用户名: 密码: 验证码:
大数据在环境技术进步中的应用
详细信息    查看官网全文
摘要
我国环境污染日益严重,政府加大环境保护力度势在必行,科学有效的环境管理对保护环境、提高环境质量起着重要的作用。环境技术进步是环境管理有效而持久的驱动力,但是,环境技术进步的测度方法一直未能实现普适性。本文首先综述了技术进步对改善环境质量的重要性,在此基础上,阐述了各学者对技术进步和偏向型技术进步测量方法的探究。然后介绍了大数据在环境管理中的应用,在其应用经验的基础上,将其引入环境技术测度领域中。大数据在该领域中的应用为环境偏向型技术进步的测度提供了新的契机,同时,大数据的应用也增加了研究的困难性。最后,基于偏向型技术进步之间的矛盾,提出最优环境技术的理念。通过利用大数据,可以合理测度偏向型技术进步,实现生产偏向型技术进步和环境偏向型技术进步的合理配比,找出可以兼顾高产出和低污染物排放量的最优偏向型技术进步模式。并进一步结合各区域的差异性和关联性,能够测算出区域最优偏向型技术进步模式,找到有效的环境管理方式,为环境管理者与政策制定者提供合理参考。
With our increasingly serious environmental pollution,the government is imperative to intensify environmental protection efforts,besides,scientific and effective environmental management plays an important role in protecting environment and improving the quality of the environment.Environmental technology progress is an effective and lasting driving force for environmental management,but the measurement method of environmental technology has not been able to achieve universal.This work first summarizes the importance of technological progress in improving the quality of the environment,on the basis of that,expounds the research of many scholars on measurement methods of the technical progress and biased technical progress.Then,the paper introduces the application of big data in environmental management.,on the basis of its application experience,introduces big data to the field of environmental technology measurement.The application of big data in this field provides a new opportunity for the measurement of environmental biased technology progress,meanwhile,the application of big data also increases the difficulty of research.Finally,based on the contradiction between the biased technological progress,the concept of optimal environmental technology is put forward.By making use of big data,biased technical progress can be reasonable measured and then production biased technology progress and environmental bias technology progress could get reasonable proportion,in addition,optimal biased technical progress model,which can take into account the high output and low pollutant emissions can be found out.Combined with the regional differences and association,regional optimal biased technical progress model can be calculated.Thereby,finding an effective way of environmental management,which provides a reasonable reference for the environmental managers and policy makers.
引文
[1]Shuhong Wang,Malin Song.Review of hidden carbon emissions,trade,and labor income share inChina,2001—2011[J].Energy Policy,2014(74):395-405.
    [2]Weitman M L.Sustainability and Technical Progress[J].The Scandinavian Journal of Economics,1997(1):1-13.
    [3]宋马林,王舒鸿.环境规制、技术进步与经济增长[J].经济研究,2013(3):122-134.
    [4]王兵,刘光天.节能减排与中国绿色经济增长——基于全要素生产率的视角[J].中国工业经济,2015(5):57-69.
    [5]Kennedy C,A P.Thirlwall;Surveys in Applied Economics:Technical Progress[J].The Economic Journal,1972,82(325):11-72.
    [6]Arnberg S,TR Bjorner.Subsititution between Energy,Capital and labour within Industrial Companies:A Micro Panel Data Analysis[J].Resource and Energy Economics,2007,Vol.29,122-136.
    [7]Ma H,LOxley,J Gibson,et al..China's Energy Economy:Technical Change,Factor Demand and Interfactor/Interfuel Substitution[J].Energy Economics,2008,Vol.30,2167-2183.
    [8]Ma H,L Oxley,J Gibsoa Substitution Possibilities and Determinants of Energy Intensity forChina[J].Energy Policy,2009,Vol.37,1793-1804.
    [9]Welsch H,C Ochsen.The Determinants of Aggregate Energy Use in West Germany:Factor Substitution,Technological Change and Trade[J].Energy Economics,2005,Vol.27,93-111.
    [10]Bentolina S,G Saint-Paul.Explaining Movements in the Labor Share[J].Contributions to Macroeconomics,2003,3(1):97-125.
    [11]宋马林,王舒鸿,汝慧萍,等.FDI绿色创新能力的统计分析[J].中国软科学,2010(5):143-151.
    [12]Caves D W,L R Christensen,W E Diewert.Multilateral Comparisons of Output,Input,and Productivity Using Superlative Index Numbers[J].The Economic Journal,1982,92(365):73-86.
    [13]Fukuyama H,W L Weber.A Directional Slacks-based Measure of Technical Inefficiency[J].Socio-Economic Planning Science,2009,43(4):274-287.
    [14]Acemoglu D.Labor and Capital-Augmenting Technical Change[J],Journal of the European Economic Association,2003,1(1),1-37.
    [15]Acemoglu D.Equilibrium Bias of Technology[J].Econometrica,2007,75(5):1371-1410.
    [16]Malin Song,Shuhong Wang,Wei Liu.A two-stage DEA approach for environmental efficiency measurement[J].Environ Monit Assess,2014,186;3041-3051.
