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学者姓名：杨隆浩
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Abstract ：
Greenhouse gas emissions are widely recognized as the primary cause of global warming, leading to a growing attention on carbon emission management. However, the existing studies still failed to propose a feasible approach to directly forecast carbon emission trends and also did not take into account both environmental regulation and efficiency improvement. Hence, this study aims to propose a novel carbon emission trend forecast model based on data-driven rule-base with considering the intensity coefficient of environmental regulation and the management efficiency of carbon emissions. Carbon emission data of 30 Chinese provinces are collected to illustrate the effectiveness of the proposed model. Results indicated that: 1) the data-driven rule-base model is able to directly forecast carbon emission trends within range from -18.54 % to 19.18 %; 2) by integrating regulation intensity, the predicted results of the model have smaller carbon emission tends, e.g., decrease of average changing rate from 0.4100 to 0.2762; 3) by further integrating efficiency improvement, the predicted results align more with the expected objectives of policy makers, i.e., the average carbon emission efficiency approximates 0.8920 and the number of provinces being effective efficiency is increased to 8. These findings also highlighted the importance of carbon emission tend forecast with environmental regulation and efficiency improvement. The proposed carbon emission trend forecast model could serve as an alternative tool for achieving dual carbon goals in the context of China.
Keyword ：
Carbon emission trend Carbon emission trend Data -driven rule -base Data -driven rule -base Efficiency improvement Efficiency improvement Environment regulation Environment regulation Forecast Forecast
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| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Hu, Haibo et al. A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement [J]. | SUSTAINABLE PRODUCTION AND CONSUMPTION , 2024 , 45 : 316-332 . |
| MLA | Yang, Long-Hao et al. "A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement" . | SUSTAINABLE PRODUCTION AND CONSUMPTION 45 (2024) : 316-332 . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Hu, Haibo , Lu, Haitian , Wang, Ying-Ming , Chang, Wen -Jun . A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement . | SUSTAINABLE PRODUCTION AND CONSUMPTION , 2024 , 45 , 316-332 . |
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扩展置信规则库(EBRB)中的规则数量和参数取值共同影响EBRB推理模型的决策准确性和计算效率.基于此,提出一种基于规则聚类和参数学习的改进EBRB推理模型,称为RCPL-EBRB模型.所提出模型的基本原理如下:首先,依据密度聚类分析对EBRB进行规则聚类来识别EBRB中无效的扩展置信规则和优化传统EBRB的建模过程;然后,以聚类所得到的规则簇(即Sub-EBRB)进行参数学习和规则推理,保证激活规则集合的一致性,从而提高RCPL-EBRB模型的决策准确性和计算效率;最后,引入非线性函数拟合和基准分类问题数据集开展模型的有效性检验和参数灵敏度分析.实验结果表明,所提出RCPL-EBRB模型比现有EBRB推理模型和传统机器学习方法具有更高的决策准确性.
Keyword ：
参数学习 参数学习 建模 建模 扩展置信规则库 扩展置信规则库 灵敏度分析 灵敏度分析 规则约减 规则约减 规则聚类 规则聚类
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| GB/T 7714 | 杨隆浩 , 陈江鸿 , 叶菲菲 et al. 基于规则聚类和参数学习的扩展置信规则库推理模型 [J]. | 控制与决策 , 2024 , 39 (08) : 2685-2693 . |
| MLA | 杨隆浩 et al. "基于规则聚类和参数学习的扩展置信规则库推理模型" . | 控制与决策 39 . 08 (2024) : 2685-2693 . |
| APA | 杨隆浩 , 陈江鸿 , 叶菲菲 , 王应明 . 基于规则聚类和参数学习的扩展置信规则库推理模型 . | 控制与决策 , 2024 , 39 (08) , 2685-2693 . |
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At the 2020 United Nations Climate Summit, China officially announced the goal to achieve carbon peaking by 2030. Exploring whether it is possible to reach the peak of carbon emissions earlier necessitates an urgent and imperative need for precise long-term forecasting of China's carbon emissions dynamics. However, the current carbon peaking predictions mostly depend on mechanical or mathematical models, which failed to consider the interdependence between carbon emissions and the time series-based patterns existed in carbon emission data. Therefore, this study presents a novel carbon peaking prediction method based on the data-driven rule-base model, which is implemented by the adaption of the extended belief rule base (EBRB) model for time series forecasting (TSF), and thus the proposed method is referred to as TSF-EBRB model. The TSF-EBRB model not only captures and measures the temporal correlations within the data throughout the processes of modeling and inference, but also consists of a novel parameter optimization model based on the temporal correlations. The study collected carbon emission data from 30 provinces in China for empirical analysis. It computed and predicted the carbon peaking trajectories of each province under three different scenarios from 2022 to 2030, validating the effectiveness and superiority of the TSF-EBRB model better than other existing carbon peaking prediction methods. The results indicated that, with policy interventions, the majority of provinces are projected to reach carbon peaking before 2030.
