Research

10 pages, 322 KiB

Open AccessArticle

A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics

by Yan Li, Huiyuan Xue, Shiyu Wei, Rongping Wang and Feng Liu

Systems 2024, 12(5), 143; https://doi.org/10.3390/systems12050143 - 23 Apr 2024

Viewed by 191

Abstract

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for [...] Read more.

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

29 pages, 2529 KiB

Open AccessArticle

Blue Sky Protection Campaign: Assessing the Role of Digital Technology in Reducing Air Pollution

by Yang Shen and Xiuwu Zhang

Systems 2024, 12(2), 55; https://doi.org/10.3390/systems12020055 - 05 Feb 2024

Cited by 1 | Viewed by 1947

Abstract

Air pollution severely threatens people’s health and sustainable economic development. In the era of the digital economy, modern information technology is profoundly changing the way governments govern, the production mode of enterprises, and the living behavior of residents. Whether digital technology can bring [...] Read more.

Air pollution severely threatens people’s health and sustainable economic development. In the era of the digital economy, modern information technology is profoundly changing the way governments govern, the production mode of enterprises, and the living behavior of residents. Whether digital technology can bring ecological welfare needs to be further studied. Based on panel data from 269 Chinese cities from 2006 to 2021, this study empirically examines the impact of digital technology on air pollution by using the two-way fixed effect model. The results show that digital technology will significantly reduce the concentration of fine particles in the air and help protect the atmospheric environment. The results are still valid after using the interactive fixed effect model and the two-stage least square method after the robustness test and causality identification. Digital technology can also reduce the air pollution by promoting green innovation, improving energy efficiency, and easing market segmentation. The effect of digital technology on reducing the concentration of fine particles in the air is heterogeneous. Digital technology plays a more substantial role in reducing pollution in resource-based cities and areas with a high degree of modernization of the commodity supply chain. The positive effect of digital technology in reducing air pollution is affected by the amount of air pollutants emitted. When the concentration of PM_2.5 in the air is high, the role of digital technology in protecting the atmosphere will be strongly highlighted. This research is a beneficial exploration of protecting the atmospheric environment by using digital technology while building an ecological civilization society. The conclusion will help urban managers, the public, and business operators entirely use modern equipment such as 5G, remote sensing, and the Internet of Things in their respective fields to protect the atmospheric environment. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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22 pages, 5711 KiB

Open AccessArticle

Complex Real-Time Monitoring and Decision-Making Assistance System Based on Hybrid Forecasting Module and Social Network Analysis

by Henghao Fan, Hongmin Li, Xiaoyang Gu and Zhongqiu Ren

Systems 2024, 12(2), 39; https://doi.org/10.3390/systems12020039 - 24 Jan 2024

Viewed by 1127

Abstract

Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. [...] Read more.

Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. Thus, this paper proposed a complex real-time monitoring and decision-making assistance system, using a hybrid forecasting module and social network analysis. Firstly, before an accurate forecasting module was constructed, text sentiment analysis and a strategy based on multiple feature selection methods and result fusion were introduced to data preprocessing. Subsequently, CNN-D-LSTM was proposed to improve the feature capture ability to make forecasting more accurate. Then, social network analysis was utilized to explore the spatial transporting characteristics, which could provide solutions to joint prevention and control in urban agglomerations. For experiment simulation, two comparative experiments were constructed for individual models and city cluster forecasting, in which the mean absolute error decreases to 7.8692 and the Pearson correlation coefficient is 0.9816. For overall spatial cluster forecasting, related experiments demonstrated that with appropriate cluster division, the Pearson correlation coefficient could be improved to nearly 0.99. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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25 pages, 19279 KiB

Open AccessArticle

Ecological Efficiency Measurement and Technical Heterogeneity Analysis in China: A Two-Stage Three-Level Meta-Frontier Network Model Based on Segmented Projection

by Ruiyue Lin, Xinyuan Wang and Yu Jiang

Systems 2024, 12(1), 22; https://doi.org/10.3390/systems12010022 - 10 Jan 2024

Viewed by 1142

Abstract

Due to persistent technological impacts on ecological efficiency (eco-efficiency) and variations in economic power and resource endowments among regions, considering regional and temporal heterogeneity becomes imperative. Ecosystems, often divided into economic production and environmental governance stages, necessitate a holistic assessment incorporating regional, temporal [...] Read more.

