摘 要
碳排放效率反映的是一种能源利用效率,即反映了在生产活动中产生生产效益的同时所引起的碳排放.碳排放效率越高表明能源利用综合效率越高.随着党的十九大的召开和淮河生态经济带上升为国家战略,淮河生态经济带在拉动中东部经济发展中的作用不可忽视.在追逐经济发展的同时,会产生大量的能源消耗,由其产生的二氧化碳对环境造成的负面影响也日益显现.在我国倡导低碳经济发展的背景下,研究淮河生态经济带碳排放效率具有较强的现实意义.
本文认真总结分析现有文献的研究成果,以可持续发展理论、低碳经济学等学科知识为支撑,以十九大报告以及相关文件政策精神为指导,应用非期望产出SBM 模型、Malmquist 指数和空间计量模型,对淮河生态经济带碳排放效率及其影响因素效应进行系统研究.主要内容和结论如下:
关于碳排放效率测度.本文选取 2013-2017 年面板数据,计算淮河生态经济带沿线城市碳排放总量,运用非期望产出 SBM 模型测度各城市碳排放效率值,随后基于分解得出碳排放效率值变动原因.结果表明,淮河生态经济带碳排放总量呈现下降趋势,其中济宁市碳排放总量最多;淮河生态经济带碳排放总效率呈现上升趋势,大部分城市年度碳排放效率值在 0.4-0.8 之间;Malmquist 指数分解结果表明技术进步是引起淮河生态经济带碳排放效率变化的主要因素.
关于碳排放效率影响因素效应分析.首先,进行全局和局部莫兰指数检验,结果表明,淮河生态经济带碳排放效率呈现显着的正向自相关性,即碳排放效率存在空间聚集效应;淮河生态经济带大部分城市具有空间依赖性和存在一定的异质性.其次,选取城镇化率、能源强度、科技创新、产业结构和对外开放水平作为碳排放效率的影响因素进行空间计量分析,并选择空间杜宾模型为拟合最优模型,结果表明,城镇化率的直接效应为正值,间接效应值为负值,说明城市的城镇化率越高则对该城市的碳排放效率的提升具有显着的促进作用,而对其他城市的碳排放效率的提高有抑制作用;产业结构的直接效应值为负值,间接效应值为正值,说明一个城市的第二产业占总产业值比例的提高对该城市碳排放效率的提高呈现抑制作用,对其他城市的碳排放效率具有促进作用;能源强度的直接效应值为正值,即一个城市单位 GDP 能耗值越高,对该城市的碳排放效率的提高具有抑制作用.技术创新的直接效应值和间接效应值均为正值,说明一个城市的R&D 经费支出占 GDP 比重越高对该城市碳排放效率的提高产生促进作用,且对其他城市碳排放效率的提高也产生促进作用;对外开放水平的直接效应值为正值说明城市的对外开放水平对该城市碳排放效率的提高产生促进效应.
关于提高淮河生态经济带碳排放效率的政策建议.分别从有效规划城镇化发展、加快产业结构优化、加大技术创新和优化外商投资质量四个方面为提高淮河生态经济带碳排放效率提出相应建议.
关键词:碳排放效率,非期望产出SBM模型,Malmquist指数,空间计量模型
ABSTRACT
The carbon emission efficiency reflects a kind of energy utilization efficiency, itreflects the carbon emission caused by the production efficiency. The higher theefficiency of carbon emission, the higher the comprehensive efficiency of energyutilization. With the convening of the 19th national congress of the communist partyof China and the promotion of Huaihe river ecological economic belt as a nationalstrategy, the role of Huaihe river ecological economic belt in promoting economicdevelopment in the central and eastern regions cannot be ignored. While pursuingeconomic development, it will produce a large amount of energy consumption, andthe negative impact of carbon dioxide on the environment is also increasinglyapparent. Under the background of China's low-carbon economic development, It isof great practical significance to study the carbon emission efficiency of Huaihe riverecological economic belt.
This paper carefully summarizes and analyzes the existing research results,according to the theory of sustainable development, low carbon economics disciplinesknowledge as the support, guided by the policy spirit of the the 18th and 19th nationalcongress of the communist party of China application of the expected output SBMmodels, and Malmquist index and spatial econometric model, the ecological economiczone of the Huaihe river system of the effect of carbon efficiency and its influencingfactors. The main contents and conclusions are as follows:
Carbon emission efficiency measure. In this paper, panel data from 2013 to 2017were selected to calculate the total carbon emissions of cities along the Huaihe rivereco-economic belt, and the carbon emission efficiency value of each city wasmeasured by using the non-expected output SBM model. Then, the reasons for thechange of carbon emission efficiency value were obtained based on decomposition.