    [17]景维民,张璐.环境管制、对外开放与中国工业的绿色技术进步[J].经济研究,2014(9):34-47.
    [18]Acemoglu D,P Aghion,L Bursztyn,et al..The Environment and Directed Technical Change[J].American Economic Review,2012a,102(1):131-166.
    [19]Acemoglu D,U Akcigit,D Hanley,et al..Transition to Clean Technology[J].2012b,MIT Working Paper.
    [20]Harrison A,2002,"Has Globalization Eroded Labor's Share?Some Cross-Country evidence".UC-Berkeley and NBER working paper,October.
    [21]Klump R,P McAdam,A Willmaa Factor substitution and factor-augmenting technical progress in the United States:a normalized supply-side system approach.Review of Economics and Statistics,2007,89(1):183-192.
    [22]Leon-Ledesma M,P McAdam,A Willmaa In dubio Pro CES:supply estimation with misspecified technical change.ECB Working Paper,2010,No.1175.
    [23]Chambers R G,R Fare,S Grosskopf.Productivity Growth in APEC Countries[J].Pacific Economic Review,1996,1(3):181-190.
    [24]Chung Y H,R Fare S.Grosskopf.Productivity and Undesirable Outputs:ADirectional Distance Function Approach[J].Journal of Environment Management,1997,51(3),229-240.
    [25]Oh Dong-hyua A global malmquist of efficiency in data envelopment analysis[J].European Journal of Operation Research,2010,130(3):498-509.
    [26]Wu X,ZhuX,Wu G Q,et al..Data mining with big data[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(1):97-107.
    [27]Tien J M Big data:Unleashing information[J].Journal of Systems Science and Systems Engineering,2013,22(2):127-151.
    [28]Manyika J,Chui M,Brown B,et al..Big data;The next frontier for innovation,competition,and productivity[M].New York:McKinsey&Company,2011.
    [29]Ozdemir V,Badr K F,Dove E.S,et al..Crowd-funded micro-grants for Genomics and"Big Data":An actionable idea connecting small(artisan)science,infrastructure science,and citizen philanthropy[J].Omics:A Journal of Integrative Biology,2013,17(4),161-172.
    [30]Russom P.Big data analytics.TDWI Best Practices Report,Fourth Quarter,2011.
    [31]Pijanowski B C,Tayyebi A,Doucette J,et al..A big data urban growth simulation at a national scale:Configuring the GIS and neural network based Land Transformation Model to run in a High Performance Computing(HPC)environment.Environmental Modelling&Software,2014,51,250-268.
    [32]Steed C A,Ricciuto D M,Shipman G,et al..Big data visual analytics for exploratory earth system simulation analysis[J].Computers&Geosciences,2013,61,71-82.
    [33]Schnase J L,Duffy D Q,Tamkin G S,et al..MERRA Analytic Services:Meeting the Big Data challenges of climate science through cloud-enabled Climate Analytics-as-a-Service[J].Computers,Environment and Urban Systems,Available online 31 January 2014.http://dx.doi.org/10.1016/j.compenvurbsys.2013.12.003.
    [34]Malin Song,Shuhong Wang.Environmental Efficiency Evaluation of China Based on a Kind of Congestion and Undesirable Output Coefficient[J].PANOECONOMICUS,2015,62(4):453-468.
    [35]董敏杰,梁泳梅,李钢.环境规制对中国出口竞争力的影响——基于投入产出表的分析[J].中国工业经济,2011(3):57-67.
    [36]周晓方,陆嘉恒,李翠平,等.从数据管理视角看大数据挑战[J].中国计算机学会通讯,2012,8(9),16-20.
    [37]Bento N,M Fontes.Spatial diffusion and the formation of a technological innovation system in the receiving country:The case of wind energy in Portugal[J].Environmental Innovation and Societal Transitions,2015,15:158-179.
    [38]宋马林,王舒鸿,汝慧萍,等.FDI绿色创新能力的统计分析[J],中国软科学,2010(5):143-151.
    [39]Malin Song,Shuhong Wang,Jie Wu,et al..A new space-time correlation coefficient and its comparison with Moran's Index on evaluation[J].Management Decision,2011,49(9):1426-1443.
    [40]邵帅,杨莉莉.自然资源开发、内生技术进步与区域经济增长[J].经济研究,2011(2):112-123.
    [41]王恕立,藤泽伟,刘军.中国服务业生产率变动的差异分析—基于区域及行业视角[J].经济研究,2015(8):73-84.
    [42]Malin Song,Shuhong Wang,Wei Liu.A new method for measuring the economic convergence and its application on central china provinces[J].Economic Research,vol.25,2012.No.4:925-936.
    [43]Malin Song,Shuhong Wang,Jie Wu,et al..A new space-time correlation coefficient and its comparison with Moran's Index on evaluation[J].Management Decision,2011,49(9):1426-1443.
    [44]李健,卫平,付军明.中国地区工业生产率增长差异及收敛性研究——基于三投入DEA实证分析[J].产业经济研究,2015(5):21-30.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700