Conference Agenda

Cities are the major driver of construction consumption emissions globally1, and growing demand for infrastructure and housing will drive this construction to unprecedent levels in the coming decades. Despite the urgent need to reduce construction emissions to meet climate goals, understanding of past emissions and future plans remain vague. There is a lack of work exploring the total amount of GHG that can be emitted by future construction, especially at the city level. To address this, we estimate historic construction consumption emissions and develop GHG budgets (the amount of cumulative future GHG that can be emitted within climate limits) for over 1000 cities with more than 1.2 billion people in developed and developing countries across the world. We compare these budgets to future housing growth in over a dozen cities to determine how construction must change to stay within global climate limits.

We estimate city-level construction emissions using a top-down approach. Starting with the Exiobase input-output model, we use national statistical data for over 40 countries to disaggregate Exiobase capital and calculate construction asset investment between 2000-2022. We then down-allocate capital investments and emissions to the city level using a regularized regression on economic proxy variables (e.g. employment, housing starts) for each city. Next, we set a budget on construction emissions for cities by extending blended sharing principles previously used in country-level budgeting. We explore how fairness, geographic definition of a city, and population dynamics change the GHG budgets. Finally, we compare the budgets to the amount of GHG that future construction could emit using published material intensity and LCA building data.

Our final emissions estimates encompass 1.4 ± 0.1 GtCO2e of construction consumption across 1000+ cities in 2020. We find that approximately 1/3rd of quantified emissions are attributed to 30 large global metropolitan areas, mainly in high-income countries. 2/3rd of emissions are attributed to middle and small sized cities which are not part of frameworks for reducing consumption emissions. City construction emissions must reduce to net-zero levels by 2040-2070 to meet a well-below 2°C global carbon budget, depending on assumptions made while budgeting. Adjusting for future population growth or the historic failure of high-income countries to meet an equitable budget reduces the budget of large cities in North America and Europe.

Regardless of assumptions, it is not possible to stay within GHG budgets and meet future construction demand with current practices in most cities. Staying within global climate limits requires radical, city-level changes in planning and building. Examples include reducing floor area to less than 25m2 per capita in future buildings, shifting towards small, multi-unit buildings, and increasing budget allocation to construction at the expense of decarbonizing other sectors up to 5-10 years quicker. Our results provide policy makers and designers with key information on trade-offs in near-term decarbonization planning.

Decarbonizing the road transport sector requires concerted efforts in clean energy production and the adoption of low-carbon powertrain technologies. Achieving a net-zero road transport sector necessitates not only the utilization of renewable energy sources for clean fuels and electricity generation but also substantial investments in advanced passenger vehicles. While battery electric vehicles (BEVs) are often regarded as the principal powertrain to achieve carbon mitigation targets in numerous countries, their optimality in decarbonizing passenger vehicles remains debatable when compared to other powertrains such as hybrid electric vehicles (HEVs) and plug-in hybrid vehicles (PHEVs) in terms of life-cycle greenhouse gas (GHG) emission reduction and total cost of ownership (TCO) across various regions. Furthermore, regional BEV promotion strategies must consider the varying levels of fuel economy, grid electricity emissions, and energy prices.

In this study, we developed a life-cycle GHG emissions and TCO model to assess the carbon emissions and abatement costs for BEVs and HEVs in comparison to internal combustion engine vehicles (ICEVs) at the U.S. state level and Chinese provincial level. The results indicate that, while BEVs have a greater potential for reducing life-cycle GHGs than HEVs, the average TCO of BEVs remains higher than that of HEVs in most U.S. states, with GHG abatement costs ranging from $134 to $2526 per metric ton CO2eq. In states such as Kentucky, West Virginia, and Wyoming, where grid electricity is more carbon-intensive, BEVs perform worse than HEVs in reducing GHG emissions. HEVs offer dual benefits by reducing both GHG emissions and TCO, resulting in negative abatement costs. Conversely, BEVs are more cost-effective than HEVs in most Chinese provinces, except for Beijing, Shanghai, Zhejiang, and Fujian. Additionally, the GHG emission intensities and abatement costs of BEVs exhibit significant regional variation, influenced by factors such as fuel economy, travel distance, energy prices, and grid carbon intensity. This analysis suggests that the U.S. should seek solutions to lower the vehicle costs of BEVs to enhance their economic competitiveness with HEVs, while China should continue decarbonizing grid electricity to promote BEV adoption for achieving a net-zero emission transport sector. The model presented is a valuable tool for researchers and industrial stakeholders to better comprehend the trade-offs in GHG emissions and TCO across regions when adopting alternative powertrain technologies.

