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学者姓名：杨隆浩
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Abstract ：
Reducing carbon emissions is an ongoing goal of the whole world and its achievement requires an outstanding approach to accurately predict future carbon emissions and explore the factors driving carbon emissions. Hence, this study proposes a driving factor decomposition-based data-driven rule-base (DFD-DDRB) approach for the aim of analyzing carbon emission reduction pathway from predictive perspective, where the approach includes three processes: 1) generating a rule-base from historical carbon emission data; 2) predicting multi-scenario carbon emissions using the rule-base; 3) providing predictive analytics for future carbon emission reduction. In empirical study, the China's provincial data from 2004 to 2021 are used to justify the applicability of the proposed approach. The experimental findings not only show that the approach can accurately predict multi-scenario carbon emissions until 2035 and reveal the factors driving carbon emissions, but also provide three implications for reducing China's carbon emissions: 1) resource endowment should be considered to establish carbon emission management policies of 30 Chinese provinces; 2) economic development effect can be regarded as the main factor driving China's future carbon emissions; 3) optimizing energy structure and consumption is much important for reducing China's provincial carbon emissions. Beside the work in China, the DFD-DDRB approach can be also used as the generic analytical framework served for some developed economies and other carbon-emitting countries. © 2025 Elsevier Ltd
Keyword ：
Carbon emissions Carbon emissions
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| GB/T 7714 | Ye, Fei-Fei , You, Rongyan , Yang, Long-Hao et al. A novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction [J]. | Computers and Industrial Engineering , 2025 , 206 . |
| MLA | Ye, Fei-Fei et al. "A novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction" . | Computers and Industrial Engineering 206 (2025) . |
| APA | Ye, Fei-Fei , You, Rongyan , Yang, Long-Hao , Lu, Haitian , Xie, Hongzhong . A novel data-driven rule-base approach with driving factor decomposition for multi-scenario prediction on carbon emission reduction . | Computers and Industrial Engineering , 2025 , 206 . |
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Smart environment is an efficient and cost-effective way to afford intelligent supports for the elderly people. Human activity recognition is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based human activity recognition model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.
Keyword ：
Accuracy Accuracy activity recognition activity recognition Belief rule base Belief rule base Bioinformatics Bioinformatics combination explosion problem combination explosion problem Correlation Correlation Data models Data models Explosions Explosions Feature extraction Feature extraction Human activity recognition Human activity recognition Predictive models Predictive models Robustness Robustness sensor sensor time correlation time correlation Vectors Vectors
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| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Nugent, Chris et al. Belief-Rule-Based System With Self-Organizing and Multi-Temporal Modeling for Sensor-Based Human Activity Recognition [J]. | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2025 , 29 (2) : 1062-1073 . |
| MLA | Yang, Long-Hao et al. "Belief-Rule-Based System With Self-Organizing and Multi-Temporal Modeling for Sensor-Based Human Activity Recognition" . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 29 . 2 (2025) : 1062-1073 . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Nugent, Chris , Liu, Jun , Wang, Ying-Ming . Belief-Rule-Based System With Self-Organizing and Multi-Temporal Modeling for Sensor-Based Human Activity Recognition . | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS , 2025 , 29 (2) , 1062-1073 . |
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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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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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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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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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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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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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为综合比较多个多指标对象在某时刻的发展状况和在不同时刻的整体发展态势,构建了基于相对熵距离的动态改进理想解法.该方法在传统理想解法基础上用改进熵值法确定不同时刻的指标权重,通过相对熵计算与理想解的距离避免了欧式距离的一些弊端,并增加考虑正负距离的相对重要性.利用基于波动性和时间度的时间权向量二次加权以推广到动态数据应用场景,最后通过实例验证该方法的可行性.
Keyword ：
动态评价方法 动态评价方法 时间权重 时间权重 理想解法 理想解法 相对熵 相对熵
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| GB/T 7714 | 李美娟 , 刘佳鸿 , 杨隆浩 et al. 基于相对熵距离的动态改进理想解法及其应用研究 [J]. | 系统科学与数学 , 2023 , 43 (1) : 174-185 . |
| MLA | 李美娟 et al. "基于相对熵距离的动态改进理想解法及其应用研究" . | 系统科学与数学 43 . 1 (2023) : 174-185 . |
| APA | 李美娟 , 刘佳鸿 , 杨隆浩 , 胡慧芳 . 基于相对熵距离的动态改进理想解法及其应用研究 . | 系统科学与数学 , 2023 , 43 (1) , 174-185 . |
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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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