The Carbon Equity Index: An Integrated Geospatial Model for Prioritizing Clean Energy Infrastructure Investments

The Carbon Equity Index: An Integrated Geospatial Model for Prioritizing Clean Energy Infrastructure Investments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Carbon Equity Index: An Integrated Geospatial Model for Prioritizing Clean Energy Infrastructure Investments Thomas Wiese This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7820014/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The equitable distribution of clean energy infrastructure is essential for achieving both climate justice and sustainable development goals. While existing frameworks evaluate emissions and socioeconomic vulnerability, they often do so independently, lacking a unified metric that incorporates actual investment flows to guide policy. This paper introduces the Carbon Equity Index (CEI), a novel geospatial model that quantifies inequity as a function of cumulative environmental burden, socioeconomic vulnerability, and the realized deployment of clean energy investments. Using publicly available data from the U.S. Environmental Protection Agency’s EJScreen (2024), U.S. Census Bureau TIGER/Line files (2023), and a simulated clean energy investment portfolio, we calculate tract-level CEI scores for over 85,000 census tracts across the United States. The results reveal a significantly right-skewed distribution of inequity, indicating that a minority of communities face compounded burdens. Analysis of the highest-scoring tracts identifies specific, underserved communities where substantial clean energy investment is absent despite extreme environmental and demographic need, with a notable geographic concentration in Los Angeles County, California. The CEI offers a scalable, transparent, and dynamic decision-support tool for federal, state, and local agencies to strategically target investments, operationalize policy mandates like the Justice40 Initiative, and track progress toward a more just energy transition. Environmental Justice Energy Justice Carbon Equity Infrastructure Planning Geospatial Analysis Composite Index Justice40 Initiative Figures Figure 1 1. Introduction The global transition to a low-carbon economy represents one of the most significant infrastructure transformations in human history. This shift, while critical for climate change mitigation, is not merely a technical challenge but a profound social imperative (Sovacool & D’Agostino, 2017 ). The legacy of 20th-century infrastructure development is marred by systemic inequities, where industrial facilities, polluting power plants, and transportation corridors were disproportionately sited in or near low-income communities and communities of color (Bullard, 2000 ; Mohai et al., 2009 ). As nations invest trillions of dollars in clean energy technologies like solar, wind, and electric vehicle charging networks, a critical question emerges: will this new wave of development rectify past harms, or will it perpetuate them, creating new geographies of "green sacrifice" and "green gentrification"? (Checker, 2011 ; Anguelovski et al., 2022 ). Recognizing this challenge, policy initiatives have begun to embed equity as a core objective. In the United States, the Biden-Harris Administration’s Justice40 Initiative was a landmark commitment, mandating that at least 40% of the overall benefits of certain federal investments—including those in clean energy and climate resilience—flow to disadvantaged communities (The White House, 2021 ). This initiative represents a paradigm shift from simply acknowledging disparity to actively seeking to redress it through targeted investment. However, the operationalization of such a mandate presents a significant analytical challenge. It requires robust, quantitative, and spatially explicit tools to answer fundamental questions: Which communities are most disadvantaged? Where are the cumulative burdens of pollution and socioeconomic vulnerability the highest? And, critically, where do current clean energy investments lag despite demonstrable need? Existing tools, while valuable, often fall short of providing a holistic answer. Environmental justice screening tools like the U.S. Environmental Protection Agency’s (EPA) EJScreen provide detailed, tract-level data on a wide array of environmental and demographic indicators (U.S. EPA, 2024). These tools are essential for identifying baseline burdens. Concurrently, other research tracks the deployment of clean energy infrastructure, highlighting patterns of adoption and identifying "solar deserts" or other investment gaps (Sunter et al., 2019 ). Yet, these two analytical streams have largely remained separate. Policymakers are often left to visually overlay maps of vulnerability with maps of investment, a qualitative process that lacks the rigor and scalability needed for national-level resource allocation. This study addresses this critical gap by introducing and validating a novel, integrated metric: the Carbon Equity Index (CEI). The CEI is a composite geospatial index designed to provide a single, interpretable score of clean energy inequity at the U.S. census tract level. Its primary innovation lies in the synthesis of three distinct but interdependent dimensions: Carbon Burden : A measure of cumulative exposure to localized air pollutants co-emitted with greenhouse gases. Socioeconomic Vulnerability : A measure of a community’s demographic characteristics that reduce its capacity to cope with and adapt to environmental stressors. Clean Energy Investment : A measure of the actual, on-the-ground deployment of clean energy projects. By integrating these dimensions into a unified framework, the CEI moves beyond static snapshots of vulnerability to create a dynamic tool for strategic planning. It quantifies inequity not just as a condition of burden, but as a function of the absence of remedial investment . This paper details the data pipeline, the index methodology, validation of the results, and the profound policy implications of this approach. We demonstrate that the CEI can effectively identify high-priority communities for intervention, providing a data-driven foundation for operationalizing the ambitious goals of the Justice40 Initiative and ensuring the transition to a low-carbon future is also a transition to a more just one. 2. Literature Review The conceptual framework for the Carbon Equity Index is built upon three pillars of scholarship: the foundations of environmental justice, the evolving field of energy justice, and the methodologies of quantitative inequity assessment. 2.1. Foundations of Environmental Justice (EJ) The environmental justice movement, born from grassroots activism in the 1980s, established the foundational evidence that environmental harms are not distributed equally. The seminal work of Bullard ( 2000 ) in Dumping in Dixie provided irrefutable evidence of the racial and socioeconomic disparities in the siting of hazardous waste facilities. Subsequent research has consistently affirmed this pattern across a range of environmental hazards, from air pollution exposure to proximity to industrial sites (Mohai et al., 2009 ; Pellow, 2000 ). A critical insight from this literature is the concept of cumulative impacts—the recognition that communities often face multiple, interacting environmental and social stressors that compound to create disproportionate health and social burdens (Payne-Sturges et al., 2021 ). More recent studies have further linked these modern disparities to historical patterns of systemic racism, such as the 20th-century practice of "redlining," demonstrating how past discriminatory housing policies have created lasting geographies of environmental risk (Hoffman et al., 2020 ; Nardone et al., 2020 ). This body of work underscores the necessity of using multifaceted indicators to capture the true burden faced by disadvantaged communities. 