Advancing the Application of a Multidimensional Sustainable Urban Waste Management Model in a Circular Economy in Mexico City

Abstract

The increase in global municipal solid waste (MSW) generation, with a significant portion being improperly managed, has resulted in detrimental socio-ecological effects. This paper addresses the challenge of implementing effective waste management practices for achieving sustainability, particularly in urban areas where the majority of global waste is generated. It emphasizes the need for a multidimensional, multiscale, and long-term approach that surpasses local solutions and linear strategies. The approach recognizes the broader impacts of waste management beyond city boundaries and promotes circularity by incorporating waste reduction, reuse, recycling, and proper disposal practices. Through an analysis of the literature on waste from 1992 to 2022, this paper aims to identify the key concepts, propose solutions, and explore sustainable waste management scenarios. This paper introduces the m-SWM4Cities model for sustainable waste management in urban areas, highlighting its methodology and application in Mexico City (mD-SWM4CDMX). The models presented in this paper incorporate essential elements and interactions, providing a comprehensive understanding of the transition to sustainable waste management. The findings highlight the importance of monitoring waste management indicators and offer insights into the waste system of Mexico City. The m-SWM4Cities model can be adapted to address specific city contexts and thus serves as a valuable tool for assessing and improving waste management practices.

1. Introduction

A major challenge in achieving sustainability nowadays is the implementation of effective waste management practices, especially in urban areas where most of the global waste is generated [1]. At the core of such sustainable and comprehensive waste management practices lie a multidimensional, multiscale, and long-term process that goes beyond local solutions and linear approaches. From an urban perspective, this approach considers the impacts beyond city boundaries and aims to address the increasing waste volumes through circularity. It encompasses various practices such as waste reduction, reuse, refurbishment, source separation, recycling, composting, energy recovery, and proper final disposition [2].

Municipal solid waste (MSW) deposited in landfills, incinerated, or composted throughout the world has increased from 0.36 billion tons in 1960 to 2.01 billion tons in 2016 [1,2,3]. It has been estimated that at least a third of such global MSW generation in 2016 was not managed in an environmentally safe manner [3]. As shown in Figure 1, waste management is expected to be even more challenging as the amount (and complexity) of MSW will reach 3.4 billion tons annually by 2050 [3]. Since MSW projections are mostly based on population growth, it is central to recognize that any eventual change on current generation patterns may either ease or aggravate further MSW-related socio-ecological impacts.

Inadequate management of municipal solid waste (MSW) has detrimental effects on socio-ecological systems [4]. Ecologically, pollution of air, water, and soil can occur [5,6]. Socioeconomically, it can impact health, local economies, and job security [6,7,8]. For example, improperly managed waste may lead to the generation of acidic leachates that concentrate organic materials and pollutants. If these leachates are not handled properly, they can flow into water bodies and soil, causing harm to ecosystems. Decomposition of organic waste in water bodies depletes oxygen, promotes the growth of harmful organisms, and reduces nutrient availability for local biodiversity [2].

Marine pollution in oceans is also another area of concern which is impacted adversely due to mismanaged MSW, improper disposal practices of the sea vessels, and the runoff from sewage and polluted streams [3]. This challenge is strongly related to the production, consumption, and inadequate disposal of plastics (in both land and oceans). According to Geyer, Jambeck, and Lavender [9], a staggering 8.3 billion metric tons of virgin plastics have been produced globally. By 2015, approximately two-thirds of such plastic volume had already become waste, with only 9% being recycled, 12% incinerated, and a significant 79% ending up in landfills or the environment [9]. Moreover, the COVID-19 pandemic led to an increased use of single-use plastics, particularly for protective gear and packaging (related to an increasingly dynamic e-commerce system). As of 23 August 2021, COVID-19-related plastic waste was estimated to be around 8.4 ± 1.4 million tons [10]. The escalation of plastic pollution has such significant socio-ecological implications [11,12] that 175 nations have agreed to develop a legally binding agreement by 2024 (UN Environment Assembly—UNEA-5 resolution of March 2022).