Keyword ：
Carbon peaking prediction Carbon peaking prediction Data-driven rule-base Data-driven rule-base Extended belief rule base Extended belief rule base Time series forecasting Time series forecasting
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| GB/T 7714 | Yang, Long-Hao , Lei, Yu-Qiong , Ye, Fei-Fei et al. Forecasting carbon peaking in China using data-driven rule-base model: An in-depth analysis across regional and economic scenarios [J]. | JOURNAL OF CLEANER PRODUCTION , 2024 , 451 . |
| MLA | Yang, Long-Hao et al. "Forecasting carbon peaking in China using data-driven rule-base model: An in-depth analysis across regional and economic scenarios" . | JOURNAL OF CLEANER PRODUCTION 451 (2024) . |
| APA | Yang, Long-Hao , Lei, Yu-Qiong , Ye, Fei-Fei , Hu, Haibo , Lu, Haitian , Wang, Ying-Ming . Forecasting carbon peaking in China using data-driven rule-base model: An in-depth analysis across regional and economic scenarios . | JOURNAL OF CLEANER PRODUCTION , 2024 , 451 . |
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针对台风灾害直接经济损失预测问题,现有的解决方法大多是基于时间序列或评估数据的预测模型,忽略了在建模过程中对历史数据的应用和模型的可解释性.鉴于此,该文将扩展置信规则库模型(EBRB)应用于台风灾害直接经济损失预测,并针对可能存在规则过量和组合爆炸问题,提出基于聚类方法与证据推理(ER)相结合的累积置信规则库(C-BRB)台风灾害经济损失预测模型.最后基于收集到的台风灾害数据进行直接经济损失预测,并通过与已有研究成果进行比较,验证基于C-BRB的台风灾害直接经济损失预测模型的有效性和可行性.
Keyword ：
可解释性 可解释性 台风灾害 台风灾害 直接经济损失预测 直接经济损失预测 累积置信规则库 累积置信规则库
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| GB/T 7714 | 张恺 , 杨隆浩 , 高建清 et al. 基于累积置信规则库推理的台风灾害直接经济损失预测 [J]. | 灾害学 , 2024 , 39 (1) : 64-68,74 . |
| MLA | 张恺 et al. "基于累积置信规则库推理的台风灾害直接经济损失预测" . | 灾害学 39 . 1 (2024) : 64-68,74 . |
| APA | 张恺 , 杨隆浩 , 高建清 , 郑晶 . 基于累积置信规则库推理的台风灾害直接经济损失预测 . | 灾害学 , 2024 , 39 (1) , 64-68,74 . |
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The performance evaluation method based on data envelopment analysis (DEA) is one of the important tools to measure the competitiveness and productivity of enterprises. However, the input and output of enterprises may contain negative data and the essence of DEA is an iterative optimization model, resulting in a low applicability of the DEA-based performance evaluation method in the real word, especially for the dilemma of evaluating enterprise performance within a limited time for new enterprises. Therefore, this study firstly develops a DEA model that can handle negative data for enterprise performance evaluation, and then further establishes a new method base on the extended belief rule-base (EBRB) model for enterprise performance online evaluation. A case study about 35 Chinese state-owned enterprises are conducted to verify the effectiveness of the proposed enterprise performance online evaluation method. Experimental results showed that the proposed method has capable of evaluating enterprise performance with accurate efficiency values better than some existing performance evaluation methods, and its computation time is significantly less than the DEA-based performance evaluation method, which guarantee that the proposed enterprise performance online evaluation method can serve as a reference for the promotion of enterprise productivity and sustainable economic development.
