Spatial–temporal characteristics and driving factors’ contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995–2021

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 2879.69 million metric tons (Mt), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2153.39 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.

Analysis of Influencing Factors and Prediction of the Peak Value of Industrial Carbon Emission in the Sichuan-Chongqing Region

“…Wei et al measured their carbon emissions and used the carbon emission coefficient method to examine the spatial features of land-use carbon emissions over the previous 20 years in different districts and counties in Gansu Province from the perspectives of the carbon ecological support coefficient and per capita carbon footprint [2]. Chu et al used the IPCC's carbon emission coefficient approach to assess greenhouse gas emissions from agriculture that are not carbon-based at the province level in a thorough manner [3]. To estimate overall agriculture net carbon emissions, Zhang and Cai employed the economywater-carbon coefficient technique, the IPCC carbon emissions accounting method, and the environmental input-output model [4].…”