How to achieve green development? A study on spatiotemporal differentiation and influence factors of green development efficiency in China.

For a long time, China 's extensive economic development model has produced a large amount of emissions, which has brought indelible damage to the environment. Green development is of vital importance for China to achieve high-quality development, and it is the core of alleviating environmental problems and promoting sustainable development. How to achieve China 's green development requires us to evaluate the level of green development in China 's provinces and analyze the reasons. In this study, an evaluation index system including undesired output of green development efficiency is constructed, and then the Supe-SBM model is used to assess the green development efficiency of 30 Chinese provinces. This paper also discusses the spatial and temporal differences as well as the factors affecting green development efficiency of green development efficiency among provinces. The findings demonstrate: (1) The green development efficiency in the eastern region is the highest, followed by the western region, while the central region has the lowest, but they all show a downward trend. (2) The spatial characteristics of green development efficiency are remarkable, according to the Global Moran's I index. However, the results of local spatial agglomeration demonstrate "small agglomeration and large dispersion," with the majority of provinces exhibiting L-L agglomeration. (3) Technological Progress, Opening Up, Urbanization Level are positively correlated with the green development efficiency. Industrial Structure, Financial Development, Energy Structure and green development efficiency are significantly negatively correlated, while Environmental Regulation shows no significant impact.

Spatiotemporal evolution and influencing factors of water resource green efficiency in the cities of the Yangtze River Economic Belt.

Effectively utilizing water resources, which is a fundamental natural resource and a vital economic resource, directly impacts how a country's economy develops. In this study, the Super-SBM model is used to calculate the city water resource green efficiency (CWRGE) of the Yangtze River Economic Belt (YREB), 108 cities that are prefecture level or higher, from 2006 to 2021. And its temporal and spatial evolution as well as its affecting variables are examined. The results indicate that, as a whole, the YREB's CWRGE has not yet achieved an effective level. The CWRGE in the YREB generally exhibits a trend of "first decreasing and then increasing, then decreasing and then increasing" and shows a "W"-shaped evolution law, and the overall trend is upward. There are just seven cities with effective data envelopment analysis (DEA), namely Changzhou, Hangzhou, Shanghai, Xuzhou, Changde, Changsha, and Yuxi. During the reporting period, the CWRGE of cities of various scales showed significant gaps: mega cities > big cities > small and medium-sized cities. From a regional perspective, the highest rate of CWRGE was found downstream of the YREB cities, then upstream, and the middle was the lowest. Spatial correlation findings demonstrated that both the agglomeration range and the outlier range were distributed, and there were mainly two positive aggregations of space forms ("high-high (H-H) type" and "low-low (L-L) type"), and the spatial distribution changed. The results of the spatiotemporal evolution demonstrate that there are more and more cities with high efficiency, as well as cities with low efficiency. From the results of the Tobit regression model, the CWRGE in the YREB are significantly improved by the economical development level, industrial scale, and water usage structure. While foreign direct investment and environmental regulation have considerable detrimental impacts, the impact of scientific and technological investment is not significant.

What were the spatiotemporal evolution characteristics and the influencing factors of urban land green use efficiency? A case study of the Yangtze River Economic Belt.

China's rapid urbanization has had a tremendous impact on the country's limited land resources, and one of the major issues of green development is how to utilize the limited land resources to maximize social, economic, and environmental advantages. From 2005 to 2019, the super epsilon-based measure model (EBM) was employed to assess the green land use efficiency of 108 prefecture-level and above cities in the Yangtze River Economic Belt (YREB), as well as investigate its spatial and temporal evolution and influential factors. The findings demonstrate that overall, urban land green use efficiency (ULGUE) in the YREB has been ineffective; in terms of city scale, megacities have the highest efficiency, followed by large cities and small and medium-sized cities; and at the regional level, downstream efficiency does have the greatest average value, followed by upstream efficiency and middle efficiency. The results of temporal and spatial evolution reveal that the number of cities with a high ULGUE is increasing in general but that their spatial characteristics are relatively dispersed. Population density, environmental regulation, industrial structure, technology input, and the intensity of urban land investment all have major beneficial effects on ULGUE, whereas urban economic development level and urban land use scale clearly have inhibitory effects. In light of the previous conclusions, some recommendations are made to continuously improve ULGUE.

