Green Technology Innovation Efficiency Research Articles

The impact of the free trade zones construction on green technological innovation efficiency ——evidence from 288 cities in Chinese

We measure the green technology innovation efficiency of 288 cities in China from static and dynamic dimensions using the super−SBM model and Malmquist−Luenberger index, and employ "Difference in Difference" (DID) model to evaluate the impact of FTZs construction on green technology innovation efficiency using panel data from 288 prefecture-level cities from 2008 to 2020. The findings show: (1) The FTZs significantly improve green technology innovation efficiency. The decomposition indexes promote the green technology innovation efficiency more from the dynamic productivity dimension (GTFP) functioning on technological advancement. (2) The FTZs can boost the efficiency of green technology innovation through industrial agglomeration, digital economy, and government financial support; (3) The effect of FTZs on the efficiency of green technology innovation differs based on the size and location of the city. Green technology innovation will reach maximum potential when promoting FTZ policy in less developed central, western, and interior regions. This study addresses whether FTZ policies can genuinely support regional green innovation and policy insights to expand opening up and enhance high-quality economic growth.

A Study on the Efficiency of Green Technology Innovation in Listed Chinese Water Environment Treatment Companies

With the continuous advancement of the economy, the issues of resource scarcity and environmental damage are becoming increasingly severe. For example, in terms of water resources, the problems of environmental pollution and ecological imbalance in the water caused by industrial and agricultural wastewater are becoming more serious. In order to reduce water pollution, protect water resources, promote ecological balance, and reduce environmental risks, it is necessary to strengthen water environment management. This study uses the Malmquist DEA model to conduct a study on the green technology innovation efficiency (GTIE) of 24 water environment governance companies from 2019 to 2022. Corporate Research & Development investment and employee compensation are used as the input indicators, while the number of color patents obtained and operating income are employed as the output indicators. The evaluation criteria include pure technical efficiency, comprehensive technical efficiency, scale efficiency, and total factor productivity. The results show that there is significant room for improvement in the GTIE of the listed Chinese water environment governance enterprises, and there are considerable differences among different enterprises. The GTIE is significantly influenced by technological progress, the enterprise size, and the equity ratio. Therefore, water environment management enterprises should enhance their efforts in technological research and development and talent training, optimize resource allocation, improve the efficiency of green technology innovation, and effectively fulfill their social responsibilities. These measures will promote the efficient utilization of ecological water, the restoration of the water environment, and the establishment of a clean ecological environment.

Environmental regulation, technology density, and green technology innovation efficiency

From the perspective of the innovation value chain, this study divides the innovation efficiency of green technology into two stages: R&D efficiency and achievement transformation efficiency. Technology density is introduced as a threshold variable to examine the influence of environmental regulation on the efficiency of green technology innovation at both stages. The findings reveal that China's overall green technology innovation efficiency (GTIE) is improving. R&D efficiency initially declined, then increased, while the efficiency of achievement transformation experienced a three-stage pattern: rise-fall-rise. The GTIE distribution across the two stages progressively increases from the northwest to the southeast, resulting in a concentrated, contiguous “line-shaped” and “block-shaped” pattern. High-efficiency areas are primarily found in the eastern coastal regions. Nationally, Environmental regulation and R&D efficiency share an inverted U-shaped relationship, with a double threshold effect of technology density. Environmental regulation does not significantly affect achievement transformation efficiency, but there is a single threshold effect due to technology density.

The impact of green finance and local regulations on industrial green innovation efficiency in China.

