The Collision of digital and green: Digital transformation and green economic efficiency

Enhancing the green economy efficiency (GEE) is crucial for building a sustainable economy. How can the rapidly advancing digital transformation contribute to this process? The paper empirically examines the direct and spatial spillover effects of digital transformation on cities' GEE in China. This study utilizes the National E-commerce Pilot City (NEPC) policy as a quasi-natural experiment of regional digital transformation and employs the staggered difference-in-differences (DID) method with heterogeneous effects. The findings reveal that (i) implementing the NEPC policy significantly increases urban GEE by 2.6%, corresponding to a 16% increase in the mean of GEE. This effect is particularly pronounced in non-resource-based cities and cities with high Internet penetration. (ii) The mechanism test shows that the pilot policy positively affects GEE by promoting green structural transformation, enhancing green innovation, and strengthening public environmental concerns. (iii) The study highlights a positive spatial spillover effect of the NEPC policy on the GEE of nonpilot cities. (iv) The adoption of the NEPC plays a pivotal role in advancing energy use and carbon emission efficiency. This paper expands the existing knowledge on the green development effects of the digital economy while offering valuable policy insights for building an "Inclusive Green Economy".

The green economic impact of a green comprehensive industry agglomeration: An example from the sports industry

The optimization of industrial structure and layout is essential for promoting the high-quality development of the regional economy. As a typical example of a green comprehensive industry, the agglomerations of the sports industry have the potential to release additional green benefits. Consequently, this paper uses the balanced panel data of 30 provinces, municipalities, and autonomous regions in China from 1998 to 2021 as samples and, based on the strategic background of China's dual-carbon target and the re-interpretation of the green economy, uses the Super-SBM model to re-measure the green economic efficiency of each region and applies the dynamic spatial Durbin model and the dynamic panel system GMM model to evaluate the direct effect, mediating mechanism, spatial spillover effect, and heterogeneity effect of sports industry agglomeration on the regional green economic efficiency. Empirical findings indicate that: (1) The improvement of green economic efficiency under China's dual-carbon target has the characteristics of dynamic accumulation, and there is a siphon effect between neighboring regions. (2) The effects of sports industry agglomeration on local green economy efficiency show an "inverted U-shape" with a positive spatial spillover effect on the green economy efficiency of neighboring regions; this conclusion is robust. (3) The green economy effect of sports industry agglomeration is more significant in the central and western regions, regions with strict environmental regulations, and regions with a higher willingness for resident participation in sports due to industrial density, compliance costs, and characteristics of sports industry development. (4) Sports industry agglomeration can promote regional green economy efficiency by escaping natural resource dependence and increasing healthy human capital; technological innovation, rationalization of industrial structure, and labor transfer serve as "inverted U-shaped" mediators between sports industry agglomeration and regional green economy efficiency. This study expands the meso- and spatial-level perspectives of the impact of the agglomeration of green industries and comprehensive industries on green development. It is of great theoretical and practical importance for promoting the construction of a regional green industrial system and the high-quality development of the green economy.

Spatiotemporal differentiation and convergence characteristics of green economic efficiency in China: from the perspective of pollution and carbon emission reduction

Accurate quantification of pollution and carbon emission reduction policies, as well as analysis of green economic efficiency (GEE), are of great significance to accelerating green economic development in China and contributing to pollution prevention and carbon peaking. Using data from 2006 to 2022, this study incorporates pollution and carbon emission reduction policies into the evaluation system, and uses a model with slacks-based measures and a directional distance function (SBM-DDF) to calculate the GEE of 30 provinces. The Dagum Gini coefficient, kernel density estimation, and spatiotemporal convergence analysis are used to analyze the spatiotemporal differentiation and convergence characteristics of GEE. The findings show that the strengths of the pollution and carbon emission reduction policies are increasing but vary greatly among the provinces. China's overall GEE has a time trend with the characteristics of "decline-fluctuation-stable." The Dagum Gini coefficient reveals the relative differences between the major regions. Both the intra-regional and inter-regional differences tend to widen over time and the latter explains most of the sources of the overall differences. Kernel density estimation shows that the absolute differences between the provinces are generally widening, whereas the absolute differences between the provinces in the central and western regions are smaller than those in the eastern region. No obvious σ convergence characteristics exist in the country overall and the three major regions, but β convergence characteristics are present in each region. The factors affecting changes in the GEE of each region are not the same. The study suggests that the China should further improve the implementation of pollution and carbon emission reduction policies, pay attention to the regional differences and convergence issues of GEE, and promote the coordinated development of green economy in different regions. This study innovatively quantifies the policies related to pollution and carbon emission reduction, providing empirical evidence for understanding the performance of pollution and carbon emission reduction policies in various regions. Furthermore, this study incorporates policies as inputs into the GEE evaluation system, reveals the spatiotemporal differentiation of GEE, thereby providing reference for green economic transformation and sustainable development.

