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

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 33128

Printed Edition Available!
A printed edition of this Special Issue is available here.

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 decomposition; simulation-optimization; machine learning; visual analytics; waste management
Special Issues, Collections and Topics in MDPI journals
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 submissions that pass pre-check are 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 2400 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 (15 papers)

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Editorial

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5 pages, 171 KiB  
Editorial
Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics
by Mariia Kozlova and Julian Scott Yeomans
Sustainability 2022, 14(3), 1655; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031655 - 31 Jan 2022
Cited by 1 | Viewed by 3210
Abstract
In practice, environmental analytics involves an integration of science, methods, and techniques involving a combination of computers, computational intelligence, information technology, mathematical modelling, and system science to address “real-world” environmental and sustainability problems [...] Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)

Research

Jump to: Editorial

20 pages, 4936 KiB  
Article
A Fuzzy-Interval Dynamic Optimization Model for Regional Water Resources Allocation under Uncertainty
by Meiqin Suo, Feng Xia and Yurui Fan
Sustainability 2022, 14(3), 1096; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031096 - 18 Jan 2022
Cited by 7 | Viewed by 1790
Abstract
In this study, a fuzzy-interval dynamic programming (FIDP) model is proposed for regional water management under uncertainty by combining fuzzy-interval linear programming (FILP) and dynamic programming (DP). This model can not only tackle uncertainties presented as intervals, but also consider the dynamic characteristics [...] Read more.
In this study, a fuzzy-interval dynamic programming (FIDP) model is proposed for regional water management under uncertainty by combining fuzzy-interval linear programming (FILP) and dynamic programming (DP). This model can not only tackle uncertainties presented as intervals, but also consider the dynamic characteristics in the allocation process for water resources. Meanwhile, the overall satisfaction from users is considered in the objective function to solve the conflict caused by uneven distribution of resources. The FIDP model is then applied to the case study in terms of water resources allocation under uncertainty and dynamics for the City of Handan in Hebei Province, China. The obtained solutions can provide detailed allocation schemes and water shortage rates at different stages. The calculated comprehensive benefits of economy, water users’ satisfaction and pollutant discharge (i.e., COD) are [2264.72, 2989.33] × 108 yuan, [87.50, 96.50] % and [1.23, 1.65] × 108 kg respectively with a plausibility degree (i.e., λopt±) ranging within [0.985, 0.993]. Moreover, the benefit from FIDP model under consideration of dynamic features is more specific and accurate than that of FILP model, whilst the water shortage rate from FIDP is [5.10, 9.10] % lower than that of FILP model. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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12 pages, 2360 KiB  
Article
Effects of Weather on Iowa Nitrogen Export Estimated by Simulation-Based Decomposition
by Vishal Raul, Yen-Chen Liu, Leifur Leifsson and Amy Kaleita
Sustainability 2022, 14(3), 1060; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031060 - 18 Jan 2022
Cited by 5 | Viewed by 1236
Abstract
The state of Iowa is known for its high-yield agriculture, supporting rising demands for food and fuel production. But this productivity is also a significant contributor of nitrogen loading to the Mississippi River basin causing the hypoxic zone in the Gulf of Mexico. [...] Read more.
The state of Iowa is known for its high-yield agriculture, supporting rising demands for food and fuel production. But this productivity is also a significant contributor of nitrogen loading to the Mississippi River basin causing the hypoxic zone in the Gulf of Mexico. The delivery of nutrients, especially nitrogen, from the upper Mississippi River basin, is a function, not only of agricultural activity, but also of hydrology. Thus, it is important to consider extreme weather conditions, such as drought and flooding, and understand the effects of weather variability on Iowa’s food-energy-water (IFEW) system and nitrogen loading to the Mississippi River from Iowa. In this work, the simulation decomposition approach is implemented using the extended IFEW model with a crop-weather model to better understand the cause-and-effect relationships of weather parameters on the nitrogen export from the state of Iowa. July temperature and precipitation are used as varying input weather parameters with normal and log normal distributions, respectively, and subdivided to generate regular and dry weather conditions. It is observed that most variation in the soil nitrogen surplus lies in the regular condition, while the dry condition produces the highest soil nitrogen surplus for the state of Iowa. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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12 pages, 427 KiB  
Article
Technical Advances in Aviation Electrification: Enhancing Strategic R&D Investment Analysis through Simulation Decomposition
by Mariia Kozlova, Timo Nykänen and Julian Scott Yeomans
Sustainability 2022, 14(1), 414; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010414 - 31 Dec 2021
Cited by 8 | Viewed by 3527
Abstract
Computational decision-making in “real world” environmental and sustainability contexts frequently requires the need to contrast numerous uncertain factors and difficult-to-capture dimensions. Monte Carlo simulation modelling has frequently been employed to integrate the uncertain inputs and to construct probability distributions of the resulting outputs. [...] Read more.
