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1、<p> 2900英文單詞,15500英文字符,中文4700字</p><p> 文獻(xiàn)出處:Wang K, Yu S, Zhang W. China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation[J]. Mathematical & Computer
2、 Modelling, 2013, 58(5-6):1117-1127.</p><p><b> 原文:</b></p><p> China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation</p><
3、p> Ke Wang, Shiwei Yuc, Wei Zhang</p><p><b> Abstract</b></p><p> Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environment
4、al performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy r
5、esources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs</p><p> Introduction</p><
6、;p> In recent years, a growing number of researches have focused on evaluating, analyzing and improving energy efficiency, which is considered a crucial approach to mitigate global warming, since global warming is on
7、e of the world’s most important environmental problems at present. And this problem is largely attributed to the emission of greenhouse gases such as carbon dioxide (CO2) which is mainly related to the burning of fossil
8、fuels. </p><p> China’s economy has rapidly developed since the implementation of economic reform and the opening up to the outside policy in 1978. China’s gross domestic product (GDP) has increased from 36
9、4.52 billion RMB in 1978 to 34 050.69 billion RMB in 2009. However, this achievement has also led to inefficient natural resource utilization and given rise to serious environmental problems. Nowadays, China has already
10、become the world’s second largest economy, the world’s largest energy consuming country an</p><p> Energy efficiency is a relative concept and there are lots of different definitions of it. According to Ang
11、, three indicators are commonly used to measure energy efficiency: thermodynamic indicators, physical-based indicators, and monetary based indicators. Monetary-based efficiency, which refers to the energy consumption per
12、 unit current output, is often used to measure the economy-wide energy efficiency at the macro level. Since any economic production activity is a joint-production process it</p><p> At the macro-economy lev
13、el, data envelopment analysis (DEA) has recently been widely applied to studying the energy and environmental efficiency as it provides an appropriate method to deal with multiple inputs and outputs in examining relative
14、 efficiency. For instance, Hu and Wang proposed a total-factor energy efficiency evaluation method using DEA, and measured the energy efficiency of 29 regions in China. Zhou et al. developed several environmental DEA tec
15、hnologies and measured the carbon em</p><p> One weakness of Hu and Wang’s energy efficiency evaluation model is that it treats energy consumption as one input and GDP as desirable output, but does not cons
