Dynamic environmental regulation threshold effect of technical progress on green total factor energy efficiency: evidence from China

Abstract

Sustainability is a strategic choice for the transition to a green economy in China. Improving green total factor energy efficiency (GTFEE) is the key to realizing the dual targets of energy saving and economic growth. This paper assesses the nonlinear influence of environmental regulation (ER) on technical innovation (TI) affecting GTFEE by establishing a dynamic panel threshold model using panel data of 271 prefecture-level cities in China from 2004-2016. Meanwhile, a system GMM approach is used to verify the channels through which TI affects GTFEE. Finally, the spatial and temporal characteristics of TI affecting GTFEE are analyzed from the perspective of ER. The empirical results are as follows: (1) TI can significantly improve GTFEE, and there is a threshold characteristic of this improvement effect, when ER is above the threshold value, TI has a greater improvement effect on GTFEE. (2) Besides directly influencing GTFEE through green technical innovation effect, TI can also indirectly influence GTFEE via channels such as economic growth effect, industrial structure upgrading effect and foreign investment effect, and the indirect influence channels show ER heterogeneity. (3) The number of cities crossing the threshold of ER in China increases year by year, which helps TI play a role in improving GTFEE, but there are still a small number of cities that do not cross the threshold during the sample period, which should attract the attention of local governments.

Do pilot free trade zones improve the green total factor productivity? Evidence from a quasi-natural experiment in China

“…Based on previous studies (Lee and Lee, 2022 ; Ding et al, 2022 ; Yu et al, 2021 ; Li et al, 2022 ; Zhu et al, 2019 ), we selected the following indicators as control variables: industrial structural upgrading (stru) is represented by the ratio of added value of the tertiary industry to that of the secondary industry; foreign investment openness (fdi) is represented by the proportion of foreign investment actually utilized in the GDP; financial development level (fin) is measured by the logarithm of the balance of RMB loans of financial institutions; infrastructure construction (infra) is expressed by the logarithm of road area of each city; marketization level (market) is represented by the proportion of the employment of urban private enterprises in the total employment of cities; urbanization level (urban) is represented by the logarithm of built-up area of each city; economic growth (economic) is expressed by the logarithm of GDP per capita, and fixed asset investment (asset) is represented by the ratio of the fixed asset investment to the total GDP of each city. Descriptive statistics for the variables are shown in Table 1 .…”

“…Second, existing studies on the factors affecting GTFP can be divided into two types: (i) evaluating the development trend of GTFP in China as a whole (Gao and Yuan, 2022 ; Tao et al, 2017 ; Zhang and Tan, 2016 ) and (ii) exploring the factors that affect GTFP, such as the level of green finance, the openness, the financial separation, the degree of foreign investment openness, the level of science and technology, the environmental supervision and regulation, the human capital, and the industrial structure (Ding et al, 2022 ; Lee and Lee, 2022 ; Song et al, 2018 ; Yu et al, 2021 ; Wu et al, 2020 ; Li et al, 2022 ; Wang et al, 2018 ; Zhao et al, 2020a , b ; Wang et al, 2021a , b , c ; Zhu et al, 2019 ; Lu et al, 2020 ). In addition, some scholars also explored the policy and institutional factors affecting GTFP from the perspectives of carbon emissions trading pilots, the government environmental information disclosure, the construction of smart city pilots, the construction of e-business pilot cities, and the Belt and Road Initiative (Huang and Chen, 2022 ; Zhao and Chen, 2022 ; Cao et al, 2021 ; Jiang et al, 2021a , b , c ; Liu and Xin, 2019a , b ).…”

Nonlinear and spatial spillover effects of the digital economy on green total factor energy efficiency: evidence from 281 cities in China

“…Next, the number of employees in each city is used as the labor input. Finally, this paper decomposes the provincial energy data to prefecture-level cities based on the weight of each city’s GDP in its province, which provides the relative microdata for empirical research (Gao et al 2021 ; Li et al 2022 ). The desirable output is gross regional product (i.e., GDP), and the unexpected output indicators Y b are industrial water discharge, industrial sulfur dioxide (SO 2 ), and industrial solid waste generation.…”

Does Human Capital Matter for China’s Green Growth?—Examination Based on Econometric Model and Machine Learning Methods

“…Its primary goal is to promote the transformation and upgrading of industrial civilization to ecological civilization [ 8 ]. However, the transition from brown economy to green economy will involve government policies [ 9 , 10 ], economic growth target pressure [ 11 , 12 ], technological progress [ 13 ], socio-cultural contexts [ 14 ], transition costs [ 15 , 16 , 17 ], and many other factors. Among many potential factors affecting green growth, human capital, known as a kind of living capital, refers to the knowledge and skill sets that workers have [ 18 , 19 , 20 ].…”