区域能源消费与经济和环境绩效——基于14个能源输出和输入大省的实证研究

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薛静静, 沈镭, 彭保发, 刘立涛. 区域能源消费与经济和环境绩效——基于14个能源输出和输入大省的实证研究. 69(10): 1414-1424
XUE Jingjing, SHEN Lei, PENG Baofa, LIU Litao. The economic and environmental performance of regional energy consumption: An empirical study on 14 major energy output and input provinces in China. Acte Geographica Sinica, 69(10): 1414-1424 复制到剪切板

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区域能源消费与经济和环境绩效——基于14个能源输出和输入大省的实证研究

薛静静¹, 沈镭², 彭保发¹, 刘立涛²

1. 湖南文理学院资源环境与旅游学院, 湖南常德 415000

2. 中国科学院地理科学与资源研究所, 北京 100101

作者简介：薛静静 (1986-), 女, 山东省聊城人, 博士, 讲师, 主要研究方向为资源经济与政策、资源生态利用。E-mail: xuejingjing22@163.com

基金:国家自然科学基金项目 (41271547); 湖南省重点建设学科“产业经济学”; 湖南文理学院重点建设学科“人文地理学”; 湖南省哲学社会科学基金项目 (13YBB159)

摘要

本文对中国的能源输出大省和能源输入大省进行了界定,运用DEA方法测度了2000-2010年能源输出和输入大省的能源消费经济绩效、环境绩效及节能潜力,对能源输出和输入大省的能源消费经济绩效、环境绩效特征进行系统归纳和比较,分析能源消费绩效形成原因,并利用Malmquist生产率指数方法对2000-2010年能源输出和输入大省的能源消费经济绩效、环境绩效变化趋势进行了深入研究,探讨提高能源输出和输入大省能源消费绩效的对策措施。研究结果表明：① 能源输出大省的能源消费绩效未达最优主要受规模效率制约,规模效率和纯技术效率对能源输入大省能源消费绩效未达最优的共同制约比较明显;② 能源输出大省的整体能源消费经济绩效改善落后于能源输入大省,但在环境绩效改善方面能源输出大省整体上强于能源输入大省;③ 技术进步是决定能源输出和输入大省能源消费绩效变化趋势的关键;④ 半数能源输出大省和大部分能源输入大省存在投入冗余现象,基于环境产出投入冗余远高于基于经济产出投入冗余,尤其是能源输入大省。

关键词: DEA和Malmquist方法; 能源输出、输入大省; 能源消费经济、环境绩效; 变化趋势; 节能潜力; 中国

The economic and environmental performance of regional energy consumption: An empirical study on 14 major energy output and input provinces in China

XUE Jingjing¹, SHEN Lei², PENG Baofa¹, LIU Litao²

1. College of Resource Environment and Tourism, Hunan University of Arts and Science, Hunan Changde 415000, China

2. Institute of Geographic Sciences and Natural Resource Research, CAS, Beijing 100101, China

Fund:National Natural Science Foundation of China, No.41271547; Key Discipline of Hunan Province-Industrial Economics; Key Discipline of Hunan University of Arts and Science-Human Geography; Philosophy and Social Science Foundation of Hunan Province, No.13YBB159

Abstract

This paper defines energy output and input provinces in China, and adopts DEA method to measure economic and environmental performance of energy consumption and energy saving potential for the energy output and input provinces from 2000 to 2010. The causes and characteristics of energy consumption performance are analyzed. The energy consumption performance of energy output provinces is compared with that of energy input provinces. Then, this paper uses the Malmquist index model to examine the changing trend of economic and environmental performance of energy consumption for the energy output and input provinces from 2000 to 2010. Finally, we discuss the strategies for promoting energy consumption performance of energy output and input provinces. The results show that, energy output provinces failing to reach optimal energy consumption performance are mainly restricted by scale efficiency, while energy input provinces are restricted by scale efficiency and pure technological efficiency. Energy consumption economic performance improvement of energy output provinces lags behind that of energy input provinces, but energy consumption environmental performance improvement of energy output provinces is better than that of energy input provinces. Technological progress is key to a changing trend of energy consumption performance. Half of the energy output provinces and most of the energy input provinces have input redundancy, and the input redundancy based on environmental output is much higher than that based on economic output, especially for the energy input provinces.

Keyword: DEA and Malmquist index methods; major energy output and input provinces; economic and environmental performance of energy consumption; changing trend; energy saving potential; China

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1 引言

中国能源资源空间分布极不均衡, 能源分布与能源消费具有“ 错位” 的空间分异特征, 区域之间需要通过能源流动不断输出或输入能源产品, 能源输出和输入是满足各方能源消费需求的前提, 由此衍生出能源输出大省和能源输入大省两类重要的研究单元。能源输出大省是指能源资源储量丰富, 且能源生产量远远大于其能源消费量的省份。能源输入大省是指能源储量较少, 且能源消费量远远大于其能源生产量的省份。近年来, 中国各地区能源消费持续增长, 区域能源消费的经济环境效应一直是研究的热点问题。关于能源消费经济产出效率的研究成果比较丰富, 主要集中于针对不同区域层面 (国家之间^[1]、三大经济区^[2]、八大经济区^{[3, 4]}、省份之间^[5]、省份^[6]、工业省份^[7]、城市^[8]等)、三次产业^[9]、行业^{[10, 11, 12]}等, 但在能源消费的环境产出效率方面基本未曾涉及。关于能源输出和输入大省, 仅有肖涛对两者的能源消耗与经济增长Granger因果关系进行了研究^[13]。作为能源资源相对丰富和极为贫乏的两大类特殊单元, 它们的能源消费效率, 即能源消费经济绩效和环境绩效如何?能源输出大省是否因资源丰富而存在浪费和效率低下, 能源资源匮乏而需要大量输入能源的省份在能源消费方面是否高效, 不同省份之间又有何差异, 几乎还没有学者专门对此方面进行过研究。

数据包络分析法 (Data Envelopment Analysis, DEA) 是评价多部门之间及多种方案之间相对有效性的一种行之有效的方法。DEA方法并不直接对数据进行综合, 决策单元的最优效率指标与投入指标值及产出指标值的量纲选取无关, 因此, 无须对数据进行无量纲化处理^[5]。无需建立指标间的函数关系, 避免了采用不恰当的函数形式而得出错误的结论, 排除了很多主观因素^[14]。投入产出松弛可以反映节能或扩大产出的潜力^[15]。Malmquist生产率指数方法将能源效率分解为纯技术效率指数、规模效率指数和技术进步指数, 可以从多角度分析能源消费效率的影响因素, 有利于深入分析各单元非有效的原因^[16]。该方法已广泛应用于国民经济^{[17, 18]}、不同行业^{[19, 20]}、城市^{[21, 22]}、企业^{[23, 24, 25]}、资源^{[26, 27, 28]}等效率评价和预警中。因此, 本研究试图采用DEA、Malmquist生产率指数方法对中国14个能源输出和输入大省的能源消费经济、环境绩效及其变化进行深入研究, 将其能源消费生产率变化指数分解为纯技术效率变化指数、规模效率变化指数和技术进步变化指数, 进行多角度探讨和分析, 并根据优化目标值, 分析能源消费节能潜力, 探讨提高能源输出、输入大省能源消费绩效的优化对策。

