Environmental Justice in the Anthropocene: A Review of Methodological Biases in Impact Assessment Studies

INTRODUCTION

The Anthropocene represents a paradigm shift in understanding human-environment interactions, marking an era where anthropogenic activities have become the dominant force shaping planetary systems (Hoffman-Hall et al., 2024; Sayyed et al., 2024). This epoch is defined by accelerated climate change, habitat destruction, pollution proliferation, and biodiversity decline, all of which intersect with social inequalities to produce uneven environmental burdens (Banerjee & Schuitema, 2022; Graefen et al., 2023; Ovienmhada et al., 2024). Environmental justice (EJ), as a framework, seeks to address these disparities by ensuring fair distribution of environmental benefits and harms, meaningful participation in decision-making, and recognition of diverse cultural and social contexts (Banerjee & Schuitema, 2023; Spuler et al., 2025). Originating from grassroots movements in the United States during the 1980s, EJ has evolved into a global discourse, particularly relevant in the Anthropocene where global-scale impacts amplify local vulnerabilities (Dhanasekar et al., 2022; Spuler & Wessel, 2024).

Impact assessment studies, encompassing environmental impact assessments (EIAs), cumulative impact assessments (CIAs), and related methodologies, are pivotal in evaluating the potential consequences of development projects, policies, and environmental changes (Leonard et al., 2020; Efremov, 2023; Sovacool et al., 2023). These studies aim to predict, mitigate, and monitor impacts on ecosystems and human communities, often serving as regulatory requirements under frameworks like the U.S. National Environmental Policy Act (NEPA) or international standards (Roque et al., 2021; Nguyen & Hoang, 2022). In the context of EJ, impact assessments provide opportunities to identify disproportionate effects on marginalized groups, such as low-income populations, indigenous communities, and people of color (Trung et al., 2022; Roque et al., 2024). However, the efficacy of these studies is compromised by methodological biases that can distort findings, perpetuate injustices, and hinder effective policy responses (Ncube et al., 2023; Bezboruah et al., 2024; Walker et al., 2024).

Methodological biases refer to systematic errors in research design, data collection, analysis, or interpretation that lead to inaccurate or incomplete conclusions (Walker et al., 2022; FigueroaValverde et al., 2023; LeFevre et al., 2023). In impact assessment research, such biases may arise from selective sampling, inadequate measurement tools, cultural insensitivities, or failure to account for cumulative effects over time (Okoro et al., 2023; Oyshi et al., 2025). For instance, quantitative approaches dominant in EIAs often prioritize measurable indicators like pollutant levels while neglecting qualitative aspects such as community perceptions of risk or cultural losses (Fitero et al., 2023; Kalyan et al., 2023). Geographic biases further exacerbate issues, with studies disproportionately focusing on certain regions or ecosystems, leaving gaps in understanding global Anthropocene dynamics (Haeffner et al., 2021; Rutten et al., 2022).

The novelty of this review lies in its emphasis on critiquing the conduct of impact assessment studies rather than synthesizing their substantive findings. While previous reviews have summarized EJ outcomes in specific contexts, such as ocean governance or urban planning (Siddiqi et al., 2022; Radonic et al., 2023; Ovienmhada et al., 2024), few have systematically dissected the methodological underpinnings that shape these outcomes. This focus is timely, as the Anthropocene demands robust, unbiased assessments to address intersecting crises like climate injustice and environmental racism (Ross et al., 2021; Banerjee & Schuitema, 2022; Xie et al., 2023).

The objectives of this review are threefold: (1) to provide a thematic overview of methodological biases in EJ-focused impact assessment studies; (2) to analyze how these biases manifest in the Anthropocene, particularly through interactions with global environmental changes; and (3) to highlight strategies for bias mitigation to enhance the equity and reliability of future research. By limiting sources to peer-reviewed journal articles, the review captures recent advancements and critiques, ensuring relevance to contemporary challenges. The structure proceeds thematically, eschewing traditional empirical sections in favor of subheadings that explore conceptual, practical, and ethical dimensions of bias.

