Green Innovation Efficiency Research Articles

The Efficiency of Green Technology Innovation and Its Influencing Factors in Wastewater Treatment Companies

To achieve the harmonious development of economic growth and the environment, nations must pay more attention to wastewater treatment and boost efficiency using green technology. This study applies the Malmquist-DEA model to assess the efficiency of green technology innovation in 19 Chinese wastewater treatment companies between 2017 and 2020. In addition, focus is placed on the core wastewater treatment technology of the companies with excellent performance, indicating how wastewater treatment companies may improve their green technology. The results of the study indicate that, first, the overall effectiveness of green technology innovation in companies follows a rising and then decreasing trend. In 2020, wastewater treatment companies’ green technology innovation efficiency was around 17.4 percent lower than in 2017. Improving technical progress is the key to boosting the total factor productivity of wastewater treatment companies. Second, based on the Tobit regression, the shareholding ratio of companies has a positive influence on the technical efficiency of companies. Therefore, China should increase innovation capacity and productivity, adopt current sewage treatment technology from overseas, and thus gradually achieve clean sewage utilization and ecological environment management.

EM Algorithm-Based Enterprise Digital Transformation: Green Innovation Efficiency of Enterprise Investment

The digital transformation of manufacturing industry refers to the integration and application of the new generation of information technology in the field of manufacturing in the context of the current digital economy development. Architecture is the foundation of the real economy, which is closely related to digital chemistry and industrial digitalization. On the one hand, high-tech manufacturing of digital information products such as terminal equipment, smart equipment, electronic components, and integrated circuits is the material basis of digital chemical industry. It is a product of traditional manufacturing, blockchain, and artificial intelligence. On the other hand, digital technologies such as simulation technology are the core of the development of manufacturing, the key to achieving precise market positioning, expanding product functions, and improving product quality and added value. Digital technology is an intellectual industry. From the perspective of investment returns, the investment behavior of enterprises is manifested as effective investment and invalid investment. If the capital cost during the investment process is lower than the company's income, the company will not increase the investment amount for any reason, so it will not receive enough investment. Due to the low threshold of investment income, investment growth exceeding a certain threshold will actually reduce investment income and lead to excessive investment. In this context, this article studies the digital transformation of corporate enterprises based on algorithms: green innovation in investment efficiency organizations. This article deeply studies the status quo and the advantages of green innovation investment in digital technology and puts forward corresponding suggestions.

Spatial Correlation Network Structure and Influencing Factors of Two-Stage Green Innovation Efficiency: Evidence from China

With the continuous progress in global sustainable development, green innovation has become the primary driving force for the development of all countries and regions. China has implemented the strategy of constructing a cross-regional green innovation network. As the spatial correlation network structure of green innovation efficiency is complicated, it is necessary to study the change rules of the network structure to coordinate regional green and innovative development. In this paper, the Super-NSBM model is used to calculate the values of two-stage green innovation efficiency of China’s industrial enterprises from 2006 to 2019. Social network analysis is used to explore the rule of changes and causes of the spatial correlation network of two-stage green innovation efficiency. Our findings are as follows. Green innovation efficiency in the two stages presents the relationship of a non-adjacent complex network, and the network of green innovation and R&D efficiency is closely interconnected. Strong hierarchical correlation breaks down when searching for the best spatial configuration relationship. The transformation efficiency of the networked cooperation of green innovation achievements is stable. In the spatial correlation of green innovation and R&D efficiency, Guangdong, Shandong, Beijing, Jiangsu and Zhejiang are at the center of the network. In the spatial correlation of transformation efficiency of green innovation achievements, Shandong, Jiangsu, Guangdong, Henan and Hubei are in the center. The northern coastal areas fall within the scope of green innovation and R&D spillover has and have a close cooperation with the green innovation spillover plate in the southern coastal areas, making green innovation achievements spill over to the Chengdu-Chongqing region and northern region. The cooperation and connection of green innovation activities conform to the rule of geographical proximity. Environmental regulation and marketization are characterized by “hierarchy”, but the economic level is “non-hierarchical”. The government can implement relevant green innovation policies according to local characteristics. Our findings are of great significance to narrow regional green innovation gaps.