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

The resilience of the electric vehicle charging infrastructure is a notable challenge as the transportation sector transitions toward electrification, driven by the need to mitigate climate change and reduce greenhouse gas emissions. This paper examines the risks posed by natural hazards and power outages to electric vehicle charging stations in the United States, with a focus on understanding how these risks impact charging infrastructure. We analyze correlations between national risk index scores and the deployment of Level 2 and DC fast charging stations, utilizing national risk index score, geospatial charging infrastructure data, and records of electric disturbance events for 2023. Our findings reveal weak but statistically significant correlations between the distribution of chargers and national risk index score, with severe weather and natural hazards emerging as primary causes of power outages that disrupt charging operations, particularly in high-risk states like Texas. The risk threshold analysis in Texas identifies a critical tipping point, a risk index score greater than 97.18, where the probability of power outages due to natural hazards increases significantly, providing a clear decision-making guideline to prioritize infrastructure fortification in high-risk and hazard-prone regions.

Many hard-to-decarbonize sectors of the economy, e.g., heavy duty transportation and industrial processing, can utilize hydrogen to reduce emissions, particularly when electrification is problematic. The Department of Energy (DOE) National Hydrogen Strategy projects the use of hydrogen to grow in the United States by up to 500% by 2050, equal to 500 million tonnes of demand per year. The hydrogen ecosystem build-out (production, conditioning, delivery, storage, and end-use) is complex and faces many challenges such as the cost of clean hydrogen (e.g., renewable and nuclear sources), technology readiness, facility siting and community acceptance, incumbent equipment replacement, and private/public sector climate policy targets. The objective of this research is to develop a fuzzy cognitive map (FCM) of a hydrogen ecosystem that informs a set of principles to address this complexity and serves to guide industry and government investment and deployment.

Currently, 95% of hydrogen production in the United States comes from steam methane reforming (SMR) of natural gas, which emits 9-11 kg CO2e/kg H2. Through the Bipartisan Infrastructure Law, the federal government has invested $8 billion in regional hubs toward demonstration and deployment of technologies for low carbon hydrogen production (less than 4 kg CO2e/kg H2). A key aim of the framework is to align technology, policy, market, and behavior drivers to accelerate sustainable hydrogen deployment. Through workshops, feedback, and engagement with over 100 stakeholders, we mapped out three main components of a hydrogen ecosystem FCM: system drivers, parameters/constraints, and sustainability performance metrics. The drivers of the system represent different variables of ecosystem design that reflect perspectives ranging from state policy to technological readiness to community engagement. The system parameters imposed on these drivers help to constrain the ecosystem network, e.g., the physical conditions of hydrogen along the supply chain, spatial variability in siting, temporal changes in demand, state/federal regulations, etc. The metrics within the FCM are objectives for the rollout of a sustainable and just ecosystem e.g., emissions abatement cost, total cost of ownership, levelized cost of hydrogen, land use, and social impact, which are minimized to help inform the creation of 12 core principles based on the relative impact of the system drivers. The FCM is mapped to all five stages of the ecosystem supply chain, resulting in a near term focus on transportation applications of green hydrogen in southeast Michigan. This work will characterize the key drivers that shape near term and long term deployment decisions in the state, and their impact on the performance metrics that measure the sustainability of the system. The principles serve as a guide for stakeholders to assess their own deployment strategy as the ecosystem continues to develop.

Rebar selection plays a critical role in the sustainability of construction projects, given its impact on durability and environmental performance. This study evaluates the environmental impacts of five commonly used rebar types: thermoplastic, thermoset, regular steel, stainless steel, and epoxy-coated steel. The research aims to identify the most sustainable material options through a comprehensive life cycle assessment in line with ISO 14040 and ISO 14044 standards.

The analysis is performed at three levels: (1) cradle-to-gate assessment of unidirectional fiber-reinforced polymer tapes used in thermoplastic rebars, focusing on input materials and energy consumption; (2) mass-based comparison of environmental impacts across all rebar types, highlighting differences in material properties and lifecycle emissions; and (3) a broader evaluation of rebar impacts within bridge deck assemblies to understand their real-world implications. Key environmental impact categories, including global warming potential, resource depletion, and toxicity, are quantified using TRACI v2.1 and IPCC 2021 frameworks.

Preliminary results indicate that thermoplastic and thermoset composite rebars offer significant advantages due to their corrosion resistance, longevity, and lower embodied impacts during production. However, they face challenges in terms of field adaptability and recyclability. Traditional steel rebars are highly recyclable but are prone to corrosion, potentially shortening their lifespan in aggressive environments. Stainless steel rebars, while durable and corrosion-resistant, incur higher production costs and environmental impacts. Epoxy-coated rebars provide moderate corrosion resistance but are susceptible to damage if the coating integrity is compromised.

This research underscores the trade-offs between durability, cost, and environmental performance among rebar types. By integrating multi-level analysis, it aims to guide material selection for bridge construction, promoting the adoption of sustainable practices in the infrastructure sector. The findings are expected to provide actionable insights for engineers, policymakers, and material manufacturers, driving greener and more resilient infrastructure development.

