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中国黄河流域地级城市碳排放的动态演变特征及其驱动因素。

Dynamic evolution characteristics and driving factors of carbon emissions in prefecture-level cities in the Yellow River Basin of China.

机构信息

School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China.

School of Public Administration, Dongbei University of Finance and Economics, Dalian, 116025, China.

出版信息

Environ Sci Pollut Res Int. 2023 May;30(25):67443-67457. doi: 10.1007/s11356-023-27190-z. Epub 2023 Apr 27.

Abstract

This paper focuses on the spatiotemporal evolution characteristics, as well as the driving factors, of carbon emissions in the prefecture-level cities in the Yellow River Basin (YB). The paper's findings will aid in promoting ecological conservation and high-quality development in the region. The initiatives undertaken in the YB are a significant national strategy towards achieving carbon peaking and carbon neutrality. To fully investigate the spatiotemporal evolution process, as well as the typical characteristics of their carbon emissions, conventional, and spatial Markov transition probability matrices were developed utilizing YB's panel data for 55 prefecture-level cities from 2003 to 2019. The generalized Divisia index decomposition method (GDIM) cleverly uses this data to conduct a complete analysis of the dynamics and driving processes influencing the change in carbon emissions in these cities. However, the evolution of carbon emissions in prefecture-level cities has reached a point of stability that maintains the original state, making it challenging to make meaningful short-term progress. The data indicates that prefecture-level cities in the YB are emitting more carbon dioxide on average. Neighborhood types in these cities significantly influence the transformation of carbon emissions. Low-emission areas can encourage a reduction in carbon emissions, whereas high-emission areas can encourage an increase. The spatial organisation of carbon emissions exhibits a "high-high convergence, low-low convergence, high-pulling low, low-inhibiting high" club convergence phenomenon. Carbon emissions rise with per capita carbon emissions, energy consumed, technology, and output scale, whereas it falls with carbon technology intensity and output carbon intensity. Hence, instead of enhancing the role of increase-oriented variables, prefecture-level cities in the YB should actively engage these reduction-oriented forces. The YB's key pathways for lowering carbon emissions include boosting research and development, promoting and applying carbon emission reduction technologies, lowering output carbon intensity and energy intensity, and improving energy use effectiveness.

摘要

本文聚焦于黄河流域(YB)地级市的碳排放时空演变特征及其驱动因素。该研究有助于推动该地区的生态保护和高质量发展。YB 的举措是实现碳达峰和碳中和的国家重大战略。为了全面研究碳排放的时空演变过程及其典型特征,利用 YB 2003-2019 年 55 个地级市的面板数据,构建了常规和空间马尔可夫转移概率矩阵。广义Divisia 指数分解法(GDIM)巧妙地利用该数据对影响城市碳排放变化的动态和驱动过程进行了全面分析。然而,地级市的碳排放演变已经达到了维持原状的稳定阶段,使得在短期内取得有意义的进展变得困难。数据表明,YB 的地级市平均二氧化碳排放量增加。城市间的邻里类型对碳排放的转化具有显著影响。低排放地区可以鼓励减少碳排放,而高排放地区则可以鼓励增加碳排放。碳排放的空间组织呈现出“高高聚集、低低聚集、高拉低、低抑高”的俱乐部收敛现象。碳排放随人均碳排放量、能源消耗、技术和产出规模的增加而上升,随碳技术强度和产出碳强度的下降而下降。因此,YB 地级市不应增强增长导向型变量的作用,而应积极利用减排导向型力量。降低 YB 碳排放的关键途径包括:加大研发力度、推广应用碳减排技术、降低产出碳强度和能源强度、提高能源利用效率。

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