Energy equity metrics are essential for evaluating and addressing disparities in access, reliability, and affordability of energy services. However, existing metrics often reflect assumptions rooted in developed nations, failing to capture the unique challenges of energy systems in sub-Saharan Africa (SSA). This study addresses this gap through a participatory research approach, engaging communities in Nigeria and South Africa via focus group discussions to document their lived experiences with energy services. A thematic analysis was conducted to identify key dimensions of energy equity specific to these contexts. The findings reveal that energy inequities in SSA are shaped by interconnected cultural, infrastructural, and systemic factors, such as limited adaptation of energy solutions to local needs and structural barriers to energy affordability. These insights highlight the need for localized, context-sensitive metrics to guide the design of equitable and sustainable energy systems. Furthermore, this work offers an enhanced evaluation framework that empowers stakeholders to comprehensively assess equity within energy systems. By bridging local voices with actionable insights, this study contributes to advancing energy justice and fostering more inclusive energy systems in SSA.

From risk assessments to impact assessments to life cycle assessments, there are a variety of assessments in the United States that measure environmental, social, economic, and/or cultural sustainability to inform decision making. These assessments shape the policies, technologies, and infrastructure projects that will determine if green energy mitigates the climate crisis or instead engenders Green Colonialism. For example, the homelands of the Anishinaabeg throughout the northern Great Lakes region in the U.S. and Canada are prime candidates for new and/or expanded mining activities to provide critical minerals for decarbonization technologies. Without procedures to ensure meaningful inclusion of Indigenous Knowledge and respect for Indigenous sovereignty, these projects risk repeating exploitative patterns of the past. Here, we examine whether current sustainability assessments ensure the just, sovereignty-affirming decision making necessary for genuine sustainability.

Responding to international and domestic directions for greater application of Indigenous Knowledge in sustainability efforts, we employ an approach that grounds Western-based analytical methods within Anishinaabe Gikendaasowin (Knowledge) on sustainability. Specifically, we evaluate how 17 different kinds of assessments (mis)align with Anishinaabe Gikendaasowin on Asemaa (tobacco), Ma’iinganag (wolves), and the Seventh Fire prophecy – teachings that guide sustainable relationships between Physical, Plant, Animal, and Human Worlds. We thematically code over 50 sources of assessment protocols with the qualitative data analysis software NVivo 20 1.6.2, identifying how assessment protocols affirm or violate Indigenous sovereignty, how assessments are connected, and what steps are needed to ensure legitimate Indigenous participation in decision making.

Employing Larsen 2018’s scalar framework for participation and the Wehipeihana Model of Indigenous evaluation (1,2) our results indicate that ~75% of assessments risk violating Indigenous sovereignty. Moreover, we find that all 17 assessments are directly connected through shared data, regulatory requirements, frameworks, or other procedural similarities. Hence, assessments cannot be considered in isolation; all assessments must affirm Indigenous sovereignty or else they risk jeopardizing the development of truly sustainable solutions. Categorizing the kinds of language across assessments that pose barriers to Indigenous participation, we conclude with practices to better weave Indigenous and Western scientific Knowledge together and generate a more robust understanding of sustainability.

(1) Larsen, R. K. Impact Assessment and Indigenous Self-Determination: A Scalar Framework of Participation Options. https://doi.org/10.1080/14615517.2017.1390874.

(2) Wehipeihana, N. Increasing Cultural Competence in Support of Indigenous-Led Evaluation: A Necessary Step toward Indigenous-Led Evaluation. https://doi.org/10.3138/cjpe.68444.

