Assessing the environmental management efficiency of manufacturing sectors: evidence from emerging economies

The comprehensive environmental efficiency of socioeconomic sectors in China: An analysis based on a non-separable bad output SBM

“…Meanwhile, the transportation sector's energy consumption is 268.73Mt standard coal (6.98%), with 536.66Mt (6.57%) of CO 2 , 7000.87 Kt (24.54%) of NO 2 , 273.65 (15.59%) Kt of BC and 20326.41Kt (11.95%) of CO. Infrastructure investment and energy consumption will be further stimulated by the huge transportation demand (Cui and Li, 2014).Therefore, the agricultural, power, industrial, residential and transportation sectors are all expected to play an important role in the reduction of air pollutant emissions in China. In the context of complex regional atmospheric pollution along with traditional coal-based air pollution, investigation into China's baseline environmental efficiency by major socioeconomic sector and a demonstration of regions with higher environmental efficiency is of great importance for the success of nationwide persistent air pollution governance in China. Many studies are making an effort to incorporate data envelopment analysis (DEA) into the evaluation of environmental efficiency for China considering undesirable factors (see appendix Table A1) and are exploring environmental performance in different sectors, including agriculture (Lin and Fei, 2015;Lin, 2016, 2017), power generation (Zhou et al, 2013b;Bi et al, 2014;Lin and Yang, 2014;Song et al, 2017), industry (He et al, 2013;Zhou et al, 2013a;Wang and Wei, 2014;Wu et al, 2014;Bian et al, 2015;Xie et al, 2016) and transportation (Cui and Li, 2015;Liu et al, 2016;Song et al, 2016), in addition to limited research regarding the residential sector without involving China (Haas, 1997;Grösche, 2009).…”

“…Most studies of agricultural efficiency evaluation target technical efficiency or energy efficiency related to CO 2 emissions reduction (Lin and Fei, 2015;Lin, 2016, 2017); however, these overlook the most significant air pollutant, NH 3 , from agricultural sources as an undesirable output. Topics related to the industrial sectors of China include the evaluation of carbon efficiency (Emrouznejad and Yang, 2016;Zhang et al, 2016) and environmental efficiency taking NO 2 and SO 2 (Wang et al, 2014;Wu et al, 2014;Bian et al, 2015) or waste gas, waste water and solid waste (He et al, 2013;Zhou et al, 2013a;Xie et al, 2016) as bad outputs, with decision making units (DMUs) varying from provinces to cities or firms in industrial sectors of China. In addition to studies considering CO 2 as an undesirable output (Lin and Yang, 2014),studies focusing on Chinese power sectors have given the most attention to emissions of SO 2 and NOx from thermal power generation (Zhou et al, 2013b;Bi et al, 2014;Song et al, 2017) Some studies confirm the need to evaluate environmental performance and sustainability in the residential sector (Haas, 1997;Grösche, 2009) but DEA analysis has not yet been applied to this sector in China, let alone taking air pollutants such as CO emitted from residents into consideration. Similarly, with the M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 5 power and industrial sectors, a growing literature has examined carbon efficiency in the transportation sector of China (Cui and Li, 2015;Liu et al, 2016), and some studies have incorporated air pollutants such as SO 2 (Song et al, 2016).…”

“…In addition, although a series of DEA models have been employed in the literature for efficiency evaluation, such as the CCR model subject to the strong hypothesis of constant returns to scale and the DDF (He et al, 2013;Zhang et al, 2008), the BCC model (Xie et al, 2016) and the RAM model (Wang et al, 2016), as well as some developed SBMs, such as weighted, dynamic, super and network SBMs (Zhou et al, 2013a;Li and Shi, 2014;Lin and Yang, 2014;Wang and Feng, 2015;Song et al, 2017); these models cannot serve our purpose of identifying China's comprehensive provincial environmental efficiency performance in major sectors, especially considering that specific bad outputs such as PM are closely related (non-separable) to specific inputs such as coal consumption. Therefore, our paper tries to fill the gaps by employing a bad output model that considers non-separable situations related to inputs leading to undesirable outputs.…”

Implementation of the Lean-Kaizen Approach in Fastener Industries Using the Data Envelopment Analysis

“…Kuah et al [35] applied DEA as a benchmarking tool to measure and evaluate inefficient areas in quality systems for improvements. Xie et al [36] applied DEA to measure environmental management efficiency of manufacturing sector. Dabestani et al [37] used DEA to rank service quality dimensions using importance-performance analysis and to compare the outcomes for the customer groups.…”

Performance Evaluation of Czech Innovative Companies: Data Envelopment Analysis Approach

“…Higher efficiency of environmental management was found in industries that have a high rate of energy consumption and produce large amounts of pollution. This is the result of continual monitoring by the Ministry of Environmental Protection (Xie et al 2015).…”