Eco-efficiency In China Research Articles

Coupling Coordination Analysis Between Farmland Transfer and Agricultural Eco-Efficiency in China

The transfer of farmland refers to the process of transferring land contracting and management rights, during which various changes occur in production entities, management behaviors, and management methods. This process fosters positive changes in grain yield and farmers’ income. However, there is no consensus on the ecological impacts of farmland transfer. The objective of this study is to explore the coupling and coordination relationship between the process of farmland transfer and regional agricultural eco-efficiency across 30 provincial units in China. The following conclusions can be drawn from the results: (1) The level of farmland transfer in China exhibits an overall upward trend, primarily influenced by the average farmland area per household in each region and the number of large grain producers, with Heilongjiang consistently maintaining the highest level. (2) The agricultural eco-efficiency value remains relatively stable at approximately 1.1, with high-efficiency areas concentrated in Beijing, Qinghai, Hainan, Shanghai, Fujian, and Guizhou, where lower agricultural carbon emissions are a significant influencing factor. (3) The coupling degree between farmland transfer and agricultural eco-efficiency in various provinces of China is predominantly in a state of adjustment, with the coupling coordination level improving year by year. However, the coupling coordination level does not demonstrate a clear “high-high” or “low-low” synchronous state with grain yield per unit area, indicating that the levels of farmland transfer and agricultural eco-efficiency have not yet established a robust interactive relationship with farmland production efficiency.

Domesticating sustainability transitions: Spatial and temporal variation of industrial eco-efficiency in China

This study analyses regional industrial sustainability transitions from global and local perspectives. We measure industrial eco-efficiency (IEE) of 280 cities in China from 2010 to 2020 and analyze potential influencing factors using geographical and temporal weighted regression model. We find that foreign investment and international trade are positively associated with IEE with diminishing effects, and then local household consumption enhances IEE on the basis of local environmental governance. The effects of international environmental regulations and technology spillover on IEE are positive, as they align with China's drive for innovation. We further categorize cities into five distinct types based on the influences of both global and local factors—dominated by local environmental regulations, driven by international standards, propelled by foreign investment, shaped by local market dynamics, and affected by multiple factors. We conclude that regional industrial sustainability transitions in China are influenced by forces operating at various scales and exhibit distinct spatiotemporal variation. The drivers of these transitions have shifted from a global scale to a local scale, and both regional conditions and the stage of industrialization significantly affect the intensity of actions undertaken by different scale factors. Finally, we propose policy recommendations, considering regional heterogeneity and the stages of industrialization.

Urban eco-efficiency of China: Spatial evolution, network characteristics, and influencing factors

Urban eco-efficiency (EE) refers to the ability of a city to balance economic growth with environmental protection through the optimal use of resources and environmental management, reflecting the city’s performance in sustainable development. Quantitative EE analysis is a crucial evaluation criterion for measuring the process of sustainable development (SD). To assess the current state of sustainable development in China, this study employs the slacks-based super efficiency model (Super-SBM) to measure the the urban eco-efficiency in China (CUEE) of 284 cities. The study utilizes Kernel Density Estimation (KDE) and Spatial Autocorrelation Analysis (SAA) to investigate the fundamental patterns of CUEE in terms of inter-city differences and relationships. Complex Network Analysis (CNA) and Link Prediction methods are applied to examine the characteristics and future trends of the CUEE network. Additionally, Geodetector and Geographically Weighted Regression (GWR) are used to analyze the influencing factors of CUEE. The findings are as follows: Overall CUEE is relatively low but shows a steady increase. The spatial association network of CUEE is becoming more complex, with increasing association strengths. The Chengdu-Chongqing Economic Circle (CCEC) is expected to further enhance its spatial association strength and may emerge as a new core area. The effectiveness of the interactions among selected factors is greater than that of individual factors. Degree centrality (X1) is identified as a core explanatory factor influencing CUEE, showing a positive correlation with CUEE. Scientific expenditure (X5) is also positively correlated with CUEE. Local fiscal budget expenditure (X3), industrial output value of foreign-invested enterprises (X7), and total cargo transportation volume (X9) are negatively correlated with CUEE. The analytical framework and research conclusions of this study offer valuable insights into the development of urban ecological efficiency in China and provide constructive recommendations for promoting sustainable urban development.

