长三角区域绿色技术创新效率的时空演化格局及驱动因素

doi:10.11821/dlyj020210102

地理研究 ›› 2021, Vol. 40 ›› Issue (10): 2743-2759.doi: 10.11821/dlyj020210102

长三角区域绿色技术创新效率的时空演化格局及驱动因素

孙燕铭¹^,²^,³(), 谌思邈³()

1.华东师范大学全球创新与发展研究院,上海 200062
2.华东师范大学崇明生态研究院,上海 202162
3.华东师范大学城市与区域科学学院,上海 200241

收稿日期:2021-02-05 修回日期:2021-07-26 出版日期:2021-10-10 发布日期:2021-12-10
通讯作者: 谌思邈（1996-）,女,湖南湘潭人,硕士研究生,主要研究方向为资源环境与绿色创新。 E-mail: 51193902006@stu.ecnu.edu.cn
作者简介:孙燕铭（1984-）,女,安徽合肥人,博士,副教授、硕士生导师,主要研究方向为绿色创新与绿色发展、资源与环境经济、区域可持续发展。E-mail: ymsun@re.ecnu.edu.cn
基金资助:
国家自然科学基金项目(71703039);上海市哲学社会科学规划青年项目(2017EJB010);中国科学院A类战略性先导科技专项(XDA20100311)

The spatio-temporal evolutionary pattern and driving forces mechanism of green technology innovation efficiency in the Yangtze River Delta region

SUN Yanming¹^,²^,³(), SHEN Simiao³()

1. Institute for Global Innovation and Development, East China Normal University, Shanghai 200062, China
2. Institute of Eco-Chongming, East China Normal University, Shanghai 202162, China
3. School of Urban & Regional Sciences, East China Normal University, Shanghai 200241, China

Received:2021-02-05 Revised:2021-07-26 Published:2021-10-10 Online:2021-12-10

摘要/Abstract

摘要：

在长三角更高质量一体化的战略背景下,绿色技术创新作为绿色发展和创新驱动两大国家战略的结合点,已成为长三角区域绿色转型发展的重要引擎。通过构建包含非期望产出的超效率SBM-DEA模型,对2010—2017年长三角区域核心城市的绿色技术创新效率进行测度,并研究其时空演化格局和驱动因素。结果显示：① 在时序演变上,长三角区域的绿色技术创新效率呈现“W”型变化特征;② 在空间演变上,长三角东南部地区的绿色技术创新效率相对稳定,而中部、西南部变动明显,整体呈现连片集聚发展特征;③ 在空间关联上,长三角区域绿色技术创新效率的区域空间联系逐渐由“极化效应”转变为“涓滴效应”,泰尔指数和基尼系数整体表现为与时序演变相反的“M”型变化特征;④ 基于长三角区域绿色技术创新投入、产出及效率测算结果,将各城市划分为高高高、高高低、高低低、低高高、低低高和低低低六种类型,进一步揭示了长三角区域绿色技术创新发展路径的区域差异;⑤ 驱动因素分析结果表明,环境规制、经济发展、产业结构、对外开放和人力资本皆对长三角区域整体的绿色技术创新效率有着显著的正向促进影响,但创新支持具有显著的负向溢出效应。

关键词: 绿色技术创新效率, 超效率SBM-DEA, 时空演化, 驱动因素, 长三角区域

Abstract:

In the strategic background of high quality integration in the Yangtze River Delta (YRD), green technology innovation, as the combination of green development and innovation-driven national strategies, has become an important engine of green transformation and development in the region. This paper, by constructing a super-efficiency SBM-DEA model that includes undesired output, measures the efficiency of green technology innovation in core cities of the YRD from 2010 to 2017, and studies its spatio-temporal evolutionary pattern and driving forces mechanism. The results show that, (1) In terms of time series evolution, the green technology innovation efficiency in the study region shows a “W”-shape pattern. (2) In terms of spatial evolution, the green technology innovation efficiency in the southeast of the YRD is relatively stable, while changes in the central and southwestern parts are obvious, showing the characteristics of continuous agglomeration and development as a whole. (3) In terms of spatial correlation, the regional spatial relationship of green technology innovation efficiency in the YRD has gradually changed from the “polarization effect” to the “trickle down effect”. As a whole, Theil index and Gini coefficient show the characteristics of an “M” change opposite to the evolution of time series. (4) Based on the measured results of input, output and efficiency of green technology innovation in the delta region, all cities are identified into six types: high-high-high, high-high-low, high-low-low, low-high-high, low-low-high, and low-low-low This further reveals regional differences in the development path of green technology innovation in the YRD. (5) The research results of driving forces mechanism show that environmental regulation, economic development, industrial structure, opening degree to the outside world, human capital and urbanization all play significant positive roles in promoting the spatio-temporal evolution of green technology innovation efficiency in the YRD, while the innovation support has a significant negative spillover effect. (6) On the whole, the green technology innovation in the study region does have a significant “Porter Hypothesis” effect, but the “pollution paradise” effect mentioned in the literature has not been found.

