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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.

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.

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.

This paper uses the super-efficiency data envelopment analysis (DEA) model to measure the higher education resource allocation efficiency (HERAE) of 30 provinces from China 2005-2018, and analyzes the regional difference and dynamic evolution law of the HERAE with Theil index and kernel density estimation, respectively. The results show that: The HERAEs of most provinces are DEA effective, but the HERAEs of a few provinces are DEA ineffective, calling for further improvement to the allocation of higher education resources in these places. There was a certain difference in the HERAE trend between eastern, central, and western regions. In the sample period, eastern region had higher HERAE than central and western regions. With the elapse of time, the internal gap of HERAE decreased to different degrees in the three regions. Eastern region had the largest gap, followed in turn by central and western regions. In addition, China’s HERAEs were polarized in time. With the passage of time, the polarization of regional HERAEs slowly weakened.

Reasonable allocation of carbon emission rights aids in the realization of the goal of carbon emission reduction. The purpose of this paper is to examine how carbon emission rights in the power sector in the Yangtze River Economic Belt (the YREB) are distributed. The YREB spans China’s eastern, central, and western areas. The levels of development and resource endowment differ significantly across regions, resulting in great heterogeneity in the YREB provinces’ carbon emission rights distribution in the power sector. The ZSG–DEA model is used in this paper to re-adjust the power sector’s carbon emission quotas in each province to achieve optimal efficiency under the country’s overall carbon emission reduction target. The results show that: (1) In most provinces, the power sector’s initial distribution efficiency is inefficient. Only Zhejiang and Yunnan have reached the production frontier, with Jiangxi and Chongqing having the lowest distribution efficiency. In the future, we should concentrate our efforts on them for conserving energy and lowering emissions; (2) The initial distribution efficiency of the power sector in the YREB’s upstream, midstream, and downstream regions is considerably different. Most upstream and downstream provinces have higher carbon emission quotas, while most midstream provinces have less, implying that the power sector in the midstream provinces faces greater emission reduction challenges; (3) The carbon emission quotas of the power industry varies greatly between provinces and shows different spatial features over time. In the early stage (2021–2027), the carbon emission quota varies substantially, while for the later stage (2027–2030), it is rather balanced. Zhejiang, Jiangsu, Sichuan, and Yunnan are more likely to turn into sellers in the market for carbon emission trading with larger carbon emission quotas. While Jiangxi and Chongqing are more likely to turn into buyers in the market for carbon emission trading with fewer carbon emission quotas. Other provinces’ carbon emission quotas are more evenly distributed. To successfully achieve China’s emission reduction target by 2030, the YREB should promote regional collaboration, optimize industrial structure, accelerate technical innovation, establish emission reduction regulations, and provide financial support based on local conditions.

Facilitating the coordinated and effective progress of green finance (GF) and ecological efficiency (EE) stands as a potent approach to support our nation in attaining sustainable development goals. This paper Utilized panel data encompassing 84 cities in Western China spanning from 2007 to 2021, this paper empirically analyzes the spatio-temporal characteristics and driving factors of the coupling coordination degree between green finance and ecological efficiency (CCD-GFEE) in western cities. The findings indicate that: (1) The level of GF demonstrates a rising trajectory, with significant regional disparities. Besides, the high level area progressively moves from the northwest to the southwest. (2) On the whole, urban EE demonstrates a relatively elevated level, but it still fails to reach DEA effectiveness. Compared to the northwest region, the southwest region has poorer efficiency. (3) The CCD-GFEE in western China showing a slight growth trend. The coupling coordination degree (CCD) in Northwest China is higher than that in Southwest China, and cities with higher CCD are primarily found in Inner Mongolia, Sichuan Province and Shaanxi Province. Within them, the CCD of Chengdu is the highest, Chongqing has achieved the largest stage leap. (4) The global Moran’s I consistently remained positive and exhibited a tendency of initially rising and subsequently falling, indicating that the spatial aggregation effect of CCD-GFEE first increased and then decreased. (5) The CCD-GFEE driving factors are examined using the spatial econometric model, and it has been observed that the impact of population size and government intervention on CCD-GFEE is negative, while the impact of industrial structure, technological progress and economic level on the coupling and coordination of CCD-GFEE is positive. Finally, the paper presents certain policy enlightenments to guide the coordinated development of GF and EE from the aspects of GF system formulation, economic construction and technological progress.

