The Relationship between Environmental Regulations and Green Economic Efficiency: A Study Based on the Provinces in China

The Relationship between Environmental Regulation, Green-Technology Innovation and Green Total-Factor Productivity-Evidence from 279 Cities in China

This paper employs the SBM-DDF method to measure the index of green total-factor productivity (GTFP), based on the panel data of 279 prefecture-level cities in China from 2007 to 2019, and constructs a spatial Durbin model (SDM) and a threshold effect to empirically test the effects of dual environmental-regulations and green technological innovation on GTFP. The results are as follows: (1) the SDM supports a nonlinear contribution of dual environmental-regulations spillover to GTFP. The relationship between formal environmental-regulation and GTFP is an inverted U-shape, while a U-shaped nonlinear relationship is found between informal environmental regulation and GTFP. (2) Green technology innovation has a significant negative moderating effect on the process of dual environmental-regulations affecting GTFP in local regions, but a positive moderating effect on informal environmental regulation in neighboring regions. (3) There is a significant green technology innovation threshold effect of dual environmental-regulations affecting GTFP. Specifically, the promotion effect of dual environmental-regulations on GFFP gradually increases as the level of green technology innovation increases.

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.

Can Green Economy and Ecological Welfare Achieve Synergistic Development? The Perspective of the "Two Mountains" Theory

China’s high-speed economic growth and severe environmental problems have resulted in a poor Environmental Performance Index and have affected China’s sustainable development and ecological welfare improvement. Therefore, exploring whether there is a certain relationship between the two and their influencing factors is an important way and a breakthrough to solve the problems regarding green economic progress and ecological welfare enhancement. To this end, by using the undesirable slack-based measure (SBM) model, this paper measures the ecological welfare performance and the green economic efficiency of 11 cities in Zhejiang Province, China, from 2000 to 2019. Through the methods of spatiotemporal evolution, coefficient of variation, coupling coordination degree, and the Tobit model, we found that: (1) The development trend of urban green economic efficiency and ecological welfare performance were both in a “U” shape that first fell and then rose; (2) The coupling coordination degree between green economic efficiency and ecological welfare performance showed a wave-like upward trend as a whole and most cities have entered a more advanced coupling coordination stage during the study period. The coefficient of variation revealed a downward trend; (3) The urbanization level, industrial structure, and government investment can promote the regional coordinated development, while the industrialization degree and the opening level had a negative impact on it; (4) The “Two Mountains” theory was beneficial to the improvement of regional urban green economic efficiency and ecological welfare performance and their coordinated development both in theory and practice. Finally, according to the findings, we offer relevant suggestions on making good use of the country’s preferential policies and informatization means from the perspective of the regional coordinated development.

A Resampling Slack-Based Energy Efficiency Analysis: Application in the G20 Economies

In order to have a sustainable economic and social development, it is important to balance economic growth and ecological environmental damage. In this article, we used the resampling model under the triangular distribution to evaluate energy efficiency, because the input/output value may have measurement errors, time lag factors, arbitrariness, and other problems, causing their own DMU to change. After these factors were taken into consideration, the resampled input/output was estimated because a super-SBM efficiency value was placed in the confidence interval. From the past-present data, for the estimated data change, the time weight was provided according to the Lucas series, and the super-SBM was time-weighted. We applied this model to a dataset of G20 economies from 2010 to 2014. To the best of our knowledge, very few studies have applied the DEA method with resampling to analyze energy efficiency. Thus, our study contributes to the methodologies for energy efficiency evaluation. We found that the overall average energy efficiency is 0.653, with substantial differences between developed economies and developing economies. The most important finding is that neither overestimation nor underestimation occurred when sampling was repeated one thousand times using 95% and 80% confidence intervals, confirming the robustness of the super-SBM model. The less energy-efficient economies should adjust their energy policies appropriately and develop new clean energy technologies in the future.