Eco-efficiency In China Research Articles

Research on Spatial Pattern Dynamic Evolution Algorithm and Optimization Model Construction and Driving Mechanism of Provincial Tourism Eco-Efficiency in China under the Background of Cloud Computing

Based on the research of spatial pattern dynamic evolution algorithm and optimization model construction and driving mechanism of provincial tourism eco-efficiency in China under the background of cloud computing, this paper takes 30 provinces in mainland China (excluding Tibet, Hong Kong, Macao, and Taiwan) as the research object and scientifically constructs the measurement index system of tourism eco-efficiency. The Super-SBM-Undesirable model is used to measure the tourism eco-efficiency of each province from 2004 to 2017, and the algorithm and model are optimized. This paper explores the spatial evolution trajectory and path of tourism eco-efficiency by using the barycentric standard deviation ellipse method and constructs a dynamic panel model to identify the factors affecting the evolution trajectory and their driving mechanisms by using the SYS-GMM method. The results show that China’s tourism eco-efficiency is at a high level and the eastern region is higher than the central and western regions. From the moving track of the center of gravity, the center of gravity of China’s tourism eco-efficiency is located in Henan province, which has experienced a process of moving from southeast to northwest. From the standard deviation ellipse, the spatial distribution direction of China’s tourism eco-efficiency presents a “northeast-southwest” pattern, and there is a further strengthening trend of deviation. There is a significant positive correlation between tourism eco-efficiency and tourism industrial structure upgrading, tourism industrial structure rationalization, tourism technology level, and tourism human capital, as well as a significant negative correlation between tourism eco-efficiency and tourism economic development level, environmental regulation intensity, and the degree of opening to the outside world, while the relationship between urbanization and tourism eco-efficiency is relatively vague.

Eco-efficiency in China's Loess Plateau Region and its influencing factors: a data envelopment analysis from both static and dynamic perspectives.

China's Loess Plateau Region (LPR) plays a significant role in national ecological security and development. Due to the advantage that relates environment with economy, eco-efficiency has become an important indicator of sustainable analysis. Using cross-level panel data for the period 2006-2017, this paper studied LPR's static eco-efficiency and dynamic performance through a combined application of DEA super-efficient slack-based measure and Malmquist Productivity Index at multi-scales. LPR's eco-efficiency was found to experience a slight increase during the study period. The value decreased roughly from east to west, with high eco-efficiency mainly distributed in provincial cities and resource-based cities. The decomposition of the Malmquist Index indicated that technological change contributed most to the improvement of eco-efficiency in the LPR. Besides, this paper explained the variations of eco-efficiency based on the integrated input-output indicators and TOBIT regression model. Economic scale, population density, government regulation, technical innovation, and openness degree were identified as positive influencing factors, while the structure of the industry and land-use intensity were found to have negative impacts on eco-efficiency. Resource-based cities were found to have stronger potentials for eco-efficiency improvement than non-resource-based cities. This paper revealed the characteristics of LPR's eco-efficiency from three perspectives: a spatiotemporal perspective, a macro-meso-micro perspective, and a static-dynamic perspective. The findings of this study hold important implications for policy makers.

Heterogeneous effect of “Belt and Road” on the two-stage eco-efficiency in China's provinces

This paper divides the industrial eco-economic system into manufacturing stage and environmental protection stage, uses the improved metabolic GM(1,1) model and TOPSIS-DEA cross-efficiency model to evaluate the two-stage eco-efficiency of China’s provinces from 2005 to 2020. On this basis, regression discontinuity is utilized to analyze the heterogeneous impact of “Belt and Road” initiative (BRI) on the eco-efficiency of industrial eco-economic system and two stages. The results show that: the eco-efficiency of industrial eco-economic system (EIES) and the eco-efficiency of manufacturing stage (EMS) have been at a relatively high level, while the eco-efficiency of environmental protection stage (EEPS) is still at a low level so far. The EIES and EMS of the provinces in eastern region are higher than those of the central, western and northeastern regions, while the EEPS of the western region is higher than that of the other three regions. The impacts of BRI on EIES and EEPS are significantly positive, while the BRI has a negative effect on EMS. In addition, there is a significant positive relationship between economic development level and EIES, EMS, and EEPS, industrial structure can promote EIES and EMS, and technological innovation has a significant positive correlation with EMS, a significant negative correlation with EEPS.