Likewise, open waste burning generates toxins and suspended particulate matter that may induce or exacerbate respiratory and neurological diseases [13], while other treatment processes can also have negative environmental impacts [14]. Also, dioxin from municipal solid waste incineration is a public health concern; therefore, understanding the behavior of such emissions has been pointed out as a key issue for improving waste management systems [15,16].

Due to the aforementioned detrimental effects, there is a growing recognition that an optimal waste management approach requires the support of a system model that comprehensively incorporates the following essential features:

(a)

The interconnectedness between various elements of waste management, including waste, resources, infrastructure, users, suppliers, and decision-makers, using flows and stocks;

(b)

The relationships between key elements of the waste management sector and components of the socio-ecological system, such as participation, institutions, processes, technology, pollutants, and the flow of matter and energy;

(c)

The interrelationships between decision-makers, suppliers, and users, aiming to promote comprehensive and collaborative action for sustainable waste management.

These aforementioned gaps identified in this paper are considered while building our model, m-SWM4Cities, a socio-ecological system conceptual model specifically crafted to enable the development of sustainable waste management strategies within urban environments. The primary objective of this research is to display the practicality and effectiveness of employing system-modeling techniques to enhance waste management practices. We illustrate the implementation of m-SWM4Cities using the case of Mexico City mD-SWM4CDMX. The output comprises a collection of scenarios of the socio-environmental consequences associated with various waste management strategies. Through these scenarios, we successfully identified the crucial thresholds pertaining to key waste management practices. Our findings demonstrate that mD-SWM4CDMX facilitates the identification of essential waste management indicators that warrant monitoring within a comprehensive and sustainable waste management strategy.

In Section 2, a concise contextualization of the role that cities play in confronting waste management challenges and their evolution is presented. This section effectively highlights the importance and limits of cities in addressing these issues. Section 3 explains the research methods and materials used in the study. In Section 4, the most relevant results are presented, including the introduction of the socio-ecological system conceptual model (m-SWM4Cities) and its implementation as a dynamic model to evaluate waste management practices in Mexico City (mD-SWM4CDMX). This section successfully establishes the framework and models used in the research. Section 5 discusses the scenarios where the mD-SWM4CDMX dynamic model is implemented. Finally, in Section 6, the principal conclusions of this paper are presented.

2. Evolution of Waste Management in Cities

The generation of waste in urban areas is intricately connected to the processes of urbanization, lifestyles, consumption patterns, and population growth [17,18]. Traditionally, waste management has predominantly concentrated on specific facets, primarily centered around technologies, management practices, as well as certain legal and economic considerations. In essence, two overarching approaches have emerged: Integrated Solid Waste Management (ISWM) and Sustainable Waste Management (SWM). ISWM emerged during the late 1940s and 1950s, mainly in response to the reality of developed countries. Since then, it has mostly prioritized waste processing methods for reducing, reusing, recycling, and disposing waste [19]. ISWM was later transplanted into developing countries with variated outcomes [9,20,21,22]. ISWM, which seeks to avoid public health problems in communities, was a first approach to integrate different aspects related to waste management, but the lineal and isolated projects did not address all relevant issues in waste management, neither solved all pressing challenges being experienced. In that sense, despite the fact that ISWM includes social, economic, and environmental aspects, it has been usually constrained to a sectorial approach and its related practices, therefore lacking a more systemic, inter- and transdisciplinary approach. In a way to transcend such limitations, more complex analytical methods are being developed nowadays. For example, by framing the waste problem from a sustainable development perspective, environmental, economic, and social aspects are now increasingly being taken into consideration [23,24,25].