2 研究对象与数据分析

2.1 研究对象

能源输出大省和能源输入大省为本文的研究对象, 以中国各省2000-2010年年均能源生产量与消费量之差为数据基础, 得出中国能源输出大省和能源输入大省, 根据数据结果我们得出, 山西、内蒙古、陕西、黑龙江、新疆和贵州6个省份为能源输出大省, 辽宁、上海、湖北、山东、浙江、河北、江苏和广东8个省份为能源输入大省 (图1)。总体来看, 2000-2010年, 能源输出大省和能源输入大省的能源消费盈余和缺口呈“ 喇叭型” 趋势, 随着能源勘探和生产技术不断进步, 能源输出大省的能源消费盈余即输出量不断增长, 能源输入大省的能源消费缺口即输入量也不断增长 (图2)。

	Figure Option '>View Download New Window
'>	图1 中国能源输出和输入大省年均能源消费盈余和缺口(2000-2010年)Fig. 1 An average annual surplus and gaps of energy consumption of China's major energy output and input provinces from 2000 to 2010

	Figure Option '>View Download New Window
'>	图2 中国能源输出和输入大省能源消费盈余、缺口的变化趋势 (2000-2010年)Fig. 2 The changing trends of energy consumption surplus and gaps of China's major energy output and input provinces from 2000 to 2010

2.2 指标选取与数据来源

本研究探讨能源输出大省和能源输入大省的能源消费经济绩效、环境绩效, 煤、石油、天然气和电力是主要的消费能源, 因此, 能源投入指标选取能源消费总量、煤炭、原油、天然气和电力消费量, 经济产出指标选取2000年基期区域不变价GDP, 环境产出指标选取工业废气排放总量 (燃料燃烧)、工业烟尘排放量、生活二氧化硫排放量、生活烟尘排放量。各省能源消费总量、煤炭、原油、天然气和电力消费数据来源于《中国能源统计年鉴》(2001-2011)。各省名义GDP、工业废气排放总量 (燃料燃烧)、工业烟尘排放量、生活二氧化硫排放量、生活烟尘排放量数据来源于《中国统计年鉴》 (2001-2011), 为使各省纵向GDP数据具有可比性, 基于2000年的不变价格计算得出各省的实际GDP。DEA模型从投入最小化或产出最大化角度对决策单元进行效率评价, 而环境安全追求环境有害物质排放的最小化, 因此对环境产出指标数据取倒数值作为DEAP2.1软件的基础输入数据。

3 研究方法

3.1 DEA方法

1978年, 著名运筹学家A.Charnes、W.W.Cooper和E.Rhodes正式提出数据包络分析方法^[29], 其模型简称CRS模型。随着对数据包络分析方法研究不断深入, 在CRS模型基础上又演化出规模报酬可变的VRS模型。假设有z个省份, 每个省份j (j = 1, 2, …, z) 均有m个投入变量和n个产出变量; x_ij > 0表示第j个省份的第i种类型输入的投入总量, i = 1, 2, ..., m; y_rj > 0表示第j个省份的第r种类型输出的输出量, r = 1, 2, ..., n; θ 代表要素资源投入产出效率综合指数, 简称综合效率指数; ε 为非阿基米德无穷小量; λ _j为权重变量, 用来判断省份的规模收益情况; S^-为松弛变量, 表示省份达到DEA有效需要减少的投入量; S⁺为剩余变量, 表示省份达到DEA有效需增加的产出量。则效率测度的数据包络分析VRS模型为：

$\begin{matrix} \{\begin{matrix} \min [θ - ε (\overset{m}{\sum_{i = 1}} s^{-} + \overset{n}{\sum_{r = 1}} s^{+})] \\ s.t. \overset{z}{\sum_{j = 1}} x_{ij} λ_{j} + s^{-} = θ x_{i}^{z} \\ \overset{z}{\sum_{j = 1}} y_{rj} λ_{j} - s^{+} = y_{r}^{z} \\ \overset{z}{\sum_{j = 1}} λ_{j} = 1 \end{matrix} \end{matrix}$ (1)

$\begin{matrix} i = 1, 2, \dots, m; r = 1, 2, \dots, n; j = 1, 2 \dots, z; λ_{j} \geq 0; s^{-} \geq 0; s^{+} \geq 0; 0 < θ \leq 1 \end{matrix}$

当存在最优解θ _j = 1时, 表明第j个省份运行在最优生产前沿面上, 该省份的产出相对于投入而言达到了综合效率最优; 当θ _j < 1时, 表明第j个省份效率无效, θ _j的值越接近于1, 则表示第j个省份的综合效率越接近有效, 反之越低。综合效率可分解为纯技术效率与规模效率的乘积, 即θ _j = θ _m × θ _n^[30], EC = θ _j为第j个省份的综合效率指数 (Technological Efficiency); PTEC = θ _m为对应省份的纯技术效率指数 (Pure Technological Efficiency); SEC = θ _n为对应省份的规模效率指数 (Scale Efficiency), 满足0 < θ _j ≤ 1; 0 < θ _m ≤ 1; 0 < θ _n ≤ 1; θ _m ≥ θ _j; θ _n ≥ θ _j。同样θ _m和θ _n的值越接近于1, 省份的纯技术效率和规模效率就越高。当θ _m = 1或θ _n = 1时, 则该省份分别为纯技术效率最优或规模效率最优。规模效率表示规模集聚水平, 反映规模收益的变化情况, 投入量偏大、偏小还是处在最适规模, 规模过大或过小都将导致平均投入的增加^[31]。纯技术效率表示要素资源的配置、利用水平, 反映对所投入资源的利用是否有效, 输出相对输入的效率情况。综合效率反映要素资源的配置、利用水平和规模集聚水平, 为以上两者的值相乘所得, 两者共同决定了决策单元的综合效率水平。

3.2 Malmquist生产率指数

Malmquist生产率变化指数可以分解为综合效率变化指数和技术进步变化指数, 技术进步变化指数表示生产技术的变化, 综合效率变化指数进一步分解为纯技术效率变化指数和规模效率变化指数。Malmquist生产率变化指数模型为：

$\begin{matrix} TFPC = EC \times TC = PTEC \times SEC \times TC \end{matrix}$ (2)

TFPC代表生产率变化指数, $\begin{matrix} EC = \frac{D_{o}^{t + 1} (x^{t + 1}, y^{t + 1})}{D_{o}^{t} (x^{t}, y^{t})} \end{matrix}$ 、 $\begin{matrix} PTEC = \frac{D_{o}^{t} (x^{t + 1}, y^{t + 1})}{D_{o}^{t} (x^{t}, y^{t})} \end{matrix}$ 、 $\begin{matrix} SEC = \frac{D_{o}^{t + 1} (x^{t + 1}, y^{t + 1})}{D_{o}^{t} (x^{t + 1}, y^{t + 1})} \end{matrix}$ 和 $\begin{matrix} TC = {[\frac{D_{o}^{t} (x^{t + 1}, y^{t + 1})}{D_{o}^{t + 1} (x^{t + 1}, y^{t + 1})} \times \frac{D_{o}^{t} (x^{t}, y^{t})}{D_{o}^{t + 1} (x^{t}, y^{t})}]}^{\frac{1}{2}} \end{matrix}$ 分别表示时期t到时期t+1的综合效率、纯技术效率、规模效率和技术进步变化指数, (x^t, y^t) 和 (x^t⁺¹, y^t⁺¹) 分别为时间t和t+1的投入产出向量, $\begin{matrix} D_{o}^{t} \end{matrix}$ 和 $\begin{matrix} D_{o}^{t + 1} \end{matrix}$ 分别为时间t和t+1的实际产出与最优产出的距离函数。