Conceptualizing methodological biases in environmental justice research

Methodological biases in environmental justice (EJ) impact assessments are inherently multifaceted, often stemming from the conceptual frameworks that guide research design (Walker et al., 2022; Hultström et al., 2023). In the Anthropocene, where human activities are deeply intertwined with natural processes, simplified models can fail to capture the complexity of socio-ecological systems, giving rise to biases that distort understanding and policy guidance (Cissé et al., 2024; Hoffman-Hall et al., 2024). For instance, studies focused on distributive justice—concerned with the fair allocation of environmental risks and benefits—frequently rely on spatial analyses. While these methods provide important insights, they can introduce selection bias by aggregating data at coarse scales, masking intra-community disparities and overlooking localized vulnerabilities (Shrestha et al., 2022; Banerjee & Schuitema, 2023).

A critical methodological challenge is the conflation of different bias types, such as confounding and measurement biases, which can skew causal inference regarding environmental impacts (Banerjee & Schuitema, 2023). Quasi-experimental designs, commonly used in impact assessments, are particularly prone to post-intervention selection biases. For example, in climate impact studies, environmental degradation often triggers migration, which alters population composition and consequently biases vulnerability analyses toward the remaining residents (Banerjee & Schuitema, 2022; Oyshi et al., 2025).

Recognition justice—emphasizing acknowledgment of diverse knowledge systems—is another area where methodological biases are prevalent. Research frameworks often privilege Western scientific paradigms, neglecting local or Indigenous knowledge systems. This is evident in ocean-related impact assessments, where insufficient integration of Indigenous knowledge leads to incomplete evaluations of marine injustices and ecological vulnerabilities (Ross et al., 2021; Ovienmhada et al., 2024; Spuler et al., 2025). Systematic reviews reveal a strong geographic bias, with a predominance of studies originating from the Global North. This Eurocentric orientation risks perpetuating narrow perspectives on Anthropocene injustices and marginalizing voices from the Global South, where environmental risks and socio-economic vulnerabilities are often most acute (Haeffner et al., 2021; Flacke et al., 2022; Chang & Vivekanand, 2024).

Such conceptual biases not only distort empirical findings but also reinforce existing power imbalances, as marginalized perspectives are frequently excluded from study design and conceptualization (Korpilo et al., 2022; Spuler & Wessel, 2024). Addressing these issues requires reflexive approaches that critically interrogate the assumptions underlying research frameworks and actively incorporate pluralistic epistemologies (Korpilo et al., 2023; Thazha et al., 2023; Sayyed et al., 2024).

Biases in data collection and measurement strategies

Data collection in EJ impact assessments is equally susceptible to methodological biases that undermine data quality, validity, and representativeness (Bodner et al., 2023a; Bezboruah et al., 2024). Measurement biases, including misclassification or mismeasurement of exposures and outcomes, are common in quantitative studies (Banerjee & Schuitema, 2023). For example, cumulative impact assessments relying on proxy indicators—such as residential proximity to industrial facilities—can misrepresent actual exposure levels, particularly for populations that are mobile or have complex activity patterns, leading to systematic underestimation of risks (Leonard et al., 2020; Brimacombe & Bodner, 2023).

Qualitative data collection, while crucial for capturing lived experiences, is also vulnerable to bias. Confirmation bias can occur when researchers selectively gather or interpret evidence that aligns with their preexisting assumptions (Haeffner et al., 2022; Kalyan et al., 2023). Participatory research aims to counteract this, yet implementation biases often arise when community involvement is tokenistic, failing to meaningfully empower participants or integrate their perspectives into study outcomes (Korpilo et al., 2022; Roque et al., 2022).