When the incentive mechanism of green finance fails to fully promote green technology innovation in industrial enterprises, local government environmental regulations become an important tool in correcting this market failure. However, due to the "follow the cost" hypothesis, the moderating effect of the local government environmental regulation is heterogeneous. In order to explore the impact mechanism of green finance development on the efficiency of green technology innovation in industrial enterprises, spatial effects as well as the heterogeneous moderating effect of local government environmental regulation, this paper systematically evaluates the development level of green finance in 30 provinces in China from 2009 to 2019. It estimates the efficiency of green technology innovation in industrial enterprises using the super-efficiency SBM model, and empirically analyzes the impact mechanism and moderating effect using the spatial Durbin model. The results show that: (1) green finance not only positively impacts the efficiency improvement of green technology innovation in industrial enterprises but also has significant spatial spillover effects; (2) local government environmental regulation has a nonlinear "inverted U-shaped" moderating effect between the green finance development and the efficiency of green technology innovation in industrial enterprises. Based on the research conclusions, this paper proposes policy recommendations from the perspectives of deepening the regional connectivity of green finance and promoting joint regulation by local governments.

Agglomeration and green technology innovation efficiency of industrial enterprises – Based on spatial statistical analysis

Agglomeration and green technology innovation efficiency of industrial enterprises – Based on spatial statistical analysis

Impact and mechanism analysis of ESG ratings on the efficiency of green technology innovation

This study examines how environmental, social, and governance (ESG) ratings affect the efficiency of green technology innovation using listed Chinese companies as a research sample. The results suggest that a higher ESG rating is beneficial for boosting the efficiency of corporate green technology innovation. The mechanism analysis reveals that the impact can be achieved by relaxing financial constraints and encouraging companies to take more risks. Moreover, heterogeneity analysis demonstrates that the impact of ESG ratings on green technology innovation varies significantly across industries. The findings shed light on how to maximize the impact of ESG ratings on green technology innovation efficiency.

From oil and mineral extraction to renewable energy: Analyzing the efficiency of green technology innovation in the transformation of the oil and gas sector in the extractive industry

This study explores the transformation process within the oil and gas sector of the extractive industry. It examines the move from conventional oil and mineral extraction methods towards renewable energy sources facilitated by advancements in green technological innovation. Using the Autoregressive Distributed Lag (ARDL) model, this research looks at the evolution of major oil and gas companies from 2010 to 2021. The issue statement focuses on identifying the drivers behind the extraction industry's transition to renewable energy. This study used the ARDL model to examine the long- and short-term connections between many factors, such as spending on renewable energy, oil output, carbon emissions, and financial performance measures. This research investigates the efficacy of the shift, as mentioned above, by analyzing its environmental and economic ramifications. The study illuminates the viability and efficacy of implementing environmentally friendly technology using a comprehensive methodology that combines environmental impact analyses with economic efficiency evaluations. Using a combination of factual data and case studies, this research brings attention to the possible advantages that may be derived from adopting green technology. These benefits include a decrease in carbon emissions, an improvement in the sustainability of resources, and the possibility of economic diversification. Furthermore, the research highlights the difficulties and obstacles impeding the transition, emphasizing the need for policies and expenditures that assist. This study ultimately adds to the academic conversation on the transition of the extractive sector towards sustainable energy solutions, providing valuable insights for policymakers, industry stakeholders, and environmental campaigners.

Retracted: Environmental Regulation, Financial Resource Allocation, and Regional Green Technology Innovation Efficiency

Retracted: Environmental Regulation, Financial Resource Allocation, and Regional Green Technology Innovation Efficiency

Spatiotemporal evolution and driving factors of urban green technology innovation efficiency in the Chengdu-Chongqing Economic Circle of China

