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<p>Synergistic emission reduction (SER) of carbon and pollutants in agriculture is crucial for addressing environmental issues, while existing studies lack comprehensive assessments to this synergy. This study develops a quantitative approach integrating carbon-pollution emission intensities with a coupling coordination model to measure SER in Chinese agriculture. Analyzing panel data from 280 prefecture-level cities (2011 − 2021), we employ spatial econometrics and convergence models to reveal spatiotemporal patterns and regional dynamics. The results indicate that (1) Agricultural carbon-emission intensity in China decreased from 33.8&#xa0;g/CNY to 21.0&#xa0;g/CNY, while the pollutant-emission intensity decreased from 3.4&#xa0;g/CNY to 2.1&#xa0;g/CNY. (2) The relationship between pollution mitigation and carbon reduction has improved, with the degree of SER increasing over time. The pattern of the SER degree exhibited a U-shaped trend from south to north. The global Moran index ranged from 0.0 to 0.2, suggesting a positive spatial autocorrelation in the SER. (3) The Theil index for the SER degree exhibited a fluctuating decline, suggesting that regional differences were narrowing. (4) The SER degree strictly followed a conditional β convergence at both national and regional levels, exhibiting spatial spillover effects. These findings suggest the potential to promote SER by focusing on their overlap and exploiting spatial convergence trends. This city-level analysis advances SER measurement methodologies and provides actionable insights for coordinating China’s agricultural environmental governance, with a methodological framework applicable to other developing economies facing similar dual environmental challenges.</p>

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Synergistic emission reduction of carbon and pollutants in agriculture: evidence from 280 Chinese cities

  • 期刊论文

  • 发表日期：

    2025年4月26日

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  • Haoyue Wu,
  • Xiangjiang Zheng,
  • Yue Meng

摘要

Synergistic emission reduction (SER) of carbon and pollutants in agriculture is crucial for addressing environmental issues, while existing studies lack comprehensive assessments to this synergy. This study develops a quantitative approach integrating carbon-pollution emission intensities with a coupling coordination model to measure SER in Chinese agriculture. Analyzing panel data from 280 prefecture-level cities (2011 − 2021), we employ spatial econometrics and convergence models to reveal spatiotemporal patterns and regional dynamics. The results indicate that (1) Agricultural carbon-emission intensity in China decreased from 33.8 g/CNY to 21.0 g/CNY, while the pollutant-emission intensity decreased from 3.4 g/CNY to 2.1 g/CNY. (2) The relationship between pollution mitigation and carbon reduction has improved, with the degree of SER increasing over time. The pattern of the SER degree exhibited a U-shaped trend from south to north. The global Moran index ranged from 0.0 to 0.2, suggesting a positive spatial autocorrelation in the SER. (3) The Theil index for the SER degree exhibited a fluctuating decline, suggesting that regional differences were narrowing. (4) The SER degree strictly followed a conditional β convergence at both national and regional levels, exhibiting spatial spillover effects. These findings suggest the potential to promote SER by focusing on their overlap and exploiting spatial convergence trends. This city-level analysis advances SER measurement methodologies and provides actionable insights for coordinating China’s agricultural environmental governance, with a methodological framework applicable to other developing economies facing similar dual environmental challenges.