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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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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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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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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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Performance evaluation is one of the most important standards to measure the competitiveness and productivity of enterprises. Although existing studies could obtain the specific values of enterprises performance based on historical data, they usually failed to effectively evaluate enterprises performance in the consideration of different indicators. Meanwhile, as the characteristics of existing performance evaluation models are uneven, how to choose a reasonable data envelopment analysis (DEA) model for enterprises performance evaluation must be considered. Therefore, a new ensemble model on the basis of homogeneous, heterogeneous, and hybrid efficiency evaluation together with the evidential reasoning (ER) approach is proposed in this study for enterprises performance evaluation, so called the ER-based ensemble model. The ER-based ensemble model can overcome the inconsistency results caused by the application of different indicators and different DEA models. In case study, 40 state-own holding enterprises in China are selected and all these enterprises are evaluated and ranked using the integrated efficiency obtained from the ER-based ensemble model. Comparative analysis demonstrates that the ER-based model is better than some traditional efficiency evaluation models in enterprises performance evaluation and performance ranking.
Keyword ：
Data envelopment analysis Data envelopment analysis efficiency ensemble efficiency ensemble efficiency evaluation efficiency evaluation enterprise performance enterprise performance evidential reasoning evidential reasoning
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| GB/T 7714 | Yang, Long-Hao , Ye, Fei-Fei , Wang, Ying-Ming et al. An ensemble model for efficiency evaluation of enterprise performance based on evidential reasoning approach [J]. | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2023 , 45 (2) : 2477-2495 . |
| MLA | Yang, Long-Hao et al. "An ensemble model for efficiency evaluation of enterprise performance based on evidential reasoning approach" . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 45 . 2 (2023) : 2477-2495 . |
| APA | Yang, Long-Hao , Ye, Fei-Fei , Wang, Ying-Ming , Huang, Yan , Hu, Haibo . An ensemble model for efficiency evaluation of enterprise performance based on evidential reasoning approach . | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS , 2023 , 45 (2) , 2477-2495 . |
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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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Given the shortage of intensive care units (ICUs) due to the coronavirus disease (COVID-19), a prediction model is essential in ensuring the availability of ICU beds. However, several challenges, such as the importance of distinguishing indicators, efficiency of ICU admission records, and the explainability and effectiveness of the prediction model, hinder the effective prediction of ICU admissions. To mitigate these challenges, an explainable decision model that uses the extended belief rule-based system is introduced to predict ICU admission. First, an indicator extraction model is proposed to measure the importance of the various indicators and obtain representative indicators. Second, a Charnes, Cooper, and Rhodes (CCR) model is constructed to measure the efficiencies of the belief rules to achieve the compact structure of an extended belief rule base. Third, a new extended belief rule-based model, optimized by parameter optimization and domain division-based rule reduction, is developed to predict ICU admission. These procedures enable the explainable decision model to adapt to big data situation, offer explanations and realize high efficiency. Finally, a case study of ICU admission during a COVID-19 outbreak is conducted to demonstrate the implementation and effectiveness of the proposed model in a comparative analysis.
Keyword ：
Coronavirus disease 2019 Coronavirus disease 2019 Data envelopment analysis Data envelopment analysis Explainable decision model Explainable decision model Extended belief rule base Extended belief rule base Random forest Random forest
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| GB/T 7714 | Zheng, Jing , Yang, Long-Hao , Wang, Ying-Ming et al. An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout [J]. | APPLIED SOFT COMPUTING , 2023 , 149 . |
| MLA | Zheng, Jing et al. "An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout" . | APPLIED SOFT COMPUTING 149 (2023) . |
| APA | Zheng, Jing , Yang, Long-Hao , Wang, Ying-Ming , Gao, Jian-Qing , Zhang, Kai . An explainable decision model based on extended belief-rule-based systems to predict admission to the intensive care unit during COVID-19 breakout . | APPLIED SOFT COMPUTING , 2023 , 149 . |
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Lymph node metastasis (LNM) constitutes one of the main prognostic factors for long-term survival in endometrial carcinoma (EC). However, the previous studies on LNM diagnosis failed to consider both model interpretability and class imbalance. In this study, the extended belief rule base (EBRB) expert system is introduced to develop a novel EBRB-based LNM diagnosis model. First, the interpretability of the EBRB expert system is investigated to demonstrate the feasibility on LNM diagnosis; Second, imbalanced learning is introduced to improve rule generation scheme for constructing base EBRBs; Third, by considering the trust of base EBRBs and base diagnoses, ensemble learning is introduced to improve rule inference scheme for diagnosing final LNM. In the case study, real EC patient data collected from Fujian Provincial Maternity and Children's Hospital are used to verify the effectiveness of the proposed EBRB-based model by comparing with the variants of rule generation schemes and rule inference schemes, as well as some machine learning-based LNM diagnosis models. The comparative results showed that the proposed EBRB-based model has better sensitivity, specificity, and geometric mean in diagnosing LNM for EC patients.
Keyword ：
Belief rule base Belief rule base Class imbalance Class imbalance Endometrial carcinoma Endometrial carcinoma Ensemble learning Ensemble learning Lymph node metastasis Lymph node metastasis
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| GB/T 7714 | Yang, Long-Hao , Ren, Tian-Yu , Ye, Fei-Fei et al. Extended belief rule base with ensemble imbalanced learning for lymph node metastasis diagnosis in endometrial carcinoma [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
| MLA | Yang, Long-Hao et al. "Extended belief rule base with ensemble imbalanced learning for lymph node metastasis diagnosis in endometrial carcinoma" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 126 (2023) . |
| APA | Yang, Long-Hao , Ren, Tian-Yu , Ye, Fei-Fei , Hu, Haibo , Wang, Hui , Zheng, Hui . Extended belief rule base with ensemble imbalanced learning for lymph node metastasis diagnosis in endometrial carcinoma . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2023 , 126 . |
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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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