16、ider any undesirable output. However, this may be unreasonable in the real production process, as the use of energy always results in the emission of pollutants, like CO2 and SO2. In addition, one limitation of Zhou et a
17、l.’s environmental efficiency evaluation method is that it just deals with energy input, desirable and undesirable outp</p><p> In recent literature, there are some DEA based studies on energy efficiency an
18、d environmental efficiency evaluation that consider total factors and pollutant emissions. Zhou et al. proposed several DEA models to evaluate the environmental efficiency of 26 OECD countries from 1995 to 1997 and eight
19、 world regions in 2002, respectively. The former use labor and primary energy consumption as two inputs, GDP as the only desirable output, and CO2, sulphur oxides (SOx), nitrogen oxides (NOx), and carbo</p><p&
20、gt; Most recently, Bian and Yang proposed several DEA models to simultaneously measure resource (energy) and environmental efficiency, and applied their models in the resource and environmental efficiency evaluation pro
21、blem of 30 Chinese provinces. Shi et al. presented three extended DEA models that treated the undesirable outputs as inputs and made them decrease with energy inputs proportionally so as to calculate the energy and envir
22、onmental overall technical efficiency, pure technical efficiency,</p><p> In this study, we propose an improved DEA model which follows Bian and Yang’s method and combines DEA window analysis in order to gi
23、ve a dynamic evaluation of the energy and environmental efficiency of 29 regions in China during the period of 2000–2008. The rest of this paper is organized as follows. Section 2 presents the DEA based performance evalu
24、ating models and DEA window analysis for total-factor energy and environmental efficiency evaluation. Section 3 presents the data and variables. The</p><p> Methodology</p><p> In this section
25、, we present a non-radial input-oriented DEA model used to evaluate the total-factor energy and environmental efficiency. In addition, we explore the total-factor energy and environmental efficiency by applying DEA windo
26、w analysis to measure the efficiency in cross-sectional and time-varying data.</p><p> Improved DEA model for evaluating the energy and environmental performance</p><p> The DEA method is a no
27、n-parametric mathematical programming approach used to evaluate a set of comparable decision-making units (DMUs). Here we use the CCR model as the basic model to examine the total-factor energy and environmental efficien