The coordinated relationship and interactive response between urban quality of life and eco-efficiency in China

It is crucial to clarify the interaction mechanism between quality of life (QOL) and eco-efficiency (EE) to achieve high-quality urban development. This study investigated the complex and dynamic interaction and coupling process between QOL and EE. By constructing two evaluation index systems, this study obtained the values of QOL and EE levels in 282 cities from 2008 to 2020. Then, the interactive relationship between QOL and EE was investigated from two perspectives: static coupling and dynamic coupling. The different development types were classified according to the regional differences between them. The results show that: (1) From 2008 to 2020, the value of QOL gradually increased, while the EE exhibited a “V-shaped” change. Medium to high levels of QOL were located in cities in the eastern coastal area, while low levels were distributed in the western, central and northeastern regions. Furthermore, medium to high levels of EE were distributed in cities in the eastern coastal and southwestern regions, whereas low levels were found in the central and northwestern regions; (2) Overall, the coupled coordination of QOL and EE got better from 2008 to 2020, showing that the relationship between them was close and that the coordination gradually got better. Consequently, the spatial change of the coupled coordination was more pronounced, the number of mildly uncoordinated cities was gradually decreasing, urban development was shifting from uncoordinated to coordinated, and the clustering characteristics of the coupled coordination degree were consistent with the spatial change characteristics; (3) Moreover, there was a Granger causality relationship between QOL and EE. This means that the degree of their mutual influence increased over time, while their development became less dependent on inertia, with obvious interaction characteristics; and (4) The values of integrated QOL and EE were separately clustered and combined with the type of coupled and coordinated urban development based on their development stage. Therefore, the research area was divided into three types: “optimization and upgrading,” “continuous improvement,” and “cultivating development.”

The threshold effect and driving path of the rapid transit system on tourism eco-efficiency in China

Rapid transit is important for the green development of China’s tourism industry because it profoundly affects the optimal structural layout and rational allocation of elements that impact regional tourism. Based on provincial panel data from 2009 to 2020, the entropy weight method, tourism carbon emission measurement model, Super efficiency slacks-based measure model and threshold regression model were used to determine the impact of the rapid transit system on tourism eco-efficiency in China. The findings showed that rapid transit system has increased steadily, while tourism eco-efficiency has fluctuated significantly, and the relative development characteristics of rapid transit system and tourism eco-efficiency gradually changed from “lagging rapid transit system” to “lagging tourism eco-efficiency.” In addition, the rapid transit system and the highway and civil aviation have a nonlinear relationship with tourism eco-efficiency, while the high-speed railway has not passed the threshold effect test, and at the regional level, the impacts of rapid transit system have heterogeneous characteristics. As the deepening construction of rapid transit system, its threshold effect on tourism eco-efficiency will change from positive “V” to positive “U” type, promoting the more efficient development of environmentally friendly tourism.

Mechanisms and Impact Effects of Digital Agriculture Development on Agricultural Eco-Efficiency in China

The green development of agriculture is an essential way to achieve high-quality agricultural development, and the development of digitalization has given new momentum to the green development of agriculture. In this study, based on the panel data of 30 provinces in China from 2011 to 2022, we measure the agricultural eco-efficiency and the level of digital agriculture development in China using the Super-SBM model with global reference and the entropy value method, respectively. The impact of the level of digital agriculture development on agricultural eco-efficiency is explored with the help of a regression model, and the mediating role of pesticide and fertilizer inputs in this impact pathway is explored using a mediating effects model. The study found that: (1) the level of digital agriculture development positively and significantly affects agricultural eco-efficiency to a relatively large extent; (2) the effect of digital agriculture development on the improvement of agriculture eco-efficiency is significantly heterogeneous in different regions; (3) pesticide and fertilizer inputs have a mediating role in this impact pathway. Therefore, the application and promotion of digital agriculture technology should be strengthened to build a green agricultural production and management system, so as to promote high-quality and sustainable development of Chinese agriculture.

How can open public data promote efficient and equitable green production? Evidence from eco-efficiency in China.