Temporal biases are particularly relevant in Anthropocene studies. Short-term data collection may overlook cumulative, long-term environmental impacts, such as biodiversity loss, gradual soil degradation, or progressive climate hazards, thereby underrepresenting slow-onset vulnerabilities (Leonard et al., 2023; Hoffman-Hall et al., 2024). Geographic sampling biases further exacerbate these limitations, as studies tend to focus on accessible or well-resourced sites, leaving many regions—such as remote, deep-sea, or underdeveloped areas—underrepresented (Haeffner et al., 2021; Ovienmhada et al., 2024). For example, marine EJ research is often concentrated on coastal zones, while offshore and deep-sea ecosystems, which are also vulnerable to industrial and climatic pressures, remain largely understudied.

Mixed-methods approaches, which integrate quantitative and qualitative data, are increasingly advocated to address these biases. However, their application is inconsistent, with a persistent quantitative dominance that may continue to marginalize community narratives and nuanced environmental experiences (Walker et al., 2022; Yu et al., 2022). Overall, these methodological challenges highlight the need for standardized protocols that emphasize inclusive sampling, robust data validation, and transparent documentation of assumptions and limitations to produce equitable and reliable EJ assessments (Bodner et al., 2023a; Oyshi et al., 2025).

Analytical biases and interpretation challenges

The analytical phases of impact assessments are particularly vulnerable to biases that can significantly affect both scientific inference and policy recommendations (Savita et al., 2022; Banerjee & Schuitema, 2023). Among the most pervasive are confounding biases, which occur when unaccounted-for variables distort observed causal relationships. These biases are especially common in non-randomized study designs, which dominate environmental and EJ research due to practical and ethical constraints (Oyshi et al., 2025). In the context of environmental justice, socioeconomic confounders—such as income inequality, housing segregation, or employment status—can obscure or exaggerate disparities in exposure among racial or marginalized communities, leading to misleading conclusions (Ross et al., 2021; Shrestha et al., 2022).

Detection and reporting biases further compromise analytical rigor. Outcome assessment biases, for example, emerge when subjective judgments influence impact ratings, often reflecting the assessor’s perspective rather than objective conditions (Leonard, 2021; Bezboruah et al., 2024). In the complex Anthropocene context, where environmental and social impacts are multidimensional and interlinked, synthesis biases in meta-analyses can compound errors. Aggregating heterogeneous studies without careful consideration of methodological differences can amplify inaccuracies, producing biased generalizations that may misinform policy (Haeffner et al., 2021; Walker et al., 2022).

Interpretation biases add another layer of complexity. Cultural biases, rooted in dominant epistemologies, and cognitive biases, such as short-termism, shape how findings are understood and prioritized (Haeffner et al., 2022). Short-termism—the tendency to focus on immediate outcomes—can undermine assessments of slow-onset phenomena like sea-level rise, biodiversity loss, or soil degradation, obscuring long-term vulnerabilities (Banerjee & Schuitema, 2022; Ovienmhada et al., 2024). In international or cross-cultural contexts, cognitive biases in risk perception influence compliance with EJ standards, particularly in construction, industrial, or urban development projects, where local perceptions of environmental risk may diverge from formal assessments (Salite et al., 2021).

Table 1. Examples of Biases in Data Collection and Analysis

Mitigating these analytical and interpretive challenges requires rigorous methodological practices. Robust sensitivity analyses, transparent reporting of assumptions and uncertainties, and explicit recognition of potential confounders are critical for producing reliable, equitable assessments. Unfortunately, many studies fall short in implementing these measures, perpetuating biased narratives and potentially reinforcing inequities rather than addressing them (Bodner et al., 2023a; Bodner et al., 2023b).

Geographic and demographic biases in study focus

Geographic biases in EJ and environmental impact research reflect broader inequalities in research funding, infrastructure, and accessibility. There is a marked skew toward studies conducted in developed regions, leaving many vulnerable areas of the Global South underrepresented (Haeffner et al., 2021; Flacke et al., 2022). This imbalance has significant consequences in the Anthropocene, where climate change, biodiversity loss, and pollution disproportionately affect under-resourced regions. For example, ocean EJ research is heavily concentrated in temperate zones, often overlooking tropical and equatorial regions where communities face acute climate injustices and ecosystem degradation (Banerjee & Schuitema, 2022; Ovienmhada et al., 2024; Spuler & Wessel, 2024).