28、cy of different regions in China.</p><p> Suppose there are n DMUs, denoted by DMUj ( j = 1, . . . , n), and each of them represents an administrative region of China. Every DMU uses m non-energy inputs xij
29、 (i = 1, 2, . . . ,m) and L energy inputs elj (l = 1, . . . , L) to produce s desirable outputs yrj (r = 1, . . . , s) along with emission of K undesirable or bad outputs bkj (k = 1, . . . , K).</p><p> In
30、the process of production, on one hand, a DMU likes to produce desirable outputs as much as possible, and to consume resource inputs as little as possible. On the other hand, the energy sources used in China are mostly n
31、on-renewable ones, e.g. coal or oil, and the burning of the energy usually generates waste gas such as CO2 and SO2 which should also be considered. Therefore, when measuring the total-factor energy and environmental effi
32、ciency, we hope to reduce the consumption of energy as mu</p><p> Note that model (1) makes the undesirable outputs proportionally decrease with energy inputs as much as possible for a given level of non-en
33、ergy inputs and desirable outputs. In model (1), the energy and environmental efficiency index θ for a region is between 0 and 1. The larger the index, the better the corresponding region performs both in energy saving a
34、nd pollutant emission reduction. If E1 = 1 (θ = 1) and all slacks sx?i , se?l , sy+r are zeros, the corresponding region is considered to be</p><p> The total-factor energy and environmental efficiency meas
35、ure presented by model (1) is a kind of radial efficiency which may have weak discriminating power in energy efficiency comparisons. Therefore, following Bian and Yang, we extend the radial energy and environmental effic
36、iency measure to a non-radial measure as:</p><p> Model (2) measure the energy and environmental efficiency (E2) by using different non-proportional adjustments for different energy inputs and pollutant out
37、puts, which account for the energy input effects (θle ) and pollutant output effects(θkb), respectively. Therefore, model (2) allows energy consumptions and pollutant emissions to be reduced with different proportions so
38、 as to let the evaluated regions reach their best practice point on the energy and environmental efficiency frontier. Here, </p><p> In model (2), only when θle = 1 and θkb = 1 for all l and k (i.e. E2 = 1)
39、, and all slacks are zeros, is the corresponding region known as energy efficient and environmental efficient. Obviously, model (2) has a higher discriminating power than model (1), thus we will use model (2) to evaluate
40、 the total-factor energy and environmental efficiency of different regions in China.</p><p> DEA window analysis for the dynamic evaluation of energy and environmental performance</p><p> In t
