甘肃省县域能源消费碳排放强度时空动态演变及其减排效果[j]。

Q2 Environmental Science
Wei-Ping Zhang, Pei-Ji Shi, Fan-Yuan Cheng
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引用次数: 0

摘要

科学估算和动态监测区域能耗碳排放及其强度的发展趋势,是制定、实施和评价区域碳减排战略的科学依据和基本保障。基于DMSP/OLS和NPP/VIIRS长时间序列夜间灯光数据,对2000 - 2020年甘肃省县域能源消费碳排放及其强度进行了数值模拟。采用非参数核密度估计、空间马尔可夫链、空间变异函数等模型分析了碳排放强度的时空动态演化特征,并利用修正系数检验了各县降低碳排放强度的有效性。结果表明:①研究期内,甘肃省能源消费碳排放强度总体呈下降趋势,2020年能源消费碳排放强度比2000年下降64.82%;②县域碳排放强度呈现明显的空间集聚特征,以陇中兰州市、河西酒泉市和陇东庆阳市为主的高碳强度地区正逐步向低碳强度地区转变。③县域碳排放强度呈现俱乐部收敛效应和空间相关性,县域碳排放强度的空间差异逐渐减小。④到2020年,甘肃省一半以上的县取得了显著的减排成效,但仍有部分县的碳排放强度低于全省平均水平,说明县级单位在推进碳减排时也应遵循共同但有区别的责任原则。研究成果为促进甘肃省区域绿色低碳转型和节能减碳提供了重要参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Spatial and Temporal Dynamic Evolution of Carbon Emission Intensity of County Energy Consumption in Gansu Province and Its Emission Reduction Effectiveness].

Scientifically estimating and dynamically monitoring the development trend of regional energy consumption carbon emissions and their intensity is the scientific basis and basic guarantee for formulating, implementing, and evaluating regional carbon reduction strategies. Based on long time-series DMSP/OLS and NPP/VIIRS nighttime light datasets, this paper simulates the carbon emissions and their intensity of energy consumption in counties in Gansu Province from 2000 to 2020. Non-parametric kernel density estimation, spatial Markov chain, spatial variation function, and other models are used to analyze the spatiotemporal dynamic evolution characteristics of carbon emission intensity, and correction coefficients are used to test the effectiveness of reducing carbon emission intensity in each county. The results follow: ① During the research period, the overall carbon emission intensity of energy consumption in Gansu Province showed a downward trend, with a 64.82% decrease in energy consumption carbon emission intensity in 2020 compared to 2000. ② The carbon emission intensity of counties showed obvious spatial agglomeration characteristics, and the high carbon intensity areas mainly in Lanzhou City in Longzhong, Jiuquan City in Hexi, and Qingyang City in Longdong are gradually transforming into low-carbon intensity areas. ③ The carbon emission intensity at the county level showed a club convergence effect and spatial correlation, and the spatial differences in carbon emission intensity at the county level gradually decreased. ④ By 2020, more than half of the counties in Gansu Province had achieved significant emission reduction results, but there were still some counties whose carbon emission intensity had decreased below the provincial average, indicating that county units should also follow the principle of common but differentiated responsibilities when promoting carbon reduction. The research results provide important references for promoting regional green and low-carbon transformation and energy conservation and carbon reduction in Gansu Province.

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来源期刊
环境科学
环境科学 Environmental Science-Environmental Science (all)
CiteScore
4.40
自引率
0.00%
发文量
15329
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