Due to persistent technological impacts on ecological efficiency (eco-efficiency) and variations in economic power and resource endowments among regions, considering regional and temporal heterogeneity becomes imperative. Ecosystems, often divided into economic production and environmental governance stages, necessitate a holistic assessment incorporating regional, temporal heterogeneity and stage distinctions. To address potential issues of a technology gap ratio (TGR) exceeding 1 within a two-stage network structure with dual heterogeneity, we introduce a segmented projection three-layer meta-frontier analysis method. In empirical study, we systematically examined eco-efficiency, emissions inefficiency and technology gaps across management, regional and temporal dimensions in 30 Chinese provinces from 2016 to 2020. Findings reveal disparities in management eco-efficiency, with the central provinces outperforming the east. Regional differences indicate advanced technology in the east, contributing to superior eco-efficiency. Temporal analysis highlights the positive role of scientific and technological development. Emissions inefficiency improvements are noted, necessitating attention toward management and regional technology levels. Eastern provinces exhibit superior emissions efficiency, emphasizing the role of regional and technological development. Recommendations include prioritizing environmental governance, strengthening regional collaborations and implementing policies to bridge technology gaps. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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26 pages, 2288 KiB

Open AccessArticle

BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

by Dayeong So, Jinyeong Oh, Insu Jeon, Jihoon Moon, Miyoung Lee and Seungmin Rho

Systems 2023, 11(9), 456; https://doi.org/10.3390/systems11090456 - 02 Sep 2023

Cited by 2 | Viewed by 1719

Abstract

The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing [...] Read more.

The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing these demands efficiently due to their limitations in modeling complex temporal dependencies and processing large amounts of data. This study presents a groundbreaking hybrid deep learning model, BiGTA-net, which integrates a bi-directional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and an attention mechanism. Designed explicitly for day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks and activation functions. Its performance is marked by the lowest mean absolute percentage error (MAPE) of 5.37 and a root mean squared error (RMSE) of 171.3 on an educational building dataset. Furthermore, it exhibits flexibility and competitive accuracy on the Appliances Energy Prediction (AEP) dataset. Compared to traditional deep learning models, BiGTA-net reports a remarkable average improvement of approximately 36.9% in MAPE. This advancement emphasizes the model’s significant contribution to energy management and load forecasting, accentuating the efficacy of the proposed hybrid approach in power system optimizations and smart city energy enhancements. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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18 pages, 1896 KiB

Open AccessArticle

Forecasting the Natural Gas Supply and Consumption in China Using a Novel Grey Wavelet Support Vector Regressor

by Xin Ma, Yanqiao Deng and Hong Yuan

Systems 2023, 11(8), 428; https://doi.org/10.3390/systems11080428 - 15 Aug 2023

Viewed by 999

Abstract

Natural gas is playing an important role in the reconstruction of the energy system of China. Natural gas supply and consumption indicators forecasting is an important decision-making support for the government and energy companies, which has attracted considerable interest from researchers in recent [...] Read more.

Natural gas is playing an important role in the reconstruction of the energy system of China. Natural gas supply and consumption indicators forecasting is an important decision-making support for the government and energy companies, which has attracted considerable interest from researchers in recent years. In order to deal with the more complex features of the natural gas datasets in China, a Grey Wavelet Support Vector Regressor is proposed in this work. This model integrates the primary framework of the grey system model with the kernel representation employed in the support vector regression model. Through a series of mathematical transformations, the parameter optimization problem can be solved using the sequential minimal optimization algorithm. The Grey Wolf Optimizer is used to optimize its hyperparameters with the nested cross-validation scheme, and a complete computational algorithm is built. The case studies are conducted with real-world datasets from 2003–2020 in China using the proposed model and 15 other models. The results show that the proposed model presents a significantly higher performance in out-of-sample forecasting than all the other models, indicating the high potential of the proposed model in forecasting the natural gas supply and consumption in China. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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27 pages, 430 KiB

Open AccessArticle

Research on the Copyright Value Evaluation Model of Online Movies Based on the Fuzzy Evaluation Method and Analytic Hierarchy Process

by Peiyi Song, Yutong Liu and Jianghua Sun

Systems 2023, 11(8), 405; https://doi.org/10.3390/systems11080405 - 07 Aug 2023

Viewed by 994

Abstract

With the rapid development of video websites, copyright management and the protection of online movies are facing severe challenges. With the strengthening of intellectual property protection, copyright value management has become necessary for the transformation of copyright value, which is of great significance [...] Read more.