IntroductionGreen technology innovation efficiency (GTIE) growth is an essential route to protect the urban ecological environment in the Chengdu-Chongqing Economic Circle (CCEC). However, the measurement and spatial driving factors of GTIE are still puzzled. MethodsThis study constructs an urban GTIE indicator system including inputs, desired outputs, and undesired outputs, and evaluates urban GTIE in the CCEC using the super-efficiency slacks-based measure (S-SBM). Then, the exploratory spatial data analysis (ESDA) method is applied to analyze the geographical distribution and spatial correlation characteristics of urban GTIE, and a spatial econometric model is used to analyze the influencing factors of urban GTIE from the perspective of spatial spillover. ResultsThe results suggest that: (1) From 2006 to 2020, the urban GTIE of CCEC has obviously increased, and its spatial distribution has a prominent unbalanced feature. (2) The urban GTIE of CCEC mainly presents a significant spatial positive correlation, mainly manifested in the "high-efficiency type" and "low-efficiency type" regional agglomeration patterns, and the spatial "core-edge" structure centering on Chengdu and Chongqing tends to be stable. (3) Economic development (ED), government support (GS), and environmental regulation (ER) can promote the urban GTIE. The negative spillover effects of external opening (EO) and ER are significant, and they have negative effects on the GTIE of neighboring cities. (4) Spatial heterogeneity analysis shows that the driving factors of urban GTIE with different efficiency levels are significantly different, and the spatial spillover effect of the driving factors of GTIE is more significant in high-efficiency cities.DiscussionThe urban GTIE in the CCEC is showing a growth trend, but it needs to narrow the gap in GTIE between cities. Firstly, cities can improve the GTIE by improving ED, strengthening GS, and enhancing ER. Secondly, cities need to pay attention to the negative spatial spillover effects of ER and EO in the process of improving the GTIE. Finally, cities with different efficiency types need to develop differentiated green innovation development strategies based on their own conditions.

Efficiency Measurement and Influencing Factors Analysis of Green Technology Innovation in Industrial Enterprises Based on DEA-Tobit Model: A Case Study of Anhui Province

With the increasingly severe environmental situation, incorporating environmental effects into the research framework of enterprise technology innovation efficiency, a two-stage DEA model was used to measure the green technology innovation efficiency of industrial enterprises in 16 prefecture-level cities in Anhui Province from 2018 to 2020, and the influencing factors were analyzed in combination with the Tobit model. The results show that the innovation efficiency in the R&D stage is significantly higher than that in the technology transfer stage, and the efficiency performance in the Wanjiang region is significantly better than that in the non-Wanjiang region. Government support, innovation atmosphere, and regional economic development level all have a significant positive impact on the efficiency value of the R&D stage of enterprises. Applied research and government support have a significant negative impact on the innovation efficiency value of the green technology conversion stage, and talent factors have a significant positive impact on this stage. In order to further improve the green technology innovation efficiency of industrial enterprises in Anhui Province, environmental protection should be taken into consideration, an evaluation index system for green technology innovation efficiency of industrial enterprises should be established, and a green economic development model should be established.

The Driving Factors of Green Technology Innovation Efficiency—A Study Based on the Dynamic QCA Method

The problems of environmental pollution and resource shortages are becoming increasingly prominent with the advances in technology and the improvements in social productivity levels. How to coordinate the innovating subjects, and strengthen the interaction and cooperation among the subjects to improve the green technology innovation efficiency (GTI efficiency) is an important issue to be solved urgently. This paper constructs a multisubject collaborative analysis framework of “government–market–society” for the GTI efficiency based on the pluralistic co-governance theory. Using 30 provinces (or regions, cities) in China from 2016 to 2020 as research samples, we analyzed the driving factors of GTI efficiency using the dynamic QCA method. Furthermore, the configuration effects of the driving factors were further analyzed in the time and spatial dimensions. The results show that: (1) The single driving factor does not constitute the requisite condition for high GTI efficiency. (2) There are three types of paths to realize high GTI efficiency: “government–market driven”, “market–society driven”, and “government–market–society synergistic promotion”. Among them, the “government–market-driven” path is subdivided into “government—internal-market driven” and “government-supported–external-market driven”. (3) In the time dimension, the “market–society driven” path has prominently enhanced its driving effect on GTI efficiency, while the driving effect of the other three paths is little changed. In the spatial dimension, the coverage of the “government-supported–external-market driven” path appears to have obvious regional differences, and the explanation degree of the central region is higher, while the other three paths do not have obvious regional differences. Based on the perspective of configuration, these research results enrich the related research on the driving factors of GTI efficiency in the time and spatial dimension. It provides enlightenment for boosting the coordinated development of regional GTI efficiency and realizing sustainable economic growth.

