The rapid economic growth of China in the last twenty years has caused a commensurate rise in atmospheric pollution which has had an impact on both the environment and public health. Since 2013, SO₂, CO₂ and nitrogen oxide levels have reached a level that may cause climate change and have adverse effects on the health of the local residents. Past environmental efficiency analyses have rarely examined economic development, air pollution and health as interacting systems; therefore, this study used a new two-stage DEA model, the Modified Undesirable EBM Two Stage DEA (Epsilon-Based Measure) to explore the environmental, economic and health efficiencies in thirty-one major cities in China. The results were as follows: while all cities needed to improve their GDP, the environmental efficiencies were continuing to rise in most cities. The health efficiency index indicated that disease efficiency had increased in most cities but declined in one third; therefore, it is necessary to strengthen treatment. The respiratory disease treatment efficiency in most cities was rising, and the room for improvement had significantly reduced. There were improvements in the mortality rate in 15 cities; however, the mortality rate treatment efficiency declined in 11 cities.

To mitigate the problems associated with climate change, the low-carbon economy concept is now being championed around the world in an effort to reduce greenhouse gas emissions and ensure sustainable economic growth. Therefore, to reduce the dependence on traditional energy sources, the Organization for Economic Co-operation and Development (OECD) has been actively promoting the use of renewable energy. Past research has tended to neglect the influence of other pollutants such as fine particulate matter (PM_2.5) and sulfur dioxide (SO₂) and have mainly been based on static analyses. To make up for these research gaps, this study examined OECD country data from 2010–2014, with labor, fixed assets, new energy, and traditional energy as the inputs, and Gross Domestic Product (GDP), carbon dioxide (CO₂), and PM_2.5 as the outputs, from which it was found: (1) the overall efficiency of the individual countries varied significantly, with nine countries being found to have efficiencies of 1 for all five years, but many others having efficiencies below 0.2; (2) in countries where there was a need for improvements in traditional energy (which here refers to coal, petroleum and other fossil energy sources), there was also a significant need for improvement in new energy sources (which here refers to clean energy which will produce pollutant emissions and can be directly used for production and life, including resources like nuclear energy and “renewable energy”); (3) countries with poor traditional energy and new energy efficiencies also had poor CO₂ and PM_2.5 efficiencies; (4) many OECD countries have made progress towards sustainable new energy developments

Environmental pollution and the associated societal health issues have attracted recent research attention. While most research has focused on the effect of air pollution on human health and local economies, few articles have discussed the environment, health, and economic development in in an integrated analysis. This paper used a Dynamic Network SBM Model to evaluate production and health efficiencies in Chinese cities and found that the production efficiency scores were slightly higher than the health efficiency scores, with the two-stage efficiency scores in most cities having significant fluctuations. Labor, fixed assets, energy, GDP, and lung disease and mortality reduction efficiencies in the first stage were generally high; however, the medical input efficiencies in the second stage were low, indicating that there was there significant room for improvement in many cities.

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.

This study utilizes the dynamic data envelopment analysis (DEA) model by considering time to measure the energy environmental efficiency of 28 countries in the European Union (EU) during the period 2006–2013. There are three kinds of variables: input, output, and carry-over. The inputs are labor, capital, and energy consumption (EC). The undesirable outputs are greenhouse gas emissions (GHE) and sulfur oxide (SOx) emissions, and the desirable output variable is gross domestic product (GDP). The carry-over variable is gross capital formation (GCF). The empirical results show that first the dynamic DEA model can measure environment efficiency and provide optimum improvement for inefficient countries, as more than half of the EU countries should improve their environmental efficiency. Second, the average overall scores of the EU countries point out that the better period of performance is from 2009 to 2012. Third, the output variables of GHE, SOx, and GDP exhibit a significant impact on environmental efficiency. Finally, the average value of others is significantly better than high renewable energy utilization (HRE) with the Wilcoxon test. Thus, the EU’s strategy for environmental energy improvement should be to pay attention to the benefits of renewable energy (RE) utilization, reducing greenhouse gas emissions (GHE), and enhancing the development of RE utilization to help achieve the goal of lower GHE.

China’s rapid economic growth is accompanied by increasing energy consumption and severe environmental problems. As sustainable development can only be achieved by reducing energy intensity, new energy and renewable energy investment, as well as improving traditional energy efficiency, is becoming increasingly important. However, past energy efficiency assessments using data envelopment analysis (DEA) models mostly focused on radial and non-radial DEA model analyses. However, traditional radial DEA models ignore non-radial slacks when evaluating efficiency values, and non-radial DEA models ignore the same proportionality as radial DEA when evaluating efficiency value slacks. To balance the radial and non-radial model characteristics and consider undesirable output, this study combines a modified Epsilou-based measure (EBM) DEA and undesirable output and proposes a modified undesirable EBM DEA model to analyze the efficiency of China’s new and traditional energy sources. The empirical results found that (1) most new energy investment in most municipalities/provinces rapidly grew from 2013 to 2016; (2) as the annual efficiency score was only 1 in Beijing, Inner Mongolia, Shanghai, and Tianjin, the other 26 municipalities/provinces need significant improvements; (3) traditional energy efficiency scores were higher than new energy efficiency; and (4) NO₂ efficiencies are slightly better than CO₂ and SO₂ efficiencies.