a systematic literature review to understand how these initiatives have been studied in the Brazilian context, followed by searches: in the Directory of Research Groups of the National Council for Scientific and Technological Development (DGP/CNPq) of Brazil; on websites open to the public and through the use of the SnowBall method. The results obtained were the identification of studies that relate LL or ULL with Brazil, their geographic scope in the country, thematic focuses and the creation of an agenda for future research, aiming at the advancement of the theme based on Brazilian studies. In addition, a list was drawn up that identified 44 Brazilian laboratories, which includes some preliminary results such as activity status and description of LL or ULL. The next step, which consists of confirming the activities of the listed laboratories, will be carried out through the application of a survey. The variables found in theory, that characterize, dimension, and describe LLs or ULLs (McCrory, Holmén, Schäpke & Holmberg, 2022; Chronéer, Stahlbröst & Habibipour, 2019; Stten & Bueren, 2017; Almirall, Lee & Warehan, 2012; Niitamo, Kulkiki, Eiksson & Hribernik, 2006), were taken into account for preparing the survey questionnaire. The aim is to gather information that portrays the characteristics of Brazilian Living Labs or Urban Living Labs, as well as to expand knowledge regarding the approach they adopt to sustainability. This work is expected to assist actors in decision-making for innovative and more sustainable solutions and transformations in developing countries.

In this study we examine how a wide yet plausible range of socioeconomic, policy, and climate futures affects agricultural crop yields in the continental United States (CONUS) over the 21st century. We use the Global Change Analysis Model - USA (GCAM-USA) to simulate 8 different scenarios consistent with the Shared Socioeconomic Pathways - Representative Concentration Pathways (SSP-RCP) scenario framework. We combine two different socioeconomic pathways with four high-resolution climate projections for the US: two instantiations (e.g., hotter and cooler climate) each for RCP8.5 and RCP4.5. These climate projections are the result of applying a Thermodynamic Global Warming (TGW) approach derived from models with greater or lesser climate sensitivity (i.e., sensitivity on ensemble mean of projected temperature) in the Coupled Model Intercomparison Project Phase 6 (CMIP6). GCAM-USA requires data inputs specifying basin-scale crop yield over time, for which we use the global crop yield model Osiris to simulate climate impacts on agricultural productivity under the four TGW climate scenarios. Osiris takes as inputs gridded precipitation and near-surface air temperature at 12 km resolution over the CONUS, atmospheric carbon dioxide concentration, and applied nitrogen (i.e., fertilizer) to generate gridded crop yields. In addition to agricultural yield, the GCAM-USA modeling also includes the dynamic impacts of these SSP-RCP combinations on energy demands and water availability.

In this study we focus on a subset of the results to highlight key insights pertaining to agriculture. Overall, we find that production of corn, fodder herb, fodder grass and oil crop are particularly sensitive to socioeconomics and climate uncertainty. We find that despite the high population growth in the US under the SSP5 scenario, total crop production in the US decreases due to a drop in international demand for fodder herb, driven by lower global population, compared to SSP3. Under the SSP3 scenario, fodder herb production in the US increases due to higher global demand (particularly in India, China, and Pakistan), and corn expands in the US (mainly northeastern river basins) due to increased national corn demands. The RCP8.5 climate has a positive impact on most crop types, especially fodder herb and fodder grass production in most of the US basins, compared to RCP4.5. However, the southeastern basins experience decreased corn production under a high-emission warmer climate. We will report on the results available up to June 2024. This research provides valuable insights into sustainable agricultural planning across diverse climate and socioeconomic scenarios.