4 结果分析

4.1 能源消费绩效分析

利用上述DEA模型和加工处理的数据, 运行DEAP2.1软件, 分别计算得出2000-2010年14个能源输出、输入大省的能源消费经济、环境绩效及其分解的结果, 即综合效率、纯技术效率和规模效率, 并对其特征进行归纳。由于数据量过大, 文中仅显示2000、2003、2006和2010年的数据 (表1-表4)。

4.1.1 能源输出大省的能源消费绩效分析能源消费经济产出综合效率最优省份占能源输出大省的50%, 能源消费环境产出综合效率最优省份所占比重略高, 由67%上升至83%。2000年山西、内蒙古、贵州的能源消费经济产出综合效率达到了DEA效率最优, 内蒙古、陕西、新疆、贵州的能源消费环境产出综合效率达到了DEA效率最优, 山西的能源消费经济绩效好而环境绩效差, 陕西和新疆的能源消费环境绩效好而经济绩效差, 黑龙江的能源消费经济绩效、环境绩效都较差, 只有内蒙古和贵州的能源消费经济绩效和环境绩效同时达到最优。2010年山西、黑龙江、贵州的能源消费经济产出综合效率达到了DEA效率最优, 山西、陕西、黑龙江、新疆、贵州的能源消费环境产出综合效率达到了DEA效率最优, 陕西和新疆的能源消费环境绩效好而经济绩效差, 内蒙古的能源消费经济绩效、环境绩效都较差, 山西、黑龙江、贵州的能源消费经济绩效和环境绩效同时达到最优 (表1, 表2)。

表1 2000-2010年中国能源输出大省的能源消费经济绩效比较 Tab. 1 The comparisons of energy consumption efficiency on economy among China's major energy output provinces from 2000 to 2010

表2 2000-2010年中国能源输出大省的能源消费环境绩效比较 Tab. 2 The comparisons of energy consumption efficiency on trash discharge among China's major energy output provinces from 2000 to 2010

2000、2010年导致各能源输出大省能源消费绩效偏低的原因大致有以下几种, 行业规模集聚水平低导致陕西、新疆的能源消费经济绩效低, 同时也导致山西的能源消费环境绩效偏低, 黑龙江 (2000年) 和内蒙古 (2010年) 的能源配置、利用水平以及行业规模集聚水平偏低导致其能源消费经济绩效、环境绩效偏低。2000、2003、2006、2010年分别有1个 (陕西)、1个 (陕西)、3个 (内蒙古、陕西、新疆)、2个 (陕西、新疆) 省份的能源消费经济产出综合效率低于全国平均水平。观察年份内能源输出大省的能源消费环境产出综合效率都高于全国平均水平 (表1, 表2)。

4.1.2 能源输入大省的能源消费绩效分析能源消费经济产出综合效率最优省份所占比重明显上升, 由2000年的38%上升至2010年的75%。能源消费环境产出综合效率最优省份所占比重较低, 由2000年的50%下降至2010年的25%。2000年分别有3个 (广东、江苏、浙江) 和4个 (广东、浙江、湖北、上海) 能源输入大省的能源消费经济产出和环境产出综合效率达到了DEA效率最优, 江苏的经济绩效好而环境绩效差, 湖北和上海的环境绩效好而经济绩效差, 辽宁、山东、浙江、河北的经济绩效和环境绩效都较差, 只有广东和浙江的能源消费经济绩效和环境绩效同时达到最优。2010年分别有6个 (广东、江苏、浙江、湖北、辽宁、上海)和2个 (浙江、上海) 能源输入大省的能源消费经济产出和环境产出综合效率达到了DEA效率最优, 广东、江苏、湖北、辽宁的经济绩效好而环境绩效差, 山东和河北的经济绩效和环境绩效都较差, 只有浙江和上海的能源消费经济绩效和环境绩效同时达到最优 (表3, 表4)。

表3 2000-2010年中国能源输入大省的能源消费经济绩效比较 Tab. 3 The comparisons of energy consumption efficiency on economy among China's major energy input provinces from 2000 to 2010

表4 2000-2010年中国能源输入大省的能源消费环境绩效比较 Tab. 4 The comparisons of energy consumption efficiency on trash discharge among China's major energy input provinces from 2000 to 2010

2000、2010年导致中国各能源输入大省能源消费绩效偏低的原因大致有以下几类, 行业规模集聚水平偏低导致河北、上海 (2000年) 的能源消费经济绩效偏低, 能源配置、利用水平以及行业规模集聚水平偏低导致山东、辽宁 (2000年)、湖北 (2000年) 的能源消费经济绩效偏低, 同时也导致河北、山东、辽宁、湖北 (2010年)、江苏、广东 (2010年) 的能源消费环境绩效偏低。2000、2003、2006、2010年分别有2个 (湖北、辽宁)、1个 (辽宁)、0个、0个省份的能源消费经济产出综合效率低于全国平均水平。观察年份内能源输入大省的能源消费环境产出综合效率都高于全国平均水平 (表3, 表4)。

4.1.3 能源输出、输入大省的能源消费绩效比较能源输出大省中能源消费经济绩效最优省份的数量不变, 而能源消费环境绩效最优省份的数量上升, 能源输入大省中能源消费经济绩效最优省份的数量上升, 而能源消费环境绩效最优省份的数量下降, 总体来看, 能源输出大省的能源消费经济绩效改善落后于能源输入大省, 但在环境绩效改善方面能源输出大省整体上强于能源输入大省。上述评价结果与现实情况相符合, 能源输出大省大多是中国经济发展水平低、发展速度慢与能源消费量较少、增长慢的地区, 能源消费经济绩效增长不明显, 但环境绩效呈现改善迹象; 而能源输入大省大多是中国经济发展水平比较高、发展速度快与能源消费基数大、增长快的地区, 大幅增长的能源消费提高了经济效益, 同时也带来巨大环境压力。能源输入大省的基于经济产出纯技术效率最优省份明显多于基于环境产出纯技术效率最优省份, 更多的追求能源消费的经济利益, 而忽略了在能源清洁利用方面的技术投入和应用。同时, 能源输出、输入大省中较多省份的能源消费规模效率较低, 能源消费配置效果不佳。重工业化产业结构、以煤炭为主的能源消费结构、能源技术水平、能源配置等因素, 共同决定了各能源消费大省以牺牲环境为代价发展经济。

4.2 能源消费绩效变化趋势及节能潜力

利用上述Malmquist生产率指数模型, 分别计算得出2000-2010年各能源输出、输入大省的能源消费综合效率变化、技术进步变化、纯技术效率变化、规模效率变化和生产率变化的指数 (表5-表8)。在计算效率的同时上述DEA方法还可以计算投入产出松弛, 即在产出不变的情况下投入可以如何最小化, 或在投入不变的情况下产出可以如何最大化, 据此本文计算2000-2010年基于经济产出和环境产出不变的情况下投入可以如何最小化, 即节能潜力 (图3)。

4.2.1 能源输出大省的能源消费绩效变化趋势及节能潜力 2000-2010年陕西、黑龙江、贵州的能源消费经济绩效呈弱改善趋势, 其中陕西和贵州的经济绩效弱改善得益于技术进步, 黑龙江的经济绩效弱改善得益于技术进步、纯技术效率和规模效率3者都提高。山西、内蒙古、新疆的能源消费经济绩效呈下降趋势, 且能源消费经济绩效变化指数低于全国平均水平, 其中山西的经济绩效下降源于技术进步下降, 新疆的经济绩效下降主要是因规模效率下降, 技术进步、纯技术效率、规模效率3者都出现下降导致内蒙古的经济绩效下降 (表5)。另外, 2000-2010年内蒙古、山西、黑龙江分别存在14, 468万t、6, 222万t、4, 311万t标煤基于经济产出节能潜力 (图3)。