The results show that the total carbon emission of Huaihe river ecological economicbelt shows a decreasing trend, among which jining city has the largest total carbonemission. The total carbon emission efficiency of Huaihe river eco-economic belt ison the rise tendency, and the annual carbon emission efficiency of most cities isbetween 0.4 and 0.8. The results of Malmquist index decomposition indicate that thetechnological progress is the main factor causing the change of carbon emissionefficiency in the Huaihe river eco-economic belt.
Analysis of factors affecting carbon emission efficiency. Firstly, the global andlocal moran index tests show that the carbon emission efficiency of Huaihe river eco-economic belt shows a significant positive spatial autocorrelation. Most cities inHuaihe river ecological economic belt have spatial dependence and some differences.Second, urbanization rate, energy intensity, science and technology innovation,industrial structure and the level of opening to the outside world as carbon and spatialeconometric analysis to the influence factors of the efficiency, and select thedoberman model for the optimal fitting model, the results showed that theurbanization rate of positive direct effect, indirect effect value is negative, the city'surbanization rate, the higher the information of the city to promote the efficiency ofcarbon emissions has significant role in promoting, and to other cities to enhance theefficiency of carbon inhibition; The direct effect value of the industrial structure isnegative, while the indirect effect value is positive, indicating that the increase of theproportion of the secondary industry in the total industrial value of a city has aninhibitory effect on the improvement of the carbon emission efficiency of that city,and an promoting effect on the carbon emission efficiency of other cities. The directeffect value of energy intensity is positive, that is, the higher the energy consumptionper unit GDP of a city is, the higher the carbon emission efficiency of the city will beinhibited. The direct effect value and indirect effect value of technological innovationare both positive, indicating that the higher the proportion of R&D expenditure inGDP in a city, the higher the carbon emission efficiency of that city will be promoted,and the higher the carbon emission efficiency of other cities will also be promoted.he direct effect value of the level of opening to the outside world is positive,indicating that the level of opening to the outside world has a promoting effect on theimprovement of the carbon emission efficiency of the city.
Policy Suggestions on improving the carbon emission efficiency of Huaihe rivereco-economic belt. In order to improve the carbon emission efficiency of Huaihe rivereco-economic belt, Suggestions are put forward from four aspects: effective planningof urbanization development, accelerating the optimization of industrial structure,increasing technological innovation and optimizing the quality of foreign investment.
Keywords:Carbon Efficiency; Non-expected Output SBM Model; MalmquistIndex; Spatial Econometric model
目 录
第一章 引言 ············································································1
第一节 研究背景及意义 ············································································1
一、研究背景 ························································································1
二、研究意义 ························································································2
第二节 相关文献综述 ···············································································3
一、国外文献综述 ··················································································3
二、国内文献综述 ··················································································4
三、文献评述 ························································································6
第三节 研究主要内容和研究方法 ································································6
一、研究主要内容 ··················································································6
二、研究方法 ························································································7
第四节 本文的创新和不足之处···································································7
一、本文的创新之处 ···············································································7
二、本文的不足之处 ···············································································8
第二章 碳排放效率基本理论 ·······················································9
第一节 基本概念·····················································································9
一、效率 ······························································································9
二、碳排放 ···························································································9
三、碳排放效率 ·····················································································9
