Special Issue "Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics"

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 November 2021.

Special Issue Editors

Prof. Dr. Julian Scott Yeomans
E-Mail Website
Guest Editor
Schulich School of Business, York University, Toronto, ON M3J 1P3, Canada
Interests: environmental informatics; simulation–optimization; machine learning; visual analytics; waste management
Dr. Mariia Kozlova
E-Mail Website
Guest Editor
School of Business and Management, LUT University, FI-53851 Lappeenranta, Finland
Interests: investment valuation; real options; simulation and system dynamics; multicriteria decision-making; renewable energy; climate change mitigation; mining; R&D

Special Issue Information

Dear Colleagues,

This Special Issue seeks applied computational analytics papers that either create new methods or provide innovative applications of existing methods to assist with sustainability analysis and environmental decision-making applications. In practice, environmental analytics is an integration of science, methods, and techniques that involves a combination of computers, computational intelligence, information technology, mathematical modelling, and system science to assess real-world, sustainability, and environmental problems. Contributions to this Special Issue should investigate novel approaches of computational analytics—be it on the side of modelling, computational solution procedures, optimization, simulation, and/or technologies—as applied to sustainability analysis and environmental decision-making. In line with the aims and scope of the Special Issue, manuscripts should emphasize both the practical relevance and the methodological contributions of the work to environmental decision-making and sustainability analysis.

Topics can include:

  • Applied computational and visual analytics procedures;
  • Simulation, optimization, and metaheuristic approaches used for environmental decision support;
  • Machine learning, information technology, and expert systems for environmental applications;
  • Methods for guidance and assistance in environmental decision-making;
  • Measures for coping with uncertainty in data, models, and decision-making;
  • Multicriteria decision making;
  • Areas of application can include all areas of environmental decision-making and sustainability, such as waste, water, energy, climate change, industrial ecology, resource recovery, and recycling.

Prof. Dr. Julian Scott Yeomans
Dr. Mariia Kozlova
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • environmental decision-making
  • simulation
  • optimization
  • computational analytics
  • visual analytics
  • sustainability
  • analysis
  • waste management
  • water resource planning
  • energy
  • climate change
  • industrial ecology
  • resource recovery
  • recycling

Published Papers (3 papers)

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Research

Article
Analytical Models for Seawater and Boron Removal through Reverse Osmosis
Sustainability 2021, 13(16), 8999; https://doi.org/10.3390/su13168999 - 11 Aug 2021
Viewed by 355
Abstract
Regarding the purification of seawater, it is necessary to reduce both the total concentration of salt and also the concentration of boron to meet purity requirements for safe drinking water. For this purpose reverse osmosis membrane modules can be designed based on experimental [...] Read more.
Regarding the purification of seawater, it is necessary to reduce both the total concentration of salt and also the concentration of boron to meet purity requirements for safe drinking water. For this purpose reverse osmosis membrane modules can be designed based on experimental data supported by computer models to determine energy efficient configurations and operating conditions. In previous studies numerical models have been suggested to predict the performance of the removal with respect to difference pressures, pH values, and temperatures. Here, an analytical model is suggested which allows for both the simplified fitting of the parameters required for predicting boron transport coefficients and also the simple equations that can be used for the design of combined seawater and boron removal systems. This modelling methodology is demonstrated through two case studies including FilmTec and Saehan membrane modules. For both cases the model is shown to be able to predict the performance with similar accuracy compared with existing finite-difference type numerical models from the literature. Full article
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Article
A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China
Sustainability 2021, 13(15), 8341; https://doi.org/10.3390/su13158341 - 26 Jul 2021
Viewed by 354
Abstract
Effective management of an urban solid waste system (USWS) is crucial for balancing the tradeoff between economic development and environment protection. A factorial ecological-extended physical input-output model (FE-PIOM) was developed for identifying an optimal urban solid waste path in an USWS. The FE-PIOM [...] Read more.
Effective management of an urban solid waste system (USWS) is crucial for balancing the tradeoff between economic development and environment protection. A factorial ecological-extended physical input-output model (FE-PIOM) was developed for identifying an optimal urban solid waste path in an USWS. The FE-PIOM integrates physical input-output model (PIOM), ecological network analysis (ENA), and fractional factorial analysis (FFA) into a general framework. The FE-PIOM can analyze waste production flows and ecological relationships among sectors, quantify key factor interactions on USWS performance, and finally provide a sound waste production control path. The FE-PIOM is applied to managing the USWS of Fujian Province in China. The major findings are: (i) waste is mainly generated from primary manufacturing (PM) and advanced manufacturing (AM), accounting for 30% and 38% of the total amount; (ii) AM is the biggest sector that controls the productions of other sectors (weight is from 35% to 50%); (iii) the USWS is mutualistic, where direct consumption coefficients of AM and PM are key factors that have negative effects on solid waste production intensity; (iv) the commodity consumption of AM and PM from other sectors, as well as economic activities of CON, TRA and OTH, should both decrease by 20%, which would be beneficial to the sustainability of the USWS. Full article
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Article
A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China
Sustainability 2021, 13(9), 4627; https://doi.org/10.3390/su13094627 - 21 Apr 2021
Cited by 1 | Viewed by 503
Abstract
In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as [...] Read more.
In this study, a C-vine copula-based quantile regression (CVQR) model is proposed for forecasting monthly streamflow. The CVQR model integrates techniques for vine copulas and quantile regression into a framework that can effectively establish relationships between the multidimensional response-independent variables as well as capture the upper tail or asymmetric dependence (i.e., upper extreme values). The CVQR model is applied to the Xiangxi River basin that is located in the Three Gorges Reservoir area in China for monthly streamflow forecasting. Multiple linear regression (MLR) and artificial neural network (ANN) are also compared to illustrate the applicability of CVQR. The results show that the CVQR model performs best in the calibration period for monthly streamflow prediction. The results also indicate that MLR has the worst effects in extreme quantile (flood events) and confidence interval predictions. Moreover, the performance of ANN tends to be overestimated in the process of peak prediction. Notably, CVQR is the most effective at capturing upper tail dependences among the hydrometeorological variables (i.e., floods). These findings are very helpful to decision-makers in hydrological process identification and water resource management practices. Full article
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