Search Results (7)

Green development is an effective way to reconcile the main contradictions between resources, environment, and regional development. Green total factor productivity (GTFP) is an important index to measure green development; an undesirable output-oriented SBM-DEA model and GML model can be used to calculate GTFP. China’s 30 provinces (municipalities and autonomous regions) are divided into three groups: eastern, central, and western. The common frontier function and group frontier function are established, respectively, to deeply explore the temporal and spatial evolution characteristics and center of gravity shift of inter-provincial green total factor productivity (GTFP) in China, and test the convergence under group frontier, to compare the convergence problems under different regions. This study aims to point out the differences in economic growth in different regions of China, foster regional coordination and orderly progress, promote China’s green development process, and improve the high-quality economic development level. According to the results, the efficiency of green development is more reasonable under the frontier groups. The average TGR in the eastern region was 0.993, indicating that it reached 99.3% of the meta-frontier green development efficiency technology. The inter-provincial GTFP in China gradually increased, with an average value of 1.043, which means China’s green development and ecological civilization construction have achieved remarkable results and the three regions showed significant differences. Judging from the shift path of the spatial center of gravity, the spatial distribution pattern of inter-provincial GTFP in China tends to be concentrated and stable as a whole. Moreover, σ convergence only exists in the western region, while absolute β convergence and conditional β convergence exist in eastern, central, and western regions, indicating that the GTFP of different regions will converge to their stable states over time. The results provide a basis for improving the efficiency of institutional allocation of environmental resources, implementing regional differentiated environmental regulation policies, and increasing the value creation of factor resources, which is of great significance for realizing the high-quality economic development in which resources, environment, and economy are coordinated in China. Full article

The mining industry is one of the pillar industries of Guangxi’s economic and social development. The output value of mining and related industries accounts for 27% of the whole district’s total industrial output value. Therefore, the mining eco-efficiency measurement in Guangxi can be of great significance for the sustainable development of Guangxi’s mining industry. This study adopted Meta-US-SBM to measure the mining eco-efficiency in Guangxi from 2008 to 2018, including economic efficiency, resource efficiency, and environmental efficiency. It used the standard deviation ellipse model to simulate the migration trend of four efficiencies in Guangxi and used GeoDetector and Tobit models to explore the internal and external factors that affect the mining eco-efficiency. The four efficiencies in Guangxi show large temporal and spatial heterogeneity, and the internal and external factors that affect the mining eco-efficiency are different. The following conclusions can be drawn. (1) Environmental efficiency and mining eco-efficiency are improving, while economic efficiency and resource efficiency are deteriorating. Cities bordering other provinces have a significantly better mining eco-efficiency than non-bordering cities. (2) The development center in Guangxi has migrated to the Beibu Gulf Economic Zone. (3) Natural resources index and mining economic scale have a great impact on the mining eco-efficiency, and with the increase of the mining economic scale, the mining eco-efficiency showed a typical “U-shaped” curve. Finally, this study put forward corresponding policy recommendations to improve the mining eco-efficiency in Guangxi from four aspects: opening-up, technological progress, regional coordination, and government control. Full article

As the largest emitter of CO₂, China has also serious air pollution issues. Is it possible to catch these two rabbits under heterogenetic conditions of urbanization? To answer this, this study examines atmospheric environmental performance (SO₂, NO_x, and PMs) of 30 major cities in China using streaming data from 2011 to 2017. A non-radial SBM-DEA approach is adopted with a meta-frontier model to evaluate regional heterogeneity in atmospheric environmental management. Our results suggest that pollution prevention and regulation policies encouraged synergic development of most cities in the economy and atmospheric environment. On average, atmospheric environmental efficiency of the cities improved from 0.556 to 0.691. However, significantly unbalanced development exists in the regions, requiring customized policies. Eastern cities achieved continuing improvement owing to stringent air pollutant emission policies. Central cities showed a strong improvement but lacked momentum after they achieved certain targets. Western cities lagged behind in the studying period due to both technology gap as well as weak regulation. Furthermore, we identify heterogeneous paths for inefficient cities to enhance their performance using benchmark information. Economically developed eastern cities, such as Beijing, Fuzhou, are facing an over-supply issue. Reshaping their economic structure may be necessary to attain better environmental performance. Central cities face diversified issues. The emphasis of different cities may vary from stringent emission policies to proactive supply-side transition to achieve strong atmospheric management performance. For under-developed cities, preferential policies for investment and tax incentives may be needed to improve their production scale for higher efficiency. Full article