Keyword ：
Data envelopment analysis Data envelopment analysis Online evaluation Online evaluation Performance Performance Rule-base Rule-base State-owned enterprises State-owned enterprises
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| GB/T 7714 | Ye, Fei-Fei , Yang, Long-Hao , Lu, Haitian et al. Enterprise performance online evaluation based on extended belief rule-base model [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
| MLA | Ye, Fei-Fei et al. "Enterprise performance online evaluation based on extended belief rule-base model" . | EXPERT SYSTEMS WITH APPLICATIONS 247 (2024) . |
| APA | Ye, Fei-Fei , Yang, Long-Hao , Lu, Haitian , Hu, Haibo , Wang, Ying-Ming . Enterprise performance online evaluation based on extended belief rule-base model . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 247 . |
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Belief rule-base (BRB) expert system is one of recognized and fast-growing approaches in the areas of complex problems modeling. However, the conventional BRB has to suffer from the combinatorial explosion problem since the number of rules in BRB expands exponentially with the number of attributes in complex problems, although many alternative techniques have been looked at with the purpose of downsizing BRB. Motivated by this challenge, in this paper, multilayer tree structure (MTS) is introduced for the first time to define hierarchical BRB, also known as MTS-BRB. MTS-BRB is able to overcome the combinatorial explosion problem of the con-ventional BRB. Thereafter, the additional modeling, inferencing, and learning procedures are proposed to create a self-organized MTS-BRB expert system. To demonstrate the development process and benefits of the MTS-BRB expert system, case studies including benchmark classification datasets and research and development (R&D) project risk assessment have been done. The comparative results showed that, in terms of modelling effectiveness and/or prediction accuracy, MTS-BRB expert system surpasses various existing, as well as traditional fuzzy system-related and machine learning-related methodologies.
Keyword ：
Belief rule base Belief rule base Combinatorial explosion problem Combinatorial explosion problem Complex problems Complex problems Expert system Expert system Multilayer tree structure Multilayer tree structure
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| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Liu, Jun et al. Belief rule-base expert system with multilayer tree structure for complex problems modeling [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 217 . |
| MLA | Yang, Long-Hao et al. "Belief rule-base expert system with multilayer tree structure for complex problems modeling" . | EXPERT SYSTEMS WITH APPLICATIONS 217 (2023) . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Liu, Jun , Wang, Ying-Ming . Belief rule-base expert system with multilayer tree structure for complex problems modeling . | EXPERT SYSTEMS WITH APPLICATIONS , 2023 , 217 . |
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规则约减和规则激活是扩展置信规则库(EBRB)推理模型优化研究中的两个重要方向.然而,现有研究成果大多存在方法参数确定主观性强和计算复杂度高等不足.为此,通过引入聚类集成和激活因子提出改进的EBRB推理模型,称为CEAF-EBRB模型.该模型先基于聚类集成对历史数据进行多次的数据聚类分析,再以簇为单位将所有历史数据生成扩展置信规则;同时,通过激活因子修正个体匹配度计算公式以及离线的方式计算激活因子取值,以确保高效地激活一致性的规则.最后,在非线性函数拟合、模式识别、医疗诊断等常见问题中验证了所提CEAF-EBRB模型的可行性和有效性,从而为决策者提供更准确的决策支持.
Keyword ：
扩展置信规则库 扩展置信规则库 激活因子 激活因子 聚类集成 聚类集成 规则激活 规则激活 规则约减 规则约减
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| GB/T 7714 | 杨隆浩 , 任天宇 , 胡海波 et al. 基于聚类集成和激活因子的扩展置信规则库推理模型 [J]. | 控制与决策 , 2023 , 38 (3) : 815-824 . |
| MLA | 杨隆浩 et al. "基于聚类集成和激活因子的扩展置信规则库推理模型" . | 控制与决策 38 . 3 (2023) : 815-824 . |
| APA | 杨隆浩 , 任天宇 , 胡海波 , 叶菲菲 , 王应明 . 基于聚类集成和激活因子的扩展置信规则库推理模型 . | 控制与决策 , 2023 , 38 (3) , 815-824 . |
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Scientific investment forecasting can effectively avoid the blind investments of environmental management. Among existing studies in developing investment forecasting models, the extended belief rule-based system (EBRBS) showed its potential to accurately predict environment investments but also exposed two challenges to be further addressed: (1) how to select antecedent attributes from various environmental indicators for the EBRBS; (2) how to optimize basic parameters of the EBRBS based on the selected antecedent attributes. Since these two challenges are connected, a bi-level joint optimization model is proposed to improve the EBRBS for better environmental investment forecasting, in which the selection of antecedent attributes is described as an upper-level optimization model using Akaike information criterion (AIC) and the optimization of basic parameters is as a lower-level optimization model using mean absolute error (MAE). Moreover, a corresponding bilevel joint optimization algorithm is proposed to solve the bi-level joint optimization model, where ensemble feature selection and swarm intelligence optimization are regarded as the engine of upperlevel and lower-level optimizations, respectively. The real environmental data collected from 2005 to 2020 of 30 Chinese provinces are studied to verify the effectiveness of the proposed approach. Experimental results show that the EBRBS with bi-level joint optimization not only can effectively predict environmental investments, but also is able to have desired accuracy better than previous investment forecasting models.