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.

In the context of the global Sustainable Development Goals, the study of tourism eco-efficiency (TE) becomes particularly important for the balance between environmental protection and economic development in tourist destinations. This paper improves the measurement method of tourism carbon emissions, uses the Super-SBM model of undesired output to measure China’s TE, and then explores the impact of new urbanization (NU) on TE. The results show that TE in China is in dynamic equilibrium in general, the agglomeration characteristics of efficiency changed from high in the east and low in the west to low in the south and high in the north, and developmental differences first increased and then decreased. NU development has a significant one-way positive impact on TE at the national level and in the eastern region. As far as impact, economic urbanization plays a great role, and ecological factors become more and more important. NU has a rapid and long-term impact on TE, and its contribution rate to developed economic regions can reach 35%. This study will provide an important reference for sustainable development of tourism under the trend of urbanization.

Rapid urbanization has resulted in the conversion of different land types, leading to a serious disruption of the balance of ecosystems, and the contradiction between land use and ecological security becomes increasingly severe. As a key factor affecting ecosystem services (ESs), improving the urban land green use efficiency (ULGUE) can effectively mitigate the decline in the ecosystem services function. This study tried to verify the profound impact of land use on ESs by measuring the coupling coordination degree (CCD) between ULGUE and ESs from the perspective of coupling coordination. However, the traditional CCD model suffers from volatility and non-comparable confidence levels in results. For this background, this study adopted the improved CCD model to measure the association between ULGUE and ESs in the Yangtze River Economic Belt (YREB). Also, a comprehensive spatial evolution of the CCD was further explored. The main conclusions are as follows: (a) The overall level of ULGUE in YREB showed an increasing trend with the distribution of ULGUE in most cities ranging from 0.71 to 1.00, showing a higher level overall and the spatial distribution character of “small aggregation, large distribution”; (b) The ESs of YREB from 2005 to 2020 demonstrated a decreasing trend and majority of cities’ current ecological protection efforts are lagging behind the extent of ecosystem damage caused by human activities; (c) The overall average CCD of YREB was largely at basically balanced level and the average CCD change ranges from 0.38 to 0.45 between 2005 and 2020, but the overall CCD had a strong spatial correlation strength. (4) To some extent, the improved CCD model solved the problems of volatility and credibility based on the traditional CCD results, which have strong rationality and scientific validity. Identifying the coupling and coordination characteristics and mechanisms can effectively provide theoretical support for the sustainable development of watersheds.

In this study, we developed an evaluation index system for green total-factor water-use efficiency (GTFWUE) which reflected both economic and green efficiencies of water resource utilization. Then we measured the GTFWUE of 30 provinces/municipalities/autonomous regions (hereafter provinces) in China (not including Tibet, Hong Kong, Macao, Taiwan as no data) from 2000 to 2018 using a minimum distance to the strong frontier model that contained an undesirable output. We further analyzed the regional differences and spatial correlations of GTFWUE using these values based on Global and Local Moran’s I statistics, and empirically determined the factors affecting GTFWUE using a spatial econometric model. The evaluation results revealed that the GTFWUE differed substantially between the regions. The provinces with high and low GTFWUE values were located in the coastal and inland areas of China, respectively. The eastern region had a significantly higher GTFWUE than the central and western regions. The GTFWUEs for all three regions (eastern, central, and western regions) decreased slowly from 2000 to 2011 (except 2005), remained stable from 2012 to 2016, and rapidly increased in 2017 before decreasing again in 2018. We found significant spatial correlations between the provincial GTFWUEs. The GTFWUE for most provinces belonged to the high-high or low-low cluster region, revealing a significant spatial clustering effect of provincial GTFWUEs. We also found that China’s GTFWUE was highly promoted by economic growth, population size, opening-up level, and urbanization level, and was evidently hindered by water endowment, technological progress, and government influence. However, the water-use structure had little impact on GTFWUE. This study fully demonstrated that the water use mode would be improved, and water resources needed to be used more efficiently and green in China. Moreover, based on the findings of this study, several policy recommendations were proposed from the aspects of cross-regional cooperation, economy, society, and institution.