How does internet development promote urban green innovation efficiency? Evidence from China

In recent years, the widespread application of the internet has become an important engine driving urban innovation and green transformation in China. Based on panel data from China's 285 prefecture-level cities from 2003 to 2018, this study used the super-epsilon-based measure (EBM)-undesirable model to evaluate urban green innovation efficiency (GIE). The dynamic spatial Durbin model (SDM), mediating effect model, threshold model, and difference in differences (DID) method were then used to investigate the spatial spillover effect, transmission mechanism, nonlinear relationship, and policy shock effect of internet development on urban GIE, respectively. Several key results were obtained. (1) Internet development can significantly promote urban GIE in China directly and indirectly by means of promoting producer services agglomeration, driving financial development, and reducing resource dependence. (2) There was a nonlinear relationship between internet development and urban GIE under different levels of producer services agglomeration, financial development and resource dependence. (3) Notably, when heterogeneity in geographical locations, infrastructure levels and innovation capabilities was considered, developed eastern regions and cities with better traditional infrastructure and higher innovation capacities enjoyed a more noticeable effect from internet development on GIE. (4) The “Broadband China” pilot policy evaluation, which was processed by the continuous DID method, further confirmed the promotional effect of internet development on urban GIE. These findings will help policy-makers better understand the relationship between internet development and urban GIE, thus the government should steadfastly continue to enhance information infrastructure, accelerate the deep integration of traditional industries with the internet, and formulate targeted policies under the regional differences, so as to promote urban technological innovation and green development in China.

The bilateral effects of foreign direct investment on green innovation efficiency: Evidence from 30 Chinese provinces

The importance of dealing with the relationship between foreign direct investment (FDI) and green innovation for sustainable development is being highlighted. However, since FDI can have both positive and negative effects on sustainable development, coupled with regional differences and different development conditions, there is uncertainty about the relationship between FDI and green innovation. Based on the panel data of 30 provinces in China from 2007 to 2018, this paper uses the two-tier stochastic frontier model to decompose the positive and negative effects of FDI on green innovation in different time periods and provinces, and considers the role of regional conditions that lead to regional differences in the green innovation effects of FDI, in order to better provide precise research findings for analyzing the relationship between FDI and green innovation in different provinces in China. First, empirical research shows that the inhibition effect of FDI on green innovation efficiency is smaller than the promotion effect, which makes the combined effect not obviously positive. The average inhibition effect of FDI on green innovation efficiency is 0.0041 and the average promoting effect is 0.0047, which makes the combined effect averaged 0.0006. Second, there are regional differences in the driving effect of FDI on green technology innovation. The western region's FDI has a greater drive effect on the efficiency of green innovation than the central and eastern regions. Third, the promoting effect character of FDI on green innovation efficiency is a “U" type with 70% percentile of the environmental regulation intensity as the inflection point. Due to the imperfection of China's economic structure at this stage, China's relatively developed regions have not yet established the market and industrial structure advantages to fully release the green innovation effect of FDI. Based on the research conclusions, to better promote green innovation efficiency, it is necessary to overcome the disadvantages of the green innovation process dominated by the central government, cultivate and develop green innovation markets, adopt incentive initiatives for foreign enterprises with green technology spillover effects, implement policies to address regional differences, and strengthen the interaction mechanism between FDI and green technology innovation in home countries.

Study on the Evaluation of Green Technology Innovation Efficiency and Its Influencing Factors in the Central Plains City Cluster of China

Green technology innovation, containing economic, social and ecological triple value effects, plays an important role in promoting regional high-quality development. In this paper, we take the Central Plains city cluster, one of China’s top ten national city clusters, as the research object and use the super-efficiency SBM-DEA model to measure and analyze its green technology innovation efficiency. The panel spatial Durbin model (SDM) is used to empirically investigate the market-based, policy and social factors that affect green technology innovation efficiency in the Central Plains city cluster. The main findings are as follows: (1) The green technology innovation efficiency in the Central Plains city cluster shows a fluctuating upward trend from 2009 to 2019, and the spatial differences are obvious, but this spatial difference has converged somewhat over time; (2) Economic development and industrial structure upgrading are the dominant market forces driving green technology innovation efficiency in the Central Plains city cluster, while opening up and enterprise performance hurt the efficiency of green technology innovation; (3) By strengthening environmental regulation and fiscal expenditures on science and technology, the government plays a guiding role in promoting green technology efficiency; (4) Human capital can provide talent support for green technology innovation to effectively promote the efficiency of green technology innovation in the Central Plains city cluster, while the impact of urbanization on green technology innovation efficiency is not significant; (5) In addition to urbanization, the market-based, policy, and social factors that affect green technology innovation efficiency in the Central Plains city cluster also present significant spatial spillover effects. To further promote green technology innovation efficiency in the Central Plains city cluster in the future, we should significantly promote the green transformation and upgrading of industrial structure, improve the quality of opening up to the outside world, strengthen environmental supervision and optimize its governance model, increase government support for green innovation, improve the talent cultivation and introduction system, and mobilize enterprises’ enthusiasm for green technology innovation.

