Research on the influencing mechanism of urbanization on carbon emission efficiency

doi:10.3969/j.issn.1004-9479.2020.03.2019211

Abstract

Abstract:

Pushing forward the urbanization process and developing the low-carbon economy is an important problem facing many countries around the world. Based on the panel data of 118 countries in the world from 2009 to 2016, the study explored the influencing mechanism of the overall sample and the country's urbanization development with different levels of urbanization on the carbon emission efficiency using STIRPAT model. The research results show that:(1) The spatial differentiation of carbon emission efficiency of the research samples was relatively obvious. Countries with higher carbon emission efficiency are mainly in Latin America, Western Europe and Africa.(2) On the whole, the level of scientific and technological innovation, per capita GDP and information level all play a significant role in promoting the efficiency of carbon emissions. The order of promotion is per capita GDP> level of scientific and technological innovation> level of informatization. The level of urbanization, the proportion of industry and the degree of openness to the outside world all have a significant inhibitory effect on the improvement of carbon emission efficiency. The degree of inhibition is urbanization level > openness > industrial proportion.(3) From a subregional point of view, the effects of various influencing factors on carbon emission efficiency in countries with different levels of urbanization show obvious heterogeneity. Based on the results, the study puts forward some suggestions, such as promoting the construction of green urbanization, paying attention to the ecological value of scientific and technological innovation, and optimizing the structure of foreign export trade.

Key words: urbanization, carbon emission efficiency, influencing mechanism, empirical analysis

摘要：

合理有序推进城镇化进程和发展低碳经济是当前全球许多国家面临的重要难题。研究基于2009—2016年全球118个国家的面板数据，利用STIRPAT模型探究了整体样本及不同等级城镇化水平国家城镇化发展对碳排放效率的影响机理。研究表明：（1）2009—2016年，研究样本碳排放效率的空间分异特征较为明显，碳排放效率较高的国家主要集中在拉丁美洲、欧洲西部及非洲，碳排放效率较低的国家基本位于北美、亚洲及大洋洲；（2）从整体来看，科技创新水平、人均GDP及信息化水平均对碳排放效率提升具有显著促进作用，促进作用大小为人均GDP>科技创新水平>信息化水平，城镇化水平、工业占比及对外开放度均对碳排放效率提升具有显著抑制作用，抑制作用大小为城镇化水平>对外开放度>工业占比；（3）分区域来看，不同等级城镇化水平国家各影响因素对碳排放效率的作用效应呈现明显的异质性。基于实证分析结果，研究提出推进绿色城镇化建设、重视科技创新生态价值、优化对外出口贸易结构等政策建议。

关键词: 城镇化, 碳排放效率, 影响机制, 实证分析

Xinjing WANG, Yu CHENG. Research on the influencing mechanism of urbanization on carbon emission efficiency[J]. World Regional Studies, 2020, 29(3): 503-511.

王鑫静, 程钰. 城镇化对碳排放效率的影响机制研究[J]. 世界地理研究, 2020, 29(3): 503-511.

