Effects of local and civil environmental regulation on green total factor productivity in China: A spatial Durbin econometric analysis

Li, Bin; Wu, Shusheng

doi:10.1016/j.jclepro.2016.10.042

Cited by 324 publications

(163 citation statements)

References 38 publications

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“…They found that environmental regulation has a significant positive impact on GTFP. Based on the data from 273 cities in China from [2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013], and using the spatial Durbin model (SDM) to test the effect of mandatory government and voluntary public environmental regulation on industrial GTFP, Li and Wu [21] found that the former has a significantly positive impact on cities with high political attributes, while the latter has a direct and indirect positive impact. Several researchers focus on other countries.…”

Section: Literature Reviewmentioning

confidence: 99%

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Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model

2018

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Environmental regulation and green innovation are two main fulcrums in the realization of green transition of industrial growth. However, few studies have done an empirical analysis of the impact of environmental regulation and green innovation on green development. Based on the theory of systematic interduality, regional industrial green development is regarded as a dynamic system composed of two subsystems: the state and the process subsystem. Using provincial industrial panel data from 2007-2015 and the spatial Durbin model under the unified analysis framework, this paper examines the role and mechanism of environmental regulation (divided into administrative environmental regulation, market-based environmental regulation, and public participation environmental regulation) in the impact of green innovation (divided into green product innovation and green craft innovation) on industrial green development. The results indicate a sharp fluctuating trend in China's overall industrial green development performance, and that China's 30 provinces can be divided into four categories, based on the development levels of two subsystems of industrial green development. There is a clear positive spatial correlation between the industrial green development performance in different provinces. Considering the impact of environmental regulation on industrial green development performance, different types of environmental regulation have different regional influences. Considering the impact of green innovation on industrial green development performance, in the absence of environmental regulation constraints, green product innovation shows a certain promotional role, and green craft innovation has a significant inhibitory effect. However, under environmental regulation constraints, market-based environmental regulation through the encouragement of green craft innovation rather than green product innovation achieves a positive impact on industrial green development.

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Section: Literature Reviewmentioning

confidence: 99%

“…In accordance with the existing literature [21,49], this paper takes into account five factors, namely, property right structure (PRO), scale of enterprise (SE), trade (TRA), capital intensity (CI) and foreign direct investment (FDI) in the control variables. Details of the variables are outlined in Table 2.…”

Section: Green Innovationmentioning

confidence: 99%

Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model

2018

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“…Thus, the variable should be considered in the model. Indus is a comprehensive influencing factor of TFP proposed by reference [32], which can reflect the industrial layout. In this paper, Indus is calculated in terms of the output value of secondary industry in proportion to GDP.…”

Section: Control Variablesmentioning

confidence: 99%

Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations

Zhao

Yuan

2020

Sustainability

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Based on the PM2.5 haze data of China's provinces between 2004-2016, this paper systematically explores the impact of haze pollution on the quality of China's economic development, as well as its transmission mechanisms. This is achieved by measuring the quality of economic development with total factor productivity. Furthermore, this paper innovatively uses precipitation as an instrumental variable for mitigating the endogeneity of the haze pollution variable, by which the impact of haze pollution on the quality of China's economic development is estimated within the framework of two-stage least squares. It is found that: the haze pollution has degraded the quality of China's economic development significantly; the labor supply loss, counter urbanization and human capital disruption are the three major transmission channels through which haze pollution affects the quality of China's economic development; strengthening government's environmental management is effective in mitigating the adverse impact of haze pollution on the economic development quality; and that China's unique fiscal decentralization system has exacerbated the negative economic effect of haze pollution. The policy implications of this paper are as follows: Improvement of economic development quality is a prerequisite for the transition of economic development mode; and the governmental management of haze is conducive to enhancing the quality of atmospheric environment and economic development, and to promoting the high-quality development of the Chinese economy.

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“…After that, Tone and Sahoo [108] extended the theoretical SBM model, and added the slacks to modify the constraint for undesirable outputs. It is argued that the DEA-SBM model is more in line with reality than the more traditional models, and is widely used in efficiency evaluations, particularly efficiency evaluations that integrate undesirable outputs [21,109,110].…”

Section: Dea-sbm Modelmentioning

confidence: 99%

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

Bossink

Chen

2019

Sustainability

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An efficiency evaluation of China's regional sustainable innovation, evaluating industrial waste and total energy consumption, is the main research subject in this paper. It focuses on a regional measurement and comparison of these undesirable outputs of Chinese firm activities, such as industrial SO 2 and CO 2 emissions. By applying a data envelopment analysis-slack-based measure (DEA-SBM) model with undesirable outputs indicators, the regional innovation efficiency was evaluated for 30 provinces in China, from 2002 to 2014. The results indicate that the sustainable innovation efficiency of overall China is still relatively low, and varies significantly in different regions. Central and Western China have similar sustainable innovation efficiencies, which are much lower than the sustainable innovation efficiency in Eastern China. Furthermore, the data indicate that regional sustainable innovation efficiency disparities among these three areas are decreasing. Based on these findings, reasons for the sustainable innovation efficiency gap among the different regions were analyzed. To scholars, this paper extends the research on regional sustainable innovation efficiency by implementing an undesirable output perspective to the DEA-SBM model. The findings also provide Chinese policy makers with useful decision support insights for regional sustainable innovation, and energy conservation and emission reduction policies.Sustainability 2020, 12, 31 2 of 21 differences can be addressed [15][16][17]. Research on regional innovation efficiency has gained increasing attention in recent years [18][19][20], but to the best of our knowledge, very few studies have been conducted to quantitatively evaluate sustainable innovation efficiency in China, by means of focusing on undesirable outputs like industrial SO 2 and CO 2 emissions [21,22]. Our study aims to contribute to the knowledge in this area, and does this by means of measuring and comparing unsustainable, unwanted, and undesirable outcomes/byproducts of Chinese regions' innovative firm activities. By evaluating the regional sustainable innovation efficiency of 30 provinces in China from 2002 to 2014, using the slack-based measure (SBM) model, this study focuses on the following research question: what are the differences between sustainable innovation efficiencies of different regions in China and which trends in these differences can be estimated?How to deal with SO 2 and CO 2 emissions and with related climate change has become one of the most urgent challenges of today's world. For example, it has been reported that global CO 2 emissions from fuel combustion has increased from 26,177 Mt in 2004 to 32,190 Mt in 2013. Many countries have committed to controlling SO 2 and CO 2 emissions and aim to develop a no-to low-emissions economy. In 2015, the Paris Climate Change Conference adopted the "Paris Agreement" to deal with climate change after 2020 [24]. More than 170 countries jointly signed the Paris Agreement in 2016. China and the United States officially ratified the...

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Effects of local and civil environmental regulation on green total factor productivity in China: A spatial Durbin econometric analysis

Cited by 324 publications

References 38 publications

Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model

Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model

Did Haze Pollution Harm the Quality of Economic Development?—An Empirical Study Based on China’s PM2.5 Concentrations

Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs

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