41、his study, we plan to measure the energy and environmental efficiency of different regions in China not only for a single year but for a time period of 2000–2008, which is considered a dynamic evaluation and could provid
42、e us with more information about the efficiency changes. Therefore, it is meaningful and practical to explore the energy and environmental efficiency by applying DEA window analysis.</p><p> DEA window anal
43、ysis, introduced by Charnes and Cooper, is a variation of the traditional DEA approach that can handle cross-sectional and time-varying data so as to measure dynamic effects. This technique operates on the principle of m
44、oving averages and establishes efficiency measures by treating each DMU in different periods as a separate unit. Under the window analysis framework, the energy and environmental performance of a region in a period can b
45、e contrasted to the performance of other regi</p><p> The DEA window analysis for energy and environmental efficiency measure in our study is presented below. A window with n × w observations is denote
46、d starting at time t (1 ≤ t ≤ T ) with window width w (1 ≤ w ≤ T ? t). In our study, there are 29 regions (provinces, autonomous regions, and municipalities) of China and a time period of 9 years (2000–2008) of efficienc
47、ies needs to be examined, so n = 29 and T = 9. The window width is supported by the number of time periods (years in this study) unde</p><p> In this study, following Halkos and Tzeremes and Zhang et al. we
48、 chose a narrow window with the width of three (w = 3) to get credible energy and environmental efficiency results. Therefore, the first three years of 2000, 2001 and 2002 construct the first window. Then the window move
49、s on a one-year period by dropping the original year and adding a new year. Thus, the next three years of 2001, 2002 and 2003 form the second window. This process continues until the last window, which contains the l<
50、/p><p> The radial and non-radial energy and environmental efficiency (E1 and E2) of 29 regions of China in each window can be obtained using DEA window analysis. For each region, each year has three values of
51、 energy and environmental efficiency, with the exceptions of 2000 and 2008, which have only one value, and 2001 and 2007, which have two values. Then, we calculate the average results of energy and environmental efficien
52、cy of each region in the same year so as to get a new efficiency result for the</p><p><b> 翻譯:</b></p><p> 基于DEA窗口模型的中國(guó)區(qū)域能源與環(huán)境效益動(dòng)態(tài)評(píng)估 </p><p> Ke Wang, Shiwei Yuc, Wei
53、 Zhang</p><p><b> 摘要</b></p><p> 近年來(lái),數(shù)據(jù)包絡(luò)分析(DEA)多用來(lái)在宏觀經(jīng)濟(jì)層面上衡量能源和環(huán)境績(jī)效。之前相關(guān)的研究有如下局限性:在計(jì)算過(guò)程中沒(méi)考慮非期望產(chǎn)出,并且在DEA的框架下未對(duì)投入要素中的能源資源和非能源資源進(jìn)行區(qū)分。因此,本文在考慮期望和非期望產(chǎn)出以及區(qū)分能源和非能源投入的聯(lián)合生產(chǎn)框架下,分析了中國(guó)區(qū)域全要素能源
