Sustainability Analytics and Modeling最新文献_第4页

The nexus between economic growth, energy use, urbanization, tourism, and carbon dioxide emissions: New insights from Singapore 经济增长、能源使用、城市化、旅游和二氧化碳排放之间的关系:来自新加坡的新见解

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100009

Asif Raihan , Almagul Tuspekova

{"title":"The nexus between economic growth, energy use, urbanization, tourism, and carbon dioxide emissions: New insights from Singapore","authors":"Asif Raihan , Almagul Tuspekova","doi":"10.1016/j.samod.2022.100009","DOIUrl":"10.1016/j.samod.2022.100009","url":null,"abstract":"<div><p>Singapore is a foremost tourist destination country experiencing continuous economic growth and rapid urbanization which is causing higher energy consumption and carbon dioxide (CO<sub>2</sub>) emissions. This study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, and tourism on CO<sub>2</sub> emissions in Singapore. Time series data from 1990 to 2019 were utilized by employing the dynamic ordinary least squares (DOLS) approach. The DOLS findings show that the long-run coefficient of economic growth is negative and significant, indicating that a 1% rise in economic growth will result in a 0.99% reduction in CO<sub>2</sub> emissions. Furthermore, the coefficient of energy use is positive and significant which reveals that an increasing 1% of energy use is linked with a rising of 0.52% CO<sub>2</sub> emissions in the long run. In addition, the long-run coefficient of urbanization is positive and significant, implying that rising urbanization by 1% causes a 1.90% increase in CO<sub>2</sub> emissions. Moreover, the coefficient of tourism is positive and significant, which specifies that an increase in tourism activities by 1% is associated with a 0.45% increase in CO<sub>2</sub> emissions in the long run. The estimated results are robust to alternative estimators such as ordinary least squares (OLS), fully modified least squares (FMOLS), and canonical cointegrating regression (CCR). Furthermore, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations toward environmental sustainability by establishing strong regulatory policy instruments to reduce environmental degradation.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100009"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259622000078/pdfft?md5=d695ae3f136fcfcc58ca369792d1148b&pid=1-s2.0-S2667259622000078-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81165573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates logit和人工神经网络在津巴布韦上市公司困境建模中的应用

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100006

Louisa Muparuri , Victor Gumbo

{"title":"On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates","authors":"Louisa Muparuri , Victor Gumbo","doi":"10.1016/j.samod.2022.100006","DOIUrl":"https://doi.org/10.1016/j.samod.2022.100006","url":null,"abstract":"<div><p>Corporate financial distress prediction is a pivotal aspect of economic development. The ability to foretell that a company will be getting into financial distress is essential for decision-makers, shareholders, and policymakers in making the best decisions and policies for sustainable development. Prediction accuracy is of paramount importance in the implementation of distress mitigation measures, a critical component attracting investment in particular to most of the developing countries in Africa. The advent of the fourth industrial revolution saw Artificial Intelligence (AI) taking centre stage in financial risk modelling. This growth has however not precluded the role of traditional statistical methods in modelling financial risk. There is a lack of consensus amongst academia and practitioners on the accuracy of these two groups of methodologies in distress prediction. Protagonists of the conventional school of thought still hold on to statistical methods being more accurate whilst the new age proponents believe AI has brought in higher levels of predictive strength and model accuracy. This study seeks to compare the accuracy of Logit and Artificial Neural Networks (ANN) in corporate distress prediction. The two modelling techniques were applied to an 8-year panel dataset from the Zimbabwe Stock Exchange. The Logit model outperformed the ANN by an overall accuracy of 92.21% compared to ANN with 85.8%. Heightened prediction accuracy is bound to improve the return to shareholders by enhancing financial risk management within emerging markets. This study also seeks to contribute to the ongoing debate on the superiority between AI techniques and statistical techniques.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100006"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259622000042/pdfft?md5=db07cd302fe69df2644ceccf1236ba1c&pid=1-s2.0-S2667259622000042-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72246662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Nature reserve optimization with buffer zones and wildlife corridors for rare species 自然保护区优化，建立珍稀物种缓冲区和野生动物走廊

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100003

Valentin Hamaide , Bertrand Hamaide , Justin C. Williams

引用次数: 3

Scheduling in a flexible flow shop with unrelated parallel machines and machine-dependent process stages: Trade-off between Makespan and production costs 具有不相关并行机器和机器相关工艺阶段的柔性流水车间中的调度：Makespan和生产成本之间的权衡

