Regional differences and convergence of green innovation efficiency in China

Regional coordinated development of low-carbon sports industry in China

This study develops a systematic modeling framework, comprising a prediction model, a super-SBM model, and a spatial autocorrelation analysis model, to explore the spatial-temporal evolution tendencies of development efficiency within China's 30 regions in the low-carbon sports industry from 2006 to 2025. This framework aims to provide theoretical insights for the formulation of more targeted policies. Based on the empirical findings, the main conclusions of this study are as follows: (1) The optimal buffer operator grey prediction model demonstrates the highest accuracy among the prediction models examined. (2) The development efficiency curves of the 30 regions exhibit a significant increasing trend from 2006 to 2021, with values generally peaking between 0.4 and 0.6. (3) Notably, the disparity in development efficiency between developed and less developed regions is expected to progressively widen. (4) The development efficiency of the low-carbon sports industry across the 30 regions typically displays high-high clustering and low-low clustering during China's four five-year plan periods. This underscores the importance and urgency of promoting regional coordinated development within the low-carbon sports industry.

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.

Impact of Environmental Protection Tax on carbon intensity in China

In the context of increasingly severe global climate change, finding effective carbon emission reduction strategies has become key to mitigating climate change. Environmental Protection Tax (EPT), as a widely recognized method, effectively promotes climate change mitigation by encouraging emission reduction behaviors and promoting the application of clean technologies. Based on data from 282 cities in China, this paper takes the official implementation of the EPT in 2018 as the policy impact and the cities with increased tax rates for air taxable pollutants as the treatment group and uses DID model to systematically demonstrate the relationship between the implementation of the EPT and carbon intensity (CI) and further explores the possible pollutant emissions and green innovation mediating effects. The findings show that (1) the implementation of EPT can effectively reduce CI by about 4.75%, and this conclusion still holds after considering the robustness of variable selection bias, elimination of other normal effects, policy setting time bias, and self-selection bias. (2) The implementation of EPT can reduce CI by reducing pollutant emissions and improving the level of green innovation. (3) There is obvious regional heterogeneity in the carbon reduction effect of EPT, and the implementation of EPT has a more significant effect on CI in medium-tax areas, low environmental concern areas, general cities, and eastern regions. This paper not only provides a new analytical perspective for systematically understanding the carbon emission reduction effect of EPT but also provides policy insights for promoting regional green transformation and advancing carbon peak carbon neutralization.

The impact of foreign direct investment on green innovation efficiency: Evidence from Chinese provinces

Improving green innovation efficiency (GIE) is the key to achieve high-quality economic development in China, and the introduction of foreign direct investment (FDI) has become an important path choice to promote the GIE. Based on the data of 30 provinces in China, this paper explores the linear and nonlinear effects of FDI on GIE from both quantity and quality perspectives, and further analyzes the mediating role of environmental regulation level. The results show that: (1) From 2011 to 2020, the GIE of all provinces in China generally shows an upward trend. (2) The quantity and quality of FDI have a significant positive impact on the improvement of GIE in China's provinces, and this impact has regional heterogeneity. (3) The quantity and quality of FDI can promote the improvement of GIE in China through the level of environmental regulation (ER). (4) With the level of knowledge accumulation and GIE as the threshold variables, the quantity and quality of FDI have a single threshold effect on the GIE of China's provinces. The conclusions of this study provide some policy implications for local governments to make full use of FDI to perform green innovation activities.

Research on the interplay between green finance and manufacturing sustainability outcomes: insights for low-carbon economy in the post-COVID-19 era

In the quest to strengthen resilient and sustainable recovery in the post-COVID-19 era, there is a huge requirement for manufacturing firms to adopt green finance which is dominated by green bond issuance. Nevertheless, published studies that provide insights on factors that influence the issuance of green bonds within manufacturing firms in the post-COVID-19 era and the impact on sustainable outcomes are currently non-existent. Therefore, this study analyzed the interrelationships that exist between the influencing factors of green bond issuance within manufacturing firms using decision-making trial and evaluation laboratory (DEMATEL) and data from Nigerian manufacturing firms. Then, a structural model of their importance levels was illustrated using interpretive structural modeling (ISM) while their impact on manufacturing sustainability outcomes was estimated with the aid of evaluation based on distance from average solution (EDAS). The study results highlight the key influencing factors of green bond issuance as environmental competencies, policy framing, low corruption, public awareness, and government support thereby signifying the criticality of strong institutions in facilitating green finance in the post-pandemic era. Besides, the study results demonstrate that green finance can significantly strengthen manufacturing sustainability in the post-COVID-19 era via green bonds by enhancing sustainable waste management, technological growth, and quality improvement as well as reducing carbon emissions. The study findings can provide a reference to decision-makers in manufacturing enterprises to predict scenarios and enact policies that facilitate the success of green finance in the post-COVID-19 era to further develop a low-carbon economy and increase competitive edge.

Regional differences, dynamic evolution, and spatial spillover effects of carbon emission intensity in urban agglomerations

Taking three major urban agglomerations in China as examples, this paper uses the Dagum Gini coefficient and its decomposition method, a Kernel density estimation method, and Markov chain and spatial Markov chain to study the regional differences, dynamic evolution characteristics, and spatial spillover effects of carbon emission intensity (CEI) of urban agglomerations, and accordingly, it proposes differentiated emission reduction and carbon reduction policies. The following results were obtained: (1) The overall CEI of the three major urban agglomerations and each individual urban agglomeration were found to have declined significantly over time, with an overall spatial pattern of "high in the north and low in the south," with inter-group differences being the main source of the overall differences. (2) The imbalance in CEI between cities was more obvious within the Beijing-Tianjin-Hebei (BTH) urban agglomeration, while the synergistic emission reduction effect of the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations increased over the study period. (3) The probability of a city maintaining a stable level of CEI was much higher than the probability of a state shift, and there was a spatial spillover effect of carbon emissions between neighboring cities. This study can provide theoretical support for the global response to greenhouse gas emissions, promoting green development and carbon reduction in various countries and urban agglomerations and providing a quantitative basis for the formulation of relevant policies.

Spatial differences, dynamic evolution, and convergence of carbon productivity in China

China is currently developing a green economy, and improving carbon productivity (CAP) is an important part of this process. The current study applied a minimum distance to strong efficient frontier (MinDS) model to measure China's CAP. The Dagum Gini coefficient and kernel density estimation methods were further used to reveal its spatial differences and dynamic evolution, while the coefficient of variation and spatial convergence models were employed to examine its convergence characteristics. The results showed significant spatial differences in China's CAP, with primarily high and low spatial distribution characteristics in the east and west, respectively. Between-regional differences were the main sources of the overall differences. Moreover, the differences between overall, eastern, central, and western regions of China all exhibited a widening trend. Although none showed σ convergence, all had significant absolute β spatial convergence and conditional β spatial convergence characteristics. Collectively, the findings of this study objectively reflect the real level, distribution characteristics, and spatial convergence characteristics of CAP in China as a whole and in each region, while also providing a reference basis for achieving peak carbon neutrality.