54、效率和環(huán)境效率。本文利用改進(jìn)的DEA模型對(duì)2000-2008年間中國(guó)29個(gè)行政區(qū)域的能源和環(huán)境效率進(jìn)行了測(cè)量。此外,應(yīng)用DEA窗口分析技術(shù)來(lái)測(cè)量橫截面和時(shí)變數(shù)據(jù)的效率,實(shí)證結(jié)果表明,中國(guó)東部地區(qū)的能源和環(huán)境效益最高,西部地區(qū)的效率最差。三個(gè)地區(qū)在效率變化上有類似的趨勢(shì),都在2000年到2008年間保持增長(zhǎng)趨勢(shì)。從能源和環(huán)境效率來(lái)看,東部地區(qū)的發(fā)展比中部和西部地區(qū)更加平衡。</p><p><b> 緒
55、論</b></p><p> 近年來(lái),由于全球變暖的問(wèn)題日益嚴(yán)重,越來(lái)越多的研究者們將關(guān)注點(diǎn)放在評(píng)估,分析和提高能源效率上,試圖通過(guò)這些研究來(lái)尋找減輕全球變暖的方法, 全球變暖主要由大量溫室氣體(如二氧化碳等)的排放造成,而溫室氣體多來(lái)自化石燃料的燃燒。</p><p> 自1978年實(shí)施改革開(kāi)放以來(lái),中國(guó)經(jīng)濟(jì)發(fā)展迅速。國(guó)內(nèi)生產(chǎn)總值從1978年的3445.2億元增加到200
56、9年的34050.99億元。但是,發(fā)展過(guò)程中低效率的自然資源利用方式造成了嚴(yán)重的環(huán)境問(wèn)題。如今,中國(guó)已經(jīng)成為世界第二大經(jīng)濟(jì)體,世界上最大的能源消耗國(guó)和世界上最大的二氧化碳排放國(guó)。為提高能源利用效率,保護(hù)環(huán)境,實(shí)現(xiàn)可持續(xù)發(fā)展,中國(guó)政府提出建設(shè)資源節(jié)約型、環(huán)境友好型社會(huì)的戰(zhàn)略目標(biāo)。中國(guó)政府還宣布,以2005年為基年,到2020年,單位GDP二氧化碳排放量要減少40%-45%。此外,隨著政府和公眾對(duì)國(guó)際環(huán)境投入越來(lái)越高的關(guān)注度,中國(guó)在國(guó)際氣候
57、變化的談判中面臨巨大的壓力。因此,考慮環(huán)境限制來(lái)衡量和提高能源效率對(duì)于中國(guó)降低能源消耗和減輕環(huán)境污染非常重要。</p><p> 能源效率是一個(gè)相對(duì)的概念,它有很多不同的定義。Ang認(rèn)為,三項(xiàng)指標(biāo)通常用于測(cè)量能源效率,分別是:熱力學(xué)指標(biāo),基于物理的指標(biāo)和基于貨幣的指標(biāo)?;谪泿胖笜?biāo)的效率是指每單位電流輸出的能源消耗量,通常用于衡量宏觀層面的整體經(jīng)濟(jì)效率。由于任何經(jīng)濟(jì)生產(chǎn)活動(dòng)都是一種聯(lián)合生產(chǎn)的過(guò)程,它通過(guò)利用能源
58、資源(如煤,石油,天然氣)和其他資源(如勞動(dòng)力和資本)產(chǎn)生理想的產(chǎn)出(GDP)和非期望產(chǎn)出,如排放的污染物(二氧化碳,二氧化硫)。因此,采用全要素效率評(píng)估模型來(lái)研究問(wèn)題是必要的。此外,由于有副產(chǎn)品污染物的存在,在衡量能源效率的同時(shí),也不能忽視環(huán)境效率,從而提供更明顯和可比較的效率分?jǐn)?shù)。因此,全要素效率評(píng)估模型也應(yīng)該能夠用來(lái)測(cè)評(píng)總的能源效率和環(huán)境效率。</p><p> 在宏觀經(jīng)濟(jì)層面,數(shù)據(jù)包絡(luò)分析(DEA)最近
59、已廣泛應(yīng)用于研究能源和環(huán)境效率,因?yàn)樗峁┝艘环N適當(dāng)?shù)姆椒▉?lái)處理多投入多產(chǎn)出的模型從而對(duì)相對(duì)效率進(jìn)行測(cè)算。Hu和Wang提出了使用DEA的全要素能效評(píng)估方法,并測(cè)算了中國(guó)29個(gè)地區(qū)的能源效率。 Zhou等開(kāi)發(fā)了幾種環(huán)境DEA技術(shù),并測(cè)量了世界上八個(gè)地區(qū)的碳排放情況。Yeh等人利用傳統(tǒng)的BCC包絡(luò)模型計(jì)算了中國(guó)大陸和臺(tái)灣能源效率中的技術(shù)效率,并利用Seiford和Zhu的方法(提高期望產(chǎn)出,降低不良產(chǎn)出)處理了不良產(chǎn)出。</p>
60、;<p> Hu和Wang的能效評(píng)估模式的缺陷是只將能源消耗作為單一投入,將GDP視為理想的產(chǎn)出,不考慮任何不良的產(chǎn)出。然而,在實(shí)際生產(chǎn)過(guò)程中這可能是不合理的,因?yàn)槟茉吹氖褂每偸菚?huì)引起污染物質(zhì)(如二氧化碳和二氧化硫)的排放。此外,Zhou等人的環(huán)境效率評(píng)估方法的缺陷是它只將能量作為投入指標(biāo),包含期望產(chǎn)出和不良產(chǎn)出,忽略了其他非能量輸入。此外,在Yeh等人的研究中,他們沒(méi)有考慮節(jié)能的最大化,因?yàn)樗鼈冊(cè)谖磪^(qū)分其他非能源投入與
61、能源投入的條件下來(lái)計(jì)算能源效率,并且將所有投入都合并在一起。在實(shí)際生產(chǎn)過(guò)程中作為投入的能源通常不可再生,但其他非能源資源(如勞動(dòng)力或資本)是可再生的。因此,對(duì)不可再生能源投入應(yīng)盡可能分開(kāi)并節(jié)省,以提高能源效率,減少污染物排放。</p><p> 在最近的文獻(xiàn)中,有一些基于DEA模型來(lái)評(píng)估能源效率和環(huán)境效率的研究考慮了總體因素和污染物排放。 Zhou等提出了幾個(gè)DEA模型,分別評(píng)估了1995年至1997年期間26