Value of dual-credit policy: Evidence from green technology innovation efficiency

The dual-credit policy (DCP), introduced by the Chinese government in 2017, aims to establish a market-based mechanism for fostering the coordinated development of energy-efficient and new energy vehicles in the automobile industry through a credit trading system. This policy plays a significant role in promoting green technology innovation, thereby contributing to the low-carbon transformation of the transportation sector in line with the dual-carbon target. However, challenges related to policy design and substantial variations among automakers during the implementation process have cast uncertainty on the effectiveness of the DCP. To address this issue, this study examines the impact and mechanisms of the DCP on the green technology innovation efficiency (GTIE) of automakers by utilizing a sample of 18 Chinese A-share listed automakers from 2012 to 2020. The analysis employs a combination of the propensity score matching (PSM) and difference-in-differences (DID) approaches. The empirical findings indicate that the introduction of the DCP significantly improves the GTIE of automakers. However, the intensity of this effect diminishes over time. The promotion of GTIE is primarily achieved by alleviating automakers’ financial constraints and increasing their management fund inputs. Furthermore, this effect is strengthened by customer concentration and supply chain concentration. Additionally, the study identifies certain enterprise characteristics that generate heterogeneity in the effects of the DCP, including enterprise ownership, region, and executive education background. The results reveal that the DCP has a more pronounced impact on private automakers, those located in eastern regions, and those with lower levels of executive education.

Spatial Correlation Network Analysis of Industrial Green Technology Innovation Efficiency in China

Exploring the spatial correlation network and its structural characteristics of China’s industrial green technology innovation efficiency is significant for promoting the coordinated development of inter-regional industrial green transformation. Based on the innovation value chain, this paper divides China’s industrial green technology innovation system into three interrelated sub-stages: technology research and development, achievement transformation, and commercialization. The NSBM model is used to measure the efficiency of industrial green technology innovation in 30 provinces and cities in mainland China from 2011 to 2020. The modified gravity model and social network analysis method are introduced to explore its spatial correlation network’s structural characteristics and evolution rules. The results show that the spatial network correlation intensity of the three stages of green technology innovation efficiency in regional industry has gradually strengthened. There is no strict hierarchical structure, and the spatial network tends to be stable. The network shows an apparent “core–edge” distribution in all three stages, with the eastern coastal and central more developed regions at the network’s core. Meanwhile, the northeastern and western remote areas are at the network’s edge and less connected with other regions’ provinces and cities. The distribution of network blocks in the three stages of green technology innovation efficiency is similar. The net benefit block mainly includes the eastern coastal and surrounding developed areas. The net spillover block mainly consists of the economically backward northwest region. The broker block is primarily distributed in the surrounding provinces and cities of the Bohai Rim. The bidirectional spillover block is mainly located in the southwest region. Finally, some suggestions are put forward to promote the coordinated improvement of regional industrial green technology innovation efficiency from the perspective of integrity, individuality, and agglomeration.

Assessing the Green Technology Innovation Efficiency in Yangtze River Delta Region under Dual Carbon Background

With the promotion of the dual carbon policy and the process of transforming and upgrading manufacturing, vigorously developing high-tech manufacturing with high-technology content and significant added value has become a powerful driving force to improve the development of the regional economy. In this regard, this research employs the two-stage network SBM-DEA method with undesirable outputs and multi-stage activities involved in the green production process. We examine the green technology innovation efficiency of high-tech manufacturing in the Yangtze River Delta (YRD) region from 2010 to 2020. We divide the activities of the high-tech manufacturing industry into two sub-stages, namely, the research and development (R&D) stage and the result transformation stage. The results are as follows: (1) The efficiency level of green technology innovation in Shanghai is at an outstanding level, having the most significant performance both in the R&D stage at 0.833 as well as in the result transformation stage at 1.006. (2) Anhui’s green technology innovation efficiency is mainly driven by the R&D stage. Green technology innovation efficiency in Jiangsu province depends primarily on the stage of result transformation, but there is still room for improvement in resource allocation. (3) Zhejiang needs to catch up in both the R&D stage and the result transformation stage, as well as to optimize the resource allocation of the green technology innovation process.

