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<p>This study provides a comprehensive assessment of global environmental performance by applying k-means time series clustering with Dynamic Time Warping (DTW) to twelve key environmental indicators from the Sustainable Consumption and Production Hotspot Analysis Tool (SCP-HAT) for 68 countries over the period 1990–2022. The analysis integrates national production and consumption footprint perspectives, considering both total and per capita values, to uncover patterns of resource use, environmental pressures, and cross-border environmental responsibility. Following data standardization and optimal cluster selection using the elbow method, three distinct clusters were consistently identified for each indicator and analytical perspective. The results reveal that China and India consistently belong to the highest-impact clusters in the total footprint analysis, with air pollution reaching approximately 30,000 kilo-DALY under the national production total perspective in recent years. In contrast, high-income economies such as the United States, Canada, Australia, and Norway dominate the highest-impact clusters in the per capita analysis, where primary energy use in the red cluster reaches approximately 600–700 PJ/capita and fossil fuel depletion remains around 35–45 (million tonnes Oil eq./capita) toward the end of the study period. Meanwhile, most Latin American and African countries consistently fall into the lowest-impact clusters across both perspectives. The simultaneous clustering of all indicators confirms persistent structural inequalities in environmental pressures driven by population size and consumption-intensive lifestyles. These findings demonstrate the value of time series clustering for detecting long-term environmental trajectories and provide evidence-based insights to support differentiated sustainability policies and international resource management strategies.</p>

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The Geography of Environmental Inequality: Thirty Years of Diverging Global Footprints

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Thelma Posadas-Paredes,
  • Francisco Javier López-Flores,
  • César Ramírez-Márquez,
  • José María Ponce-Ortega

摘要

This study provides a comprehensive assessment of global environmental performance by applying k-means time series clustering with Dynamic Time Warping (DTW) to twelve key environmental indicators from the Sustainable Consumption and Production Hotspot Analysis Tool (SCP-HAT) for 68 countries over the period 1990–2022. The analysis integrates national production and consumption footprint perspectives, considering both total and per capita values, to uncover patterns of resource use, environmental pressures, and cross-border environmental responsibility. Following data standardization and optimal cluster selection using the elbow method, three distinct clusters were consistently identified for each indicator and analytical perspective. The results reveal that China and India consistently belong to the highest-impact clusters in the total footprint analysis, with air pollution reaching approximately 30,000 kilo-DALY under the national production total perspective in recent years. In contrast, high-income economies such as the United States, Canada, Australia, and Norway dominate the highest-impact clusters in the per capita analysis, where primary energy use in the red cluster reaches approximately 600–700 PJ/capita and fossil fuel depletion remains around 35–45 (million tonnes Oil eq./capita) toward the end of the study period. Meanwhile, most Latin American and African countries consistently fall into the lowest-impact clusters across both perspectives. The simultaneous clustering of all indicators confirms persistent structural inequalities in environmental pressures driven by population size and consumption-intensive lifestyles. These findings demonstrate the value of time series clustering for detecting long-term environmental trajectories and provide evidence-based insights to support differentiated sustainability policies and international resource management strategies.