第二节 相关理论····················································································10
一、低碳经济理论 ·················································································10
二、可持续发展理论 ·······················································································11
三、生态经济理论···························································································11
四、碳排放效率影响因素的理论分析 ··································································11
第三章 淮河生态经济带碳排放效率分析 ·······································15
第一节 淮河生态经济带碳排放量测定·························································15
一、测定方法 ·······················································································15
二、淮河生态经济带碳排放量变动分析······················································17
第二节 淮河生态经济带碳排放效率测度分析················································21
一、测度方法 ·······················································································21
二、指标选取和数据说明 ········································································24
三、实际测度分析 ·················································································26
第四章 淮河生态经济带碳排放效率影响因素分析····························31
第一节 空间计量模型介绍········································································31
一、空间回归分析基础 ···········································································31
二、空间自相关性检验 ···········································································31
三、空间权重矩阵 ·················································································33
四、空间计量模型选择 ···········································································34
第二节 变量选取及数据来源·····································································35
一、变量选取 ·······················································································35
二、数据来源 ·······················································································36
第三节 碳排放效率的空间自相关性诊断······················································36
一、碳排放效率的全局自相关检验 ····························································37
二、碳排放效率的局部莫兰指数散点图······················································37
第四节 模型估计与结果分析·····································································39
一、模型估计及选择 ··············································································39
二、结果分析 ·······················································································40
第五章 主要结论及建议 ····························································43
第一节 主要结论····················································································43
一、淮河生态经济带碳排放效率测算及其分解·············································43
二、碳排放效率值影响因素分析结果 ·························································44
第二节 政策建议····················································································45
一、有效规划城镇化发展 ········································································45
二、加快产业结构优化,走可持续发展经济················································45
三、加大技术创新,实现区域联动发展······················································46
四、提高外商引进质量 ···········································································46
参考文献 ·················································································47
附录 ·······················································································51
致谢 ·······················································································55
第一章 引言
第一节 研究背景及意义
一、研究背景
社会经济发展的基础是对能源的使用,能源的开发利用拉动经济发展.当今世界各国的经济处在高速增长状态,因此对能源的使用也随之增加.由于对能源的过度使用一些环境污染问题也随之显现,其中包括光化学烟雾、酸雨等,同时煤炭能源的大量消耗会带来二氧化碳的排放,二氧化碳浓度超出地球承受范围时便会造成温室效应,温室效应对生态社会和经济社会带来的负面影响不可忽视.因此自上世界 90 年代开始,低碳经济不断被国家关注.2003 年英国能源白皮书首次提出低碳经济(Low-Carbon Economy)概念,低碳经济是以可持续发展理念为指导,通过相应产业结构、技术创新、资源配置等方法,提高能源使用效率,降低能源的消耗,减少温室气体排放,达到经济发展和生态环境保护和谐发展的双赢目标.
在"低碳减排"提出的大背景下,"低碳经济"、"低碳技术"、"低碳城市"等相关概念和政策相继出现,生态文明建设是人类发展中必不可少的课题,其关键是将能源与经济发展结合起来.根据结合我国具体国情和实际发展,党的十八大报告提出将生态文明建设纳入中国特色社会主义道路事业总体布局,这是对自然规律和人与自然关系再认识的崭新成果.随后,党的十九大报告在十八大基础上提出新的要求即我国社会主要矛盾发生转变,即要创造更多物质财富和精神财富以满足人民日益增长的美好生活需要,也要提供更多优质生态产品以满足人民日益增长的优美生态环境需要,在发展经济的同时要着重强调绿色经济、低碳经济的发展,对中国经济的发展提出新的要求和期待.
由于中国幅员辽阔、地大物博,为了充分发挥区域资源禀赋和地理区位优势、实现区域经济协调发展和可持续发展,改善生态环境,实现共同富裕,因此划分经济带进行发展.淮河流域是处于长江流域和黄河流域之间,经济发展总体相对滞后,是我国中东部最具发展潜力的地区之一.2018 年 10 月 18 日国务院发布《国务院关于淮河生态经济带发展规划的批复》①,原则同意《淮河生态经济带发展规划》.批复中对淮河生态经济带提出的要求是达到整个经济带和谐绿色发展的目标,即在实现经济发展的同时注重生态环境的保护,淮河生态经济带作为一个整体,区域间应相互扶持,加快现代化经济体系建设的步伐,将其打造成一个绿色发展、美丽宜居、人民幸福的生态经济带.淮河生态经济带旨在推动形成人与自然和谐发展的现代化建设新格局,促进经济绿色、健康发展.绿色发展则是以低碳、效率、和谐、持续为目标的经济增长和社会发展方式.党的十九大将淮河生态经济带建设提升为国家战略,淮河生态经济带建设对促进我国东中部地区经济社会发展具有重要意义.当前,淮河生态经济带的发展相比长江经济带仍存在不足之处.相比于长江经济带,淮河生态经济带在经济水平发展、产业结构和能源结构调整上有较大的提升空间.而淮河生态经济带作为新的国家发展战略,必然要关注碳排放的问题.
发展低碳经济是淮河生态经济带建设的主要内容和重要途径,因此淮河经济带的碳排放效率理应得到重视.由于经济发展基础、资源配置方式、技术进步水平等因素存在地区差异,因而淮河经济带内各地区碳排放效率也必然存在非均衡性.定量测度淮河经济带内各地区碳排放效率及其影响因素,对于反映其碳排放效率空间分布差异特征、揭示其主要影响因素并进而寻求优化路径,促进淮河生态经济带绿色低碳经济发展具有重要作用.
