Spatial-Temporal Pattern, Influencing Factors and Spatial Spillover Effect of Rural Energy Carbon Emissions in China

In the context of the “dual carbon” strategy, clarifying the current characteristics, spatial-temporal pattern and influencing factors of rural energy carbon emissions can provide important support for effectively promoting rural low-carbon development.

Carbon emission factor method is used to measure rural energy carbon emissions in China effectively, and analyze its temporal and spatial characteristics. Then, the autocorrelation model is used to explore its spatial correlation pattern. Finally, the introduction of STIRPAT extended model is used to analyze the main factors affecting its intensity changes and the spatial spillover effect.

China's total rural energy carbon emissions are in a continuous upward trend, with an increase of 77.55% in 2019 compared with 2005, which is mainly attributed to the increase in rural residents' domestic energy consumption. Rural energy carbon emission intensity has increased slightly during the investigation period. Although there are some inter-annual fluctuations, the overall fluctuations are small. In 2019, there were significant inter-provincial differences in rural energy carbon emissions, with Hebei leading the way and Ningxia at the bottom. Compared with 2005, only 5 provinces were in a downward trend. In 2019, Beijing ranked first in rural energy carbon emission intensity, while Hainan ranked last, with the latter even less than one tenth of the former. Since 2008, China's rural energy carbon emissions have shown obvious and stable spatial dependence, as well as local spatial clustering, with a small and relatively stable number of high-high concentration provinces and a lager and growing number of low-low concentration provinces. Among the social factors, the increase of rural affluence can lead to an increase of rural energy carbon emission intensity, while agricultural technology progress and rural labor force structure variables have a dampening effect, with only rural affluence showing a spatial spillover effect in a negative direction. Among the economic factors, the increase in the rural financial agglomeration and the improvement of agricultural development level both lead to the increase of rural energy carbon emission intensity, and both have spatial spillover effects, with the former positive and the latter negative. While agricultural financial investment does not have a direct effect but shows a negative spatial spillover effect. Among the industry-level factors, the increase of agricultural industry agglomeration leads to the increase of rural energy carbon emission intensity, but at the same time, it also presents a negative spatial spillover effect.

The total amount and intensity of rural energy carbon emissions in China are on the rise, with significant inter-provincial differences. China's rural energy carbon emissions show obvious spatial dependence and spatial heterogeneity. Rural energy carbon emissions are affected by a combination of social, economic and industrial factors.