How does green innovation drive urban carbon emission efficiency? -Evidence from the Yangtze River Economic Belt

被引:84
作者
Fang, Guochang [1 ,2 ]
Gao, Zhengye [2 ]
Wang, Li [2 ]
Tian, Lixin [3 ]
机构
[1] Nanjing Univ Finance & Econ, Sch Publ Finance & Taxat, Nanjing 210023, Jiangsu, Peoples R China
[2] Nanjing Univ Finance & Econ, Sch Econ, Nanjing 210023, Jiangsu, Peoples R China
[3] Jiangsu Univ, Sch Math Sci, Zhenjiang 212013, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Green innovation; Carbon emission efficiency; Spatial Durbin model; Yangtze river economic belt; SLACKS-BASED MEASURE; CO2; EMISSIONS; ENERGY; TECHNOLOGY; PERFORMANCE; CONSUMPTION; ENVIRONMENT; PROVINCES; IMPACT;
D O I
10.1016/j.jclepro.2022.134196
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper explores the relationship between green innovation and urban carbon emission efficiency (CEE). Based on the panel data of 108 cities in the Yangtze River Economic Belt, the spatiotemporal evolution pattern of green innovation and urban CEE is investigated. The spatial Durbin model shows that green innovation has a positive and significant effect on CEE. The conclusions are robust after considering different spatial weight matrices and possible patent hysteresis. Furthermore, it is found that environmental regulation and industrial upgrading play a significant role in strengthening the relationship between green innovation and CEE. Heterogeneity analysis shows that green innovation has a more significant effect on CEE in the middle and lower reaches of the Yangtze River and in the period of 2012-2017. In addition, the impact of green innovation on CEE is linear, while the impact of general innovation on CEE is U-shaped, reflecting the important driving role of green innovation for CEE. The findings in this study offer a reference for improving the efficiency of spatial allocation of technical elements, which are conducive to urban green and low-carbon transformation.
引用
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页数:15
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共 65 条
[1]   Competing engines of growth: Innovation and standardization [J].
Acemoglu, Daron ;
Gancia, Gino ;
Zilibotti, Fabrizio .
JOURNAL OF ECONOMIC THEORY, 2012, 147 (02) :570-601
[2]   The Environment and Directed Technical Change [J].
Acemoglu, Daron ;
Aghion, Philippe ;
Bursztyn, Leonardo ;
Hemous, David .
AMERICAN ECONOMIC REVIEW, 2012, 102 (01) :131-166
[3]   The Green Solow model [J].
Brock, William A. ;
Taylor, M. Scott .
JOURNAL OF ECONOMIC GROWTH, 2010, 15 (02) :127-153
[4]   Revisiting the environmental Kuznets curve in China: A spatial dynamic panel data approach [J].
Chang, Hsuan-Yu ;
Wang, Wei ;
Yu, Jihai .
ENERGY ECONOMICS, 2021, 104
[5]   County-level CO2 emissions and sequestration in China during 1997-2017 [J].
Chen, Jiandong ;
Gao, Ming ;
Cheng, Shulei ;
Hou, Wenxuan ;
Song, Malin ;
Liu, Xin ;
Liu, Yu ;
Shan, Yuli .
SCIENTIFIC DATA, 2020, 7 (01)
[6]   How technological innovations affect urban eco-efficiency in China: A prefecture-level panel data analysis [J].
Chen, Weidong ;
Si, Wen ;
Chen, Zhan-Ming .
JOURNAL OF CLEANER PRODUCTION, 2020, 270
[7]   One man's loss is another's gain: Does clean energy development reduce CO2 emissions in China? Evidence based on the spatial Durbin model [J].
Chen, Yang ;
Shao, Shuai ;
Fan, Meiting ;
Tian, Zhihua ;
Yang, Lili .
ENERGY ECONOMICS, 2022, 107
[8]   Bigger cities better climate? Results from an analysis of urban areas in China [J].
Cheng, Lu ;
Mi, Zhifu ;
Sudmant, Andrew ;
Coffman, D'Maris .
ENERGY ECONOMICS, 2022, 107
[9]   The carbon dioxide emissions of firms: A spatial analysis [J].
Cole, Matthew A. ;
Elliott, Robert J. R. ;
Okubo, Toshihiro ;
Zhou, Ying .
JOURNAL OF ENVIRONMENTAL ECONOMICS AND MANAGEMENT, 2013, 65 (02) :290-309
[10]   Cleaner production indicator system of petroleum refining industry:From life cycle perspective [J].
Cui, Yuanyuan ;
Yang, Lan ;
Shi, Lei ;
Liu, Guangxin ;
Wang, Yutao .
JOURNAL OF CLEANER PRODUCTION, 2022, 355