This study pays more attention to the energy consumption saving, environmental pollution, and health efficiency improvement. We employ the Slack-based measure of Dynamic network Data Envelopment Analysis (DEA) model (DNSBM) to assess the impact of forestry area on annual and overall energy and health efficiency in 2 intertemporal stages, and also put forward on direction and magnitude to be improved respect to the slack variables. For the empirical study, this study employs the 13 countries in the Association of Southeast Asian Nations Plus Three Cooperation (hereinafter referred to as APT) during 2011-2015. From the empirical evidence, it is not easy to raise gross domestic product while reducing energy consumption and PM2.5 emissions to improve energy efficiency. What makes people neglect is the impact of reduced forestry area on health efficiency. Optimistically, all economies are able to adopt measures from policy and technical perspectives, for instance, appropriately adjust energy-related policies, energetically develop innovative energy technologies, and preserve forestry areas, to create a harmonious atmosphere featuring economic development, environmental conservation, and national health and well-being.

This study introduces the translation adjustment model of Seiford and Zhu (2002) into dynamic DEA models to measure and analyze the dynamic energy efficiency of Asia-Pacific Economic Cooperation (APEC) economies from 2010 to 2014. The APEC economies are divided into annual energy and overall energy efficiency ratings, and improvement directions are proposed for the different variables. With the proposal of magnitude, this study discusses the changes in intertemporal conversion variables and proposes suggestions for improvement. Finally, this study analyzes the implications of energy investment and the efficiency policies of APEC economies. The results show that economies with the lowest overall energy efficiency ratings have great potential for improvement. Reducing capital stock, labor, fossil fuel consumption, and CO2 emissions while increasing GDP can increase energy efficiency ratings. However, economies do not want to reduce the state’s capital stock, and labor and population birth adjustments are difficult. Energy efficiency can only start by adjusting the consumption of fossil fuels, CO2 emissions, and GDP. The results indicate that to improve energy efficiency and reduce fossil fuel consumption and CO2 emissions, economies are expected to increase their GDP unless they enact cuts through policy and technical approaches, appropriately adjust their energy policies, and actively develop new energy technologies to effectively reduce CO2 emissions and achieve optimal energy efficiency.

This study applies the dynamic slacks-based measure (DSBM) and the total-factor agricultural efficiency (TFAE) to explore the overall agricultural production efficiency of 30 administrative regions and the eastern, central, and western regions of China from 2012 to 2016. The previous literature has mainly focused on China’s economic development and experience, but as the economy continues to grow, more food is needed and agricultural labor is shifting to urban areas. Little attention has been paid to the impact of limited agricultural land on agricultural production efficiency. Therefore, this paper uses the agricultural land area as the carry-over variable and uses agricultural labor, total agricultural machinery power, rural electricity consumption, agricultural fertilizer use, and agricultural GDP as variables to discuss the efficiency of agricultural production in different regions. The empirical results show that from 2012 to 2016, the best administrative region in terms of overall agricultural production efficiency in China was the east. In terms of the overall analysis of the region, the east had the highest overall agricultural production efficiency, while the central region had the lowest. The input variable that needed the most improvement was rural electricity consumption, with the largest adjustment in rural electricity consumption being observed in Hebei and Liaoning provinces of the eastern region. Furthermore, from 2012 to 2016, both overall agricultural production efficiency and agricultural GDP showed upward trends. However, adjustments are still needed for other relevant agricultural input variables to effectively allocate resources and improve the overall agricultural production efficiency.

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The purpose of this study is to explore the impact of pollution control on industrial production efficiency in 31 provinces and cities in the Yellow River and Non-Yellow River basins in China from 2013 to 2017, using the methods of the directional distance function (hereinafter referred to as DDF) and the technology gap ratio (hereinafter referred to as TGR) in parallel, while taking the industrial production sector (labor force, total capital formation, energy consumption and industrial water consumption) and the pollution control sector (wastewater treatment funds and waste gas treatment funds) as input variables. Undesirable outputs (total wastewater discharge, lead, SO2 and smoke and dust in wastewater) and an ideal output variable (industrial output value) are taken as output variables. It is found that the total efficiency of DDF in the Non-Yellow River Basin is 0.9793, which is slightly better than 0.9688 in the Yellow River Basin. Among the 17 provinces and cities with a total efficiency of 1, only Shandong and Sichuan are located in the Yellow River Basin. The TGR values of 31 provinces, cities and administrative regions are less than 1, and the average TGR value of the Yellow River Basin is 0.3825, which is lower than the average TGR value of the Non-Yellow River Basin of 0.5234. We can start by improving the allocation of manpower and capital, implementing the use of pollution prevention and control funds, improving the technical level of industrial production, improving pollutant emission, and increasing output value to improve overall efficiency performance. This study uses the parallel method, taking the industrial production department and the pollution control department as inputs, to objectively evaluate the changes in industrial production efficiency and technology gap in the Yellow River and Non-Yellow River basins, which is conducive to mastering the situation of pollution control and industrial production efficiency, and provides the reference for SDG-6- and SDG-9-related policy making.