Spatio-temporal evolution and driving factors of green innovation efficiency in the Chinese urban tourism industry based on spatial Markov chain.

Green innovation in the tourism industry is a sustainable development concept for resource conservation and environmental optimization. The effective measurement of green innovation efficiency in the tourism industry and an accurate understanding of its spatial relationship was significantly important for promoting its sustainable development. Using the SBM-undesirable model, kernel density estimation, and a spatial Markov chain, we explored the spatio-temporal evolution characteristics and influencing mechanisms of urban tourism green innovation efficiency (TGIE) in China between 2000 and 2020. We found that (1) the temporal and spatial changes of TGIE were generally at a lower than medium level and fluctuated throughout country, with a transition in the east, collapse in the middle, and stagnation in the northeast. (2) The dynamic evolution of TGIE always exhibited polarization, but regional coordination was gradually enhanced with strong stability, although it was difficult to achieve leap-forward development. The cities with spatial upward transfer were concentrated mainly in the central and western region and while there were few cities with a downward adjustment, there were obvious asymmetrical spatial spillover effects. (3) The driving factors of TGIE were the overall economic level, industrial structure, government regulation, and education level. These factors had a significant positive relationship with TGIE, while the degree of opening up to the outside world has no significant effect, but the degree of influence, mechanism, and conditions of each factor were strongly regional.

Exploration and future trends on spatial correlation of green innovation efficiency in strategic emerging industries under the digital economy: A social network analysis.

With digital technological change and the increasing frequency of interregional innovation links, the spatial correlation and diversity of strategic emerging industries' green innovation efficiency (SEI-GIE) need to be explored in depth. This paper innovatively constructs the SEI-GIE input-output index system under digital economy. The proposed grey model FINGBM(1,1) with ω-order accumulation and weighted initial value optimization realizes effective prediction of 7 input-output indicators of 30 provinces in China from 2021 to 2025. Super-SBM-DEA, gravity model, and social network analysis are applied to explore spatial network structure's dynamic process of SEI-GIE from 12th to 14th Five-Year-Plan period (2011-2025). Empirical results show that (1) Under the effect of digital economy, the SEI-GIE in China generally shows a U-shaped fluctuation trend, in which the growth trend in the central region is obvious, and the western region shows significant fluctuations. (2) The spatial correlation network of SEI-GIE presents a complex and stable center-periphery circle. Particularly, the overall increase in network efficiency highlights the strong small-world characteristics. (3) Beijing, Shanghai, Zhejiang and Jiangsu have always been in the leading core position, with strong influence and control; And Tianjin's core position in the network will decline. Additionally, Guangxi and Chongqing have great potential, but Guangdong needs to strengthen its radiation effect. (4) Block model shows that plate-I (Beijing, Tianjin) receive spatial spillovers from others, while plates-III,IV have significant spillover effects. This study provides theoretical reference for policymakers from a network perspective to promote development of China's SEI-GIE.

The effect of producer service agglomeration externalities on urban green innovation efficiency in China.

For China's urban industrial layout and green innovation, determining which types of producer service agglomeration externalities (PSAE) are most conducive to the rise of green innovation efficiency (GIE) has theoretical and practical relevance. Based on the panel data of 274 prefecture-level cities in China from 2006 to 2019, this paper adopted the super-SBM model with unexpected output to evaluate the urban GIE. It used the Moran I index to judge the spatial auto-correlation of GIE, followed by the spatial Durbin model (SDM) to examine the spatial spillover effect of PSAE on the urban GIE. Then we used a comprehensive threshold effects model to investigate the non-linear relationship. The results demonstrate that Marshall-Arrow-Romer externality (MARE) and Jacobs externality (JACE) are curbing the urban GIE. Porter externality (PORTERE) inhibits the region and promotes the surrounding area. Furthermore, these effects also depend on the stage of economic development.

The impact of digital infrastructure on provincial green innovation efficiency-empirical evidence from China.

In the epoch of the digital economy, technological innovation and energy conservation are significantly facilitated by digital infrastructure, leading to substantial improvements in green innovation efficiency at the provincial level. This study employed the feasible generalized least square (FGLS) method to examine the effects of digital infrastructure on the green innovation efficiency across 30 provinces in the Chinese mainland, utilizing panel data from 2011 to 2020. Additionally, this investigation delves into the intervening role of industrial structure upgrading and the amplifying effects of environmental regulation and human capital on the process. Findings indicate that, to begin with, digital infrastructure contributes to the meaningful enhancement of green innovation efficiency within provinces. Subsequently, the industrial structure upgrading partially mediates the impact of digital infrastructure on the efficiency of provincial green innovation. Lastly, both human capital and environmental regulations amplify the beneficial influence of digital infrastructure on the effectiveness of green innovation at the provincial level. This study provides valuable insights into the mechanisms through which digital infrastructure boosts green innovation efficiency, aiding policymakers in formulating appropriate policies to augment digital infrastructure, thereby promoting provincial green innovation efficiency.