The phenomena of “large energy consumption, high carbon emission, and serious environmental pollution” are against the goals of “low energy consumption, low emissions” in China’s industrial sector. The key to solving the problem lies in improving total factor energy efficiency (TFEE) and carbon emission efficiency (TFCE). Considering the heterogeneity of different sub-industries, this paper proposes a three-stage global meta-frontier slacks-based measure (GMSBM) method for measuring TFEE and TFCE, as well as the technology gap by combining meta-frontier technology with slacks-based measure (SBM) using data envelopment analysis (DEA). DEA can effectively avoid the situation where the technology gap ratio (TGR) is larger than unity. This paper uses the three-stage method to empirically analyze TFEE and TFCE of Anhui’s 38 industrial sub-industries in China from 2012 to 2016. The main findings are as follows: (1) Anhui’s industrial sector has low TFEE and TFCE, which has great potential for improvement. (2) TFEE and TFCE of light industry are lower than those of heavy industry under group-frontier, while they are higher than those of heavy industry under meta-frontier. There is a big gap in TFEE and TFCE among sub-industries of light industry. Narrowing the gap among different sub-industries of light industry is conducive to the overall improvement in TFEE and TFCE. (3) The TGR of light industry is significantly higher than that of heavy industry, indicating that there are sub-industries with the most advanced energy use and carbon emission technologies in light industry. And there is a bigger carbon-emitting technology gap in heavy industry, so it needs to encourage technology spillover from light industry to heavy industry. (4) The total performance loss of industrial sub-industries in Anhui mainly comes from management inefficiency, so it is necessary to improve management and operational ability. Based on the findings, some policy implications are proposed. Full article

China has become the second-largest economy in the world; however, the price of its rapid economic development has been a rise in serious environmental pollution, with air quality being a major public issue in many regions. However, few previous energy and environmental sustainability studies have included the Air Quality Index (AOI) and in particular CO₂ and PM_2.5 emissions in their calculations and few have included regional differences, as these are difficult to describe using radial and non-radial methods. In this paper, DEA (Data Envelopment Analysis) is used to assess the energy and economic efficiencies of Chinese provinces and cities, in which the environmental pollution source variable is CO₂, and the main methods applied are radial (CCR or BCC) and non-radial SBM (Slacks Based Measures). Different from past studies, this study used both a Meta Undesirable EBM (Epsilon-Based measure) method to overcome the radial and non-radial errors and geographical differences and AQI environmental pollution indicators to accurately assess the economic, energy, and environmental efficiencies. It was found that: (1) Guangzhou and Shanghai had the best four-year efficiencies, (2) the energy efficiency differences in each city were large and there was a significant need for improvements, (3) the GDP efficiencies in each city were high, indicating that all cities had strong economic development, (4) the CO₂ efficiencies indicated that around half the cities had had sustained improvements, (5) the AQI efficiencies in each city were low and there was a significant need for improvement, and (6) the technological differences between the cities were large, with the efficiencies in the high-income cities being much higher than in the low-income cities. Full article

With the rapid development of its economy, environmental governance is becoming more important in China. The Yangtze River Economic Belt (YREB), as the world’s largest inland shipping channel, can lead the country’s regional green economy development. As most research on China’s environmental efficiency focuses on provinces or the east and west regions, this paper examines its energy input and output and environmental effects from the aspects of YREB and non-YREB, breaking through the limitations of previous studies that only used cross-section or panel data for environmental assessment. This paper employs the meta-frontier dynamic SBM model, selects fixed assets as carry-over indicators, and considers the interrelationships between the dynamics variables during 2014–2016. The results are as follows: The overall energy efficiency and CO₂ emission efficiency of YREB are higher than those of non-YREB. The difference in energy consumption, CO₂, and AQI efficiency is large, but the performance of YREB is generally better than that of non-YREB. After setting the meta-frontier, non-YREB is better than YREB, for the main reason that the technology gap values of YREB are smaller than those of non-YREB. Our findings thus suggest that YREB should strengthen technical exchanges and promotion within its region, thereby decreasing regional technology differences, while non-YREB should address environment protection and CO₂ emissions and advocate a low-carbon production mode. Full article

The present paper investigates the impact of pollution information transparency index (PITI) on eco-efficiency using a novel panel dataset covering 109 key environmental protection prefecture-level cities in China over the period 2008–2015. We apply an extended data envelopment analysis (DEA) model, simultaneously incorporating metafrontier, undesirable outputs and super efficiency into slack-based measure (Meta-US-SBM) to estimate eco-efficiency. Then, the bootstrap Granger causality approach is utilized to test the unidirectional Granger causal relationship running from PITI to eco-efficiency. Results of DEA model show that there exist significant spatiotemporal disparities of eco-efficiency, on average, the eco-efficiency in eastern region is relative higher than those of central/western region. Estimates of ordinary least square (OLS) method, quantile regression, and spatial Durbin model document that the evidence of an inverted-U-shaped relation between PITI and eco-efficiency is supported, and the turning points vary from 0.3370 to 0.4540 with different model specifications. Finally, supplementary analysis of panel threshold model also supports the robust findings. Policy implications are presented based on the empirical results. Full article