成渝城市群碳排放空间关联结构演化及影响因素

王晓平; 冯庆; 宋金昭

PDF(881 KB)

中国环境科学 ›› 2020, Vol. 40 ›› Issue (9) : 4123-4134.

环境影响评价与管理

成渝城市群碳排放空间关联结构演化及影响因素

王晓平, 冯庆, 宋金昭

作者信息 +

The spatial association structure evolution of carbon emissions in Chengdu-Chongqing urban agglomeration and its influence mechanism

WANG Xiao-ping, FENG Qing, SONG Jin-zhao

Author information +

文章历史 +

摘要

本文基于社会网络分析法（SNA）以及二次分配程序（QAP）方法，利用成渝城市群2005~2016年面板数据，对成渝城市群碳排放空间关联性及影响因素进行研究.结果表明：①成渝城市群碳排放空间关联性显著，呈现出复杂的网络结构形态，样本期内，网络密度由0.16增长至0.68，关联关系数从38个增长为162个.②重庆、成都、绵阳和南充等城市位于网络的中心地位，发出了较多的关联关系，同时发挥着中介作用.③碳排放空间关联网络被划分成为5个层级，层级结构整体较为稳定，然而第一层级与第二层级存在较为严重的断层现象.④空间距离、人口数量差异以及经济水平差异是碳排放关联性的主要驱动因素.城市间空间距离越近、人口数量与经济水平差异越大，越容易产生碳排放关联关系.

Abstract

Based on social network analysis (SNA) and quadratic assignment procedure (QAP) regression method, the spatial correlation of carbon emission and its influence mechanism in Chengdu-Chongqing urban agglomeration were studied referring to panel data from 2005~2016. The results showed that: ① A significant spatial correlation of carbon emissions in Chengdu-Chongqing urban agglomeration was observed, presenting a complex network pattern. During the sample period, the network density increased from 0.16 to 0.68, and the number of connections increased from 38 to 162. ②Cities like Chengdu, Chongqing, Mianyang and Nanchong were located in the center of the network, with more correlations being developed that played an intermediary role. ③ The spatial correlation network of carbon emissions was divided into five levels, whose overall hierarchy was relatively stable. However, there were serious faults in the first and second level. ④ In addition, difference in spatial distance, population and economic development were the main driving force of the correlation of carbon emissions. The closer the space distance, and the greater the difference regarding population and the economic development, the easier carbon emissions correlation between cities can be formed.

导出引用

王晓平, 冯庆, 宋金昭. 成渝城市群碳排放空间关联结构演化及影响因素[J]. 中国环境科学. 2020, 40(9): 4123-4134

WANG Xiao-ping, FENG Qing, SONG Jin-zhao. The spatial association structure evolution of carbon emissions in Chengdu-Chongqing urban agglomeration and its influence mechanism[J]. China Environmental Science. 2020, 40(9): 4123-4134

