基于三阶段SBM-DEA模型的中国区域碳排放效率研究

结合非期望产出的SBM-DEA模型与SFA方法,提出基于非期望产出的三阶段SBM-DEA模型,用于对全要素碳排放效率的测算。该模型具有非径向和非角度的特点,而且能剔除外部环境和随机因素的影响,使效率评价更接近实际情况。运用该模型对2000~2012年我国各区域二氧化碳排放绩效进行了评价与分析。结果表明,全国平均碳排放效率不高,但从2006年呈现加速上升趋势,表现了低碳经济良好的发展态势。剔除外部环境和随机因素影响后的碳减排效率将进一步下降。分区域来看,我国碳排放效率呈现出“东>中>西”的格局,且东部的优势有所扩大。基于对投入、产出冗余以及外部环境因素影响的分析表明,提升能源、资本、人员的配置效率,加强东部地区与中西部地区之间的人才和技术交流,有利于提升碳排放效率。
关键词:碳排放效率;三阶段DEA;SBM模型;SFA回归     

考虑非期望产出的我国对外开放机场生产效率评估

针对已有对机场生产效率评估中忽视非期望产出这一问题,研究构建了非期望产出模型SBM-Undesirable,选择我国对外开放机场为研究对象,对其机场生产效率进行评估,并在此基础上对对外开放机场投入产出要素进行定量调整.研究结果表明:在机场生产运营过程中存在非期望产出的情况下,采用SBM-Undesirable模型对其进行效率评价,能够避免传统DEA模型的缺陷,提高效率评价的准确性.通过模型分析发现我国62个对外开放机场中有18个机场生产效率有效,而采用传统CCR模型进行机场生产效率评估时,只有12个机场生产效率有效,说明非期望产出对外开放机场生产效率有显著的影响;我国对外开放机场的生产效率有显著的区域特征,我国区域对外开放机场生产效率得分排序为华中、西南、西北、华南、华东、华北、东北;通过对我国生产效率无效的对外开放机场分析,发现各个对外开放机场普遍存在投入冗余、效益产出不足和非期望产出过多的情况,优化投入产出配置能力、减少机场投诉数量等非期望产出和增加机场有效产出是改善机场生产效率的现实途径.     

考虑非期望产出的我国对外开放机场生产效率评估北大核心

针对已有对机场生产效率评估中忽视非期望产出这一问题,研究构建了非期望产出模型SBM-Undesirable,选择我国对外开放机场为研究对象,对其机场生产效率进行评估,并在此基础上对对外开放机场投入产出要素进行定量调整.研究结果表明:在机场生产运营过程中存在非期望产出的情况下,采用SBM-Undesirable模型对其进行效率评价,能够避免传统DEA模型的缺陷,提高效率评价的准确性.通过模型分析发现我国62个对外开放机场中有18个机场生产效率有效,而采用传统CCR模型进行机场生产效率评估时,只有12个机场生产效率有效,说明非期望产出对外开放机场生产效率有显著的影响;我国对外开放机场的生产效率有显著的区域特征,我国区域对外开放机场生产效率得分排序为华中、西南、西北、华南、华东、华北、东北;通过对我国生产效率无效的对外开放机场分析,发现各个对外开放机场普遍存在投入冗余、效益产出不足和非期望产出过多的情况,优化投入产出配置能力、减少机场投诉数量等非期望产出和增加机场有效产出是改善机场生产效率的现实途径.     

非期望产出的DEA效率评价

将非期望产出作为投入应用到传统DEA模型上,解决了非期望产出生产活动的效率评价问题.结合生产可能集,将非期望产出直接反映到生产可能集中,建立了基于投入导向的径向和非径向两种DEA模型.并对两种DEA模型效率值的大小关系、相对有效性的等价性问题进行了证明,指出非径向DEA模型更能准确的实现效率定量评价.     