Key words: green technology innovation efficiency, super-efficiency SBM-DEA, spatio-temporal evolution, driving factor, Yangtze River Delta

孙燕铭, 谌思邈. 长三角区域绿色技术创新效率的时空演化格局及驱动因素[J]. 地理研究, 2021, 40(10): 2743-2759.

SUN Yanming, SHEN Simiao. The spatio-temporal evolutionary pattern and driving forces mechanism of green technology innovation efficiency in the Yangtze River Delta region[J]. GEOGRAPHICAL RESEARCH, 2021, 40(10): 2743-2759.

图/表 8

表1

图1

表2

图2

图3

表3

表4

表5

绿色技术创新效率驱动因素SDM的直接效应、空间效应和总效应

		PM_2.5					gree
		（1）	（2）	（3）	（4）		（5）	（6）	（7）	（8）
直接效应	er	1.031^* (0.570)	2.039^* (1.137)	-0.548 (1.909)	1.138^* (0.630)		1.733^* (0.914)	0.035 (0.185)	-0.267 (0.228)	-0.205^* (0.106)
	tech	-0.744^* (0.395)	0.502^** (0.236)	0.130 (0.204)	-0.046 (0.082)		-0.474 (0.415)	0.477^* (0.256)	0.070 (0.232)	-0.100 (0.086)
	ec	3.090^*** (0.729)	-0.208 (0.287)	-0.850 (0.843)	-0.062 (0.235)		3.031^*** (0.597)	-0.250 (0.272)	-1.774 (1.413)	0.076 (0.235)
	is	-0.154^*** (0.023)	0.041^* (0.024)	0.150^*** (0.049)	0.007 (0.013)		-0.196^*** (0.040)	0.032 (0.024)	0.089 (0.055)	0.005 (0.013)
	fc	10.545 (6.776)	-3.374 (2.514)	10.095^** (4.761)	3.478^** (1.499)		0.729 (4.151)	-0.837 (2.388)	11.992 (7.407)	4.141^*** (1.491)
	tale	0.103^** (0.048)	0.019 (0.025)	-0.085^* (0.050)	0.033^** (0.013)		0.087^** (0.041)	0.008 (0.025)	-0.056 (0.066)	0.034^*** (0.013)
	urb	-2.377^*** (0.531)	-0.205 (0.634)	-5.898^*** (2.005)	0.830^* (0.437)		-1.843^*** (0.552)	-0.081 (0.634)	-4.694 (3.031)	0.994^** (0.445)
间接效应	er	3.042^* (0.815)	-4.355^*** (1.668)	-7.021^* (3.766)	-3.438^*** (1.177)		7.858^** (3.879)	0.873^** (0.366)	-2.439 (2.202)	0.597^** (0.297)
	tech	-3.475^* (0.580)	0.680 (0.531)	-0.646 (0.509)	-0.755^*** (0.239)		-4.036^* (2.268)		0.584 (0.650)	-2.153 (1.723)	-0.714^** (0.280)