Due to the great demand of plastics and deficient waste management systems around the world, a considerable amount of plastic polymers end up in marine ecosystems. The environmental impacts of marine plastic pollution are vast, specially to marine fauna. A considerable part of this waste enters the ocean through river channels, both in the form of macroplastics and microplastics. Consequently, scientific community has focused on estimating the presence of plastic in river, warning the associated hazards. There is a need to explore new methodologies to refine these estimates and reduce potential errors. At the end of the last decade, a novel approach for detecting macroplastics has been explored: the use of artificial intelligence. In this context, and based on the state-of-the-art of the field, the present research focuses on the adaptation of the YOLOv8 pre-trained model through the application of transfer learning technique for the detection of macroplastics in aquatic environments. Thus, the main objective is to estimate the amount of macroplastic types present in the study section and determine what percentage of these are transported to the ocean. Consequently, we analyze the fluctuations in the detected amount of macroplastics in each record over the course of the hydrological year. A 1.1 km stretch from the Rímac River has been selected as the case study, whose waters flow through the city of Lima and discharge into the Pacific Ocean, representing one of the main rivers of the capital of Peru. Visual recording campaigns have been established, which include images and videos from the use of a drone during the period of a hydrological year (Set/23-Aug/24). Therefore, the temporality and bimodal behavior of the river are considered for detecting macroplastic. The images collected to date (~ 2000) were used to generate a 3D model (orthophoto model) of the study section. This model facilitates the identification and monitoring areas where there is a greater density of plastic waste. Additionally, the model enables the identification of 8 types of polymer classes present along the slopes and within the course of the river. Being Yolov8 a supervised model, the previously filtered images have been processed to create labels, that will be utilized as input to the model. Initial findings demonstrate that variations in each time window (imaging campaign) are caused by the natural washing of the watercourse, especially during flood stages. Certainly, YOLOv8, which relies on convolutional neural networks, differs from other architectures in its processing speed and ability to perform detections in a single pass through an image and it is currently used in a diverse range of applications in the field of object detection. Therefore, once trained for the detection and classification of macroplastics, it has the potential to expand its coverage to a broader area and extrapolate this methodology to other watercourses, providing practical utility in real-time monitoring of plastic debris.

Using a large (36 M) database of new and used vehicles listed online at 60,000 dealerships in the U.S. between 2016 and 2022, we provide high resolution, nation-wide estimates of resale value and mileage in the United States for battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and conventional gasoline vehicles (CVs). Previous estimates of both of these important metrics are based on limited or outdated data, or indirect measurements such as self-reported data from surveys. For resale value, we find that BEVs and PHEVs depreciate at faster rates than CVs and HEVs, though Tesla BEVs are a notable exception, depreciating slower than many CVs. Newer model year BEVs and those with larger ranges have significantly higher retention rates than older models with smaller ranges. Subsidies for new BEVs have a modest effect on resale market prices, with the $7,500 federal subsidy lowering resale prices by 3% on average. Disruptions from the COVID19 pandemic have affected affordability across all vehicles, with mean listing prices rising 37% and 39% for CVs and BEVs, respectively, from January 2020 to March 2022 in inflation-adjusted 2019 dollars. For mileage, we find that while HEVs and PHEVs are driven comparably to CVs, BEVs are driven significantly less, with BEV cars traveling on average 3,400 fewer miles per year and BEV SUVs traveling 2,900 fewer miles per year. Higher-range BEVs are driven further: 1,265 more annual miles for every additional 100 miles of driving range. We also find that the annual mileage gap between CV and BEV cars shrinks to just 660 miles in states with a $1.00 per gallon higher mean gasoline price. Our results indicate that while current BEVs are not being driven as much as conventional vehicles, they approach parity under conditions that already exist for some BEVs in some locations.

The rapid increase in food delivery services in China has resulted in a significant rise in the consumption of food and beverage containers (FaBCs), which has led to detrimental environmental effects. To promote sustainability within this sector, the adoption of circular economy (CE) strategies by consumers can play a crucial role.

To provide evidence for policy-making and ascertain the range of consumer-induced environmental impact reductions through CE measures, this study undertakes the following steps: Firstly, it builds a bottom-up community-level plastic FaBCs dataset in Guangzhou, China, maps the material flow of FaBCs and subsequently evaluates its current and alternative environmental impacts via a life cycle assessment. During the period of June to August 2022, a total of 131 orders from 45 restaurants were collected in the studied community.