Effective management of municipal solid waste (MSW) in cities of the Global South poses significant challenges due to their substantial population sizes, projected urban population growth, limited access to technologies and financial resources, and constrained capacities for proper waste management and informal waste practices. The metropolitan area of the Mexico Valley serves as an illustrative example [26]. Nonetheless, cities play a pivotal role in advancing sustainable MSW management by implementing initiatives to reduce waste, with specific emphasis on materials such as plastics [27], and by fostering the development of economically viable systems that promote the closure of material cycles [28,29]. Within urban settings, the accumulation of material waste can be perceived as a reservoir that offers valuable resources to be reclaimed in the future through recycling, energy recovery, and strategic depositing and concentration of substances [30,31]. Remarkably, the quantity of materials amassed in older settlements, predominantly in developed countries [31], already surpasses the current volume of waste being generated [32]. Hence, the adoption of a preventive waste management approach that considers a transition to the socio-ecological system of sustainability is necessary. This approach diverges from mainstream practices that primarily concentrate on immediate waste reduction or linear recycling methodologies.

The ISWM approach progressively evolved into Sustainable Waste Management (SWM), particularly in some European and Asian countries such as Germany, Spain, Japan, and China [33,34,35]. The advantages of SWM approach include its ability to identify, the key components, available technologies, actors involved (formal and informal), and prevailing dynamics from a more systemic approach, with the purpose of enabling a positive interaction among technical, legal, environmental, managerial, sociocultural, financial, and economic issues. In other words, a multidimensional and systemic approach to sustainable waste management aims to stimulate positive synergies among existing, but not always evident, nexuses to enhance waste management and also to take advantage of potential co-benefits, and reduce trade-offs. This is perhaps the main challenge in the implementation of sustainable waste management practices, particularly in contexts of high informality and lack of regulation enforcement.

One example is urban mining, a concept that emphasizes the interaction between consumption and waste management over time [30], which holds potential benefits for both cities in the Global North and the Global South [36]. Its relevance is particularly pronounced for cities striving to achieve the Sustainable Development Goals (SDGs) and bridge the infrastructure gap, as substantial quantities of materials and energy will be required [36]. While maximizing the resource and economic value of waste streams is a crucial aspect of sustainable waste management, it represents only a fraction of the broader circular economy framework [37,38,39]. Waste management practices are intricately influenced by a combination of socioeconomic, political, sociocultural, economic, and technological factors, leading to variations and challenges between the Global North and the Global South [40]. The complexity of waste flows, marked by the involvement of multiple stakeholders and considerations of environmental justice, exemplify the diverse opportunities and intricacies found within waste management practices in different cities, such as Mexico City and Santiago de Chile [26], and collaborative urban mining initiatives in Brazil [41].

This initial comparison between ISWM and SWM allows us to identify the key elements and indicators necessary for developing a system dynamics model to achieve sustainable waste management. At the city level, this model represents the socio-ecological urban system as a complex interplay of stocks and flows between the ecological and social subsystems (Figure 2). It encompasses the interactions of these flows across different spatial and temporal scales, involving not only the ecological subsystem within the urban environment (including both biotic and abiotic factors) but also non-urban ecosystems where waste may be transferred. Furthermore, the model considers the influence of various factors such as economic structure, urban form, informality, and evolving sociocultural practices on waste generation and management. Additionally, the model examines the potential impacts of waste management practices on local health, quality of life, and urban adaptation, considering aspects such as urban vulnerability to flooding and overall resilience [42,43,44]. As highlighted by Haberl et al. [45], the ecological and social subsystems coevolve in a non-directional manner, constituting two distinct spheres of causality. From this perspective, socio-ecological systems comprise a biophysical sphere of causation, governed by natural laws, and a cultural sphere of causation, perpetuated through symbolic communication. Accordingly, we represented the social sphere as a “subsystem” embedded within the ecological subsystem. Together, these components form the socio-ecological urban system described.