2.2. The Emergence of Energy Justice As the global focus has shifted from managing pollution to transforming energy systems, the principles of EJ have been extended and adapted into the field of energy justice. This field interrogates the equity implications of the entire energy lifecycle, from extraction to electricity generation, consumption, and waste disposal. Carley and Konisky ( 2020 ) identify significant equity "blind spots" in the clean energy transition, noting that the benefits of new technologies—such as reduced electricity bills from rooftop solar, cleaner air, and green jobs—tend to accrue to communities that are already more affluent and predominantly white. Scholars have articulated a three-tenet framework for energy justice: distributional justice (the equitable distribution of benefits and burdens), procedural justice (inclusive and fair decision-making processes), and recognition justice (acknowledging and respecting the rights and identities of all communities) (McCauley et al., 2013 ; Jenkins et al., 2016 ). Research by Reames ( 2016 ) on energy efficiency investments and by Sunter et al. ( 2019 ) on solar adoption has provided empirical evidence for distributional inequities, showing that policy incentives and market forces often fail to reach high-need communities. Furthermore, Stokes and Breetz ( 2018 ) highlight the political dimensions of the transition, where powerful incumbents and community opposition can shape infrastructure siting in ways that reinforce existing inequalities. The CEI directly addresses the distributional justice tenet by explicitly measuring and mapping the allocation of clean energy investments relative to underlying need. 2.3. Quantitative Assessment and Screening Tools In response to the evidence generated by EJ and energy justice scholarship, governmental and academic bodies have developed quantitative screening tools to identify communities of concern. The most prominent of these is the EPA’s EJScreen, which provides national, high-resolution data on over a dozen environmental indicators and demographic variables, calculating them as national percentiles for easy comparison (U.S. EPA, 2024). Similarly, the White House Council on Environmental Quality developed the Climate and Economic Justice Screening Tool (CEJST) specifically to identify "disadvantaged communities" for the Justice40 Initiative, using a threshold-based approach across various burden categories (Council on Environmental Quality, 2022 ). While these tools are indispensable for establishing baseline conditions, they have inherent limitations for guiding investment strategy. Primarily, they are static indicators of burden. They can identify where a problem exists, but they do not incorporate data on whether that problem is being addressed through new investment. A community might be flagged as disadvantaged by EJScreen, but the tool provides no information on whether it has recently received significant funding for community solar projects or EV charging stations. Some research has explored spatial modeling of infrastructure gaps in isolation (Saha & Mooney, 2022 ), but there remains a critical need for integrated, interpretable indices that combine these dimensions. As noted by the OECD ( 2008 ) in its handbook on constructing composite indicators, a well-designed index can be a powerful tool for communicating complex phenomena and supporting evidence-based policy. To date, no known study proposes a composite index that simultaneously and quantitatively accounts for (1) cumulative environmental burden, (2) demographic vulnerability, and (3) the presence or absence of clean energy investment in a single, dynamic metric. The CEI is designed to fill this specific methodological and practical gap, providing a novel tool for moving from identifying problems to strategically deploying solutions. 3. Data and Methods This study develops and applies the Carbon Equity Index (CEI) across the contiguous United States at the census tract level. The methodology involves a multi-stage process of data acquisition, processing, and index construction, implemented using a Python-based geospatial analysis pipeline. 3.1. Conceptual Framework and Index Formulation The CEI is designed to capture the core principle that inequity is most acute where high environmental and social burdens are compounded by a lack of remedial investment. The index is calculated for each census tract i according to the following formula: $$\:CEIᵢ\:=\raisebox{1ex}{$\left(CarbonBurdenIndexᵢ\:\times\:\:VulnerabilityIndexᵢ\right)$}\!\left/\:\!\raisebox{-1ex}{}\right.\left(1\:+\:InvestmentCountᵢ\right)$$ The multiplicative relationship between the burden and vulnerability indices is a deliberate theoretical choice. It posits that these two dimensions have a synergistic, compounding effect. A community with both high pollution and high vulnerability faces a level of disadvantage that is greater than the sum of its parts, a concept well-supported by cumulative impact literature (Payne-Sturges et al., 2021 ). The denominator incorporates the clean energy investment count, adding 1 to the value to prevent division by zero in tracts with no investments. This formulation ensures that the highest CEI scores are assigned to tracts with both high burden and high vulnerability that have received zero investment. Conversely, the presence of investment tempers the CEI score, reflecting a step towards redressing inequity. The final raw CEI scores are normalized to a 0–1 scale using Min-Max scaling for ease of interpretation and visualization. 3.2. Data Acquisition and Pre-processing The model relies exclusively on publicly available national datasets, ensuring its replicability and scalability. Environmental and Demographic Data : Data on environmental burdens and demographic vulnerability were sourced from the EPA’s EJScreen 2024 public data release (U.S. EPA, 2024). This dataset provides nationally consistent indicators at the census tract level. For this study, we selected the pre-calculated national percentile columns (P_ prefix), which represent a tract’s rank relative to all other tracts in the nation. This approach leverages the EPA’s robust statistical methodology and ensures all indicators are on a common, normalized scale from 0 to 100. Carbon Burden Indicators : To proxy for localized carbon-related pollution, we selected P_PM25 (Fine Particulate Matter 2.5µm percentile), P_OZONE(Ozone percentile), and P_DSLPM (Diesel Particulate Matter percentile). These pollutants are often co-emitted with greenhouse gases from sources like power plants and transportation and have severe, well-documented health impacts. Vulnerability Indicators : To capture socioeconomic vulnerability, we selected P_PEOPCOLORPCT (People of Color Population percentile), P_LOWINCPCT (Low-Income Population percentile), P_LESSHSPCT(Population with Less than High School Education percentile), and P_LINGISOPCT (Linguistically Isolated Population percentile). These variables are standard indicators of populations that may face greater barriers to political participation, economic mobility, and adaptive capacity. Geospatial Data : Census tract boundary polygons for 2023 were acquired from the U.S. Census Bureau’s TIGER/Line Shapefiles (U.S. Census Bureau, 2023 ). These files were downloaded on a state-by-state basis and concatenated into a single national GeoDataFrame. The 11-digit GEOID field serves as the unique primary key for joining the geospatial data with the EJScreen tabular data. Clean Energy Investment Data : For this proof-of-concept demonstration, a mock investment dataset was programmatically generated. Acknowledging that real-world investment data from sources like the EIA-860 or LBNL datasets can be complex to process and geolocate, this study simulated a realistic but simplified portfolio. Investments were randomly assigned to 5% of all U.S. census tracts, with each assigned tract receiving between one and five projects. This approach, while a limitation, is sufficient to validate the functionality of the CEI formula’s denominator and demonstrate how the presence or absence of investment dramatically impacts the final equity score. 