Figures/Tables 4

References 22

1	张宗斌,汤子玉,辛大楞.城市化与城市规模对中美对外直接投资区位选择的影响研究.中国人口·资源与环境,2019,29(12):158-167.
	Zhang Z,Tang Z,Xin D.Research on the effect of urbanization and city size on location selection of outward foreign direct investment of China and the United States.China Population,Resources and Environment,2019,29(12):158-167.
2	王锋,秦豫徽,刘娟,等.多维度城镇化视角下的碳排放影响因素研究——基于中国省域数据的空间杜宾面板模型.中国人口·资源与环境,2017,27(9):151-161.
	Wang F, Qin Y, Liu J, et.al. A study on the influencing factors of carbon emissions from the perspective of multi-dimensional urbanization:based on the spatial Dubin panel model of China provinces. China Population,Resources and Environment, 2017,27(9):151-161.
3	方创琳,鲍超,乔标.城市化过程与生态环境效应.北京:科学出版社,2008.
	Fang C, Bao C, Qiao B. The Urbanization Process and Ecological Environment Effect. Beijing: Science Press,2008.
4	陈占明,吴施美,马文博,等.中国地级以上城市二氧化碳排放的影响因素分析:基于扩展的STIRPAT模型.中国人口·资源与环境,2018,28(10):45-54.
	Chen Z, Wu S, Ma W, et, al. Driving forces of carbon dioxide emission for China's cities: empirical analysis based on extended STIRPAT Model.China Population,Resources and Environment,2018,28(10):45-54.
5	冯冬,李健.我国三大城市群城镇化水平对碳排放的影响.长江流域资源与环境,2018,27 (10):2194-2200.
	Feng D, Li J. Impacts of Urbanization on Carbon Dioxide Emissions in the Three Urban Agglomerations of China. Resources and Environment in the Yangtze Basin, 2018,27 (10):2194-2200.
6	Zhou C,Wang S,Wang J.Examining the influences of urbanization on carbon dioxide emissions in the Yangtze River Delta, China:Kuznets curve relationship. Science of the Total Environment, 2019,675:472-482.
7	Wang Z, Cui C,Peng S.How do urbanization and consumption patterns affect carbon emissions in China? A decomposition analysis. Journal of Cleaner Production,2019,211:1201-1208.
8	王星.中国城镇化对碳排放的影响——基于省级面板数据的分析.城市问题,2016,35(7):23-29.
	Wang X. Influences on carbon emission by China's urbanization:based on the analysis with Chinese provincial panel data. Urban Problems, 2016,35(7):23-29.
9	Alam S,Fatima A,Butt M S. Sustainable development in Pakistan in the context of ener- gy consumption demand and environmental degradation. Journal of Asian Economics, 2007,18(5):825-837.
10	Paul Adjei Kwakwa,Alhassan Hamdiyah. The effect of energy and urbanisation on carbon dioxide emissions: evidence from Ghana. OPEC Energy Review,2018,42(4):301-330.
11	王志强,张惠.人口城镇化下碳排放机理与治理框架.企业经济,2017,36(6):11-16.
	Wang Z,Zhang H. Carbon emission mechanism and governance framework under population urbanization. Enterprise Economy, 2017,36(6):11-16.
12	韩秀艳,孙涛,高明.新型城镇化建设、能源消费增长与碳排放强度控制研究.软科学, 2018,32(9):90-93.
	Han X, Sun T, Gao M. Study on the new urbanization construction, energy consumption growth and carbon emission intensity controlling. Soft Science, 2018,32(9):90-93.
13	王锋,林翔燕,刘娟,等.城镇化对区域碳排放效应的研究综述.生态环境学报,2018,27(8):1576-1584.
	Wang F, Lin X, Liu J, et, al. Research progress on the impact of regional urbanization on carbon emissions. Ecology and Environmental Sciences, 2018,27(8):1576-1584.
14	秦耀辰,荣培君,杨群涛,等.城市化对碳排放影响研究进展.地理科学进展,2014,33(11):1526-1534.
	Qin Y, Rong P, Yang Q,et, al.Research progress of impact of urbanization on carbon emissions.Progress in Geography,2014,33(11):1526-1534.
15	刘希雅,王宇飞,宋祺佼,等.城镇化过程中的碳排放来源.中国人口·资源与环境,2015, 25(1):61-66.
	Liu X, Wang Y, Song Q, et, al. Study on carbon emissions sources in China's urbanization process. China Population, Resources and Environment, 2015,25(1):61-66.
16	魏梅,尹艳冰.碳排放效率的库兹涅茨曲线及双收敛假说的检验.海南大学学报：人文社会科学版, 2012, 30(1):114-118.
	Wei M, Yin Y.The Kuznets Curve of carbon emission efficiency and the testing on the double convergence hypothesis.Humanities & Social Sciences Journal of Hainan University, 2012,30(1):114-118.
17	王坤,黄震方,曹芳东.中国旅游业碳排放效率的空间格局及其影响因素.生态学报,2015,35(21):7150-7160.
	Wang K, Huang Z, Cao F. Spatial pattern and influencing factors of carbon dioxide emissions efficiency of tourism in China. Acta Ecologica Sinica, 2015,35(21):7150-7160.
18	张腾飞.城镇化对中国碳排放效率的影响.重庆：重庆大学,2016.
	Zhang T. The impact of urbanization on China’s carbon emission efficiency—Theory and Empirical Study. Chongqing： Chongqing university,2016.
19	王鑫静,程钰,丁立,等.“一带一路”沿线国家科技创新对碳排放效率的影响机制研究.软科学,2019,33(6): 72-78.
	Wang X, Cheng Y, Ding L, et, al. Research on the impact mechanism of technological innovation on carbon emission efficiency in countries along The Belt and Road. Soft Science, 2019,33(6):72-78.
20	金娜,仇方道,袁荷.江苏省碳排放效率时空格局及驱动因素.地域研究与开发,2018,37 (4):144-149.
	Jin N, Qiu F, Yuan H. Spatial pattern and influencing factors of carbon dioxide emissions efficiency in Jiangsu Province. Areal Research and Development, 2018,37 (4):144-149.
21	何平,倪苹.中国城镇化质量研究.统计研究,2013,30(6):11-18.
	He P, Ni P. Study on the quality of China's urbanization. Statistical Research, 2013,30(6):11-18.
22	程钰,王晶晶,王亚平,等.中国绿色发展时空演变轨迹与影响机理研究.地理研究,2019,38(11):2745-2765.
	Cheng Y, Wang J, Wang Y, et,al. A comparative research of the spatial-temporal evolution track and influence mechanism of green development in China. Geographical Research, 2019,38(11):2745-2765.

