The decarbonization of our energy system will, absent large-scale deployment of carbon capture, inevitably involve ratcheting down economic activity in fossil energy production and use. This shift away from fossil energy will negatively impact communities that rely on fossil energy for jobs, tax revenue, and economic activity. There is also substantial county-level variation at the in terms of potential impact (due to the concentrated nature of fossil energy resources) and sensitivity (due to differing levels of economic inequality within a state). In this work, we propose a novel methodology to measure the potential impact of losing jobs in the energy sector that leverages machine learning and employment, renewable energy data, and socio-economic data. Using this framework, we identify that several counties of the Marcellus-Utica shale play region of western Pennsylvania, West Virginia, and Ohio will be uniquely challenged by the fossil transition. One strategy to reduce this potential impact is directed and intentional investment in these areas. We again leverage county-level data on socioeconomics, the cost of installing and operating wind turbines, and maximum wind generating capacity, to build an optimization framework designed to maximize the production of jobs across the state of Ohio. By weighting counties on the basis of county-level exposure and vulnerability during the transition away from fossil fuels, our model allows for policy-makers to prioritize investment in areas that are most likely to be left behind in the transition to a more renewable energy system.

Water supply systems can use flexible pumping operations as an energy storage resource, filling elevated water storage and then using gravitational potential energy to distribute water to urban customers, similar to pumped hydropower systems. Shifting pump operating times can maximize renewable energy consumption and decarbonize water supply. Despite the potential, previous studies have been limited to a small set of benchmarking systems and have not significantly accounted for impacts on water quality or supply reliability. This study aims to investigate the opportunities and limitations of using excess water storage in water supply systems as a virtual battery to store energy and reduce carbon emissions. As a part of this research, we estimated the energy storage capacity of several water distribution systems based on their current network configuration. We also investigated the tradeoffs between energy storage capacity, embedded carbon emissions, water quality, and reliability using a multiobjective optimization framework, analyzing two contrasting case studies. Preliminary results indicate that water utilities can leverage existing water storage to provide energy services while maintaining service reliability and quality and that increasing operational storage can expand energy flexibility.

Macro energy system optimization models are important tools for understanding the possible outcomes of deep decarbonization pathways and can inform the impacts of policy mechanisms that aim to reach net-zero greenhouse gas emissions by mid-century. Given that current U.S. federal climate policy is insufficient to achieve carbon neutrality, state-level decarbonization efforts are of increasing importance. In this research, we use a comprehensive energy systems model (Temoa) to explore the implications of achieving decarbonization goals through either federal or state action. We consider two carbon budget policy scenarios: (1) 23 climate-friendly states set net-zero carbon dioxide emissions targets by 2050; (2) a federal policy that matches the greenhouse gas emissions levels of the state-policy, but with the added flexibility of this being a national constraint. Both scenarios achieve a carbon dioxide emissions reduction of approximately 40% by 2050, but the spatial distribution of emissions limits varies by scenario. Through this investigation, we found that state-driven net-zero carbon pathways select a different least-cost set of technologies relative to the federal policy. Specifically, through state-level climate policy, we see significant deployment of nuclear power in the northeast region, whereas, through federal-level policy, we see an increase in southeast biomass with carbon capture and sequestration. Further, traditional fuel use increased within the federal scenario’s transport sector. This study offers new understanding of the risks associated with technology lock-in under different decarbonization pathways, highlighting the importance of early planning for long-term action.

Energy and mobility systems must transition to cost-effective reliance on resources, processes, and options that are more environmentally and socially benign. These transitions must address the myriad ways in which some communities are underserved and marginalized (e.g., “charging deserts”, discriminatory siting of infrastructure, more adverse health consequences of exposure to pollutants). Physical, natural, and social scientific researchers must partner with communities and organizations to assure that infrastructure transitions equitably elevate environmental, social, and health conditions. This talk presents a project that combines environmental engineering, environmental science, social sciences, public health, and policy analysis in a collaboration between Ohio State University, the City of Columbus, Franklin County Public Health, the Mid-Ohio Regional Planning Commission, and the Electrification Coalition. The project partners with fifteen underserved communities in the Columbus Metropolitan Area to (a) understand their needs for electrification and mobility and evaluate the effectiveness of current efforts; (b) understand how knowledge, perception, and values vary and design and evaluate interventions that inform individual and domicile-level decisions; and (c) develop strategies to guide transition investment and deployment to improve health, environmental, and social conditions over time for marginalized communities across the urban-exurban-rural gradient. In particular, the project develops scenarios of changes in electricity demand and generation capacity resulting from electrification and mobility shifts; models the air pollution, and subsequent health impacts, of changes in tailpipe and electricity generation infrastructure; and uses cost-effectiveness and benefit-cost analyses to evaluate and support policy decisions to guide the just transition to electrified home energy and mobility.

Electric vehicles (EVs) are up to twice as emissions-intensive to produce than internal combustion engine vehicles. Industry and transport must decarbonize if we are to avoid the worst consequences of climate change; therefore, this study examines the opportunities and barriers to EV production decarbonization in the United States. Our study combines a detailed literature review on energy efficiency and decarbonization opportunities (e.g., electricity grid decarbonization) and material efficiency opportunities (e.g., enhanced recycling techniques to use more secondary feedstocks). Combined with semi-structured interviews with automotive industry experts, we determine potential life-cycle opportunities for reducing EV manufacturing carbon footprints. We also adapt the Argonne National Lab’s widely used Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model to quantify Global Warming Potential (GWP) and Cumulative Energy Demand (CED) impacts. We model five scenarios and compare them with the baseline, which is representative of the current status of EV manufacturing. The first scenario focuses on the environmental advantages of improving prevalent automotive manufacturing process yields. The second scenario investigates the environmental impacts of using lightweight materials. The third scenario identifies the environmental advantages of using renewable energy sources for manufacturing & EV charging, and the fourth scenario focuses on improving end-of-life recycling rates. The last scenario combines scenarios one to four, illustrating the potential capacity of maximizing the environmental benefits of using low-carbon manufacturing and closed-loop recycling for EVs. From these scenarios, we draw a set of design, research, and policy recommendations for maximizing EV benefits.