In Mexico on January 2019, a document titled “National Vision towards Sustainable Management: Zero Waste” was issued, coinciding with a change in federal authority [46]. This document outlines the National Waste Vision and its fundamental principles, aiming to transform the conventional waste management system into a circular economy model that promotes the rational utilization of natural resources and fosters sustainable development throughout the country [46]. However, it took over a year for the quantitative and qualitative foundation of the National Waste Vision to be published in the form of the “Basic Diagnosis for Integrated Waste Management” [47]. Notably, while the discourse and objective of the national waste vision focus on achieving a “circular economy model,” the basic diagnosis continues to be presented and structured from the perspective of “integrated waste management”. In 2021, Mexico City faced significant waste management challenges as it generated 12,355 tons per day, with a per capita generation of 1.071 kg/person/day [48]. These challenges stem from the increasing waste generation, the size of the population (including both residents and floating population), and various socio-economic, political, environmental, and infrastructural factors that influence waste management operations. The National Waste Vision sets integrated solid waste management as the preferred model for Mexico City, while also expressing an intention to transition towards the “Zero Waste” paradigm [49].

3. Materials and Methods

The overall conceptualization of the socio-ecological system (Figure 2) was considered during the development of m-SWM4Cities. This paper adopts a four-stage methodology, which involves (1) conducting an extensive review of relevant scientific literature, (2) undertaking a quantitative and qualitative meta-analysis to inform subsequent steps, (3) formulating a theoretical model conceptualization referred to as m-SWM4Cities, and (4) creating a dynamic model that was specifically implemented for the case study of Mexico City (mD-SWM4CDMX). We explain the process in this section.

In the first stage, peer-reviewed literature was gathered from Scopus (considering all databases). The search considered “waste” and “sustainable management” comprising the fields of “article title, abstract, keywords”. Search was further redefined by considering only those articles that were in the final publication stage and were written in English. The result was a total of 5985 documents for the period of 1992 to 2022. No publication was found under those search parameters before 1994. From the 5985 documents, only those classified as “papers” were selected (reducing their number to 4779). A manual selection followed to prioritize those relevant for a quantitative and qualitative analysis, retrieving a total of 418 papers.

In the second stage, the analysis focused on the examination of waste narratives employed in the 418 selected papers. Each paper was carefully reviewed and encoded using Atlas.ti^® software (version 8.1.0 for Mac) to facilitate subsequent analysis, which involved a detailed examination of each paper’s paragraphs in order to comprehend the narrative and manually assign one or multiple codes representing semantical concepts. Furthermore, the relationships between codes and groups of codes (clusters) were identified within each paper. By synthesizing the narratives and codes from the analyzed papers, a cognitive map in the form of a semantic network was constructed using Atlas.ti software. This semantic network serves as a comprehensive representation of the relationships among concepts identified in the analyzed papers (see Figure 3). The intensity of the semantic relationship between concepts is determined by the number of coincidences (co-occurrences) observed among the elements, thereby elucidating the dynamics of concept linkages within the analytical frameworks presented in the literature (as shown in Results section). Moreover, this co-occurrence analysis allows for the identification of weaker or less addressed relationships, as well as trends in the relationships between concepts, providing a visual representation of the current state of the art in scientific literature on sustainable waste management, facilitating the identification of existing gaps, weak elements, and relationships.

In the third stage, building upon the findings of the quantitative and qualitative analysis, we developed the theoretical conceptualization of our Multidimensional Model for Sustainable Waste Management for Cities (m-SWM4Cities, Figure 4). This model aims to address existing gaps in the literature while reinforcing elements and relationships that may benefit from a more robust understanding of sustainability [50]. By integrating various dimensions and considering the multifaceted nature of sustainability, m-SWM4Cities strives to provide a comprehensive framework for enhancing waste management practices in urban settings.

Finally, in the fourth stage, the developed multidimensional model, m-SWM4Cities, was applied to the case of Mexico City, encompassing the socio-ecological system elements relevant to waste management in urban contexts. The m-SWM4Cities model served as the fundamental framework for constructing a dynamic flow and stocks model tailored specifically to the circumstances of Mexico City, using VensimPLE^® V6.4b software (mD-SWM4CDMX, Figure 5). The conceptualization of the mD-SWM4CDMX model was founded on two essential assumptions: (1) the presence of a sample of verifiable scientific information on waste management in the vast body of scientific literature, and (2) the recognition that sustainable waste management processes predominantly operate within an environment characterized by substantial levels of uncertainty. These assumptions align with the principles of exploratory modeling [51]. In line with these assumptions, the modeling process integrates information and elements derived from the concepts and their interrelationships (semantics) identified during the second stage of analysis. The variables and equations employed in the applied dynamic model (mD-SWM4CDMX) are presented in Appendix A.