表5 基于Malmquist指数的2000-2010年中国各能源输出大省能源消费经济绩效的变化 Tab. 5 The change of energy consumption efficiency on economy of each major energy output province of China from 2000 to 2010 based on Malmquist index

2000-2010年中国能源输出大省的能源消费环境绩效均呈下降趋势, 其中内蒙古、陕西、新疆的能源消费环境绩效变化指数低于全国平均水平。山西、陕西、黑龙江、新疆、贵州的能源消费环境绩效下降, 主要是因技术进步出现下降; 内蒙古的能源消费环境绩效下降源于技术进步、纯技术效率和规模效率3者都出现下降(表6)。2000-2010年内蒙古、山西、黑龙江分别存在 25, 924.357万t、22, 646.097万t、5, 612.41万t标煤基于环境产出节能潜力 (图3)。

表6 基于Malmquist指数的2000-2010年中国各能源输出大省能源消费环境绩效的变化 Tab. 6 The change of energy consumption efficiency on trash discharge of each China's major energy output province from 2000 to 2010 based on Malmquist index

4.2.2 能源输入大省的能源消费绩效变化趋势及节能潜力 2000-2010年河北、湖北、辽宁和上海的能源消费经济绩效呈弱改善趋势, 其中河北的经济绩效弱改善得益于规模效率的提高, 湖北的经济绩效弱改善得益于纯技术效率和规模效率的提高, 上海的经济绩效弱改善得益于技术进步和规模效率的提高, 辽宁的经济绩效弱改善得益于技术进步、纯技术效率和规模效率3者都提高。广东、江苏、浙江、山东的能源消费经济绩效呈下降趋势, 且能源消费经济绩效变化指数低于全国平均水平, 其中广东、江苏、浙江的经济绩效下降源于技术进步下降, 山东的经济绩效下降源于技术进步和规模效率的下降 (表7)。2000-2010年辽宁、河北、湖北分别存在23, 082万t、5, 717万t、3, 561万t标煤基于经济产出节能潜力 (图3)。

表7 基于Malmquist指数的2000-2010年中国各能源输入大省能源消费经济绩效的变化 Tab. 3 The change of energy consumption efficiency on economy of each China's major energy input provinces from 2000 to 2010 based on Malmquist index

2000-2010年中国能源输入大省的能源消费环境绩效均呈下降趋势, 其中广东、浙江、山东、湖北的能源消费环境绩效变化指数低于全国平均水平。浙江、辽宁、上海的能源消费环境绩效下降主要是因技术进步出现下降, 山东的能源消费环境绩效下降主要源于技术进步和规模效率下降, 江苏和河北的能源消费环境绩效下降主要是因技术进步和纯技术效率下降, 广东、湖北的能源消费环境绩效下降源于技术进步、纯技术效率和规模效率3者都出现下降 (表8)。2000-2010年河北、辽宁、江苏、广东、湖北分别存在122, 583.164万t、63, 320.923万t、59, 329.484万t、43, 294.874万t、14, 770.361万t标煤基于环境产出节能潜力 (图3)。

表8 基于Malmquist指数的2000-2010年中国各能源输入大省能源消费环境绩效的变化 Tab. 4 The change of energy consumption efficiency on trash discharge of each China's major energy input province from 2000 to 2010 based on Malmquist index

由上述分析可知, 无论是能源输出大省还是能源输入大省, 技术进步都是决定能源消费绩效变化趋势的关键。技术进步对能源输出大省陕西、黑龙江、贵州以及能源输入大省辽宁、上海的能源消费经济绩效改善作用重大。14个能源输出和输入大省的能源消费环境绩效均出现下降, 技术进步变化指数降低是重要原因。从图3也可以看出, 2000-2010年基于环境产出节能潜力远高于基于经济产出节能潜力, 尤其是能源输入大省, 说明近年来促进节能减排的技术研发及应用并不到位。能源输出和输入大省中节能潜力较大省份的二次产业和 (或) 煤炭消费所占比重都偏高, 因此, 加快能源利用技术进步步伐, 促进产业结构和能源消费结构优化是减少能源浪费、节能的关键。

	Figure Option '>View Download New Window
'>	图3 2000-2010年中国能源输出和输入大省基于经济产出、环境产出节能潜力分析Fig. 3 The energy saving potential analysis of each China's major energy output and input province from 2000 to 2010 based on economy output and trash discharge

5 结论与建议

本文利用DEA和Malmquist生产率指数方法对2000-2010年中国14个能源输出和输入大省的能源消费经济、环境绩效及其变化趋势、节能潜力进行了深入研究。研究结论如下：① 能源输出大省的能源消费绩效未达最优主要受规模效率制约, 规模效率和纯技术效率对能源输入大省能源消费绩效未达最优的共同制约比较明显; ② 能源输出大省的整体能源消费经济绩效改善落后于能源输入大省, 但在环境绩效改善方面能源输出大省整体上强于能源输入大省; ③ 技术进步是决定能源输出和输入大省能源消费绩效变化趋势的关键; ④ 半数能源输出大省和大部分能源输入大省存在投入冗余现象, 基于环境产出投入冗余远高于基于经济产出投入冗余, 尤其是能源输入大省。

规模不经济或 (和) 能源配置、技术应用水平低导致部分省份能源消费绩效低下, 二次产业或 (和) 煤炭消费所占比重较高是其能源消费绩效低下的重要深层次原因, 能源消费绩效变化趋势主要受技术进步水平影响。应积极推动产业结构优化升级, 提升高新技术产业和现代服务业的发展比重, 引导资产重组向新技术和循环经济生产方式方向发展, 促进能源消费合理优化配置。提高企业产能利用率, 推动现有企业重组整合, 在保证地区重型支柱产业健康发展的同时, 循序淘汰行业落后闲置产能, 提高能源利用规模效率, 增加清洁能源消费比重。能源工业是能源输出大省的重要支柱产业, 应依托丰富的能源资源以及技术优势发展能源深加工产业, 提高能源附加值, 增加清洁、高效能源产品的供应和消费, 走园区化、规模化、集约化、一体化的发展路子, 提高能源消费经济绩效和环境绩效。多数能源输出、输入大省存在能源利用不充分, 在不影响经济健康发展的基础上适当减少能源投入量, 可以节约能源, 提高能源消费经济绩效和促进节能减排的实际效果, 走能源节约型的可持续发展道路。

The authors have declared that no competing interests exist.