二、研究意义
我国社会主要矛盾的转化揭示人民对生态环境的向往越来越强.在实现经济发展目标的同时加强对生态环境的保护,强调人与自然和谐共处,共同发展.淮河生态经济带作为新的国家发展战略,对中东部绿色经济发展具有重要意义,因此本文对淮河生态经济带沿线城市碳排放效率进行测度,观察其近几年变化趋势及变化原因,同时分析影响碳排放效率原因和效应,为淮河生态经济带沿线城市科学合理地制定减排计划及为促进淮河生态经济带绿色低碳经济发展提供新的思路,对于淮河生态经济带实现经济质量和速度的共同发展具有十分重要的理论意义和现实意义.
(一)理论意义
目前淮河生态经济带的发展正处在重要阶段,绿色经济对其意义重大,但从碳排放效率角度对淮河生态经济带绿色发展做出分析的研究较少.因此本文从这一角度出发,首先,本文采用 IPCC 清单法测度淮河生态经济带二氧化碳排放量,并将其作为非期望产出;采用将非期望产出和 SBM 模型结合起来的非期望产出SBM 模型对淮河生态经济带碳排放效率进行测度,将碳排放作为松弛变量引入到此模型中,使其可以有效的对沿线城市碳排放效率水平进行比较分析和评价;其次,基于 Malmquist 指数分解方法探寻淮河生态经济带沿线城市碳排放效率差异的原因;最后,在碳排放效率影响因素的分析中将空间效应考虑在内,在分析过程中建立空间计量模型,从多角度综合考察相关影响因素对淮河生态经济带沿线城市碳排放效率的空间影响及其作用.以上分析丰富了关于碳排放效率研究的内容,相关研究思路可以推广到相关学科的研究中.
(二)现实意义
科学客观的评价碳排放的效率水平和影响因素是衡量淮河生态经济带沿线城市低碳减排与经济发展之间平衡关系的重要依据.基于现有相关数据的基础上,定量测度淮河生态经济带碳排放效率值,分析各城市之间存在的差异,分析碳排放效率动态变化趋势及变化原因,从空间角度度量相关因素对碳排放效率影响的方向及大小.根据上述分析有助于了解淮河生态经济带碳排放效率相关问题的现状,根据相关结论对淮河生态经济带沿线城市科学合理地制定减排计划及为促进淮河生态经济带绿色低碳经济发展提供新的思路,推动建立淮河生态经济带绿色经济发展,对于淮河生态经济带绿色发展的政策制定和指导具有一定的现实意义.
…………由于本文篇幅较长,部分内容省略,详细全文见文末附件
第五章 主要结论及建议
第一节 主要结论
本文选取淮河生态经济带沿线 27 个城市基于 2013-2017 年面板数据基础上,将二氧化碳排放量作为非期望产出,建立相应的投入产出指标体系,运用MaxDEA 软件,通过建立非期望产出 SBM 模型,测度沿线城市 2013-2017 年的碳排放效率值,了解各城市碳排放效率的变化趋势;同时通过对 DEA-Malmquist指数的分解得到碳排放效率的动态变化,较为清晰地解释碳排放效率值变动的原因.在碳排放效率值测度的基础上,运用空间计量等相关理论基础对碳排放效率的影响因素进行实证分析.
一、淮河生态经济带碳排放效率测算及其分解
(一)各城市碳排放量
2013-2017 年淮河生态经济带沿线城市碳排放总量在前四年呈现下降趋势,其中 2014-2015 年下降幅度最大,说明低碳经济、绿色发展理念在淮河生态经济带沿线城市中贯彻的较为有效,在随后一年中呈现轻微的上升趋势,此现象可能是由于经济的快速发展带动能源的消耗加大.
对各城市碳排放总量观测可知,2013-2017 年碳排放总量最大的是济宁市,较最后一名随州市相差约 360 倍,因此可以看出淮河生态经济带碳排放量在城市之间差异较大,且碳排放总量较高的城市大多集中在江苏省和山东省.在碳排放量年增长率方面,年均增长率最高的亳州市达到 38.9%,最低的是随州市为-20.41%,其中碳排放总量年增长率为负值的达到 17 个城市,占总城市之比62.96%.
(二)各城市碳排放效率值
在现有 DEA 相关知识和实践的基础上,采用非期望产出 SBM 模型及DEA-Malmquist 指数对对碳排放效率进行有效测度和分析.
