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基于超效率SBM的中国省际工业化石能源效率评价及影响因素分析
引用本文:宫大鹏,赵涛,慈兆程,姚浩. 基于超效率SBM的中国省际工业化石能源效率评价及影响因素分析[J]. 环境科学学报, 2015, 35(2): 585-595
作者姓名:宫大鹏  赵涛  慈兆程  姚浩
作者单位:天津大学管理与经济学部, 天津 300072;天津大学管理与经济学部, 天津 300072;东北财经大学金融学院, 大连 116025;大连理工大学运载工程与力学学部, 大连 116024
基金项目:国家自然科学基金(No.71373172)
摘    要:工业占据我国最大的化石能源终端消耗,评价中国工业化石能源效率对于提高我国工业化石能源效率和实现节能减排目标具有重要意义.本文运用包含非期望产出的超效率SBM模型评价中国30个省(直辖市、自治区)和三大区域(东部、中部、西部)2006—2011年间的工业化石能源效率,运用Tobit回归模型分析工业化石能源效率的影响因素,并给出各区域节能减排潜力.研究结果表明:北京、天津、上海等地区每年的工业化石能源效率指数均远远领先于其他省份;东部区域效率值以明显的优势高于中部和西部区域;西部区域的效率值比较平稳;中部区域内部各地区效率值比东部和西部区域集中.Tobit回归分析结果表明,地区GDP、经济结构、外商直接投资、工业新增固定投资和地理位置位于东部对地区工业化石能源效率有积极影响;产业结构和地理位置位于中部对地区工业化石能源效率有消极影响;地区人均GDP对地区工业化石能源效率没有显著性影响.东部地区油品和天然气节能潜力较大,中部地区煤炭节能潜力较大,西部地区煤炭和天然气节能潜力较大;中部和东部地区是工业CO2减排的重点.基于以上研究本文给出了3点建议提升工业能源效率.

关 键 词:数据包络分析(DEA)  超效率SBM  工业化石能源效率  工业节能减排潜力  Tobit回归
收稿时间:2014-04-21
修稿时间:2014-06-17

Evaluation of regional industrial fossil energy efficiency in China based on super SBM and factors analysis
GONG Dapeng,ZHAO Tao,CI Zhaocheng and YAO Hao. Evaluation of regional industrial fossil energy efficiency in China based on super SBM and factors analysis[J]. Acta Scientiae Circumstantiae, 2015, 35(2): 585-595
Authors:GONG Dapeng  ZHAO Tao  CI Zhaocheng  YAO Hao
Affiliation:College of Management and Economics, Tianjin University, Tianjin 300072;College of Management and Economics, Tianjin University, Tianjin 300072;School of Finance, Dongbei University of Finance and Economics, Dalian 116025;Faculty of Vehicle Engineer and Mechanics, Dalian University of Technology, Dalian 116024
Abstract:Industry accounts for the largest fossil energy terminal consumption in China. Evaluation of industrial fossil energy efficiency is therefore necessary for China to achieve energy reduction targets and improve industrial fossil energy efficiency. This paper employs super SBM (slacks-based measure) with undesirable outputs to calculate the industrial fossil energy efficiency for 30 regions in China from 2006 to 2011. Tobit regression model is used to analyze the impact factors of industrial fossil energy efficiency, and potential energy savings and potential CO2 emission reductions are also calculated. The results show that metropolitans such as Beijing, Tianjin and Shanghai have higher efficiency scores, and energy efficiency of eastern area is much higher than that of central and western areas. Energy efficiency in the western is relatively stable, while that in the central area is more concentrated. Tobit regression analysis shows that regional GDP, regional economic structure, foreign direct investment, newly increased fixed assets and location in the eastern area have a positive impact on industrial fossil energy efficiency. Regional industry structure and location in the central area have a negative impact on industrial fossil energy efficiency, while regional per capita GDP has no significant impact on industrial fossil energy efficiency. In terms of energy consumption saving, eastern area has larger potential on oil and gas, while central area should concentrate on coal and west area on coal and gas. Central and eastern areas are the key area for CO2 emission reduction. Based on the above analysis we provides three suggestions in improving industrial energy efficiency.
Keywords:data envelopment analysis(DEA)  super SBM  industrial fossil energy efficiency  industrial fossil energy conservation and emission reduction  Tobit regression
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