With the rapid development of video websites, copyright management and the protection of online movies are facing severe challenges. With the strengthening of intellectual property protection, copyright value management has become necessary for the transformation of copyright value, which is of great significance for the healthy development of the industry. Based on the current development status of China’s online movie offerings, online movies were collected from China’s three major video platforms between 2016 and 2018 as research objects, and a set of scientific and effective online movie copyright value assessment methods and systems are proposed through the fuzzy comprehensive evaluation method, analytic hierarchy process, Delphi method, and empirical research. In this study, using data collected through a questionnaire survey, a fuzzy evaluation method is applied to establish the evaluation index of the copyright value of online movies. Moreover, according to the Delphi method, expert suggestions are collected, the indexes are scientifically corrected in the market, and the weights of the copyright value evaluation index of online movies both before and after broadcasting are calculated using the analytic hierarchy process. On this basis, by applying big data analysis, the communication effect index, prebroadcast value score evaluation index, and postbroadcast value evaluation index are deeply analyzed, and the copyright value evaluation model of online movies both before and after broadcasting is established. Finally, based on market feedback data, the evaluation models are revised and empirically tested to verify the scientificity and rationality of the copyright evaluation method proposed in this study. The results show that the proposed methods and systems for evaluating the copyright value of online movies are scientific and effective. This study provides new insights for all types of movie and television production organizations and video playback platforms on how to design effective copyright value evaluation models and practice methods for online movies. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

16 pages, 2102 KiB

Open AccessArticle

A Novel DGM(1, N) Model with Interval Grey Action Quantity and Its Application for Forecasting Hydroelectricity Consumption of China

by Ye Li, Hongtao Ren, Shi Yao, Bin Liu and Yiming Zeng

Systems 2023, 11(8), 394; https://doi.org/10.3390/systems11080394 - 01 Aug 2023

Cited by 1 | Viewed by 771

Abstract

This paper addresses the issue of the conventional DGM(1, N) model’s prediction results not taking into account the grey system theory pri1nciple of the “non-uniqueness of solutions”. Firstly, before presenting the interval grey action quantity, the practical significance of grey action quantity is [...] Read more.

This paper addresses the issue of the conventional DGM(1, N) model’s prediction results not taking into account the grey system theory pri1nciple of the “non-uniqueness of solutions”. Firstly, before presenting the interval grey action quantity, the practical significance of grey action quantity is examined. In the DGM(1, N) model, the grey action quantity is transformed into an interval grey action quantity. Then, the calculation of the parameters uses the least squares method. A DGM(1, N,

\otimes_{c}

) model containing interval grey action is then built, and meanwhile, the program code for DGM(1, N,

\otimes_{c}

) is provided. Lastly, the aforementioned model is used to forecast the hydroelectricity consumption of China. The findings indicate that it produces more rational outcomes than the traditional DGM(1, N) model. Overall, the research carries significant pragmatic implications for broadening the conceptual underpinnings of multivariate grey forecasting models and enhancing their structural arrangement. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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23 pages, 4727 KiB

Open AccessArticle

Derivation of Optimal Operation Factors of Anaerobic Digesters through Artificial Neural Network Technology

by Yumeng Bao, Ravindranadh Koutavarapu and Tae-Gwan Lee

Systems 2023, 11(7), 375; https://doi.org/10.3390/systems11070375 - 22 Jul 2023

Cited by 1 | Viewed by 1105

Abstract

The anaerobic digestion of sewage sludge in South Korean wastewater treatment plants is affected by seasonal factors and other influences, resulting in lower digestion efficiency and gas production, which cannot reach optimal yields. The aim of this study was to improve the digestion [...] Read more.

The anaerobic digestion of sewage sludge in South Korean wastewater treatment plants is affected by seasonal factors and other influences, resulting in lower digestion efficiency and gas production, which cannot reach optimal yields. The aim of this study was to improve the digestion efficiency and gas production of sludge anaerobic digestion in a wastewater treatment plant (WWTP) by using data mining techniques to adjust operational parameters. Through experimental data obtained from the WWTP in Daegu City, South Korea, an artificial neural network (ANN) technology was used to adjust the range of the organic loading rate (OLR) and hydraulic retention rate (HRT) to improve the efficiency and methane gas production from anaerobic sludge digestion. Data sources were normalized, and data analysis including Pearson correlation analysis, multiple regression analysis and an artificial neural network for optimal results. The results of the study showed a predicted 0.5% increase in digestion efficiency and a 1.3% increase in gas production at organic loads of 1.26–1.46 kg/m³ day and an HRT of 26–30 days. This shows that the ANN model that we established is feasible and can be used to improve the efficiency and gas production of sludge anaerobic digestion. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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17 pages, 831 KiB

Open AccessArticle

Risk Control for Synchronizing a New Economic Model

by Reza Behinfaraz, Abdolmehdi Bagheri, Amir Aminzadeh Ghavifekr and Paolo Visconti

Systems 2023, 11(7), 373; https://doi.org/10.3390/systems11070373 - 20 Jul 2023

Viewed by 835

Abstract

Risk analysis in control problems is a critical but often overlooked issue in this research area. The main goal of this analysis is to assess the reliability of designed controllers and their impact on applied systems. The chaotic behavior of fractional-order economical systems [...] Read more.