Carbon neutrality and green technology innovation efficiency in Chinese textile industry

The present trend of rapid expansion in the textile industry is highly polluting and energy intensive, and the government intervention is necessitated. This study develops a Data Envelopment Analysis (DEA) model, examines the green technology innovation of listed textile enterprises, and explores its determining factors in order to address the issue of carbon emission reduction in the textile sector. The research sample for this study is obtained from 42 listed textile enterprises from 2017 to 2020. The research demonstrates: (1) the green technology innovation efficiency of domestic listed textile enterprises remains marginal; (2) from 2017 to 2020, the mean value of total factor productivity (TFP) and its decomposition index of listed enterprises in China's textile industry exhibit a downward trend, primarily as a consequence of the slowdown of technological progress; (3) the green technology of the entire industry is in recession from 2017 to 2020; (4) green technology innovation efficiency is inversely correlated with government subsidies, but positively correlated with net profit and asset size of enterprises. In light of the above information, enterprises should boost their ability to innovate with green technologies and implement a research and development (R&D) management development strategy that gives equal consideration to both economic and environmental benefits.

Research on the Improvement Path of Regional Green Technology Innovation Efficiency in China Based on fsQCA Method

Improvements in green technology innovation efficiency is the core factor to promote to shape new advantages in resource-saving and environmental friendliness under the new pattern of double-cycle development. It is also the main driving force needed to establish a high-quality development model of an efficient and sustainable economy. Taking 30 provinces of China as case samples, this paper establishes the appraisal system of green technology innovation efficiency. The first step is using the three-stage DEA model to measure green technology innovation efficiency. Then, according to the configuration perspective, the paper uses fuzzy set qualitative comparative analysis (fsQCA) to explore multiple paths for promoting green technology innovation efficiency. The findings are as follows: (1) A single factor of environmental support or technology supply cannot effectively stimulate the improvement of green technology innovation efficiency. Therefore, the impacting factors must be matched to jointly improve green technology innovation efficiency. (2) There are three configuration paths for high green technology innovation efficiency. Namely, they are the driven by economic environment and environmental regulation type; the driven by industrial structure and supply of finance type; and the driven by industrial structure, supply of finance, and supply of manpower type. (3) The paths to generate non-high green technology innovation efficiency can be summarized as one. The shortage of human resources and a poor economic environment are the main reasons for the inhibition of improvements in green technology innovation efficiency; additionally, the configuration of high and non-high green technology innovation efficiency is asymmetrical. On the one hand, our results are helpful for the study of the efficiency of regional green technology innovation at the provincial level. On the other hand, the results also provide practical solutions and a theoretical basis for provinces to promote regional green technology innovation efficiency under the new economic normal.

Research on Green Technology Innovation Efficiency of Industrial Enterprises in Qinghai Province

Incorporating the concept of green innovation into the technical activities of industrial enterprises is the key to breaking through the constraints of the environment and resources and ensuring the sustainable development of the industry. In this paper, the emissions of three industrial wastes and investment in pollution control are included in the research, and the two-stage Super-NSBM model is used to study the green technology innovation efficiency of industrial enterprises in Qinghai Province. The results show that from 2006 to 2020, the overall level of green technology innovation efficiency of industrial enterprises in Qinghai Province is low, the efficiency value fluctuates greatly, and the total efficiency value and sub-stage efficiency value are unstable; the green technology innovation of industrial enterprises in Qinghai Province in 2020, the efficiency is weak and effective. The total efficiency value and the efficiency value of the technological innovation research and development stage are ranked first in the western region, but the efficiency value of the technological innovation achievement transformation stage is lower than that of Guangxi, Chongqing and Shanxi; the green technology innovation investment redundancy phenomenon is prominent, and resources has not been optimally allocated and utilized; the transformation efficiency of knowledge output in the process of marketization is low; the problem of environmental pollution is still severe.

