Energy consumption, environmental pollution, and technological innovation efficiency: taking industrial enterprises in China as empirical analysis object

Miao, Cheng-lin; Meng, Xiao-na; Duan, Meng-meng; Wu, Xinyu

doi:10.1007/s11356-020-09537-y

Cited by 57 publications

(19 citation statements)

References 26 publications

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“…The 2018 Global Environmental Performance Index scores and rankings of 180 countries and regions show that China has a comprehensive score of 50.74, ranking 120th overall and ranking 177th in air quality. Environmental pollution caused by energy consumption is an important factor restricting the sustainable development of China's economy and has brought huge economic losses to China [8]. Highlighted by the World Health Organization, haze pollution significantly reduces the per capita GDP, damages the quality of economic development, and directly causes a greater threat to physical health, traffic safety, and climate change [9,10].…”

Section: Introductionmentioning

confidence: 99%

Digital Economy and Environmental Quality: Evidence from 217 Cities in China

Wen

2021

Sustainability

123

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With the rapid development of the digital economy, understanding the relationship between the digital economy and the environment is increasingly important for sustainable development. This study mainly studies the nexus of digital economy development and environmental quality for 217 cities in China from 2003 to 2018. Specifically, this study first evaluates the degree of coupling coordination between the digital economy system and the environmental system. Results show that the coupling coordination degree between the digital economy system and the environmental system shows a fluctuating rise from 2003 to 2018. Furthermore, this study empirically investigates the impact of digital economy development on PM2.5, which represents environmental quality, and results show that digital economy development significantly reduces PM2.5. The above finding is still valid by using the quasi-natural experiment of broadband China for the robustness test. Urbanization, population density, and economic development have caused threshold effects. When the urbanization rate and population density exceed a certain threshold, the digital economy significantly inhibits PM2.5. In contrast, when the per capita GDP exceeds a certain threshold, the digital economy significantly promotes PM2.5. The digital economy mainly affects pollutant emissions through direct and technological effects.

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Section: Introductionmentioning

confidence: 99%

Digital Economy and Environmental Quality: Evidence from 217 Cities in China

Wen

2021

Sustainability

123

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“…Battese and Coelli used the SFA method to evaluate and analyze the regional innovation efficiency [28]; Diaz and Sánchez utilized the SFA model to assess the innovation efficiency of the small and medium-sized manufacturing industry [29]. Hong et al utilized the SFA method to assess the innovation efficiency of the high technology industry [30]; The SFA method was used to measure the efficiency of green innovation for China's high technical industries [31]; The RAGA-PP-SFA method, which based on traditional SFA method, was used to evaluate the green technology innovation efficiency of the manufacturing industry which are the high-end [32]; The SFA method was used to assess the efficiency of green innovation for heavy polluting sectors which located in China [33]; The technological innovation efficiency of industrial enterprises was assessed through using the SFA method [34].…”

Section: Introductionmentioning

confidence: 99%

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

Yang

Zhu

2022

Sustainability

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Green innovation, which combines “innovation-driven” and “green development,” is one of the most powerful ways to overcome resource and environmental constraints and enhance manufacturing industry sustainability. Based on the innovation value chain perspective, the green innovation process of manufacturing industry is decomposed into two stages: green scientific and technological R&D and achievement transformation. Then, using the three-stage DEA and Malmquist index model to measure the green innovation performance of China’s manufacturing industry, and compare its regional heterogeneity from the dual perspectives of static efficiency and dynamic productivity. In addition, this paper further discusses the improvement path of green innovation performance of China’s manufacturing industry. The findings are as follows: (1)The green innovation efficiency of manufacturing industry in China is at a comparatively low degree and has great potential for improvement. Moreover, it shows apparent regional heterogeneity: The green innovation efficiency in the eastern region is higher than that in the western region, and both are higher than that in the center region, confirming the phenomenon of “central collapse”. (2)The green innovation productivity of China’s manufacturing industry shows a “W-type” dynamic evolution tendency, with green technological progress as the key driving factor, while the green technical efficiency does not clearly exhibit a “catch-up effect”. Additionally, it shows significant regional heterogeneity: green innovation productivity in the western region is higher than that in the central and eastern regions, indicating a potential “backwardness advantage”. (3) The eastern region of China is located in combination IV, which indicates that it has a high rate of green innovation efficiency but a low rate of green innovation productivity; the central region is located in combination III, which indicates that it has a low rate of both green innovation efficiency and productivity; and the western region is located in combination II, which indicates that it has a low rate of green innovation efficiency but a high rate of green innovation productivity. Last but not least, this paper puts forward three kinds of paths for the improvement of the green innovation performance of China’s manufacturing industry: unilateral breakthrough, step-by-step and stimulating jumping type.

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“…For example, some scholars estimated the input efficiency of industrial environmental management in China using SFA models based on data from three types of industrial wastes and explored the overall characteristics of input efficiency [ 12 ]. Other scholars used stochastic frontier analysis (SFA) models to analyze the effects of energy consumption and environmental pollution on the efficiency of technological innovation in industrial enterprises [ 13 ]. Moreover, some scholars have used the improved DEA method to measure the efficiency of environmental governance.…”

Section: Literature Reviewmentioning

confidence: 99%

How to Evaluate Investment Efficiency of Environmental Pollution Control: Evidence from China

Xiao-chun

Long

Sun

et al. 2022

IJERPH

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Clarifying the efficiency of investment in environmental pollution control is conducive to better control of environmental pollution. Based on panel data of 30 provinces and cities in China from 2008 to 2017, this study combines the three-stage super-efficient SBM-DEA model and the Global-Malmquist-Luenberger index to measure the efficiency of investment in environmental pollution control in China and analyze regional differences. The results show that: First, the investment efficiency of environmental pollution control in China shows a rising trend year by year, but there are significant differences among provinces and regions; the presence of random factors and environmental variables makes the control efficiency underestimated. Second, excluding the effects of both, the national investment efficiency of environmental pollution control has improved significantly, but still has not reached the optimal effect; the gap between provinces and regions has narrowed while the investment efficiency of environmental pollution control has improved, and there is still an unbalanced situation. Third, the main driver of the year-on-year improvement in China’s environmental pollution control efficiency is technological progress; compared with northeastern China, technological progress has a more significant role in promoting eastern, central, and western China. Finally, based on the results, this paper focuses on making suggestions to promote environmental pollution control in China in terms of making regional cooperation, making good environmental protection investment and strengthening environmental protection technology research and development.

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Energy consumption, environmental pollution, and technological innovation efficiency: taking industrial enterprises in China as empirical analysis object

Cited by 57 publications

References 26 publications

Digital Economy and Environmental Quality: Evidence from 217 Cities in China

Digital Economy and Environmental Quality: Evidence from 217 Cities in China

Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China

How to Evaluate Investment Efficiency of Environmental Pollution Control: Evidence from China

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