Impact of environmental regulation intensity on green innovation efficiency in the Yellow River Basin, China

In this paper, a model for the slacks-based measure of super-efficiency in data envelopment analysis (SBM-DEA) was used to measure the green innovation efficiency (GIE) of 79 cities in the Yellow River Basin (YRB) from 2003 to 2018. In addition, the panel threshold model was employed to test the nonlinear relationship between environmental regulation intensity (ERI) and GIE. According to the results, the average GIE in the YRB was 0.68. However, the GIE exhibited a rising and fluctuating temporal trend and the following spatial trend: “lower reaches > upper reaches > middle reaches.” Moreover, ERI had a double threshold effect on GIE in the YRB. That is, ERI had a significant positive impact on GIE only when ERI ranged between 0.595 and 0.899. Furthermore, the upper, middle, and lower reaches of the YRB exhibited different nonlinear correlations between ERI and GIE. Therefore, it is essential to implement environmental regulations that are appropriate for the actual conditions of each region. In addition, the per capita income, teacher resources, industrial structure, and actual utilization of foreign capital are significant factors affecting GIE in the YRB. These findings can help governments formulate suitable environmental policies and promote green innovation in the YRB, thereby contributing toward the goal of global sustainable development.

The Threshold Effect of Knowledge Diversity on Urban Green Innovation Efficiency Using the Yangtze River Delta Region as an Example.

Green innovation in the Yangtze River Delta is closely related to higher-quality integrated development, and knowledge diversity is crucial to the realization of regional green technology innovation and development. This study measured the green innovation efficiency of cities in the Yangtze River Delta region from 2010 to 2018 utilizing the Super-SBM model based on undesired outputs. In addition, this study used patent data to investigate regional knowledge deversity, including related variety, and unrelated variety, and to examine the spatio-temporal characteristics of green innovation efficiency and the threshold effect of knowledge diversity. The results demonstrated that related variety was positively correlated with the efficiency of urban green innovation, which was in line with extant studies. Unrelated variety was accompanied by an increase in urban science and technology investment and expansion of urban scale, and the negative effect of knowledge unrelated variety was significantly weakened. This study deepened the understanding of the mechanism of action of diversity, which is conducive to the sustainable development goals as regards the formulation of policies related to green innovation and the development of various types of cities.

The "Local Neighborhood" Effect of Environmental Regulation on Green Innovation Efficiency: Evidence from China.

Current research has generally concentrated on the motivations of environmental policies on local green innovation while ignoring the effect they may have on green innovation in neighboring places. To obtain a thorough understanding and explanation of the influencing mechanism of environmental regulation (ER) on green innovation efficiency (GIE), the super-slack based measure-data envelopment analysis (Super-SBM-DEA) method was applied to evaluate Chinese provinces’ GIE, a spatial Durbin model was developed to evaluate the effect of ER on GIE from the perspective of the “local neighborhood” effect, and a mediating effect model was built to analyze the transmission mechanism of the neighborhood effect of ER on GIE. The study indicated that China’s regional GIE is high in the east and low in the west, with large spatial variability and significant positive spatial clustering characteristics. The effect of ER on local GIE is “U” shaped, while the influence on green innovation efficiency in neighboring areas is an inverted “U” shape. The influence of environmental regulation on GIE in neighboring areas is mainly achieved through the transfer of local polluting industries to neighboring areas. Based on the results, policy recommendations from the perspectives of choosing environmental regulation tools and transferring polluting industries are made to promote and realize the coordinated development of ER and green innovation.

Impact of Green Innovation Efficiency on Carbon Peak: Carbon Neutralization under Environmental Governance Constraints.