1.各城市碳排放效率测度值
2013-2017 年间,淮河生态经济带沿线大部分城市的年度效率均值在 0.4-0.8之间,说明存在较大的投入产出改进空间.其中平顶山市、淮安市、随州市、滁州市等的年度碳排放效率均值接近 1,说明这些城市实现了对劳动力、固定资本、能源投入的充分利用,在拉动经济增长的同时降低了碳排放总量,实现了碳排放技术的提高;而淮南市、孝感市、枣庄市、淮北市等的碳排放效率值较低,其中淮北市、淮南市是资源型城市,是安徽省最大的煤矿资源城市,对煤炭的采掘加工量巨大,对碳排放量产生一定的负面影响,同时技术水平也有待提高.
就省份而言,湖北省的碳排放效率平均值较高且较为稳定,这与湖北省结合自身区域优势、政策优势在发展低碳经济方面做出的探索密不可分;江苏省的平均碳排放效率值呈现先下降后上升趋势,江苏省经济发展较为快速,但在发展中可能存在技术水平不足、产业结构不合理的现象,使投入资本并未得到有效利用;河南省、安徽省、山东省的碳排放效率值均处在较低的水平.
2.碳排放效率分解
通过分析可得,影响淮河生态经济带沿线城市碳排放综合效率的主要由于技术进步.淮河生态经济带沿线各城市的综合效益指数均大于 1,说明碳排放效率总的呈现上升趋势,其中规模效益增加和技术进步是拉动碳排放效率上升的主要动力.
就省份而言,5 个省市的综合效率值均大于 1,可看出碳排放效率处于上升状态,其中江苏省的技术效率和规模报酬呈现上升趋势,而并未实现技术的进步,江苏省内 6 个城市的技术进步均小于 1,拉低安徽省和湖北省碳排放投入产出有效性的主要是技术进步.河南省在技术进步和技术效益方面实现了增长,山东省达到了技术进步、技术效益和规模效益增长的目标.
二、碳排放效率值影响因素分析结果
在碳排放效率值测度的基础上,通过依据相关理论基础选择相关影响因素建立空间计量模型分析得到影响因素的方向及大小.得到以下结论:
(一)空间自相关检验
根据全局和局部空间自相关检验结果可知,2013-2017 年间淮河生态经济带沿线城市碳排放效率在空间上聚集分布较为稳定.淮河生态经济带碳排放效率出现显着的空间正相关性,即碳排放效率高的城市呈现"聚集"现象即碳排放效率高的城市出现"优势聚集"而碳排放效率低的城市出现"劣势聚集".2013-2017年间落入 H-H 和 L-L 象限的城市依次为 74.07%、66.67%、66.67%、70.37%和62.96%,说明城市间碳排放效率存在依赖性,但同时存在一定的差异.
(二)影响因素分析
通过对淮河生态经济带沿线城市碳排放效率的实证分析得,SDM-FE 模型为拟合最优模型.由于回归系数显着不为 0,因此分解为直接效应、间接效应和总效应分析各因素对碳排放效率的影响方向及大小.
1. 城镇化率的空间溢出效应
城镇化率的直接效应和间接效应值依次为 0.231 和-0.174,说明城镇化率的提高对该城市的碳排放效率的提高产生促进作用,对其他城市的碳排放效率产生抑制影响.
2. 产业结构的空间溢出效应
产业结构的直接效应值和间接效应值依次为-0.187 和 0.136 且分别在 1%和5%的显着性水平下通过检验,一个城市的第二产业占总产业值的比例的提高对该城市的碳排放效率的提高呈现抑制作用,对其他城市的碳排放效率具有促进作用.
3. 能源强度的空间溢出效应
能源强度的直接效应值为负值,其间接效应和总效应未通过显着性检验,即一个城市的单位 GDP 能耗值越高对该城市的碳排放效率的提高具有抑制作用.
4. 技术创新的空间溢出效应
技术创新的直接效应值和间接效应值均为正值且均均通过 1%显着性水平下的检验,即一个城市的 R&D 经费支出占 GDP 的比重越高对该城市的碳排放效率的提高产生促进作用,且对其他城市的碳排放效率的提高也产生促进作用.
5. 对外开放水平的空间溢出效应
对外开放水平的直接效益值和总效应值依次为 0.140 和-0.196 且显着,及一个城市的对外开放水平对该城市碳排放效率的提高产生促进效应,但总体上城市的对外开放水平对碳排放率的提高起抑制作用.