Risk analysis in control problems is a critical but often overlooked issue in this research area. The main goal of this analysis is to assess the reliability of designed controllers and their impact on applied systems. The chaotic behavior of fractional-order economical systems has been extensively investigated in previous studies, leading to advancements in such systems. However, this chaotic behavior poses unpredictable risks to the economic system. This paper specifically investigates the reliability and risk analysis of chaotic fractional-order systems synchronization. Furthermore, we present a technique as a new mechanism to evaluate controller performance in the presence of obvious effects. Through a series of simulation studies, the reliability and risk associated with the proposed controllers are illustrated. Ultimately, we show that the suggested technique effectively reduces the risks associated with designed controllers. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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19 pages, 21420 KiB

Open AccessArticle

Analysis of Spatial and Temporal Evolution Characteristics and Influencing Factors of the Water Footprint in Xinjiang from 2000 to 2020

by Shijie Wang, Xiaoying Lai and Xinchen Gu

Systems 2023, 11(7), 349; https://doi.org/10.3390/systems11070349 - 07 Jul 2023

Cited by 1 | Viewed by 896

Abstract

Xinjiang is home to one of the most serious resource-based water shortages, and at the same time, it is an important main production area of grain, cotton, and high-quality fruits and vegetables in China, placing a heavy burden on water resources. Based on [...] Read more.

Xinjiang is home to one of the most serious resource-based water shortages, and at the same time, it is an important main production area of grain, cotton, and high-quality fruits and vegetables in China, placing a heavy burden on water resources. Based on this, this paper determines the basic condition of water resources in regions of Xinjiang using the water footprint method. It then identifies the drivers of water footprint changes using the population scale effect, policy support effect, investment–output effect, economic structure effect, water use efficiency effect, and water use structure effect via the LMDI decomposition model. Finally, this paper illustrates the trajectory of the regional water footprint through individual stochastic convergence. This study found the following: (1) The water footprint of Xinjiang showed a fluctuating upward trend, and the total water footprint varied significantly between regions. From a compositional standpoint, most regions were dominated by the agricultural water footprint, while spatially, the regional water footprint had a high distribution trend in the south and a low distribution in the north. (2) The driving effects of the water footprint, policy support, population scale, and water use structure were incremental, while the effects of water use efficiency, economic structure, and investment output were decremental. (3) Most regions in Xinjiang showed individual stochastic convergence trends, indicating that regions converged to their respective compensating difference equilibrium levels. In this regard, it is necessary to strengthen R&D and the promotion of water use technology, further optimize the industrial structure, and leverage the positive effect of government investment to alleviate the regional water constraint dilemma and promote high-quality regional economic development. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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21 pages, 1549 KiB

Open AccessArticle

Do Environmental Taxes Affect Carbon Dioxide Emissions in OECD Countries? Evidence from the Dynamic Panel Threshold Model

by Abdullah Sultan Al Shammre, Adel Benhamed, Ousama Ben-Salha and Zied Jaidi

Systems 2023, 11(6), 307; https://doi.org/10.3390/systems11060307 - 14 Jun 2023

Cited by 6 | Viewed by 1949

Abstract

The latest decades have been marked by rapid climate change and global warming due to the release of greenhouse gas emissions into the atmosphere. Environmental taxes have emerged as a cost-effective way to tackle environmental degradation. However, the effectiveness of environmental taxes in [...] Read more.

The latest decades have been marked by rapid climate change and global warming due to the release of greenhouse gas emissions into the atmosphere. Environmental taxes have emerged as a cost-effective way to tackle environmental degradation. However, the effectiveness of environmental taxes in reducing pollution remains a topic of ongoing debate. The purpose of this paper is to examine empirically the effects of various environmental tax categories (energy, pollution, resource and transport) on CO₂ emissions in 34 OECD countries between 1995 and 2019. The dynamic panel threshold regression developed by Seo and Shin (2016) is implemented to assess whether the impact of environmental taxes on CO₂ emissions depends on a given threshold level. The locally weighted scatterplot smoothing analysis provides evidence for a nonlinear association between environmental taxes and CO₂ emissions. The analysis indicates the existence of one significant threshold and two regimes (lower and upper) for all environmental tax categories. The dynamic panel threshold regression reveals that the total environmental tax, energy tax and pollution tax reduce CO₂ emissions in the upper regime, i.e., once a given threshold level is reached. The threshold levels are 3.002% of GDP for the total environmental tax, 1.991% for the energy tax and 0.377% for the pollution tax. Furthermore, implementing taxes on resource utilization may be effective but with limited environmental effects. Based on the research results, it is recommended that countries in the OECD implement specific environmental taxes to reduce greenhouse gas emissions. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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14 pages, 680 KiB

Open AccessArticle

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

by Shanshan Jiang, Ruiting Dong, Jie Wang and Min Xia

Systems 2023, 11(6), 305; https://doi.org/10.3390/systems11060305 - 13 Jun 2023

Cited by 9 | Viewed by 3494

Abstract

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning [...] Read more.

