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学者姓名：郭菊娥
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Abstract ：
With unconventional oil and gas booming in commercial development, its inevitable environmental damage has aroused the public's vigilance. To support the regulation improvement and early-warning system building, it is of great need to learn the regular patterns in recurrent violations both for practitioners and governments. In this respect, we utilized the "Oil and Gas Compliance Report" from the Pennsylvania Department of Environmental Protection from 2000 to 2019, a total of 5737 violation records, to dig out the historical violation patterns. Through LDA (Latent Dirichlet Allocation) analysis combined with the decision tree model, our research attained the following conclusions: first, we find that the LDA themes of violations as "Erosion and sediment" and "Water pollution" are critical factors for "Failed" enforcement results. Therefore, policymakers and practitioners should pay more attention to those two types of accidents. Second, it is noted that counties are also one of the essential features that matter the enforcement results. Third, we need to consider the role of economic punishment dialectically, while it is not a significant feature for successful enforcement results. That is to say, a monetary penalty may not necessarily improve the effectiveness of the company's measurements.
Keyword ：
Decision tree Environmental violation Environment pollution LDA Text mining Unconventional oil and gas
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| GB/T 7714 | Bi, Dan , Guo, Ju-E , Zhao, Erlong et al. Identifying environmental and health threats in unconventional oil and gas violations: evidence from Pennsylvania compliance reports [J]. | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2021 , 29 (15) : 22742-22755 . |
| MLA | Bi, Dan et al. "Identifying environmental and health threats in unconventional oil and gas violations: evidence from Pennsylvania compliance reports" . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 29 . 15 (2021) : 22742-22755 . |
| APA | Bi, Dan , Guo, Ju-E , Zhao, Erlong , Sun, Shaolong , Wang, Shouyang . Identifying environmental and health threats in unconventional oil and gas violations: evidence from Pennsylvania compliance reports . | ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH , 2021 , 29 (15) , 22742-22755 . |
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Abstract ：
Short-term load forecasting is crucial for power demand-side management and the planning of the power system. Considering the necessity of interval-valued time series modeling and forecasting for the power system, this study proposes an interval decomposition-reconstruction-ensemble learning approach to forecast interval-valued load, in terms of the concept of 'divide and conquer'. First, bivariate empirical mode decomposition is applied to decompose the original interval-valued data into a finite number of bivariate modal components for extracting and identifying the fluctuation characteristics of data. Second, based on the complexity analysis of each bivariate modal component by multivariate multiscale permutation entropy, the components were reconstructed for capturing inner factors and reduce the accumulation of estimation errors. Third, long short-term memory is utilized to synchronously forecast the upper and the lower bounds of each bivariate component and optimized by the Bayesian optimization algorithm. Finally, generating the aggregated interval-valued output by ensemble the forecasting results of the upper and lower bounds of each component severally. The electric load of five states in Australia is used for verification, and the empirical results show that the forecasting accuracy of our proposed learning approach is significantly superior to single models and the decomposition-ensemble models without reconstruction. This indicates that our proposed learning approach appears to be a promising alternative for interval load forecasting. © 2021 Elsevier Ltd
Keyword ：
Brain Electric power plant loads Forecasting Information management Long short-term memory
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| GB/T 7714 | Yang, Dongchuan , Guo, Ju-e , Sun, Shaolong et al. An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting [J]. | Applied Energy , 2021 , 306 . |
| MLA | Yang, Dongchuan et al. "An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting" . | Applied Energy 306 (2021) . |
| APA | Yang, Dongchuan , Guo, Ju-e , Sun, Shaolong , Han, Jing , Wang, Shouyang . An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting . | Applied Energy , 2021 , 306 . |
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Electricity demand forecasting plays a fundamental role in the operation and planning procedures of power systems, and the publications related to electricity demand forecasting have attracted more and more attention in the past few years. To have a better understanding of the knowledge structure in the field of electricity demand forecasting, we applied scientometric methods to analyze the current state and the emerging trends based on the 831 publications from the Web of Science Core Collection during the past 20 years (1999-2018). Employing statistical description analysis, cooperative network analysis, keyword co-occurrence analysis, co-citation analysis, cluster analysis, and emerging trend analysis techniques, this study gives a comprehensive overview of the most critical countries, institutions, journals, authors, and publications in this field, cooperative networks relationships, research hotspots, and emerging trends. The results can provide meaningful guidance and helpful insights for researchers to enhance the understanding of crucial research, emerging trends, and new developments in electricity demand forecasting.
