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<p>City-resolved CO₂ emissions are needed to support urban mitigation planning and to track progress toward climate targets, yet long-term, consistent administrative-unit inventories remain scarce in Southeast Asia. We develop an annual CO₂ emissions dataset for 4,413 GADM admin-3 units across 11 Southeast Asian countries for 2000–2020. We downscale EDGAR admin-2 emission totals using a proxy-based allocation framework that integrates nighttime lights, impervious surface information, and urban–rural settlement distributions to represent economic activity, built form, and consumption-related patterns. To address strong regional heterogeneity and uneven sample sizes, we implement an adaptive dual-model strategy that selects province-specific or pooled (“hybrid”) models using cross-validated performance. Among evaluated algorithms, gradient boosting provides the best out-of-fold fit. Final city-level estimates are obtained by normalizing predicted intensities within each admin-2 unit and enforcing a strict mass-balance constraint so that city totals exactly match EDGAR baselines. The resulting panel supports analyses of urban emission dynamics, hotspot identification, and policy-relevant comparison across Southeast Asia.</p>

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City-level carbon emissions data in Southeast Asia from 2000 to 2020

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Xingyu Chen,
  • Yuhao Ba

摘要

City-resolved CO₂ emissions are needed to support urban mitigation planning and to track progress toward climate targets, yet long-term, consistent administrative-unit inventories remain scarce in Southeast Asia. We develop an annual CO₂ emissions dataset for 4,413 GADM admin-3 units across 11 Southeast Asian countries for 2000–2020. We downscale EDGAR admin-2 emission totals using a proxy-based allocation framework that integrates nighttime lights, impervious surface information, and urban–rural settlement distributions to represent economic activity, built form, and consumption-related patterns. To address strong regional heterogeneity and uneven sample sizes, we implement an adaptive dual-model strategy that selects province-specific or pooled (“hybrid”) models using cross-validated performance. Among evaluated algorithms, gradient boosting provides the best out-of-fold fit. Final city-level estimates are obtained by normalizing predicted intensities within each admin-2 unit and enforcing a strict mass-balance constraint so that city totals exactly match EDGAR baselines. The resulting panel supports analyses of urban emission dynamics, hotspot identification, and policy-relevant comparison across Southeast Asia.