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns. In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed. Among them, fraudulent transactions are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data. Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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27 pages, 3604 KiB

Open AccessArticle

Spatiotemporal Hybrid Air Pollution Early Warning System of Urban Agglomeration Based on Adaptive Feature Extraction and Hesitant Fuzzy Cognitive Maps

by Xiaoyang Gu, Hongmin Li and Henghao Fan

Systems 2023, 11(6), 286; https://doi.org/10.3390/systems11060286 - 02 Jun 2023

Cited by 1 | Viewed by 1073

Abstract

Long-term exposure to air pollution will pose a serious threat to human health. Accurate prediction can help people reduce exposure risks and promote environmental pollution control. However, most previous studies have ignored the spatial spillover of air pollution, i.e., that the current region’s [...] Read more.

Long-term exposure to air pollution will pose a serious threat to human health. Accurate prediction can help people reduce exposure risks and promote environmental pollution control. However, most previous studies have ignored the spatial spillover of air pollution, i.e., that the current region’s air quality is also correlated with that of geographically adjacent areas. Therefore, this paper proposes an innovative spatiotemporal hybrid early warning system based on adaptive feature extraction and improved fuzzy cognition maps. Firstly, a spatial spillover analysis model based on the Moran index and local gravitational clustering was proposed to capture the diffusion and concentration characteristics of air pollution between regions. Then, an adaptive feature extraction model based on an optimized Hampel filter was put forward to process and correct the outliers in the original series. Finally, a hesitant fuzzy information optimized fuzzy cognitive maps model was proposed to forecast the air quality of urban agglomeration. The experimental results show that the air quality forecasting accuracy of urban agglomerations can be significantly improved when the geographical conditions and other interactions among cities are comprehensively considered, and the proposed model outperformed other benchmarks and can be used as a powerful analytical tool during urban agglomeration air quality management. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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20 pages, 6489 KiB

Open AccessArticle

Renewable Energy-Based DC Microgrid with Hybrid Energy Management System Supporting Electric Vehicle Charging System

by Harin M. Mohan and Santanu Kumar Dash

Systems 2023, 11(6), 273; https://doi.org/10.3390/systems11060273 - 29 May 2023

Cited by 7 | Viewed by 3062

Abstract

Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested [...] Read more.

Growing Electric vehicle (EV) ownership leads to an increase in charging stations, which raises load demand and causes grid outages during peak hours. Microgrids can significantly resolve these issues in the electrical distribution system by implementing an effective energy management approach. The suggested hybrid optimization approach aims to provide constant power regardless of the generation discrepancy and should prevent the early deterioration of the storage devices. This study suggests using a dynamic control system based on the Fuzzy-Sparrow Search Algorithm (SSA) to provide a reliable power balance for microgrid (MG) operation. The proposed DC microgrid integrating renewable energy sources (RES) and battery storage system (BSS) as sources are designed and evaluated, and the findings are further validated using MATLAB Simulink simulation. In comparing the hybrid SSA strategy with the most widely used Particle Swarm Optimization (PSO)-based power management, it was observed that the hybrid SSA approach was superior in terms of convergence speed and stability. The effectiveness of the given energy management system is evaluated using two distinct modes, the variation of solar irradiation and the variation of battery state of charge, ensuring the microgrid’s cost-effective operation. The enhanced response characteristics indicate that the Fuzzy-SSA can optimise power management of the DC microgrid, making better use of energy resources. These results show the relevance of algorithm configuration for cost-effective power management in DC microgrids, as it saves approximately 7.776% in electricity expenses over a year compared to PSO. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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18 pages, 3992 KiB

Open AccessArticle

Monitoring and Early Warning of SMEs’ Shutdown Risk under the Impact of Global Pandemic Shock

by Xiaoliang Xie, Xiaomin Jin, Guo Wei and Ching-Ter Chang

Systems 2023, 11(5), 260; https://doi.org/10.3390/systems11050260 - 19 May 2023

Cited by 59 | Viewed by 1839

Abstract

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This [...] Read more.