Over a billion metric tons of waste and biomass are projected to be available in a future mature market in the United States. These resources represent an opportunity to decouple chemical production from conventional fossil fuel feedstocks, but such a broad solution space can also make for challenging decision-making. This talk will showcase how key analysis tools such as techno-economic analysis, life cycle assessment, material flow analysis, and multi-criteria decision analysis can be used to benchmark the costs, environmental impacts, and circularity of new innovations as well as to identify opportunities for prioritization and optimization. Using a series of examples related to chemical manufacturing, we will explore how analysis can guide where and how to leverage waste carbon in supply chains towards a future circular economy.

There have been many studies of how to decarbonize road transportation, but comparatively few on the best path for rail decarbonization in the United States. US railroads rely on diesel and have been resistant to electrification with catenary. Hydrogen, batteries, and biofuels are under consideration as options for decarbonization. The route from the Port of Los Angeles to Barstow (the Cajon Sub) is a good case study because of its high utilization and its uneven topography.

This analysis considered the net present cost over the lifetime of a locomotive of five technologies for traction along the Cajon Sub: Diesel, the current mainstay; biodiesel, a drop-in replacement with lower carbon intensity; hydrogen, a widely considered option for decarbonization of heavy freight; batteries, which are also widely used for transportation electrification; and catenary, a widely used but capital intensive way to decarbonize rail systems. It considered the net present value in three methods of calculation: private cost to a company without considering policies; the credit cost, meaning the private cost after considering the sale of credits under California’s low-carbon fuel standard and tax credits for hydrogen under the Inflation Reduction Act; and the social cost, considering the total cost to society of air pollution and greenhouse gas emissions. It conducted a Monte Carlo simulation which deployed wide ranges of values for inputs to produce a range of outputs for all fifteen NPV cost calculations. It also conducted analysis of the same route with only one train per day, to simulate options for lightly utilized railways.

The ranges for costs of all options at least partially overlapped, but clear trends were visible. The analysis found that for all three methods of calculation, on average, electrification with catenary is the lowest cost option, with battery electrification as a close second. The use of hydrogen fuel cells is the third lowest cost option, but with noticeably higher cost than the electric options. This is true even when ignoring externalities, and even more the case when considering them. When considering the full social cost of operation, diesel is the highest cost option, while when considering only the private cost, with or without tax credits and LCFS credits, biodiesel is the highest cost option while diesel is the second highest.

In the simulation with only one train per day, battery electric operation was the lowest cost option, for all three methods of calculation. Catenary was by far the highest cost option in this simulation, though if extremely low cost of catenary can be achieved, it could be competitive with other options. The choice between the other three depends on the method of calculation.

This study finds that over the lifespan of a typical locomotive, electrification through batteries or catenary is typically the most cost-effective option for rail operation, in most cases by a substantial margin. This holds true for both highly and lightly utilized rail corridors, and whether or not policies to reduce emissions are considered. It bolsters the case for electrification of land transportation.

As the world advances, the demand for middle distillates, particularly jet fuel and diesel grows rapidly, driven by factors such as rising global demand for transportation and energy fuels. This demand translates to an increase in CO2 emissions as the current fuels’ sources are fossils. Notably, hard-to-abate sectors such as aviation, shipping, and heavy hauling transport are challenging to transition away from fossil fuels since the current option, being electrification is challenging in these sectors. Therefore, an alternative solution such as biofuels can be a sustainable substitute for fossil fuels to decarbonize these sectors. Renewable feedstocks like lignocellulosic biomass can be transformed into low-carbon fuels to partially meet the existing demands of fuels and energy for hard-to-decarbonize sectors. In the last 30 years, a plethora of conversion pathways and catalytic upgrading alternatives for lignocellulosic materials have been discovered. However, a crucial decision in chemical process design, which involves the selection of methods for process representation, simulation, and optimization in a cost-effective and sustainable manner becomes challenging. Typical approaches such as detailed simulations or lifecycle analyses struggle to efficiently explore all possible alternatives because of the huge design space.

Hence, to fill this gap, in this research, we developed a novel superstructure-based optimization framework and mathematical models to study the diverse pathways for middle distillates production. Our superstructure contains 500 catalysts, 165 biomass upgrading chemistries and 320 individual biofuels. Notably, our superstructure allows three optimal decisions: the choice of catalysts, biomass upgrading chemistries, and blends of middle distillates. We use machine learning like the graph neural networks to parameterize the properties of biofuels that have not been tested experimentally as jet fuels or diesel. Then, the rationally designed blends of the transformed biomass molecules, identified via optimization, can be used as novel fuel products, such as diesel and jet fuels, which could match or outperform existing fossil fuel counterparts. We formulate this integrated process and product design as a multi-objective mixed integer non-linear programming problem. Our multi-objectives are aimed at identifying the minimum selling price of our desired fuels and their minimum greenhouse gas emissions (CO2 equivalent).

Using the developed framework, we examine the tradeoffs between the economics (cost) and environmental sustainability of different biofuel designs. We propose a good compromise between these competing objectives ensuring profitability, lower emissions, and resilience of our biofuel design solutions. This research aims to inform policymakers and industries on novel product blends which are cost-effective and environmentally sustainable for decarbonizing the transportation and energy sector, contributing to a net-zero carbon economy.