Computational decision-making in “real world” environmental and sustainability contexts frequently requires the need to contrast numerous uncertain factors and difficult-to-capture dimensions. Monte Carlo simulation modelling has frequently been employed to integrate the uncertain inputs and to construct probability distributions of the resulting outputs. Visual analytics and data visualization can be used to support the processing, analyzing, and communicating of the influence of multi-variable uncertainties on the decision-making process. In this paper, the novel Simulation Decomposition (SimDec) analytical technique is used to quantitatively examine carbon emission impacts resulting from a transformation of the aviation industry toward a state of greater airline electrification. SimDec is used to decompose a Monte Carlo model of the flying range of all-electric aircraft based upon improvements to batteries and motor efficiencies. Since SimDec can be run concurrently with any Monte Carlo model with only negligible additional overhead, it can easily be extended into the analysis of any environmental application that employs simulation. This generalizability in conjunction with its straightforward visualizations of complex stochastic uncertainties makes the practical contributions of SimDec very powerful in environmental decision-making. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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15 pages, 439 KiB  
Article
Why Do Companies Need Operational Flexibility to Reduce Waste at Source?
by Yara Kayyali Elalem, Isik Bicer and Ralf W. Seifert
Sustainability 2022, 14(1), 367; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010367 - 30 Dec 2021
Viewed by 2526
Abstract
We analyze the environmental benefits of operational flexibility that emerge in the form of less product waste during the sourcing process by reducing overproduction. We consider three different options for operational flexibility: (1) lead-time reduction, (2) quantity-flexibility contracts, and (3) multiple sourcing. We [...] Read more.
We analyze the environmental benefits of operational flexibility that emerge in the form of less product waste during the sourcing process by reducing overproduction. We consider three different options for operational flexibility: (1) lead-time reduction, (2) quantity-flexibility contracts, and (3) multiple sourcing. We use a multiplicative demand process to model the evolutionary dynamics of demand uncertainty. We then quantify the impact of key modeling parameters for each operational-flexibility strategy on the waste ratio, which is measured as the ratio of excess inventory when a certain operational-flexibility strategy is employed to the amount when an offshore supplier is utilized without any operational flexibility. We find that the lead-time reduction strategy has the maximum capability to reduce waste in the sourcing process of buyers, followed by the quantity-flexibility and multiple-sourcing strategies, respectively. Thus, our results indicate that operational-flexibility strategies that rely on the localization of production are key to reducing waste and improving environmental sustainability at source. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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15 pages, 3612 KiB  
Article
Ex-Ante Study of Biofuel Policies–Analyzing Policy-Induced Flexibility
by Inka Ruponen, Mariia Kozlova and Mikael Collan
Sustainability 2022, 14(1), 147; https://0-doi-org.brum.beds.ac.uk/10.3390/su14010147 - 23 Dec 2021
Cited by 1 | Viewed by 1973
Abstract
A variety of policy types are available to foster the transition to a low-carbon economy. In every sector, including transportation, heat and power production, policymakers face the choice of what type of policy to adopt. For this choice, it is crucial to understand [...] Read more.