The study conducted a community-based sample collection of food and beverage container (FaBCs) waste, which estimated that the average FaBCs waste generated per order was 95.73g. This waste was primarily composed of container, cutlery, and cup waste, which amounted to 64.93g/order, 17.30g/order, and 13.50g/order, respectively. The findings also revealed that the total quantity of FaBCs waste generated in the study area was estimated to be 436.7 tons in 2022, with polypropylene (PP) accounting for the majority of the composition at 73.9%. The total FaBCs waste generated in the study community can result in 1,300.39 tons of CO2 emissions, 2.09 tons of NOX emissions, 0.58 tons of PM2.5 emissions, 2.50 tons of SO2 emissions, 1.65 tons of BOD, and 7,750 m3 of water consumption in 2022. The adoption of the circular economy compound scenario, which incorporates refuse, reduce, reuse, and recycling elements, can result in a reduction effect ranging from 29.9% to 66.9% compared to the business-as-usual (BAU) scenario.

The study finds that all examined consumers' CE efforts show a visible environmental mitigation contribution, particularly in the accumulative CE strategies. By providing first-hand data on the material flow of FaBCs at the community level, this study offers a comprehensive understanding of the current state of FaBCs waste generation and its environmental impacts. As such, policymakers should consider implementing policies and regulations that promote the adoption of CE strategies by consumers, such as the use of reusable food containers. Such measures can contribute to the sustainable transformation of the food delivery industry in China, promoting environmental sustainability and achieve carbon-neutral target.

We describe a newly developed detailed industry module in the Global Change Analysis Model (GCAM). GCAM-USA is an open-source human-Earth systems model that represents key interactions across economic, energy, water, and land systems with state-level energy and socio-economic detail for the United States embedded within a global model. The GCAM-USA detailed industry module is developed to provide insights on the US industrial sector responses to changing prices, policies, and technology options. State-level industrial output is simulated for 12 sub-sectors that each consume a state-specific mix of 11 energy services. Sectoral service demand calibrated with projections of industrial indicators of the Energy Information Administration’s Annual Energy Outlook out to 2050. The new industrial sector representation, which are integrated with GCAM's existing building, transportation, and energy transformation sectors, allows integrated analysis of the energy system, emissions, and mitigation at the US state level.

Decarbonizing the U.S. electric power sector will require massive deployment of clean energy infrastructure, including utility-scale solar photovoltaics (solar PV) and other renewables. This deployment, though, must comply with local zoning ordinances, which impose a nationwide patchwork of restrictions on where deployment can actually occur. While zoning restrictions on deployment may be developed for legitimate purposes to protect public health and safety, they could impede or increase the costs of decarbonization of the electric power sector, but no research in this area exists. We quantify the role of utility-scale solar zoning ordinances on power sector decarbonization across the Great Lakes region (Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin) by integrating a first-of-a-kind database of 6,300 rural community zoning ordinances into a power system planning model. Our results indicate zoning ordinances can play a pivotal role in shaping sub-regional and regional decarbonization outcomes. Relative to no ordinances, solar zoning ordinances reduce total available capacity of solar PV by 52% (or 1.6 TW) across our region. Currently, however, the biggest zoning barrier to deployment is zoning ordinances which are silent on utility-scale solar, often interpreted as a de facto ban. This absence of guidelines decreases available capacity by 31% across the region and up to 59% at the state level. Outright bans—by explicitly disallowing solar—contributes another 6% reduction across the region and up to 13% reduction at the state level. Deployment restrictions translate to up to 4 GW greater investment needs and 5.6% greater PV investment costs to achieve a 10% PV generation target. Starker shifts occur at the state level, e.g. Wisconsin sees a 40% reduction in PV investments due to zoning restrictions; these investments shift to other states with laxer ordinances, e.g. Illinois. Our results underscore the need for planning that aligns local zoning laws with state and regional decarbonization objectives.