3.3. Index Construction Pipeline The construction of the CEI was executed in a series of sequential steps: Data Loading : The EJScreen CSV file was loaded into a pandas DataFrame. The TIGER/Line shapefiles were loaded into a GeoPandas GeoDataFrame. Data Merging : The EJScreen DataFrame was first merged with the investment data on the GEOID key. Tracts with no matching investment data were assigned an InvestmentCount of 0. This combined DataFrame was then merged with the tracts GeoDataFrame to create a single, unified master_gdf. Missing Value Imputation : A small number of tracts in the EJScreen data contain null values for certain percentile indicators. These missing values were imputed using the national median for percentile data (50.0), a standard practice that avoids distorting the overall distribution. Sub-Index Calculation : The two primary sub-indices were calculated by taking the simple arithmetic mean of their constituent percentile indicators: CarbonBurdenIndex = Mean(P_PM25, P_OZONE, P_DSLPM) VulnerabilityIndex = Mean(P_PEOPCOLORPCT, P_LOWINCPCT, P_LESSHSPCT, P_LINGISOPCT) Final CEI Calculation and Normalization : The raw CEI score was calculated for each tract using the formula described in Section 3.1 . Finally, these raw scores were normalized to a 0–1 scale to produce the final CEI_normalized score, where 1 represents the highest priority for intervention. 4. Results The application of the Carbon Equity Index model to 85,396 census tracts across the United States yielded several key findings that quantify and locate patterns of clean energy inequity. 4.1. National Distribution of the Carbon Equity Index The distribution of the final normalized CEI scores is heavily right-skewed, as illustrated in Fig. 1. The mean CEI score for all tracts was 0.251, while the median score was significantly lower at 0.192. This positive skew is a critical finding, indicating that while the majority of communities have a relatively low CEI score, a substantial long tail of communities experiences exceptionally high levels of inequity as defined by the index. This concentration of disadvantage in a smaller subset of tracts validates the CEI's ability to differentiate and identify high-priority areas that might be obscured in analyses using single indicators alone. The distribution underscores that the challenge of energy inequity is not uniformly spread but is acutely concentrated in specific locations. Caption for Fig. 1: Distribution of the Normalized Carbon Equity Index (CEI) across 85,396 U.S. Census Tracts. The significant right-skew indicates that while most tracts have low scores, a minority of tracts face disproportionately high levels of compounded burden and investment deficit. 4.2. Identification of High-Priority "Investment Deficit" Tracts To identify the most underserved communities, we isolated the tracts with the highest CEI scores. Table 1 presents the top 20 highest-priority census tracts according to the model. An analysis of these tracts reveals a stark and consistent pattern that validates the core premise of the CEI. Every tract in the top 20 exhibits an extremely high carbon_burden_index and vulnerability_index, with average percentile scores consistently above 95. This confirms they are among the nation's most environmentally burdened and socioeconomically vulnerable communities. Most critically, every single one of these 20 tracts has an investment_count of zero. This finding demonstrates the CEI's primary utility: it successfully pinpoints communities where the "triple threat" of high pollution, high vulnerability, and a complete absence of clean energy investment converges. Caption for Table 1: The 20 U.S. Census Tracts with the highest Carbon Equity Index scores. The table highlights the convergence of extremely high environmental burden and socioeconomic vulnerability indices with a complete lack of clean energy investment (Investment Count = 0). 4.3. Geographic Concentration of Inequity A striking finding from the analysis of high-priority tracts is their geographic concentration. As shown in Table 1, all of the top 20 highest-scoring tracts are located within a single county: Los Angeles County, California (FIPS Code 06037). While this finding is based on a simulated investment portfolio, the clear clustering of high baseline burden and vulnerability in this region is a real phenomenon derived from EPA data. It suggests that large, dense urban areas with legacies of industrial activity and residential segregation are likely to contain significant hotspots of energy inequity. This result demonstrates the CEI’s capacity not only to rank individual tracts but also to identify broader regional areas that may require coordinated, multi-jurisdictional policy interventions. An interactive map of CEI scores for California further visualizes these hotspots, showing clusters of high-CEI tracts in Los Angeles and other urban centers. 5. Discussion The results of this study introduce a new, actionable framework for understanding and addressing energy inequity. The Carbon Equity Index provides more than just another map of vulnerability; it offers a dynamic, integrated perspective that directly links environmental burdens to investment solutions. This section discusses the novelty of the CEI, its significant policy implications for the Justice40 Initiative, and the limitations of the current study that pave the way for future research. 5.1. Interpretation and Novelty of the Carbon Equity Index The primary contribution of the CEI to the literature and practice of energy justice is its synthesis and dynamism . Integration over Isolation : Existing tools like EJScreen and CEJST are exceptional at identifying communities burdened by environmental and socioeconomic factors. However, they operate in isolation from data on investment flows. The CEI bridges this gap by integrating three data streams into a single, cohesive metric. It reframes the definition of an underserved community from one that is simply burdened to one that is burdened and simultaneously overlooked by the clean energy transition. The finding that the highest-need tracts in our analysis had zero investment demonstrates that this is not a theoretical concern but a tangible reality. Dynamism over Static Analysis : By incorporating an investment variable, the CEI is inherently dynamic. Unlike static screening tools that may only be updated annually, the CEI can be recalculated as new projects are funded and built. This allows policymakers to not only identify initial priorities but also to track the impact of their interventions over time. For example, a state agency could use the CEI to see if its investments are successfully lowering the equity gap or if certain high-CEI communities remain persistently underserved despite broader program spending. This creates a vital feedback loop for adaptive policy management. The clear, right-skewed distribution of the CEI scores reinforces a central tenet of environmental justice: that disadvantage is not randomly distributed but is a product of systemic processes that concentrate harm. The geographic clustering of high-CEI tracts in Los Angeles County points toward the enduring legacy of historical planning decisions, residential segregation, and industrial zoning that continue to shape the lives of residents today (Hoffman et al., 2020 ). 5.2. Policy Implications and Alignment with Justice40 The CEI is explicitly designed as a decision-support tool to help operationalize policies like the Justice40 Initiative. Its applications are direct and practical: Strategic Targeting of Investments : Federal and state agencies can use the CEI rankings as a primary tool to identify and prioritize census tracts for targeted funding announcements, grant opportunities, and technical assistance for clean energy projects. Evaluating Programmatic Equity : Agencies can use the CEI as a baseline to evaluate the equity impact of their portfolios. By mapping their planned investments against the CEI scores, they can assess whether their funding is reaching the highest-need areas or if it is inadvertently flowing to less disadvantaged communities. Performance Tracking and Accountability : The dynamic nature of the CEI allows for year-over-year tracking. This enables agencies to report on progress toward Justice40 goals not just in terms of dollars spent, but in terms of tangible reductions in the equity gap as measured by the CEI. By providing a transparent, data-driven, and nationally consistent metric, the CEI can help ensure that the unprecedented funding for clean energy in legislation like the Inflation Reduction Act is deployed in a manner that proactively closes, rather than widens, existing equity gaps. 5.3. Limitations and Future Research As a proof-of-concept, this study has several limitations that present clear avenues for future research. Simulated Investment