A variety of policy types are available to foster the transition to a low-carbon economy. In every sector, including transportation, heat and power production, policymakers face the choice of what type of policy to adopt. For this choice, it is crucial to understand how different mechanisms incentivize investments in terms of improving their profitability, shaping the flexibility available for investors, and how they are affected by the surrounding uncertainty. This paper focuses on transportation-biofuel policies, particularly on the financial incentives put on the bio-component of fuel and the combination of using penalties and tax-relief. Delivery of vital policymaking insights by using two modern simple-to-use profitability analysis methods, the pay-off method and the simulation decomposition method, is illustrated. Both methods enable the incorporation of uncertainty into the profitability analyses, and thus generate insight about the flexibilities involved, and the factors affecting the results. The results show that the combination of penalties and tax-relief is a way to steer fuel-production towards sustainability. The two methods used for analysis complement each other and provide important insights for analysis and decision-making beyond what the commonly used profitability analysis methods typically provide. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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18 pages, 615 KiB  
Article
Profitability Determinants of Unlisted Renewable Energy Companies in Germany—A Longitudinal Analysis of Financial Accounts
by Maria-Kristiine Luts, Jyrki Savolainen and Mikael Collan
Sustainability 2021, 13(24), 13544; https://0-doi-org.brum.beds.ac.uk/10.3390/su132413544 - 07 Dec 2021
Viewed by 2509
Abstract
The fight against a climate crisis has urged nations and the global community to cut emissions and to define ambitious environmental goals. This has highlighted the importance of the renewable energy (RE) industry. Germany has been one of the most active countries in [...] Read more.
The fight against a climate crisis has urged nations and the global community to cut emissions and to define ambitious environmental goals. This has highlighted the importance of the renewable energy (RE) industry. Germany has been one of the most active countries in RE adoption. In this vein, the purpose of this research is to study and identify key profitability determinants of unlisted German electricity-producing RE-companies, many of which have been supported by the German Feed-in Tariff (FIT). A multi-year analysis based on panel data from 783 companies for the years 2010–2018 is used. The results show that both company- and industry-specific profitability determinants are statistically significant, but the company-specific determinants seem to be more important. The results shed new light on what drives the profitability of private German RE companies during the period of financial aid from the government and are of use to managers, regulators and investors alike, e.g., when the effects of different regulatory climates and industry environments, as well as states of business life cycle are considered. Furthermore, the implications of this study have wider environmental and economic importance as the performance of the RE companies is critical in achieving the emission targets of the energy industry and ensuring a more sustainable energy production for the future. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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18 pages, 1123 KiB  
Article
Steering Renewable Energy Investments in Favor of Energy System Reliability: A Call for a Hybrid Model
by Mariia Kozlova and Alena Lohrmann
Sustainability 2021, 13(24), 13510; https://0-doi-org.brum.beds.ac.uk/10.3390/su132413510 - 07 Dec 2021
Cited by 2 | Viewed by 2167
Abstract
The global increase in electricity supply volatility due to the growing share of intermittent renewable energy sources together with recent extreme weather events draws attention to energy system reliability issues and the role of renewable energy sources within these systems. Renewable energy deployment [...] Read more.
The global increase in electricity supply volatility due to the growing share of intermittent renewable energy sources together with recent extreme weather events draws attention to energy system reliability issues and the role of renewable energy sources within these systems. Renewable energy deployment strategies have already become a key element in debates on future global energy systems. At the same time, more extensive use of renewable energy sources implies a higher dependence on intermittent power, which puts the reliability of the electricity system at risk. Policymakers are introducing measures to increase the reliability of energy systems. Paradoxically, support for renewable energy and analyses of energy system reliability have been dealt with by two different and rarely overlapping research approaches. As a result, renewable energy promotion has often been designed without accounting for system reliability. To our knowledge, a model that captures those investment incentives and allows for tuning such financial support does not exist. This paper introduces a hybrid model that can potentially steer renewable energy investments in favor of energy system reliability. We demonstrate the idea of reliability-based support for renewable energy sources in action using a stylized case. Depending on the complementarity of different renewable energy power outputs available in the system, such reliability-based support can substantially reduce the necessity for greater backup capacity, can cut the overall costs of the energy system, and can reduce its environmental footprint. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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27 pages, 8141 KiB  
Article
Accuracy and Predictive Power of Sell-Side Target Prices for Global Clean Energy Companies
by Christoph Lohrmann and Alena Lohrmann
Sustainability 2021, 13(22), 12746; https://0-doi-org.brum.beds.ac.uk/10.3390/su132212746 - 18 Nov 2021
Cited by 1 | Viewed by 1401
Abstract
Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s [...] Read more.