The heterogeneous effects of different technological innovations on eco-efficiency: Evidence from 30 China’s provinces

Eco-efficiency (EE) is an important indicator to reflect the level of sustainable economic growth. This article places contribution to the existing literature by examining the relationship between innovation and eco-efficiency by incorporating three different innovation indicators: invention patent, utility patent and design patent. The dataset covers a balanced panel of 30 China’s provinces over the period of 2003–2017. Super efficiency slack-based model (SSBM) and super efficiency epsilon-based model (SEBM) under meta-frontier is used to measure the eco-efficiency. While, Bootstrap Truncation Regression is employed to analyze the different impacts of innovations on eco-efficiency. Regarding efficiency, the results indicate that eco-efficiency in China is at a decreasing trend overall, and there exists a certain heterogeneity among different regions. The results of Bootstrap Truncation Regression show that the invention patent and design patent exert positive effects on China’s eco-efficiency while utility patent inhibits the improvement of efficiency score during the sample period. Moreover, the estimation validates the existence of EKC (Environmental Kuznets curve) theory for China based on the extended STIRPAT model. Inward foreign direct investment (IFDI) has both pollution halo effect and pollution haven effect in China, while the effect of outward foreign direct investment (OFDI) on eco-efficiency is different across different regions. A U-shaped relationship is found between environmental regulation and eco-efficiency. On the basis of results, government should formulate strict environmental regulation and impose targeted subsidies on green technological innovation to promote green economic growth.

China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach

From the Kyoto Protocol to the Copenhagen Conference and the Paris Agreement, eco-environmental problems have gradually become a matter of common concern worldwide. Eco-efficiency (EE) is an essential indicator for measuring levels of sustainable development. This study uses an epsilon-based measure (EBM) model with undesirable outputs to evaluate the EEs of 30 Chinese provinces during the research period 2008 to 2017, and a spatial Durbin model (SDM) to search for the impact factors of EE. The results indicate that most provinces in China have a low EE level. The EE value of the eastern area is higher than are those for the central, western, or northeastern areas. The EE in China as a whole demonstrates an inverted V-shaped trend with a high point in 2011. The SDM shows that economic development level, foreign trade dependence, and technological progress exert significant positive effects on EE, while population density exerts significant negative influences on EE. This paper provides scientific bases for the formulation of policies resulting in sustainable development.

Evaluation and improvement of agricultural eco-efficiency in China

Evaluation and improvement of agricultural eco-efficiency in China

Evaluation of the Heterogeneity Effect of Fertilizer Use Intensity on Agricultural Eco-Efficiency in China: A Panel Quantile Regression Approach

Evaluation of the Heterogeneity Effect of Fertilizer Use Intensity on Agricultural Eco-Efficiency in China: A Panel Quantile Regression Approach

The impact of the spatio-temporal neighborhood effect on urban eco-efficiency in China