中图分类号： X321

参考文献

[1] Han F, Xie R, Lu Y, et al. The effects of urban agglomeration economies on carbon emissions:Evidence from Chinese cities[J]. Journal of Cleaner Production, 2018,172:1096-1110.
[2] Mi Z F, Zhang Y K, Guan D B, et al. Consumption-based emission accounting for Chinese cities[J]. Applied Energy, 2016,184:1073-1081.
[3] Wang Y, Zhao H, Duan F M, et al. Initial provincial allocation and equity evaluation of China's carbon emission rights-based on the improved TOPSIS method[J]. Sustainability, 2018,10(4):982.
[4] 肖金成,汪阳红,张燕.成渝城市群空间布局与产业发展研究[J]. 全球化, 2019,(8):30-48,134. Xiao J C, Wang Y H, Zhang Y. Research on spatial layout and industrial development of Chengdu-Chongqing Urban Agglomeration[J]. Globalization, 2019,(8):30-48,134.
[5] 张永年,潘竟虎.基于DMSP/OLS数据的中国碳排放时空模拟与分异格局[J]. 中国环境科学, 2019,39(4):1436-1446. Zhang Y N, Pan J H. Spatio-temporal simulation and differentiation pattern of carbon emissions in China based on DMSP/OLS nighttime light data[J]. China Environmental Science, 2019,39(4):1436-1446.
[6] 赵领娣,吴栋.中国能源供给侧碳排放核算与空间分异格局[J]. 中国人口·资源与环境, 2018,28(2):48-58. Zhang L D, Wu D. Carbon emission accounting and spatial heterogeneity pattern of China's energy supply side[J]. China Population, Resources and Environment, 2018,28(2):48-58.
[7] 李海萍,龙宓,李光一.基于DMSP/OLS数据的区域碳排放时空动态研究[J]. 中国环境科学, 2018,38(7):2777-2784. Li H P, L M, Li G Y. Spatial-temporal dynamics of carbon dioxide emissions in China based on DMSP/OLS nighttime stable light data[J]. China Environmental Science, 2018,38(7):2777-2784.
[8] 刘贤赵,高长春,张勇,等.中国省域碳强度空间依赖格局及其影响因素的空间异质性研究[J]. 地理科学, 2018,38(5):681-690. Liu X Z, Gao C C, Zhang Y, et al. Spatial dependence pattern of carbon emission intensity in China's provinces and spatial heterogeneity of its influencing factors[J]. Scientia Geographica Sinica, 2018,38(5):681-690.
[9] 刘贤赵,高长春,张勇,等.中国省域能源消费碳排放空间依赖及影响因素的空间回归分析[J]. 干旱区资源与环境, 2016,30(10):1-6. Liu X Z, Gao C C, Zhang Y, et al. Spatial dependence of China's provincial carbon emissions from energy consumption and its spatial regression analysis[J]. Arid Land Resources and Environment, 2016, 30(10):1-6.
[10] 陈操操,蔡博峰,孙粉,等.京津冀与长三角城市群碳排放的空间聚集效应比较[J]. 中国环境科学, 2017,37(11):4371-4379. Chen C C, Cai B F, Sun F, et al. Spatial agglomeration effects of carbon dioxide emissions between Beijing-Tianjin-Heibei region and Yangtze River Delta region[J]. China Environmental Science, 2017, 37(11):4371-4379.
[11] 佟昕,李学森,佟琳,等.中国碳排放空间格局的时空演化——基于动态演化及空间集聚的视域[J]. 东北大学学报(自然科学版), 2016,37(11):1668-1672. Tong X, Li X S, Tong L, et al. Spatial-time evolution of carbon emission patterns in China:based on dynamic evolution and spatial agglomeration[J]. Northeastern University (Natural Science), 2016, 37(11):1668-1672.
[12] 胡雪瑶,焦文献,陈兴鹏,等.河南省能源消费碳排放动态变化及空间分异[J]. 地域研究与开发, 2017,36(4):147-152. Hu X Y, Jiao W X, Chen X P, et al. The dynamics and spatial differentiation research of carbon emission from energy consumption in Henan province[J]. Areal Research and Development, 2017,36(4):147-152.
[13] 朱妮,张艳芳,位贺杰.县域尺度下能源产区能源消费碳排放强度空间分异——以陕西省榆林市为例[J]. 地域研究与开发, 2014, 33(6):164-169. Zhu N, Zhang Y F, Wei H J. Spatial differences of carbon emissions intensity on the county level:a case study of Yulin city, Shaanxi province[J]. Areal Research and Development, 2014,33(6):164-169.
[14] 张丽君,李宁,秦耀辰,等.基于DPSIR模型的中国城市低碳发展水平评价及空间分异[J]. 世界地理研究, 2019,28(3):85-94. Zhang L J, Li N, Qin Y C, et al. The low-carbon city evaluation and its spatial differentiation based on the DPSIR[J]. World Regional Studies, 2019,28(3):85-94.