	ec	11.711^** (0.815)	0.386 (0.525)	0.671 (2.519)	1.312^** (0.610)		7.082^** (3.394)		-0.013 (0.571)	6.309 (13.990)	0.641 (0.652)
	is	-0.168^* (0.036)	0.018 (0.046)	0.266^* (0.146)	0.119^*** (0.034)		-0.238 (0.146)		0.109^* (0.060)	0.371 (0.525)	0.154^*** (0.042)
	fc	142.537^*** (7.145)	5.327 (4.794)	17.238 (18.830)	13.656^*** (4.332)	79.210^*** (27.684)		3.886 (5.834)	10.885 (51.132)	12.084^** (4.996)
	tale	0.455^* (0.052)	-0.045 (0.039)	-0.240 (0.308)	0.034 (0.029)	0.253 (0.158)		-0.063 (0.044)	-0.448 (0.579)	-0.006 (0.033)
	urb	5.670^*** (0.662)	-1.292 (1.344)	-12.801^** (5.835)	1.035 (1.305)	6.950^*** (2.198)		-0.482 (1.610)	-5.945 (26.850)	2.174 (1.523)
总效应	er	4.073^** (1.789)	-2.316^*** (0.687)	-7.569^*** (2.473)	-2.300^*** (0.729)	9.590^** (4.618)	0.907^** (0.409)	-2.705 (2.342)	0.392 (0.334)
	tech	-4.219^* (0.337)	1.182^** (0.582)	-0.517 (0.431)	-0.801^*** (0.263)	-4.510^* (2.552)		1.061 (0.741)	-2.083 (1.831)	-0.814^*** (0.307)
	ec	14.802^** (5.935)	0.178 (0.361)	-0.178 (2.664)	1.251^** (0.547)	10.114^*** (0.807)		-0.263 (0.445)	4.535 (14.800)	0.718 (0.602)
	is	-0.322^*** (0.103)	0.059^* (0.034)	0.416^*** (0.131)	0.127^*** (0.030)	-0.436^*** (0.155)		0.141^*** (0.050)	0.459 (0.539)	0.159^*** (0.039)
	fc	153.082^** (60.055)	1.953 (4.397)	27.333 (20.512)	17.134^*** (4.526)	79.939^* (6.725)		3.049 (5.648)	22.877 (56.253)	16.225^*** (5.302)
	tale	0.558^** (0.277)	-0.026 (0.042)	-0.326 (0.324)	0.068^** (0.033)	0.339^* (0.185)		-0.055 (0.050)	-0.504 (0.620)	0.028 (0.037)
	urb	3.293 (2.118)	-1.497 (1.402)	-18.699^*** (6.305)	1.865 (1.431)	5.107^** (2.274)		-0.563 (1.764)	-10.639 (28.806)	3.169* (1.688)
	sigma_2e	0.003	0.035	0.011	0.067	0.004	0.038	0.017	0.068
	rho	-0.655^***	-1.146^***	-0.940^***	-0.554^***	-0.899^***	-0.774^***	-0.209^***	-0.343^***