Target prices are often provided as a support for stock recommendations by sell-side analysts which represent an explicit estimate of the expected future value of a company’s stock. This research focuses on mean target prices for stocks contained in the Standard and Poor’s Global Clean Energy Index during the time period from 2009 to 2020. The accuracy of mean target prices for these global clean energy stocks at any point during a 12-month period (Year-Highest) is 68.1% and only 46.6% after exactly 12 months (Year-End). A random forest and an SVM classification model were trained for both a Year-End and a Year-Highest target and compared to a random model. The random forest demonstrates the best results with an average accuracy of 73.24% for the Year-End target and 81.15% for the Year-Highest target. The analysis of the variables shows that for all models the mean target price is the most relevant variable, whereas the number of target prices appears to be highly relevant as well. Moreover, the results indicate that following the rare positive predictions of the random forest for the highest target return groups (“30% to 70%” and “Above 70%”) may potentially represent attractive investment opportunities. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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14 pages, 5605 KiB  
Article
Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers
by Michael Binns and Hafiz Muhammad Uzair Ayub
Sustainability 2021, 13(21), 12191; https://0-doi-org.brum.beds.ac.uk/10.3390/su132112191 - 04 Nov 2021
Cited by 5 | Viewed by 1576
Abstract
Various modeling approaches have been suggested for the modeling and simulation of gasification processes. These models allow for the prediction of gasifier performance at different conditions and using different feedstocks from which the system parameters can be optimized to design efficient gasifiers. Complex [...] Read more.
Various modeling approaches have been suggested for the modeling and simulation of gasification processes. These models allow for the prediction of gasifier performance at different conditions and using different feedstocks from which the system parameters can be optimized to design efficient gasifiers. Complex models require significant time and effort to develop, and they might only be accurate for use with a specific catalyst. Hence, various simpler models have also been developed, including thermodynamic equilibrium models and empirical models, which can be developed and solved more quickly, allowing such models to be used for optimization. In this study, linear and quadratic expressions in terms of the gasifier input value parameters are developed based on linear regression. To identify significant parameters and reduce the complexity of these expressions, a LASSO (least absolute shrinkage and selection operator) shrinkage method is applied together with cross validation. In this way, the significant parameters are revealed and simple models with reasonable accuracy are obtained. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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15 pages, 809 KiB  
Article
Eco-Efficiency for the G18: Trends and Future Outlook
by Perry Sadorsky
Sustainability 2021, 13(20), 11196; https://0-doi-org.brum.beds.ac.uk/10.3390/su132011196 - 11 Oct 2021
Cited by 4 | Viewed by 1736
Abstract
Eco-efficiency is an important ecological indicator for tracking the progress of how countries’ environmental-adjusted economic activity changes over time. The objective of this research is to calculate country-level eco-efficiency for a group of 18 major countries (G18) that are part of the G20. [...] Read more.