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[17]	Guo Qingwang, Zhao Zhiyun, Jia Junxue. The analysis of provincial total productivity in China. The Journal of World Economy, 2005, (5): 46-53. [郭庆旺, 赵志耘, 贾俊雪. 中国省份经济的全要素生产率分析. 世界经济, 2005, (5): 46-53. ] [本文引用:1] [CJCR: 2.247]
[18]	Zhao Xiaolei, Shao Shuai, Yang Lili. Administrative systems and the increase in economic growth efficiency in China's development zones: An empirical analysis based on Malmquist index and GMM estimation. Journal of Finance and Economics, 2011, 37(8): 4-15. [赵晓雷, 邵帅, 杨莉莉. 管理体制与中国开发区经济发展效率增长: 基于Malmquist指数和GMM的实证分析. 财经研究, 2011, 37(8): 4-15. ] [本文引用:1] [CJCR: 1.433]
[19]	Yuan Yijun, Liu Hao, Bai Nan. Total factor productivity measurement of China's producer service: Based on Malmquist index. China Soft Science, 2009, (1): 159-167. [原毅军, 刘浩, 白楠. 中国生产性服务业全要素生产率测度-基于非参数Malmquist指数方法的研究. 中国软科学, 2009, (1): 159-167. ] [本文引用:1] [CJCR: 1.968]
[20]	Zhang Jianhua, Wang Peng. Chinese banks, generalized Malmquist productivity index. Economic Research Journal, 2010, (8): 128-139. [张健华, 王鹏. 中国银行业广义Malmquist生产率指数研究. 经济研究, 2010, (8): 128-139. ] [本文引用:1] [CJCR: 8.619]
[21]	Liu Yong. Evaluating Chinese urban material metabolism efficiency based on DEA-Malmquist. Journal of Natural Resources, 2010, 25(1): 12-17. [刘勇. 基于DEA-Malmquist的我国城市物质代谢效率评价. 自然资源学报, 2010, 25(1): 12-17. ] [本文引用:1] [CJCR: 2.407]
[22]	Li Peng, Ma Zhanxin. The evaluation of city competitiveness in Shand ong province. Energy Procedia, 2011, 5: 472-476. [本文引用:1]
[23]	Zheng Junjun, Han Xiao, Pan Ziyi. Analysis on TFP growth and convergence in real estate development companies based on Malmquist index method. China Soft Science, 2013, (3): 141-151. [郑君君, 韩笑, 潘子怡. 基于Malmquist指数的房地产开发企业全要素生产率变动及收敛性研究. 中国软科学, 2013, (3): 141-151. ] [本文引用:1] [CJCR: 1.968]
[24]	Xu Erming, Gao Huai. Evaluation and dynamic evolution of competitiveness of iron and steel firms of China. China Industrial Industry, 2004, (11): 40-46. [徐二明, 高怀. 中国钢铁企业竞争力评价及其动态演变规律分析. 中国工业经济, 2004, (11): 40-46. ] [本文引用:1] [CJCR: 0.547]
[25]	Udaya S, Pakkala T P M, Mallikarjunappa T. A modified directional distance formulation of DEA to assess bankruptcy: An application to IT/ITES companies in India. Expert Systems with Applications, 2012, 39(1/2): 1988-1997. [本文引用:1] [JCR: 1.854]
[26]	Liao Huchang, Dong Yiming. Utilization efficiency of water resources in 12 western provinces of China based on the DEA and Malmquist TFP index. Resources Science, 2011, 33(2): 273-279. [廖虎昌, 董毅明. 基于DEA和Malmquist指数的西部12省水资源利用效率研究. 资源科学, 2011, 33(2): 273-279. ] [本文引用:1] [CJCR: 2.123]
[27]	Pang Ying, Li Shuchao, Zhou Lei. et al. Resources allocation efficiency of food production and its regional difference in China: Empirical analysis of dynamic non-parametric Malmquist index approach. Economic Geography, 2008, 28(1): 113-117. [庞英, 李树超, 周蕾等. 中国粮食生产资源配置效率及其区域差异: 基于动态Malmquist指数的经验. 经济地理, 2008, 28(1): 113-117. ] [本文引用:1]
[28]	Hu Jinli, Wang Shichuan, Ye Fangyu. Total-factor water efficiency of regions in China. Resources Policy, 2006, 31(4): 217-230. [本文引用:1]
[29]	Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research, 1978, (6): 429-444. [本文引用:1] [JCR: 2.038]
[30]	Banker R D. Estimating most productive scale size using Data Envelopment Analysis. European Journal of Operational Research, 1984, 17: 35-44. [本文引用:1] [JCR: 2.038]
[31]	You Heyuan, Wu Cifang. Carbon emission efficiency and low carbon optimization of land use: Based on the perspective of energy consumption. Journal of Natural Resources, 2010, 25(11): 1875-1884. [游和远, 吴次芳. 土地利用的碳排放效率及其低碳优化: 基于能源消耗的视角. 自然资源学报, 2010, 25(11): 1875-1884. ] [本文引用:1] [CJCR: 2.407]

2012

0.0

2.123

Genhua

, Qin

Siyi.

Comparative research of total-factor energy efficiency in BRICS based on DEA and Tobit models.

Resources Science, 2012, 34(3): 533-540.

[胡根华, 秦嗣毅. “金砖国家”全要素能源效率的比较研究-基于DEA-Tobit模型. 资源科学, 2012, 34(3): 533-540. ]

Energy and technology are regarded as two important factors for the development of economy. Under the background of low-carbon economy, growth of energy consumption has become a serious problem and the friction caused by contest over energy resources has also grown in intensity and frequency. Therefore, improvement of energy efficiency has become an urgent task for a nation which is willing to develop its economy. Take the BRICS ( Brazil, Russia, India, China and South Africa) for example, they have become a group with rapid economic development and significant role in economy field. At the same time, the demand of energy has also been increasing rapidly with economic development. Therefore, it is meaningful to do some researches on how to improve the energy efficiency of BRICS. With consumption of primary energy, capital stock, total employees and technology as input indices and GDP as output index, this paper has selected the BRICS'panel data from 2003 to 2010 to evaluate the total-factor energy efficiency in the five countries by using input-oriented super-efficiency DEA model and compare the differences among their energy efficiencies. Then, it has analyzed the influences of technical progress, industrial structure and structure of energy consumption to the total-factor energy efficiency of BRICS from the viewpoints of technology, economic structure and energy factor based on Tobit model. The empirical results show that: 1) The BRICS'total-factor energy efficiency exhibits a low level on the whole but there are significant differences among them, i.e. Brazil's total-factor energy efficiency is the highest among the five nations, followed by South Africa and India, China and Russia are positioned in the third, the fourth and the last respectively; 2) There exists significant negative correlations between energy efficiency and industrial structure and structure of energy consumption, i.e. the higher the ratios of the secondary industry and raw coal consumption are, the lower the total-factor energy efficiency is; 3) Structure of energy consumption has relatively greater impact on all the five countries, and optimizing economic structure only has relatively greater impact on China and India. In general, BRICS have great potential of energy saving and their total-factor energy efficiency can definitely be further improved. In addition, BRICS should improve their total-factor energy efficiency from the aspects of technical progress, economic structure and energy factors based on their own conditions, thus strengthening their economic competitive advantages under the background of low-carbon economy.

School of Economics, Guangdong University of Business Studies, Guangzhou 510320, China;School of Economics, Guangdong University of Business Studies, Guangzhou 510320, China;National Economics Research Center, Guangdong University of Business Studies, Guangzhou 510320, China

选取“金砖国家”2003年-2010年面板数据,文章首先在规模报酬不变的条件下运用基于投入导向的超效率DEA模型测算“金砖国家”全要素能源效率,并比较分析五国能源效率之间的差异;然后,采用Tobit模型来分析技术进步、产业结构、能源消费结构对“金砖国家”全要素能源效率的影响。研究表明,“金砖国家”全要素能源效率整体水平不高,但各国存在显著差异,且中国与俄罗斯能源效率都很低,因而节能潜力很大;另外“, 金砖国家”全要素能源效率与产业结构和能源消费结构均存在显著的负相关关系,其中能源消费结构对各国全要素能源效率的影响均较大,技术进步因素对各国全要素能源效率具有一定程度的影响,而产业结构对中国和印度全要素能源效率影响较大。

... 关于能源消费经济产出效率的研究成果比较丰富,主要集中于针对不同区域层面 (国家之间^[1]、三大经济区^[2]、八大经济区^[3,4]、省份之间^[5]、省份^[6]、工业省份^[7]、城市^[8]等)、三次产业^[9]、行业^[10,11,12]等,但在能源消费的环境产出效率方面基本未曾涉及 ...

