Dynamic changes and convergence of China’s regional green productivity:A dynamic spatial econometric analysis

Low-carbon economic development is at the heart of the post-pandemic green recovery scheme worldwide. It requires economic recovery without compromising on the environment, implying a critical role that green productivity plays in achieving the carbon neutrality goal. Green productivity measures the quality of economic growth with consideration for energy consumption and environmental pollution. This study employs the slacks-based measure directional distance function (SBM-DDF) approach and the Malmquist-Luenberger (ML) index to calculate green productivity and its components of 30 provinces in China between 2001 and 2018. Using a spatial panel data model, we empirically analyzed the conditional β-convergence of China's green productivity. We found that overall, since 2001, China's green productivity has demonstrated a continuous upward trend. When taking into account spatial factors, China's green productivity demonstrates a significant conditional β-convergence. In terms of regional effects, the results indicate that the green productivity of the eastern and western regions demonstrates club convergence, implying a more balanced green economic development. Moreover, the convergence rate of China's green productivity increases with the addition of environmental regulation variable, and so the corresponding convergence time decreases. It indicates that environmental regulations help to facilitate the convergence of China's green productivity, narrowing the gap between the regional green economic development. The findings provide guideline for achieving a low-carbon development and carbon neutrality from a regional green productivity perspective

The governance-production nexus of eco-efficiency in Chinese resource-based cities:A two-stage network DEA approach

For decades, resource-based cities in China have significantly contributed to China's socio-economic development. The heavy resource dependence of resource-based cities inevitably leads to a series of environmental problems. Mitigating environmental impacts in an unthinking manner might be disruptive for economic development. Improving eco-efficiency has been a crucial solution for protecting the environment while mitigating its negative economic impact. However, the method commonly used to evaluate the eco-efficiency – that is, the black-box data envelopment analysis (DEA) – cannot examine the inefficiencies of the internal structure, and as a result, the underlying management defects are unclear. To open the black box, this study presents a two-stage network DEA framework incorporating government and industrial sectors and measures the eco-efficiency of 84 resource-based cities during the post-financial crisis period (2007–2015). The results indicate that the average eco-efficiency of China's resource-based cities shows a promising increase, and there is a positive relationship between governance efficiency and production efficiency. The decreasing trend of governance efficiency in the Central, Western, and Northeast regions after 2014 shows the low quality of the government sector in the usage of fiscal income. Proactive disclosure of how the government sector conducts public business and spends taxpayers' money should be made to increase transparency, attract more entrepreneurial resources to carry out production activities, and further improve sustainability. The two-stage network DEA framework helps obtain more insights into the internal management defects of the government and industrial sectors and enhance their cooperation to improve the eco-efficiency precisely

A Decomposed Data Analysis Approach to Assessing City Sustainable Development Performance: A Network DEA Model with a Slack-Based Measure

This paper deals with urban sustainable development in China. We propose a network data envelopment analysis (DEA) model with a slack-based measure (SBM) to analyze the eco-efficiency of 284 Chinese cities, enabling us to find a way to open the “black box” in conventional DEA models and introduce social well-being factors into the model, and depict the role of local government in providing public service and improving social well-beings. We set up a framework of urban development by dividing the process of into two steps. The first stage is a production system translating inputs and natural resources into GDP and waste production, which will be inputs to the second stage for distribution and consumption to realize social welfare and environmental protection. The results show eco-efficiency of Chinese cities experienced a significant decrease from 2005 to 2016, which should be mainly attributed to the distribution and consumption processes. Structural differences are described by regions, administrative level and clusters. These results are compared with an existing urban sustainability index system developed by McKinsey and an ANOVA approach is conducted to reveal differences between cities across regions and clusters. This article sheds new light on the understanding of urban sustainable construction and development in China regarding the service performance of local government. View Full-Tex

Regional differences and spatio-temporal convergence of environmental regulation efficiency in the Yellow River Basin, China

Environmental regulation efficiency facilitates environmental governance performance assessment, ecological protection, and high-quality development. Herein, based on the panel data of 75 cities in the Yellow River Basin from 2007 to 2020, this paper constructed an evaluation index system and measured the environmental regulation efficiency using a super-EBM hybrid distance model. We analyzed the regional differences and dynamic evolution characteristics of environmental regulation efficiency with the help of Dagum’s Gini coefficient decomposition and kernel density estimation methods. Furthermore, a spatial econometric model explored the spatio-temporal convergence of environmental regulation efficiency. The main findings show that the environmental regulation efficiency of the overall Yellow River Basin and the upper, middle, and lower reaches showed an increasing trend with significant within-region spatial differences. The differences between all regions had a narrowing trend. The primary source of spatial differences in environmental regulation efficiency was the intensity of transvariation. The dynamic evolution characteristics of environmental regulation efficiency in different regions were quite different, and the spatial polarization phenomenon was more evident in the upper reaches. Except for the overall Yellow River Basin, all regions existed σ convergence. The results of spatial convergence estimation indicated absolute and conditional β convergence in all regions. The findings provide a factual reference for policies related to establishing policy systems for environmental regulation efficiency and green coordinated development in similar regions of the world