Spatiotemporal differentiation of carbon emission efficiency and influencing factors: From the perspective of 136 countries.

The severity of the global climate issue is rising, primarily as a result of excessive carbon dioxide emissions. Climate change is a global problem. How to reduce carbon dioxide emissions while promoting social and economic development is a problem that all countries need to face. This study examines global carbon emission efficiency in order to make recommendations for comprehensively improving global low-carbon development level. We extend the research scale of carbon emission efficiency from countries, regions, economic belts and sectors to the world, which can show the differences of countries and has theoretical guiding significance for global low-carbon development. This study calculates the carbon emission efficiency for 136 countries from 2000 to 2019 using the Super-EBM model. The discussion that follows examines the temporal and spatial characteristics of carbon emissions efficiency in 136 countries from the perspective of countries, developed and developing countries, and regions. Finally, the Tobit model is used to comprehensively analyze the factors that affect carbon emission efficiency. The results show that: (1) There are great differences in carbon emission efficiency among countries and regions. Only a few countries reach the production frontier, mainly in Europe, which are Switzerland, Luxembourg, Iraq, Norway, Denmark and the United Kingdom. The carbon emission efficiency of most countries is not ideal, being mainly concentrated in Asia and Africa, and has not achieved significant improvement over time. Asia has the lowest carbon emission efficiency. Mongolia, Ukraine, Iran, Angola, Belarus and Uzbekistan are the key governance areas for global energy conservation and carbon emissions reduction. (2) Developed countries have the much higher average carbon emission efficiency than developing countries. Combined with the industrial development stages of developed and developing countries, this is in line with the environmental Kuznets curve (EKC) hypothesis. The average carbon emission efficiency gap between developing and developed countries shows a trend of "first narrowing and then widening", which demonstrates that developing countries' reliance on energy input to boost their economies will improve carbon emission efficiency, but only temporarily. (3) Urbanization level, foreign trade and proportion of renewable energy effectively improve the carbon emission efficiency, while industrial structure and proportion of electricity users have an inhibitory effect on the carbon emission efficiency. Global low-carbon development should be hastened by strengthening international cooperation, optimizing industrial structure, promoting urbanization and foreign trade, and adjusting the energy structure.

Green Total-Factor Energy Efficiency of Construction Industry and Its Driving Factors: Spatial-Temporal Heterogeneity of Yangtze River Economic Belt in China.

With the proposal of the "carbon peak, carbon neutral" goal, energy efficiency has become one of the key means to achieve energy conservation and emission reduction at this stage. The construction industry, as a cornerstone of China's economy, is characterized by serious overcapacity, energy waste, and pollution. As a result, academic research on its energy efficiency is gaining traction. This paper employed the Super-EBM model considering undesirable output to evaluate the green total-factor energy efficiency of the construction industry (CIGTFEE) in the Yangtze River Economic Belt (YREB) from 2003 to 2018. The spatial-temporal evolution characteristics and spatial heterogeneity of CIGTFEE were analyzed in detail through geospatial analysis. Finally, the driving factors of CIGTFEE were analyzed through a spatial econometric model. The results indicated that, during the sample research period, the CIGTFEE showed a holistic growth trend with volatility. By region, the downstream CIGTFEE grew sharply until 2006 and then remained fairly stable, while the midstream conformed to the "M" trend and the upstream region showed an inverted u-shaped trend; From the perspective of spatial differentiation, the CIGTFEE in YREB shows a significant spatial agglomeration situation, while the spatial agglomeration degree weakened. It existed a ladder-shaped change trend, with the regional average CIGTFEE from high to low levels as follows: Downstream, Midstream, and Upstream, and showed an obvious polarization in the upstream and downstream. From the analysis of the driving factors, CIGTFEE is significantly promoted by economic growth, energy structure, and human capital and suppressed by urbanization level, yet the impact of technological progress and the level of technology and equipment is not significant. In summary, province-specific policies based on spatial and temporal heterogeneity were proposed to improve the CIGTFEE of YREB.