Digital finance and green innovation efficiency: empirical data from Chinese listed manufacturing companies.

Amid the flourishing digital economy, digital finance overcomes the constraints of the conventional financial model and largely improves the supply efficiency and use of funds. This provides new opportunities for manufacturing corporations to improve their green innovation efficiency. Employing Chinese Shenzhen and Shanghai A-share listed manufacturing corporations between 2011 and 2021, this paper conducts an empirical analysis to study the effect of digital finance on corporate green innovation efficiency. Discoveries suggest that digital finance significantly improves manufacturing corporations' green innovation efficiency. After a few robustness tests, the results are still accurate. According to a mechanism analysis, digital finance increases the effectiveness of green innovation in manufacturing corporations by removing financing constraints. According to the heterogeneity analysis, the impact of digital finance on manufacturing corporations exhibits distinctive financial and geographical regional heterogeneity, particularly accentuated in Zhejiang Province and the central and western regions. This paper can provide a valuable reference for digital finance in supporting manufacturing corporations in green innovation ventures and improving the level of green innovation in the context of digitalization.

From green finance to sustainable innovation: how to unleash the potential of China's high-tech industry.

Green finance is acknowledged as a critical policy tool in China's sustainable development sector, with the goal of lowering the financial burden associated with ecological transformation for Chinese firms. This research examines the impact of green finance on the green innovation efficiency of the high-tech industry in China, within the context of carbon neutrality. Using a panel dataset covering 30 provinces, autonomous regions, and municipalities in China from 2013 to 2021, we analyze the effects of green finance on green innovation efficiency. Our findings indicate that green finance significantly improves the green innovation efficiency of the high-tech industry, even after robustness testing. Furthermore, this paper also explores the threshold effect of industrial agglomeration on the relationship between green finance and green innovation efficiency, specifically in terms of specialization, diversity, and competition. We verify that green finance reduces the costs of green transformation for enterprises, leading to a substantial improvement in the green innovation efficiency of the high-tech industry. These results shed light on the factors influencing green innovation efficiency and provide theoretical insights and implications for policymakers, entrepreneurs, and financial institutions to reconcile economic growth and sustainability goals.

What is affecting the improvement of green innovation efficiency in the old industrial base: evidence from Northeast China.

Green innovation is an important driving force for high-quality development and is vital for reinvigorating the old industrial bases in Northeast China. As such, this study investigates the spatial-temporal evolution characteristics and factors influencing green innovation efficiency (GIE) in Northeast China from 2005 to 2020, using the super-efficient EBM-Malmquist model, kernel density estimation, and random forest model. The results show the following. (1) The "growth effect" of technological change is the main force driving GIE improvement; the "horizontal effect" of pure technical efficiency change has started to play an important role; and the club convergence characteristics of GIE in Northeast China have started to be optimized. (2) GIE in Northeast China shows significant spatial differentiation. The urban agglomeration of Mid-southern Liaoning and Harbin-Changchun has had high values for GIE, indicating that green innovation has a cyclic cumulative effect and the spatial pattern of green innovation needs to be further optimized. (3) The random forest model is more accurate and provides more trustworthy results compared with the traditional multiple linear regression model. The results of random forest model measurement illustrate that the number of digital economy enterprises, public finance expenditure, GDP per capita, and vegetation coverage play a positive role in promoting GIE. The proportion of the non-farm population and industrial agglomeration plays a negative role in GIE. In the same period, the contribution of the number of digital economy enterprises≥0.41, public expenditure ≥0.47, GDP per capita≥0.39, and vegetation coverage≥0.36 to GIE reach maximum values and then remain unchanged.

The impact of technology transfer on the green innovation efficiency of Chinese high-tech industry.