Water Resource Utilization Efficiency (WRUE) for Prefecture-Level Cities of Jiangxi, China: A Three-Stage DEA and Bootstrap DEA Approach

In the present study, For China’s prefecture-level cities of Jiangxi, their respective Water Resource Utilization Efficiency (WRUE) from 2006 to 2015 was evaluated in this study, by applying a three-stage DEA and bootstrap DEA model. The results revealed that a comprehensive efficiency of water resources utilization in prefecture-level cities of Jiangxi remained at a relatively low level. The scale of investment in these prefecture-level cities is a key factor restricting their WRUE. However, we found that external environmental variables greatly influenced this efficiency. The spatial changes of DEA effective cities presented the evolution character of change from clear contiguous to complicated scattered. Moreover, our results suggest the bootstrap method can provide more accurate results for WRUE than those from a three-stage DEA. Finally, based on these results, corresponding policy recommendations are put forward in this study

Research on regional differences and influencing factors of Chinese industrial green technology innovation efficiency based on Dagum Gini coefficient decomposition

Industrial green technology innovation has become an important content in achieving high-quality economic growth and comprehensively practicing the new development concept in the new era. This paper measures the efficiency of industrial green technology innovation and regional differences based on Chinese provincial panel data from 2005 to 2018, using a combination of the super efficiency slacks-based measure (SBM) model for considering undesirable outputs and the Dagum Gini coefficient method, and discusses and analyses the factors influencing industrial green technology innovation efficiency by constructing a spatial econometric model. The results show that: firstly, industrial green technology innovation efficiency in China shows a relatively stable development trend, going through three stages: “stationary period”, “recession period” and “growth period”. However, the efficiency gap between different regions is obvious, specifically in the eastern > central > western regions of China, and the industrial green technology efficiency innovation in the central and western regions is lower than the national average. Secondly, regional differences in the efficiency of industrial green technology innovation in China are evident but tend to narrow overall, with the main reason for the overall difference being regional differences. In terms of intra-regional variation, variation within the eastern region is relatively stable, variation within the central region is relatively low and shows an inverted ‘U’ shaped trend, and variation within the western region is high and shows a fluctuating downward trend. Thirdly, the firm size, government support, openness to the outside world, environmental regulations and education levels contribute to the efficiency of industrial green technology innovation. In addition, the industrial structure hinders the efficiency of industrial green technology innovation, and each influencing factor has different degrees of spatial spillover effects

A Two-Stage DEA Model to Evaluate the Technical Eco-Efficiency Indicator in the EU Countries

This paper evaluates the evolution of eco-efficiency for the 27 European Union (EU) countries over the period 2008–2018, provided the traditional high concerns of the EU concerning the economic growth-environmental performance relationship. The EU has triggered several initiatives and regulations regarding environmental protection over the years, but as well the Sustainable Development Goals demand it. Under this setting, we conduct a two-stage analysis, which computes eco-efficiency scores in the first stage for each of the pairs EU 27-year, through the nonparametric method data envelopment analysis (DEA), considering the ratio GDP per capita and greenhouse gas emissions (GHG). In the second stage, scores are used as a dependent variable in the proposed fractional regression model (FRM), whose determinants considered were eight pollutants (three greenhouse gases and five atmospheric pollutants). CO2/area and N2O/area effects are negative and significant, improving the eco-efficiency of the EU 27 countries. When the efficient European countries are excluded from the estimations, the results evidence that CO2/area and CH4/area decrease the DEA score. The country with the lowest GHG emissions and pollutant gases was Ireland, being the country within the considered period that mostly reduced emissions, particularly SOx and PM10, increasing its score.info:eu-repo/semantics/publishedVersio

Monitoring domestic material consumption at subnational level: Enabling the territorial perspective

162 p.The growing awareness that business as usual is both, unwise and unsustainable, has placed the role of the environment and the efficient use of natural resources at the centre of political and economic strategies. At the same time, mitigation strategies and monitoring frameworks geared to sustainability are generally implemented at national or supranational levels, failing short in providing significant guidance for local policy makers. This thesis provides a methodology for scaling national environmental indicators to lower levels considering territorial heterogeneity. Hence, it provides a regional database for resource consumption that represents a critical input to expand the understanding on the complex relationship between resource consumption, territorial contexts and socioeconomic drivers. The analysis highlights the existence of a significant technological gap between urban and rural regions, the latter struggling the most to recover from economic crises and to retain human capital. Going further, a closer inspection on the impacts of socioeconomic drivers on resource efficiency across different regional economic structures reveals that increased access to capital would generate higher resource efficiency returns in material-intensive economies, compared with service-based economies. Differently, increased agglomeration levels represent the best resource efficiency leverage across urban territories.Overall, the thesis brings into discussion a renewed interest for the consideration of territorial aspects for a better understanding of the dialectics between the underlying forces driving regional resource efficiency and the different opportunities and challenges that regions might face according to their specific endowments