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An Empirical Study on Industrial Ecological Efficiency in Arid

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An Empirical Study on Industrial Ecological Efficiency in Arid An Empirical Study on Industrial Ecological Eciency in Arid Resource Exploitation Region of Northwest China Xudong Zhou ( [email protected] ) Luzhou Vocational and Technical College https://orcid.org/0000-0001-8683-6746 Zhengkai Xue Luzhou Vocational and Technical College Jumeniyaz Seydehmet Luzhou Vocational and Technical College Research Article Keywords: Industrial ecological eciency, SBM-Undesirable model, Malmquist index, Input-output redundancy, Arid and resource-developing regions Posted Date: March 11th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-235836/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 An empirical study on industrial ecological efficiency in arid 2 resource exploitation region of northwest China 3 4 Xudong Zhou*a Zhengkai Xuea Jumeniyaz Seydehmetb 5 6 a Luzhou Vocational and Technical College; Luzhou, Sichuan Province, China, 7 b Jumeniyaz Seydehmet1. Collage of Geographical Science and Tourism, Xinjiang Normal University, Urumqi, 8 Xinjiang, China. 9 10 11 Abstract1 12 Located in the northwest of China, Xinjiang is a typical arid desert area and mineral resources 13 development zone. Lacking water resources and a fragile ecological environment restricts the 14 sustainable development of the region. Based on the industrial panel data of Xinjiang from 2001 to 15 2015, this paper uses the Undesirable Output SBM model, Malmquist index model, and Tobit 16 regression model to comprehensively and systematically measure and evaluate the industrial eco- 17 efficiency and its change characteristics from provincial, regional and prefectural levels. The results 18 show that:(1) The level of industrial eco-efficiency in Xinjiang is generally low, lower than the 19 national average, but it has been rising steadily over time, from 0.36 in 2001 to 1.00 in 2008, and 20 from 0.41 in the "Tenth Five-Year Plan" period to 0.99 in the "Twelfth Five-Year Plan" period. (2) 1 1 * Corresponding author. Luzhou Vocational and Technical College, Luzhou , Sichuan ,China Email: [email protected] 1 21 The industrial ecological efficiency of Xinjiang is not balanced in space. Northern Xinjiang is larger 22 than that of eastern Xinjiang and southern Xinjiang. The 14 prefectures have uneven and 23 asynchronous development, which can be divided into two development modes: industrial region 24 and agriculture and animal husbandry region. (3) Through the decomposition analysis of the 25 Malmquist index, it is found that the technology progress index is the restriction factor of the 26 changing trend of TFP, while the technical efficiency index and the pure technical efficiency index 27 are the promoting factors. (4) The main factors causing the loss of ecological efficiency are 28 industrial sulfur dioxide emissions, industrial nitrogen oxide emissions, total industrial water 29 consumption, general industrial solid waste. It can be seen that the emission of air pollutants and 30 excessive industrial water are the main problems in the region. (5) Industrial ecological efficiency 31 is positively correlated with industrial development level, scientific and technological innovation, 32 industrial structure, and environmental planning, and negatively correlated with opening up and 33 industrial agglomeration degree. (6) Xinjiang is an extremely arid and water-scarce region. These 34 are the key and prerequisite of saving water resources and strengthening the comprehensive 35 utilization of water resources. Water- saving should be given top priority no matter in industrial 36 areas, or agricultural and animal husbandry areas. 37 Keywords: Industrial ecological efficiency, SBM-Undesirable model, Malmquist index; Input- 38 output redundancy, Arid and resource-developing regions 39 1. Background 40 Since the implementation of the "Tenth Five-Year Plan" in 2001, Xinjiang has witnessed rapid 41 economic development with an average growth rate of 17% in industrial GDP. However, many 42 problems have been exposed. Xinjiang is located on the western border of China and the hinterland 2 43 of Eurasia (Fig1), with desert area accounting for nearly 60% of China's desert area. It is one of the 44 world's drought centers with a fragile ecological environment. Xinjiang is an important corridor of 45 the Silk Road Economic Belt. Meanwhile, coal, oil, natural gas, and other mineral resources are 46 abundant (Ma, 2013), and about 60% of prefectures in Xinjiang are listed as resource-based 47 prefectures and regions by the Chinese government (2013). Problems such as excessive resource 48 consumption, environmental pollution, and ecological imbalance are becoming increasingly serious, 49 which have become the bottleneck that further restricts the sustainable development of Xinjiang. 50 Therefore, it is very important to scientifically measure the ecological level and efficiency of 51 economic development in this region, which is also the focus of policymakers at all levels. 