基于三阶段SM-DEA-Malmquist指数的长江经济带碳排放效率研究

碳排放问题近年来备受各界学者所关注。为评价长江经济带区域内碳排放绩效,本文结合非期望产出的三阶段SBM-DEA模型和Malmquist指数对2011~2019年长江经济带11省市碳排放效率测算及动静态分析,该模型剔除了环境因素和随机变量的影响,使得测算结果更加符合实际情况。研究表明:长江经济带碳排放绩效呈整体上升态势,且存在东部>中部>西部差异化特征,内部管理对碳排放效率提升的正向作用明显,加大东中西部地区的人才引入及技术交流频度,有助于提升碳排放效率。     

基于LMDI的中国碳排放影响因素分解研究

在对中国2000-2012年不同产业部门、不同能源消费类型的碳排放量进行估算的基础上,利用Kaya恒等式改进模型结合LMDI分解法对中国碳排放量的影响因素进行分解分析.结果显示:经济产出水平、能源结构和人口规模对碳排放的影响是正向的,能源强度和产业结构对碳排放量的影响是负向的.最后提出节能减排的对策建议.     

我国海运出口贸易碳排放影响因素的对数指数分解研究

对2005-2013年我国的碳排放进行了对数指数分解,得出海运出口贸易规模、能源效率、能源结构、碳排放强度是构成碳排放的四个因素,其中海运出口规模和碳排放强度是碳排放的正向拉动关系,能源结构和能源效率是碳排放的负向抑制关系.运用协整检验证明四个因素与碳排放之间具有长期稳定的关系,运用Granger因果检验证明海运出口贸易规模、能源结构、能源效率是碳排放的Granger原因.在此基础上,提出减少海运业碳排放的政策建议.     

中国城市土地利用效率的动态测度及影响因素——基于DEA-Tobit两步法的分析

利用数据包络模型(DEA)对中国大陆30个省、市、自治区2001—2010年城市土地利用效率进行了测度与评价,并运用Tobit回归模型对其影响因素进行了深入分析.结果表明:①中国城市土地利用效率总体呈现上升趋势,除少数省区效率值低于0.5以外,其余均保持在0.5-1之间;②城市土地利用效率前沿面分析结果显示,2001—2010年处于生产前沿面的省、市、自治区的数量保持稳定,但其空间分布却发生了一些变化,10年间东部地区减少2个,西部地区增加2个,中部地区一直处于零水平的状态;③城市土地利用过程中高投入低产出局面的扭转、非农从业人员效率的提高是城市土地利用效率改善的基本内核和关键所在;④经济规模对城市士地利用效率呈现正向促进作用,低层次的产业结构和不断增加的人口密度呈现负面影响,政府规制、空间集聚水平、城市规模和区位条件的影响并不显著.     

基于DEA模型的中国区域能源与环境效率评估

数据包络分析已经被广泛应用于研究中国各省市的能源与环境效率.在以前研究文献中,模拟弱可处置性建模时,隐含的假设所有决策单元采用统一均匀的减少系数.但是在实际生产过程中,面对边际减排成本最低的决策单元,减少非期望产出,也通常会带来成本效益,而且决策者也往往更倾向于减少那些带来成本效益更低的决策单元.因此,为解决这一实际问题,引进不同决策单元的非期望产出采用非均匀比例减少的基础上,考虑非能源投入和能源投入的划分,来构建评估中国各个省市能源和环境效率的生产可能集,然后利用改善的DEA模型计算2016年中国其中30个省市的能源与环境效率.结果表明,我国不同区域,相同区域不同省市之间的能源与环境效率都存在着较大差异.     

居民生活私人交通碳排放驱动因素的“三级分解”模型

突破以往采用难以保证精确性估算数据的局限,根据居民生活私人交通所消耗的能源种类,利用官方统计年鉴中的数据,在保证数据真实性的基础上,基于扩展的IPAT与LMDI模型,构建一个包含居民私人交通碳排放强度、碳排放结构、交通消费倾向、收入、家庭规模与户数6个因素的居民私人交通碳排放驱动因素的"三级分解模型",对居民生活私人碳排放及其驱动因素进行核算.结果建议进一步降低居民私人交通碳排放强度,提高居民能源消费的利用效率;同时鼓励发展小排量与新能源汽车消费,并且大力发展公共交通,倡导居民出行多采用公共交通工具出行,引导节能减排的出行方式.     

