Abstract
Comprehending the spatial–temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China’s strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial–temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.
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Data availability
The datasets generated during the current study are available from the corresponding author on reasonable request.
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YY C: methodology; validation; data curation; writing—original draft; writing—review and editing; visualization
DW Q: resources; writing—review and editing; supervision
XL Z: investigation; conceptualization
YC G: conceptualization; resources
CY Z: supervision; data curation
LJ T: supervision; data curation
JW Z: supervision; data curation
XL L: supervision; data curation
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Chu, Yy., Zhang, Xl., Guo, Yc. et al. Spatial–temporal characteristics and driving factors’ contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995–2021. Environ Sci Pollut Res 31, 19779–19794 (2024). https://doi.org/10.1007/s11356-024-32359-1
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DOI: https://doi.org/10.1007/s11356-024-32359-1