Qi Huibo, Shen Xinyi, Long Fei, Liu Meijuan, Gao Xiaowei
College of Economics and Management, Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang Agricultural & Forestry University, Zhejiang Province Key Cultivating Think Tank, Hangzhou, 310000, China.
College of Economics and Management, Zhejiang Agricultural & Forestry University, Hangzhou, 310000, China.
Environ Sci Pollut Res Int. 2023 Jan;30(4):10136-10148. doi: 10.1007/s11356-022-22790-7. Epub 2022 Sep 7.
Zhejiang Province is a "demonstration area for high-quality development and construction of common prosperity" in China. Moreover, the county is the basic unit and power source for the economic development of Zhejiang Province. Therefore, the research on the spatial-temporal characteristics and influencing factors of county-level carbon emissions is of great significance for Zhejiang Province to achieve the strategic goal of carbon peak and carbon neutrality. Based on the carbon emissions and socio-economic data of 62 counties in Zhejiang Province from 2014 to 2020, the spatial dependence and agglomeration of county-level carbon emissions are analyzed through the spatial autocorrelation test and local spatial autocorrelation test respectively. According to the spatial-temporal characteristics of county-level carbon emissions revealed by the index of Moran's I and local Moran's I, the spatial error STIRPAT model is used to study the influencing factors of county-level carbon emissions in Zhejiang Province, China. The main results are as follows: (1) The total amount of county-level carbon emissions of 62 counties fluctuates from 259.69 to 326.28 million tons and shows a growth trend. (2) Moran's I index is between 0.369 and 0.399. The county-level carbon emissions have a significant spatial correlation, and the spatial agglomeration trend is relatively stable, which is consistent with the hypothesis of the geographical polarization effect. (3) High-high agglomeration counties are concentrated in the northeast of Zhejiang Province, while low-low agglomeration counties are mainly in the southwest. (4) The relationship between county per capita GDP and carbon emissions has not been "decoupled," because when other variables remain unchanged, the county's total carbon emissions will increase by 2.866% for every 1% increase in the county's per capita GDP; the increase of the proportion of secondary industry contributes to the decline of carbon emissions, and the low-carbon effect brought by large-scale industrial development as well as scientific and technological innovation has not yet appeared. (5) The estimate of the spatial coefficient λ was 0.324, which illustrates that the carbon emission of a single county is positively affected by the carbon emission of the neighboring counties, and other socio-economic factors affecting carbon emission among counties also have a spatial correlation. Therefore, the policy of realizing regional coordinated development as well as the carbon peaking and carbon neutrality goals should not only focus on industrial layout, but also take a dynamic and comprehensive consideration from a spatial perspective.
浙江省是中国“高质量发展建设共同富裕示范区”。而且，县域是浙江省经济发展的基本单元和动力源泉。因此，研究县级碳排放的时空特征及影响因素对浙江省实现碳达峰、碳中和战略目标具有重要意义。基于2014—2020年浙江省62个县的碳排放及社会经济数据，分别通过空间自相关检验和局部空间自相关检验分析县级碳排放的空间依赖性和集聚性。依据莫兰指数I（Moran's I）和局部莫兰指数（local Moran's I）揭示的县级碳排放时空特征，采用空间误差STIRPAT模型研究中国浙江省县级碳排放的影响因素。主要结果如下：（1）62个县的县级碳排放总量在2.5969亿吨至3.2628亿吨之间波动，呈增长趋势。（2）莫兰指数I在0.369至0.399之间。县级碳排放存在显著的空间相关性，空间集聚趋势相对稳定，这与地理极化效应假说相符。（3）高高集聚型县集中在浙江省东北部，而低低集聚型县主要在西南部。（4）县域人均GDP与碳排放之间尚未“脱钩”，因为在其他变量不变的情况下，县域人均GDP每增长1%，县域碳排放总量将增长2.866%；第二产业比重的增加有助于碳排放的下降，大规模产业发展以及科技创新带来的低碳效应尚未显现。（5）空间系数λ的估计值为0.324，这表明单个县的碳排放受到相邻县碳排放的正向影响，各县之间影响碳排放的其他社会经济因素也存在空间相关性。因此，实现区域协调发展以及碳达峰、碳中和目标的政策不仅应关注产业布局，还应从空间角度进行动态、全面的考量。
