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<p>A spatiotemporal inventory of CO₂ emissions is essential for countries aiming to reduce carbon output. However, rising urban industrial activity and daily energy consumption present major challenges. Accurately understanding the spatiotemporal dynamics of urban CO₂ emissions is crucial for identifying sources and enabling targeted mitigation, yet this topic has received limited attention. This study introduces a CO₂ Emission Spatial Index (CESI), developed by integrating MODIS and VIIRS-DNB data, and applies a zonal classification approach to map urban CO₂ emissions across Europe from 2012 to 2021. Results indicate that CESI combined with zonal classification improves spatial estimation accuracy compared with nighttime light data alone, with all <i>R</i>² values exceeding 0.96. The analysis reveals significant spatiotemporal variation in Europe’s urban CO₂ emissions during the study period. Additionally, a new normalized indicator integrating population, GDP, and land use (PGL<sub>index</sub>) is proposed, and its coupling coordination relationship with CESI is examined. The results show that several northeastern European countries experience pronounced imbalances. These findings provide valuable insights for local authorities, supporting more precise spatial analysis and more effective CO₂ reduction policies.</p>

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Mapping urban CO2 emission spatiotemporal dynamics in Europe via a nighttime light composite index

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Wei Guo,
  • Xiaoling Wei,
  • Ximin Cui,
  • Xuesheng Zhao,
  • Jinke Liu,
  • Xueqian Gao,
  • Mengjun Chao,
  • Valerie Graw,
  • Andreas Rienow

摘要

A spatiotemporal inventory of CO₂ emissions is essential for countries aiming to reduce carbon output. However, rising urban industrial activity and daily energy consumption present major challenges. Accurately understanding the spatiotemporal dynamics of urban CO₂ emissions is crucial for identifying sources and enabling targeted mitigation, yet this topic has received limited attention. This study introduces a CO₂ Emission Spatial Index (CESI), developed by integrating MODIS and VIIRS-DNB data, and applies a zonal classification approach to map urban CO₂ emissions across Europe from 2012 to 2021. Results indicate that CESI combined with zonal classification improves spatial estimation accuracy compared with nighttime light data alone, with all R² values exceeding 0.96. The analysis reveals significant spatiotemporal variation in Europe’s urban CO₂ emissions during the study period. Additionally, a new normalized indicator integrating population, GDP, and land use (PGLindex) is proposed, and its coupling coordination relationship with CESI is examined. The results show that several northeastern European countries experience pronounced imbalances. These findings provide valuable insights for local authorities, supporting more precise spatial analysis and more effective CO₂ reduction policies.