In response to the challenges posed by rapid urbanization, this study explored relationships between urban metabolism (UM) and digital twin (DT) technologies, aiming to advance sustainable urban development. A pilot high-fidelity DT representing a university building and its surrounding streetscape was crafted by integrating CAD design drawings and Building Information Modeling (BIM) technologies using Unreal Engine. The DT incorporated both physical and analytical environments and it was further enhanced by integrating a database developed for the University of Pittsburgh annual greenhouse gas (GHG) inventory. The created model allows for high-fidelity visualization of the building's behavior during routine activities, providing a meaningful basis for comparison with UM analyses.

This innovative approach holds promise for sustainable urban design and planning, harmonizing diverse data streams through DT technologies. The potential impact of this research extends to the thorough tracking, mapping, and analysis of critical resource flows such as construction materials, water, energy, and waste, implementing circular economy strategies within the built environment. The use of UM may facilitate resource-efficient opportunities and resource recovery and reuse to minimize the environmental footprint of urban developments.

Results show how the integration of DT technologies and UM analysis streamlines data collection processes, supporting its standardization and fostering sustainable urban practices. This study represents a potential advancement to promote circularity objectives in the built environment. By adopting this framework, cities can pave the way for new production and consumption patterns that prioritize the responsible use of natural resources, contributing to a more sustainable and resilient future.

The energy infrastructure that supports cooling requirements in buildings is anticipated to undergo rapid change and growth in the coming decades. Climate goals drive ambitious electrification targets and warrant the conversion of existing energy systems. As space cooling in commercial buildings consumes 11% of the total electricity and is expected to see a growth of 38% by 2050, gaining a deeper understanding of cooling requirements in existing buildings becomes imperative to improve energy efficiency, resilience, and flexibility.

This study presents simple and scalable data-driven solutions to characterize the cooling requirements in a portfolio of 119 co-located commercial buildings in a warm-summer Mediterranean climate. Factoring out geography-driven differences provides a unique opportunity to characterize cooling needs of a heterogeneous set of buildings. Cooling loads (MJ/m2) show 7-34x variation among different classes of commercial buildings over the years, with the highest loads in medical buildings and the lowest in commercial residential buildings. We create interpretable regressions for cooling loads that can be used as data-driven benchmarks. Our models capture daily variability: they explain over 70% of variance for buildings that collectively represent 85-94% of the portfolio’s overall cooling load in five different years. Our results indicate that there is a strong usage-driven heterogeneity across buildings, both in base load intensity, which we define as the cooling use intensity at 18°C (64.4°F), and in sensitivity to the drivers that we identify. The portfolio’s base load cooling intensity is 2.6-3.0 MJ/m2 and we identify Outside Air Temperature (OAT) as the most important driver of cooling consumption. Consumption increases by 7.6-9.8% for every 1°C (~1.8°F) increase in average daily OAT. A weekend indicator variable is the next most significant feature, especially in small-sized buildings. Overall consumption reduces by 7-8% on the weekends. Other weather-related variables including solar radiation, wind, and relative humidity have a smaller influence on cooling load in the region.

The methods presented here provide building researchers and managers with a new set of analytics for developing their own data-driven building performance indicators, which can be used for targeting demand response and efficiency measures, identifying unusual consumption behavior, and opportunities for energy retrofitting. Further, these models can provide benchmarks for predicting future energy demand growth in response to changing climate.

High quality, recently updated emissions data are crucial for earth systems models to represent the impact of anthropogenic emissions on the environment and human health.

The Community Emissions Data System (CEDS) was created to produce readily updateable historical emissions data sets by combining existing energy data and inventory data. The first CEDS dataset extending to 2014 was used in CMIP6, with an updated version out to 2019 released in 2021. We report on an updated global anthropogenic emission inventory dataset (1750 – 2022) of aerosol (BC, OC), aerosol and ozone precursor compounds (SO2, NOx, NH3, CO, NMVOC), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). CEDS reports annual country-total emissions for 71 sectors and 8 fuel categories and monthly gridded (.5 x .5 degree for all years, and .1 x .1 degree for recent years) emission fluxes for 8 sectors. It is anticipated that a subsequent release of this dataset will be used as the historical anthropogenic emissions forcing data set for CMIP7. It is, therefore, important that modelers test and evaluate this data to identify any issues that should be resolved before CMIP7 production runs begin.