Keyword ：
citespace electricity demand forecasting knowledge mapping scientometric visualization
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| GB/T 7714 | Yang, Dongchuan , Guo, Ju-e , Li, Jie et al. Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight [J]. | FRONTIERS IN ENERGY RESEARCH , 2021 , 9 . |
| MLA | Yang, Dongchuan et al. "Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight" . | FRONTIERS IN ENERGY RESEARCH 9 (2021) . |
| APA | Yang, Dongchuan , Guo, Ju-e , Li, Jie , Wang, Shouyang , Sun, Shaolong . Knowledge Mapping in Electricity Demand Forecasting: A Scientometric Insight . | FRONTIERS IN ENERGY RESEARCH , 2021 , 9 . |
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This study explores a supply chain with one finance-constrained upstream startup supplier that invests in quality innovation and one downstream manufacturer that sells to consumers under demand uncertainty. The survival and profit-seeking cases are investigated for the startup supplier. The manufacturer supports quality innovation by the startup supplier via an investment-sharing contract. The results show that when making quality investment decisions, the survival-seeking supplier focuses on financial constraints, whereby large constraints pressure the firm to invest more in quality innovation. By contrast, profit-seeking suppliers are encouraged by a large potential market. Investment-sharing contracts are proven to facilitate "win-win" vertical R&D cooperation in the supply chain. Last, by introducing a revenue-sharing contract and comparing it with an investment-sharing contract, our numerical analyses show how the manufacturer should select the appropriate contract as key parameters change.
Keyword ：
investment sharing profit seeking quality innovation revenue sharing survival seeking
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| GB/T 7714 | Xing, Guangyuan , Xia, Bing , Guo, Jue . Contract choice for upstream innovation in a finance-constrained supply chain [J]. | INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH , 2021 , 29 (3) : 1897-1914 . |
| MLA | Xing, Guangyuan et al. "Contract choice for upstream innovation in a finance-constrained supply chain" . | INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 29 . 3 (2021) : 1897-1914 . |
| APA | Xing, Guangyuan , Xia, Bing , Guo, Jue . Contract choice for upstream innovation in a finance-constrained supply chain . | INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH , 2021 , 29 (3) , 1897-1914 . |
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Abstract ：
The availability of tourism-related big data increases the potential to improve the accuracy of tourism demand forecasting but presents significant challenges for forecasting, including curse of dimensionality and high model complexity. A novel bagging-based multivariate ensemble deep learning approach integrating stacked autoencoder and kernel-based extreme learning machine (B-SAKE) is proposed to address these challenges in this study. By using historical tourist arrival data, economic variable data, and search intensity index (SII) data, we forecast tourist arrivals in Beijing from four countries. The consistent results of multiple schemes suggest that our proposed B-SAKE approach outperforms the benchmark models in terms of level accuracy, directional accuracy, and even statistical significance. Both bagging and stacked autoencoder can effectively alleviate the challenges brought by tourism big data and improve the forecasting performance of the models. The ensemble deep learning model we propose contributes to tourism demand forecasting literature and benefits relevant government officials and tourism practitioners.
