基于动态社会网络的能源转型路径评估多属性群决策建模研究

doi:10.16381/j.cnki.issn1003-207x.2022.1025

Abstract

Abstract:

The goal of “carbon peaking and carbon neutral” is a new goal and requirement for China in the new development stage， and promoting the green energy transition of the whole society is an inevitable choice to achieve the goal. The evaluation and selection of energy transition paths involves a comprehensive quantitative evaluation of different energy transition paths， but this evaluation method faces the challenges of multi-dimensional variables， multi-structure coupling and multi-level structure. Aiming at the evaluation and selection of energy transition path， a multi-attribute group consensus decision-making model is constructed based on dynamic social network and public big data attribute mining in a complex cognitive language environment. First， a social network trust relationship based on double hierarchy linguistic terms is constructed， and a method for determining experts’ weights based on dynamic social network trust relationships is proposed， the dynamic trust relationships of experts are affected by their degree of consensus during a consensus iteration. Secondly， natural language processing technology is used to mine and process big data of public behavior preferences in social platforms to establish an evaluation attribute system， then a group consensus model based on the trust reward and punishment mechanism and integrating the trust relationship of social networks is proposed to promote consensus among experts. Finally， the proposed method and model are applied to the actual decision-making problem of energy transition path assessment. Among them， the green electrification high-growth transformation path scored the highest， with 0.7301， is chosen as the optimal energy transition path. Subsequently， a comparative analysis is conducted in many aspects， and simulation analysis is also carried out. The effectiveness and practicability of this research in solving the energy transition path assessment are highlighted and enlightenment and suggestions in this field are given.

Key words: dynamic social network, trust propagation equation, TF-IDF algorithm, group consensus, energy transition path assessment

CLC Number:

C934

Xunjie Gou, Xinru Xu, Zeshui Xu. Research on Multi-attribute Group Decision-making Modeling of Energy Transition Path Evaluation Based on Dynamic Social Network[J]. Chinese Journal of Management Science, 2025, 33(6): 346-359.

Figures/Tables 7

能源转型路径方案

方案内容

绿色电气化高增长转型路径

( $A 1$ )

我国以“十四五”规划期间的实施力度来推行“十四五”规划文件中的碳减排措施，并可借鉴发达国家的经验。重点对家庭、商业建筑和大型公共建筑进行节电，降低单位能源强度;加大新能源和清洁能源车船装备应用力度，发展城市绿色货运配送等措施并引导居民绿色出行;强制控制机动车总量和保有量增长并大力发展公共交通系统

绿色能源基础复苏转型路径(

A 2

)

我国以“十四五”规划期间的实施力度来推行“十四五”规划文件中的碳减排措施,重点大力开发新能源和可再生能源，如太阳能、水能、生物质能和风能;提高能源使用效率和减少燃油量;所有的烹饪燃料完全被天然气取代，部分工业用煤、柴油和燃料油被天然气取代

绿色产业高速发展转型路径(

A 3

)

我国以发达国家碳减排措施为标杆,强力贯彻推进各项碳减排措施。重点调整工业结构，用低能耗代替高耗能业、能源梯级利用和余热回收，降低单位产品生产能耗，实现经济效益高、污染程度低的绿色经济