Relative to the previous release (O’Rourke et al 2020), this update extends emissions from 2019 to 2022; details aluminum, iron and steel, and non-ferrous metal productions; utilizes point source data from the OMI satellite and other sources to refine gridded emissions; updates the methodology for estimating seasonality of emissions to address COVID anomalies; and updates all relevant data sources. In addition to providing new estimates for recent years, this update revises historical estimates from previous versions of the data set. We also report on analysis exploring the uncertainty involved in extending emissions to most recent years using aggregate data trends. This data set was released in early 2024 and the next update is planned for late summer/early fall 2024.

Natural gas accounted for 40% of US electricity generation in 2020, up from 22% in 2008, showing the highest growth rate among electricity sources despite efforts to reduce fossil fuel consumption. Burning natural gas to produce electricity is projected to continue, even in planned decarbonization roadmaps. Thorough accounting of the climate change impacts of natural gas from cradle to grave is thus crucial to guide the energy transition towards climate change mitigation. Despite the many studies that have determined the climate change impacts of the natural gas power system, none comprehensively include direct land use change (DLUC) effects. The climate change impacts of DLUC emerge from loss of original biomass, soil organic carbon loss, change in net primary productivity, and altering the surface albedo of the site, which are variable site to site. Well pads at the stage of natural gas extraction occupy 7% to 12% of land in the natural gas system, but their construction on remote lands with minimal prior development may incur notable DLUC impacts. Well pads also differ in their lifetimes, total gas production, altered areas, and extent of earth flattening, which further affect the life cycle climate change impacts of natural gas. This study is the first life cycle assessment (LCA) of natural gas-producing well pads that incorporates the spatial variations in these parameters and DLUC effects, using geographically specific data of existing well pads. Natural gas production in New Mexico was chosen as a case study for this geospatial LCA. The state has a varied geography with different land covers, each storing varying amounts of carbon, and a history of natural gas extraction that extends to today. Active gas wells constructed in New Mexico between 1950 and 2020 and with a minimum average daily gas production of 0.5 Mcf, were selected, totaling 24,991 wells. A pipeline of functions was developed in R to conduct LCA on large numbers of gas wells automatically while collecting geospatial data. The pipeline was first tested with a randomized subset of 32 of the gas wells, situated on different types of land cover in New Mexico. The results suggest that the impact of DLUC can be substantial in regions with rich organic carbon or high productivity, like forests. Subsequently, machine learning techniques, including object detection from maps and satellite imagery, were integrated into the workflow to delineate the spatial extents of the thousands of well pads efficiently for site-specific data collection. Training datasets were developed by manually delineating well pads and surrounding areas. The effects of machine learning with separate training datasets by land cover type relative to one training dataset were investigated, as were the effects of training dataset size on machine learning delineation accuracy. The results indicate prospective best practices for geospatial data collection for large numbers of relatively small areas using machine learning. The variation in DLUC impacts by well pad site and relationships between well pad features and DLUC impacts will also be discussed, in order to guide future development and remediation efforts.

In alignment with the IPCC's Sixth Climate Assessment Report, which underscores the significance of carbon sequestration in saline aquifers to achieve net zero CO2 emissions by 2050, this study conducts a nationwide geospatial assessment of Colombia's carbon sequestration potential. Deep-saline aquifers are considered potential CS sites due to their global availability and cost-effectiveness. However, the geological heterogeneity of underground formations substantiallyy influences storage potential and costs, necessitating the consideration of local variability when estimating techno-economic resources for CS deployment.

The methodical approach used for this analysis integrates geospatial analysis with cost estimation in three main stages: a) identifying feasible areas with proximity algorithms and geostatistical methods, b) Then, characterizing those areas in terms of storage potential using the volumetric approach equation and Montecarlo simulations (CO2 screen tool, NETL 2017), and c) modeling storage cost using a proxy from Rio Frio, Texas (using the NETL,2017 model).