Nowadays, concurrent attention to economic development and ecological issues is becoming an important trend. In this paper, we measure the eco-efficiency of 285 Chinese cities from 2003 to 2019 using a non-radial directional distance function and the data envelopment analysis method, based on which we analyze the club convergence of cities' eco-efficiency using the logt test; we estimate the impact of open public data platforms on eco-efficiency and its convergence using a multi-period difference in difference model and panel-ordered logit model, respectively. We find that, first, open public data platforms improve cities' eco-efficiency by about 6.5%, and the impact mechanisms include scale efficiency, technical efficiency, and total factor productivity, or, at the micro level, increasing the economic agglomeration degree, boosting the amount of foreign investment used, and increasing green innovation level. Second, there are three convergence clubs of eco-efficiency in China's cities, whose average eco-efficiency trends are above, close to, and below average, respectively. Third, public data platforms significantly increase the probability of cities belonging to the convergence clubs of high and medium eco-efficiency (Clubs 1 and 2) and decrease the probability of belonging to the low one (Club 3). However, the mechanisms only include technical efficiency and total factor productivity, or the amount of foreign investment used and the green innovation level at the micro level.

Study on the relationship between informatization and marine eco-efficiency in China–taking 11 coastal provinces as examples

This article focuses on 11 coastal provinces in China and selects relevant data from 2008 to 2021. The improved entropy method is used to calculate the level of informationization development in each province. The Malmquist index is used to calculate the marine ecological efficiency of each province. The panel vector autoregression (PVAR) model is established to study the dynamic relationship between informationization and marine ecological efficiency. The results show that: (1) There is a long-term equilibrium relationship between informationization and marine ecological efficiency, and informationization is a Granger cause of marine ecological efficiency at the 5% significance level. (2) Overall, informationization has a promoting effect on marine ecological efficiency, but with a certain lag; the improvement of marine ecological efficiency also benefits the development of informationization. (3) There is a bidirectional relationship between informationization and marine ecological efficiency, and informationization has become the main influencing factor of marine ecological efficiency.

Reassessment of industrial eco-efficiency in China under the sustainable development goals: A meta two-stage parallel entropy dynamic DDF-DEA model

As the path of industrial eco-efficiency (IEE) needs to be re-examined from a multi-dimensional perspective, this research opens the internal “black box” of China's industrial eco-economic system driven by sustainable development goals (SDGs) and decomposes IEE into industrial production and sustainable development stages. It further divides the industrial production stage into two parallel subsystems of water use efficiency (WUE) and energy efficiency (EE) and derives SDG6 efficiency (SDG6E) and SDG7 efficiency (SDG7E) in the sustainable development stage respectively. The paper proposes a meta two-stage parallel entropy dynamic DDF-DEA model to re-measure IEE of 30 provinces in China from 2015 to 2021, discusses the relationship between overall efficiency and stage efficiency, and by using the Tobit model explores various types of influencing factors by subregion. The results show that the modified DDF-DEA model can explore China's IEE more accurately under SDGs. China's IEE is more stable from 2015 to 2021, with the efficiency value always fluctuating between 0.59 and 0.64, the overall level is low, and there is more room for improvement. There are significant inter-provincial differences in IEE and efficiency inertia. In more than 75% of the provinces SDG7E constrains IEE improvement, while the dynamic evolution of IEE and its subsystem efficiency varies across regions. Environmental regulation is a common factor affecting IEE across the country and in the east, central, and west regions, whereas the level of economic development, openness to the outside world, and urbanization rate are differential factors affecting IEE across the country and in the three major regions. This study offers new perspectives for exploring the path of IEE enhancement and providing empirical references for the collaborative construction of industrial ecological civilization.

Evaluation of China’s provincial eco-efficiency with the explainable boosting machine (EBM) model and Tobit regression

The explainable boosting machine (EBM) model measures China’s provincial eco-efficiency, and the Tobit regression model reveals internal driving factors to provide consultation for promoting China’s green development based on the panel data of 30 provinces from 2000 to 2018. The findings of this article are as follows: provincial-level regional ecological efficiency is low, growth is slow, regional differentiation is significant, and development still has a trend of incoordination and multi-polarization. From the perspective of global autocorrelation, the Moran index is significantly positive, and the province eco-efficiency of the first grade presents a positive spatial correlation and has the characteristics of spatial agglomeration. The regression analysis results show that the economic level, the FDI, the level of technological innovation, and the level of human capital are the main influencing factors of eco-efficiency, and there are spatial differences. Relevant suggestions are based on the status and influencing factors of the unbalanced development heterogeneity of provincial eco-efficiency in China.