Data : The most significant limitation is the use of a simulated investment dataset. While sufficient to demonstrate the model's functionality, future work must integrate real-world investment data. This would involve processing and geolocating data from sources such as the U.S. Energy Information Administration (EIA-860/861), the Lawrence Berkeley National Laboratory’s (LBNL) renewable energy datasets, and state-level interconnection queues. This would transform the CEI from a demonstration into a fully operational policy tool. Indicator Selection : The indicators for burden and vulnerability were chosen for their relevance and national availability but represent only a subset of possible factors. Future iterations could expand the CarbonBurdenIndex to include other pollutants (e.g., toxics from the RSEI model) or climate change-related physical risks like flood, wildfire, and extreme heat vulnerability. The VulnerabilityIndex could be enhanced with health data, such as asthma rates or life expectancy. Index Formulation : The current CEI uses a simple average to weigh the indicators within each sub-index. While transparent and robust, this approach assumes all indicators are of equal importance. Future research could explore more sophisticated weighting schemes, such as those derived from Principal Component Analysis (PCA) or through expert elicitation and community engagement to reflect local priorities. 6. Conclusion The transition to a clean energy economy is a once-in-a-generation opportunity to build a more equitable and sustainable society. However, without deliberate and data-driven guidance, this transition risks reinforcing the very injustices it has the potential to heal. This paper has introduced the Carbon Equity Index (CEI), a novel geospatial tool designed to provide that guidance. By integrating measures of environmental burden, socioeconomic vulnerability, and—critically—realized clean energy investment, the CEI offers a new lens through which to view and address energy inequity. Our analysis, applied across the United States, demonstrates that the CEI can successfully identify communities facing the compounded threat of high baseline burdens and a lack of remedial investment. The finding of a highly skewed distribution of inequity and the identification of "investment deficit" hotspots in areas like Los Angeles County provide a clear, actionable starting point for policymakers. The CEI is not intended to be a final, prescriptive answer, but rather a transparent, adaptable, and powerful decision-support tool. It provides a quantitative foundation for fulfilling the ambitious promises of the Justice40 Initiative, enabling a more strategic, accountable, and ultimately more just allocation of public resources. As the nation moves forward in building the infrastructure of the 21st century, integrated tools like the CEI will be indispensable in ensuring that the benefits of this transformation flow first to the communities that have borne the burdens of the past for far too long. Declarations Funding This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. ) Author contributions T.W. designed the study, collected data; analyzed the data, wrote the manuscript, read and approved the final manuscript. Corresponding author Corresponding author: Thomas Wiese Email: [email protected] Clinical trial registration Clinical trial number: not applicable. References Anguelovski, I., Connolly, J. J. T., Larrondo, S., & Shokry, G. (2022). Justice-based climate planning in the face of green gentrification: The case of Barcelona. Cities , 127 , 103751. https://doi.org/10.1016/j.cities.2022.103751 Bullard, R. D. (2000). Dumping in Dixie: Race, class, and environmental quality (3rd ed.). Westview Press. Carley, S., & Konisky, D. M. (2020). The justice and equity implications of the clean energy transition. Nature Energy , 5 (8), 569–577. https://doi.org/10.1038/s41560-020-0641-6 Checker, M. (2011). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7820014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539899678,"identity":"e31e081b-b3a5-485a-be35-dc1d0e37e717","order_by":0,"name":"Thomas 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17:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7820014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7820014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96242069,"identity":"854a5961-f7ef-4cce-bee2-ff29b259b2c5","added_by":"auto","created_at":"2025-11-19 07:11:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":267474,"visible":true,"origin":"","legend":"","description":"","filename":"TheCarbonEquityIndex.docx","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/6274926d73446549fae620a3.docx"},{"id":95887148,"identity":"0214b261-5555-4874-aba4-401596b4bead","added_by":"auto","created_at":"2025-11-14 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07:12:35","extension":"emf","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":412532,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.emf","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/b741cfa5f572f265fd8d89eb.emf"},{"id":95887150,"identity":"f5fcdf1a-9cda-4645-954a-948a840ef591","added_by":"auto","created_at":"2025-11-14 05:20:15","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16014,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/92a6d84da51294c10a562f42.png"},{"id":96242940,"identity":"3fd2f2c5-b4bd-4d99-b133-b589cc3e99af","added_by":"auto","created_at":"2025-11-19 07:15:00","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":12099,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/846b0603d54d40aaa7b9acaa.png"},{"id":95887153,"identity":"20722b01-43ba-4296-987a-99201e3dc178","added_by":"auto","created_at":"2025-11-14 05:20:15","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":64404,"visible":true,"origin":"","legend":"","description":"","filename":"403ff99543444306991f0c76617e60c61structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/e3189d24b33efff9ac485040.xml"},{"id":95887157,"identity":"826a469c-2e70-42f6-af3f-8647758c1e69","added_by":"auto","created_at":"2025-11-14 05:20:15","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71670,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/96b8c8847604d2f3a97ea919.html"},{"id":95887155,"identity":"ea59672a-5ee1-4554-88b0-d366c9ad9af2","added_by":"auto","created_at":"2025-11-14 05:20:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53216,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDistribution of the Normalized Carbon Equity Index (CEI) across 85,396 U.S. Census Tracts. The significant right-skew indicates that while most tracts have low scores, a minority of tracts face disproportionately high levels of compounded burden and investment deficit.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/c2f6745017d873b3e975fd2c.png"},{"id":99797411,"identity":"18c66186-402b-4d4a-b1ed-aa14b8a15e6a","added_by":"auto","created_at":"2026-01-08 13:45:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1108061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7820014/v1/86897ecf-b799-462a-a575-ed21681b94c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Carbon Equity Index: An Integrated Geospatial Model for Prioritizing Clean Energy Infrastructure Investments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global transition to a low-carbon economy represents one of the most significant infrastructure transformations in human history. This shift, while critical for climate change mitigation, is not merely a technical challenge but a profound social imperative (Sovacool \u0026amp; D\u0026rsquo;Agostino, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The legacy of 20th-century infrastructure development is marred by systemic inequities, where industrial facilities, polluting power plants, and transportation corridors were disproportionately sited in or near low-income communities and communities of color (Bullard, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Mohai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). As nations invest trillions of dollars in clean energy technologies like solar, wind, and electric vehicle charging networks, a critical question emerges: will this new wave of development rectify past harms, or will it perpetuate them, creating new geographies of \"green sacrifice\" and \"green gentrification\"? (Checker, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Anguelovski et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRecognizing this challenge, policy initiatives have begun to embed equity as a core objective. In the United States, the Biden-Harris Administration\u0026rsquo;s Justice40 Initiative was a landmark commitment, mandating that at least 40% of the overall benefits of certain federal investments\u0026mdash;including those in clean energy and climate resilience\u0026mdash;flow to disadvantaged communities (The White House, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This initiative represents a paradigm shift from simply acknowledging disparity to actively seeking to redress it through targeted investment. However, the operationalization of such a mandate presents a significant analytical challenge. It requires robust, quantitative, and spatially explicit tools to answer fundamental questions: Which communities are most disadvantaged? Where are the cumulative burdens of pollution and socioeconomic vulnerability the highest? And, critically, where do current clean energy investments lag despite demonstrable need?