China's industrial green total-factor energy efficiency and its influencing factors: a spatial econometric analysis.

The sustainable development of China's economy is bottlenecked by resource shortage and environmental pollution. As the leading resource consumer and pollutant source, the industrial sector needs to improve its energy efficiency. This paper establishes a super epsilon-based measure (Super-EBM) model with bad outputs like environmental cost and evaluates the industrial green total-factor energy efficiencies (IGTFEEs) of 30 provinces in China during 2000-2017. Unlike previous research, the main contribution of this paper is to choose four environmental pollutants as bad outputs (industrial carbon dioxide, industrial sulfur dioxide, industrial chemical oxygen demand, industrial solid waste). By contrast, the previous studies mostly only take one environmental pollutant as bad output, i.e., the bad outputs are not fully measured. Then, the spatiotemporal dynamics and spatial correlations of the IGTFEEs were analyzed, and the influencing factors of IGTFEE were examined empirically with a spatial econometric model. Finally, this paper adopts generalized method of moments (GMM) to solve the endogenous problem, trying to assure the robustness of estimation results. The results show significant provincial differences in IGTFEE. Most eastern coastal provinces achieved satisfactory IGTFEEs, while most inland provinces had undesirable IGTFEEs. Eastern region achieved the highest IGTFEE, followed by central region; western region had the lowest IGTFEE. The IGTFEE improved over time in some provinces while worsened greatly in some provinces. The IGTFEE in most provinces need to be further improved. Global Moran's I values indicate that the provincial IGTFEEs were clustered in space, rather than randomly distributed. Local indication of spatial association (LISA) map reflects significant local spatial clustering of provincial IGTFEEs. In addition, IGTFEE is significantly promoted by industrial structure, technological innovation, human capital, opening-up, and energy structure yet significantly suppressed by ownership structure and environmental regulation. Considering the endogeneity, GMM results show that the estimation results of the model were robust. Specific policy recommendations include vigorously developing high-tech industries, deepening state-owned enterprises reform, diverting more funds to research and development, cultivating versatile talents, introducing environmentally-friendly foreign capital, accelerating the implementation of clean energy development strategy, and widening the fund channels of pollution control investment.

Spatiotemporal changes in efficiency and influencing factors of China's industrial carbon emissions.

The industrial sector is the backbone of China's national economy. The industrial carbon emission efficiency (ICEE) of China is directly related to the achievement of carbon emission reduction targets. This paper reports on the use of the minimum distance (min-SBM) method to determine the ICEE of 30 provinces in China during 1998-2015, as well as the use of a spatial econometric method to investigate the convergence and influencing factors of the regional ICEE. The results indicate significant regional differences in the ICEE. The provinces with higher average values of ICEE are located in the eastern coastal areas, whereas the provinces with lower average values of ICEE are located in the central and western inland regions. The results of the spatial autocorrelation index reveal that China's inter-provincial ICEE exhibits significant spatial autocorrelation characteristics, and its spatial distribution demonstrates a certain regularity. The local indicators of spatial association diagram further illustrate that most provinces in China have high and low agglomeration values. With the introduction of the spatial effect, the absolute and conditional convergence rates increase. In addition to the non-significant industrial structure effect, the level of economic development, foreign direct investment, technological progress, and government intervention demonstrate a positive impact on the ICEE convergence, whereas the energy consumption structure has a negative impact. This work investigates the cause for the regional gap in China's current ICEE. Suggestions for improving the efficiency of China's industrial carbon emissions and narrowing the regional gap are provided, which serve as a reference value for China to achieve the peak of carbon dioxide emissions before 2030.