Promoting technology transfer is an important strategic measure for China to promote industrial innovation. However, there is little research exploring the influence of technology transfer on the green innovation efficiency (GIE) of China's high-tech industry (HTI). From the perspective of process, green innovation in HTI is a continuous three-stage system including research and development (R&D), commercialization, and diffusion. Therefore, we measure the GIE of China's HTI by using a three-stage network data envelopment analysis (NDEA) model considering environmental pollution and establish a series of regression models to investigate the role of the two main ways of technology transfer, domestic technology acquisition (DTA) and foreign technology introduction (FTI), in improving the GIE of HTI. The results show that the average GIE of China's HTI is 0.7727 from 2011 to 2020. Except for Jiangsu, Guangdong, Qinghai, and Xinjiang, green innovation in HTI in other provinces in China is inefficient. DTA has significantly promoted GIE in HTI. FTI has a positive impact on the GIE of HTI but is not statistically significant. The robustness test confirmed these results. This study is helpful to understand the differences between the effects of DTA and FTI on the GIE of China's HTI, to provide a basis for adjusting technology transfer policies.

Fiscal decentralization, local government environmental protection preference, and regional green innovation efficiency: evidence from China.

Green technological innovation has gained in importance in regional policy making towards gaining competitive advantage and sustainable development. This paper used the data envelopment analysis method to calculate regional green innovation efficiency in China, and empirically tested the effect of fiscal decentralization through Tobit model. The regression results show that the local governments with higher fiscal autonomy would prefer to strengthen environmental protection; thus, the regional green innovation efficiency was improved. After the guidance of relevant national development strategies, these effects became more apparent. Our research provided theoretical support and practical guidance for promoting regional led green innovation, improving environmental quality, achieving carbon neutrality, and promoting the high-quality and sustainable development.

Spatial-temporal differentiation of coupling coordination degree for green finance and green innovation efficiency: a case study in China.

Continued investment in finance and innovation is beneficial to economic development, and the joining of green system can accelerate the process of economic recovery from environmental distress. To better enhance the relationship of green finance and green innovation, it is vital to demonstrate the synergy between the two thoroughly. Thirty provinces in China are selected to examine the coupling coordination relationship between the two, specifically testing the spatial aggregation and evolutionary differences in the coupling coordination by adopting the coupling coordination degree (CCD) model, spatial autocorrelation, and kernel density estimation. Conclusions of the paper show that green finance is calculated by the EW-TOPSIS method, and the overall score of provinces is low. Using super-SBM model to evaluate green innovation, the uneven distribution of efficiency is obvious, although it is gradually increasing. The CCD in most provinces is in low-level or basic coordination, with significant regional heterogeneity. The global Moran's index becomes gradually evident with time. The local Moran scatter diagram presents a downward trend from east to west, but with more L-L aggregation provinces emerging in 2020. The center of the national kernel density curve gradually shifts to the right, indicating that the national overall synergy level is improving. Deepening the understanding of the empirical results facilitates the formulation of reasonable policies that fit the four major regions.

Threshold Effect of Environmental Regulation and Green Innovation Efficiency: From the Perspective of Chinese Fiscal Decentralization and Environmental Protection Inputs.

In the context of China's 14th Five-Year Plan and 2035 visionary goals of national economic and social development, to achieve the national dual carbon goals, an innovation-driven green development strategy must be implemented, and the relationship between environmental regulation and green innovation efficiency must be clarified. Based on the DEA-SBM model, in this study, we measured the green innovation efficiency of 30 provinces and cities in China from 2011 to 2020 by introducing environmental regulation as the core explanatory variable, and two threshold variables, environmental protection input and fiscal decentralization, to empirically analyze the threshold effect of environmental regulation on green innovation efficiency. We found that: (1) The green innovation efficiency of 30 provinces and municipalities in China is spatially distributed as strong in the east and weak in the west. (2) A double-threshold effect exists with environmental protection input as the threshold variable. Environmental regulation showed an inverted N-shaped relationship of first inhibiting, then promoting, and finally inhibiting green innovation efficiency. (3) A double-threshold effect exists with fiscal decentralization as the threshold variable. Environmental regulation showed an inverted N-shaped relationship of inhibiting, promoting, and then inhibiting green innovation efficiency. The study results provide theoretical guidance and practical reference for China to achieve the dual carbon goal.

Impact of Manufacturing Agglomeration on the Green Innovation Efficiency-Spatial Effect Based on China's Provincial Panel Data.