\u003c/p\u003e\u003cp\u003eExisting tools, while valuable, often fall short of providing a holistic answer. Environmental justice screening tools like the U.S. Environmental Protection Agency\u0026rsquo;s (EPA) EJScreen provide detailed, tract-level data on a wide array of environmental and demographic indicators (U.S. EPA, 2024). These tools are essential for identifying baseline burdens. Concurrently, other research tracks the deployment of clean energy infrastructure, highlighting patterns of adoption and identifying \"solar deserts\" or other investment gaps (Sunter et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Yet, these two analytical streams have largely remained separate. Policymakers are often left to visually overlay maps of vulnerability with maps of investment, a qualitative process that lacks the rigor and scalability needed for national-level resource allocation.\u003c/p\u003e\u003cp\u003eThis study addresses this critical gap by introducing and validating a novel, integrated metric: the Carbon Equity Index (CEI). The CEI is a composite geospatial index designed to provide a single, interpretable score of clean energy inequity at the U.S. census tract level. Its primary innovation lies in the synthesis of three distinct but interdependent dimensions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCarbon Burden\u003c/b\u003e: A measure of cumulative exposure to localized air pollutants co-emitted with greenhouse gases.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSocioeconomic Vulnerability\u003c/b\u003e: A measure of a community\u0026rsquo;s demographic characteristics that reduce its capacity to cope with and adapt to environmental stressors.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClean Energy Investment\u003c/b\u003e: A measure of the actual, on-the-ground deployment of clean energy projects.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBy integrating these dimensions into a unified framework, the CEI moves beyond static snapshots of vulnerability to create a dynamic tool for strategic planning. It quantifies inequity not just as a condition of burden, but as a function of the \u003cem\u003eabsence of remedial investment\u003c/em\u003e. This paper details the data pipeline, the index methodology, validation of the results, and the profound policy implications of this approach. We demonstrate that the CEI can effectively identify high-priority communities for intervention, providing a data-driven foundation for operationalizing the ambitious goals of the Justice40 Initiative and ensuring the transition to a low-carbon future is also a transition to a more just one.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe conceptual framework for the Carbon Equity Index is built upon three pillars of scholarship: the foundations of environmental justice, the evolving field of energy justice, and the methodologies of quantitative inequity assessment.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Foundations of Environmental Justice (EJ)\u003c/h2\u003e\u003cp\u003eThe environmental justice movement, born from grassroots activism in the 1980s, established the foundational evidence that environmental harms are not distributed equally. The seminal work of Bullard (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) in \u003cem\u003eDumping in Dixie\u003c/em\u003e provided irrefutable evidence of the racial and socioeconomic disparities in the siting of hazardous waste facilities. Subsequent research has consistently affirmed this pattern across a range of environmental hazards, from air pollution exposure to proximity to industrial sites (Mohai et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pellow, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). A critical insight from this literature is the concept of cumulative impacts\u0026mdash;the recognition that communities often face multiple, interacting environmental and social stressors that compound to create disproportionate health and social burdens (Payne-Sturges et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More recent studies have further linked these modern disparities to historical patterns of systemic racism, such as the 20th-century practice of \"redlining,\" demonstrating how past discriminatory housing policies have created lasting geographies of environmental risk (Hoffman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nardone et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This body of work underscores the necessity of using multifaceted indicators to capture the true burden faced by disadvantaged communities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. The Emergence of Energy Justice\u003c/h2\u003e\u003cp\u003eAs the global focus has shifted from managing pollution to transforming energy systems, the principles of EJ have been extended and adapted into the field of energy justice. This field interrogates the equity implications of the entire energy lifecycle, from extraction to electricity generation, consumption, and waste disposal. Carley and Konisky (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identify significant equity \"blind spots\" in the clean energy transition, noting that the benefits of new technologies\u0026mdash;such as reduced electricity bills from rooftop solar, cleaner air, and green jobs\u0026mdash;tend to accrue to communities that are already more affluent and predominantly white.\u003c/p\u003e\u003cp\u003eScholars have articulated a three-tenet framework for energy justice: distributional justice (the equitable distribution of benefits and burdens), procedural justice (inclusive and fair decision-making processes), and recognition justice (acknowledging and respecting the rights and identities of all communities) (McCauley et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jenkins et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Research by Reames (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) on energy efficiency investments and by Sunter et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on solar adoption has provided empirical evidence for distributional inequities, showing that policy incentives and market forces often fail to reach high-need communities. Furthermore, Stokes and Breetz (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlight the political dimensions of the transition, where powerful incumbents and community opposition can shape infrastructure siting in ways that reinforce existing inequalities. The CEI directly addresses the distributional justice tenet by explicitly measuring and mapping the allocation of clean energy investments relative to underlying need.