Impact of carbon lock-in on green economic efficiency: Evidence from Chinese provincial data

Carbon lock-in is a major obstacle to transforming carbon-based energy systems toward carbon peaking and neutralization, affecting the green economy. However, its impacts and paths on green development are unclear, and it is difficult to represent carbon lock-in using a single indicator. This study measures five types of carbon lock-ins and their comprehensive effect using the entropy index of 22 indirect indicators in 31 Chinese provinces during 1995-2021. Moreover, green economic efficiencies are measured using a fuzzy slacks-based model considering undesirable outputs. The panel Tobit models are used to test the impacts of carbon lock-ins on green economic efficiencies and their decompositions. Our results show that provincial carbon lock-ins in China range from 0.20 to 0.80, with notable type and regional differences. Overall carbon lock-in levels are similar, but the severity of different carbon lock-in types varies, with social behavior being the most serious. However, the overall trend of carbon lock-ins is declining. Low pure green economic efficiencies, rather than scale efficiencies, contribute to China's worrisome green economic efficiencies, but they are decreasing and accompanied by regional gaps. Carbon lock-in hinders green development, but a specific analysis is needed for different carbon lock-in types and development phases. It is biased to assume that all carbon lock-ins hinder sustainable development, as some are even necessary. The impacts of carbon lock-in on green economic efficiency depend more on its effect on technology than on scale change. Implementing various measures to unlock carbon and maintaining reasonable levels of carbon lock-in can promote high-quality development. This paper may promote the development of new unlocking CLI measures and sustainable development policies.

Can outward foreign direct investment improve China's green economic efficiency?

Under the constraints of energy and environment, improving green economic efficiency (GEE) has become the key path to promote the sustainable economic development. Among the driving factors of GEE, the role of outward foreign direct investment (OFDI) is worth exploring. In this paper, we adopt the inter-provincial panel data of China from 2011 to 2019 and System Generalized Method of Moments (SYS-GMM) to explore the influence of OFDI on GEE. We find that OFDI significantly improves China's GEE, and reverse technology spillover through direct investment in developed countries is an important way for OFDI to promote GEE. Regional heterogeneity test shows that OFDI significantly promotes GEE in eastern China; however, the promotion effect is not significant in midwestern China. Besides, the promoting effect of OFDI on GEE has been further improved after 2016. We further adopt panel threshold model and find that when the financial development (FD) and human capital (HUM) exceeds 2.0954 and 0.0290, respectively, the promoting effects of OFDI on GEE are greatly enhanced. We suppose that the above conclusions can provide guidance for policymakers to optimize OFDI and improve GEE.

Research on the spatiotemporal evolution and influencing factors of green economic efficiency in the Yangtze River Economic Belt

In the Yangtze River Economic Belt (YREB), the green economy is an essential to sustainable economic development. In this study, we calculated a comprehensive index of environmental pollution based on the global entropy weight method and used the super slacks-based measure (SBM) model to estimate the green economic efficiency (GEE) of provinces and cities in the YREB from 2005 to 2018. Subsequently, we explored temporal and spatial evolution characteristics combined with the Theil and Moran indexes, and adopted the spatial Dubin model to analyze its influencing factors. We divided the YREB into three watersheds to facilitate the analysis. The results show that the GEE in the YREB initially decreased and then increased, and the difference among the three major watersheds was higher than that within the watershed. We found a positive spatial autocorrelation in the development level of the green economy in the YREB. While industrial structure had a negative effect, economic development, scientific and technological level, and environmental regulation all had a positive effect on GEE. Finally, we offered policy recommendations to improve the level of green development in the YREB.