Under environmental governance constraints, in order to explore the quantitative contribution of green innovation efficiency to carbon peak and carbon neutralization at the urban level, this paper uses the unexpected Super-SBM model to measure the green innovation efficiency of each prefecture-level city based on the panel data of 40 prefecture-level cities in the Yangtze River Delta from 2010 to 2019. Furthermore, the panel fixed effect model is constructed, and the two-stage least squares estimation method is used for empirical research. It is found that green innovation efficiency can significantly reduce carbon emissions in the Yangtze River Delta, promote carbon emissions in the Yangtze River Delta to reach an early peak, and achieve the long-term goal of carbon neutrality as soon as possible. This conclusion is still stable after solving the endogenous problem and the influence of outliers. The results of regional heterogeneity analysis show that green innovation efficiency has remarkable effects on carbon emission reduction in Anhui and Zhejiang Provinces, and the emission reduction effect in Zhejiang Province is greater than that in Anhui Province. In addition, there exists obvious heterogeneity between different quantiles for the impact of green innovation efficiency on carbon emissions, showing an “inverted U” shape, and its intensity in the context of medium carbon emissions is greater than that of low carbon and high carbon emissions.

Impacts of environmental constraint target on green innovation efficiency: Evidence from China

• Green innovation efficiency is calculated by Super efficiency slacks-based model. • Green innovation efficiencies of China's provinces have upward trends. • Environmental constraint target positively effects green innovation efficiency. • The mediating effect of industrial structure upgrading is verified. Green innovation has attracted more attention all over the world under the background of resource shortage and environmental pollution. This study explores effects of environmental constraint target on green innovation efficiency by super efficiency slacks-based measure (SBM), panel Tobit and instrumental variables-two stage least square (IV-2SLS) models. Our empirical evidence analyses 30 provinces, China from 2007 to 2018. Green innovation efficiencies of China's provinces show an upward trend, as well as performance a significant cluster effect. Moreover, this paper offers multiple empirical supports for the favorable impacts of environmental constraint target on green innovation efficiency. Finally, environmental constraint target promotes industrial structure upgrading significantly, and then achieves the improvement of green innovation efficiency.

Study on the Spatial and Temporal Evolution Patterns of Green Innovation Efficiency and Driving Factors in Three Major Urban Agglomerations in China—Based on the Perspective of Economic Geography

Sustainable development has become a global consensus, and green innovation is the key starting point, and it has become a ballast stone and stabilizer for regional ecological environmental protection and high-quality economic development. Based on GIS tools and multi-method models, this paper studies the spatio-temporal characteristics and influence mechanism of green innovation in three major urban agglomerations in China from 2010 to 2019 from the perspective of economic geography. The study found that: (1) the green innovation efficiency of the three major urban agglomerations in China is in a fluctuating upward trend as a whole, with obvious spatial disequilibrium; (2) from the spatial point of view, the characteristics of global spatial agglomeration distribution have positive spatial correlation, and the law of local spatial autocorrelation is obvious, and the spatio-temporal pattern transitions from “low efficiency, big difference” to “high efficiency, small difference” during the study period; (3) there are obvious spatial spillover and diffusion effects on the green innovation efficiency of the three major urban agglomerations in China as a whole. However, the spatial dependence of green innovation efficiency is inconsistent in China’s three major urban agglomerations; the YRD Urban region and the PRD Urban region show a positive impact, while the JJJ Urban region shows a negative impact; (4) the level of economic development, the operating environment of science and technology, and the guiding factors of government system function with significant differences and regional spatial heterogeneity on the efficiency of green innovation in the three major urban agglomerations in China.

Regional green innovation efficiency and dynamic evolution of Chinese industrial enterprises: a three-stage super-efficiency DEA method based on cooperative game.

The level of development of industrial enterprises is related to a country's or region's overall competitiveness. It is critical to assess the green innovation efficiency of regional industrial enterprises scientifically and effectively in order to improve a country's overall green innovation capability. The green innovation system is divided into three sub-stages in this paper: technology development, economic transformation, and environmental protection. Based on the theory of innovation value chain, a three-stage super-efficiency DEA model of the cooperative game including shared inputs and undesirable outputs is constructed to calculate the overall efficiency, three sub-stages efficiency, and dynamic evolution of green innovation of industrial enterprises in China's provincial administrative regions and eight economic zones from 2015 to 2019 (divided by the time of ultimate output). The results indicate that (1) in terms of overall efficiency, the efficiency of green innovation is not high, and there are clear regional differences, as evidenced by the following states: the middle reaches of the Yangtze River economic zone > the eastern coastal economic zone > the southern coastal economic zone > the northern coastal economic zone > the northeastern economic zone > the northwestern economic zone > the middle reaches of the Yellow River economic zone, and the overall efficiency of the southwestern economic zone fluctuates around the average level of China; (2) from the standpoint of various stages, economic transformation stage efficiency > overall efficiency > technology development stage efficiency > environmental protection stage efficiency. The improvement of overall efficiency is largely dependent on the high efficiency of the economic transformation stage, but low efficiency in the environmental protection stage results in overall low efficiency; (3) from the perspective of the dynamic evolution trend, the overall efficiency and three sub-stages have been improved to varying degrees. However, due to the low efficiency of the environmental protection stage, there is still a long way to go to achieve the goal of innovation-driven development; (4) based on the classification analysis, it was determined that the green innovation efficiency of industrial enterprises in only a few regions belongs to the "three high innovation type," which must take targeted measures to improve the inefficient innovation process links.