Demographic biases further compound these gaps. Research frequently neglects intersectional factors, including gender, age, indigeneity, and disability, which are crucial for understanding the differential impacts of environmental change (Ross et al., 2021; Spuler et al., 2025). Participatory frameworks are designed to counter such biases, yet they often fall short in practice. Participant recruitment tends to favor accessible, educated, or socially connected individuals, limiting the representativeness of findings and marginalizing those most affected (Korpilo et al., 2022; Roque et al., 2022).

Cumulative impact assessments, essential for capturing the multi-layered consequences of Anthropocene processes, are also affected by geographic and demographic biases. Incomplete historical data, unrecorded exposures, and overlooked legacy injustices distort the understanding of cumulative risks, particularly in communities with long-standing environmental vulnerabilities (Leonard et al., 2020; Brimacombe & Bodner, 2023). This geographic-demographic mismatch not only hampers scientific accuracy but also undermines the equitable distribution of environmental protections, highlighting the urgent need for targeted research in underrepresented regions and populations (Flacke et al., 2022; Hoffman-Hall et al., 2024).

Participatory approaches and their methodological limitations

Participatory research has been widely recognized as a crucial avenue for advancing procedural justice in environmental justice (EJ) assessments. By involving communities directly in the research process, participatory approaches aim to ensure that affected populations have a voice in decisions that influence their environments and well-being. Such involvement, in principle, enhances inclusivity, transparency, and democratic decision-making. However, despite these advantages, participatory research is not immune to biases and methodological limitations (Korpilo et al., 2022).

One notable challenge is the presence of power imbalances within participatory settings. In many cases, dominant stakeholders—such as government agencies, corporations, or vocal community members—can overshadow marginalized voices, leading to performance biases where the perspectives of less powerful participants are underrepresented or ignored (Roque et al., 2022; Graefen et al., 2023). This dynamic not only compromises the fairness of the process but also risks skewing research findings toward the priorities of more influential actors.

In studies addressing the Anthropocene, participatory methods often struggle to integrate diverse ontologies and epistemologies. Recognition biases arise when dominant worldviews—typically those aligned with Western scientific paradigms—are prioritized over local, Indigenous, or alternative ways of knowing (Sayyed et al., 2024; Spuler et al., 2025). As a result, research outcomes may fail to capture the full spectrum of community experiences and environmental understandings, limiting the relevance and legitimacy of the findings.

Methodologically, participatory approaches face practical constraints that can limit their scalability. Resource limitations, time constraints, and logistical challenges often confine participatory studies to small-scale, localized cases (Yu et al., 2022; Kalyan et al., 2023). While such studies provide rich, context-specific insights, their findings are not always generalizable, which can hinder broader policy application. Moreover, critical interpretive syntheses suggest that although participatory tools enhance inclusivity, they carry the risk of co-optation: without genuine empowerment, participation can be tokenistic, serving institutional agendas more than community interests (Dhanasekar et al., 2022; Korpilo et al., 2022).

In formal impact assessment frameworks such as the National Environmental Policy Act (NEPA), additional procedural barriers further restrict the incorporation of EJ principles. These include limited windows for public input, complex technical language, and formalized processes that privilege stakeholders with resources and expertise (Roque et al., 2021; Efremov, 2023). Addressing these challenges requires hybrid methodologies that combine participatory approaches with rigorous bias identification and mitigation strategies, ensuring that participation genuinely advances equity rather than merely fulfilling procedural obligations (Bodner et al., 2023b; Korpilo et al., 2023).