[15] 陈江龙,李平星,高金龙.1990~2014年泛长三角地区能源利用碳排放时空格局及影响因素[J]. 地理科学进展, 2016,35(12):1472-1482. Chen J L, Li P X, Gao J L. Spatiotemporal patterns and influencing factors of carbon emissions in the Pan-Yangtze River Delta region, 1990~2014[J]. Progress in Geography, 2016,35(12):1472-1482.
[16] 苑韶峰,唐奕钰.低碳视角下长江经济带土地利用碳排放的空间分异[J]. 经济地理, 2019,39(2):190-198. Yuan S F, Tang Y Y. Spatial differentiation of land use carbon emission in the Yangtze River Economic Belt based on low carbon perspective[J]. Economic Geography, 2019,39(2):190-198.
[17] 王少剑,苏泳娴,赵亚博.中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素[J]. 地理学报, 2018,73(3):414-428. Wang S J, Su Y X, Zhao Y B. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions[J]. Acta Geographica Sinica, 2018,73(3):414-428.
[18] 张翠菊,柏群,张文爱.中国区域碳排放强度影响因素及空间溢出性——基于空间杜宾模型的研究[J]. 系统工程, 2017,35(10):70-78. Zhang C J, Bai Q, Zhang W A. The influencing factors of regional carbon emission intensity in China and spatial spillover[J]. Systems Engineering, 2017,35(10):70-78.
[19] 刘佳骏,史丹,汪川.中国碳排放空间相关与空间溢出效应研究[J]. 自然资源学报, 2015,30(8):1289-1303. Liu J J, Shi D, Wang C. A study on spatial spillover and correlation effect of carbon emissions across 30provinces in China[J]. Natural Resources, 2015,30(8):1289-1303.
[20] 王凯,邵海琴,周婷婷,等.中国旅游业碳排放效率及其空间关联特征[J]. 长江流域资源与环境, 2018,27(3):473-482. Wang K, Shao H Q, Zhou T T, et al. A study on carbon emissions efficiency of tourism and its spatial correlation characteristics in China[J]. Resources and Environment in the Yangtze Basin, 2018,27(3):473-482.
[21] 张帅,袁长伟,赵小曼.中国交通运输碳排放空间聚类与关联网络结构分析[J]. 经济地理, 2019,39(1):122-129. Zhang S, Yuan C W, Zhao X M. Spatial clustering and correlation network structure analysis of transportation carbon emissions in China[J]. Economic Geography, 2019,39(1):122-129.
[22] 周莹莹,孙玉宇.长江经济带城市碳排放的空间关联性研究[J]. 北京交通大学学报(社会科学版), 2018,17(2):52-60. Zhou Y Y, Sun Y Y. Research on spatial correlation of carbon emissions in cities of Yangtze River Economic Belt[J]. Beijing Jiaotong University (Social Sciences Edition), 2018,17(2):52-60.
[23] 崔玮,苗建军,邹伟.武汉城市圈土地利用空间关联的碳排放效率及其收敛性分析[J]. 长江流域资源与环境, 2016,25(12):1824-1831. Cui W, Miao J J, Zou W. Carbon emission efficiency of spatial association and its convergence of land use in Wuhan Urban Agglomeration[J]. Resources and Environment in the Yangtze Basin, 2016,25(12):1824-1831.
[24] 刘军.社会网络分析导论[M]. 北京:社会科学文献出版社, 2004:6-28. Li J. Introduction to social network analysis[M]. Beijing:Social Sciences Academic Press, 2004:6-28.
[25] 刘华军,刘传明,孙亚男.中国能源消费的空间关联网络结构特征及其效应研究[J]. 中国工业经济, 2015,(5):83-95. Liu H J, Liu C M, Sun Y N. Spatial correlation network structure of energy consumption and its effect in China[J]. China Industrial Economics, 2015,(5):83-95.
[26] 邹嘉龄,刘卫东.2001~2013年中国与"一带一路"沿线国家贸易网络分析[J]. 地理科学, 2016,36(11):1629-1636. Zou J L, Liu W D. Trade network of China and countries along "Belt and Road Initiative"areas from 2001 to 2013.[J]. Scientia Geographica Sinica, 2016,36(11):1629-1636.
[27] 马述忠,任婉婉,吴国杰.一国农产品贸易网络特征及其对全球价值链分工的影响-基于社会网络分析视角[J]. 管理世界, 2016, (3):60-72. Ma S Z, Ren W W, Wu G J. Characteristics of a country's agricultural trade network and its impact on the division of Labor in global value chains:a social network analysis perspective[J]. Management World, 2016,(3):60-72.
[28] Liu W, Xu J, Li J. The influence of poverty alleviation resettlement on rural household livelihood vulnerability in the western mountainous areas[J]. Sustainability, 2018,10(8):2793.
[29] 王珏,陈雯,袁丰.基于社会网络分析的长三角地区人口迁移及演化[J]. 地理研究, 2014,33(2):385-400. Wang J, Chen W, Yuan F. Human mobility and evolution based on social network:an empirical analysis of Yangtze River Delta[J]. Geographical Research, 2014,33(2):385-400.