表5

参考文献 32

[1]	Braun E, Wield D. Regulation as a means for the social control of technology. Technology Analysis & Strategic Management, 1994, 6(3):259-272. DOI: 10.1080/09537329408524171. doi: 10.1080/09537329408524171
[2]	Fussler C, James P. Driving eco-innovation: A breakthrough discipline for innovation and sustainability. Pitman Pub, 1996, 6(5):297-297. DOI: 10.1002/(SICI)1099-0836(199711)6: 5<297:AID-BSE128>3.0.CO;2-R. doi: 10.1002/(SICI)1099-0836(199711)6: 5<297:AID-BSE128>3.0.CO;2-R
[3]	Mirata M, Emtairah T. Industrial symbiosis networks and the contribution to environmental innovation. Journal of Cleaner Production, 2005, 13(10-11):993-1002. DOI: 10.1016/j.jclepro.2004.12.010. doi: 10.1016/j.jclepro.2004.12.010
[4]	孙燕铭, 梅潇, 谌思邈. 长三角城市群绿色技术创新的时空格局及驱动因素研究. 江淮论坛, 2021, (1):13-22, 61.
	[ Sun Yanming, Mei Xiao, Shen Simiao. Research on the spatio-temporal pattern and driving factors of green technology innovation in Yangtze River Delta Urban agglomeration. Jianghuai Tribune, 2021, (1):13-22, 61.] DOI: 10.3969/j.issn.1001-862X.2021.01.003. doi: 10.3969/j.issn.1001-862X.2021.01.003
[5]	孙燕铭, 孙晓琦. 长三角城市群工业绿色全要素生产率的测度及空间分异研究. 江淮论坛, 2018, (6):60-67.
	[ Sun Yanming, Sun Xiaoqi. Financing and risk management of low carbon demonstration project: Case study of oxyfuel carbon capture project. Jianghuai Tribune, 2018, (6):60-67] DOI: 10.3969/j.issn.1001-862X.2018.06.011. doi: 10.3969/j.issn.1001-862X.2018.06.011
[6]	肖黎明, 高军峰, 韩彬. 中国省际绿色创新效率的空间溢出效应: 同质性和异质性检验. 工业技术经济, 2018, 37(4):30-38.
	[ Xiao Liming, Gao Junfeng, Han Bin. Spatial spillover effects of provincial green innovation efficiency in China-homogeneity and heterogeneity test. Journal of Industrial Technological Economics, 2018, 37(4):30-38.] DOI: 10.3969/j.issn.1004910X.2018.04.004. doi: 10.3969/j.issn.1004910X.2018.04.004
[7]	曹霞, 于娟. 绿色低碳视角下中国区域创新效率研究. 中国人口·资源与环境, 2015, 25(5):10-19.
	[ Cao Xia, Yu Juan. Regional innovation efficiency in China from the green low-carbon perspective. China Population, Resources and Environment, 2015, 25(5):10-19.] DOI: 10.3969/j.issn.1002-2104.2015.05.002. doi: 10.3969/j.issn.1002-2104.2015.05.002
[8]	周亮, 车磊, 周成虎. 中国城市绿色发展效率时空演变特征及影响因素. 地理学报, 2019, 74(10):2027-2044. doi: 10.11821/dlxb201910006
	[ Zhou Liang, Che Lei, Zhou Chenghu. Spatio-temporal evolution and influencing factors of urban green development efficiency in China. Acta Geographica Sinica, 2019, 74(10):2027-2044.] DOI: 10.11821/dlxb201910006. doi: 10.11821/dlxb201910006
[9]	车磊, 白永平, 周亮, 等. 中国绿色发展效率的空间特征及溢出分析. 地理科学, 2018, 38(11):1788-1798. doi: 10.13249/j.cnki.sgs.2018.11.006
	[ Che Lei, Bai Yongping, Zhou Liang, et al. Spatial characteristics and spillover analysis of green development efficiency in China. Scientia Geographica Sinica, 2018, 38(11):1788-1798.] DOI: 10.13249/j.cnki.sgs.2018.11.006. doi: 10.13249/j.cnki.sgs.2018.11.006
[10]	陈斌, 李拓. 财政分权和环境规制促进了中国绿色技术创新吗?. 统计研究, 2020, 37(6):27-39.
	[ Chen Bin, Li Tuo. Do fiscal decentralization and environmental regulation promote green technology innovation in China?. Statistical Research, 2020, 37(6):27-39.] DOI: 10.19343/j.cnki.111302/c.2020.06.003. doi: 10.19343/j.cnki.111302/c.2020.06.003
[11]	梁中, 昂昊. 中国绿色技术创新效率演化及其空间治理. 财贸研究, 2019, 30(8):16-25, 63.