Spatiotemporal Differentiation and Influencing Factors of Green Technology Innovation Efficiency in the Construction Industry: A Case Study of Chengdu–Chongqing Urban Agglomeration

In order to support the green and low-carbon transformation of China’s construction industry and accomplish the dual carbon objective, it is vital to accelerate green technology innovation. Therefore, this paper takes the Chengdu–Chongqing urban agglomeration of China as the study area, using the super-efficiency slacks-based measure (SBM)model and the gravity model to assess the efficiency of green technology innovation in the construction industry, utilizing geographical detectors to investigate the drivers of green technology innovation in the construction industry further. Additionally, we consider each influencing factor’s level of impact on the efficiency of green technology innovation in the construction sector both under the single factor and double factor scenarios. The findings indicate that there is a considerable difference in the efficiency of green technology innovation in the Chengdu–Chongqing metropolitan agglomeration’s construction industry, and the trend is upward. In addition, the research area exhibited spatially heterogeneous characteristics in terms of the efficiency of green technology innovation in the construction industry, and the spatial spillover effect was significantly limited by distance. Further research revealed that environmental legislation, economic development, public environmental concern, urbanization level, and foreign direct investment were the primary driving factors of green technology innovation efficiency in the construction sector, and industrial size was the potential driving factor. The spatial and temporal differentiation of the green technology innovation efficiency in the construction industry was also more affected by the interaction between the dominating factor and the prospective factor than by either factor acting alone. The research’s findings are useful in advancing the green and low-carbon transformation of the construction sector in the Chengdu–Chongqing metropolitan agglomeration by offering theoretical support and decision-making reference.

Industrial green technology innovation efficiency of China

The industrial green technology innovation of a region is of great significance for promoting the high-quality development of the regional economy and promoting the construction of ecological civilization. Based on the panel data of 30 provinces (except Tibet) in China from 2010 to 2020, this paper uses the Data Envelopment Analysis model adding unexpected output to measure China’s industrial green technology innovation efficiency (IGTIE) under the common Frontier and regional Frontier. The results show that: 1) The industrial green technology innovation efficiency generally shows an upward trend, which is mainly due to technical progress and efficiency improvement. Under the common Frontier, IGTIE has an average annual growth of 6.54%, and under the regional Frontier, IGTIE has an average annual growth of 6.77%. 2) Whether under the common Frontier or the regional Frontier, the central region has obvious advantages in IGTIE compared with the western region and the eastern region. 3) Under the two frontiers, the western region is dominated by efficiency change. Finally, according to the empirical analysis results, specific policy recommendations are put forward for the improvement of China’s future industrial green technology innovation efficiency.

Accelerating efficiency of green technology innovation on carbon mitigation under low-carbon regulation

According to the transaction cost theory for carbon mitigation, green technology innovation must achieve energy conservation and emission reduction through appropriate low-carbon regulation. As a consequence, this study compared the punishment effect of the carbon tax and the reward effect of carbon subsidy on the performance of green technology innovation in China’s provincial industries from 2006 to 2017. We also elucidated that the enterprises’ green technology innovation restricts and stimulates the role of low-carbon regulation on green efficiency in the case of the double threshold effect of carbon dioxide emission. Based on this framework, the integrated tax-subsidy policy for carbon mitigation has a significant differential effect on green efficiency, which changed from an inhibition effect in the high-carbon stage to a promotion effect in the low-carbon stage. And the validity of low-carbon regulations increases with the reduction degree of carbon dioxide emission. But it all depends on whether low-carbon regulations stimulate the enterprises’ willingness to green technology innovation, and whether innovations generate incentives that both increase carbon dioxide emissions and compensate for their innovation cost. Otherwise, enterprise performance should adopt follow strategy more than green technology innovation. Therefore, we further suggested that offsetting the costs of green technology innovation through carbon subsidies would improve the willingness of enterprises to make technological progress and increase the likelihood of carbon reductions which can help the peak in China’s carbon emissions.