62、個(gè)經(jīng)合組織國(guó)家的環(huán)境效率,以及2002年世界上8個(gè)地區(qū)的環(huán)境效率。前者應(yīng)用非徑向DEA方法將勞動(dòng)和一次能源消耗作為兩個(gè)投入指標(biāo),GDP作為唯一期望的產(chǎn)出,二氧化碳,硫氧化物,氮氧化物和一氧化碳作為不良產(chǎn)出。后者簡(jiǎn)單地選擇總能源消耗,利用基于徑向DEA方法,將GDP和二氧化碳排放量作為單一的投入要素,期望產(chǎn)出和不良產(chǎn)出。 Zhou和Ang在聯(lián)合生產(chǎn)框架內(nèi)提出了幾種DEA線性規(guī)劃模型,用于衡量經(jīng)濟(jì)范圍的能源效率和使用能源和非能源投入以及期望
63、和非期望產(chǎn)出。此外,根據(jù)能效綜合效率或“能源混合效應(yīng)”對(duì)效率的定義,提出了能效指標(biāo),能效績(jī)效指標(biāo),平均能源利用效能指標(biāo)和加權(quán)平均能源利用效能指數(shù)三個(gè)能效指標(biāo)。然而,上述研究只是單獨(dú)評(píng)估能源效率或環(huán)境效率,未將能源效率和環(huán)境效率進(jìn)行整體評(píng)估。</p><p> 最近,Bian和Yang提出了幾種DEA模型,同時(shí)測(cè)量能源效率和環(huán)境效率,并用他們的模型來(lái)研究中國(guó)30個(gè)省份的資源和環(huán)境效率評(píng)估問(wèn)題。 Shi等提出了三個(gè)
64、擴(kuò)展DEA模型,將不良產(chǎn)出作為投入,并按比例減少能源投入,從而計(jì)算中國(guó)28個(gè)行政區(qū)域的能源和環(huán)境整體技術(shù)效率、純技術(shù)效率和規(guī)模效率。Wang等開(kāi)發(fā)了一種混合能源經(jīng)濟(jì)-環(huán)境效率模型,試圖按比例增加理想產(chǎn)出并同時(shí)減少不必要的產(chǎn)出,從而計(jì)算匯總效率。然而,Bian和Yang的研究被認(rèn)為是一個(gè)靜態(tài)分析,因?yàn)樗鼈冎皇呛饬苛艘荒甑目?jī)效,不能從中看出效率的變化趨勢(shì)。 Shi和Wang等人評(píng)估了多期工作效率,但他們只是計(jì)算了每年不同地區(qū)的效率,然后簡(jiǎn)單
65、地比較了不同年份的表現(xiàn),技術(shù)進(jìn)步被忽略,不同年份的效率差異不大。此外,Shi等人和Wang等人的DEA模型在評(píng)估能源和環(huán)境效率時(shí)具有較弱的辨別力。</p><p> 在本研究中,我們提出了一種改進(jìn)的DEA模型,遵循Bian和Yang的方法,并結(jié)合DEA窗口分析,以便對(duì)2000-2008年期間中國(guó)29個(gè)地區(qū)的能源和環(huán)境效率進(jìn)行動(dòng)態(tài)評(píng)估。 本文的其余部分安排如下。 第2節(jié)介紹了基于DEA的績(jī)效評(píng)估模型和全要素能源利
66、用效率及環(huán)境效率評(píng)估的DEA窗口分析。 第3節(jié)介紹數(shù)據(jù)和變量。 然后在第4節(jié)分析了2000年至2008年中國(guó)不同地區(qū)和區(qū)域內(nèi)的能源和環(huán)境效率。第5節(jié)是本文的結(jié)論。</p><p><b> 方法論</b></p><p> 在本節(jié)中,我們提出了一種用于評(píng)估全要素能源和環(huán)境效率的非徑向、輸入導(dǎo)向DEA模型。 此外,我們通過(guò)應(yīng)用DEA窗口模型來(lái)分析和測(cè)量橫截面和時(shí)變數(shù)
67、據(jù)的效率,探討全要素能源和環(huán)境效率。</p><p> 用于評(píng)估能源和環(huán)境績(jī)效的改進(jìn)的DEA模型</p><p> DEA方法是一種用于評(píng)估一組可比較的決策單元(DMU)的非參數(shù)數(shù)學(xué)規(guī)劃方法。 在這里,我們使用CCR模型作為檢驗(yàn)中國(guó)不同地區(qū)全要素能源效率和環(huán)境效率的基本模型。</p><p> 假設(shè)有n個(gè)DMU,用DMUj(j = 1,...,n)表示,它們分
68、別代表不同的行政區(qū)域。 每個(gè)DMU使用m個(gè)非能源輸入指標(biāo)xij(i = 1,2,...,m)和L個(gè)能源輸入指標(biāo)elj(l = 1,...,L),這些投入要素產(chǎn)生期望產(chǎn)出yrj(r = 1,...)以及非期望產(chǎn)出bkj(k = 1,...,K)。</p><p> 在生產(chǎn)過(guò)程中,一方面,DMU希望盡可能地產(chǎn)生期望產(chǎn)出,并盡可能少地消耗投入的資源;另一方面,中國(guó)使用的能源大都是不可再生能源(如煤或油等)。燃燒之后通
69、常會(huì)產(chǎn)生廢氣(如二氧化碳和二氧化硫),排放物也應(yīng)該被考慮。因此,在計(jì)算全要素能源和環(huán)境效率時(shí),我們希望盡可能地減少能量消耗,以獲得所需的輸出和非能量輸入。而對(duì)于非期望產(chǎn)出,我們希望輸出的越少越好。但是,在標(biāo)準(zhǔn)DEA模型中不允許減少污染物,對(duì)于這個(gè)問(wèn)題,有這樣一些解決方法,例如使用非期望產(chǎn)出的倒數(shù),將非期望產(chǎn)出轉(zhuǎn)換為投入要素來(lái)處理,或者,投入產(chǎn)出的分類不變,將非期望產(chǎn)出通過(guò)數(shù)學(xué)方法轉(zhuǎn)換成期望產(chǎn)出。在我們對(duì)能源效率和環(huán)境效率的研究中,非期望