变量含义	平均值	最小值	最大值	标准差
碳排放效率/（PPP美元/千克）	4.76	0.78	16.67	2.62
城镇化水平/%	64.05	16.48	100.00	20.60
技术水平	36.87	14.55	68.30	10.75
人均可支配收入/现价国际元	22906.15	755.41	129349.92	21449.65
产业结构/%	28.36	9.38	96.62	11.66
信息化水平	5.09	0.88	8.93	2.06
对外开放度/%	90.92	20.72	410.17	54.76

变量含义	平均值	最小值	最大值	标准差
碳排放效率/（PPP美元/千克）	4.76	0.78	16.67	2.62
城镇化水平/%	64.05	16.48	100.00	20.60
技术水平	36.87	14.55	68.30	10.75
人均可支配收入/现价国际元	22906.15	755.41	129349.92	21449.65
产业结构/%	28.36	9.38	96.62	11.66
信息化水平	5.09	0.88	8.93	2.06
对外开放度/%	90.92	20.72	410.17	54.76

[1]	Linjing MA, Peisu LI, Yahong WU. How does urbanization affect informal employment? [J]. World Regional Studies, 2022, 31(6): 1273-1284.
[2]	Shaoyan CAI. Knowledge map analysis on the study of Chinese family migration [J]. World Regional Studies, 2022, 31(3): 649-661.
[3]	Xinyu YANG, Fangqu NIU. Spatio-temporal coupling process of urbanization and resource-environmental pressure in Liaoning Province [J]. World Regional Studies, 2022, 31(2): 317-328.
[4]	Yang YANG, Xiaolan TANG, Yanyan JIA, Qiaoqiao Zhan. Spatial-temporal coupling coordination degree and driving factors of population, land and economy urbanization in the Yangtze Basin [J]. World Regional Studies, 2021, 30(5): 978-990.
[5]	Nanchen CHU, Pingyu ZHANG, Xiangli WU, He LI. Measurement and spatial differentiation of urbanization development in Russia [J]. World Regional Studies, 2021, 30(2): 257-266.
[6]	Jiemin LIU, Bin LV. The development strategies of new town in Africa [J]. World Regional Studies, 2021, 30(2): 276-285.
[7]	Liang CHANG, Rongjun AO. Spatio-temporal pattern and influencing mechanism analysis of population ageing in China's border counties [J]. World Regional Studies, 2021, 30(2): 410-421.
[8]	Kun WANG, Shenjing HE. Urban political ecology in Anglophone literature: Progress and theoretical inquires [J]. World Regional Studies, 2021, 30(2): 286-296.
[9]	Chudan GU, Fenglong WANG, Feng LUO. Difference in and determinants of subjective well-being for Chinese urban and rural residents [J]. World Regional Studies, 2021, 30(1): 179-191.
[10]	Qianbo WU, Congcong HUI. Urbanization characteristics and spatial pattern of population in central cities of China in the 21st century [J]. World Regional Studies, 2020, 29(3): 523-535.
[11]	Rongtian ZHANG, Chuanwu LI. An investigation in the typical counties of Anhui [J]. World Regional Studies, 2020, 29(1): 112-119.
[12]	Yan TONG, Yujun PAN, Meifen ZHANG, Luyan XU. Analysis on coupling and coordination between population urbanization and land urbanization in Yunnan Province [J]. World Regional Studies, 2020, 29(1): 120-129.
[13]	Xiaofan WU, Guang LIN. From urban to suburban: American suburbanization driven by the federal government after World War II [J]. World Regional Studies, 2020, 29(1): 77-85.