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.