The impacts of decision uncertainty on municipal solid waste management

Factors in enhancing environmental governance for marine plastic litter abatement in Manila, the Philippines: A combined structural equation modeling and DPSIR framework

This empirical study examines the factors enhancing environmental governance for marine plastic litter abatement in Manila, the Philippines. We use a combined covariance-based hybrid structural equation modeling (SEM) and DPSIR framework, with data collected via an online survey from 456 barangays in Manila, the Philippines. The survey was processed and analyzed using a combined model, validated through interviews and focused group discussions. With Higher-Order Model good internal consistency (0.917) and achieved measures of CFI (0.992), RMSEA (0.036), and SRMR (0.019), the findings revealed that environmental governance (COVID-19 waste), community participation, socio-economic factors, and solution measures have positively affected marine plastic litter (MPL) abatement. Notwithstanding, environmental governance (SWM policies and guidelines) has a negative impact on MPL abatement. There is, however, no link between waste infrastructure and MPL abatement. The findings provide significant perspectives in Manila to enhance environmental governance for MPL abatement. This paper presents policy-actions implications drawn from DPSIR-SEM.

Practical Challenges and Opportunities for Marine Plastic Litter Reduction in Manila: A Structural Equation Modeling

Land-based plastic pollution has increased to the level of an epidemic due to improper plastic waste management, attributed to plastic waste flux into the marine environment. The extant marine plastic litter (MPL) literature focuses primarily on the monitoring and assessment of the problem, but it fails to acknowledge the link between the challenges and opportunities for MPL reduction. The study aimed to examine the practical challenges and opportunities influencing the reduction of marine plastic litter in Manila in the Philippines. Data collected through an online survey from 426 barangays were analyzed using structural equation modeling (SEM) and were then validated using interviews and focused group discussions. Good internal consistency (0.917) and convergent and discriminant validity were achieved. The empirical study has established structural model fit measures of RMSEA (0.021), SRMR (0.015), CFI (0.999), and TLI (0.994), with a good parsimonious fit of the chi-square/degrees of freedom ratio of 1.190. The findings revealed that environmental governance regarding waste management policies and guidelines, COVID-19 regulations for waste management, community participation, and socio-economic activities have positively affected marine plastic litter leakage and solution measures. Environmental governance significantly and partially mediates the effects of, e.g., COVID-19-related waste and socio-economic activities on MPL leakage. However, there is no relationship between the waste management infrastructure and environmental governance. The findings shed light on how to enhance environmental governance to reduce marine plastic litter and address Manila’s practical challenges.

A hybrid mathematical modelling approach for energy generation from hazardous waste during the COVID-19 pandemic

The COVID-19 virus in a short time has caused a terrible crisis that has been spread around the world. This crisis has affected human life in several dimensions, one of which is a sharp increase in urban waste. This increase in waste volume during the pandemic, in addition to the intense increase in costs associated with the risks of virus contagion through infectious waste. In this study, a hybrid mathematical modelling approach including a Bi-level programming model for infectious waste management has been proposed. At the higher level of the model, government decisions regarding the total costs related to infectious waste must be minimized. At this level, the collected infectious waste is converted into energy, the revenue of which is returned to the system. The lower level relates to the risks of virus contagion through infectious waste, which can be catastrophic if ignored. This study has considered the low, medium, high and very high prevalence scenarios as key parameters for the production of waste. In addition, the uncertainty in citizens' demand for waste collection was also included in the proposed model. The results showed that by energy production from waste during the COVID-19 pandemic, 34% of the total cost of collecting and transporting waste can be compensated. Finally, this paper obtained useful managerial insights using the data of Kermanshah city as a real case.

Hazardous infectious waste collection and government aid distribution during COVID-19: A robust mathematical leader-follower model approach

The outbreak of COVID-19 has posed significant challenges to governments across the world. The increase in hazardous infectious waste (HIW) caused by the pandemic is associated with the risk of transmitting the virus. In this study, hazardous waste includes infectious waste generated both by individuals and by hospitals during the COVID-19 pandemic. To control the outbreak by maintaining social distance and home quarantine protocols, daily necessities and health supplies must be provided to the people affected. Governments play an essential role in the management of the crisis, creating an elaborate plan for collecting HIW and providing necessities and health supplies. This paper proposes a leader-follower approach for hazardous infectious waste collection and government aid distribution to control COVID-19. At the top level of the model, government policies are designed to support people by distributing daily necessities and health supplies, and to support contractors by waste collection. The lower level of the model is related to the operational decisions of contractors with limited capacities. Due to the potential risk of virus transmission via contaminated waste, the proposed model considers the complications imposed on contractors at the lower level. Applying a stochastic programming approach, four possible scenarios are examined, dependent of the severity of the outbreak. As a solution approach, the Benders decomposition method is combined with Karush-Kuhn-Tucker conditions. The results show that government support, in addition to much better management of citizen demand, can control the spread of the virus by implementing quarantine decisions.

A novel cooperative model in the collection of infectious waste in COVID-19 pandemic

Purpose

Production of waste has been increased exponentially due to world industrialization and urban and machine life expansion. On the other hand, the outbreak of the COVID-19 coronavirus quickly became a global crisis. This crisis has added a large amount of waste to urban waste. The purpose of this study is to create cooperation between municipal waste collector contractors.

Design/methodology/approach

Thus, a mathematical model is proposed under uncertain conditions, which includes the volume of municipal waste and infectious waste including personal protective equipment and used equipment for patients. To reduce total costs, the results are evaluated with four cooperative game theory methods such as Shapley value, t value, core center and least core. Ultimately, the saved cost by cooperation in each coalition is allocated fairly among the contractors. Finally, a comparison was made between the solution methods based on the value of the objective function and the solution time.