Emissions from fossil fuel power plants lead to considerable health damages for communities located nearby and downwind. In the United States, more than 5,000 deaths are caused each year due to exposure to fine particulate matter (PM2.5) resulting from power generation, disproportionately impacting low-income communities and people of color. Currently, the electricity system is undergoing substantial changes, with renewable generators coming online and older fossil fuel plants being retired. To maximize the health benefits of power plant retirements, and reduce disparities in health impacts, data on the health damages caused by each power plant is needed. In this work, we develop an inventory of air pollution-related health damages segmented by race, ethnicity, and income for each power plant in the United States. We collect data on annual emissions of primary PM2.5, sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), and volatile organic compounds (VOCs) from the Emissions & Generation Resource Integrated Database and the national Emissions Inventory, and use InMAP, a reduced complexity air quality model, to estimate gridded changes in annual average PM2.5 concentration due to each individual plant’s emissions. We use a log-linear concentration response function from Krewski et al. to estimate associated premature mortality. We overlay gridded mortality with demographic characteristics at the census tract level to assess the distribution of mortality by race, ethnicity and income. For each power plant, we aggregate the number of annual deaths in each demographic group. We find that just over 50 power plants (mainly coal plants) cause more than half of annual deaths from air pollution related to power generation. While coal plants cause most of the mortality overall, natural gas plants are responsible for more than 40% of mortality in Asian, Latino, and Pacific Islander populations. We identify the power plants causing the highest number of mortalities overall and for each demographic group. The Parish Coal Plant in Fort Bend County, TX causes the most deaths in Asian, Latino, Pacific Islander, and Mixed-Race populations, while the Martin Lake Coal Plant in Rusk County, TX causes the most deaths in Black and Native American populations. The Labadie Coal Plant causes the most deaths in White populations and in the population as a whole. These results suggest that prioritizing air quality and health improvements in distinct demographic groups would lead to different plant retirement strategies than those targeting population-wide reductions in air pollution-related health damages. Given current and historical air quality and health disparities, we suggest that plant retirement strategies should take into account which groups would benefit. These results provide a basis for considering different plant retirement options as the transition to clean electricity continues.

The transition to electric vehicles (EVs) is necessary for reducing transportation greenhouse gas emissions and combating climate change. In the US, California is leading this transition to EVs, with 35% of all new EVs sold in the country being sold in California as of 2024. EVs offer benefits that include lower operational costs and zero tailpipe emissions. However, access to EVs has not been uniform across socioeconomic and demographic groups. Understanding the distribution of benefits and burdens of the EV transition is necessary to facilitate an equitable transition by ensuring that the benefits of electrification are accessible to all and that low-income and marginalized communities are not burdened with air quality-related health impacts and higher transportation costs of internal combustion engine vehicles (ICEs). To support this understanding, we introduce a high-resolution fleet turnover model that operates at the zipcode level to analyze vehicle fleet evolution and EV adoption patterns across California. Unlike previous fleet turnover models that use aggregate state or national-level data, our model incorporates local variations in vehicle ownership and retirement patterns. We use the Kaplan-Meier estimator to create zipcode-level age-specific vehicle survival probabilities and use linear regression to forecast vehicle sales at the zipcode level using vehicle registration data. We validate our high-resolution fleet turnover model and analyze two scenarios: a business-as-usual scenario and a scenario implementing California's Advanced Clean Cars II (ACCII) mandate that bans the sale of all ICE vehicles after 2035. Our analysis reveals important disparities in vehicle age and EV adoption across income levels and race/ethnicities. By 2050 in the business-as-usual scenario, high-income communities (>$100,000 median income) are projected to achieve 40-55% EV adoption rates, while low-income communities (<$50,000) reach only 8-15%. Asian communities show the highest EV adoption rates in both income categories, with high-income Asian populations reaching approximately 55% adoption in 2050. In contrast, Hispanic and Black communities in low-income areas show the lowest adoption rates at approximately 8% and 9% respectively. Under the ACCII scenario, EV adoption rates in lower-income communities would need to increase by 70-80% compared to the business-as-usual scenario, while higher-income communities would need to increase by only 10-20% by 2050. These findings highlight the importance of considering local socioeconomic and demographic factors when designing policies to support an equitable transition to electric vehicles.