2011

0.0

1.691

2010

0.0

2.407

Liu

Litao

, Shen

Lei.

Spation-temporal evolution pattern and influential factor of regional total fact or energy efficiency in China.

Journal of Natural Resources, 2010, 25(12): 2142-2153.

[刘立涛, 沈镭. 中国区域能源效率时空演进格局及其影响因素分析. 自然资源学报, 2010, 25(12): 2142-2153. ]

The most important contributions of this paper attempt to advance the measurement of TFEE by introducing environmental impact associated with energy consumption as one of the inputs into the evaluation. We employ data envelopment analysis (DEA) to evaluate TFEE of 30 administrative regions in China (not including Tibet, Hong Kong, Macao and Taiwan due to no data) for the period 1997-2007. In our DEA model, coal, crude oil, natural gas, electricity and fuel combustion emissions from industrial sector are the five inputs and real GDP is the single output. Based on spatial analysis techniques with GIS software, we investigate the spatio-temporal evolution pattern of regional TFEE. On the basis of panel data at the provincial level in the period of 1998-2007, we develop the fixed-effect model of TFEE to further explore influential factors and their underlying mechanisms. Results are as follows:1) regional TFEE of China improves constantly, while inter-provincial disparity of TFEE continues to widen and a significant divergent trend is found. 2) The spatial pattern of high-TFEE area in China evolutes from "line" of the south coast to the "surface" of the southern area. 3) The empirical analysis of fixed-effect model at national-level shows that there is significant positive correlation between technological progress, economic openness, energy consumption structure (independent variables )and TFEE(dependent variable), while there is a significant negative relationship between industrial structure, marketization level and energy resource endowments (independent variables ) and TFEE(dependent variable). 4) The empirical analysis of fixed-effect model at national-level shows that technological progress has the most positive effect on regional TFEE, and the other two main factors affecting TFEE of most regions are industrial structure and marketization level.

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China

将化石能源消费中产生的环境影响作为投入要素引入到全要素能源效率(Total-Factor Energy Efficiency,TFEE)的研究之中,是不断完善能效评价的重要途径。论文基于数据包络分析(Data Envelopment Analysis,DEA),选取煤炭、原油、天然气、电力及燃料燃烧工业废气排放量作为投入变量,真实GDP(不变价)作为单一产出变量,借助于GIS空间分析技术,刻画了1997—2007年来中国区域全要素能源效率的时空演进格局。在此基础上,论文利用1998—2007年中国省际面板数据,通过建立TFEE固定影响模型进一步解析了中国区域TFEE的影响因素及其内在作用机理。研究表明:①中国区域TFEE整体水平不断改进,但区间差异持续扩大,区域分异态势显著;②中国TFEE高效区空间分布从南部沿海"线"状向南部片区"面"状格局演进;③全国层面上看,技术进步、经济开放性及能源消费结构与TFEE存在显著正相关关系,而产业结构、市场化水平及能源资源禀赋则与之存在显著负相关关系;④区域层面上看,技术进步成为各区域改进TFEE最为关键的一环,与TFEE存在显著正相关关系,而产业结构及市场化水平对绝大多数区域TFEE存在显著影响。

2013

0.0

2.123

2013

1.42

0.0

... DEA方法并不直接对数据进行综合,决策单元的最优效率指标与投入指标值及产出指标值的量纲选取无关,因此,无须对数据进行无量纲化处理^[5] ...

2010

0.0

2.937

Zhao

Yuan

, Hao

Lisha

, Yang

Zuying.

Regional differentiation of energy efficiency and its causes in Jiangsu.

Acta Geographica Sinica, 2010, 65(8): 919-928.

[赵媛, 郝丽莎, 杨足膺. 江苏省能源效率空间分异特征与成因分析. 地理学报, 2010, 65(8): 919-928. ]

Improving energy efficiency is an essential way for Jiangsu to break out energy predicament. This paper adopts DEA model to measure the CRS technical efficiency, pure technical efficiency and scale efficiency of energy utilization of 13 cities in Jiangsu in the year of 2007, under the framework of total factors productivity analysis. Then, this paper analyzes the regional differentiation and the spatial pattern of the CRS technical efficiency, pure technical efficiency and scale efficiency of energy utilization in Jiangsu, and divides 13 cities into 4 regional types of energy efficiency according to the incidence relation of these three types of energy efficiency. The causes of energy efficiency differentiation in Jiangsu are analyzed with coefficient of variation (CV), energy consumption per unit GDP and the large-and-medium-size enterprises concentration index, as well as the characteristics of industrial structure of each city.Finally, target values of input/output obtained through DEA model are used to discuss the strategies for promoting energy efficiency of each city. The results show that, the spatial distribution of energy efficiency in Jiangsu is characterized by high-value in the south and low-value in the north, as well as west-east differentiation, and that there is a light manufacturing DEA effectiveness pole in North Jiangsu and a heavy manufacturing DEA effectiveness pole in South Jiangsu respectively. It is scale energy efficiency that mainly determines the CRS technical efficiency as well as its spatial patterns in Jiangsu. Energy consumption per unit GDP could be a general indicator to reflect regional pure technical energy efficiency, and the large- and medium-sized enterprises concentration index could be a general indicator to reflect regional scale energy efficiency. The pure technical energy efficiency of cities with high proportion of heavy manufacturing is relatively low, and the scale energy efficiency of cities lying in the transition zones or the edge areas is also relatively low; input redundancy is relatively ubiquity in Jiangsu, and regional energy efficiency could be promoted by the approach of optimizing investment environment, advancing industrial structure adjustment, highlighting the unique feature of industrial development, promoting the quality of human resources or increasing the concentration degree of industrial development, and so on.

School of Geographical Science, Nanjing Normal University, Nanjing 210046, China

提高能源效率是江苏省突破能源困局的重要途径。文章采用数据包络分析法(Data Envelopment Analysis, DEA),在全要素生产力分析框架下,测度2007 年江苏省各地级市能源利用的综合效率、技术效率和规模效率,分析3 类能源效率的空间分异特征,并根据3 类能源效率的关联关系划分能源效率的地域类型;进而采用变异系数(CV)、单位GDP能耗和大中型企业集中指数(CI) 等指标,结合各地级市的产业结构特征,分析江苏省能源效率地域分异成因;最后,根据DEA模型的投入产出优化目标值(Targets),探讨提高各地级市能源效率的对策措施。研究结果表明：① 江苏省能源效率总体上呈南高北低、东西分化的分布特征,并存在轻工业型和重工业型两大“DEA有效”极核;② 能源规模效率是制约江苏省能源综合效率的主要方面;③ 单位GDP能耗可以集中反映区域能源技术效率水平,大中型企业集中指数可以集中反映区域能源规模效率水平;④ 高耗能产业比重高的区域其能源技术效率相对较低, 居于经济区过渡地带或边缘区位的区域其能源规模效率相对较低;⑤ 全省普遍存在生产要素投入冗余现象,可通过优化投资环境、产业结构高级化、突出特色产业、提升产业技术、提高劳动力素质及提高产业集中程度等途径来提高区域的能源利用效率。

2008

0.0

2010

0.0

1.129

Zhi

, Li

Guoping.

