交通运输低碳化能力影响因素众多且相互关联,如何识别和区分其影响因素是学术界研究的焦点。针对传统决策试验与评价实验室方法(DEMATEL)的缺点进行了改进,提出了适合于交通运输低碳化能力影响因素识别的RBF-DEMATEL方法,利用RBF神经网络计算目标指标和影响因素指标之间的权值来得到直接关联矩阵,然后利用传统DEMATEL方法分析交通运输低碳化能力的影响因素。本文利用RBF-DEMATEL进行了实证分析,结果证实了方法的可行性,从而为提升交通运输低碳化能力提供理论支撑。RBF-DEMATEL方法丰富了影响因素研究的理论与方法,为有效地提取根本型影响因素提供了可能性。
Abstract
It is a hot academic topic to identify the influencing factors of transportation low-carbonization capacity which are numerous and interrelated. This study improves the traditional Decision-making Trial and Evaluation Laboratory (DEMATEL) method according to its boundedness and proposes the RBF-DEMATEL method which is suitable for the influencing factors identification of transportation low-carbonization capacity. It exploits the RBF neural network to calculate the weights between object index and influencing factor index and uses the weights to get the direct-relation matrix, then takes advantage of the traditional DEMATEL method to study the influencing factors of transportation low-carbonization capacity. The empirical analysis shows that this method is feasible and can supply theoretical support in improving the transportation low-carbonization capacity, so the RBF-DEMATEL method enriches the theory and method in studying the influencing factors and provides the possibility of extracting the fundamental influencing factors effectively.
关键词
RBF-DEMATEL /
交通运输低碳化能力 /
影响因素
Key words
RBF-DEMATEL /
transportation low-carbonization capacity /
influencing factors
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基金
国家自然科学基金"低碳港口生成机理及评价模式研究"(71273037);国家自然科学基金"不可再生资源全生命周期效率提升的能力建设模式研究"(71073016)。
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