Sustainability Analytics and Modeling最新文献

Singapore is a foremost tourist destination country experiencing continuous economic growth and rapid urbanization which is causing higher energy consumption and carbon dioxide (CO₂) emissions. This study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, and tourism on CO₂ 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 CO₂ 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% CO₂ 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 CO₂ 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 CO₂ 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.

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.

The creation of protected areas is an important public policy strategy for protecting species, as mentioned in the Aichi targets. This paper formulates spatial integer programming (set covering) models to protect both rare species (arbitrarily defined here as species breeding in 1% or less of the territory) and common species. Spatial constraints are used to form buffer zones around core areas that protect rare species, and cost-efficient corridors linking these buffered cores are then designed. The models are applied on a portion of the State of Oregon and results are evaluated in light of the expected United Nations post-2020 targets for area-based conservation measures. It is estimated that such results aimed at covering all species and enhancing protection of at-risk species seem locally consistent with the global biodiversity objectives for 2030.

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.

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.

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 $\lambda$ 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.

This paper deals with a dual channel inventory model where capacity of the market of a particular product is uncertain. The demand rates of the traditional consumers and the online consumers are segregated from total capacity of the market. The trust vale and acceptance rate of the product by the online consumers and utility functions comprising of value of the product, trust value of the online purchasing and some costs associated with risk, traffic, opportunities, etc., determine the demand rates of the products in offline and online channel. In this model, one retailer has offline and online options to sale the products. The objective of the retailer is to find out optimal pricing, order lot size and reorder point for maximizing jointly the average expected profit from offline and online channel. A mathematical model is analyzed to find out the optimal values of variables. Finally, a numerical example is illustrated to justify the proposed model.