基于改進的超效率SBM中國工業(yè)能源效率評價
發(fā)布時間：2018-09-11 19:22
【摘要】：中國經(jīng)濟的快速增長導(dǎo)致大量能源消耗和CO_2排放。如今在CO_2排放和能源消耗方面,中國已經(jīng)超過美國,成為世界上最大的國家。為實現(xiàn)可持續(xù)發(fā)展、提高能源效率和控制溫室氣體排放,中國政府提出了建設(shè)環(huán)境友好型和資源節(jié)約型社會的戰(zhàn)略目標(biāo)。工業(yè)占據(jù)我國最大的終端能源消耗,占全國能源終端消費比例的70%,工業(yè)的能源利用情況對我國總體的能源利用情況有著重要的影響。本文對基于松弛變量視角的超效率SBM模型進行了改進,引入了非期望產(chǎn)出使得該模型更貼近生產(chǎn)實際,得出的結(jié)果更加合理。在本文改進的模型基礎(chǔ)上結(jié)合逆DEA方法,計算出SBM有效地決策單元的能源最小節(jié)省量,用于構(gòu)建對SBM有效的決策單元進行評價的能源效率指標(biāo)。本文主要結(jié)論如下:中國各地區(qū)的工業(yè)能源效率情況差別明顯,其中天津、北京、上海和廣東每年的效率值均高于1,遠遠領(lǐng)先于其他省份,我國各個省的效率值分布差異很明顯;東部能源效率值以明顯的優(yōu)勢高于中部和西部,中部區(qū)域排第二,西部區(qū)域能源效率值排最后;Tobit回歸分析結(jié)果表明地區(qū)GDP、地區(qū)人均GDP、經(jīng)濟結(jié)構(gòu)、外商直接投資、工業(yè)新增固定投資和地理位置位于東部對地區(qū)工業(yè)能源效率有積極影響,地區(qū)產(chǎn)業(yè)結(jié)構(gòu)和人口密度對地區(qū)工業(yè)能源效率有消極的影響,地理位置位于中部對工業(yè)能源效率沒有顯著影響;節(jié)能潛力較高的地區(qū)為河北、河南、山東等地區(qū),山東、河南、河北等是減排潛力最大的地區(qū);區(qū)域節(jié)能潛力為中部和西部交替居首位和第二位,東部地區(qū)位居第三,區(qū)域減排潛力為中部區(qū)域CO_2減排潛力最大,西部區(qū)域第二,東部區(qū)域最后�；谝陨系慕Y(jié)論和文中的分析,對于提升工業(yè)能源效率給出如下建議:提升效率水平較低地區(qū)的經(jīng)濟活動總量;根據(jù)地區(qū)能源產(chǎn)出的特點來調(diào)整地區(qū)工業(yè)結(jié)構(gòu);加快我國能源結(jié)構(gòu)調(diào)整和工業(yè)企業(yè)電能替代;定量化企業(yè)的碳排放額度,開放碳排放的交易市場。
[Abstract]:China's rapid economic growth has led to massive energy consumption and CO_2 emissions. China has now overtaken the United States as the world's largest country in terms of CO_2 emissions and energy consumption. In order to achieve sustainable development, improve energy efficiency and control greenhouse gas emissions, the Chinese government has put forward the strategic goal of building an environmentally friendly and resource-efficient society. Industry occupies the largest terminal energy consumption in China, accounting for 70% of the national energy terminal consumption. The energy utilization of industry has an important impact on the overall energy utilization in China. In this paper, the super-efficiency SBM model based on relaxation variable is improved, and the non-expected output is introduced to make the model more close to the production practice, and the result is more reasonable. Based on the improved model and inverse DEA method, the minimum energy saving of SBM efficient decision making unit is calculated, which is used to construct the energy efficiency index for evaluating the effective decision making unit of SBM. The main conclusions of this paper are as follows: there are significant differences in the industrial energy efficiency among different regions in China. Among them, Tianjin, Beijing, Shanghai and Guangdong all have efficiency values higher than 1 per year, far ahead of other provinces. The distribution of efficiency values in various provinces in China is very obvious; the energy efficiency values in the east are significantly higher than those in the central and western regions, and the central regions rank second. The results of Tobit regression analysis show that the per capita GDP, economic structure, foreign direct investment (FDI), new fixed investment in industry and location in the east have a positive effect on the industrial energy efficiency in the GDP, region. The regional industrial structure and population density have a negative impact on the regional industrial energy efficiency, the geographical location in the central region has no significant impact on the industrial energy efficiency, and the regions with high energy saving potential are Hebei, Henan, Shandong, Shandong, and Henan. Hebei and so on are the regions with the greatest emission reduction potential, the regional energy saving potential is the first and second in the central and western regions, the eastern region is the third, the regional emission reduction potential is the largest in the central region and the second in the western region. Last in the eastern region. Based on the above conclusions and the analysis in this paper, the following suggestions are given to improve the industrial energy efficiency: to promote the total economic activity in areas with low efficiency level, to adjust the regional industrial structure according to the characteristics of regional energy output; Speed up the adjustment of energy structure and electric power substitution of industrial enterprises, quantify the carbon emission quota of enterprises, and open up the trading market of carbon emissions.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F424
本文編號：2237642
[Abstract]:China's rapid economic growth has led to massive energy consumption and CO_2 emissions. China has now overtaken the United States as the world's largest country in terms of CO_2 emissions and energy consumption. In order to achieve sustainable development, improve energy efficiency and control greenhouse gas emissions, the Chinese government has put forward the strategic goal of building an environmentally friendly and resource-efficient society. Industry occupies the largest terminal energy consumption in China, accounting for 70% of the national energy terminal consumption. The energy utilization of industry has an important impact on the overall energy utilization in China. In this paper, the super-efficiency SBM model based on relaxation variable is improved, and the non-expected output is introduced to make the model more close to the production practice, and the result is more reasonable. Based on the improved model and inverse DEA method, the minimum energy saving of SBM efficient decision making unit is calculated, which is used to construct the energy efficiency index for evaluating the effective decision making unit of SBM. The main conclusions of this paper are as follows: there are significant differences in the industrial energy efficiency among different regions in China. Among them, Tianjin, Beijing, Shanghai and Guangdong all have efficiency values higher than 1 per year, far ahead of other provinces. The distribution of efficiency values in various provinces in China is very obvious; the energy efficiency values in the east are significantly higher than those in the central and western regions, and the central regions rank second. The results of Tobit regression analysis show that the per capita GDP, economic structure, foreign direct investment (FDI), new fixed investment in industry and location in the east have a positive effect on the industrial energy efficiency in the GDP, region. The regional industrial structure and population density have a negative impact on the regional industrial energy efficiency, the geographical location in the central region has no significant impact on the industrial energy efficiency, and the regions with high energy saving potential are Hebei, Henan, Shandong, Shandong, and Henan. Hebei and so on are the regions with the greatest emission reduction potential, the regional energy saving potential is the first and second in the central and western regions, the eastern region is the third, the regional emission reduction potential is the largest in the central region and the second in the western region. Last in the eastern region. Based on the above conclusions and the analysis in this paper, the following suggestions are given to improve the industrial energy efficiency: to promote the total economic activity in areas with low efficiency level, to adjust the regional industrial structure according to the characteristics of regional energy output; Speed up the adjustment of energy structure and electric power substitution of industrial enterprises, quantify the carbon emission quota of enterprises, and open up the trading market of carbon emissions.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：F224;F424
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