| 中国城市减污降碳协同效应的时空演变格局及影响因素 |
| 摘要点击 716 全文点击 63 投稿时间：2024-12-22 修订日期：2025-04-07 |
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| 中文关键词 减污降碳 协同效应 时空演变 空间马尔可夫链 核密度 时空地理加权回归模型（GTWR） |
| 英文关键词 pollution reduction and carbon reduction synergistic effect spatial-temporal evolution spatial Markov chain kernel density GTWR model |
| DOI 10.13227/j.hjkx.20260204 |
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| 中文摘要 |
| 以降碳为重点推动城市减污降碳协同增效是实现经济社会绿色转型的重要抓手. 综合使用包含非期望产出的SBM-DEA模型和耦合协调度模型测度中国城市减污降碳协同效应，运用空间马尔可夫链和数据可视化等方法研究中国城市2004~2022年减污降碳协同效应的时空演变格局，使用时空地理加权回归模型（GTWR）、核密度曲线与地图可视化等方法考察城市减污降碳协同效应影响因素的时空异质性. 结果表明：①中国城市减污降碳协同效应总体呈上升趋势，2014年之前波动下降，随后进入快速增长区间. ②中国城市减污降碳协同效应具有较强的稳定性，其演变过程具有渐进性，邻域减污降碳协同效应水平与本城市向上转移概率呈现正相关，而与向下转移概率呈现负相关. ③城市减污降碳协同效应呈现东北>东部>西部>中部的空间分异格局，高水平区域逐渐向东部和东北地区集聚，中西部地区整体稳中有进，低水平区域以山西省为中心逐渐收敛. ④GTWR的回归结果表明，产业结构、能源效率和技术创新对减污降碳协同效应呈现促进作用，人口规模、经济发展和能源结构则表现为抑制作用，各影响因素具有明显的时空异质性. |
| 英文摘要 |
| Environmental pollution and carbon emissions predominantly stem from fossil fuel consumption， sharing common sources and processes. This inherent co-production relationship establishes a scientific foundation for implementing coordinated governance to enhance the Synergistic Effect of Pollution Reduction and Carbon Reduction （SEPCR）， thereby accelerating socio-economic green transition. However， existing literature lacks comprehensive assessments of SEPCR at the urban level in China and seldom examines its influencing factors from a spatiotemporal heterogeneity perspective. To address this gap， this study comprehensively uses the SBM-DEA model with unexpected outputs and coupling coordination model to measure the SEPCR of cities in China. The spatial Markov chain and data visualization methods are used to analyze the spatiotemporal evolution pattern of SEPCR in Chinese cities from 2004 to 2022. The spatiotemporal geographic weighted regression model （GTWR）， kernel density curve， and map visualization methods are used to investigate the spatiotemporal heterogeneity of factors influencing the SEPCR. The study revealed that： ① The overall SEPCR was on the rise， with a fluctuating decline prior to 2014 before entering a rapid growth period. ② The SEPCR exhibited strong stability， with a gradual evolution process. SEPCR levels of neighboring cities were positively correlated with the upward transfer probability of the city， while negatively correlated with the downward transfer probability of the city. ③ The SEPCR showed a spatial differentiation pattern of northeast>east>west>central regions， with high-value clusters concentrating in eastern coastal and northeastern regions， while central and western regions were generally stable and emerging progress. Low-value areas centered in Shanxi Province showed spatial convergence. ④ The regression results of GTWR indicate that industrial structure， energy efficiency， and technological innovation had a promoting effect on the SEPCR， while population expansion， economic development， and energy structure mainly exhibited an inhibitory effect. Each influencing factor demonstrated significant spatiotemporal heterogeneity. |