Spatial and temporal differences and influencing factors of building eco-efficiency in China

This study utilizes the global super-efficiency EBM model to evaluate the eco-efficiency of China's construction industry based on panel data spanning from 2007 to 2021. Furthermore, it examines regional disparities and influential factors using the Dagum Gini coefficient method and panel Tobit model. The findings reveal a sequential decline in efficiency across the eastern, central, and western regions. The overall disparity demonstrates an initial downward fluctuation followed by an upward trend, with the primary contribution shifting from inter-regional variation to hypervariable density. Importantly, the internal discrepancy within the western region surpasses that of other regions. The developmental stage of the construction industry and the composition of energy consumption have a substantial positive impact on its ecological efficiency. In addition, the level of economic development, degree of openness to international markets, and environmental regulations exhibit a positive impact on both the overall construction industry and specifically within the eastern region. Consequently, region-specific measures should be implemented to address disparities in eco-efficiency within the construction industry and promote its high-quality development.

Impact and Spatial Effect of Socialized Services on Agricultural Eco-Efficiency in China: Evidence from Jiangxi Province

Agricultural eco-efficiency (AEE) is a crucial indicator of the green development of agriculture. Agricultural socialized services (AS) provide services for the agricultural production process and they promote the effective input of production factors, such as science and technology, talent, information, and capital, into the agricultural production chain, deepening the division of labor and injecting vitality into agricultural development. We measured AEE based on field research data in Jiangxi Province, China. We also constructed an endogenous switching model to explore the impact of AS on AEE. Our results show that, based on the counterfactual assumption, the AEE increased by 13.19% among farmers who adopted the services compared to those who did not. From the perspective of scale and structural differences, the larger the scale of agricultural cultivation, the stronger the impact of AS on AEE. Furthermore, a large share of cash crops was found to inhibit the impact of AS on AEE. We also investigated whether farmers in close proximity to each other affect their neighbors through knowledge dissemination and technology spillover. The extent of the impact of AS on AEE depended on distance thresholds: it was more pronounced when we increased the distance threshold. Our results suggest that the government should improve the AS system, provide more public welfare services, and appropriately subsidize AS organizations. The AS for food crops should be emphasized; however, those for cash crops should not be ignored.

Spatial–Temporal Characteristics and Influencing Factors of Marine Fishery Eco-Efficiency in China: Evidence from Coastal Regions

Marine fishery is an important part of China’s maritime power strategy. Improving the ecological efficiency of marine fishery is the inevitable way to achieve the sustainable development of fishery. Based on the perspective of industrial sustainable development, this study used the Super-SBM model to evaluate the ecological efficiency of marine fishery in 11 coastal provinces of China from 2011 to 2020. Combined with Malmquist index, Moran index and other methods, the spatial and temporal evolution characteristics were analyzed. On this basis, the Tobit panel model was used to explore the influencing factors of marine fishery eco-efficiency. The results show that: (1) From 2011 to 2020, the marine fishery eco-efficiency in the 10 coastal provinces and cities of China shows a clear trend of improvement, and the efficiency values in high-efficiency areas remain basically stable. The relative gap between efficient and inefficient regions remains significant. (2) From the perspective of spatial distribution characteristics, the ecological efficiency of marine fishery in coastal provinces and cities in China had no obvious spatial correlation, and showed a trend of cross-distribution between high-efficiency regions and low-efficiency regions. (3) The change of marine fishery eco-efficiency is the result of a variety of influencing factors. Fishery industrial structure, scientific and technological support levels and environmental regulation play a role in promoting the improvement of marine fishery eco-efficiency. Therefore, optimizing the structure of the fishery industry, improving environmental regulation and increasing investment in science and technology are all effective measures for local governments to improve the eco-efficiency of marine fisheries.

Exploring the effects of market-oriented reforms on industrial land use eco-efficiency in China: Evidence from a spatial and non-linear analysis