Intersecting biases in cumulative impact assessments

Cumulative impact assessments (CIAs) are designed to capture the complex synergies and interactions of multiple environmental and social stressors, particularly in Anthropocene contexts where ecosystems and human communities are deeply intertwined. Despite their importance, CIAs are highly susceptible to intersecting biases, which can undermine the accuracy and fairness of assessments (Leonard et al., 2020; Brimacombe & Bodner, 2023).

One form of bias arises from confounding multiple exposures. Communities facing layered injustices—such as industrial pollution, socio-economic marginalization, and limited healthcare access—may experience overlapping environmental risks that standard assessment methods fail to disentangle (Shrestha et al., 2022; Banerjee & Schuitema, 2023). Consequently, risk estimates may be biased, underestimating the cumulative burden borne by vulnerable populations.

Methodological analyses of EJ literature reveal a disproportionate focus on distributional outcomes, such as exposure levels and pollutant concentrations, while procedural and recognition dimensions are frequently neglected (Haeffner et al., 2021; Flacke et al., 2022). In policy contexts, the language of public participation often limits true empowerment. Most legislation favors consultative or advisory participation over collaborative engagement, reinforcing procedural biases and reducing the potential for community-driven solutions (Nguyen & Hoang, 2022; Efremov, 2023).

The convergence of these biases amplifies social and environmental vulnerabilities. Addressing them requires integrated frameworks that simultaneously account for distributional, procedural, and recognition dimensions, providing a holistic approach to bias mitigation in CIAs (Walker et al., 2022; Bodner et al., 2023a).

Ethical implications of biases in anthropocene research

The ethical stakes of bias in EJ and Anthropocene research are substantial. Biases embedded in assessment design, execution, and interpretation can perpetuate harm, further marginalizing already vulnerable populations (Banerjee & Schuitema, 2022; Hoffman-Hall et al., 2024). Cognitive biases in research, such as short-termism or confirmation bias, can exacerbate these harms by prioritizing immediate outcomes over long-term justice (Salite et al., 2021; Haeffner et al., 2022).

Ethical frameworks in EJ research emphasize the necessity of bias-aware study designs that explicitly prioritize equity and inclusivity (Flacke et al., 2022; Korpilo et al., 2023). However, prevailing tendencies toward simpler study designs—driven by resource limitations or methodological conservatism—often undermine these ideals, compromising the credibility and ethical integrity of research (Savita et al., 2022; Trung et al., 2022).

To uphold ethical standards, transparency and accountability must be central to participatory and cumulative impact research. This includes documenting methodological choices, power dynamics, and decision-making processes, as well as actively addressing and reporting potential biases. By doing so, assessments can fulfill their intended purpose of promoting justice rather than inadvertently entrenching inequality (Ross et al., 2021; Spuler & Wessel, 2024).

RESULTS AND DISCUSSION

The thematic exploration of methodological biases in environmental justice (EJ) impact assessment studies reveals a pervasive pattern of systematic errors that compromise the integrity of research in the Anthropocene. These biases, ranging from conceptual oversimplifications to analytical distortions, not only skew empirical outcomes but also perpetuate structural inequalities by marginalizing vulnerable communities in decision-making processes (Hoffman-Hall et al., 2024; Sayyed et al., 2024). In an era where human-induced environmental changes intensify disparities, the failure to address these flaws undermines the core principles of EJ—distributive, procedural, and recognition justice (Sovacool et al., 2023; Ovienmhada et al., 2024). For instance, selection biases in data collection often exclude indigenous perspectives, leading to incomplete assessments of cumulative impacts from resource extraction and climate adaptation projects (Leonard et al., 2020; Leonard, 2021). This exclusion is particularly acute in the Global South, where geographic biases favor urban or accessible sites, ignoring rural and remote areas disproportionately affected by biodiversity loss and sea-level rise (Flacke et al., 2022).

