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

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

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

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

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

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

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

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