第二节 政策建议
基于现有文献及相关知识的梳理,通过对淮河生态经济带碳排放效率的测度及影响因素分析结果分析,针对淮河生态经济带碳排放效率的提高以及低碳经济发展等问题,提出以下建议:
一、有效规划城镇化发展
由实证分析结果可知,城镇化率对淮河生态经济带城市内部的碳排放效率起到促进作用,说明现阶段淮河生态经济带各城市应合理提高城镇人口占总人口规模,实现科技随着人口流动而集中,也要避免因人口过于聚集造成公共设施和资源的过度使用,从而有效提高淮河生态经济带碳排放效率.同时淮河生态经济带各城市之间应合理分配城市流动人口,实现碳排放效率的共同提高.
二、加快产业结构优化,走可持续发展经济
随着经济的快速发展,各城市不应片面的注重经济发展速度,而应该将重心放在经济发展质量上.整体来看,淮河生态经济带沿线城市产业结构对碳排放效率的提高呈抑制作用,可知大力发展第二产业不利于低碳经济的发展.因此淮河生态经济带各城市应注重产业结构的优化,逐步稳健的实现第二产业向第三产业过渡,实现重工业向轻工业的过渡.淮河生态经济带城市间应相互协调,不应盲目攀比 GDP 值而忽视能源消耗问题,各级政府针对第二产业相关企业加大力度宣传可持续发展理论和低碳经济理论,做到淮河生态经济带产业结构普遍优化.
三、提高技术创新,实现区域联动发展
技术进步是提高碳排放效率的一个重要因素,技术的提高可以有效的减少能源消耗和污染排放.淮河生态经济带沿线城市应鼓励发展技术创新,给予相关企业创新支持,使用清洁能源发展低能耗高产出企业,适当关闭高能耗低产出企业.同时各城市应注重科技人才的引进,加大企业管理.淮河生态经济带作为一个发展总体,各城市之间应互帮互助,实现技术共享,企业间也应相互扶持,实现企业与企业之间,城市与城市之间的共同发展.
四、提高外商引进质量
由分析结果可知,对外开放水平对淮河生态经济带碳排放效率呈现抑制作用.说明淮河生态经济带城市在引进外商时门槛较低,外商投资可能为能源消耗较高、技术较为落后的企业,造成能源消耗量增加对能源效率低,因此拉低当地碳排放效率,在淮河生态经济带沿线城市中连云港市为沿海城市,因此政府在引进外商投资时,应适量提高引进标准,减少高消耗企业的加入.同时在引进外资是应着重引进技术创新企业,有效利用国外先进技术提高城市内部企业发展.
参考文献
[1]成刚.数据包络分析方法与 MaxDEA 软件[M].北京:知识产权出版社,2014,149-220.
[2]陈强.高级计量经济学及 Stata 应用(第二版)[M].北京:高等教育出版社,2014.
[3]《淮河生态经济带发展规划》获国务院批复[J].现代城市研究,2018(11):131.
[4]陆敏,王增武.基于 DEA 模型的中国碳排放管制效率研究[J].生态经济,2019,35(06):13-17.
[5]王兆峰,杜瑶瑶.基于 SBM-DEA 模型湖南省碳排放效率时空差异及影响因素分析[J].地理科学,2019,39(05):797-806.
[6]姜国刚,阮婉妮,郭铁军.基于三阶段 DEA 的江苏石化产业碳排放效率分析[J].环境科学与技术,2019,42(03):172-179.
[7]李小胜,胡正陶,张娜,宋马林."十二五"时期中国碳排放全要素生产率及其影响因素研究[J].南开经济研究,2018(05):76-94.
[8]郭四代,钱昱冰,赵锐.西部地区农业碳排放效率及收敛性分析--基于SBM-Undesirable 模型[J].农村经济,2018(11):80-87.
[9]张强."丝绸之路经济带"中国段交通运输业碳排放效率分析--基于Luenberger 生产率指数法[J].湖南大学学报(社会科学版),2018,32(05):78-84.
[10]李健,邓传霞.基于 SDDF 的中国省区二氧化碳排放效率及减排潜力测度[J].软科学,2015,29(03):70-73.
[11]雷玉桃,杨娟.基于 SFA 方法的碳排放效率区域差异化与协调机制研究[J].经济理论与经济管理,2014(07):13-22.