Heterogeneous environmental regulations and green economic efficiency in China: the mediating role of industrial structure

Industrial upgrading is the key to promoting green economic efficiency. Coordination between environmental regulations and industrial structure can lead to sustainable economic growth. However, insufficient attention has been paid to the mechanism by which environmental regulation (ER) promote green economic efficiency (GEE) under the mediating role of industrial structure optimization. Using robust and comprehensive measures of green economic efficiency, we assess how various environmental regulations affect green economic efficiency as well as the intermediate effect of industrial structure of a certain province with provincial panel data during the period 2003-2017. The results of dynamic panel two-step system generalized method of moments (GMM) confirm the heterogeneous effects of the three types of ER, namely control-and-command regulation, market-based regulation, and voluntary regulation on GEE in China. The spatial analysis demonstrates that control-and-command and voluntary regulations significantly accelerate GEE in inland provinces, while they have insignificant effect in coastal provinces. Based on the mediating analysis, we find that market-based regulation is conducive to GEE through both advanced and rationalized industrial structure, whereas control-and-command regulation improves GEE through advanced industrial structure only. The voluntary-based regulation on one hand stimulates GEE through advanced industrial structure, but on other hand impedes it through rationalized industrial structure. The results could provide critical insights and a theoretical basis for policy makers in reasonable optimization of industrial structure and guaranteeing green economic efficiency.

Impact of green credit on green economy efficiency in China

With urgent desire of promoting sustainable development, this study explored the impact and affecting mechanism of green credit on green economy efficiency through spatial lag model. Panel data from 2008 to 2019 of 30 selected provinces and cities in China were collected to portray the green economy efficiency with application of Sup-SBM DEA including unexpected outputs. The results show that (1) green credit had significant promoting effect on green economy efficiency, while time-space inconsistency existed. (2) During the promoting process, level of marketization and environmental regulation generated threshold effects, which were positively correlated with regional green economy efficiency. (3) Industrial structure upgrading and environmental investment played positive intermediary roles between green credit and green economy efficiency. (4) Considering endogenous issues, the positive effect still existed though the promotion effect was weakened. This study further enriches the literature and provides deep insights into the policy design when formulating the green economy path.

Technology imports, independent innovation, and China's green economic efficiency: an analysis based on spatial and mediating effect

Environmental quality and economic growth are important factors that need to be balanced for sustainable development, especially in developing countries where technology is relatively backward. Many studies have shown that technology imports may be beneficial to economic growth, but once the resources and environment are taken into consideration, the role of technology imports becomes blurred. Based on provincial panel data of China from 2004 to 2019, this paper investigates the influence mechanism of domestic and foreign technology imports on the green economic efficiency (GEE) of 30 provinces in China. There are two main conclusions: First, GEE is spatially related and the impact of technology imports on GEE has a significant spillover effect. Besides, the relationship between technology imports and GEE is non-linear, both in terms of direct and indirect effects. Second, independent innovation plays an important role in the influence mechanism of technology imports on GEE. As the level of independent innovation increased, the impact of technology imports on GEE turns from negative to positive, and it is strengthened through the channel of "transfer-absorption-diffusion-re-innovation." In this regard, some measures should be taken to enhance the role of technology imports in improving GEE.

Assessment of green economic efficiency in China using analytical hierarchical process (AHP)

Global health, as well as worldwide development regimes, was seriously threatened by the COVID-19 pandemic and Delta variant outbreaks. In addition to pledging to adapt to and mitigate climate change, experts, economists, and policymakers expressed their determination to do so. Green growth and sustainable development have become the focus of policymakers and governments. The progress toward green economic efficiency (GEE), which will benefit the economy, society, and environment, continues. In terms of green growth and development, implementing environmental regulations and policies has been one of the most challenging aspects of the process. China, the world's second-largest economy, has begun its journey to GEE. Nonetheless, the green economy faces many challenges. The objective of the study is to use AHP analysis to analyze environmental regulation and GEE in China. Accordingly, the study identified three alternative approaches to achieve GEE by analyzing four criteria and ten sub-criteria in the context of environmental regulations in China. The analytical hierarchy process (AHP) has been used to rank criteria, sub-criteria, and alternative approaches. According to the model, China's best path to GEE is through resource efficiency and green purchasing strategies. This article offers an insightful assessment of sustainable development in the Chinese economy.