Findings

The results indicate that the proposed cooperative method increases cost savings and reduces the fine of residual waste. Therefore, it can be mentioned that this kind of cooperation would finally result in more incentives for contractors to form larger coalitions. Genetic algorithms were used to solve the large-scale model.

Originality/value

The proposed model boosts the current understanding of waste management in the COVID-19 pandemic. The paper adds additional value by unveiling some key future research directions. This guidance may demonstrate possible existing and unexplored gaps so that researchers can direct future research to develop new processes.

Bayesian decision network modeling for environmental risk management: A wildfire case study

Environmental decision-making requires an understanding of complex interacting systems across scales of space and time. A range of statistical methods, evaluation frameworks and modeling approaches have been applied for conducting structured environmental decision-making under uncertainty. Bayesian Decision Networks (BDNs) are a useful construct for addressing uncertainties in environmental decision-making. In this paper, we apply a BDN to decisions regarding fire management to evaluate the general efficacy and utility of the approach in resource and environmental decision-making. The study was undertaken in south-eastern Australia to examine decisions about prescribed burning rates and locations based on treatment and impact costs. Least-cost solutions were identified but are unlikely to be socially acceptable or practical within existing resources; however, the statistical approach allowed for the identification of alternative, more practical solutions. BDNs provided a transparent and effective method for a multi-criteria decision analysis of environmental management problems.

Implementing Circular Economy in municipal solid waste treatment system using P-graph

Municipal solid waste (MSW) is one of the issues associated with the growth of economic and urban population. The aim of this study is to develop an integrated design of waste management systems in support of a Circular Economy by P-graph (a bipartite graphical optimisation tool) as an effective decision support tool. The case study considers four MSW compositions based on different country income levels. Solving the P-graph model identifies the most suitable treatment approaches, considering the economic balance between the main operating cost, type, yield, quality of products, as well as the GHG emission (externality cost). The identification of near-optimal solutions by P-graph is useful in dealing with the trade-offs between conflicting objectives, e.g. local policy and practical implementation, that are difficult to monetise. For a lower-income country, the optimal solution includes a combination of at source separation, recycling, incineration (heat, electricity), anaerobic digestion (biofuel, digestate) and the landfill. It avoids an estimated 411 kg CO_2eq/t of processed MSW and achieves a potential profit of 42 €/t of processed MSW. The optimisation generally favours mechanical biological treatment as the country income level rises, which affects the composition of the MSW. The relative prices of biofuel, electricity and heat (>20%) cause a significant impact on the highest-ranking treatment structure and overall profit. This study shows that the developed framework by P-graph is an effective tool for MSW systems planning. For future study, localised data inputs can be fed into the proposed framework for a customised solution and economic feasibility assessment.

Switching Coal-Fired Thermal Power Plant to Composite Fuel for Recovering Industrial and Municipal Waste: Combustion Characteristics, Emissions, and Economic Effect

Combustion characteristics were studied experimentally for single droplets of fuel slurries based on wet coal processing waste with municipal solid waste components (cardboard, plastic, rubber, and wood) and used turbine oil. We established the ignition delay time for three various groups of fuel compositions in motionless air at 600–1000 °C. The minimum values are 3 s, and the maximum ones are 25 s. The maximum temperatures in the droplet vicinity reach 1300 °C during fuel combustion for compositions with 10% of used oil. The combustion temperatures of fuel compositions without oil are 200–300 °C lower. The concentrations of anthropogenic emissions in flue gases do not exceed those from dry coal combustion. Adding used oils to composite fuels reduces the concentrations of dioxins and furans in flue gases when municipal solid waste in the fuel burns out due to high combustion temperatures. Based on the experimental research findings, we have elaborated a strategy of combined industrial and municipal waste recovery by burning it as part of composite fuels, as illustrated by three neighboring regions of the Russian Federation with different industrial structures and levels of social development. This strategy suggests switching three typical coal-fired thermal power plants (one in each of the regions) to composite liquid fuel. It will reduce the hazard of waste to the environment and decrease the consumption of high-quality coals for power generation. Implementing the developed strategy for 25 years will save 145 Mt of coal and recover 190–260 Mt of waste. The positive economic effect, considering the modernization of fuel handling systems at thermal power plants and the construction of a fuel preparation plant, will make up 5.7 to 6.9 billion dollars, or 65–78%, respectively, of the main costs of three thermal power plants operating on coal within the identical period.

Managing the uncertainty problems of municipal solid waste disposal

In waste management systems, several parameters such as the rate of waste production, disposal facility, treatment cost, and their relations may be uncertain and can influence the associated optimization processes. These uncertainty problems in waste management were addressed by using various inexact programming methods. For example, fuzzy, stochastic programming, and interval programming techniques were generally used for solving the uncertainty-related waste management problems. The analysis revealed that the efficiency of waste management system can be maximized by the proper use of these optimization techniques. In this approach, an uncertainty problem is reduced into several subproblems with sureness by using the minimax regret optimization technique. And these subproblems are focused on a calculation where the lament of not getting the goal is minimized. The analysis also revealed that the fuzzy-stochastic method was increasingly used for dealing with the waste management system uncertainty in recent times. This paper gives an overview of dealing with the uncertainty problems of waste disposal in urban areas. An indication of the solid waste disposal problems and its management in conjunction with the repercussion of the investigation is described. The rationale and setting of the uncertainty issues in proper waste management are detailed. The applications of fuzzy analysis approach and integrated waste management in dealing with the uncertainty problems are presented. The applications of these techniques in diverse case studies worldwide are discussed and finally, the conclusions of the literature analysis are summarized.