The COVID-19 outbreak devastated business operations and the world economy, especially for small and medium-sized enterprises (SMEs). With limited capital, poorer risk tolerance, and difficulty in withstanding prolonged crises, SMEs are more vulnerable to pandemics and face a higher risk of shutdown. This research sought to establish a model response to shutdown risk by investigating two questions: How do you measure SMEs’ shutdown risk due to pandemics? How do SMEs reduce shutdown risk? To the best of our knowledge, existing studies only analyzed the impact of the pandemic on SMEs through statistical surveys and trivial recommendations. Particularly, there is no case study focusing on an elaboration of SMEs’ shutdown risk. We developed a model to reduce cognitive uncertainty and differences in opinion among experts on COVID-19. The model was built by integrating the improved Dempster’s rule of combination and a Bayesian network, where the former is based on the method of weight assignment and matrix analysis. The model was first applied to a representative SME with basic characteristics for survival analysis during the pandemic. The results show that this SME has a probability of 79% on a lower risk of shutdown, 15% on a medium risk of shutdown, and 6% of high risk of shutdown. SMEs solving the capital chain problem and changing external conditions such as market demand are more difficult during a pandemic. Based on the counterfactual elaboration of the inferred results, the probability of occurrence of each risk factor was obtained by simulating the interventions. The most likely causal chain analysis based on counterfactual elaboration revealed that it is simpler to solve employee health problems. For the SMEs in the study, this approach can reduce the probability of being at high risk of shutdown by 16%. The results of the model are consistent with those identified by the SME respondents, which validates the model. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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23 pages, 1981 KiB

Open AccessArticle

Path Optimization of Technological Innovation Efficiency Improvement in China’s High-Tech Industries Based on QCA and GA-PSO-BP Neural Network

by Yuanyuan Kou, Huiying Chen, Kai Liu, Yanping Zhou and Huajie Xu

Systems 2023, 11(5), 233; https://doi.org/10.3390/systems11050233 - 07 May 2023

Viewed by 1328

Abstract

Innovation is the main driving force to promote national technological progress. It is of great significance to explore the optimal path to improve innovation efficiency by using the qualitative method and neural network prediction model to promote the high-quality development of the national [...] Read more.

Innovation is the main driving force to promote national technological progress. It is of great significance to explore the optimal path to improve innovation efficiency by using the qualitative method and neural network prediction model to promote the high-quality development of the national economy. This study focuses on high-tech industries in the eastern, central and western regions of China; a factor-dependent research framework for innovation efficiency improvement in high-tech industries is constructed in China. The fuzzy-set qualitative comparative analysis method (QCA) is used to explore multiple paths to enhance the innovation efficiency of China’s high-tech industries. Then, a GA-PSO-BP neural network is used to construct an optimization model for the enhancement path of technological innovation efficiency, which clarifies the optimal path for the enhancement of innovation efficiency of high-tech industries in the eastern, central and western regions of China. Finally, innovation management strategies for high-tech industries are presented with regional features. The study finds that none of the individual conditions are necessary to promote the innovation efficiency of China’s high-tech industries, and only the linkage effect of the factors can achieve the goal of improving the innovation efficiency level of China’s high-tech industries. There are four configuration paths to improve the innovation efficiency of China’s high-tech industries, which are: “Multinational company (MNC) innovation—economic development—government support”; “MNC innovation—government support”; “economic development—government support”; and “economic development”. The characteristics of regional heterogeneity make differences in the optimal paths of innovation efficiency improvement in high-tech industries in eastern, central and western regions of China. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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17 pages, 4353 KiB

Open AccessArticle

Risk Connectedness among International Stock Markets: Fresh Findings from a Network Approach

by Ki-Hong Choi and Seong-Min Yoon

Systems 2023, 11(4), 207; https://doi.org/10.3390/systems11040207 - 19 Apr 2023

Cited by 1 | Viewed by 2157

Abstract

In this study, we analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness [...] Read more.

In this study, we analyze the upside and downside risk connectedness among international stock markets. We characterize the connectedness among international stock returns using the Diebold and Yilmaz spillover index approach and compute the upside and downside value-at-risk. We document that the connectedness level of the downside risk is higher than that of the upside risk and stock markets are more sensitive when the stock market declines. We also find that specific periods (e.g., the global financial crisis, the European debt crisis, and the COVID-19 turmoil) intensified the spillover effects across international stock markets. Our results demonstrate that DE, UK, EU, and US acted as net transmitters of dynamic connectedness; however, Japan, China, India, and Hong Kong acted as net receivers of dynamic connectedness during the sample period. These findings provide significant new information to policymakers and market participants. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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19 pages, 6826 KiB

Open AccessArticle

A Delphi Study on Technical and Socio-Economic Perspectives of Nanotechnology and ICT Industries Relations

by Vinodh Rida Arumugam, Boon-Kwee Ng and Kavintheran Thambiratnam

Systems 2023, 11(4), 190; https://doi.org/10.3390/systems11040190 - 08 Apr 2023

Cited by 1 | Viewed by 1218

Abstract

By using the Delphi technique and a case study on Malaysia’s nanotechnology research and Information and Communication Technology (ICT) industries, this paper aims to determine the development and convergence of nanotechnology and ICT innovation systems from the perspective of science-industry relations. A total [...] Read more.