Fuzzy-based multi-criteria decision analysis of environmental regulation and green economic efficiency in a post-COVID-19 scenario: the case of China

The COVID-19 pandemic outbreak posed serious threats not only to global health but also to the worldwide development regime. The experts, economists, policymakers, and the governments expressed their pledges and determinations to adapt and mitigate climate change. Policymakers and governments have started adopting green growth and development strategies. The progress moves further to achieve green economic efficiency (GEE) to achieve economic, social, and environmental development. One of the major challenges has been promulgating and strictly implementing environmental regulations and policies vis-à-vis green growth and development. China, having the second largest economy, has started its voyage to achieve GEE. However, there are multiple challenges on the way to the green economy. The objective of the present stud is to analyze environmental regulation and GEE in China using fuzzy-based multi-criteria decision analysis. To serve this purpose, the study identifies 5 alternative strategies to achieve GEE while considering 10 criteria and 48 sub-criteria in the context of environmental regulations in China. The Fuzzy Analytical Hierarchy Process (AHP) has been employed to rank criteria and sub-criteria to the goal. The Fuzzy VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method has been used to rank the alternative strategies of GEE. The proposed model unveiled resource efficiency and green purchasing as the best strategy to achieve GEE in the Chinese economy followed by local production. The study provides a comprehensive insight into the green development process to achieve GEE in the Chinese economy in the post-COVID-19 world.

Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model

Due to the pressure of global ecological degradation, the coordination of economic increase and ecological protection has drawn attention from policymakers and practitioners. Green economic efficiency (GEE) is a comprehensive index to measure economic, social, and environmental development. As China is the second-biggest economy in the world with high-energy consumption, it is necessary to investigate its green economy efficiency. In this paper, we innovatively adopt a super-SBM (slacks-based measure) model with undesirable outputs to calculate the GEE in 30 provinces of China between 2008 and 2017, and then comprehensively apply a spatial Dubin model (SDM) to investigated its influencing factors. The results showed that the overall GEE in China during the study period was at a low level with significant regional differences. The inter-regional GEE generally showed a gradient decreasing pattern of "East-Middle-West", which demonstrates a gradual decline from the East to the West in China. The trend of the national GEE initially dropped and then gradually stabilized over the study period. Foreign trade dependence and direct investment had significant positive effects on the GEE, while the secondary industry and urbanization level had a significant negative effect.

China's energy consumption and green economy efficiency: an empirical research based on the threshold effect

Using the panel data of China's provinces from 2005 to 2016, the paper adopts the ultra-efficiency model to measure the green economy efficiency of each province. Then, the paper used the panel threshold model to study the impact of science and technological input on the green economy efficiency with the energy consumption intensity as a threshold variable. The results show that the green economy efficiency in China's provinces is low and is in a downward trend. When the energy consumption intensity is the threshold variable, the single-threshold effect is significant. When the energy consumption intensity is lower than the threshold value, the impact of science and technological input on green economic efficiency is not significant, and the energy consumption intensity has a significant impact on green economic efficiency with science and technological input and energy consumption intensity as the core explanatory variables and with energy consumption intensity as the threshold variable. When the energy consumption intensity crosses the threshold value, the impact of science and technological input and energy consumption intensity on green economic efficiency becomes significant. According to the above empirical results, this paper proposes corresponding countermeasures and suggestions.

How does China's Western Development Strategy affect regional green economic efficiency?

Narrowing regional economic gaps and constructing an environmentally friendly society are two major objectives of China's current economic policies. Promoting green development in resource-based regions is a global issue. Focusing on China's Western Development Strategy (WDS), this study first calculates the provincial green economic efficiency (GEE) in China. The synthetic control method is adopted to evaluate the net effect of WDS on regional GEE. The transmission mechanisms are then investigated in perspective of the interregional flow of innovation factors. The results show that: (1) The GEE in coastal areas of China is generally higher than that of western China; (2) The WDS can improve the overall regional GEE but the effect decays over time and through the diversity of the regions; (3) WDS can improve regional GEE by introducing innovation factors into the western regions, further improving the regional industrial structure, urbanization, and labor quality; (4) The optimal scale of innovation factors flowing into the WDS regions is calculated. The transmission mechanisms will have a positive effect on the GEE of the western regions simultaneously only if the inflow scale of the innovation factors varies on the interval (0.347, 0.618). The paper concludes with targeted policies to promote regional green development.