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100010

Ali Hasani , Seyed Mohammad Hassan Hosseini , Shib Sankar Sana

{"title":"Scheduling in a flexible flow shop with unrelated parallel machines and machine-dependent process stages: Trade-off between Makespan and production costs","authors":"Ali Hasani , Seyed Mohammad Hassan Hosseini , Shib Sankar Sana","doi":"10.1016/j.samod.2022.100010","DOIUrl":"https://doi.org/10.1016/j.samod.2022.100010","url":null,"abstract":"<div><p>This paper aims to tackle bi-objective scheduling problem in a flexible flow shop containing unrelated parallel machines in the first stage. Due to the different technology levels of the parallel machines, their process speeds and production costs vary to each other. Therefore, minimizing the maximum completion time (Makespan) and the total production cost are considered as two objective functions. In addition, setup times are considered sequence-dependent and this system considers machine-dependent process steps and the process steps of an order depend on the assigned machine in the first stage. First, the problem is described and formulated as a bi-objective mathematical model. Since the problem is known to be strongly NP-hard, an approximate solution method is introduced based on the Non-dominated Sorting Genetic Algorithm (NSGA-II) to provide proper solutions for decision makers. The performance of the proposed solution method is investigated in comparison to another powerful multi-objective algorithm (SPEA 2) by solving different test problems. The computational results using various metrics such as Error Ratio (ER) and Generational Distance (GD) show the effectiveness of the proposed method in terms of optimality. The other indices such as Spacing (S), Diversification (D), and Mean Ideal Distance (MID) emphasize the superiority of the proposed algorithm compared to the rival algorithm in solving medium and large instances. In addition, supplementary analysis provided proper trade-off between two objectives for managers to select the best solution based on their preferences.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100010"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266725962200008X/pdfft?md5=90c9f1336451ee2ff9efb454155dc876&pid=1-s2.0-S266725962200008X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72293636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Optimization of industrial energy consumption for sustainability using time-series regression and gradient descent algorithm based on historical electricity consumption data 基于历史用电量数据的时序回归和梯度下降算法的工业可持续性能耗优化

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100004

Richard Opoku , George Y. Obeng , Louis K. Osei , John P. Kizito

{"title":"Optimization of industrial energy consumption for sustainability using time-series regression and gradient descent algorithm based on historical electricity consumption data","authors":"Richard Opoku , George Y. Obeng , Louis K. Osei , John P. Kizito","doi":"10.1016/j.samod.2022.100004","DOIUrl":"10.1016/j.samod.2022.100004","url":null,"abstract":"<div><p>Optimizing electricity consumption to minimize wastage and reduce cost is a major challenge in many industries. This is because, in many cases, the effect of the independent variables contributing to the total electricity consumption and cost are latent. The purpose of this study is to apply numerical techniques to identify and optimize these independent variables in order to improve sustainable energy management in industries to minimize wastage. Regression analysis was first applied to identify and decouple the independent variables to determine their individual effects on electricity consumption and cost. A cost function called the Mean Square Error (MSE) was then used to optimize these independent variables using gradient descent algorithm (GDA). In a case study, the developed approach that combines time series regression analysis with gradient descent optimization was used to analyze the electricity consumption data of an oil distribution company for the period 2015 to 2018. The results showed potential electricity savings of 124,684 kWh and cost savings of US$ 25,375 annually, when the facility is operated at optimum parameters of 0.95 power factor, 260 kVA maximum demand and 25,000 kWh active electricity consumption. The novelty of this study is that a procedure that combines time series regression analysis (RA) and gradient descent algorithm (GDA) has been developed and applied to decouple and optimize the independent variables that affect electricity consumption in an industry.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100004"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259622000029/pdfft?md5=28d76d2b4df77319e87e807973be6b77&pid=1-s2.0-S2667259622000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80169348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A three-layer supply chain integrated production-inventory model with idle cost and batch shipment policy 具有闲置成本和批量装运策略的三层供应链集成生产库存模型

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100011

S. Khanra , S.K. Ghosh , C. Pathak

{"title":"A three-layer supply chain integrated production-inventory model with idle cost and batch shipment policy","authors":"S. Khanra , S.K. Ghosh , C. Pathak","doi":"10.1016/j.samod.2022.100011","DOIUrl":"https://doi.org/10.1016/j.samod.2022.100011","url":null,"abstract":"<div><p>The paper describes an integrated/centralised supply chain model consisting of one supplier, one manufacturer and one retailer within a finite time horizon. The manufacturer produces, at a finite rate, in each lot. The lot production rate in a batch increases with a rate <span><math><mi>λ</mi></math></span> in successive batch and the produced items are supplied to the retailer. The objective of the proposed model is to optimize the average total profit under the consideration of the proportional increase in the size of successive shipments within a batch production run and the production time of the supplier. The corresponding average profits of the supplier, the manufacturer and the retailer and the average total profit of integrated model are obtained. The results obtained in the numerical examples clearly establish that it is always beneficial in terms of profit when the size of the successive shipment is a variable. Therefore, size of the successive shipment should be variable in order to get more profit. A sensitivity analysis of the optimal solution with respect to changes of the parameter values is also carried out to strengthen the proposed model.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100011"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259622000091/pdfft?md5=5d6a80c7ede9354b6dec29a928785a0c&pid=1-s2.0-S2667259622000091-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72246666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sale through dual channel retailing system— A mathematical approach 双渠道零售系统的销售——一种数学方法