[12]刘兴华,廖翠萍,黄莹,谢鹏程.基于 STIRPAT 模型的广州市建筑碳排放影响因素及减排措施分析[J].可再生能源,2019,37(05):769-775.
[13]王剑,薛东前,马蓓蓓.基于 GFI 模型的西安市能源消费碳排放因素分解研究[J].干旱区地理,2018,41(06):1388-1395.
[14]王雅楠,谢艳琦,谢丽琴,陈伟.基于 LMDI模型和 Q 型聚类的中国城镇生活碳排放因素分解分析[J].环境科学研究,2019,32(04):539-546.
[15]曹俊文,姜雯昱.基于 LMDI 的电力行业碳排放影响因素分解研究[J].统计与决策,2018,34(14):128-131.
[16]蒋毅一,彭林,赵爽,刘琳.基于空间计量的中国省域火电行业碳排放效率分析[J].山东财经大学学报,2019,31(02):31-42+83.
[17]马大来.中国农业能源碳排放效率的空间异质性及其影响因素--基于空间面板数据模型的实证研究[J].资源开发与市场,2018,34(12):1693-1700+1765.
[18] 李晓灿 . 可 持 续 发 展 理 论 概 述 与 其 主 要 流 派 [J]. 环境与发展,2018,30(06):221-222.
[19]蒋伟.《我们共同的未来》简介[J].城市环境与城市生态,1988(01):46-47.
[20]张立军,徐红梅,张同建.浅析低碳经济的内涵--基于经济学与非经济学的视角[J].萍乡高等专科学校学报,2013,30(05):12-14.
[21] 张同建 . 实 验 经 济 学 研 究 浅 述 [J]. 十 堰 职 业 技 术 学 院 学报,2009,22(06):21-23.
[22]李明星,张同建.基于 BSC 的煤炭企业核心能力 KPI 体系研究[J].财会通讯,2010(29):36-37.
[23]刘良灿,张同健.论互惠性偏好理论的内涵及其在和谐社会构建中的作用[J].兰州石化职业技术学院学报,2010,10(03):44-47.
[24]张军,吴桂英,张吉鹏.中国省级物质资本存量估算:1952-2000[J].经济研究,2004(10):35-44.
[25]徐丹丹,刘超,董莹.我国国有固定资本存量测算及其规模变迁分析[J].价格理论与实践,2017(06):79-81.
[26]耿世刚,孟卫东,尹凡.低碳城市建设与产业转型升级的对接研究[J].云南社会科学,2019(04):153-158.
[27]张梅,黄贤金,揣小伟.中国城市碳排放核算及影响因素研究[J].生态经济,2019,35(09):13-19+74.
[28]丁凡琳,陆军,赵文杰.城市居民生活能耗碳排放测算及空间相关性研究- -基于 287 个地级市的数据[J].经济问题探索,2019(05):40-49.
[29]韩晶,王赟,陈超凡.中国工业碳排放绩效的区域差异及影响因素研究--基于省域数据的空间计量分析[J].经济社会体制比较,2015(01):113-124.
[30]程叶青,王哲野,张守志,叶信岳,姜会明.中国能源消费碳排放强度及其影响因素的空间计量[J].地理学报,2013,68(10):1418-1431.
[31]梅林海,蔡慧敏.中国南北地区生活消费人均碳排放影响因素比较--基于空间计量分析[J].生态经济,2015,31(07):45-50.
[32]孙群英,朱震锋,曹玉昆.低碳经济视域下中国省级区域绿色创新能力评价分析--以黑龙江省为例[J].林业经济,2019,41(11):34-42.
[33]章金霞,白世秀.碳信息披露对企业全要素碳排放效率影响研究[J].生态经济,2019,35(12):13-18.
[34]李慧,李玮,姚西龙.中国省域全要素碳排放效率空间特征与动态收敛性研究[J].科技管理研究,2019,39(19):98-103.
[35]郗永勤,吉星.我国工业行业碳排放效率实证研究--考虑非期望产出SBM 超效率模型与 DEA 视窗方法的应用[J].科技管理研究,2019,39(17):53-62.
[36]孙耀华,何爱平,彭硕毅,杨叶飞.碳强度减排指标约束下碳排放权的省际分配效率研究[J].统计与信息论坛,2019,34(06):74-81.
[37]杨雪,马粟粟,卢亚丽.碳排放约束下的物流效率评价--以"一带一路"背景下内陆十省市为例[J].生态经济,2019,35(06):66-71.