The abundant supply of industrial land has played an important role in China's large-scale urbanization and industrialization. A large amount of industrial land is characterized by low-efficiency use and high industrial-pollution emissions. To improve industrial land use eco-efficiency (ILUE), the Chinese government is continuously strengthening the market-oriented reforms of industrial land transactions. Different from previous studies that assess the effectiveness of such an effort based on non-spatial and linear perspectives, this study explores the spatial and non-linear effects of the market-oriented reforms on ILUE, using data of 99 cities in China collected from 2009 to 2020. We evaluate the market-oriented reforms with two indicators of industrial land premium rate (ILPR) and the ratio of bidding, auction, and listing (RBAL) in industrial land transactions, then measure the spatial and non-linear effects using the spatial panel data model. The results show that the rising ILPR in adjacent cities negatively affected local ILUE, but the RBAL had no obvious spatial effects. The relationship between ILPR and ILUE showed a U-shaped curve; that between RBAL and ILUE was an inverted U-shape. The effects were heterogeneous across regions. The rising ILPR of adjacent cities positively affected local ILUE in the east, while negatively in the central region. The increasing RBAL had no noticeable spatial effects except in the central region. The western region did not show significant spatial effects. The relationships between ILUE and the two indicators showed U-shaped curves of different shapes in the three regions. The findings help to improve future ILUE by managing the industrial land markets in better ways.

Spatial Disequilibrium and Dynamic Evolution of Eco-Efficiency in China’s Tea Industry

Eco-efficiency is a significant target for evaluating the agricultural ecosystem and measuring sustainable agricultural development through quantitative analysis. It is also an essential part of constructing the ecological tea garden, which offers a directional function in realizing the green development of the tea industry. After measuring the eco-efficiency of China’s tea industry using the super-efficiency SBM model, this paper analyzes the spatial disequilibrium and dynamic evolution trend of the eco-efficiency in China’s tea industry through the method of Dagum Gini Coefficient and Kernel Density Estimation. The results show that the level of eco-efficiency in China’s tea industry was improved overall, and the spatial disequilibrium was significantly reduced. The differences within the tea region decreased as follows: tea regions in Southwest China, South China, south of the Yangtze River, and north of the Yangtze River; the overall difference in the eco-efficiency in the tea industry mainly comes from the contribution of the interregional difference in tea regions, and the second contribution comes from the intraregional difference in tea regions and the difference in super-variable density. The eco-efficiency of the tea industry has been improved both nationally and within the top four tea regions; the disequilibrium between areas and within the tea region has been largely alleviated, but there is still room to optimize the input–output structure and promote the eco-efficiency.

Exploring the Role of Foreign Direct Investment and Environmental Regulation in Regional Ecological Efficiency in the Context of Sustainable Development

Eco-efficiency (EE) is an important indicator of regional sustainable development, which reflects the efficiency of regional economic development in using resources effectively to reduce environmental pressures, and foreign direct investment and environmental regulation are very important in promoting regional economic growth and enhancing eco-efficiency. In this paper, we chose China’s panel data from 2009 to 2021, measured China’s regional eco-efficiency using the super-efficient SBM model, and explored environmental regulation and the impact of FDI on EE in different regions using the Tobit model, with conclusions as follows: (1) The average value of national EE in China during 2009–2021 was about 0.631, which was at a low level, and there were significant differences between regions, with the highest EE in the eastern region, and the central and western regions being lower than the national average. (2) FDI at the national level had a significant promoting effect on regional EE, with an elasticity coefficient of 0.0213, which verifies that the “pollution paradise” effect does not exist at national level. FDI promoted EE in the eastern region, while not being significant in the other two regions. The impact of the environmental regulation act on EE at the national level did not pass the significance test, but the impact passed the significance test with positive coefficients for both the eastern and central regions, while in the western region it was not significant. (3) Financial investment in science and technology promoted EE in the national, east, and central regions significantly, while not being significant in the western region. The economic development level of all regions was positively correlated with EE; the impact of urbanization on EE was significantly positive in national, central, and western regions, but was not significant in the eastern region. The industrial structure of all regions was not conducive to the improvement of EE, with the western region having the most negative impact on EE. The study in this paper represents an important addition and refinement to research in related fields.

Research on Spatial Correlation Evolution of Marine Ecological Efficiency Based on Social Network and Spatial Correlation Matrix Model