Keyword ：
bagging economic variables ensemble deep learning search intensity index stacked autoencoder tourism demand forecasting
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| GB/T 7714 | Sun, Shaolong , Li, Yanzhao , Guo, Ju-e et al. Tourism demand forecasting: An ensemble deep learning approach [J]. | TOURISM ECONOMICS , 2021 . |
| MLA | Sun, Shaolong et al. "Tourism demand forecasting: An ensemble deep learning approach" . | TOURISM ECONOMICS (2021) . |
| APA | Sun, Shaolong , Li, Yanzhao , Guo, Ju-e , Wang, Shouyang . Tourism demand forecasting: An ensemble deep learning approach . | TOURISM ECONOMICS , 2021 . |
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Abstract ：
System dynamics is a well-established methodology to analyze the behavior of complex systems through computer simulation. Different from the traditional system dynamics, in this paper, the peak shaving and frequency control reserve constraints are incorporated into power generation mix planning to ensure system security and efficiency. However, it is an intractable task to consider this multi-market equilibrium and multi-period coupling planning problem in system dynamics. To ameliorate this inherent drawback, a novel planning model based on the rolling horizon optimal approach is proposed. First, by adding the constraints to the objective function, the Hamilton function is constructed to calculate the optimality conditions, and then the primal planning problem can be converted to a capital recovery problem. Considering the stochastic characteristics in the future market, a rolling horizon approach is adopted to update the planning information and optimize the power mix continuously. Next, an approximate gradient based on Pontryagin's minimum principle is developed to simplify the optimal iteration processes. To demonstrate its feasibility, the sensitivity analysis suggests that the reduction of renewable energy cost and the wide allocation of flexible resources are two major factors to achieve a high-level penetration of renewable energy. © 2021 Elsevier Ltd
Keyword ：
Commerce Iterative methods Sensitivity analysis Stochastic systems System theory
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| GB/T 7714 | Tang, Lei , Guo, Jue , Zhao, Boyang et al. Power generation mix evolution based on rolling horizon optimal approach: A system dynamics analysis [J]. | Energy , 2021 , 224 . |
| MLA | Tang, Lei et al. "Power generation mix evolution based on rolling horizon optimal approach: A system dynamics analysis" . | Energy 224 (2021) . |
| APA | Tang, Lei , Guo, Jue , Zhao, Boyang , Wang, Xiuli , Shao, Chengcheng , Wang, Yifei . Power generation mix evolution based on rolling horizon optimal approach: A system dynamics analysis . | Energy , 2021 , 224 . |
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In this study, we focus our attention on the forecasting of daily PM2.5 concentrations. According to the principle of "divide and conquer," we propose a novel decomposition ensemble learning approach by integrating ensemble empirical mode decomposition (EEMD), artificial neural networks (ANNs), and adaptive particle swarm optimization (APSO) for forecasting PM2.5 concentrations. Our proposed decomposition ensemble learning approach is formulated exclusively to deal with difficulties in quantitating meteorological information with high volatility, irregularity, and complicacy. This decomposition ensemble learning approach mainly consists of three steps. First, we utilize EEMD to decompose original time series of PM2.5 concentrations into a specific amount of independent intrinsic mode functions (IMFs) and residual term. Second, the ANN, whose connection parameters are optimized by APSO algorithm, is employed to model IMFs and residual terms, respectively. Finally, another APSO-ANN is applied to aggregate the forecast IMFs and residual term into a collection as the final forecasting results. The empirical results show that the forecasting of our decomposition ensemble learning approach outperforms other benchmark models in terms of level accuracy and directional accuracy.
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| GB/T 7714 | Xing, Guangyuan , Sun, Shaolong , Guo, Jue . A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting [J]. | DISCRETE DYNAMICS IN NATURE AND SOCIETY , 2020 , 2020 . |
| MLA | Xing, Guangyuan et al. "A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting" . | DISCRETE DYNAMICS IN NATURE AND SOCIETY 2020 (2020) . |
| APA | Xing, Guangyuan , Sun, Shaolong , Guo, Jue . A New Decomposition Ensemble Learning Approach with Intelligent Optimization for PM2.5 Concentration Forecasting . | DISCRETE DYNAMICS IN NATURE AND SOCIETY , 2020 , 2020 . |
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Abstract ：
Investment on product greenness in green supply chain is always restricted by the emerging supplier' financial constraints, so manufacturers always share the suppliers' investment to encourage the suppliers' green innovation. Based on the two-stage cooperation model between one manufacturer and one emerging supplier, and the assumption that emerging suppliers need to reach a certain survival threshold at the end of each period, this paper studies investment on product greenness and sustainability of cooperation in the supply chain. The impacts of consumers' preference for greenness (CPG), market volatility, financial constraints, and investment-sharing proportion are also examined. It was found that when market volatility and CPG exist at the same time, compared with the deterministic environment, emerging suppliers will improve (or reduce) the wholesale price and greenness at the same time to balance the short-term bankruptcy risk and the long-term profit, and suppliers' green investment would be stimulated by the increasing demand uncertainty. Besides, when suppliers' financial constraints increase, manufacturers will also increase its sharing proportion of green investment. Lastly, there always exists an investment-sharing proportion that optimizes the sustainability of cooperation and profits jointly.
