Abstract
Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an “adder” distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China’s rural energy consumption carbon emission intensity shows a trend of “Inverted N-shaped.” The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.
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Notes
In 2030, the carbon emission intensity per unit of GDP will decrease by more than 65% compared with that in 2005, the proportion of non-fossil energy consumption will reach about 25%, and the total installed capacity of wind power and solar power will reach more than 1.2 billion kilowatts.
It should be noted that the article only calculates the direct emissions of 17 fossil fuels consumed by the agricultural sector, regardless of the emissions related to electricity or heat. This is because the emissions of electricity and heat have been calculated from the production side and allocated to their respective power plants. The inclusion of the calculation will lead to a biased accounting of the final total carbon emissions from agricultural energy consumption (Shan (2020)).
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The authors acknowledge financial support from the National Natural Science Foundation of China (71663045), the Social Science Fund Project of Xinjiang Production and Construction Corps (22YB22), and the graduate research and innovation project of Tarim University (TDGRI202264). The usual disclaimers apply.
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WX: conceptualization, data collation, software, writing—original draft, writing—review and editing. YM: data curation, validation, writing—original draft, writing—review and editing. YG: writing—review and editing, data curation, validation. XS: theoretical analysis, writing—review and editing, funding acquisition, guidance and supervision. YH: funding acquisition, guidance and supervision.
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Xia, W., Ma, Y., Gao, Y. et al. Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China. Environ Sci Pollut Res 31, 7751–7774 (2024). https://doi.org/10.1007/s11356-023-31539-9
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DOI: https://doi.org/10.1007/s11356-023-31539-9