Most developed countries in the world are maritime powers. This article constructs an ecological efficiency evaluation system based on the characteristics of the ocean itself while taking into account the relationship between land and sea. Based on social network analysis, the relationship between China’s marine ecological efficiency is regarded as a social network system, and the roles and positions played by 11 coastal cities in the network are analyzed from a relational perspective. Next, based on the unexpected super efficiency model to measure the ocean efficiency value in China’s coastal areas, we explore its spatiotemporal evolution characteristics, and measure the ocean ecological efficiency while incorporating ecological environmental pollution as an unexpected output into the evaluation system. Then, the spatial incidence matrix of marine ecological efficiency is calculated through the modified gravity model, and the characteristics of network structure are described with the help of the social network method. In addition, ArcGIS software is used to visualize the spatial evolution process. Finally, QAP regression is used to explore the key factors affecting the spatial correlation network of marine ecological efficiency in China. The results show the following: (1) In terms of time, the marine eco-efficiency of most provinces is not high, and the difference between provinces is obvious, but on the whole, it shows a fluctuating upward trend. (2) From the perspective of space, the overall displacement of the center of marine eco-efficiency in China is large in the north–south direction and small in the east–west direction, and the center of marine eco-efficiency is always concentrated near the Yangtze River Delta. (3) On the whole, the spatial correlation network of China’s marine eco-efficiency is becoming more and more complex. The number of correlation relationships and network density is increasing, and the network framework is gradually maturing. The spatial adjacency matrix, the difference in economic development level, and the difference in population distribution level can significantly promote the formation and development of the spatial correlation network of marine eco-efficiency. However, the differences in the level of opening to the outside world and in the structure of marine industries restrain their spatial networks. The difference between marine science and technology levels is not significant.

A study on the effects of natural resource abundance and foreign direct investment on regional eco-efficiency in China under the target of COP26

Regional eco-efficiency emphasizes the harmonious development of the economy, society and the natural environment, which is of great significance in the construction of an ecological civilization. This study analyzed the data of 30 regions in China from 2009 to 2021, measured regional eco-efficiency by using the super-efficient SBM model and then verified the influence of resource abundance and foreign direct investment (FDI) on regional eco-efficiency using the Tobit model. The conclusions were as follows. (1) The eco-efficiency level in China shows a slow upward trend from 2009 to 2021, and the national average value is low, at 0.519. The eco-efficiency levels of three regions—east, central and west—increased between 2009 and 2021, with the highest eco-efficiency in the east and the lowest in the west. (2) The negative elasticity coefficients of natural resource abundance confirm the existence of the “resource curse” in the eco-efficiency domain in the central and western regions and nationwide, but not in the eastern region. (3) The impact of FDI intensity on eco-efficiency nationwide is negative, but not significant. Eastern and western regions enjoy enhanced regional eco-efficiency, but the effect in the central region is negative. (4) Human capital promotes eco-efficiency in all regions; urbanization in the eastern and western regions and nationwide promotes eco-efficiency, whereas it inhibits eco-efficiency in the central region. Economic growth cannot increase eco-efficiency nationwide, with a positive promotion effect in the eastern and central regions but a negative promotion effect in the western ones. Moreover, green finance can significantly improve national and eastern regional eco-efficiency, but has a non-significant promotion effect in the central region and significant inhibiting effects in the west. Finally, in this paper, the corresponding countermeasures proposed for improving eco-efficiency are described.

Exploring the Impacts of Urbanization on Eco-Efficiency in China

It is of significant importance to conduct research on the relationship between urbanization and eco-efficiency (EE), for it can aid policy making for urban and regional sustainable development. This paper studied the effects of urbanization on the EE in 30 provinces of China from 2008 to 2019. Using the epsilon-based measure (EBM) model with undesirable outputs, this study measured the EE of China’s provinces before empirically analyzing the effects of urbanization on EE. Conclusions could be drawn: the annual mean EE of the eastern region was the highest (0.837), followed by those of the central region (0.653) and western region (0.570), and that of the northeast region remained the lowest (0.438). Zooming into the provinces and cities, the EEs of Beijing, Shanghai, and Fujian were at the production frontier surface, with a high level of EE during the study period, while those of Gansu, Ningxia, and Xinjiang were generally at a lower level. Empirical analysis showed that the effects of urbanization on EE in China presented a U-shaped relationship, having a negative correlation first and then reversing to a positive one. At present, China is in the early negative stage, and the turning point has yet to come. Considering the control variables, the economic development level, technological progress, and foreign direct investment have positively influenced eco-efficiency. Overall, the paper may shed light on related studies and provide relevant policy suggestions to promote EE through a new urbanization strategy.

Energy Eco-Efficiency in China’s Oil and Gas Resource-Based Enterprises and Its Influencing Factors: a Data Envelopment Analysis from Static and Dynamic Perspectives

Energy Eco-Efficiency in China’s Oil and Gas Resource-Based Enterprises and Its Influencing Factors: a Data Envelopment Analysis from Static and Dynamic Perspectives