Causes and Evolution Characteristics of Green Innovation Efficiency Loss: The Perspective of Factor Mismatch under Local Government Competition

The objective of this paper is to measure the efficiency loss of green innovation caused by local government competition and explore its causes and evolution characteristics. Based on the equimarginal principles such as the deviation of the allocation of green innovation output factors, this paper uses the panel data of China’s provinces (excluding Tibet, Hong Kong, Macao and Taiwan) from 2000 to 2020 and employs the spatial panel measurement model and the Kernel density estimation. The study finds that first, local government competition causes the mismatch of local innovation factors not only locally but also in neighboring regions. Second, the mismatch of innovative talents and capital caused by local government competition lowers the green innovation efficiency. Third, the shortage of innovative talents caused by local government competition is the main reason for the loss of green innovation efficiency in Beijing, Tianjin, and Shanghai. Fourth, the degree of efficiency loss of green innovation at the provincial level in China is heterogeneous in government competition strategies, and the loss due to tax competition is the most significant. Fifth, although the loss of green innovation efficiency generally decreases yearly, in the future, the institutional competition will still hinder the improvement of green innovation efficiency in the eastern, central and western regions of China. Our policy suggestions include promoting regional cooperation and cultivating innovative talents to further improve the efficiency of green innovation.

Evaluation of green technology innovation efficiency in a regional context: A dynamic network slacks-based measuring approach

As green technology innovation efficiency is considered an effective indicator to evaluate energy conservation and emission mitigation, the question of how to estimate it has become a hot topic. Research primarily calculates the cross-section efficiency from a static perspective or the network efficiency alone; however, few studies have considered dynamic characteristics and the network structure of the innovation process simultaneously. To address this gap, this paper employs slacks-based dynamic network data envelopment analysis to calculate the overall, intertemporal, and divisional green technology innovation efficiency covering China's 30 provinces over 2012–2019. The results indicate that (1) a strong spatial dependence of efficiency scores exists, accounting for 60 % of the aggregate statistics. (2) China's overall scores are low, and inefficient regions need to improve efficiency by at least 31.41 % to become efficient. (3) Economically developed regions, provinces with advanced manufacturing, and nonresource-based areas have relatively high scores. (4) The period scores fluctuate greatly up and down around the overall scores, showing a significant spatial imbalance within consecutive multiple periods. (5) Regarding divisional scores, the internal structure of green technology innovation efficiency in China is inferior in R&D and superior in commercialization. Generally, policy-makers should improve local conditions for green technology innovation efficiency.

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

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.

Exploring the impact of energy factor prices and environmental regulation on China's green innovation efficiency.

This study adopts super-DEA model to measure the current level of green innovation efficiency in Chinese provinces. At the same time, Tobit model is used to analyze the impact of energy factor prices and environmental regulation on its efficiency, the results are as follows: (1) Chinese green innovation efficiency average is 0.545, which is at a low level, but shows a steady upward trend. The results have shown that large differences exist in different provinces. Average value of efficiency is highest in the eastern and lowest in the western, and low green innovation efficiency is focus on main provinces of producing coal-based mineral resources. (2) The price change of energy factors inhibits the national green innovation efficiency. The impact on the eastern part of China is positive, but did not pass the significance test, and in central region is negative and also did not pass test, but in the western, it inhibits green innovation efficiency significantly. Environmental regulation has a negative effect on the national green innovation efficiency, the impact coefficient of east is positive, and also positive in the central part, but it fails to pass significance test, while in the west is negative. It can be seen that there are significant regional differences in the impact of the two on the efficiency of green innovation. This paper proposes that we can pay attention to the promotion of environmental regulation, encourages enterprises to actively carry out green innovation activities, continues to deepen the market-oriented reform of energy prices, and increases credit support and introduce financial resources for corporate R&D activities to explore policies to improve the efficiency of regional green innovation.