属性	候选关键词及TF-IDF值
产业发展 $B 1$	(“产业”, 0.0244), (“企业”, 0.0227),(“高质量”, 0.0159), (“经济”, 0.0153),(“产能”, 0.0064),(“数字化”, 0.0064),(“基建”, 0.00624),(“制造”, 0.0057),(“智能化”, 0.0045), (“5G”, 0.0043),(“制造业”,0.0038),(“经济社会”, 0.0034),(“增速”, 0.0032),(“效率”, 0.0026),(“钢铁”, 0.0028),(“生产”, 0.0081),(“化工”, 0.0023),(“军工”, 0.0027),(“投产”, 0.0023),(工业”, 0.0064),(“供应链”, 0.0037),(“产业链”, 0.0114),(“成本”, 0.0054)
电力推广 $B 2$	(“绿色”, 0.0467), (“技术”, 0.0167),(“电力”, 0.0130),(“发电”, 0.0104),(“电网”, 0.0072),(“国网”, 0.0056),(“充电”, 0.0052), (“用电”, 0.0049),(“电力系统”, 0.0046), (“分布式”, 0.0041),(“电站”, 0.0038),(“供电”, 0.0037), (“火电”, 0.0035),(“电动”, 0.0029), (“核电”, 0.0027), (“电气”, 0.0028), (“锂电”, 0.0025),( “煤电”, 0.0026),( “电源”, 0.0024),(“电价”, 0.0024) (“风电”, 0.014),(“特高压”, 0.0035),(“电源”, 0.0024)
绿色环保 $B 3$	(“低碳”, 0.0320),(“转型”, 0.0178), (“氢能”, 0.0149),(“排放”, 0.0142),(“清洁”, 0.0117),(“节能”, 0.0105), (“环保”, 0.0099),(“减排”, 0.0082),(“减碳”, 0.0065),(“绿电”, 0.005), (“零碳”, 0.0046), (“降碳”, 0.0029),(“生态环境”, 0.0043),(“示范区”, 0.0023),(“节约用电”, 0.0023), (“回收”, 0.0028),(“循环”, 0.0029),(“健康”, 0.0024),(“高耗能”, 0.0023), (“生态”, 0.0102),(限电”, 0.0036)
国家政策 $B 4$	(“碳中和”, 0.04453),(“碳达峰”, 0.0280),( “中国”, 0.0170),(“科技”, 0.0156),(“政策”, 0.0148),(“创新”, 0.0138),(“战略”, 0.0125),(“十四五”, 0.0118),(“两会”, 0.0089),(“规划”, 0.0076), (“政府”, 0.0034),(“解决方案”, 0.0035),(“共同富裕”, 0.0032), (“稳中求进”, 0.0023),(“技术创新”, 0.0023),(“改革”, 0.0045),(“双减”, 0.0041),(“机制”, 0.0039),(“双控”, 0.0102),(“会议”, 0.0064)
能源结构 $B 5$	(“能源”, 0.0431),(“新能源”, 0.0352),(“光伏”, 0.0295),(“储能”, 0.01930),(“新型”, 0.0069),(“再生能源”, 0.0053),(“制氢”, 0.0033),(“风光”, 0.0033),(“天然气”, 0.003),(“太阳能”, 0.0030),(“用能”, 0.0029),(“新型”, 0.0069),(“资源”, 0.0075),(“能耗”, 0.0082), (“石油”, 0.0023),(“赋能”, 0.0044),(“供需”, 0.0030),(“燃料电池”, 0.0087), (“结构”, 0.0046),(“高耗能”, 0.0024)

方法	迭代次数	方案得分	方案排序
1	3	{0.7941，0.592，0.6812}	$A 1 > A 3 > A 2$
2	-	{0.6580，0.6144，0.4710}	$A 1 > A 2 > A 3$
3	2	{0.8011，.0.574，0.6489}	$A 1 > A 3 > A 2$
4	3	{0.6950，0.5823，0.6481}	$A 1 > A 3 > A 2$

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方法对比	方法1^[30]	方法2^[31]	方法3^[11]	方法4^[32]	本文方法
专家权重	未考虑专家之间的社会网络	直接社会网络确定专家权重	专家动态权重	社会网络确定专家及社区权重	信任奖惩机制下动态社会网络确定专家权重
语言信息	犹豫模糊语言	分布式语言	概率语言术语集	直觉模糊数	双层语言术语集
属性权重	初始给定属性权重信息	基于扩展TOPSIS的属性权重确定方法	初始给定属性权重信息	初始给定属性权重信息	基于公共数据挖掘的属性权重确定方法
共识反馈	群决策信息反馈	不执行意见调整反馈	意见、权重双重共识反馈机制	群决策信息反馈	信任度、个性化意见反馈机制

[1]	HAN Wei, SUN Yong-he, MIAO Bin. A Group DEMATEL Decision-making Method with Incomplete Judgment Information [J]. Chinese Journal of Management Science, 2021, 29(5): 231-239.
[2]	WANG Wei-ming, DENG Xiao, XU Hai-yan. A New Index Weights Calculation Method of Group DEMATEL Based on Three-dimensional Density Operator [J]. Chinese Journal of Management Science, 2021, 29(12): 179-190.
[3]	LIU Jin-pei, YANG Hong-wei, CHEN Hua-you, ZHOU Li-gang. Group Decision Making with Interval Multiplicative Linguistic Preference Relations based on Cross-efficiency DEA and Group Consensus [J]. Chinese Journal of Management Science, 2020, 28(2): 190-198.

Research on Multi-attribute Group Decision-making Modeling of Energy Transition Path Evaluation Based on Dynamic Social Network

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