Spatial autocorrelation and driving factors of carbon emission density of crop production in China.

Abstract

Mitigating carbon emissions from crop production is essential for addressing global warming. At a macro-level, existing studies have often relied on the calculation of carbon emission intensity of crop production to understand comparable carbon effects between regions. However, this approach obscures the differences in crop planting scale and natural attributes across regions, leaving room for improvement in the methods and scope of analysis. To extend the existing research, we proposed an idea for calculating the carbon emission density of crop production based on planting area. Additionally, we developed an analytical framework for driving factors of carbon emission density of crop production from a spatial interaction perspective. The provincial carbon emission density of crop production in mainland China between 2000 and 2020 was calculated, and spatial econometric models were utilized to investigate the spatial autocorrelation and driving factors. The results indicate that the national average carbon emission density of crop production was 1.462 t/hm2 annually. Over 21years, the carbon emission density of agricultural materials, rice cultivation, soil management, and straw burning evolved from 0.384 to 0.470 t/hm2, 0.409 to 0.367 t/hm2, 0.171 to 0.169 t/hm2, and 0.317 to 0.448 t/hm2, respectively. The global Moran's index indicated a positive spatial autocorrelation of carbon emission density of crop production and the subdivided carbon sources among provinces. Regarding direct effects, an increase in the proportion of paddy fields in cropland composition and irrigation efficiency would significantly promote the carbon emission density, while factors such as cropland area, multiple cropping, agricultural personnel numbers, departmental proportion, and disaster degree would decrease the local carbon emission density. Certain factors, such as cropland area and agricultural disasters, had a spatial spillover effect on carbon emission density between provinces. The study suggests harnessing key drivers and spatial spillover effects to achieve regional low-carbon crop production.