小波神经网络模型预测二氧化碳+水溶液体系界面张力

二氧化碳+水溶液体系界面张力(IFT)是影响地层中气水两相运移特性的重要参数之一,对二氧化碳捕集、埋存至关重要.为了快速准确地确定二氧化碳+水溶液体系IFT,对已有IFT实验结果进行了统计整理,得到了1 677组样本数据,考虑了压力,温度,气体中甲烷、氮气含量,水溶液中一价阳离子(Na+,K+)浓度、二价阳离子(Ca2+,Mg2+)浓度6个因素对IFT的影响,建立了小波神经网络（WNN）预测模型对二氧化碳+水溶液体系IFT进行预测.模拟结果表明,随机选取839组数据作为训练集样本,得到的小波神经网络结构为6-16-1,该模型预测IFT的平均绝对误差(M_MAE)、平均相对误差(M_MARE)、方差(M_MSE)和相关度(R2)分别为1.23 mN/m,3.30%,2.30 mN2/m2,0.988.与最新提出的多元拟合模型和BP神经网络模型对比结果表明,小波神经网络模型预测精度最高.     

基于灰色Malmquist-DEA的中国粮食生产效率分析

选取全国各省粮食生产指标面板数据,对数据进行灰色拟合处理,采用数据包络分析(DEA)法,测算各区域的粮食生产效率,得到投入产出体系的要素冗余值;同时测算了各区域的全要素生产率,并对Malmquist指数进行分解,分析各区域的效率变化和技术变化趋势.结果显示:东、西部地区粮食生产投入要素大量冗余,要素配置效率不高,中部地区要素利用率较高,要素配置趋于合理;效率变化对全要素生产率的作用不明显,而技术进步是全要素生产率增长的关键驱动因素.     

Centralised reallocation of emission permits using DEA

In this paper a data envelopment analysis (DEA) approach to the problem of emission permits reallocation is presented. It can be used with conventional command and control as well as with an allowance market. It uses a centralized point of view, which represents the common good. In the model it is assumed that firms produce two types of outputs: desirable outputs (i.e. good outputs with positive value for consumers) and undesirable outputs (i.e. bad outputs with negative value for consumers, such as emissions of pollutants). The proposed approach has three phases, which correspond to three objectives that are pursued lexicographically. The three objectives are maximizing aggregated desirable production, minimizing undesirable total emissions and minimizing the consumption of input resources. The relative priority of these objectives is defined by the regulator. The whole approach is units-invariant and does not require information on input and output prices. The approach is applied on a dataset from the Swedish pulp and paper industry.     

Estimating output gains by means of Luenberger efficiency measures

In this paper we propose a new measure of input allocative efficiency that we estimate using directional distance functions. Our new measure compares the gain in output if a firm reduces technical inefficiency for the direct production possibility set and the gain in output if the firm reduces technical inefficiency for the indirect production possibility set. Because the directional distance function uses a translated origin, the gain in output from an optimal reallocation of inputs can be estimated for non-radial expansions in output. We estimate efficiency for Japanese banks during 1992–1999. The gains in outputs from reducing allocative inefficiency by reallocating inputs are greater than the gains in outputs that can be attained by reducing technical inefficiency.     

Stochastic resonance with different periodic forces in overdamped two coupled anharmonic oscillators

We study the stochastic resonance phenomenon in the overdamped two coupled anharmonic oscillators with Gaussian noise and driven by different external periodic forces. We consider (i) sine, (ii) square, (iii) symmetric saw-tooth, (iv) asymmetric saw-tooth, (v) modulus of sine and (vi) rectified sinusoidal forces. The external periodic forces and Gaussian noise term are added to one of the two state variables of the system. The effect of each force is studied separately. In the absence of noise term, when the amplitude f of the applied periodic force is varied cross-well motion is realized above a critical value (f_c) of f. This is found for all the forces except the modulus of sine and rectified sinusoidal forces. For fixed values of angular frequency ω of the periodic forces, f_c is minimum for square wave and maximum for asymmetric saw-tooth wave. f_c is found to scale as Ae^0.75ω + B where A and B are constants. Stochastic resonance is observed in the presence of noise and periodic forces. The effect of different forces is compared. The stochastic resonance behaviour is quantized using power spectrum, signal-to-noise ratio, mean residence time and distribution of normalized residence times. The logarithmic plot of mean residence time τ_MR against 1/(D − D_c) where D is the intensity of the noise and D_c is the value of D at which cross-well motion is initiated shows a sharp knee-like structure for all the forces. Signal-to-noise ratio is found to be maximum at the noise intensity D = D_max at which mean residence time is half of the period of the driving force for the forces such as sine, square, symmetric saw-tooth and asymmetric saw-tooth waves. With modulus of sine wave and rectified sine wave, the SNR peaks at a value of D for which sum of τ_MR in two wells of the potential of the system is half of the period of the driving force. For the chosen values of f and ω, signal-to-noise ratio is found to be maximum for square wave while it is minimum for modulus of sine and rectified sinusoidal waves. The values of D_c at which cross-well behaviour is initiated and D_max are found to depend on the shape of the periodic forces.     