	[ Liang Zhong, Ang Hao. Evolution of green technology innovation efficiency and its spatial governance in China. Finance and Trade Research, 2019, 30(8):16-25, 63.] DOI: 10.19337/j.cnki.34-1093/f.2019.08.002. doi: 10.19337/j.cnki.34-1093/f.2019.08.002
[12]	肖黎明, 张仙鹏. 强可持续理念下绿色创新效率与生态福利绩效耦合协调的时空特征. 自然资源学报, 2019, 34(2):312-324.
	[ Xiao Liming, Zhang Xianpeng. Spatio-temporal characteristics of coupling coordination between green innovation efficiency and ecological welfare performance under the concept of strong sustainability. Journal of Natural Resources, 2019, 34(2):312-324.] DOI: 10.31497/zrzyxb.20190208. doi: 10.31497/zrzyxb.20190208
[13]	闫华飞, 肖静, 冯兵. 长江经济带工业绿色技术创新效率的时空分异研究. 重庆社会科学, 2020, (3):6-17.
	[ Yan Huafei, Xiao Jing, Feng Bing. Research on the spatial-temporal differentiation of industrial green technology innovation efficiency in the Yangtze River Economic Belt. Chongqing Social Sciences, 2020, (3):6-17.] DOI: 10.19631/j.cnki.css.2020.003.001. doi: 10.19631/j.cnki.css.2020.003.001
[14]	黄磊, 吴传清. 长江经济带城市工业绿色发展效率及其空间驱动机制研究. 中国人口·资源与环境, 2019, 29(8):40-49.
	[ Huang Lei, Wu Chuanqing. Study on urban industrial green development efficiency and its spatial driving mechanism in the Yangtze River Economic Belt. China Population, Resources and Environment, 2019, 29(8):40-49.] DOI: 10.12062/cpre.20190320. doi: 10.12062/cpre.20190320
[15]	成琼文, 贺显祥, 李宝生. 绿色技术创新效率及其影响因素: 基于 35个工业行业的实证研究. 中南大学学报: 社会科学版, 2020, 26(2):97-107.
	[ Cheng Qiongwen, He Xianxiang, Li Baosheng. Green technology innovation efficiency and its influencing factors: An empirical study of 35 industrial industries in China. Journal of Central South University: Social Science Edition, 2020, 26(2):97-107.] DOI: 10.11817/j.issn.1672-3104.2020.02.011. doi: 10.11817/j.issn.1672-3104.2020.02.011
[16]	段德忠, 杜德斌, 刘承良. 上海和北京城市创新空间结构的时空演化模式. 地理学报, 2015, 70(12):1911-1925. doi: 10.11821/dlxb201512005
	[ Duan Dezhong, Du Debin, Liu Chengliang. Spatial-temporal evolution mode of urban innovation spatial structure: A case study of Shanghai and Beijing. Acta Geographica Sinica, 2015, 70(12):1911-1925.] DOI: 10.11821/dlxb201512005. doi: 10.11821/dlxb201512005
[17]	Charnes A, Cooper W W, Rhodes E. Measuring efficiency of decision making units. European Journal of Operational Research, 1978, 6(2):429-444. DOI: 10.1016/0377-2217(78)90138-8. doi: 10.1016/0377-2217(78)90138-8
[18]	Tone K. A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 2002, 143(1):32-41. DOI: 10.1016/ S0377-2217(01)00324-1. doi: 10.1016/ S0377-2217(01)00324-1
[19]	陆铭, 冯皓. 集聚与减排: 城市规模差距影响工业污染强度的经验研究. 世界经济, 2014, 37(7):86-114.
	[ Lu Ming, Feng Hao. Agglomeration and emission reduction: An empirical study on the influence of urban scale gap on industrial pollution intensity. World Economy, 2014, 37(7):86-114.] DOI: CNKI:SUN:SJJJ.0.2014-07-006. doi: CNKI:SUN:SJJJ.0.2014-07-006
[20]	沈坤荣, 金刚, 方娴. 环境规制引起了污染就近转移吗?. 经济研究, 2017, 52(5):44-59.
	[ Shen Kunrong, Jin Gang, Fang Xian. Does environmental regulation cause pollution to transfer nearby?. Economic Research Journal, 2017, 52(5):44-59.] DOI: CNKI:SUN:JJYJ.0.2017-05-012. doi: CNKI:SUN:JJYJ.0.2017-05-012
[21]	Copeland B R, Taylor M S. Trade, growth and the environmen. Wisconsin Madison-Social Systems, 2003. DOI: 10.4337/9781781008201.00013. doi: 10.4337/9781781008201.00013
[22]	Conrad K, Wastl D. The impact of environmental regulation on productivity in German industries. Empirical Economics, 1995, 20(4):615-633. DOI: 10.1007/BF01206060. doi: 10.1007/BF01206060