[38]张德钢.市场分割对碳排放效率的影响研究--基于固定效应面板随机前沿模型[J].软科学,2018,32(09):94-97.
[39]刘勇,张俊飚,张露.基于 DEA-SBM 模型对不同稻作制度下我国水稻生产碳排放效率的分析[J].中国农业大学学报,2018,23(06):177-186.
[40]陈振,徐瑶瑶,翟振杰,黄松.基于 SBM-DEA 模型的河南省农业生产效率分析[J].河南农业大学学报,2019,53(04):647-652.
[41]马粟粟. "一带一路"背景下我国内陆十省市低碳物流效率评价研究[D].华北水利水电大学,2019.
[42]王钊,王良虎.碳排放交易制度下的低碳经济发展--基于非期望 DEA 与DID 模型的分析[J].西南大学学报(自然科学版),2019,41(05):85-95.
[43] William H Golove,Lee J Schipper. Restraining carbon emissions: measuring energy useand efficiency in the USA[J]. Energy Policy,1997,25(7).
[44] Kaya Y,Yokobori K.Global environment,energy,and economic development held at theunited nations university[R].Tokyo,1993.
[45] B.W Ang. Is the energy intensity a less useful indicator than the carbon factor in thestudy of climate change?[J]. Energy Policy,1999,27(15).
[46] Xuhui Ding,Juhua He,Xinyuan Yu. Spatial Differentiation of Carbon EmissionEfficiency of "Silk Road Economic Belt" based on Environmental Regulation[J]. IOP ConferenceSeries: Earth and Environmental Science,2018,208(1).
[47] Joshua Ignatius,M.-R. Ghasemi,Feng Zhang,Ali Emrouznejad,Adel Hatami-Marbini.Carbon efficiency evaluation: An analytical framework using fuzzy DEA[J]. European Journal ofOperational Research,2016,253(2).
[48] Toshiyuki Sueyoshi,Mika Goto. Should the US clean air act include CO 2 emissioncontrol?: Examination by data envelopment analysis[J]. Energy Policy,2010,38(10).
[49] Marklund Per-Olov,Samakovlis Eva. What is driving the EU Burden-sharing Agreement:efficiency or equity?[J]. Journal of Environmental Management,2006,85(2).
[50] Wen Lei,Shao Hengyang. Analysis of influencing factors of the carbon dioxideemissions in China's commercial department based on the STIRPAT model and ridgeregression.[J]. Environmental science and pollution research international,2019.
[51] Xiongfeng Pan,Xianyou Pan,Yang Ming,Jing Zhang. The effect of regional mitigation of carbon dioxide emission on energy efficiency in China, based on a spatial econometricsapproach[J]. Carbon Management,2018,9(6).
[52] Ang B W,Zhang F Q,Choi K H.Factorizing changes in energy and environ-mentalindicators through decomposition[J].Energy,1998,2(6):489-495.
[53] Md. Sharif Hossain.Panel estimation for CO2 emissions, energy consumption, economicgrowth, trade openness and urbanization of newly industrialized countries[J]. Energy Policy . 2011(11).
[54] Paelinck J,Klaassen L.Spatial Econometrics[M].Farnborough:Saxon House,1979.
[55] Charnes A,Cooper W W,Rhodes E,Measuring the efficiency of decision makingunits.European journal of operational research,1978,2(6):429-444.
[56] Banker R D,Charnes A,Cooper W W.Some Models for Estimating Technical and ScaleInefficiencies in Data Envelopment Analysis.Management Science,1984,30(9):1078-1092.
[57] Tonk K. A slacks-based measure of efficiency in data envelopment analysis.EuropeanJournal of Operational Research,2001,130(3):498-509.
[58] Tone K.Dealing with undesirable outputs in DEA:A slacks-based measure(SBM)approach.GRIPS Research Report Series,2003.
[59] Fare R,Grosskopf S,Lindgren B,Roos P.Productivity changes in Swedish pharamacies1980-1989 A non-parametric Malmquist approach[J].Journal of ProductivityAnalysis,1992(3):85-101.
[60] Goldsmith,Raymond W.A Perpetual Inventory of National Wealth[R].NBER Studies inIncome and Wealth.New York:National Bureau of Economic,1951.
[61] Anselin L.Spatial externalities,spatial multipliers,and spatial econometrics.Internationalregional science review,2003,26(2):153-166.
[62] Anselin L,Smirnov O.Efficient algorithms for constructing proper higher order spatiallag operators.Journal of Regional Science,1996,36(1):67-89.
