本文選題：數(shù)據(jù)包絡(luò)分析(DEA) + 異質(zhì)性��； 參考：《山西大學(xué)》2017年碩士論文

【摘要】：目前的現(xiàn)實(shí)背景是:世界各國(guó)都面臨著生態(tài)環(huán)境問(wèn)題愈發(fā)嚴(yán)重的能源資源危機(jī)。2009年后,中國(guó)成為了全世界消耗能源最多的國(guó)家,也是發(fā)展中國(guó)家中排放CO2最多的,節(jié)省能源消耗和減少?gòu)U氣排放是長(zhǎng)時(shí)間以來(lái)留給中國(guó)的非常重要的歷史任務(wù)和使命,這個(gè)是我國(guó)必須肩負(fù)起的。所以這就使得合理系統(tǒng)得測(cè)算我國(guó)的省域能源利用效率以及在結(jié)果之上提出有針對(duì)性的能源政策,給我國(guó)有智慧地走未來(lái)道路賦予了非同一般的現(xiàn)實(shí)意義。先測(cè)算能源效率然后提出能夠提升能源效率的方法是目前大量文獻(xiàn)的主要關(guān)注點(diǎn)。本文不同于傳統(tǒng)的DEA方法,考慮能源現(xiàn)實(shí)環(huán)境中決策單元的投入產(chǎn)出指標(biāo)的非同質(zhì)現(xiàn)象,先將決策單元內(nèi)部元素拆分為一系列和決策單元有著相同的投入產(chǎn)出指標(biāo)的互斥的子決策單元;然后采納DEA交叉效率方法計(jì)算子決策單元效率;最后通過(guò)子決策單元加權(quán)平均效率得出決策單元綜合效率,以此來(lái)測(cè)算中國(guó)31個(gè)省份2010年到2014年的能源效率排名。最后得出5年來(lái)依然是中西部被東部遠(yuǎn)遠(yuǎn)領(lǐng)先的結(jié)論。雖然三個(gè)區(qū)域能源效率值有所起伏,但整體上能源效率都有所增加。本論文的創(chuàng)新之處有以下3個(gè)方面:(1)在多流程DEA模型中測(cè)算子決策單元的流程效率時(shí),采用DEA交叉效率模型代替?zhèn)鹘y(tǒng)的CCR模型,交叉效率模型采用相對(duì)被評(píng)價(jià)單元的自我評(píng)價(jià)和他人評(píng)價(jià)兩種方法融合的辦法來(lái)測(cè)算決策單元的相對(duì)效率,減弱了經(jīng)典DEA模型結(jié)論有過(guò)多一致的效率值從而無(wú)法準(zhǔn)確判斷效率大小的弊端。(2)在選用多流程DEA模型計(jì)算出2010年到2014年中國(guó)各省域能源效率后,采用灰色關(guān)聯(lián)TOPSIS法對(duì)31個(gè)省份的能源效率值進(jìn)行排序,最終得出綜合排序。由于TOPSIS法有著需要決策的方案和最佳值的貼近度不能從形狀上反映的缺陷,灰色關(guān)聯(lián)剛好是反映變化形勢(shì)的貼近程度從而彌補(bǔ)了缺陷,所以通過(guò)灰色關(guān)聯(lián)與TOPSIS相結(jié)合,可以彌補(bǔ)灰色關(guān)聯(lián)和TOPSIS法各自的缺點(diǎn),提升決策結(jié)果的全面性。(3)區(qū)別于傳統(tǒng)方法的投入指標(biāo)選取,前人的文獻(xiàn)中,在測(cè)算能源效率時(shí),能源資源是作為一個(gè)投入指標(biāo)測(cè)算的,而本文將能源拆分為煤、天然氣和石油三個(gè)投入指標(biāo)進(jìn)行測(cè)算,雖測(cè)算結(jié)果與前人文獻(xiàn)有所差異,但可以從更具體的能源資源使用情況分析各省域的能源效率問(wèn)題,得出更具針對(duì)性的結(jié)論。
[Abstract]:At present, the realistic background is that every country in the world is facing an increasingly serious energy and resource crisis of ecological and environmental problems. After 2009, China has become the most energy-consuming country in the world and the largest emitter of CO2 among developing countries. Saving energy consumption and reducing exhaust emissions is a very important historical task and mission for China for a long time, which our country must shoulder. Therefore, this makes it reasonable and systematic to measure the energy efficiency of our country and put forward the targeted energy policy on the basis of the results, which gives our country an extraordinary practical significance to take the future path intelligently. First measuring energy efficiency and then proposing methods to improve energy efficiency is the main focus of a large number of literatures. In this paper, different from the traditional DEA method, we consider the input-output index of the decision making unit in the energy realistic environment. First, the internal elements of the decision making unit are divided into a series of mutually exclusive decision units with the same input-output index as the decision making unit, and then the efficiency of the sub-decision unit is calculated by using the DEA cross-efficiency method. Finally, the comprehensive efficiency of Decision-making units is obtained by weighted average efficiency of sub-Decision-making units, and the energy efficiency rankings of 31 provinces in China from 2010 to 2014 are calculated. Finally came to the conclusion that the central and western regions are still far ahead of the east in the past five years. Although the energy efficiency of the three regions fluctuates, the overall energy efficiency has increased. The innovations of this paper are as follows: 1) when measuring the process efficiency of sub-decision units in the multi-process DEA model, the DEA cross-efficiency model is used instead of the traditional CCR model. The cross-efficiency model measures the relative efficiency of the decision making unit by combining the self-evaluation of the relative evaluated unit and the evaluation of others. It weakens the drawback that the classical DEA model has too many consistent efficiency values so that it can not accurately judge the efficiency. (2) after selecting the multi-process DEA model to calculate the energy efficiency in the provinces of China from 2010 to 2014, The energy efficiency value of 31 provinces is sorted by grey correlation TOPSIS method, and finally the comprehensive ranking is obtained. Because the TOPSIS method has the defect that the scheme and the best value's closeness cannot be reflected from the shape, the grey relation is just the close degree to reflect the changing situation, so it combines the grey correlation with TOPSIS. It can make up for the shortcomings of grey correlation and TOPSIS method, and improve the comprehensive decision result. It is different from the traditional method in selecting input index. In previous literature, energy resources are calculated as an input index in energy efficiency measurement. In this paper, energy is divided into three input indexes, coal, natural gas and petroleum. Although the calculation results are different from the previous literatures, we can analyze the energy efficiency in each province from the more specific situation of energy resources use. Draw more targeted conclusions.
【學(xué)位授予單位】：山西大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：F224;F426.2

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