Does Internet development have a spatial fluctuation spillover effect on green total factor productivity in China? A Spatial-SAR-ARCH model

被引:8
作者
Dong, Kangyin [1 ]
Wang, Jianda [1 ]
Ren, Xiaohang [2 ]
机构
[1] Univ Int Business & Econ, Sch Int Trade & Econ, Beijing, Peoples R China
[2] Cent South Univ, Sch Business, Changsha, Peoples R China
关键词
Green total factor productivity; Internet development; SARspARCH model; Spatial fluctuation spillover; Heterogeneity; China; C31; L86; Q56; TECHNOLOGICAL-PROGRESS; ECONOMIC-GROWTH; CO2; EMISSIONS; IMPACT; CONSUMPTION; POLLUTION; ENERGY; USAGE;
D O I
10.1108/MEQ-08-2022-0226
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
PurposeThe purpose of this study is to examine the spatial fluctuation spillover effect of green total factor productivity (GTFP) under the influence of Internet development.Design/methodology/approachUsing panel data from 283 cities in China for the period 2003-2016, this paper explores the spatial fluctuation spillover effect of internet development on GTFP by applying the spatial autoregressive with autoregressive conditional heteroscedasticity model (SARspARCH).FindingsThe results of Moran's I test of the residual term and the Bayesian information criterion (BIC) value indicate that the GTFP has a spatial fluctuation spillover effect, and the estimated results of the SARspARCH model are more accurate than the spatial autoregressive (SAR) model and the spatial autoregressive conditional heteroscedasticity (spARCH) model. Specifically, the internet development had a positive spatial fluctuation spillover effect on GTFP in 2003, 2011, 2012 and 2014, and the volatility spillover effect weakens the positive spillover effect of internet development on GTFP. Moreover, Internet development has a significant positive spatial fluctuation spillover effect on GTFP averagely in eastern China and internet-based cities.Research limitations/implicationsThe results of this study provide digital solutions for policymakers in improving the level of GTFP in China, with more emphasis on regional synergistic governance to ensure growth.Originality/valueThis paper expands the research ideas for spatial econometric models and provides a more valuable reference for China to achieve green development.
引用
收藏
页码:741 / 770
页数:30
相关论文
共 79 条
[1]  
Bank, 2021, GDP CAP GROWTH ANN
[2]   Stranded assets and stranded resources: Implications for climate change mitigation and global sustainable development [J].
Bos, Kyra ;
Gupta, Joyeeta .
ENERGY RESEARCH & SOCIAL SCIENCE, 2019, 56
[3]  
CAICT, 2020, CHIN INT IND DEV TRE
[4]   Volatility spillovers for spot, futures, and ETF prices in agriculture and energy [J].
Chang, Chia-Lin ;
Liu, Chia-Ping ;
McAleer, Michael .
ENERGY ECONOMICS, 2019, 81 :779-792
[5]   Measuring green total factor productivity of China's agricultural sector: A three-stage SBM-DEA model with non-point source pollution and CO2 emissions [J].
Chen, Yufeng ;
Miao, Jiafeng ;
Zhu, Zhitao .
JOURNAL OF CLEANER PRODUCTION, 2021, 318
[6]   How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel quantile regression [J].
Cheng, Cheng ;
Ren, Xiaohang ;
Dong, Kangyin ;
Dong, Xiucheng ;
Wang, Zhen .
JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2021, 280
[7]   Can low-carbon city construction facilitate green growth? Evidence from China's pilot low-carbon city initiative [J].
Cheng, Jinhua ;
Yi, Jiahui ;
Dai, Sheng ;
Xiong, Yan .
JOURNAL OF CLEANER PRODUCTION, 2019, 231 :1158-1170
[8]   Agglomeration economy and the growth of green total-factor productivity in Chinese Industry [J].
Cheng, Zhonghua ;
Jin, Wei .
SOCIO-ECONOMIC PLANNING SCIENCES, 2022, 83
[9]   Information Rigidity and the Expectations Formation Process: A Simple Framework and New Facts [J].
Coibion, Olivier ;
Gorodnichenko, Yuriy .
AMERICAN ECONOMIC REVIEW, 2015, 105 (08) :2644-2678
[10]   Spatial econometric analysis of China's PM10 pollution and its influential factors: Evidence from the provincial level [J].
Dong, Kangyin ;
Hochman, Gal ;
Kong, Xianli ;
Sun, Renjin ;
Wang, Zhiyuan .
ECOLOGICAL INDICATORS, 2019, 96 :317-328