We have identified 160,000 km² of feasible locations for Carbon sequestration distributed across six basins after screening previously published and open technical data in 14 continental sedimentary basins and overlapping layers relating to surface exclusion areas and leakage risk minimization. The results suggest a theoretical total capacity exceeding 1.1 Tera tonnes CO2 in P50 (0.11- 1.9 Tera tonnes) and an effective capacity of 65,000 million tonnes (Mt) of CO2 (16,000-183,000 M tonnes) predominantly concentrated in Colombia's eastern region, specifically the Llanos basin, where efficient storage capacity 29,000 Mt in P50. Subsequently, modeling of storage costs generates representative CO2 storage cost curves, indicating that up to 20 Gt of CO2 can be stored in the Llanos basin at a levelized cost below $20 per Metric ton.

This comprehensive assessment yields valuable insights into CS technical feasibility and economic viability in Colombia, providing crucial insights for policy decisions and future investments in carbon management strategies, especially in offset carbon markets. This research highlights the significant role of CS in global climate mitigation efforts, presenting a pathway toward carbon neutrality for Colombia and, potentially, for the global south community.

This work summarizes the U.S. Department of Energy National Energy Technology Laboratory's (NETL) modeling and analysis of some of the key decarbonization opportunities in the natural gas supply chain. In recent years, there has been growing interest in reducing methane emissions, with a specific focus on emissions from natural gas infrastructure, due to the much higher global warming potential of methane as compared to carbon dioxide. To enable deeper insights into the emissions mitigation potential of existing natural gas system technologies, NETL developed a natural gas decarbonization tool that incorporates data from the U.S. Environmental Protection Agency’s (EPA) Natural Gas STAR Program, studying 18 methane mitigation strategies for various emissions sources (including compressors, dehydrators, flaring, etc.) across the production through distribution segments of the natural gas supply chain. Additional emissions mitigation strategies provided in recent literature are investigated as part of this work. For grounding our analysis, this work reviews recent EPA regulations for curbing methane emissions from oil and natural gas operations through a detailed study of the final new source performance standards and emissions guidelines published in December 2023 and incorporates these standards into our modeling framework. The new regulations cover a range of emissions sources, exhibiting some overlap with the sources covered by the 18 methane mitigation strategies.

Disclaimer:

This project was funded by the United States Department of Energy, National Energy Technology Laboratory an agency of the United States Government, in part, through a support contract. Neither the United States Government nor any agency thereof, nor any of its employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

Exposure to risks associated with the production and usage of products, particularly oil, poses significant threats to both ecological systems and human health. Notable examples include the Gulf War Oil Spill (1991) and the Deepwater Horizon Oil Spill (2010). However, numerous smaller-scale oil spills, occurring annually, contribute to substantial oil releases. These incidents span various life cycle stages of oil production—drilling, transportation, refining, and usage—each affecting different environmental compartments (e.g., oceans, rivers, roads) and involving distinct products (e.g., crude oil, refined products). Understanding the environmental impacts of historical oil spill incidents associated with different life cycle stages and processes of oil production can help us better assess the environmental risks of producing and consuming oil, which is not captured by the existing life cycle framework.

To fill this gap, our study first developed a detailed oil spill incidents database, covering 1967 to 2023. We quantified the released amount (RA) of oil spills recorded by the National Oceanic and Atmospheric Administration (NOAA). Subsequently, we utilized life cycle impact indicators in ReCiPe to evaluate the environmental impacts of these spills. Our findings reveal that approximately 6 million gallons of oil have been spilled in North America, equating to the volume of three Deepwater Horizon spills. The human toxicity of the annually spilled oil is comparable to the release of hundreds of tons of dichlorobenzene (DCB), while its ecotoxicities are equivalent to tens of thousands of tons DCB. Additionally, we observed that the global warming potential and ozone formation potential of spilled oil are highly dependent on the ratio of its light fraction in the spilled oil products.

