Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path

Zhu, Lin; Luo, Ji-An; Dong, Qingli; Yang, Zhao; Wang, Yunyue; Yong, Wang

doi:10.1016/j.techfore.2021.120890

Cited by 129 publications

(46 citation statements)

References 59 publications

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“…Second, existing research on GTI focuses more on the overall research at the national level in China, but little on the regional heterogeneity of GTI, which results in a fragmentary understanding of the regional heterogeneity of GTI [ 72 , 73 , 74 ]. Last, the analyses of the impact of GTI on environmental issues in the literature do not consider spatial correlation [ 73 , 75 , 76 , 77 ]. Considering that China is a country with significant regional differences in resource endowments, economic structures and policy environments, ignoring spatial spillover effects will inevitably lead to biased results.…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Zeng

et al. 2022

IJERPH

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The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

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Section: Literature Reviewmentioning

confidence: 99%

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Zeng

et al. 2022

IJERPH

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“…Nevertheless, traditional DEA methods, such as those proposed by CCR and BCC, as well as the slacks-based measure (SBM) approach, consider the operation a "black box," a characterization that cannot appropriately capture the innovation process (Banker et al, 1984;Charnes et al, 1978;Pastor, Ruiz, & The Economics and Finance Letters, 2022, 9(2): 244-256 Sirvent, 1999;Tone, 2001). Therefore, scholars have developed many other methods to calculate innovation efficiency, including network DEA (Kang, Feng, Chou, Wey, & Khan, 2022;Min et al, 2020;Wang, Pan, Pei, Yi, & Yang, 2020;Zhou & Xu, 2022), dynamic DEA (Chen, Kou, & Fu, 2018;Jiang, Ji, Shi, Ye, & Jin, 2021), super DEA (Chen, Liu, Gong, & Xie, 2021;Zhu et al, 2021), inverse DEA with frontier changes (Chen et al, 2021;Kutty, Kucukvar, Abdella, Meb, & Nco, 2022), parallel DEA (Xiong, Yang, Zhou, & Wang, 2022), Zero-Sum Gains DEA (Bouzidis & Karagiannis, 2022), DEA combined with the Malmquist-Luenberger Index (Zhang & Vigne, 2021), DEA with common weights (Arman, Jamshidi, & Hadi-Vencheh, 2021;Wang, Wu, & Chen, 2019), generalized DEA (Li, He, Shan, & Cai, 2019), and others. It is worth noting that, of all these methods, dynamic network DEA is the only one to consider the dynamic and network features of the innovation process simultaneously (Tone & Tsutsui, 2014).…”

Section: Literature Reviewmentioning

confidence: 99%

Measuring Regional Innovation Efficiency in China using a Dynamic Network DEA Model

Wang

Zhang²

2022

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With the explosive growth in R&D investments and patent applications in recent decades, has China truly achieved improved innovation quality? To answer this question, it is necessary to correctly estimate China’s innovation efficiency. However, when measuring innovation efficiency, the dynamic and network features of the innovation process are seldom considered simultaneously. Therefore, this paper employs the method of dynamic network data envelopment analysis to estimate the overall, period, and sub-stage innovation efficiency of China’s 30 provinces between 2012 and 2016. We conclude that: (1) There is a regional imbalance in the overall scores, for example, developed provinces are more efficient than less developed areas. (2) The period and sub-stage values are not high in each period and represent a gap among the various provinces. (3) For most provinces, scores in the R&D stage are higher than those in the commercialization phase, indicating an uneven distribution of the innovation structure. Accordingly, policymakers should focus on innovation efficiency indicators, encourage innovation according to local conditions, and facilitate the long-run enhancement of both R&D and commercialization.

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“…According to existing literature, we believe there is a complex dynamic interaction between environmental regulation, R&D investment, and green technology innovation. On the one hand, government environmental regulation interacts with R&D investment by enterprises and is likely to affect the level of green technology innovation to variable degrees [ 26 ]. On the other hand, green technology advances will to a certain extent, encourage policymakers to increase the intensity of environmental regulation to the extent consistent with the existing level of technology, and make various factors of enterprise R&D investment more inclined to the “green” direction, resulting in a new cycle of technological advancement and environmental protection.…”

Section: Introductionmentioning

confidence: 99%

Environmental regulation, R&D investment, and green technology innovation in China: Based on the PVAR model

Zhang

Chen

2022

PLoS ONE

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The unreasonable economic development model of human beings has caused the environmental pollution problem to become increasingly serious. In order to achieve a positive relationship and interaction between environmental regulation, research and development (R&D) investment, and green technology innovation, and effectively solve the “strange circle” problem between high-quality economic development and environmental pollution in China and even the world, this paper takes the panel data of industrial enterprises above designated size in Chinese mainland 31 provinces from 2009 to 2019 as a research sample. The comprehensive index of R&D investment and green technology innovation was established by the entropy method, and the panel vector autoregressive (PVAR) model was constructed from the dynamic endogenous perspective, and the dynamic interaction and regional heterogeneity between environmental regulation, R&D investment, and green technology innovation were empirically analyzed by using impulse response function and variance decomposition. We obtain the following findings: (1) Environmental regulation has a two-way interaction relationship with R&D investment and green technology innovation, and R&D investment has a promotion effect on the “green degree” of technological innovation, but its role is still weak and has lagging characteristics. (2) There is significant regional heterogeneity in the dynamic responses of the eastern, central and western parts of China. (3) In the long run, environmental regulation has a “negative crowding out effect” on R&D investment in the central region, and the phenomenon of “central collapse” still exists but will gradually weaken. Environmental regulation has a “positive innovation compensation effect” on green technology innovation. Green technology innovation and R&D investment have an obvious “Pareto improvement” effect on environmental regulation, especially in the eastern region. The conclusions of this study help to clarify the dynamic interaction between environmental regulation, R&D investment, and green technology innovation, further improve environmental regulatory policies and green technology innovation R&D decision-making, and provide an effective way to achieve green and sustainable development in China and other parts of the world.

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Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path

Cited by 129 publications

References 59 publications

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

The Impact of Green Technology Innovation on Carbon Emissions in the Context of Carbon Neutrality in China: Evidence from Spatial Spillover and Nonlinear Effect Analysis

Measuring Regional Innovation Efficiency in China using a Dynamic Network DEA Model

Environmental regulation, R&D investment, and green technology innovation in China: Based on the PVAR model

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