Since urban eco-efficiency is known to be influenced by the contemporaneous efficiency level in the vicinity, one must allow for possible spatio-temporal correlations between such efficiencies. This article assessed the urban eco-efficiency of 282 prefecture-level cities in China from 2004 to 2016 using a super-efficiency model based on the non-radial directional distance function (Super-NDDF) and then conducted neighborhood correlation tests using the spatio-temporal Moran’s I index (STMI). After a variety of parameter identification and econometric tests, the spatio-temporal Durbin model (STDM) and partial differential method were used to investigate the impact of the spatio-temporal neighborhood effect on urban eco-efficiency. The results showed that compared with the traditional spatial econometric model, the spatial coefficient ρ of the STDM increased by 75%, indicating that the time lag and accumulation of neighborhood effects could not be ignored. After the setting of the spatio-temporal weight matrix in the time dimension was changed, the diminishing marginal neighborhood effect on urban eco-efficiency was found as the temporal window increases and the attenuation boundary appeared within approximately five years. Additionally, in contrast to the statistical insignificance of the neighborhood effect on the strict spatial dimension, environmental regulation had a significant positive impact on the eco-efficiency of neighboring cities when the neighborhood effect was considered in a spatio-temporal context, suggesting a time lag in the neighboring demonstration effect of environmental regulation. The obvious increase in the ratio of indirect effects to direct effects indicated that the spatio-temporal neighborhood effect exacerbated the inertia of neighborhood dependence evolution and strengthened the positive spatial externalities of industrial structure upgrading and technological progress.

Spatial-temporal differences and evolution of eco-efficiency in China’s forest park

Forest parks are typical areas where human-land relationships interact specially. The research on eco-efficiency is the basis for the formulation and implementation of inclusive and sustainable development policies and measures. The eco-efficiency of forest parks shows a strong spatial and temporal difference and evolution pattern because of uneven economic development. Relevant data of China’s forest parks in 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan) are taken as the research object. Super-SBM-Malmquist model with undesirable outputs is utilized to measure and analyze the dynamic changes of eco-efficiency. In addition, spatial heterogeneity is analyzed by using a spatial model. The results show that the eco-efficiency in 2007–2017 has made some improvement in fluctuations while there are still obvious differences in eco-efficiency between provinces and regions. After decomposition, it is found that the comprehensive eco-efficiency of economically developed regions is mainly driven by pure technical efficiency while in economically underdeveloped regions is mainly driven by scale efficiency. In terms of spatial evolution, the eco-efficiency shows a tendency of high-value and low-value agglomeration; some neighboring provinces are closely related, and the “Matthew effect” is significant. In terms of the moving path of the barycenter, it moves to the southeast on the whole. The movement trajectory of the comprehensive efficiency and pure technical efficiency in each year is roughly U-shaped evolution, and the scale efficiency is roughly M-shaped evolution. Finally, reasonable and effective suggestions for the future forest parks management and the reverse of the spatial imbalance of the eco-efficiency are put forward on the basis of empirical evidence, which is of great significance to the optimal allocation and sustainable development of tourism resources in forest parks.

Analysis of industrial eco-efficiency and its influencing factors in China

Industry is the largest sector for energy consumption and pollution emissions in China. Thus, improving industrial eco-efficiency is necessary for China to achieve sustainable development. Based on panel data from 31 industrial sectors from 2001 to 2015, a three-stage data envelopment analysis model was used to empirically explore industrial eco-efficiency and its influencing factors from the perspective of industrial heterogeneity. The results show that the overall level of industrial eco-efficiency in China is not high, first declining and then rising during the study period. Low eco-efficiency was mainly due to low scale efficiency. After removing the influences of external environmental factors and noise, industry profit rates, ownership structures, and foreign direct investments were all significantly and positively correlated with eco-efficiency. Environmental regulations were significantly and negatively correlated, while the intensity of research and development exhibited no linear relationship. Industrial heterogeneity significantly affects eco-efficiency. Capital-intensive industries had the highest eco-efficiencies, followed by resource-intensive industries and labor-intensive industries, respectively. Comparison of technical efficiency (TE) before and after adjustment on a panel of 31 industries in China from 2001 to 2015.

The effect of crop diversity on agricultural eco-efficiency in China: A blessing or a curse?