The implications of these biases extend beyond academic validity to real-world policy and governance. Impact assessments inform critical decisions in sectors like energy transition and water management, yet biased methodologies can result in policies that exacerbate environmental racism and socioeconomic vulnerabilities (Banerjee & Schuitema, 2022; Banerjee & Schuitema, 2023; Oyshi et al., 2025). In the context of just transition plans, for example, perceptual biases among stakeholders can overlook the lived experiences of affected workers, leading to inequitable distribution of benefits from low-carbon initiatives (Banerjee & Schuitema, 2022). Similarly, in stormwater infrastructure and urban planning, biases in green infrastructure assessments fail to advance EJ, perpetuating cycles of infrastructure decay and health disparities in marginalized neighborhoods (Banerjee & Schuitema, 2023; Oyshi et al., 2025). The Anthropocene's interconnected crises amplify these issues, as short-term analytical biases ignore long-term cumulative effects, such as compounded heat exposure in prison landscapes or water insecurity post-disasters (Roque et al., 2021; Ovienmhada et al., 2024).

Ethical considerations further highlight the urgency of bias mitigation. Methodological assumptions in bias adjustment for climate models involve value judgements that can inadvertently prioritize certain knowledge systems over others, raising questions about usable information for EJ advocacy (Spuler et al., 2025). Western cultural biases in conservation studies undermine indigenous stewardship practices, treating them as monolithic and ignoring diverse epistemologies (Leonard et al., 2020). This recognition deficit not only distorts impact evaluations but also erodes trust between researchers and communities, hindering participatory approaches essential for resilient adaptation (Roque et al., 2022; Radonic et al., 2023; Roque et al., 2024). Moreover, cognitive biases in observational studies, akin to those in public health, can lead to negative effectiveness interpretations if not addressed, paralleling skewed EJ outcomes (Bodner et al., 2023a).

Interdisciplinary integration offers a pathway to redress these biases. Socio-hydrology and water governance research emphasize representation justice, advocating for inclusive agendas that capture human agency and methodological pluralism (Haeffner et al., 2021; Yu et al., 2022). Public participation GIS tools demonstrate potential in assessing multiple EJ dimensions, incorporating landscape values and sensory data to counter geographic skews (Korpilo et al., 2022; Korpilo et al., 2023). However, challenges persist in scaling participatory methods, where power imbalances can introduce new biases if partnerships are short-term or tokenistic (Radonic et al., 2023). Community-based participant-observation and digital participatory methods provide promising models, enabling real-time EJ measurement and household perceptions in underrepresented regions (Shrestha et al., 2022; Roque et al., 2024).

Policy implications underscore the need for reflexive practices in impact assessments. Frameworks like the WAMPUM adaptation for tribal nations illustrate how incorporating indigenous frameworks can mitigate biases in water security evaluations (Leonard, 2021). Similarly, satellite data applications for EJ require community organizer input to ensure data justice, avoiding top-down geospatial distortions (Ovienmhada et al., 2024; Sayyed et al., 2024). In coastal flooding and urban resilience, adaptive capacity building through participatory monitoring addresses biases in vulnerability assessments (Bezboruah et al., 2024). Ultimately, confronting these biases demands a shift from quantitative dominance to hybrid methodologies that value qualitative narratives and ethical accountability (Haeffner et al., 2022; Bodner et al., 2023b).

Table 2. Strategies for Bias Mitigation

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Banerjee, A., & Schuitema, G. (2023). Spatial justice as a prerequisite for a just transition in rural areas? The case study from the Irish peatlands. Environment and Planning C: Politics and Space, 41(6), 1096–1112. doi:10.1177/23996544231173113

Bezboruah, K., Sakalker, A., Hummel, M., Jenewein, O., Masten, K., & Liu, Y. (2024). Building adaptive capacity to address coastal flooding: The case of a small Texas city. Environmental Science & Policy, 151, 103599. doi:10.1016/j.envsci.2023.103599

Bodner, K., Irvine, M. A., Kwong, J. C., & Mishra, S. (2023a). Observed negative vaccine effectiveness could be the canary in the coal mine for biases in observational COVID-19 studies. International Journal of Infectious Diseases, 131, 111–114. doi:10.1016/j.ijid.2023.05.013