By using the Delphi technique and a case study on Malaysia’s nanotechnology research and Information and Communication Technology (ICT) industries, this paper aims to determine the development and convergence of nanotechnology and ICT innovation systems from the perspective of science-industry relations. A total of 25 experts have provided their opinions and consensus on the present stage and possible future scenarios of nanotech-ICT development from four dimensions: technology landscape, economic viability, governance, and social acceptance. Results from two survey rounds indicate that the Malaysian ICT innovation system is presently economically viable and easily accepted by the market. The best-case scenario can be achieved with the help of nanotechnology. This would also require the implementation of policies and regulations from government. Although industrial and social adoption and the acceptance of nanotechnology are already strong, government is responsible for creating various programs to ensure greater awareness and development of knowledge. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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23 pages, 5027 KiB

Open AccessArticle

Time and Frequency Spillovers between the Green Economy and Traditional Energy Markets

by Lili Zhao, Wenke He, Anwen Wang and Fangfei Zhu

Systems 2023, 11(3), 153; https://doi.org/10.3390/systems11030153 - 17 Mar 2023

Cited by 5 | Viewed by 1721

Abstract

The green economy is aimed at decreasing the dependence of the global economy on traditional fossil energy, thereby resolving conflicts between economic development and environmental issues and achieving sustainable economic development. Thus, the relation between the green economy and traditional energy markets is [...] Read more.

The green economy is aimed at decreasing the dependence of the global economy on traditional fossil energy, thereby resolving conflicts between economic development and environmental issues and achieving sustainable economic development. Thus, the relation between the green economy and traditional energy markets is of great importance for both policymakers and portfolio managers. In this study, we investigate the dynamic spillover effects between the green economy and traditional energy markets by applying time and frequency spillover measures based on the TVP-VAR model. The results reveal a strong spillover relationship between the green economy and traditional energy system, and the spillover direction is mainly from green economy markets to traditional energy markets. Our analysis further reveals the heterogeneity of these spillover effects, both within green economy markets and between these markets and traditional energy markets. The performance of the U.S. green economy market is similar to that of Europe, whereas the Asian green economy market is more complex. The frequency domain results demonstrate that the spillover effects are mainly dominated by short-term (1–5 days) components, whereas medium- and long-term components have less of an effect. In addition, we find a sharp increase in the level of spillover effects during the COVID-19 pandemic. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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21 pages, 2694 KiB

Open AccessArticle

Carbon Reduction Countermeasure from a System Perspective for the Electricity Sector of Yangtze River Delta (China) by an Extended Logarithmic Mean Divisia Index (LMDI)

by Jianfeng Chen, Junsong Jia, Lin Wang, Chenglin Zhong and Bo Wu

Systems 2023, 11(3), 117; https://doi.org/10.3390/systems11030117 - 23 Feb 2023

Cited by 2 | Viewed by 1268

Abstract

The electricity sector is a complex system, especially in the Yangtze River Delta (YRD) of China. Thus, the carbon dioxide (CO₂) emission of YRD’s electricity sector during 2000–2020 was first calculated and then evaluated from two systematical dimensions of cross-region and [...] Read more.

