“…Wu et al measured China's industrial energy efficiency using several environmental DEA models accounting for CO 2 emissions [21]. Valadkhani [22] adopted a multiplicative extension of environmental DEA models to measure environmental efficiency changes of the world's major polluters. Shi et al [23] measured industrial energy efficiency and the maximum energy-saving potential of 28 administrative regions in China.…”
Section: Studies Of Data Envelopment Analysis In Environmental Researchmentioning
This paper estimates China's water utilization efficiency using the directional distance function to take into account the environmental degradation affecting the economy. We further analyze the spatial correlation and the factors influencing the utilization efficiency using spatial panel data models. The results show that water utilization efficiency in China differs between provinces and regions. For example, water utilization efficiency in the eastern coastal provinces is significantly higher than that of inland provinces. The pattern of spatial auto-correlation Moran's I index presents significant spatial auto-correlation and evident cluster tendencies in China's inter-provincial water utilization. Factors that contribute to water utilization efficiency include economic development, technological progress, and economic openness. Negative factors affecting water utilization efficiency arise from industrial structure, government interference, and water resources endowment. In addition, the price of water resources is insignificant. The improvement of water utilization efficiency is essential to sustainable economic development. To raise the utilization efficiency of water resources, China should focus on transforming its industrial restructure, advancing technological development, enhancing economic openness, and encouraging entrepreneurial innovations. Moreover, establishing a mechanism to encourage water conservation and reduce wastewater pollution will further increase water utilization efficiency.
“…Wu et al measured China's industrial energy efficiency using several environmental DEA models accounting for CO 2 emissions [21]. Valadkhani [22] adopted a multiplicative extension of environmental DEA models to measure environmental efficiency changes of the world's major polluters. Shi et al [23] measured industrial energy efficiency and the maximum energy-saving potential of 28 administrative regions in China.…”
Section: Studies Of Data Envelopment Analysis In Environmental Researchmentioning
This paper estimates China's water utilization efficiency using the directional distance function to take into account the environmental degradation affecting the economy. We further analyze the spatial correlation and the factors influencing the utilization efficiency using spatial panel data models. The results show that water utilization efficiency in China differs between provinces and regions. For example, water utilization efficiency in the eastern coastal provinces is significantly higher than that of inland provinces. The pattern of spatial auto-correlation Moran's I index presents significant spatial auto-correlation and evident cluster tendencies in China's inter-provincial water utilization. Factors that contribute to water utilization efficiency include economic development, technological progress, and economic openness. Negative factors affecting water utilization efficiency arise from industrial structure, government interference, and water resources endowment. In addition, the price of water resources is insignificant. The improvement of water utilization efficiency is essential to sustainable economic development. To raise the utilization efficiency of water resources, China should focus on transforming its industrial restructure, advancing technological development, enhancing economic openness, and encouraging entrepreneurial innovations. Moreover, establishing a mechanism to encourage water conservation and reduce wastewater pollution will further increase water utilization efficiency.
“…Comparing equity financing efficiency among China's seven strategic emerging industries, Zhai (2012) finds that both the market and investors are attentive to the low-carbon clean technology sector, which leads to increased financial support for these industries. DEA has also been widely applied to other topics, such as environmental challenges and efficient allocation (Kim and Kim, 2012;Voltes-Dorta, et al, 2013;Sueyoshi and Goto, 2014 a;Ederer, 2015;Houshyar et al, 2012;Huang et al, 2014;Li and Lin, 2015;Mousavi-Avval et al, 2011 a;Valadkhani, et al, 2016;Vlontzos et al, 2014;Wang et al, 2014a;Sueyoshi and Goto, 2014 b;Yang and Pollitt, 2009;Mousavi-Avval et al, 2011b;Song et al, 2013;Nassiri and Singh, 2009).…”
Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.
“…Эмпирические исследования подтверждают более высокую экологическу ю эффективность развитых стран и регионов [Gómez-Calvet et al, 2014;Rashidi et al, 2015;Valadkhani et al, 2016;Vlontzos, 2014]. Например, в Китае [Chen, 2015;Shi et al, 2010;Wang et al, 2012; более эффективны восточные районы.…”
Section: краткий обзор исследований устойчивого развитияunclassified
Climate change as well as ecological and social problems requires new goals and instruments of economic policy, based on the principles of sustainable development. However, over the past 20 years, an increase in energy prices has resulted in the raw material growth model prevailing in Russia. Has this growth led to sustainable regional development? We propose an approach to evaluating ecological efficiency of the Russian regions as the ratio of the output of non-primary goods and services to the input of resources (labor, capital, raw materials, and environmental costs). This is a new indicator of the quality of economic growth. The sustainable development model, combining growth of GRP per capita and ecological efficiency, has been observed for more than half of the period in most regions. The eco-efficiency of the average region has been growing since 2003, except crisis periods, following an increase of the services sector share and the closure of inefficient pollution-intensive factories. According to the econometric results, ecological efficiency was growing faster in densely populated regions with a high share of high-tech services, investment attractiveness and intensive technology implementation (Moscow; Saint Petersburg; Sverdlovsk, Tomsk, Belgorod and Kaliningrad regions etc.); it decreased in most northern and Siberian regions. Great potential for raising eco-efficiency remains in most regions. In general, the results of regional development in Russia do not contradict the principles and goals of sustainable development (SDGs), although it was largely achieved due to the system of inter-budget transfers, distributing the oil rent surplus among the regions. In the future, an increase in investments in the non-primary sector, energy efficiency and public transportation will be required. Corresponding changes can be accelerated in the context of an emerging economic crisis caused by the pandemic and falling oil prices.
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