Green Innovation’s Promoting Impact on the Fusion of Industry and Talent: The Case of Pharmaceutical Industry in the Yangtze River Economic Belt of China

This study aims to explore the promoting impact of green innovation on the fusion of industry and talent (FIT). The primary objectives of the study also include showing how FIT affects the Yangtze River Economic Belt of China and evaluating the development status of three subsystems: the pharmaceutical industry, talent support, and green innovation. In this study, an index system comprising 28 indicators is established to characterize the three subsystems, based on which a comprehensive evaluation model is used to assess the development of each subsystem. A fusion model is used to explore the current status of FIT and the role that green innovation plays in this, based on panel data obtained for 11 provinces and cities in the Yangtze River Economic Belt from 2010 to 2019. The results suggest that: (1) the three subsystems in the Belt have all maintained growth, though the development score for the pharmaceutical industry fluctuated greatly and has been somewhat unstable, while growth trends for talent support and green innovation have been stable; (2) the extent of FIT is low, with nearly half of the provinces and cities lacking organization, with a typical spatial pattern of higher levels in the downstream region and lower levels in the upstream region. The downstream region has obvious advantages in the degree of FIT, while the upstream region has a more optimistic growth trend; and (3) green innovation stimulated the development of FIT in the Belt, with a “strong and stronger” trend depending on the foundation of FIT. To promote FIT, the government should (1) focus on enhancing the development and efficiency of green innovation to help promote FIT; (2) promote the stable and sustainable growth of the pharmaceutical industry as well as talent’s support to consolidate the foundation of fusion; and (3) implement regional coordinated development and interaction policies to narrow the regional gap.

The Influence of the Policy of Replacing Environmental Protection Fees with Taxes on Enterprise Green Innovation—Evidence from China’s Heavily Polluting Industries

This paper analyzes the impact of the policy of replacing environmental protection “fees” with “taxes” on enterprise green innovation based on the Chinese A-share listed companies sample from 2015 to 2019. This paper tries to analyze the factors that may affect the level of green innovation of enterprises and the ability of enterprise green innovation (GI) under the background of the implementation of this policy. This paper adopts the difference-in-differences method (DID), takes 1 January 2018 as the time variable demarcation boundary and uses the heavily polluting industry as the dummy variable boundary, conducts group research on the experimental variables, and observes and analyzes the impact of heavily polluting industries and non-heavy pollution before and after the implementation of the policy. It is found that the policy significantly improves green innovation and the R&D efficiency of green innovation of enterprises in heavy pollution industries. Further research reveals that after the implementation of the policy, large enterprises and private enterprises, compared with SMEs and state-owned enterprises, lay more stress on improving green innovation technology. In the end, it examines the relationship between senior executives’ academic research experience and enterprises’ green innovation and finds that senior executives’ academic research experience can not only promote green innovation, but also improve the R&D efficiency of green innovation. The research results of this paper provide a theoretical basis for decision makers and enterprise management in formulating rules and managing enterprises.

Research on the Role and Mechanism of Digital Finance in Improving the Performance of Industrial Green Innovation

The slack-based measure–Malmquist index model (SBM Malmquist) including unexpected output was used to calculate the performance of China’s provincial industrial green innovation from 2011 to 2019, and a systematic general moment method (GMM) with a dynamic panel model was constructed to empirically test the effect and mechanism of digital finance on the performance of industrial green innovation. The results show that the performance of China’s industrial green innovation has shown an upward trend in recent years, mainly due to improvements in technical efficiency. Digital finance can significantly promote the performance of industrial green innovation, and the improvement of technical efficiency is greater than that of technological progress. From the perspective of structure, the coverage, use depth, and digitization degree have significantly promoted the performance of industrial green innovation, and the digitization degree has the greatest effect. In areas with backward and traditional financial development, digital finance can better promote the improvement in the performance of industrial green innovation and technical efficiency, and the role of the universal benefit is obvious. The mechanism test found that financial efficiency played a partial intermediary effect in the process of digital finance, affecting the performance of industrial green innovation, while residents’ consumption mainly played a masking role. This provides a useful reference for the development and optimization of digital finance and the realization of the drive towards regional industrial green innovation.