A study on the regional difference of Chinese urban energy efficiency and its influential factors.

Economic Theory and Business Management, 2010, (7): 17-23.

[李治, 李国平. 中国城市能源效率差异特征及影响因素分析. 经济理论与经济管理, 2010, (7): 17-23. ]

本文运用1995-2006年中国210个主要地级市的面板数据,首先采用DEA模型测算了城市全要素能源效率,然后分析了城市全要素能源效率差异特征,结果显示,城市能源效率变动趋势呈现四个较为明显"倒U型"特征,城市间差距扩大转折点出现在2000年左右,而地区间城市差距存在一定的趋同性.城市能源效率水平并不稳定,低效率高投入城市将是节能减排的重点.本文最后采用Tobit模型分析了资源禀赋、平均气温、产业结构、技术水平及各类政策因素与能源效率之间的关系,回归结果说明,应区别对待不同因素在不同条件下引起能源效率变化的影响.

2012

0.0

0.5316

2010

0.0

1.433

2011

3.258

0.0

2007

2.743

0.0

Azadeh

, Amalnick M

, Ghaderi S

et al.

An integrated DEA PCA numerical taxonomy approach for energy efficiency assessment and consumption optimization in energy intensive manufacturing sectors.

Energy Policy, 2007, 35(7): 3792-3806.

Abstract This paper introduces an integrated approach based on data envelopment analysis (DEA), principal component analysis (PCA) and numerical taxonomy (NT) for total energy efficiency assessment and optimization in energy intensive manufacturing sectors. Total energy efficiency assessment and optimization of the proposed approach considers structural indicators in addition conventional consumption and manufacturing sector output indicators. The validity of the DEA model is verified and validated by PCA and NT through Spearman correlation experiment. Moreover, the proposed approach uses the measure-specific super-efficiency DEA model for sensitivity analysis to determine the critical energy carriers. Four energy intensive manufacturing sectors are discussed in this paper: iron and steel, pulp and paper, petroleum refining and cement manufacturing sectors. To show superiority and applicability, the proposed approach has been applied to refinery sub-sectors of some OECD (Organization for Economic Cooperation and Development) countries. This study has several unique features which are: (1) a total approach which considers structural indicators in addition to conventional energy efficiency indicators; (2) a verification and validation mechanism for DEA by PCA and NT and (3) utilization of DEA for total energy efficiency assessment and consumption optimization of energy intensive manufacturing sectors.

2012

0.0

1.012

... 关于能源输出和输入大省,仅有肖涛对两者的能源消耗与经济增长Granger因果关系进行了研究^[13] ...

2001

2.038

0.0

Dyckhoff

, Allen

Measuring ecological efficiency with data envelopment analysis.

European Journal of Operational Research, 2001, (2): 312-325.

The measurement of ecological efficiency provides some important information for the companies’ environmental management. Ecological efficiency is usually measured by comparing environmental performance indicators. Data envelopment analysis (DEA) shows a high potential to support such comparisons, as no explicit weights are needed to aggregate the indicators. In general, DEA assumes that inputs and outputs are ‘goods’, but from an ecological perspective also ‘bads’ have to be considered. In the literature, ‘bads’ are treated in different and sometimes arbitrarily chosen ways. This article aims at the systematic derivation of ecologically extended DEA models. Starting from the assumptions of DEA in production theory and activity analysis, a generalisation of basic DEA models is derived by incorporating a multi-dimensional value function f . Extended preference structures can be considered by different specifications of f , e.g. specifications for ecologically motivated applications of DEA.

Technical University of Aachen, Templergraben 64, D-52064 Aachen, Germany

... 无需建立指标间的函数关系,避免了采用不恰当的函数形式而得出错误的结论,排除了很多主观因素^[14] ...

2006

0.0

Shi

Dan.

Regional differences in China's energy efficiency and conservation potentials.

China Industrial Economy, 2006, (10): 49-58.

[史丹. 中国能源效率的地区差异与节能潜力分析. 中国工业经济, 2006, (10): 49-58. ]

能源效率的区域差别与国际差别有着根本的不同,前者是当前的技术经济水平下通过采取措施可能实现的节能潜力.以国内最高能源效率为比较标准计算中国的节能潜力不仅比以国际先进水平为标准更符合实际,而且还可以做到能源资源公平合理地配置.中国能源效率较高的省市主要集中在东南沿海地区,能源效率最低的地区主要是煤炭资源比较丰富、以煤炭消费为主的内陆省区.提高中国的能源效率需要改变目前地区自我平衡的能源配置方式,使能源流向效率高的地区.各地区在制定节能措施时既要考虑影响能源效率的一般性因素,也要考虑本地区的特殊因素,另外,中国也不能不分地区地实行同一节能降耗的目标.

... 投入产出松弛可以反映节能或扩大产出的潜力^[15] ...

1984

1.859

0.0

... Malmquist生产率指数方法将能源效率分解为纯技术效率指数、规模效率指数和技术进步指数,可以从多角度分析能源消费效率的影响因素,有利于深入分析各单元非有效的原因^[16] ...

2005

0.0

2.247

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2011

0.0

1.433

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2009

0.0

1.968

Yuan

Yijun

, Liu

Hao

, Bai

Nan.

Total factor productivity measurement of China's producer service: Based on Malmquist index.

China Soft Science, 2009, (1): 159-167.

[原毅军, 刘浩, 白楠. 中国生产性服务业全要素生产率测度-基于非参数Malmquist指数方法的研究. 中国软科学, 2009, (1): 159-167. ]

在构建1997-2005年中国27个省市区生产性服务业面板数据的基础上,本文利用非参数Malmquist指数方法考察了中国生产性服务业全要素生产率的变化原因、地区差异与变动趋势.研究表明,中国生产性服务业仍表现为粗放型增长方式;全要素生产率呈现负增长,但下降的速度在逐年放缓;分析期内导致生产性服务业全要素生产率下降的原因不同,前期为技术进步,后期为技术效率;东部地区全要素生产率下降的速度要远低于中西部地区;相对而言,信息传输、计算机服务和软件业、租赁和商务服务业的全要素生产率较高.

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2010

0.0

8.619

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2010

0.0

2.407

Liu

Yong.

Evaluating Chinese urban material metabolism efficiency based on DEA-Malmquist.

Journal of Natural Resources, 2010, 25(1): 12-17.

[刘勇. 基于DEA-Malmquist的我国城市物质代谢效率评价. 自然资源学报, 2010, 25(1): 12-17. ]

运用数据包络分析与麦氏指数(DEA-Malmquist)，从“技术效率”、“技术变动效率”和“全要素生产率”三个方面，对我国34个城市从2005年到2007年的物质代谢效率进行实证分析，结果表明：物质代谢效率的总体水平不高，被评估的34个城市中有19个城市在评价期间是无效率的，主要分布在西部地区；并且整体而言，“全要素生产率”和“技术变动效率”要优于“技术效率”；结果还表明：34个城市物质代谢的“技术效率”普遍处于中等水平，没有呈现出明显的空间分布差异，但是“技术变动效率”则在长三角地区的城市中体现出较为明显的优势，最后，提出了针对性的建议。

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2011

0.0

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2013

0.0

1.968

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2004

0.0

0.547

Erming

, Gao

Huai.

Evaluation and dynamic evolution of competitiveness of iron and steel firms of China.

China Industrial Industry, 2004, (11): 40-46.