70、產(chǎn)出主要是由生產(chǎn)過(guò)程中的化石燃料燃燒產(chǎn)生的,如果能源消耗減少,應(yīng)該可以減少其排放量。因此,與Shi等人的方法相似,我們首先利用以下徑向DEA模型,來(lái)測(cè)量全要素能源效率和環(huán)境效率。</p><p> 對(duì)于給定的非能量輸入和期望的輸出,公式(1)使非期望產(chǎn)出盡可能地按照能源輸入成比例地減小。 在公式(1)中,一個(gè)區(qū)域的能源效率和環(huán)境效率指數(shù)θ介于0和1之間。該值越大,表明相應(yīng)區(qū)域在節(jié)能減排上就做的越好。如果E1 =
71、 1(θ= 1),并且所有松弛sx?i , se?l , sy+r都為零,則相應(yīng)的區(qū)域被認(rèn)為是能量和環(huán)境效率很高,不能再降低其能量消耗率和污染物的排放量。 如果E1 <1(θ<1)和(或)一些松弛不為零,則相應(yīng)的區(qū)域是能量和環(huán)境效率較低,并且該區(qū)域仍然具有降低能源利用率和污染物量排放的潛力。</p><p> 公式(1)提出的全要素能源效率和環(huán)境效率的測(cè)度方法在能源效率的比較中具有較弱辨別力的徑向效
72、率。 因此,按照Bian和Yang的方法,我們將徑向能源和環(huán)境效率測(cè)度擴(kuò)展為非徑向測(cè)量:</p><p> 公式(2)通過(guò)調(diào)整不同能源輸入和污染物輸出的比例來(lái)測(cè)量能量和環(huán)境效率(E2),分別考慮了能源輸入效應(yīng)(θle)和污染物輸出效應(yīng)(θkb)。因此,公式(2)允許以不同比例減少能源消耗和污染物排放,從而使評(píng)估區(qū)域處于能源效率和環(huán)境效率的生產(chǎn)前沿面上。在這里,我們必須指出,在公式(2)中,通過(guò)調(diào)整不同的非比例來(lái)
73、評(píng)估能源效率和環(huán)境效率,并且通過(guò)決策者計(jì)算統(tǒng)一效率,在本文中,分配給這兩個(gè)效率的權(quán)重均設(shè)為1/2。然而,決策者也可以為他們分配不同的權(quán)重,以便在統(tǒng)一的效率方程式中對(duì)能源利用績(jī)效或環(huán)境保護(hù)績(jī)效提出不同的偏好。</p><p> 在公式(2)中,只有對(duì)于所有l(wèi)和k,都滿足θle = 1且θkb = 1(即E2 = 1),并且所有松弛為零時(shí),才能被稱為能量效率和環(huán)境效率的對(duì)應(yīng)區(qū)域。 顯然,公式(2)具有比公式(1)更
74、高的識(shí)別力,因此我們將使用公式(2)來(lái)評(píng)估中國(guó)不同地區(qū)的全要素能源效率和環(huán)境效率。</p><p> 基于DEA窗口模型的能源和環(huán)境績(jī)效動(dòng)態(tài)評(píng)估</p><p> 本文測(cè)評(píng)中國(guó)不同地區(qū)的能源和環(huán)境效率的時(shí)間跨度不僅僅只有一年,而是2000-2008年這一時(shí)間段,這被認(rèn)為是一種動(dòng)態(tài)的評(píng)估方式,可以為我們提供更多關(guān)于效率變化的信息。 因此,通過(guò)應(yīng)用DEA窗口分析來(lái)探索能源和環(huán)境效率是有實(shí)用
75、性并且有意義的。</p><p> 由Charnes和Cooper提出的DEA窗口分析是傳統(tǒng)DEA方法的一個(gè)變體,可以處理橫截面數(shù)據(jù)和時(shí)變數(shù)據(jù),以便測(cè)量動(dòng)態(tài)效果。這種方法基于移動(dòng)平均原則,通過(guò)將不同時(shí)期的每個(gè)DMU視為單獨(dú)的單元來(lái)計(jì)算效率。在窗口分析的框架下,一段時(shí)間內(nèi)某一區(qū)域的能源效率和環(huán)境績(jī)效可以與其他地區(qū)同一時(shí)段的表現(xiàn)或者自身其他時(shí)期的表現(xiàn)來(lái)進(jìn)行對(duì)比。 因此,應(yīng)用這種技術(shù),我們可以通過(guò)一系列重疊窗口來(lái)研究
76、不同地區(qū)和不同時(shí)間的能源和環(huán)境效率。</p><p> 我們用以研究能源和環(huán)境效率測(cè)量的DEA窗口分析如下:窗口寬度w(1≤w≤T-t),時(shí)間t (1 ≤ t ≤ T ),表示具有n×w個(gè)觀測(cè)值的窗口。在我們的研究中,中國(guó)有29個(gè)地區(qū)(省,自治區(qū),直轄市)需要計(jì)算9年間(2000-2008年)的效率,所以n = 29,T = 9。受到分析的時(shí)間段(本研究的年數(shù))的支持,時(shí)間段以跨期間的方式構(gòu)想。據(jù)Zh
77、ang等人指出,由于在給定窗口內(nèi)的特定年份的每個(gè)區(qū)域彼此相互測(cè)量,所以DEA窗口分析的隱含假設(shè)在是在每個(gè)窗口內(nèi)的分析期間都沒(méi)有技術(shù)變化。這被認(rèn)為是這個(gè)研究方法的存在的一個(gè)普遍的問(wèn)題。因此,必須使用窄的窗口寬度來(lái)解決問(wèn)題。 Charnes等人提出將三到四個(gè)時(shí)間段作為窗口寬度來(lái)測(cè)量效率值時(shí)可以在格式化和穩(wěn)定性方面獲得最佳平衡。</p><p> 在本研究中,遵循Halkos、Tzeremes和Zhang等人提出的方
78、法,我們選擇了寬度為3(w = 3)的狹窄窗口,以獲得可靠的能源和環(huán)境效率結(jié)果。 因此,以2000年,2001年和2002年這個(gè)三年構(gòu)建第一窗口。 然后,窗口將在一年內(nèi)移動(dòng),刪除原始年份并添加新的一年。 因此,2001年,2002年和2003年的未來(lái)三年形成了第二個(gè)窗口。 這個(gè)過(guò)程一直持續(xù)到包括2006年,2007年和2008年的最后三年的最后一個(gè)窗口被構(gòu)建。 最后,我們一共獲得了包含每個(gè)區(qū)域的7個(gè)窗口,每個(gè)窗口中的DMU(我們研究中的
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