Wastewater treatment plants (WWTP) play a major role in nitrogen management today. If the nitrogen in the wastewater discharge is not treated, it can cause various environmental and human health problems such as increased N2O pollution, eutrophication, and algae blooms. Currently, many WWTPs rely on nitrification-denitrification technologies to transform nitrogen in wastewater typically in the form of ammonia into diatomic nitrogen. However, this process is highly energy-intensive and produces an unusable product. As WWTPs start to transform into water resource recovery facilities (WRRF), there will be a need to develop technologies that can transform waste nitrogen into high-value products. Doing so can help develop a nitrogen-circular economy.

One potential pathway for recovering nitrogen from wastewater is by utilizing cyanophycin-accumulating organisms (CAOs) to transform waste nitrogen into cyanophycin. Cyanophycin is a naturally occurring biopolymer used by bacteria as a nitrogen storage compound and consists of the amino acids aspartate and arginine. Because of this structure, it has the potential to directly be used as a microbial protein source. However, the potential impacts of this technology have been unexplored. By quantifying the potential impacts of this new pathway, its feasibility compared to incumbent technologies can be understood.

Using life cycle and techno and economic analysis, the impact of implementing this technology at WWTPs that treat 25, 50, and 100 million gallons per day (MGD) of wastewater is explored. For each of the different sizes, a Monte Carlo simulation with 1000 iterations was used to evaluate the impact of different process parameters. Global warming potential (GWP), water consumption (WC), and minimum selling price (MSP) of the produced microbial protein were calculated and compared against traditional protein sources, and baseline nitrogen recovery technologies. Initial results suggest an MSP of $0.32 to $0.95, GWP of 0.04 kgCO2eq to 0.2 kgCO2eq, and WC of 0.2 L/kg MP and 0.9 L/kg MP, which makes the technology competitive against different protein sources. Additionally, with a cost of nitrogen recovery between $1.29 and $3.48, it is competitive against nitrification-denitrification for treating influent nitrogen. WWTPs can use the results from this study to evaluate the potential of the technology for their specific facility.

The majority of today’s wastewater treatment infrastructure is aging. Wastewater is no longer viewed solely as a waste stream but as an opportunity for resource recovery, reflected by a change of nomenclature from wastewater treatment plant (WWTP) to water resource recovery facility (WRRF). Nearly doubling biogas production compared to a conventional WWTP, anaerobic membrane bioreactors (AnMBRs) have been extensively studied as energy self-sufficient WRRFs, but there remains a lack of viable solutions to handle their relatively high ammonium emissions, propensity for membrane fouling, and large membrane cost. Additionally, many previous assessments of AnMBRs excluded direct dinitrogen oxide (N2O) emissions because of uncertainty. By neglecting N2O, previous studies also disregarded more than half of greenhouse gas (GHG) emissions at WRRFs performing biological nitrification. Some of the greatest reductions in GHG emission can be made in addressing direct N2O emissions, but these reductions will go unnoticed if direct N2O emissions are excluded from an assessment. This work leverages concurrent techno-economic analysis (TEA) and lifecycle assessment (LCA) of two novel anaerobic WRRFs to evaluate solutions to the obstacles of ammonia emissions, membrane cost, and membrane fouling including a discussion on direct N2O emissions.

Two WRRF utilizing novel high-flux/low-cost cloth membrane AnMBRs were assessed with different nitrogen removal options. The first WRRF utilized a novel ammonium ion exchange and ammonia electrolysis process while the second WRRF utilized biological nitrogen removal. Both WRRFs were compared to a conventional AnMBR WRRF with biological nitrogen removal and an activated sludge WRRF with denitrification. Experimental data from a pilot study at the Urbana and Champaign Sanitary District was used to develop and validate an engineering process model used for TEA and LCA. The system boundary of TEA and LCA included all treatment steps and solids processing. The functional unit of this study was 1 m3 of municipal wastewater incorporating influent and effluent quality. Results were calculated for treatment cost ($∙m−3) and GHG emissions (kg CO2-eq∙m−3).

