China's provincial carbon emission driving factors analysis and scenario forecasting

IF 5.4 Q1 ENVIRONMENTAL SCIENCES
Siyao Li, Lili Yao, Yuchi Zhang, Yixin Zhao, Lu Sun
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引用次数: 0

Abstract

Studying the drivers of China's carbon emissions at the provincial level can clarify differences in carbon emissions due to initial resource endowments and explore pathways to achieve China's 2030 carbon peak and 2060 carbon-neutral commitments (China's 30.60 decarbonization target). In this paper, the carbon emissions of 30 provinces in China during 2000–2019 were calculated using the emission coefficient method. The LMDI model was used to investigate each province's carbon emission drivers. On this basis, the STIRPAT model is used to predict the carbon emissions of each province under three scenarios: low carbon, baseline, and high carbon. The results show that: (1) China's carbon emissions have significant regional differences, and the trend of total carbon emissions is consistent with that of per capita carbon emissions; (2) Economic development contributes the most to regional carbon emission; (3) China's carbon emission trend can be divided into four patterns: gathering type, discrete type, overlapping type, and idiotype. The results enrich the research on carbon emission drivers and forecasts, provide targeted policy recommendations for China to coordinate regional economic development, energy conservation, and carbon emission reduction, and explore a path for China to achieve the 30.60 decarbonization goal.

中国省级碳排放驱动因素分析与情景预测
研究中国省级碳排放的驱动因素,可以明确初始资源禀赋导致的碳排放差异,探索实现中国2030年碳峰值和2060年碳中和承诺(中国30.60脱碳目标)的路径。本文采用排放系数法计算了 2000-2019 年中国 30 个省份的碳排放量。利用 LMDI 模型研究了各省的碳排放驱动因素。在此基础上,利用 STIRPAT 模型预测了低碳、基准和高碳三种情景下各省的碳排放量。结果表明(1)中国的碳排放具有显著的地区差异,碳排放总量的变化趋势与人均碳排放的变化趋势一致;(2)经济发展对地区碳排放的贡献最大;(3)中国的碳排放趋势可分为聚集型、离散型、重叠型和特异型四种模式。研究结果丰富了碳排放驱动因素和预测研究,为中国协调区域经济发展、节能减排提供了有针对性的政策建议,为中国实现 30.60 的低碳化目标探索了路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental and Sustainability Indicators
Environmental and Sustainability Indicators Environmental Science-Environmental Science (miscellaneous)
CiteScore
7.80
自引率
2.30%
发文量
49
审稿时长
57 days
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