Loss of crop diversity has been recognized as a serious threat to agricultural sustainability. This paper considers crop diversity to be a special input in the production process of agricultural goods. Quantile regression was applied to analyze the effects of crop diversity on agricultural output, non-point source pollution and Agricultural Eco-Efficiency (AEE) in China. We found that crop diversity had an inverted U-shaped impact on agricultural output and non-point source pollution, and an overall positive effect on AEE nationally. Crop diversity had positive effects on AEE in the Eastern and Central regions but some inhibitory effects in the Northeast (where cropping is less diverse) and West of China. Under rainfall stress, AEE increased with more crop diversity in Northeast China, but the effects were reversed elsewhere. To promote AEE and sustainable agricultural development, crop diversification should be reduced in Northeast and Western China, but more so in Eastern and Central China.

Regional Green Eco-Efficiency in China: Considering Energy Saving, Pollution Treatment, and External Environmental Heterogeneity

The economy in China has gradually transformed from a stage of high-speed development into one of high-quality development. The current study considers the economic environment, energy saving, and pollution treatment in an integrated way to measure eco-efficiency and external environmental heterogeneity. A modified three-phase data envelopment analysis (DEA) model is constructed to measure ecological efficiency while eliminating interference from both statistical noise and the external environment. The first phase uses a two-stage production structure DEA model considering nondiscretionary input and undesirable output. The model was applied to data for the year 2015 in 30 administrative regions in China, including municipalities, provinces, and autonomous regions. The results of this study show that many factors influence these regions’ eco-efficiency in China, including the levels of economic development, technological innovation, environmental regulation, and industrial structure. Finally, implications and suggestions are given to provincial governments from the perspectives of different industries and of provincial ecological–economic development.

Study on Spatial-Temporal Distribution and Dynamic Evolution of Eco-Efficiency in China's Coastal Provinces

Yu, W.; Chen, T.; Yu, S., and Wang, H., 2020. Study on spatial-temporal distribution and dynamic evolution of eco-efficiency in China's coastal provinces. In: Gong, D.; Zhang, M., and Liu, R. (eds.), Advances in Coastal Research: Engineering, Industry, Economy, and Sustainable Development. Journal of Coastal Research, Special Issue No. 106, pp. 454–458. Coconut Creek (Florida), ISSN 0749-0208.The dynamic trends and convergence of eco-efficiency were analyzed using the super-efficiency data envelopment analysis model (US-DEA) with undesirable output and the σ convergence model based on the panel data of 11 coastal provinces in China from 2008 to 2017 in accordance with the eco-efficiency evaluation characteristics of coastal areas. A Markov transition probability model was introduced to analyze the transition probability of eco-efficiency in the time domain and predict the future eco-efficiency evolution of coastal areas in China. The results show that: (1) The eco-efficiency of each province in coastal areas is increasing year after year, presenting a convergence trend. (2) The eco-efficiency of coastal areas is transitioning gradually rather than suddenly. The upward transition probability is greater than the downward transition probability, indicating that the positive probability of eco-efficiency is greater than the negative probability. (3) The eco-efficiency of coastal areas in China will increase year after year in the future based on the prediction of the eco-efficiency transition probability.

How technological innovations affect urban eco-efficiency in China: A prefecture-level panel data analysis

This paper utilizes SBM-undesirable model and Malmquist index to examine the urban eco-efficiency in China and analyzes the regional heterogeneity. Through the panel data of 273 prefecture-level cities during 2007–2017 in China, the spatial Durbin model is used to examine the influencing factors of urban eco-efficiency associated with technological innovations. The results show that the average values of urban eco-efficiency of the East, Central, and West regions are 0.93, 0.88, and 0.90, respectively. The Moran’s I of eco-efficiency is around 0.8, which shows a strong spatial heterogeneity. It is found that higher innovative ability can increase urban eco-efficiency. Education investment and innovation talents have a U-shape relationship with urban eco-efficiency, while capital investment and innovation performance have an inverted U-shape relationship with it. This research outlines the relationship between technological innovations and urban eco-efficiency, offering policy implications for China’s eco-construction and sustainable urban development.

Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years

Chinese agricultural output has been multiple under the intensive input of production factors since the reform and opening-up. Such a growth pattern that realizes high output through high input results in increasingly prominent environmental pollution problems. Considering the provincial panel data in China during 1978–2017 as the research units and taking agriculture in broad sense as the study object, the agricultural eco-efficiency (AEE) was measured by the Super-SBM Model, and the influencing factors were screened out from agricultural basic condition, agricultural industrial structure, agricultural development potential and agricultural input strength. The findings indicated that agricultural expected output and unexpected output were synchronously increased, while the change of input factors was totally different and gradually transferred to materiality from resources. In 1978–2017, AEE was increased to 0.713 from 0.405, with an increase of about 76%. And it approximately underwent four stages, including free development, reform promotion, market regulation and policy incentives. Under the resource restraint and policy incentives, AEE showed that Northeast, East and South China were higher than the national average level. North and Central China basically fitted for the national average level, and Southwest and Northwest China were lower than the national average level. Also, it was successively present in some spatial characteristics including polarization, differentiation, agglomeration and reconstruction on the provincial scale. The magnitude and direction of influencing factors indicated that the introduction of subsidy policies for compound fertilizers, an increase of farmers’ incomes, optimization of agricultural plantation structure, and maintenance of stable agricultural product prices could effectively improve AEE.

Measurement and Spatio-temporal evolution of China’s Regional Eco-efficiency

Based on panel data from 2008 to 2017, the Super-SBM model that considers undesired output has been to measure the eco-efficiency of 30 provinces in China. Meanwhile, the spatial autocorrelation analysis is incorporated into the spatiotemporal evolution law in order to calculate the global and local Moran’s I index, and combining the Moran scatter plot to explore the spatiotemporal evolution and agglomeration characteristics of regional eco-efficiency. The results show that the overall level of China’s eco-efficiency has improved, but the regional differences are obvious, shows a gradient descending pattern of “East, Northeast, Middle and West”. The regions with familiar eco-efficiency level showed the volatility clustering phenomenon, and that the regions and their neighbors have potentially similar trends. Eco-efficiency in China was improved followed by the improvement of local polarization situation and regional differences have narrowed.

Measuring Regional Eco-Efficiency in China (2003-2016): A "Full World" Perspective and Network Data Envelopment Analysis.

Eco-efficiency enhancement is an inherent requirement of green development and an important indicator of high-quality development in general. It aims to achieve the coordinated development of nature, the economy, and society. Therefore, eco-efficiency measurements should focus on not only total factor input, but also process analysis. Based on the “full world” model in ecological economic theory, this study constructed a theoretical framework for a composite economic-environmental-social system that reflects human welfare and sustainability. To this end, using network data envelopment analysis (DEA), this study established a staged eco-efficiency evaluation model that uses economic, environmental, and social factors to measure the overall and staged eco-efficiency of China’s provinces from 2003 to 2016 and analyze its spatiotemporal characteristics. A geographically weighted regression (GWR) model was also used to analyze the influencing factors of eco-efficiency changes and the spatial differentiation in their effect intensity. The findings were as follows: (1) China’s overall eco-efficiency is still at a low level. It varies significantly from region to region, and only three regions are at the frontier of production. The eastern region has the highest eco-efficiency, followed by the central region, and the gap between the central and western regions has gradually narrowed. In terms of staged efficiency, the level of eco-efficiency in the production stage is less than in the environmental governance stage, which is less than that in the social input stage. (2) In terms of the efficiency of each stage, the efficiency level of the production stage showed a downward trend throughout the entire process, and the decline in the central and western regions was more obvious. The social input stage and the environmental governance stage both showed upward trends. The social input stage showed a higher level, and the increase was relatively flat during the period of study. Efficiency continued to rise during the environmental governance stage from 2003 to 2010 and rose overall, but with some fluctuations from 2011 to 2016. (3) Geographically weighted regression showed that the effects of the influencing factors on eco-efficiency had obvious spatial heterogeneity. The factors affecting overall, production stage, and social input eco-efficiency were, in order of effect intensity from high to low, economic growth level, marketization level, and social input level. In terms of environmental governance, social input level had the greatest impact, followed by economic growth; marketization level did not show a significant impact.