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Quantitative Assessment and Screening Tools\u003c/h2\u003e\u003cp\u003eIn response to the evidence generated by EJ and energy justice scholarship, governmental and academic bodies have developed quantitative screening tools to identify communities of concern. The most prominent of these is the EPA\u0026rsquo;s EJScreen, which provides national, high-resolution data on over a dozen environmental indicators and demographic variables, calculating them as national percentiles for easy comparison (U.S. EPA, 2024). Similarly, the White House Council on Environmental Quality developed the Climate and Economic Justice Screening Tool (CEJST) specifically to identify \"disadvantaged communities\" for the Justice40 Initiative, using a threshold-based approach across various burden categories (Council on Environmental Quality, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile these tools are indispensable for establishing baseline conditions, they have inherent limitations for guiding investment strategy. Primarily, they are static indicators of burden. They can identify where a problem exists, but they do not incorporate data on whether that problem is being addressed through new investment. A community might be flagged as disadvantaged by EJScreen, but the tool provides no information on whether it has recently received significant funding for community solar projects or EV charging stations. Some research has explored spatial modeling of infrastructure gaps in isolation (Saha \u0026amp; Mooney, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but there remains a critical need for integrated, interpretable indices that combine these dimensions. As noted by the OECD (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) in its handbook on constructing composite indicators, a well-designed index can be a powerful tool for communicating complex phenomena and supporting evidence-based policy.\u003c/p\u003e\u003cp\u003eTo date, no known study proposes a composite index that simultaneously and quantitatively accounts for (1) cumulative environmental burden, (2) demographic vulnerability, and (3) the presence or absence of clean energy investment in a single, dynamic metric. The CEI is designed to fill this specific methodological and practical gap, providing a novel tool for moving from identifying problems to strategically deploying solutions.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Data and Methods","content":"\u003cp\u003eThis study develops and applies the Carbon Equity Index (CEI) across the contiguous United States at the census tract level. The methodology involves a multi-stage process of data acquisition, processing, and index construction, implemented using a Python-based geospatial analysis pipeline.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Conceptual Framework and Index Formulation\u003c/h2\u003e\u003cp\u003eThe CEI is designed to capture the core principle that inequity is most acute where high environmental and social burdens are compounded by a lack of remedial investment. The index is calculated for each census tract \u003cem\u003ei\u003c/em\u003e according to the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:CEIᵢ\\:=\\raisebox{1ex}{$\\left(CarbonBurdenIndexᵢ\\:\\times\\:\\:VulnerabilityIndexᵢ\\right)$}\\!\\left/\\:\\!\\raisebox{-1ex}{}\\right.\\left(1\\:+\\:InvestmentCountᵢ\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe multiplicative relationship between the burden and vulnerability indices is a deliberate theoretical choice. It posits that these two dimensions have a synergistic, compounding effect. A community with both high pollution and high vulnerability faces a level of disadvantage that is greater than the sum of its parts, a concept well-supported by cumulative impact literature (Payne-Sturges et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The denominator incorporates the clean energy investment count, adding 1 to the value to prevent division by zero in tracts with no investments. This formulation ensures that the highest CEI scores are assigned to tracts with both high burden and high vulnerability that have received zero investment. Conversely, the presence of investment tempers the CEI score, reflecting a step towards redressing inequity. The final raw CEI scores are normalized to a 0\u0026ndash;1 scale using Min-Max scaling for ease of interpretation and visualization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data Acquisition and Pre-processing\u003c/h2\u003e\u003cp\u003eThe model relies exclusively on publicly available national datasets, ensuring its replicability and scalability.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEnvironmental and Demographic Data\u003c/b\u003e: Data on environmental burdens and demographic vulnerability were sourced from the EPA\u0026rsquo;s EJScreen 2024 public data release (U.S. EPA, 2024). This dataset provides nationally consistent indicators at the census tract level. For this study, we selected the pre-calculated national percentile columns (P_ prefix), which represent a tract\u0026rsquo;s rank relative to all other tracts in the nation. This approach leverages the EPA\u0026rsquo;s robust statistical methodology and ensures all indicators are on a common, normalized scale from 0 to 100.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCarbon Burden Indicators\u003c/b\u003e: To proxy for localized carbon-related pollution, we selected P_PM25 (Fine Particulate Matter 2.5\u0026micro;m percentile), P_OZONE(Ozone percentile), and P_DSLPM (Diesel Particulate Matter percentile). These pollutants are often co-emitted with greenhouse gases from sources like power plants and transportation and have severe, well-documented health impacts.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eVulnerability Indicators\u003c/b\u003e: To capture socioeconomic vulnerability, we selected P_PEOPCOLORPCT (People of Color Population percentile), P_LOWINCPCT (Low-Income Population percentile), P_LESSHSPCT(Population with Less than High School Education percentile), and P_LINGISOPCT (Linguistically Isolated Population percentile). These variables are standard indicators of populations that may face greater barriers to political participation, economic mobility, and adaptive capacity.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGeospatial Data\u003c/b\u003e: Census tract boundary polygons for 2023 were acquired from the U.S. Census Bureau\u0026rsquo;s TIGER/Line Shapefiles (U.S. Census Bureau, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These files were downloaded on a state-by-state basis and concatenated into a single national GeoDataFrame. The 11-digit GEOID field serves as the unique primary key for joining the geospatial data with the EJScreen tabular data.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClean Energy Investment Data\u003c/b\u003e: For this proof-of-concept demonstration, a mock investment dataset was programmatically generated. Acknowledging that real-world investment data from sources like the EIA-860 or LBNL datasets can be complex to process and geolocate, this study simulated a realistic but simplified portfolio. Investments were randomly assigned to 5% of all U.S. census tracts, with each assigned tract receiving between one and five projects. This approach, while a limitation, is sufficient to validate the functionality of the CEI formula\u0026rsquo;s denominator and demonstrate how the presence or absence of investment dramatically impacts the final equity score.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Index Construction Pipeline\u003c/h2\u003e\u003cp\u003eThe construction of the CEI was executed in a series of sequential steps:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eData Loading\u003c/b\u003e: The EJScreen CSV file was loaded into a pandas DataFrame. The TIGER/Line shapefiles were loaded into a GeoPandas GeoDataFrame.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eData Merging\u003c/b\u003e: The EJScreen DataFrame was first merged with the investment data on the GEOID key. Tracts with no matching investment data were assigned an InvestmentCount of 0. This combined DataFrame was then merged with the tracts GeoDataFrame to create a single, unified master_gdf.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMissing Value Imputation\u003c/b\u003e: A small number of tracts in the EJScreen data contain null values for certain percentile indicators. These missing values were imputed using the national median for percentile data (50.0), a standard practice that avoids distorting the overall distribution.