[徐二明, 高怀. 中国钢铁企业竞争力评价及其动态演变规律分析. 中国工业经济, 2004, (11): 40-46. ]

提出企业竞争力差异的根源是基于企业的技术资本、组织资本和社会资本有机结合基础之上的企业动态能力运用效果不同的观点,建立了一个企业竞争力的内在决定因素及其作用机制的理论分析框架.在此基础上,利用数据包络分析(DEA)方法和Malmquist指数,针对我国大中型钢铁企业,根据企业价值创造的效率和有效性分析评价了我国大中型钢铁企业竞争力及其动态演变趋势.

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2012

1.854

0.0

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2011

0.0

2.123

Liao

Huchang

, Dong

Yiming.

Utilization efficiency of water resources in 12 western provinces of China based on the DEA and Malmquist TFP index.

Resources Science, 2011, 33(2): 273-279.

[廖虎昌, 董毅明. 基于DEA和Malmquist指数的西部12省水资源利用效率研究. 资源科学, 2011, 33(2): 273-279. ]

The drought which occurred over southwestern China in the early 2010 highlights the situation of inefficient utilization of water resources over these regions. Although it resulted in part from natural factors, people are more clearly aware that the water ecosystem across the region is generally vulnerable, and the ability of drought control is weak. Inadequate infrastructure construction associated with reserving and allocating water resources, which is indispensible to mitigate the uneven spatial and temporal distribution of water resources over there, appears to aggravate water crisis in this region. This paper attempts to examine causes of low efficiency of water utilization across the western regions of China and solutions on how to deal with the grim situation. First, we selected GDP, total investment in fixed assets, annual water supply, and population using water as input-output indicators, and made use of data envelopment analysis to investigate the performance of water resources utilization in 12 western provinces in years 2007 and 2008. Subsequently, we analyzed time series data of the 12 western provinces from 1999 to 2008 using the Malmquist total factor productivity index method. The innovation of this work lies in the dynamic process of analysis. Results show that the utilization efficiency of water resources in Sichuan, Shanxi, Xinjiang, Inner Mongolia, and Guangxi is slightly higher. In order to effectively address an acute shortage of water, other provinces can refer to the experience of the five provinces. Tibet, in particular, should make more efforts to improve the water use efficiency. The total factor productivity index of the utilization efficiency of water resources across western China was found to be 0.956. This means a decreasing trend in water utilization efficiency year by year. Furthermore, the results indicate that technology is the dominant factor restraining water use efficiency for the 12 western provinces. Some suggestions are given including increasing investment in science and technology to improve the overall utilization efficiency of water resources, and expanding the scale of production and optimizing the industrial structure. This paper provides a basis for formulating reasonable water exploitation and protection strategies and policies and would be helpful for achieving the sustainable and coordinated development of resources, economy, and society in western China.

Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China;Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650093, China

2010年初的西南大旱凸显出了西部地区水资源利用效率低下的现状。本文从分析西部12省入手，采用地区生产总值、固定资产投资总额、全年供水量、用水人口等作为投入产出指标，先运用数据包络分析方法对西部12省2007年和2008年的水资源利用效率进行分析和评价，然后运用Malmquist指数方法对1999年至2008年西部12省的序列数据进行了分析。研究发现，四川、陕西、新疆、内蒙古和广西的水资源利用效率要稍微高些，其他省份应参考这5省的经验进行调整，西藏尤其应该加大调整力度；西部地区整体水资源利用效率的TFP指数为0.956，呈逐年衰退之势；技术是制约西部12省水资源利用效率的主导因素，要提高水资源利用效率首先应该加大科技投入。扩大生产规模，优化产业结构也是水资源效率提高的必由之路。

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2008

0.0

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

2006

0.0

... 该方法已广泛应用于国民经济^[17,18]、不同行业^[19,20]、城市^[21,22]、企业^[23,24,25]、资源^[26,27,28]等效率评价和预警中 ...

1978

2.038

0.0

... Rhodes正式提出数据包络分析方法^[29],其模型简称CRS模型 ...

1984

2.038

0.0

... _n^[30],EC = #cod#x003b8 ...

2010

0.0

2.407

You

Heyuan

, Wu

Cifang.

Carbon emission efficiency and low carbon optimization of land use: Based on the perspective of energy consumption.

Journal of Natural Resources, 2010, 25(11): 1875-1884.

[游和远, 吴次芳. 土地利用的碳排放效率及其低碳优化: 基于能源消耗的视角. 自然资源学报, 2010, 25(11): 1875-1884. ]

Carbon emission efficiency is payid more attention to the effect of carbon emission which is considered as cost on desired output, but some indicators, such as total carbon emission and carbon emission per unit land area, neglect this effect. In this paper, the key consideration of carbon emission is the carbon emission brought by energy which is consumed in land use. Based on the input-oriented model of CCR and BCC, the carbon emission efficiency of land use that is composed of the total efficiency, technical efficiency, scale efficiency and returns to scale is calculated. The computed results indicate that: 1) the total efficiencies of carbon emission in Inner Mongolia, Fujian, Guangxi and Qinghai were 1.0000, and they were DEA efficient among the 30 provinces(not including Tibet, Hong Kong, Macao and Taiwan due to no data). The total efficiency of carbon emission in Shanghai was 0.0469, and it was the least among the 30 provinces. The total efficiency of carbon emission in the other provinces was from 0.0682 to 0.8965. 2) There was a relationship between the features of land use and the distribution of technical efficiency and scale efficiency, the efficiency value of carbon emission was also affected by the features of land use. The distribution of regions whose scale efficiencies were efficient, was smaller than the distribution of regions whose technical efficiencies were efficient. 3) The provinces whose scale efficiencies were efficient, different from the provinces with constant returns to scale. Therefore, the returns to scale of the provinces should be taken into account if the scale efficiency of carbon emission efficiency of land use is wanted to improve. In order to insure DEA efficiency of carbon emission of land use in all provinces, the inputs optimization and outputs optimization of land use in 26 provinces which are not DEA efficient are designed. The optimization based on the perspective of low carbon also calculates the input redundancy and output shortage. Finally, according to the results of low carbon optimization, measures are taken to make land use low carbon emission. The measures are composed of the energy input control in land use and land output optimization based on optimal allocation of land resource. Carbon emission efficiency of land use can achieve DEA efficiency if these measures are implemented.

Institute of Land Science and Property Management, Zhejiang University, Hangzhou 310029, China

碳排放效率可以弥补碳排放总量等指标对碳排放作为成本对期望产出作用考虑不足的弱点。研究以土地利用中能源消耗产生的碳排放为基础,基于投入导向的CCR与BCC模型测算土地利用碳排放的总效率、技术效率、规模效率与规模报酬。研究结果表明:30个省份中仅内蒙古、福建、广西、青海土地利用碳排放总效率有效;技术效率与规模效率的效率值及其分布与土地利用特征存在联系,规模效率有效地区分布远小于技术有效;规模效率有效省份与规模报酬不变省份存在不一致,改善土地利用碳排放规模效率需要考虑地区规模报酬所处阶段。因此对26个碳排放非DEA有效省份的土地利用从投入与产出进行低碳优化,并给出投入冗余度与产出不足率。最后针对低碳优化结果,设计包含土地利用能源投入控制以及基于土地资源配置的产出优化的土地利用低碳排放对策,以实现碳排放效率有效。

... 规模效率表示规模集聚水平,反映规模收益的变化情况,投入量偏大、偏小还是处在最适规模,规模过大或过小都将导致平均投入的增加^[31] ...