Keyword ：
emerging suppliers financial constraints green supply chain investment-sharing contracts uncertain investment
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| GB/T 7714 | Xing, Guangyuan , Xia, Bing , Guo, Jue . Sustainable Cooperation in the Green Supply Chain under Financial Constraints [J]. | SUSTAINABILITY , 2019 , 11 (21) . |
| MLA | Xing, Guangyuan et al. "Sustainable Cooperation in the Green Supply Chain under Financial Constraints" . | SUSTAINABILITY 11 . 21 (2019) . |
| APA | Xing, Guangyuan , Xia, Bing , Guo, Jue . Sustainable Cooperation in the Green Supply Chain under Financial Constraints . | SUSTAINABILITY , 2019 , 11 (21) . |
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Abstract ：
通过构建包含风险投资者、风险投资家和风险企业家三方的委托代理模型对比分析了不同内部融资模式对风险投资家股权投资策略的影响效应.发现债权融资模式可以遏制风险企业家的冒险投资行为,但是对风险企业家股权激励较低;股权融资模式下风险投资家存在冒险投资的可能性同时会对风险企业家提供比债权模式更高的股权激励;有限合伙人制度下通过提高投资收益率要求可以有效遏制风险投资家的冒险投资行为,与股权融资模式相比,风险投资家在更高的股权激励下才会参与投资,并且会在付出更多努力的同时向风险企业家提供更高的股权激励,从而降低整体投资风险.
Keyword ：
风险投资 股权投资策略 融资模式 委托代理模型
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| GB/T 7714 | 薛力 , 郭菊娥 . 风险投资机构内部融资模式对风险投资家股权投资策略的影响 [J]. | 系统管理学报 , 2018 , (2) : 201-207 . |
| MLA | 薛力 et al. "风险投资机构内部融资模式对风险投资家股权投资策略的影响" . | 系统管理学报 2 (2018) : 201-207 . |
| APA | 薛力 , 郭菊娥 . 风险投资机构内部融资模式对风险投资家股权投资策略的影响 . | 系统管理学报 , 2018 , (2) , 201-207 . |
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Abstract ：
在提前订购和延时采购两种情景下,分别考虑最大化期望收益和最小化违约概率两种决策目标,对受资金约束零售商的最优采购量和相匹配的融资策略进行分析,结果表明,相比于以最大化期望收益为决策目标,以最小化违约概率为决策目标的零售商在提前订购中将奉行“保守”的采购量和融资策略(仅耗尽自有资金量采购而不融资),而在延时采购中采取“激进”的采购量和融资策略(与最大化期望收益下一致);在此基础上,对不同决策目标下受资金约束零售商的最优采购时机问题进行研究发现,在最大化期望收益下,零售商的最优采购时机由产品采购成本、市场容量均值、市场容量方差、自有资金量、银行贷款利率等多个参数共同决定,而在最小化违约概率下,零售商将始终选择延时采购.
Keyword ：
采购策略 采购时机 零售商 资金约束
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| GB/T 7714 | 史金召 , 郭菊娥 , Richard Y.K.FUNG et al. 不同决策目标下受资金约束零售商的最优采购策略研究 [J]. | 中国管理科学 , 2018 , (5) : 98-108 . |
| MLA | 史金召 et al. "不同决策目标下受资金约束零售商的最优采购策略研究" . | 中国管理科学 5 (2018) : 98-108 . |
| APA | 史金召 , 郭菊娥 , Richard Y.K.FUNG , 夏兵 . 不同决策目标下受资金约束零售商的最优采购策略研究 . | 中国管理科学 , 2018 , (5) , 98-108 . |
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