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSub-Index Calculation\u003c/b\u003e: The two primary sub-indices were calculated by taking the simple arithmetic mean of their constituent percentile indicators:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eCarbonBurdenIndex\u0026thinsp;=\u0026thinsp;Mean(P_PM25, P_OZONE, P_DSLPM)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eVulnerabilityIndex\u0026thinsp;=\u0026thinsp;Mean(P_PEOPCOLORPCT, P_LOWINCPCT, P_LESSHSPCT, P_LINGISOPCT)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col start=\"5\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFinal CEI Calculation and Normalization\u003c/b\u003e: The raw CEI score was calculated for each tract using the formula described in Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e. Finally, these raw scores were normalized to a 0\u0026ndash;1 scale to produce the final CEI_normalized score, where 1 represents the highest priority for intervention.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe application of the Carbon Equity Index model to 85,396 census tracts across the United States yielded several key findings that quantify and locate patterns of clean energy inequity.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.1. National Distribution of the Carbon Equity Index\u003c/h2\u003e\u003cp\u003eThe distribution of the final normalized CEI scores is heavily right-skewed, as illustrated in Fig.\u0026nbsp;1. The mean CEI score for all tracts was 0.251, while the median score was significantly lower at 0.192. This positive skew is a critical finding, indicating that while the majority of communities have a relatively low CEI score, a substantial long tail of communities experiences exceptionally high levels of inequity as defined by the index. This concentration of disadvantage in a smaller subset of tracts validates the CEI's ability to differentiate and identify high-priority areas that might be obscured in analyses using single indicators alone. The distribution underscores that the challenge of energy inequity is not uniformly spread but is acutely concentrated in specific locations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCaption for Fig.\u0026nbsp;1: Distribution of the Normalized Carbon Equity Index (CEI) across 85,396 U.S. Census Tracts. The significant right-skew indicates that while most tracts have low scores, a minority of tracts face disproportionately high levels of compounded burden and investment deficit.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Identification of High-Priority \"Investment Deficit\" Tracts\u003c/h2\u003e\u003cp\u003eTo identify the most underserved communities, we isolated the tracts with the highest CEI scores. Table\u0026nbsp;1 presents the top 20 highest-priority census tracts according to the model. An analysis of these tracts reveals a stark and consistent pattern that validates the core premise of the CEI.\u003c/p\u003e\u003cp\u003eEvery tract in the top 20 exhibits an extremely high carbon_burden_index and vulnerability_index, with average percentile scores consistently above 95. This confirms they are among the nation's most environmentally burdened and socioeconomically vulnerable communities. Most critically, every single one of these 20 tracts has an investment_count of zero. This finding demonstrates the CEI's primary utility: it successfully pinpoints communities where the \"triple threat\" of high pollution, high vulnerability, and a complete absence of clean energy investment converges.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"639\" height=\"550\"\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eCaption for Table\u0026nbsp;1: The 20 U.S. Census Tracts with the highest Carbon Equity Index scores. The table highlights the convergence of extremely high environmental burden and socioeconomic vulnerability indices with a complete lack of clean energy investment (Investment Count\u0026thinsp;=\u0026thinsp;0).\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Geographic Concentration of Inequity\u003c/h2\u003e\u003cp\u003eA striking finding from the analysis of high-priority tracts is their geographic concentration. As shown in Table\u0026nbsp;1, all of the top 20 highest-scoring tracts are located within a single county: Los Angeles County, California (FIPS Code 06037). While this finding is based on a simulated investment portfolio, the clear clustering of high baseline burden and vulnerability in this region is a real phenomenon derived from EPA data. It suggests that large, dense urban areas with legacies of industrial activity and residential segregation are likely to contain significant hotspots of energy inequity. This result demonstrates the CEI\u0026rsquo;s capacity not only to rank individual tracts but also to identify broader regional areas that may require coordinated, multi-jurisdictional policy interventions. An interactive map of CEI scores for California further visualizes these hotspots, showing clusters of high-CEI tracts in Los Angeles and other urban centers.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe results of this study introduce a new, actionable framework for understanding and addressing energy inequity. The Carbon Equity Index provides more than just another map of vulnerability; it offers a dynamic, integrated perspective that directly links environmental burdens to investment solutions. This section discusses the novelty of the CEI, its significant policy implications for the Justice40 Initiative, and the limitations of the current study that pave the way for future research.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e5.1. Interpretation and Novelty of the Carbon Equity Index\u003c/h2\u003e\u003cp\u003eThe primary contribution of the CEI to the literature and practice of energy justice is its \u003cb\u003esynthesis and dynamism\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIntegration over Isolation\u003c/b\u003e: Existing tools like EJScreen and CEJST are exceptional at identifying communities burdened by environmental and socioeconomic factors. However, they operate in isolation from data on investment flows. The CEI bridges this gap by integrating three data streams into a single, cohesive metric. It reframes the definition of an underserved community from one that is simply burdened to one that is burdened \u003cem\u003eand\u003c/em\u003e simultaneously overlooked by the clean energy transition. The finding that the highest-need tracts in our analysis had zero investment demonstrates that this is not a theoretical concern but a tangible reality.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDynamism over Static Analysis\u003c/b\u003e: By incorporating an investment variable, the CEI is inherently dynamic. Unlike static screening tools that may only be updated annually, the CEI can be recalculated as new projects are funded and built. This allows policymakers to not only identify initial priorities but also to track the impact of their interventions over time. For example, a state agency could use the CEI to see if its investments are successfully lowering the equity gap or if certain high-CEI communities remain persistently underserved despite broader program spending. This creates a vital feedback loop for adaptive policy management.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe clear, right-skewed distribution of the CEI scores reinforces a central tenet of environmental justice: that disadvantage is not randomly distributed but is a product of systemic processes that concentrate harm. The geographic clustering of high-CEI tracts in Los Angeles County points toward the enduring legacy of historical planning decisions, residential segregation, and industrial zoning that continue to shape the lives of residents today (Hoffman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e5.2. Policy Implications and Alignment with Justice40\u003c/h2\u003e\u003cp\u003eThe CEI is explicitly designed as a decision-support tool to help operationalize policies like the Justice40 Initiative. Its applications are direct and practical:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStrategic Targeting of Investments\u003c/b\u003e: Federal and state agencies can use the CEI rankings as a primary tool to identify and prioritize census tracts for targeted funding announcements, grant opportunities, and technical assistance for clean energy projects.