Industrial Chemistry
Open Access

Sustainable waste management; Multi-criteria decision-making (MCDM); Municipal solid waste (MSW); Recycling and composting; Waste-to-energy

Introduction

Municipal solid waste management (MSWM) is one of the most critical challenges faced by cities worldwide, especially as rapid urbanization continues to increase the amount of waste generated daily. Sustainable management of municipal solid waste (MSW) is essential for minimizing the environmental impact, conserving natural resources, and protecting public health [1-4]. To address the complexity of sustainable waste management, multi-criteria decision-making (MCDM) methods offer a structured and systematic approach to evaluate and choose among different strategies. These methods can help local governments and waste management authorities balance environmental, economic, and social factors while achieving sustainability goals [5-7].

Understanding municipal solid waste and sustainability

Municipal solid waste (MSW) typically refers to waste generated from households, commercial establishments, institutions, and small-scale industries. It includes a wide range of materials, such as food waste, paper, plastic, metals, glass, and organic matter. Managing this waste stream in a sustainable manner is crucial for preventing pollution, reducing greenhouse gas emissions, minimizing land use for landfills, and fostering a circular economy [8].

Challenges in municipal solid waste management

Traditional waste management practices, such as landfilling and incineration, have led to environmental and public health concerns. Landfills often result in soil and groundwater contamination, and they produce methane emissions, a potent greenhouse gas. Incineration, while reducing the volume of waste, can generate harmful air pollutants and toxic ash. The limitations of these conventional methods have pushed cities toward exploring more sustainable waste management approaches [9-10].

However, implementing sustainable waste management systems presents numerous challenges:

Financial constraints: Sustainable waste management infrastructure, such as advanced recycling facilities, composting plants, and waste-to-energy technologies, requires significant capital investment. This can be a major barrier for municipalities with limited budgets.

Public participation: Sustainable waste management relies heavily on the active participation of citizens in waste segregation, recycling, and reduction practices. Public awareness and engagement are essential to the success of any eco-friendly waste management program.

Technical feasibility: Not all technologies are universally applicable due to variations in waste composition, local climate conditions, and availability of resources.

Regulatory and policy support: Municipalities need strong regulations and policies that promote sustainability, such as extended producer responsibility (EPR), incentives for waste reduction, and penalties for non-compliance.

Given the complex interplay of these factors, a systematic approach is required to evaluate the trade-offs between different waste management options and identify the most sustainable solution.

Multi-criteria decision-making (MCDM) methods

Multi-criteria decision-making (MCDM) is a set of methods used to evaluate and prioritize multiple conflicting criteria in decision-making processes. In the context of MSWM, MCDM methods help stakeholders assess various waste management options based on a range of environmental, economic, social, and technical criteria. These methods are particularly useful because they allow decision-makers to consider both quantitative and qualitative factors.

Some of the common MCDM methods used in sustainable waste management include:

Analytic hierarchy process (AHP): AHP is a widely used MCDM method that helps prioritize alternatives by breaking down complex decisions into a hierarchy of criteria and sub-criteria. It involves pairwise comparisons of criteria to assign relative weights, which are then used to rank the alternatives.

Technique for order of preference by similarity to ideal solution (TOPSIS): TOPSIS evaluates alternatives based on their distance from an ideal solution (the best possible option) and a negative ideal solution (the worst possible option). Alternatives are ranked based on their relative closeness to the ideal solution.

Elimination and choice translating reality (ELECTRE): ELECTRE is a family of outranking methods that compare alternatives based on their performance in relation to criteria. It identifies alternatives that outperform others in most criteria and eliminates those that perform poorly.

Multi-attribute utility theory (MAUT): MAUT evaluates alternatives based on the utility or satisfaction they provide with respect to different criteria. Each alternative is assigned a utility score based on how well it satisfies the criteria.

Fuzzy logic approaches: Fuzzy logic-based MCDM methods are used when there is uncertainty or imprecision in the criteria or alternatives. These methods apply fuzzy sets and membership functions to evaluate alternatives in scenarios with incomplete or ambiguous data.

Application of MCDM in sustainable waste management

MCDM methods are highly versatile and can be applied to various stages of the waste management decision-making process, including waste collection, treatment, and disposal. Here’s how MCDM can be applied to assess eco-friendly municipal solid waste management options:

Defining objectives and criteria

The first step in applying MCDM is to define the objectives and criteria for decision-making. In sustainable MSWM, common objectives might include reducing landfill waste, minimizing greenhouse gas emissions, lowering operational costs, and promoting resource recovery.

Environmental criteria: Greenhouse gas emissions, energy consumption, resource recovery rate, reduction in land use for landfills, etc.

