Towards green economic recovery: how to improve green total factor productivity

被引:16
作者
Lu, Dongdong [1 ]
Wang, Zilong [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Sch Econ & Management, Nanjing 210000, Peoples R China
基金
英国科研创新办公室;
关键词
Green economic recovery; Green total factor productivity; New digital infrastructure; Industrial agglomeration; Environmental regulation; Government environmental preference; ENERGY-CONSUMPTION; CHINA; GROWTH; INDUSTRIES; EFFICIENCY; POLICY;
D O I
10.1007/s10644-023-09515-7
中图分类号
F [经济];
学科分类号
02 ;
摘要
Achieving green economic recovery is crucial to improving environmental quality and sustainable development. This study examines the influence of new digital infrastructure on green total factor productivity (GTFP) using panel data from 30 regions in China from 2008 to 2019. The results are as follows: (1) New digital infrastructure has a significant improvement effect on GTFP. After a series of robustness tests, the conclusion is still valid. (2) The improvement effect of new digital infrastructure on GTFP shows significant heterogeneity. In regions with high industrial agglomeration, high environmental regulation and strong government environmental preference, the improvement effect of new digital infrastructure on GTFP is more obvious. (3) New digital infrastructure improves GTFP through green technology innovation and factor allocation optimization. The government should strengthen the fiscal incentives for green technology development while increasing R&D investment in fiscal expenditure, thus promoting green economic recovery.
引用
收藏
页码:3163 / 3185
页数:23
相关论文
共 103 条
[1]   Productivity Spillovers Across Countries and Industries: New Evidence From OECD Countries [J].
Badinger, Harald ;
Egger, Peter .
OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 2016, 78 (04) :501-521
[2]  
Battese GE., 1995, Empir Econ, V20, P325, DOI [10.1007/BF01205442, DOI 10.1007/BF01205442]
[3]   Do R & D activities matter for productivity? A regional spatial approach assessing the role of human and social capital [J].
Bengoa, Marta ;
Martinez-San Roman, Valeriano ;
Perez, Patricio .
ECONOMIC MODELLING, 2017, 60 :448-461
[4]   Market segmentation, resource misallocation and environmental pollution [J].
Bian, Yuanchao ;
Song, Kaiyi ;
Bai, Junhong .
JOURNAL OF CLEANER PRODUCTION, 2019, 228 :376-387
[5]   How does e-commerce city pilot improve green total factor productivity? Evidence from 230 cities in China [J].
Cao, Xiguang ;
Deng, Min ;
Li, Haokuang .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2021, 289
[6]   The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective [J].
Cassetti, Gabriele ;
Boitier, Baptiste ;
Elia, Alessia ;
Le Mouel, Pierre ;
Gargiulo, Maurizio ;
Zagame, Paul ;
Nikas, Alexandros ;
Koasidis, Konstantinos ;
Doukas, Haris ;
Chiodi, Alessandro .
ENERGY, 2023, 263
[7]   Green Total Factor Productivity Growth and Its Determinants in China's Industrial Economy [J].
Chen, Chaofan ;
Lan, Qingxin ;
Gao, Ming ;
Sun, Yawen .
SUSTAINABILITY, 2018, 10 (04)
[8]   The impact of low-carbon city pilot policy on the total factor productivity of listed enterprises in China [J].
Chen, Hao ;
Guo, Wei ;
Feng, Xue ;
Wei, Wendong ;
Liu, Hanbin ;
Feng, Yan ;
Gong, Weiyi .
RESOURCES CONSERVATION AND RECYCLING, 2021, 169
[9]   Is the digital economy driving clean energy development? -New evidence from 276 cities in China [J].
Chen, Pengyu .
JOURNAL OF CLEANER PRODUCTION, 2022, 372
[10]   Consistent estimation of the fixed effects stochastic frontier model [J].
Chen, Yi-Yi ;
Schmidt, Peter ;
Wang, Hung-Jen .
JOURNAL OF ECONOMETRICS, 2014, 181 (02) :65-76