| 中国县域碳排放时空演变与异质性 |
| 摘要点击 2077 全文点击 538 投稿时间：2022-02-19 修订日期：2022-04-29 |
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| 中文关键词 县域|碳排放|时空演变|异质性|影响机制|时空地理加权回归 |
| 英文关键词 county|carbon emissions|spatial-temporal evolution|heterogeneity|influencing mechanism|geographically and temporally weighted regression |
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| 中文摘要 |
| 县域是实现减排控碳的关键空间单元,研究并揭示县域碳排放的时空演变特征和影响机制对于实现"双碳"目标具有重要意义.以县域作为分析单元,运用数理统计和面板数据回归模型等方法,分析2000~2017年中国县域碳排放时空演变和异质性特征,探究其影响机制.结果表明:① 2000~2017年碳排放年均增速为7.12%,历经"大幅上升-缓慢上升-高位波动"3个发展阶段,最终稳定在90×108 t左右;在县域尺度上表现为显著正向空间自相关.②普通面板回归模型显示,GDP、建设用地规模、人口规模、人均GDP和人均金融机构存款余额和碳排放关系显著,前三者对碳排放的促进作用最为强烈.③时空地理加权回归模型拟合优度较高,除国民生产总值在全局上稳定表现为促进作用以外,其余影响因素的作用方向和强度均在时空上发生了较大转变;表明我国不同类型县域间碳排放水平和主要影响因素各异.该研究一定程度上揭示了县域碳排放的演变特征和异质性,有助于优化"双碳"目标的空间实施路径. |
| 英文摘要 |
| Counties are the key spatial units in achieving the reduction and control of carbon emissions. It is of great significance to study and reveal the spatial-temporal evolution characteristics and influencing mechanism of carbon emissions for realizing the "carbon peak and carbon neutral" goal. In this study, the spatial-temporal evolution and heterogeneity of carbon emissions at the county level in China from 2000 to 2017 were analyzed by using mathematical statistics and panel data regression modeling, and the influencing mechanism was explored. The results showed that: ① from 2000 to 2017, the annual growth rate of carbon emissions was 7.12%, which experienced the three stages of "sharp rise, slow rise, and high fluctuation" and finally stabilized at approximately 90×108 t. At the county scale, there was a significant positive spatial autocorrelation. ② The general panel regression model showed that GDP, construction land area, population, per capita GDP, and per capita deposit balance of financial institutions were significantly correlated with carbon emissions, and the former three had the strongest promoting effect on carbon emissions. ③ The goodness of fit of the geographically and temporally weighted regression model was high, and the direction and intensity of the other impact factors changed greatly in spatial-temporal characteristics, except that GDP showed a stable promoting effect globally. The results showed that carbon emission levels and main influencing factors varied among counties in China. This study revealed the heterogeneity of carbon emissions at the county level, which is helpful to optimize the spatial-temporal implementation path of the "dual carbon" target. |
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