Improving the efficiency of green innovation has become an urgent issue in the transformation of manufacturing industries in most developing countries within the context of increasing resource scarcity and environmental constraints. As an important feature of manufacturing development, agglomeration also plays a substantial role the promotion of technological progress and green transformation. Taking China as an example, this paper investigates the spatial impact of manufacturing agglomeration (MAGG) on green innovation efficiency (GIE). We first measure the level of MAGG and GIE in 30 provinces (autonomous regions and municipalities) in China during the period from 2010 to 2019, and then we utilize the spatial Durbin model in order to empirically test the spatial effect and heterogeneity based on theoretical analysis. The findings demonstrate that (1) the overall GIE in China has maintained a steady increase, and the level of MAGG slowly decreased from 2010 to 2019 with characteristics of obvious regional non-equilibrium and spatial correlations; (2) MAGG has a significant effect on the improvement of GIE nationally; (3) under the constraints of regional heterogeneity, the impacts of MAGG on GIE show significant differences between eastern, central and western China; (4) in terms of industry heterogeneity, high-tech MAGG can significantly enhance local GIE, while the indirect effect of non-high-tech MAGG is significantly negative. Our findings not only contribute to the advancement of studies pertaining to industry agglomeration and innovation, but also present policy implications for China and the world at large in terms of the development of high-quality and green economy.

Digital economy, spatial spillover and industrial green innovation efficiency: Empirical evidence from China.

The digital economy is pushing more efficient and greener production and innovation processes, as well as quickening the mobility of production factors, which would have a critical impact on improving industrial green innovation efficiency. Based on the panel data of 30 Chinese provinces from 2005 to 2019, this study established a comprehensive index system to assess the level of provincial digital economy development, and adopted the SBM-DEA model including non-expected output to evaluate industrial green innovation efficiency, then adopted the Global Moran's I and Local Moran's I to test whether there is spatial autocorrelation, followed by the spatial Durbin model (SDM) and the mediating effect test model to investigate the direct impact, spatial spillover effect and indirect transmission mechanism of the digital economy on industrial green innovation efficiency. The results show that: both the development level of the digital economy and industrial green innovation efficiency show positive spatial autocorrelation; The digital economy not only has a significant direct role in promoting industrial green innovation efficiency but also has a spatial spillover effect; The digital economy can improve industrial green innovation efficiency by promoting manufacturing structure upgrading and stimulating enterprises' green technology innovation. The findings of this paper are helpful for policymakers to clarify the relationship between the digital economy and industrial green innovation efficiency and provide favorable policy directions for developing the digital economy to promote industrial green innovation efficiency.

Regional differences and convergence of green innovation efficiency in China.

Green innovation facilitates high-quality economic development and ecological environmental protection. Herein, the minimum distance to strong efficient frontier (MinDS) model was used to measure green innovation efficiencies (GIEs) of 30 Chinese provinces over a period of 21 years (2000-2020). Gini coefficient decomposition and kernel density estimation methods were used to analyze the regional differences of GIE. Spatial correlation was estimated to analyze spatial-spillover effects and spatial convergence of the GIE. China's GIE has shown an increasing trend with significant spatial differences in GIE among provinces. Regional differences and transvariation intensity are the primary sources of spatial differences in GIE. Regional differences in GIE have decreased, except for eastern regions. The results of spatial convergence estimation suggest spatial absolute and conditional convergence in all regions. Therefore, for the GIE improvement in China, the effects of economic level, industrial structure, and environmental regulations must be considered.

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.

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.

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.

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.

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.

Does green credit policy promote the green innovation efficiency of heavy polluting industries?-empirical evidence from China's industries.

Whether green credit policy is conducive to improving the green innovation efficiency of heavy polluting industries is of great significance for China's sustainable economic development and the construction of ecological civilization. This paper uses China's Green Credit Guidelines to conduct a quasi-natural experiment based on relevant panel data of industries from 2007 to 2018. Specifically, it employs the Super-SBM model including non-expected output to measure the green innovation efficiency of 35 industries in China, and constructs the propensity score matching difference-in-difference model to explore how green credit policy impact on the green innovation efficiency of heavy polluting industries. The results show that the coefficient of difference-in-difference ([Formula: see text]) was 0.262, which was significant at the 1% level; the coefficient of [Formula: see text] was not significant; the coefficient of [Formula: see text] was 0.490, which was significant at the 1% level; and the coefficient of [Formula: see text] was 0.173, which was significant at the 1% level, indicating that green credit policy significantly contributes to the green innovation efficiency of heavy polluting industries, though with a lag effect. Further study finds that green credit policy pushes heavy polluting industries to improve green innovation efficiency by increasing financing cost and R&D investment; meanwhile, the heterogeneity test shows that the higher the state-owned share of the industry, the greater the positive effect of green credit policy on its green innovation efficiency. Finally, in order to accelerate the implementation of green credit policy and promote the green innovation efficiency of heavy polluting industries, banks can guide heavy polluting industrial enterprises through credit to carry out green technological transformation, heavy polluting industries should raise awareness of green innovation, and government should encourage heavy polluting industrial enterprises to carry out green innovation, and guide and supervise the state-owned enterprises in particular, so that they can improve cleanliness and achieve green economic development.