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100008

Shib Sankar Sana

引用次数: 27

Applying probabilistic mathematical modeling approach and AI technique to investigate serious train accidents in Japan 应用概率数学建模方法和人工智能技术调查日本严重列车事故

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100005

Tatsuo Oyama , Masashi Miwa

{"title":"Applying probabilistic mathematical modeling approach and AI technique to investigate serious train accidents in Japan","authors":"Tatsuo Oyama , Masashi Miwa","doi":"10.1016/j.samod.2022.100005","DOIUrl":"https://doi.org/10.1016/j.samod.2022.100005","url":null,"abstract":"<div><p>We investigated data for serious train accidents (STAs) in Japan to elucidate their causes and consequences and to improve countermeasures for reducing the number of STAs. We used statistical data on the STAs occurring in Japan from 1987 to 2018, which included the frequency, types, causes, and consequences of the STAs, along with additional derailment, collision, and casualty data. We investigated the historical trend of the STAs using various probabilistic mathematical modeling approaches, such as Markov models, logit regression models, Bayesian approaches, and artificial-intelligence techniques. We showed that the number of casualties in STAs involving collisions was significantly larger than that for accidents not involving collisions. Thus, the statistical analysis indicated that preventing train collisions is the most important and necessary measure for reducing damage to passengers. Additionally, we proposed several countermeasures for ensuring the safety of passengers in Japan, e.g., install automatic train stops for all railway companies of Private Railway and terminate the use of ground-level crossings without gates. We evaluated the effectiveness of these countermeasures from various viewpoints.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100005"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259622000030/pdfft?md5=be636475407a8e592c7bcf41c55c0d08&pid=1-s2.0-S2667259622000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72246664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Political stability effect on environment and weak sustainability in Asian countries 政治稳定对环境的影响和亚洲国家的弱可持续性

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2022.100007

Lotfali Agheli , Vahid Mohamad Taghvaee

{"title":"Political stability effect on environment and weak sustainability in Asian countries","authors":"Lotfali Agheli , Vahid Mohamad Taghvaee","doi":"10.1016/j.samod.2022.100007","DOIUrl":"10.1016/j.samod.2022.100007","url":null,"abstract":"<div><p>A healthy and clean environment is a place for healthy production and consumption. Environmental quality decreases due to human intervention. In addition to human activities, the type of governance system and political regime causes the destruction or improvement of the environment. This paper investigates the role of political stability on weak sustainability in a sample of 43 Asian countries during 2000–2019. Control variables are government size, trade, and population density. Based on the results, political stability and lack of violence have positive effect on Adjusted Net Saving (ANS) ratio, while government size affects ANS negatively. In addition, trade impacts on weak sustainability in the current sample, rejecting pollution haven hypothesis. The effect of population density on ANS is positive. This effect is unexpected which requires further studies. According to our findings, Asian countries should follow stabilizing policies, especially in political races and campaigns.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100007"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259622000054/pdfft?md5=2bd8db84be782b1cd34f7f4033728969&pid=1-s2.0-S2667259622000054-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81869629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Local integrated air quality predictions from meteorology (2015 to 2020) with machine and deep learning assisted by data mining 利用数据挖掘辅助的机器和深度学习，从气象学(2015年至2020年)进行当地综合空气质量预测

Sustainability Analytics and Modeling Pub Date : 2022-01-01 DOI: 10.1016/j.samod.2021.100002

David A. Wood

{"title":"Local integrated air quality predictions from meteorology (2015 to 2020) with machine and deep learning assisted by data mining","authors":"David A. Wood","doi":"10.1016/j.samod.2021.100002","DOIUrl":"10.1016/j.samod.2021.100002","url":null,"abstract":"<div><p>Overall air quality local indices can usefully be established by combining normalised values of common individual pollutant values. This reveals distinctive seasonal trends that are strongly influenced by local meteorological conditions. A newly compiled dataset for 2015 to 2020 covering Dallas County (USA), combining six pollutants into a combined local area benchmark (CLAB), is assessed in terms of eleven meteorological variables. It is possible to distinguish the effects of lock-down induced impacts in the CLAB index and some of its component pollutants during 2020. Nine machine learning and three deep learning algorithms are compared in their abilities to predict CLAB from the meteorological variables on supervised and unseen bases. Prediction results for 2019 and 2020 are distinctive for annual and quarterly timeframes. In-depth prediction outlier analysis using a transparent data-matching algorithm provides insight to the few data records for which CLAB is not accurately predicted from ground-level meteorological data.</p></div>","PeriodicalId":101193,"journal":{"name":"Sustainability Analytics and Modeling","volume":"2 ","pages":"Article 100002"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667259621000023/pdfft?md5=37b03ebad1223de314e4249cea1bb045&pid=1-s2.0-S2667259621000023-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86120157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7