Keyword ：
Bi-level joint optimization Bi-level joint optimization Environmental investment Environmental investment Extended belief rule-based system Extended belief rule-based system Forecasting model Forecasting model
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| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Wang, Ying-Ming et al. Extended belief rule-based system using bi-level joint optimization for environmental investment forecasting [J]. | APPLIED SOFT COMPUTING , 2023 , 140 . |
| MLA | Yang, Long-Hao et al. "Extended belief rule-based system using bi-level joint optimization for environmental investment forecasting" . | APPLIED SOFT COMPUTING 140 (2023) . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Wang, Ying-Ming , Lan, Yi-Xin , Li, Chan . Extended belief rule-based system using bi-level joint optimization for environmental investment forecasting . | APPLIED SOFT COMPUTING , 2023 , 140 . |
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The evaluation of inter-provincial carbon emission efficiency and the analysis of its influencing factors hold great practical significance for reducing carbon emissions and promoting sustainable development in ecological management. To address the shortcomings of existing research in the classification evaluation of carbon emission efficiency and account for the impacts of different environmental regulatory policies on carbon emissions, this paper aims to examine the impact of formal and informal environmental regulations on carbon emission efficiency. This is accomplished by utilizing a combination of the data envelopment analysis (DEA) model, entropy weighting, and k-means cluster analysis methods. The fixed-effects model is also applied to examine the influences of different factors on carbon emission efficiency under different categories. To conduct the case studies, carbon emission management data from 30 provinces in China are collected, and the results show the following: (1) Formal environmental regulations exhibit a "U-shaped" relationship with carbon emission efficiency, whereas informal environmental regulations have an "inverted U-shaped" relationship with carbon emission efficiency. (2) Under the cluster analysis of carbon emission efficiency, formal environmental regulations are found to have a stronger incentive effect on inter-provincial carbon efficiency compared to informal environmental regulations. This study carries significant theoretical and practical implications for China's timely attainment of its double-carbon target.
Keyword ：
carbon emissions carbon emissions cluster analysis cluster analysis efficiency evaluation efficiency evaluation entropy weight method entropy weight method environmental regulations environmental regulations
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| GB/T 7714 | Ye, Feifei , You, Rongyan , Lu, Haitian et al. The Classification Impact of Different Types of Environmental Regulation on Chinese Provincial Carbon Emission Efficiency [J]. | SUSTAINABILITY , 2023 , 15 (15) . |
| MLA | Ye, Feifei et al. "The Classification Impact of Different Types of Environmental Regulation on Chinese Provincial Carbon Emission Efficiency" . | SUSTAINABILITY 15 . 15 (2023) . |
| APA | Ye, Feifei , You, Rongyan , Lu, Haitian , Han, Sirui , Yang, Long-Hao . The Classification Impact of Different Types of Environmental Regulation on Chinese Provincial Carbon Emission Efficiency . | SUSTAINABILITY , 2023 , 15 (15) . |
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Environmental governance cost prediction can avoid blind investment and waste of resources and achieve effective cost planning for sustainable development of resources and environment. For the sake of solving the problem that most previous studies failed to consider the causal relationship and data reliability of environmental governance inputs and outputs, a new environmental governance cost prediction method is proposed under the framework of the evidential reasoning (ER) rule with three improvements comparing to existing methods: (1) the causal relationship of environmental governance inputs and outputs is embedded into evidence representation for better extracting knowledge from data; (2) the efficiency about the minimum inputs to achieve the maximum outputs is used to evaluate the data reliability of environmental governance inputs and outputs; and (3) a new analytical ER rule is investigated to optimize the process of evidence combination. Hence, the new method includes the calculation of belief distributions, evidence reliabilities, and evidence weights, as well as the combination of evidences to predict environmental governance costs. In the case study, the data of 30 provinces in Mainland China from 2005 to 2020 are collected to verify the effectiveness of the new method. Results show a high level of accuracy of the new method over other existing methods.
Keyword ：
Cost prediction Cost prediction Environmental governance Environmental governance Evidential reasoning (ER) rule Evidential reasoning (ER) rule Reliability Reliability Weight Weight
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| GB/T 7714 | Ye, Fei-Fei , Yang, Long-Hao , Uhomoibhi, James et al. Evidential reasoning rule for environmental governance cost prediction with considering causal relationship and data reliability [J]. | SOFT COMPUTING , 2023 , 27 (17) : 12309-12327 . |
| MLA | Ye, Fei-Fei et al. "Evidential reasoning rule for environmental governance cost prediction with considering causal relationship and data reliability" . | SOFT COMPUTING 27 . 17 (2023) : 12309-12327 . |
| APA | Ye, Fei-Fei , Yang, Long-Hao , Uhomoibhi, James , Liu, Jun , Wang, Ying-Ming , Lu, Haitian . Evidential reasoning rule for environmental governance cost prediction with considering causal relationship and data reliability . | SOFT COMPUTING , 2023 , 27 (17) , 12309-12327 . |
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