Economic criteria: Capital costs, operational and maintenance costs, revenue from recycled materials, etc.

Social criteria: Public health impact, job creation, social acceptance, etc.

Technical criteria: Reliability, scalability, adaptability to local conditions, etc.

Identifying and evaluating alternatives

Next, different waste management alternatives are identified and evaluated against the criteria. Common eco-friendly alternatives include:

Recycling and material recovery: Sorting and processing recyclable materials to divert them from landfills.

Composting and anaerobic digestion: Organic waste is processed to create compost or biogas, which can be used as fertilizer and energy, respectively.

Waste-to-energy (WTE) technologies: Technologies like incineration, gasification, or pyrolysis convert waste into energy, thereby reducing the volume of waste sent to landfills and generating electricity or heat.

Source reduction and reuse: Promoting waste reduction at the source by encouraging minimal packaging, reusable containers, and repair of goods.

Each alternative is assessed based on the previously defined criteria using the selected MCDM method. For example, AHP could be used to rank alternatives based on their environmental and economic performance, while TOPSIS could compare the alternatives’ distance from the ideal scenario of sustainability. Ranking and Selecting the Optimal Solution. Once the alternatives are evaluated, MCDM methods provide a ranking that helps decision-makers identify the most sustainable waste management option. The alternative that best satisfies the criteria and achieves the highest ranking is recommended for implementation.

Sensitivity analysis

One of the key advantages of MCDM methods is the ability to conduct sensitivity analysis. This allows decision-makers to assess how changes in the weight or importance of criteria affect the ranking of alternatives. For example, if public health impacts are given higher importance, the ranking of waste management options may change, leading to a different optimal solution.

Conclusion

Sustainable municipal solid waste management is essential for addressing the environmental and social challenges posed by urbanization and increasing waste generation. Multi-criteria decision-making (MCDM) methods provide a robust framework for evaluating and selecting eco-friendly waste management options. By systematically analyzing different waste management alternatives based on environmental, economic, social, and technical criteria, MCDM helps local authorities make informed and balanced decisions that promote sustainability and public welfare. As cities continue to grow, adopting MCDM approaches in waste management will be crucial for achieving long-term sustainability goals.

References

1. Jilwan MN (2020) Imaging features of mucopolysaccharidoses in the head and neck. Int J Pediatr Otorhinolaryngol 134: 110022.

Indexed at, Google Scholar, Crossref

2. Grabowski GA (2012) Gaucher disease and other storage disorders. Am Soc Program 2012: 13-8.

Indexed at, Google Scholar, Crossref

3. Murugesan V, Chuang WL, Liu J, Lischuk A, Kacena K, et al. (2016) Glucosylsphingosine is a key biomarker of Gaucher disease. Am J Hematol 11: 1082-1089.

Indexed at, Google Scholar, Crossref

4. Bultron G, Kacena K, Pearson D, Boxer M, Yang M, et al. (2010) The risk of Parkinson’s disease in type 1 Gaucher disease. J Inherit Metab Dis 33: 167-173.

Indexed at, Google Scholar, Crossref

5. Horowitz M, Wilder S, Horowitz Z, Reiner O, Gelbart T, et al. (1989) The human glucocerebrosidase gene and pseudogene: structure and evolution. BMC research notes 4: 87-96.

Indexed at, Google Scholar, Crossref

6. Winfield SL, Tayebi N, Martin BM, Ginns EI, Sidransky E et al. (1997) Identification of three additional genes contiguous to the glucocerebrosidase locus on chromosome 1q21: implications for Gaucher disease. Genome Res 7: 1020-1026.

Indexed at, Google Scholar, Crossref

7. Koprivica V, Stone DL, Park JK, Callahan M, Frisch A, et al. (2000) Analysis and classification of 304 mutant alleles in patients with type 1 and type 3 Gaucher disease. Am J Hum Genet 66: 1777-1786.

Indexed at, Google Scholar, Crossref

8. Zhang J, Chen H, Kornreich R, Yu C (2019) Prenatal Diagnosis of Tay-Sachs Disease. Methods Mol Biol 1885: 233-250.

Indexed at, Google Scholar, Crossref

9. Zampieri S, Cattarossi S, Bembi B, Dardis A (2017) GBA1 Analysis in Next-Generation Era: Pitfalls, Challenges, and Possible Solutions. J Mol Diagnost 19: 733-741.

Indexed at, Google Scholar, Crossref

10. Yoshida S, Kido J, Matsumoto S, Momosaki K, Mitsubuchi H, et al. (1990) Prenatal diagnosis of Gaucher disease using next-generation sequencing. Pediatr Int 58: 946-9.

Indexed at, Google Scholar, Crossref