[23]	Porter M E, C van der Linde . Toward a new conception of the environment competitiveness relationship. Journal of Economics Perspectives, 1995, 9(4):97-118. DOI: 10.2307/2138392. doi: 10.2307/2138392
[24]	Popp D. International innovation and diffusion of air pollution control technologies: The effects of NO_x and SO₂ regulation in the US, Japanand Germany. Journal of Environmental Economics and Management, 2006, 51(1):46-71. DOI: 10.1016/j.jeem.2005.04.006. doi: 10.1016/j.jeem.2005.04.006
[25]	董直庆, 王辉. 环境规制的“本地-邻地”绿色技术进步效应. 中国工业经济, 2019, (1):100-118.
	[ Dong Zhiqing, Wang Hui. The effect of “local-neighborhood” green technology progress on environmental regulation. China Industrial Economics, 2019, (1):100-118.] DOI: 10.19581/j.cnki.ciejournal.2019.01.006. doi: 10.19581/j.cnki.ciejournal.2019.01.006
[26]	梁圣蓉, 罗良文. 国际研发资本技术溢出对绿色技术创新效率的动态效应. 科研管理, 2019, 40(3):21-29.
	[ Liang Shengrong, Luo Liangwen. The dynamic effect of international R&D capital technology spillover on the efficiency of green technology innovation. Science Research Management, 2019, 40(3):21-29.] DOI: CNKI:SUN:KYGL.0.2019-03-003. doi: CNKI:SUN:KYGL.0.2019-03-003
[27]	汪克亮, 孟祥瑞, 杨宝臣, 等. 基于环境压力的长江经济带工业生态效率研究. 资源科学, 2015, 37(7):1491-1501.
	[ Wang Keliang, Meng Xiangrui, Yang Baochen, et al. The industrial eco-efficiency of the Yangtze River Economic Zone based on environmental pressure. Resources Science, 2015, 37(7):1491-1501.] DOI: 1007-7588(2015)07-1491-11 doi: 1007-7588(2015)07-1491-11
[28]	Qin Xionghe, Sun Yanming. Cross-regional comparative study on environmental-economic efficiency and driving forces behind efficiency improvement in China: A multistage perspective. International Journal of Environmental Research and Public Health, 2019, 16(7):1160. DOI: 10.3390/ijerph16071160. doi: 10.3390/ijerph16071160
[29]	余振, 龚惠文, 胡晓辉. 可持续性转型地理研究综述与展望. 地理科学进展, 2021, 40(3):498-510. doi: 10.18306/dlkxjz.2021.03.013
	[ Yu Zhen, Gong Huiwen, Hu Xiaohui. Geography of sustainability transitions: A sympathetic critique and research agenda. Progress in Geography, 2021, 40(3):498-510.] DOI: 10.18306/dlkxjz.2021.03.013. doi: 10.18306/dlkxjz.2021.03.013
[30]	辛晓华, 吕拉昌. 中国主要城市技术创新影响环境污染的空间分异与机理. 地理科学, 2021, 41(1):129-139. doi: 10.13249/j.cnki.sgs.2021.01.014
	[ Xin Xiaohua, Lv Lachang. Spatial differentiation and mechanism of technological innovation on environmental pollution in major cities of China. Scientia Geographica Sinica, 201, 41(1):129-139.] DOI: 10.13249/j.cnki.sgs.2021.01.014. doi: 10.13249/j.cnki.sgs.2021.01.014
[31]	张桅, 胡艳. 长三角地区创新型人力资本对绿色全要素生产率的影响: 基于空间杜宾模型的实证分析. 中国人口·资源与环境, 2020, 30(9):106-120.
	[ Zhang Wei, Huyan. The effect of innovative human capital on green total factor productivity in the Yangtze River Delta: An empirical analysis based on the spatial Durbin model. China Population, Resources and Environment, 2020, 30(9):106-120.] DOI: 10.12062/cpre.20200408. doi: 10.12062/cpre.20200408
[32]	范擎宇, 杨山, 胡信. 耦合视角下长三角地区城镇化协调度的时空特征及交互机制. 地理研究, 2020, 39(2):289-302. doi: 10.11821/dlyj020181167
	[ Fan Qinyu, Yang Shan, Hu Xin. Spatio-temporal characteristics and interaction mechanism of urbanization coordination degree in the Yangtze River Delta from a coupling perspective. Geographical Research, 2020, 39(2):289-302.] DOI: CNKI:SUN:DLYJ.0.2020-02-007. doi: CNKI:SUN:DLYJ.0.2020-02-007