Dynamics, differences, influencing factors of eco-efficiency in China: A spatiotemporal perspective analysis

Eco-efficiency can effectively measure the relationship between economy, resources, environment, and development. Understanding eco-efficiency is of great practical significance for decision makers tasked with addressing and delivering sustainable socioeconomic development. Based on an “undesirable output Slacks-Based Measure models,” this study evaluated the eco-efficiency of 285 Chinese cities during the period 2004–2014, analyzing spatiotemporal dynamics and influencing factors using a Spatial Autocorrelation Panel Data Model. The main results are as follows: From the spatial perspective, eco-efficiency in Chinese cities generally evidenced an M-shaped trend (increasing-decreasing-increasing-decreasing), and imbalanced spatiotemporal dynamics. Furthermore, the urban eco-efficiency generally presented a distinct convergence of HH cluster and LL cluster clubs, with the latter being the most dominant. From the regional point of view, there existed a decreasing trend in the efficiencies of cities, from eastern China to central and western China. In addition, we identified significant differences in the eco-efficiency of different cities in China, and the coefficient of variation of eco-efficiency showed a general decrease. The results of our estimation of the factors affecting urban eco-efficiency showed that the economic development level, the industrial structure, import and export trade, and the information level all had significant positive influence, and local government expenditure, social retail sales of consumer goods, and infrastructure all had a negative effect on urban eco-efficiency. This paper puts forward numbers of suggestions for ways to promote social, ecological, and economic development in Chinese cities, based on our findings.

The spatial and temporal evolution of provincial eco-efficiency in China based on SBM modified three-stage data envelopment analysis.

Eco-efficiency plays a significant role in expressing how efficient the economic activity consumes nature's goods and services. To accurately measure eco-efficiency, the method slack-based measure modified three-stage data envelopment analysis (DEA) is adopted to evaluate environmental conditions in China's 30 provinces from year 2004 to 2016. This study treats carbon emissions and three industrials wastes as undesirable outputs and excludes the influences from external environment and random errors when make adjustments. Based on the results, this study makes the following conclusions: Firstly, industrial structure, trade openness, and population have negative effects on eco-efficiency while technology investment, urbanization process, foreign direct investment, and fiscal decentralization have positive effects on eco-efficiency. Secondly, the eco-efficiency for most provinces after adjusted is lower than the pre-adjusted, which indicates the overestimation in eco-efficiency when using traditional approaches. Thirdly, the eco-efficiency in China showed a clear geographical step distribution, with the highest eco-efficiency in the east area, followed by the central, northwest, and southwest regions.

The Double-Edged Sword of Urbanization and Its Nexus with Eco-Efficiency in China.

Urbanization has made tremendous contributions to China’s economic development since its economic reforms and opening up. At the same time, population agglomeration has aggravated environmental pollution and posed serious challenges to China’s environment. This article empirically investigates the impacts of China’s urbanization on eco-efficiency, comprehensively reflecting economic growth, resource input, and waste discharge. We first measured the provincial eco-efficiency in China from 2005 to 2015 using the Super Slack-Based model (Super-SBM). We then constructed a spatial model to empirically analyze the effects of urbanization on eco-efficiency at the national level, and at four regional levels. The results indicated that the regional eco-efficiency in China has fluctuated, but is generally improving, and that a gap between regions was evident, with a trend toward further gap expansion. We observed an effect of spatial spillover in eco-efficiency, which was significant and positive for the whole country, except for the western region. The influence of urbanization on China’s eco-efficiency exhibited a U-curve relationship. The changing trend in the eastern, central, and western regions was the same as that in the whole country; however, the trend exhibited an inverted U-curve relationship in the northeastern region. To the best of our knowledge, covering a time period of 2005–2015, this article is the first of its kind to study the impact of urbanization on eco-efficiency in China at both the national and regional levels. This study may help policy-makers to create sustainable policies that could be helpful in balancing urbanization and the ecological environment.