Resources use and greenhouse gas emissions in urban economy: Ecological input–output modeling for Beijing 2002

The embodiment of natural resources and greenhouse gas emissions for the urban economy of Beijing economy 2002 by a physical balance modeling is carried out based on an extension of the economic input–output table into an ecological one integrating the economy with its various environmental driving forces. Included resources and greenhouse gas emissions belong to six categories as energy resources in terms of primary energy and secondary energy; water resource; emissions of CO₂, CH₄, and N₂O; exergy in terms of energy sources, biological resources and minerals; and solar emergy and cosmic emergy in terms of climate resources, soil, energy sources, and minerals.     

Incorporating performance measures with target levels in data envelopment analysis

Data envelopment analysis (DEA) is a technique for evaluating relative efficiencies of peer decision making units (DMUs) which have multiple performance measures. These performance measures have to be classified as either inputs or outputs in DEA. DEA assumes that higher output levels and/or lower input levels indicate better performance. This study is motivated by the fact that there are performance measures (or factors) that cannot be classified as an input or output, because they have target levels with which all DMUs strive to achieve in order to attain the best practice, and any deviations from the target levels are not desirable and may indicate inefficiency. We show how such performance measures with target levels can be incorporated in DEA. We formulate a new production possibility set by extending the standard DEA production possibility set under variable returns-to-scale assumption based on a set of axiomatic properties postulated to suit the case of targeted factors. We develop three efficiency measures by extending the standard radial, slacks-based, and Nerlove–Luenberger measures. We illustrate the proposed model and efficiency measures by applying them to the efficiency evaluation of 36 US universities.     

Improving weak efficiency frontiers in the fuzzy data envelopment analysis models

Evaluating the performance of activities or organization by common data envelopment analysis models requires crisp input/output data. However, the precise inputs and outputs of production processes cannot be always measured. Thus, the data envelopment analysis measurement containing fuzzy data, called “fuzzy data envelopment analysis”, has played an important role in the evaluation of efficiencies of real applications. This paper focuses on the fuzzy CCR model and proposes a new method for determining the lower bounds of fuzzy inputs and outputs. This improves the weak efficiency frontiers of the corresponding production possibility set. Also a numerical example illustrates the capability of the proposed method.     

基于DEA模型的中国省会城市开发效益分析

利用CCR-DEA和BCC-DEA模型估算了我国省会城市的开发效益,结果显示我国大多数省会城市处于DEA无效状态.从整体上看DEA有效的单元为太原、呼和浩特和石家庄等9个城市,无论其纯技术效率还是规模效率都达到1,仅占整体省会城市的约29%.利用皮尔逊相关系数测算了3种效率与投入指标的关系,结果发现我国城市开发效益主要依赖固定资本的大量投入,与城市开发规模相关性不大,人力资本的投入还未能在城市效益上起到关键作用.最后对提高省会城市开发效益提出政策建议.     

Technical efficiency estimation with multiple inputs and multiple outputs using regression analysis

Regression and linear programming provide the basis for popular techniques for estimating technical efficiency. Regression-based approaches are typically parametric and can be both deterministic or stochastic where the later allows for measurement error. In contrast, linear programming models are nonparametric and allow multiple inputs and outputs. The purported disadvantage of the regression-based models is the inability to allow multiple outputs without additional data on input prices. In this paper, deterministic cross-sectional and stochastic panel data regression models that allow multiple inputs and outputs are developed. Notably, technical efficiency can be estimated using regression models characterized by multiple input, multiple output environments without input price data. We provide multiple examples including a Monte Carlo analysis.