| 城乡融合发展网络结构特征及其碳排放效应 |
| 摘要点击 1687 全文点击 57 投稿时间：2024-10-29 修订日期：2025-03-04 |
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| 中文关键词 城乡融合 碳排放 网络结构 空间关联 中国 |
| 英文关键词 urban-rural integration carbon emissions network structure spatial correlation China |
| DOI 10.13227/j.hjkx.202410260 |
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| 中文摘要 |
| 探究城乡融合发展空间关联网络结构特征及其对碳排放强度的影响，能够为城乡融合发展与区域碳减排的深度融合提供科学依据. 选取2011~2022年中国281个城市作为样本，构建城乡融合发展水平综合评价指标体系，测度分析中国城乡融合发展水平时空分异特征，采用社会网络分析法分析城乡融合发展空间关联网络结构特征，运用固定效应模型与门槛模型检验城乡融合发展空间关联网络中心度的碳排放效应. 结果表明：①2011~2022年，中国城乡融合发展水平整体呈现“先上升后下降”的时序变化特征，“由东向西梯度递减”的空间变化格局. ②市域尺度下，中国城乡融合发展水平呈现明显的空间关联网络特征，整体结构较为稳定，关联关系数量与整体网络密度值呈现上升趋势，京津冀和长三角等城市群处于网络核心位置. ③城乡融合发展空间关联网络中心度对碳排放强度存在显著的负向影响作用，结果在稳健性检验后依旧成立，此影响在不同经济带、地理区位与城市规模存在显著差异. ④城乡融合发展空间关联网络中心度对碳排放强度的影响存在以碳排放绩效与地区经济发展水平为门槛的双重门槛效应. 当碳排放绩效或地区经济发展水平大于门槛值时，城乡融合发展空间关联网络中心度的碳减排效应增强. |
| 英文摘要 |
| Exploring the structural characteristics of the spatial correlation network of urban-rural integrated development and its impact on carbon emission intensity can provide a scientific basis for the deep integration of urban-rural integrated development and regional carbon reduction. This study selected 281 cities in China from 2011 to 2022 as samples to construct indicators for measuring and analyzing the spatiotemporal differentiation characteristics of the level of urban-rural integration development in China. The social network analysis method was used to analyze the spatial correlation network structure characteristics of urban-rural integration development， and the fixed effects model and threshold effect model were used to test the carbon emission effect of spatial correlation network centrality. The results indicate that： ① From 2011 to 2022， the overall level of integrated urban-rural development in China showed a temporal pattern of “first increasing and then decreasing” and a spatial pattern of “gradient decreasing from east to west.” ② At the city scale， the level of integrated urban-rural development in China presented obvious spatial correlation network characteristics， with a relatively stable overall structure. The number of correlation relationships and the overall network density value showed an upward trend， and urban agglomerations such as the Beijing Tianjin Hebei and Yangtze River Delta were at the core of the network. ③ There was a significant relationship between the spatial correlation network for urban-rural integration development and carbon emission intensity， which varied significantly in different economic belts， geographical locations， and urban sizes. This result was found to be stable after a series of robustness tests. ④ The centrality of the spatial correlation network for urban-rural integration development had a dual threshold effect on carbon emission intensity with carbon emission performance and regional economic development level as the threshold. When the carbon emission performance and regional economic development level exceeded the threshold value， the carbon emission reduction effect of the centrality of the urban-rural integrated development spatial correlation network was enhanced. |