[30] 方大春,孙明月.高铁时代下长三角城市群空间结构重构-基于社会网络分析[J]. 经济地理, 2015,35(10):50-56. Fang D C, Sun M Y. The reconstruction of the spatial structure of the Yangtze River Delta City Group in the High-speed Rail Era-based on the social network analysis[J]. Economic Geography, 2015,35(10):50-56.
[31] 彭芳梅.粤港澳大湾区及周边城市经济空间联系与空间结构-基于改进引力模型与社会网络分析的实证分析[J]. 经济地理, 2017, 37(12):57-64. Peng F M. Economic spatial connection and spatial structure of Guangdong-Hong Kong-Macao Greater Bay and the surrounding area cities-an empirical analysis based on improved gravity model and social network analysis[J]. Economic Geography, 2017,37(12):57-64.
[32] 彭本红,武柏宇,谷晓芬.电子废弃物回收产业链协同治理影响因素分析-基于社会网络分析方法[J]. 中国环境科学, 2016,36(7):2219-2229. Peng B H, Wu B Y, Gu X F. Influence factors analysis of cooperative governance for the e-waste recycling industry chain-based on social network analysis[J]. China Environmental Science, 2016,36(7):2219-2229.
[33] 孙亚男,刘华军,刘传明,等.中国省际碳排放的空间关联性及其效应研究——基于SNA的经验考察[J]. 上海经济研究, 2016,(2):82-92. Sun Y N, Liu H J, Liu C M, et al. Research on spatial association of provinces carbon emission and its effects in China[J]. Shanghai Journal of Economics, 2016,(2):82-92.
[34] 曾冰.中国省际金融发展的空间网络结构及其驱动机制研究[J]. 金融发展研究, 2019,(10):14-21. Zeng B. Research on spatial network structure and driving mechanism of interprovincial finance development in China[J]. Financial Development Research, 2019,(10):14-21.
[35] 邵璇璇,姚永玲.长江中游城市群的空间网络特征及其影响机制[J]. 城市问题, 2019,(10):15-26. Shao X X, Yao Y L. Spatial network characteristics and its influence mechanism of urban agglomeration in the middle reaches of the Yangtze River[J]. Urban Problems, 2019,(10):15-26.
[36] 张雪薇,宗刚,赵蓉.高铁时代中国城市群创新空间相关性研究-基于空间关联网络格局的分析[J]. 价格理论与实践, 2019,(7):140-143. Zhang X W, Zong G, Zhao R. Analysis of innovative spatial correlation network of Chinese urban agglomeration in High-speed rail era[J]. Price:Theory &Practice, 2019,(7):140-143.
[37] 李敬,陈澍,万广华,等.中国区域经济增长的空间关联及其解释——基于网络分析方法[J]. 经济研究, 2014,49(11):4-16. Li J, Chen S, Wan G H, et al. Study on the spatial correlation and explanation of regional economic growth in China-based on analytic network process[J]. Economic Research, 2014,49(11):4-16.
[38] 杨桂元,吴齐,涂洋.中国省际碳排放的空间关联及其影响因素研究-基于社会网络分析方法[J]. 商业经济与管理, 2016(4):56-68,78. Yang G Y, Wu Q, Tu T. Researchs of China's regional carbon emission spatial correlation and its determinants:based on the method of social network analysis[J]. Journal of Business Economics, 2016,(4):56-68, 78.
[39] 苏凯,陈毅辉,范水生,等.市域能源碳排放影响因素分析及减碳机制研究-以福建省为例[J]. 中国环境科学, 2019,39(2):859-867. Su K, Chen Y H, Fan S S, et al. Influencing factors and reduction mechanism of carbon emissions at the city-range:an empirical study on Fujian province[J]. China Environmental Science, 2019,39(2):859-867.
[40] 景侨楠,侯慧敏,白宏涛,等.自上而下的城市能源消耗碳排放估算方法[J]. 中国环境科学, 2019,39(1):420-427. Jing Q N, Hou H M, Bai H T, et al. A top-bottom estimation method for city-level energy-related CO₂ emissions[J]. China Environmental Science, 2019,39(1):420-427.
[41] 国家统计局.中国能源统计年鉴[M]. 北京:中国统计出版社, 2005-2016. National Statistical Bureau. Energy statistics yearbook of China[M]. Beijing:China Statistics Press, 2005-2016.
[42] 四川省统计局.四川统计年鉴[M]. 北京:中国统计出版社, 2005-2016. Sichuan Statistical Bureau. Statistical yearbook of Sichuan[M]. Beijing:China Statistics Press, 2005-2016.
[43] 重庆市统计局.重庆统计年鉴[M]. 北京:中国统计出版社, 2005-2016. Chongqing Statistical Bureau. Statistical yearbook of Chongqing[M]. Beijing:China Statistics Press, 2005-2016.
[44] 付汉良,刘晓君.再生水回用公众心理感染现象的验证及影响策略[J]. 资源科学, 2018,40(6):1222-1229. Fu H L, Liu X J. Verification and influence strategies of residents'spiritual cognition of recycled water reuse[J]. Resources Science, 2018,40(6):1222-1229.