Bodner, K., Knight, J., Hamilton, M. A., & Mishra, S. (2023b). Testing whether higher contact among the vaccinated can be a mechanism for observed negative vaccine effectiveness. American Journal of Epidemiology, 192(8), 1335–1340. doi:10.1093/aje/kwad080

Brimacombe, C., Bodner, K., & Michalska-Smith, M. (2023). Shortcomings of reusing species interaction networks created by different sets of researchers. PLoS Biology, 21(4), e3002068. doi:10.1371/journal.pbio.3002068

Chang, J., & Vivekanand, W. (2024). Studying the relationship between moral sensitivity and moral performance of nursing students. Journal of Integrated Nursing and Palliative Care, 5, 62–67. doi:10.51847/IljINMMyUU

Cissé, C., Konaré, M. A., & Samaké, M., Togola, I. (2024). Exploring the anti-inflammatory potential of Sericanthe chevalieri and Ceiba pentandra as natural antitussives for children. Special Journal of Pharmacognosy, Phytochemistry & Biotechnology, 4, 49–58. doi:10.51847/0OVczhRtw1

Dhanasekar, P., Rajayyan, J. S., Veerabadiran, Y., Kumar, K. S., Kumar, K. S., & Chinnadurai, N. (2022). Evaluation of alum and purification process of water by coagulation method. Bulletin Pioneer Research in Medical and Clinical Science, 1(2), 1–6. doi:10.51847/R8GyfOmMDh

Efremov, A. (2023). Relieving psychosomatic pain and negative emotions through dehypnosis. Asian Journal of Individual and Organizational Behavior, 3, 18–24. doi:10.51847/BPFsWgpeFd

FigueroaValverde, L., Rosas-Nexticapa, M., AlvarezRamirez, M., Lopez-Ramos, M., MateuArmand, V., & Lopez-Gutierrez, T. (2023). Prostate cancer trends in southern Mexico: Statistical data from 1978 to 2020. Asian Journal of Current Research in Clinical Cancer, 3(2), 1–7. doi:10.51847/Vd5RkEeAhh

Fitero, A., Negruț, N., Cseppento, D. C. N., MirelaTit, D., Negru, P. A., Bustea, C., Radu, A. F., & Bungau, S. G. (2023). Inequities in antiviral therapy for diabetic individuals affected by COVID-19. Annals of Pharmacy Practice and Pharmacotherapy, 3, 9–20. doi:10.51847/BAIbQWifek

Flacke, J., Maharjan, B., Shrestha, R., & Martinez, J. (2022). Environmental inequalities in Kathmandu, Nepal—Household perceptions of changes between 2013 and 2021. Frontiers in Sustainable Cities, 4, 835534. doi:10.3389/frsc.2022.835534

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Haeffner, M., Hames, F., Barbour, M. M., Reeves, J. M., Platell, G., & Grover, S. (2022). Expanding collaborative autoethnography into the world of natural science for transdisciplinary teams. One Earth, 5(2), 157–167. doi:10.1016/j.oneear.2022.01.005

Haeffner, M., Hellman, D., Cantor, A., Ajibade, I., Oyanedel-Craver, V., Kelly, M., Schifman, L., & Weasel, L. (2021). Representation justice as a research agenda for socio-hydrology and water governance. Hydrological Sciences Journal, 66(11), 1611–1624. doi:10.1080/02626667.2021.1979697

Hoffman-Hall, A., White-Newsome, J. L., Filippelli, G. M., McAdoo, B. G., Cannon, C., Reano, D., & Ovienmhada, U. (2024). Surviving global change: GeoHealth, marginalized communities, and environmental justice in the Anthropocene. GeoHealth, 8(2), e2023GH000974. doi:10.1029/2023GH000974