52 Ecological efficiency is the ratio of increased value to increased environmental impact (Schaltegger 53 et al., 1990), which has gained high attention in the study of sustainable development (Jollands et 54 al., 2004). It has become one of the research hotspots of Chinese and foreign scholars and industrial 55 ecology (Hahn et al.,2010; Lv et al., 2006) and has become an important analytical tool to measure 56 sustainable development. 57 Industrial ecological efficiency is defined as the ratio of the total amount of products produced 58 by industrial enterprises in a certain region to resource consumption and environmental impact (Gao 59 et al.,2011). At first, it was applied to the enterprise level. With the deepening of the research, it 60 gradually developed to the micro and macro aspects. Foreign countries focused on the micro aspects 61 such as the research on industrial enterprises and products. (Dahlstrom et al., 2005) focused on the 62 trend of eco-efficiency in the steel and aluminum industry; (Huppes et al., 2007) focused on the 63 Dutch oil and gas products; (Korhonen et al., 2004) focused on the ecological efficiency of the 64 power plant, (Aanand Dave et al., 2016) focused on the application of eco-efficiency model in 3 65 furniture manufacturers. In contrast, China focuses on large scale studies in cities and regions. (Gao 66 et al., 2004) (Lu et al., 2004), and (Wang et al., 2016) measured and evaluated the industrial 67 ecological efficiency of 30 provinces in China Other scholars have studied the industrial ecological 68 efficiency in Beijing (Wang et al., 2008), Shandong (Lu et al., 2014)], Sichuan (Liu et al., 2014) and 69 Hunan (Zhang et al., 2015). The relatively mature research methods of industrial ecological 70 efficiency mainly include traditional data envelopment analysis (DEA) (Gao et al., 2011&Wang et 71 al., 2011) and super- efficiency DEA model method (Fu et al., 2013&Li et al., 2018). However, the 72 traditional DEA model does not consider the slack of input and output in the evaluation of ecological 73 efficiency, and it may lead to the deviation of ecological efficiency measurement due to the choice 74 of radial and Angle (Hu.,2016 &Pan et al., 2013). Therefore, non-radial and non-angular SBM 75 models have been gradually applied and developed in recent years. Dan Pan and Ruiyao Ying used 76 the SBM-Undesirable model to measure the spatial and temporal distribution of agricultural eco- 77 efficiency in 30 provinces of China (Pan et al., 2013). Biao Hu and Yeteng Fu (Hu et al., 2016) 78 conducted an empirical study on the ecological efficiency of 30 provinces in China. Xiaodong Ma 79 et al. (Ma et al., 2018), Yong Zhou et al. (Zhou et al., 2020) and Song Hu et al. (Hu, 2016) 80 respectively applied and discussed the SBM model. 81 At present, there are few and incomplete research data on industrial ecological efficiency in 82 Xinjiang. Weiping Jia (Jia,2016) took Tianye of Xinjiang as the research object and discussed the 83 connotation and application of ecological efficiency of the Chlor-alkali chemical industry in 84 Xinjiang. This paper mainly uses the SBM model, Malmquist Index, and Tobit model to 85 comprehensively and systematically measure and evaluate the industrial eco-efficiency and its 86 change characteristics from provincial, regional and prefectural levels. The goal of the present study 4 87 was to explore annual change trends in industrial ecological efficiency, spatial distribution patterns, 88 and analyze the existing shortcomings, explore the ways of improvement and promotion, to provide 89 a reference for the sustainable development of resource-developing prefectures located in arid areas. 90 91 2. Materials and methods 92 2.1 Research Methods 93 2.1.1 SBM-Undesirable model 94 95 SBM-Undesirable model proposed by (Jia, 2016 &Tone,2001) Tone is a method to measure 96 ecological efficiency. Compared with the traditional data envelope analysis model, the SBM model 97 can take wastewater, waste gas, solid waste, and other unexpected outputs generated in the process 98 of economic development into account, making the calculation results more accurate and effective. 99 It effectively solves the difficulties encountered by the traditional DEA model when the input and 100 output elements increase and the corresponding slack is considered, and the deviation of ecological 101 efficiency calculation is caused by the choice of radial and Angle. The model formula is as follows: 102 1 푁 푥 1 − ∑푛=1 푆푛 /푋푛0 푁 푦 푈 푀푖푛휌 = 푀 푚 퐼 푖 1 푚=1 푆 푆 1 + (∑ ⁄ 푚0 + ∑푖=1 ⁄ 푖0) 푀 + 1 푦 푈 103 푁 퐾 푥 푦 푠. 푡. ∑ 푍푘 푋푛푘 + 푆푛 = 푋푛0,푛 = 1,2,…,푁; ∑푍푘 푦푚푘 − 푆푚 = 푦푚0, 푛 = 1,2, … , 푀 푛=1 푘=1 104 퐾 퐾 푢 푥 푦 푢 ∑ 푍푘 푈푚푘 + 푆푖 = 푈푖0,푖 =1,2,…,퐼;∑푍푘 = 1; 푍푘 ≥ 0; 푆푛 ≥ 0; 푆푚 ≥ 0; 푆푖 ≥ 0 푘=1 푘=1 105 106 Here, Xn0 represents the input variable of DMU, Ym0 represents the expected output variable 107 of DMU, and UI0 represents the unexpected output variable of DMU.
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