The novel WRRFs with cloth membranes achieve energy self-sufficiency by lowering transmembrane pressure and reduce the cost of water treatment through AnMBR from 1.21 $∙m−3 to 0.92–0.93 $∙m−3, depending on nitrogen removal technology. In this range, the novel WRRFs are competitive with traditional—activated sludge—wastewater treatment with denitrification. Both novel WRRFs achieve energy self-sufficiency, but their GHG emissions vary. Their lower energy requirements are offset by increased consumption of chemicals and materials, mainly for ion exchange and electrolysis. Excluding direct N2O emissions, the WRRF utilizing the ion exchange process has higher emissions than an activated sludge WRRF (0.35 kg CO2-eq∙m−3 compared to 0.19–0.22 kg CO2-eq∙m−3). In contrast, the WRRF utilizing biological nitrogen removal presented lower emission than an activated sludge WRRF (0.08 kg CO2-eq∙m−3). However, when direct N2O emissions are accounted for, the ion exchange WRRF stands out with the lowest emissions because it avoids biological nitrogen removal and has no direct N2O emissions. This work emphasizes that a comprehensive approach, including emission from electricity, the chemical supply chain, and direct N2O and CH4, is required to understand environmental impact.

Wastewater treatment plants (WWTPs) are significant industrial emitters, contributing 44 million metric tonnes of CO2 annually, primarily due to their elevated energy consumption. This has sparked interest in exploring innovative technologies capable of treating contaminated waters while reducing greenhouse gas (GHG) emissions. Algal Turf Scrubber (ATS) systems emerge as a promising wastewater treatment technology that can simultaneously remove nutrients from contaminated waters and provide a biomass co-product for chemicals, nutraceuticals, and biofuels. However, ATS systems have been traditionally used to treat surface waters, limiting the scalability of the technology. The effluent discharged from primary treatment, in existing WWTPs, is a potential source of high nutrient loads, where algal wastewater treatment effectively removes nitrogen and phosphorus while addressing concerns around GHG emissions. This study aims to compare the economic and environmental implications of ATS WWTPs with conventional WWTPs in terms of nitrogen and phosphorus removal.

By integrating engineering process modeling with Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA), this study evaluates the economic feasibility and environmental impacts of ATS WWTPs. The process model leverages geographically resolved weather and point-source nutrient datasets to model the wastewater treatment process and biomass harvesting for multiple sites across the continental United States. The mass and energy flows of each ATS facility were modeled based on nutrient availability and used to inform the TEA and LCA. In the TEA, costs are subjected to a discounted cash-flow rate of return analysis that includes capital expenses, operational costs, and potential revenues from the biomass co-product. The LCA considers the life cycle environmental impacts of the ATS system within the system boundary and across different environmental metrics such as acidification, eutrophication, air quality, and human health impacts. Results from the TEA and LCA, including the minimum wastewater treatment selling prices (MWWTSPs) and global warming potential (GWP), are then compared to those of conventional WWTPs to assess the competitiveness of algal wastewater treatment.

Preliminary modeling indicates significant potential for ATS systems to replace secondary and tertiary WWTP infrastructure. Results include MWWTSP ($/m^3) and GWP (g CO2-eq/m^3) for three point-sources in Alabama, Minnesota, and Montana. ATS treatment costs were determined to be 0.45, 1.07, and 2.93 $/m^3 in Alabama, Minnesota, and Montana, respectively. ATS systems demonstrated cost competitiveness when compared to the MWWTSP of conventional WWTPs (0.94 $/m^3). Sites in northern latitudes exhibited higher MWWTSPs due to lower operational days and suboptimal conditions for algal growth. The primary factor contributing to increased MWWTSPs is the combination of high nutrient loads with low biomass productivity. The GWP of electricity use was notably lower for ATS WWTPs at 14.21 g CO2-eq/m^3 compared to conventional WWTPs (125.35 g CO2-eq/m^3). Initial conclusions support the potential economic and environmental advantages of ATS over conventional WWTPs, offering competitive costs that can be reduced by increased productivity and the integration of high-value co-products. Additionally, the lower electricity use of ATS WWTPs implies a potential reduction in GWP.