The electricity sector is a complex system, especially in the Yangtze River Delta (YRD) of China. Thus, the carbon dioxide (CO₂) emission of YRD’s electricity sector during 2000–2020 was first calculated and then evaluated from two systematical dimensions of cross-region and the whole process (production, trade, transmission, and consumption) by an extended logarithmic mean Divisia index (LMDI). (1) During 2000–2020, the CO₂ emission of YRD’s electricity sector increased from 228.12 Mt to 807.55 Mt, with an average annual growth rate of 6.52%. Compared to other regions, the YRD’s electricity mix effect had the strongest mitigation impact on CO₂ growth. Therefore, it is important for YRD to build a low-carbon electricity system itself, including the de-carbonization of electricity production and the carbon reduction of the electricity-use process. (2) Nationally, electricity trade had an overall mitigating impact on emission growth during 2000–2020. This result means that cross-regional cooperation or trade in the electricity sector is beneficial to emission reduction. So, it is important to improve the national power grids to promote trade. (3) Jiangsu had the largest CO₂ emissions, while Anhui had the fastest average annual growth rate (9.71%). Moreover, the economic activity effect was the most significant driver in all provinces, especially in Jiangsu and Anhui. Thus, Jiangsu and Anhui should strive to improve the quality of economic growth while vigorously cutting carbon emissions. (4) Electricity transmission loss had an overall driving impact on emission growth in each YRD province, especially in Zhejiang and Anhui. Meanwhile, electricity structure, electricity trade, and electricity intensity were the inhibiting factors. Particularly, the inhibiting effect of Shanghai’s electricity structure was notably weak (−2.17 Mt). So, Shanghai should try hard to increase the proportion of renewable energy, while Zhejiang and Anhui should upgrade their electricity transmission equipment. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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23 pages, 7810 KiB

Open AccessArticle

Spatial-Temporal Evolution and Driving Factors of Regional Green Development: An Empirical Study in Yellow River Basin

by Fuli Zhou, Dongge Si, Panpan Hai, Panpan Ma and Saurabh Pratap

Systems 2023, 11(2), 109; https://doi.org/10.3390/systems11020109 - 20 Feb 2023

Cited by 4 | Viewed by 1656

Abstract

The sustainable development of the Yellow River Basin (YRB) is regarded as a national strategy for China. Previous literature has focused on the green efficiency measurement of YRB, ignoring its evolution process and influential mechanism. This paper tries to disclose the spatial-temporal evolution [...] Read more.

The sustainable development of the Yellow River Basin (YRB) is regarded as a national strategy for China. Previous literature has focused on the green efficiency measurement of YRB, ignoring its evolution process and influential mechanism. This paper tries to disclose the spatial-temporal evolution of green efficiency and its influential mechanism of the YRB region by proposing a novel integrated DEA-Tobit model to fill the gap. Based on the development path of the YRB region, the multi-period two-stage DEA model is adopted to evaluate the green development efficiency (GDE) from provincial and urban dimensions. In addition, the panel Tobit model is developed to investigate the influential factors of the GDE for the YRB region. The GDE in the YRB region shows an unbalanced state where the downstream is best, followed by the middle and upstream. The unbalanced development also exists within the province. Both Henan and Shandong Province achieved the optimal value, while cities in these two provinces show lower green efficiency. The results also show that economic development, technological innovation and foreign capital utilization obviously affect the GDE of the YRB region positively, while industrial structure, urbanization levels and environmental regulation have negative effects. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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33 pages, 3173 KiB

Open AccessArticle

A Novel System Based on Selection Strategy and Ensemble Mode for Non-Ferrous Metal Futures Market Management

by Sibo Yang, Wendong Yang, Kai Zhang and Yan Hao

Systems 2023, 11(2), 55; https://doi.org/10.3390/systems11020055 - 19 Jan 2023

Cited by 1 | Viewed by 1510

Abstract

Non-ferrous metals, as one of the representative commodities with large international circulation, are of great significance to social and economic development. The time series of its prices are highly volatile and nonlinear, which makes metal price forecasting still a tough and challenging task. [...] Read more.

Non-ferrous metals, as one of the representative commodities with large international circulation, are of great significance to social and economic development. The time series of its prices are highly volatile and nonlinear, which makes metal price forecasting still a tough and challenging task. However, the existing research focus on the application of the individual advanced model, neglecting the in-depth analysis and mining of a certain type of model. In addition, most studies overlook the importance of sub-model selection and ensemble mode in metal price forecasting, which can lead to poor forecasting results under some circumstances. To bridge these research gaps, a novel forecasting system including data pretreatment module, sub-model forecasting module, model selection module, and ensemble module, which successfully introduces a nonlinear ensemble mode and combines the optimal sub-model selection method, is developed for the non-ferrous metal prices futures market management. More specifically, data pretreatment is carried out to capture the main features of metal prices to effectively mitigate those challenges caused by noise. Then, the extreme learning machine series models are employed as the sub-model library and employed to predict the decomposed sub-sequences. Moreover, an optimal sub-model selection strategy is implemented according to the newly proposed comprehensive index to select the best model for each sub-sequence. Then, by proposing a nonlinear ensemble forecasting mode, the final point forecasting and uncertainty interval forecasting results are obtained based on the forecasting results of the optimal sub-model. Experimental simulations are carried out using the datasets copper and zinc, which show that the present system is superior to other benchmarks. Therefore, the system can be used not only as an effective technique for non-ferrous metal prices futures market management but also as an alternative for other forecasting applications. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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