低碳运输系统分析的混合方法:整合CRITIC-DEMATEL和深度学习功能
1Transportation College, Northeast Forestry University, Harbin, 150040 China.
概括
本研究介绍了一种混合方法,用于评估中国的低碳运输发展,确定可持续物流和供应链的关键因素.
科学领域:
- 环境科学 环境科学
- 运输工程 运输工程
- 可持续发展 可持续发展 可持续发展
背景情况:
- 人们越来越担心中国物流和运输部门的能源消耗和碳排放.
- 全球越来越重视可持续发展目标和绿色交通倡议.
- 中国政府的举措,以促进低碳运输系统.
研究的目的:
- 量化评估中国低碳运输系统的发展水平.
- 识别这些系统中的关键影响因素及其相互联系.
- 提出一种新的混合方法来评估可持续的交通发展.
主要方法:
- 混合方法结合了通过标准相关性 (CRITIC) 的标准重要性,决策试验和评估实验室 (DEMATEL) 和深度学习.
- 使用CRITIC来确定客观的权重,减少DEMATEL的主观性.
- 使用人工神经网络来完善权重准确性和客观性.
主要成果:
- 准确的定量评估低碳运输发展水平.
- 确定影响可持续运输的关键因素.
- 阐明这些因素之间复杂的相互关系.
- 通过中国的案例研究和敏感性分析验证混合方法.
结论:
- 拟议的混合方法为评估低碳运输发展提供了强大而客观的方法.
- 确定了关键因素及其联系,可以为促进中国可持续运输的政策提供信息.
- 该方法提供了一种有价值的决策工具,用于实现绿色物流和供应链.
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