Eco-efficiency is an important ecological indicator for tracking the progress of how countries’ environmental-adjusted economic activity changes over time. The objective of this research is to calculate country-level eco-efficiency for a group of 18 major countries (G18) that are part of the G20. First, the data envelope analysis (DEA) method is used to calculate eco-efficiency scores. Second, the Malmquist productivity index (MPI) is used to examine how eco-efficiency changes over time. Eco-efficiency is forecast to the year 2040 using automated forecasting methods under a business-as-usual (BAU) scenario. Over the period 1997 to 2040, eco-efficiency varies widely between these countries with some countries reporting positive growth in eco-efficiency and other countries reporting negative growth. Eco-efficiency leaders over the period 1997 to 2019 and 2019 to 2040 include Australia, Brazil, France, Germany, Great Britain, Italy, Japan, Russia, and the United States. Laggards include Canada, China, India, and Indonesia. These laggard countries recorded negative growth rates in eco-efficiency over the period 1997 to 2019 and 2019 to 2040. Negative eco-efficiency growth points to a worsening of environmental sustainability. Large variations in eco-efficiency between countries make it more difficult to negotiate international agreements on energy efficiency and climate change. For the G18 countries, the average annual change in MPI over the period 1997 to 2019 was 0.5%, while the forecasted average annual change over the period 2019 to 2040 was a 0.1% decrease. For the G18 countries, there has been little change in eco-efficiency. The G18 are an important group of developed and developing countries that need to show leadership when it comes to increasing eco-efficiency. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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22 pages, 3654 KiB  
Article
Development of a Cyberinfrastructure for Assessment of the Lower Rio Grande Valley North and Central Watersheds Characteristics
by Linda Navarro, Ahmed Mahmoud, Andrew Ernest, Abdoul Oubeidillah, Jessica Johnstone, Ivan Rene Santos Chavez and Christopher Fuller
Sustainability 2021, 13(20), 11186; https://0-doi-org.brum.beds.ac.uk/10.3390/su132011186 - 11 Oct 2021
Viewed by 1434
Abstract
Lower Laguna Madre (LLM) is designated as an impaired waterway for high concentrations of bacteria and low dissolved oxygen. The main freshwater sources to the LLM flow from the North and Central waterways which are composed of three main waterways: Hidalgo/Willacy Main Drain [...] Read more.
Lower Laguna Madre (LLM) is designated as an impaired waterway for high concentrations of bacteria and low dissolved oxygen. The main freshwater sources to the LLM flow from the North and Central waterways which are composed of three main waterways: Hidalgo/Willacy Main Drain (HWMD), Raymondville Drain (RVD), and International Boundary & Water Commission North Floodway (IBWCNF) that are not fully characterized. The objective of this study is to perform a watershed characterization to determine the potential pollution sources of each watershed. The watershed characterization was achieved by developing a cyberinfrastructure, and it collects a wide inventory of data to identify which one of the three waterways has a major contribution to the LLM. Cyberinfrastructure development using the Geographic Information System (GIS) database helped to comprehend the major characteristics of each area contributing to the watershed supported by the analysis of the data collected. The watershed characterization process started with delineating the boundaries of each watershed. Then, geospatial and non-geospatial data were added to the cyberinfrastructure from numerous sources including point and nonpoint sources of pollution. Results showed that HWMD and IBWCNF watersheds were found to have a higher contribution to the water impairments to the LLM. HWMD and IBWCNF comprise the potential major sources of water quality impairments such as cultivated crops, urbanized areas, on-site sewage facilities, colonias, and wastewater effluents. Full article
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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23 pages, 17702 KiB  
Article
Analytical Models for Seawater and Boron Removal through Reverse Osmosis
by Michael Binns
Sustainability 2021, 13(16), 8999; https://0-doi-org.brum.beds.ac.uk/10.3390/su13168999 - 11 Aug 2021
Cited by 3 | Viewed by 1594
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
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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18 pages, 8169 KiB  
Article
A Factorial Ecological-Extended Physical Input-Output Model for Identifying Optimal Urban Solid Waste Path in Fujian Province, China
by Jing Liu, Yongping Li, Gordon Huang, Yujin Yang and Xiaojie Wu
Sustainability 2021, 13(15), 8341; https://0-doi-org.brum.beds.ac.uk/10.3390/su13158341 - 26 Jul 2021
Cited by 3 | Viewed by 1760
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
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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22 pages, 4709 KiB  
Article
A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China
by Huawei Li, Guohe Huang, Yongping Li, Jie Sun and Pangpang Gao
Sustainability 2021, 13(9), 4627; https://0-doi-org.brum.beds.ac.uk/10.3390/su13094627 - 21 Apr 2021
Cited by 5 | Viewed by 2046
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
(This article belongs to the Special Issue Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics)
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