类型	一级指标	二级指标	三级指标
投入	资本要素	R&D经费	R&D内部经费支出
	劳动力要素	R&D从业人数	R&D人员全时当量
	资源要素	资源消耗	工业用水总量、工业用电总量、城市建成区面积
产出	期望产出	创新效益	发明专利申请量
		经济效益	新产品销售收入
		环境效益	工业固体废物综合利用率、生活垃圾无害化处理率
	非期望产出	环境污染	工业二氧化硫排放量、工业废水排放量、工业烟尘粉尘排放量

年份	2010	2011	2012	2013	2014	2015	2016	2017
Moran's I	-0.325^**	-0.052^**	-0.143^**	-0.419^**	-0.078^**	0.002^**	0.241^**	0.234^**
泰尔指数	0.187	0.290	0.151	0.152	0.177	0.164	0.297	0.284
基尼系数	0.317	0.413	0.285	0.290	0.297	0.290	0.417	0.399

变量名称	变量符号	Mean	Std.	Min	Max
绿色技术创新效率	GTIE	0.754	0.470	0.051	2.349
环境规制	er	3.823	0.293	2.978	4.247
创新支持	tech	0.797	0.667	0.026	5.179
经济发展	ec	11.155	0.489	9.764	12.201
产业结构	is	50.542	7.407	26.320	74.730
对外开放	fc	0.053	0.033	0.003	0.188
人力资本	tale	14.677	19.408	0.500	84.080
城镇化	urb	0.430	0.225	0.118	1

长三角区域绿色技术创新效率的时空演化格局及驱动因素

The spatio-temporal evolutionary pattern and driving forces mechanism of green technology innovation efficiency in the Yangtze River Delta region

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 32

相关文章 15

Metrics

本文评价

推荐阅读 0

变量名称	变量符号	衡量指标
环境规制	er	PM_2.5年均浓度的对数（逆向）
创新支持	tech	地方财政一般预算内科学技术支出占GDP比例(%)
经济发展	ec	人均国民生产总值的对数
产业结构	is	第二产业增加值占GDP的比值（%）
对外开放	fc	外商实际投资总额/GDP(%)
人力资本	tale	各地区高校在校生人数(万人)
城镇化	urb	年末城镇人口/年末总人口(%)

[1]	王少剑, 田莎莎, 蔡清楠, 伍慧清, 吴璨熹. 产业转移背景下广东省工业碳排放的驱动因素及碳转移分析[J]. 地理研究, 2021, 40(9): 2606-2622.
[2]	杨子生, 杨人懿, 刘凤莲. 基于贫困分级的云南省城乡收入差距时空演化与影响因素研究[J]. 地理研究, 2021, 40(8): 2252-2271.
[3]	王列辉, 张楠翌, 林羽珊. 全球高端航运服务业的时空演化及影响因素分析[J]. 地理研究, 2021, 40(3): 708-724.
[4]	卢淑莹, 陶卓民, 李涛, 濮蓉, 荣慧芳. 泛长三角区域入境游客空间格局与意象研究[J]. 地理研究, 2021, 40(1): 263-278.
[5]	吴健生, 罗可雨, 赵宇豪. 深圳市近20年城市景观格局演变及其驱动因素[J]. 地理研究, 2020, 39(8): 1725-1738.
[6]	李建豹,黄贤金,孙树臣,揣小伟. 长三角地区城市土地与能源消费CO₂排放的时空耦合分析[J]. 地理研究, 2019, 38(9): 2188-2201.
[7]	曾通刚, 赵媛. 中国老龄事业发展水平时空演化及其与经济发展水平的空间匹配[J]. 地理研究, 2019, 38(6): 1497-1511.
[8]	苏康传, 杨庆媛, 张佰林, 张忠训. 山区农村土地利用转型与小农经济变迁耦合机理[J]. 地理研究, 2019, 38(2): 399-413.
[9]	张海洲, 陆林, 张大鹏, 虞虎, 张潇. 环莫干山民宿的时空分布特征与成因[J]. 地理研究, 2019, 38(11): 2695-2715.
[10]	周笑, 王鹏飞. 中国工业发展的资源环境压力空间分异演化及影响因素[J]. 地理研究, 2018, 37(8): 1541-1557.
[11]	张国俊, 王珏晗, 庄大昌. 广州市产业生态化时空演变特征及驱动因素[J]. 地理研究, 2018, 37(6): 1070-1086.
[12]	刘承良, 管明明. 基于专利转移网络视角的长三角城市群城际技术流动的时空演化[J]. 地理研究, 2018, 37(5): 981-994.
[13]	叶士琳, 曹有挥, 王佳韡, 祁新华. 长江沿岸港口物流发展格局演化及其机制[J]. 地理研究, 2018, 37(5): 925-936.
[14]	刘斌全, 吴威, 苏勤, 张璐璐. 中国铁路运输效率时空演化特征及机理研究[J]. 地理研究, 2018, 37(3): 512-526.
[15]	丁俊, 王开泳. 珠三角城市群工业生产空间的时空演化及驱动机制[J]. 地理研究, 2018, 37(1): 53-66.