Hultström, M., Becirovic-Agic, M., & Jönsson, S. (2023). Gum Arabic alleviates aluminum chloride-induced kidney damage via XRCC1 upregulation and Ki67/P53 downregulation. Special Journal of Pharmacognosy, Phytochemistry & Biotechnology, 3, 21–30. doi:10.51847/BXt3sRWLfV

Kalyan, B., Diaz, A. D., McAdams, J. H., & Carrasquillo, M. E. (2023). Can community-based participatory action research fulfill environmental justice principles in Newark, NJ? Environmental Research Letters, 18(9), 094035. doi:10.1088/1748-9326/acef7a

Korpilo, S., Kaaronen, R. O., Olafsson, A. S., & Raymond, C. M. (2022). Public participation GIS can help assess multiple dimensions of environmental justice in urban green and blue space planning. Applied Geography, 148, 102794. doi:10.1016/j.apgeog.2022.102794

Korpilo, S., Nyberg, E., Vierikko, K., Nieminen, H., Arciniegas, G., & Raymond, C. M. (2023). Developing a multi-sensory public participation GIS (MSPPGIS) method for integrating landscape values and soundscapes of urban green infrastructure. Landscape and Urban Planning, 230, 104617. doi:10.1016/j.landurbplan.2022.104617

LeFevre, G. H., Hendricks, M. D., Carrasquillo, M. E., McPhillips, L. E., Winfrey, B. K., & Mihelcic, J. R. (2023). The greatest opportunity for green stormwater infrastructure is to advance environmental justice. Environmental Science & Technology, 57(48), 19088–19093. doi:10.1021/acs.est.3c04051

Leonard, K. (2021). WAMPUM adaptation framework: Eastern coastal Tribal Nations and sea level rise impacts on water security. Climate and Development, 13(9), 842–851. doi:10.1080/17565529.2020.1867680

Leonard, K., Aldern, J. D., Christianson, A., Ranco, D., Thornbrugh, C., & Loring, P. A. (2020). Indigenous conservation practices are not a monolith: Western cultural biases and a lack of engagement with Indigenous experts undermine studies of land stewardship. EcoEvoRxiv. doi:10.32942/osf.io/pj4km

Leonard, K., David-Chavez, D., Smiles, D., & Jennings, L., Alegado, R. A. (2023). Water back: A review centering rematriation and Indigenous water research sovereignty. Water Alternatives, 16(2), 125–171.

Ncube, M., Sibanda, M., & Matenda, F. R. (2023). The influence of AI and the pandemic on BRICS nations: South Africa’s economic performance during crisis. Annals of Organizational Culture, Communications and Leadership, 4, 17–24. doi:10.51847/lrMvYTE3OF

Nguyen, D. T., & Hoang, T. H. (2022). The influence of organizational capabilities on operational efficiency: A study of Vietnamese businesses. Asian Journal of Individual and Organizational Behavior, 2, 15–20. doi:10.51847/PapKxH2ZYU

Okoro, C., Adebayo, F., & Bello, A. (2023). Enhanced clinical decision-making in early prostate cancer recurrence using [18F]Fluciclovine PET/CT. Asian Journal of Current Research in Clinical Cancer, 3(2), 36–50. doi:10.51847/Nuh7wPRHUj

Ovienmhada, U., Diongue, A., Pellow, D. N., & Wood, D. (2024). Satellite remote sensing for environmental data justice: Perspectives from anti-prison community organizers on the uses of geospatial data. Environmental Justice, 17(3), 181–192. doi:10.1089/env.2023.0012

Oyshi, F. H., Chhour, K., Womack, F., & Carrasquillo, M. E. (2025). The cost of aging water infrastructure and environmental racism: The Jackson, Mississippi water crisis and access to funding. Environmental Justice, 18(5), 324–330. doi:10.1089/env.2023.0034

Radonic, L., Jacob, C., Kalman, R., & Lewis, E. Y. (2023). It’s a sprint, not a marathon: A case for building short-term partnerships for community-based participatory research. Qualitative Research, 23(2), 380–398. doi:10.1177/14687941211008723

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