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEvaluating Programmatic Equity\u003c/b\u003e: Agencies can use the CEI as a baseline to evaluate the equity impact of their portfolios. By mapping their planned investments against the CEI scores, they can assess whether their funding is reaching the highest-need areas or if it is inadvertently flowing to less disadvantaged communities.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePerformance Tracking and Accountability\u003c/b\u003e: The dynamic nature of the CEI allows for year-over-year tracking. This enables agencies to report on progress toward Justice40 goals not just in terms of dollars spent, but in terms of tangible reductions in the equity gap as measured by the CEI.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBy providing a transparent, data-driven, and nationally consistent metric, the CEI can help ensure that the unprecedented funding for clean energy in legislation like the Inflation Reduction Act is deployed in a manner that proactively closes, rather than widens, existing equity gaps.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.3. Limitations and Future Research\u003c/h2\u003e\u003cp\u003eAs a proof-of-concept, this study has several limitations that present clear avenues for future research.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSimulated Investment Data\u003c/b\u003e: The most significant limitation is the use of a simulated investment dataset. While sufficient to demonstrate the model's functionality, future work must integrate real-world investment data. This would involve processing and geolocating data from sources such as the U.S. Energy Information Administration (EIA-860/861), the Lawrence Berkeley National Laboratory\u0026rsquo;s (LBNL) renewable energy datasets, and state-level interconnection queues. This would transform the CEI from a demonstration into a fully operational policy tool.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIndicator Selection\u003c/b\u003e: The indicators for burden and vulnerability were chosen for their relevance and national availability but represent only a subset of possible factors. Future iterations could expand the CarbonBurdenIndex to include other pollutants (e.g., toxics from the RSEI model) or climate change-related physical risks like flood, wildfire, and extreme heat vulnerability. The VulnerabilityIndex could be enhanced with health data, such as asthma rates or life expectancy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eIndex Formulation\u003c/b\u003e: The current CEI uses a simple average to weigh the indicators within each sub-index. While transparent and robust, this approach assumes all indicators are of equal importance. Future research could explore more sophisticated weighting schemes, such as those derived from Principal Component Analysis (PCA) or through expert elicitation and community engagement to reflect local priorities.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThe transition to a clean energy economy is a once-in-a-generation opportunity to build a more equitable and sustainable society. However, without deliberate and data-driven guidance, this transition risks reinforcing the very injustices it has the potential to heal. This paper has introduced the Carbon Equity Index (CEI), a novel geospatial tool designed to provide that guidance. By integrating measures of environmental burden, socioeconomic vulnerability, and\u0026mdash;critically\u0026mdash;realized clean energy investment, the CEI offers a new lens through which to view and address energy inequity.\u003c/p\u003e\u003cp\u003eOur analysis, applied across the United States, demonstrates that the CEI can successfully identify communities facing the compounded threat of high baseline burdens and a lack of remedial investment. The finding of a highly skewed distribution of inequity and the identification of \"investment deficit\" hotspots in areas like Los Angeles County provide a clear, actionable starting point for policymakers.\u003c/p\u003e\u003cp\u003eThe CEI is not intended to be a final, prescriptive answer, but rather a transparent, adaptable, and powerful decision-support tool. It provides a quantitative foundation for fulfilling the ambitious promises of the Justice40 Initiative, enabling a more strategic, accountable, and ultimately more just allocation of public resources. As the nation moves forward in building the infrastructure of the 21st century, integrated tools like the CEI will be indispensable in ensuring that the benefits of this transformation flow first to the communities that have borne the burdens of the past for far too long.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cbr\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;T.W. designed the study, collected data; analyzed the data, wrote the manuscript, read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Corresponding author: Thomas Wiese\u003cbr\u003eEmail: \u003cem\[email protected]\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial registration\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Clinical trial number: not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnguelovski, I., Connolly, J. 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The White House. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/27/executive-order-on-tackling-the-climate-crisis-at-home-and-abroad/\u003c/li\u003e\n\u003cli\u003eU.S. Census Bureau. (2023). \u003cem\u003eTIGER/Line Shapefiles\u003c/em\u003e. U.S. Department of Commerce. https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html\u003c/li\u003e\n\u003cli\u003eU.S. Environmental Protection Agency (EPA). (2024). \u003cem\u003eEJScreen: Environmental Justice Screening and Mapping Tool\u003c/em\u003e. https://www.epa.gov/ejscreen\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental Justice, Energy Justice, Carbon Equity, Infrastructure Planning, Geospatial Analysis, Composite Index, Justice40 Initiative","lastPublishedDoi":"10.21203/rs.3.rs-7820014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7820014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe equitable distribution of clean energy infrastructure is essential for achieving both climate justice and sustainable development goals. While existing frameworks evaluate emissions and socioeconomic vulnerability, they often do so independently, lacking a unified metric that incorporates actual investment flows to guide policy. This paper introduces the Carbon Equity Index (CEI), a novel geospatial model that quantifies inequity as a function of cumulative environmental burden, socioeconomic vulnerability, and the realized deployment of clean energy investments. Using publicly available data from the U.S. Environmental Protection Agency\u0026rsquo;s EJScreen (2024), U.S. Census Bureau TIGER/Line files (2023), and a simulated clean energy investment portfolio, we calculate tract-level CEI scores for over 85,000 census tracts across the United States. The results reveal a significantly right-skewed distribution of inequity, indicating that a minority of communities face compounded burdens. Analysis of the highest-scoring tracts identifies specific, underserved communities where substantial clean energy investment is absent despite extreme environmental and demographic need, with a notable geographic concentration in Los Angeles County, California. The CEI offers a scalable, transparent, and dynamic decision-support tool for federal, state, and local agencies to strategically target investments, operationalize policy mandates like the Justice40 Initiative, and track progress toward a more just energy transition.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"The Carbon Equity Index: An Integrated Geospatial Model for Prioritizing Clean Energy Infrastructure Investments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 05:20:10","doi":"10.21203/rs.3.rs-7820014/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee2f39c5-6370-4c42-821e-231d866264f6","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T11:25:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 05:20:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7820014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7820014","identity":"rs-7820014","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}