CAJ | 학술논문

本文认为区域生态效率是个相对概念,会随着经济和环境的变化呈现相对有效性,相对于某一阶段的最优在下一阶段未必是最优,如何识别并判断出区域生态效率所处的生命周期阶段成为地方政府制定发展政策的依据。基于能源约束的区域相对生态效率识别法运用DEA神经网络识别模型,能够有效地分析和识别在有限能源输入下区域的阶段性相对生态效率,可以成为生态效率衡量方法的有益补充。实证研究表明,该方法具有识别准确、可操作性强等特点,具有明显优势和可行性,在实践中有广泛的应用推广价值。
본문인위구역생태효솔시개상대개념,회수착경제화배경적변화정현상대유효성,상대우모일계단적최우재하일계단미필시최우,여하식별병판단출구역생태효솔소처적생명주기계단성위지방정부제정발전정책적의거。기우능원약속적구역상대생태효솔식별법운용DEA신경망락식별모형,능구유효지분석화식별재유한능원수입하구역적계단성상대생태효솔,가이성위생태효솔형량방법적유익보충。실증연구표명,해방법구유식별준학、가조작성강등특점,구유명현우세화가행성,재실천중유엄범적응용추엄개치。
Regional eco-efficiency is regarded as a relative concept.This paper presented the relative eco-efficiency with the change of economy and environment.That means it could not the optimal compared with the predicted optimum in the future.It would be a basis of policy decision making of local governments according to identify the life cycle stage of current regional eco-efficiency.As a beneficial supplement of the eco-efficiency measurement,this paper set up an energy restricted DEA Neural Network recognition model which can effectively analyze and identify the current relative eco-efficiency of a region.It is an obvious advantage method of fast convergence,accurate identification,extensive application and promotion value in practice that the empirical research showed.