4. Results

4.1. Analysis of Semantical Concepts

Atlas.ti analysis was structured around three clusters of codes: (1) sustainability, (2) waste management, and (3) methodological aspects. The sustainability cluster is integrated using the following codes: sustainability (Ss), “environmental” (En), “politics” (Pl), “social” (Sc) and “economic” (Ec) aspects. The waste management cluster is composed of “waste management” (Wm), “processes” (Pr), “technologies” (Tc), “infrastructure” (In) and “materials” (Ma) codes. The third one comprises just one code on “methodology” (Mt). In

Table 1. Semantical concepts associate with codes: Ss, En, Ec, Sc, Pl and Mt.

Ss_	En_	Ec_	Sc_	Pl_	Mt_
sustainability	environmental	products	recovery	municipal	systems
development	impacts	economics	social	policy	analysis
indicators	emissions	costs	communities	government	models
regional	reduce	value	problems	public	methods
states	resources	efficiency	stakeholders	institutional	scenarios
zero waste	greenhouse gas	companies	health	private	assessment
pickers	gas	markets	households	informal	framework
risks	effective	consumption	knowledge	organizations	variables
country	ecological	capital	workers	legislation	evaluation
solutions	methane	consumers	population	regulations	scientific
monitoring	carbon	business	networks	authorities	hierarchy
national	carbon dioxide	financial	behaviors	actors	components
complex	renewable	incentives	people	associated
opportunities	pollution	prices	concerns	agencies
	cleaner	investment	human	accountable
	hazardous	savings	residents	rights
	natural	tax	education	governance
	climate	pay	prevention	illegal
	conservation	bank	relationships
	air	amounts	participants
	habitat		responsibility
			learning

Source: authors’ own elaboration.

and

Table 2. Semantical concepts associate with codes: Wm, Pr, In, Tc and Ma.

Wm_	Pr_	In_	Tc_	Ma_
waste	recycling	cities	technologies	energy
management	solid	facilities	composting	materials
waste management system	landfill	urban	incineration	food
operations	process	sources	wte	paper
implementation	collection	industry	anaerobic	wastewater
strategies	generation	service	innovation	organic
alternatives	treatment	sites	combustion	cycle
integrated	separation	hotels	digestion	biomass
action	disposal	agricultural	engineering	fuels
flow	transportation	equipment	gasification	electricity
	selected		chemical	metals
	composition		vermicomposting	plastic
	transfer		biological	power
	residues		biodegradable	heat
				glass
				biogas
				ash
				sewage
				oil

Source: authors’ own elaboration.

the most relevant semantical concepts associated with each of the 11 codes are presented.

The results according to codes, summarized in

Table 3. Quantitative analysis of linguistic codes.

Clusters/Codes	Frequency	Number of Interactions	% Absolute	% Relative to the Cluster
Sustainability Cluster (subtotal)		30,838	37%	100%
Sustainability	3664	4%	12%	12%
Environmental	7846	9%	25%	24%
Economical	9524	11%	31%	33%
Social	5210	6%	17%	15%
Politics	4595	6%	15%	15%
Waste Management Cluster (subtotal)		45,577	55%	100%
Waste management	4832	6%	11%	10%
Processes	17,867	21%	39%	38%
Infrastructure	3334	4%	7%	8%
Technologies	5071	6%	11%	10%
Materials	14,474	17%	32%	34%
		0%
Methodology Cluster/Code (subtotal)	6992	8%	100%	100%
Total codes interactions		83,407	100%

Source: authors’ own elaboration.

, show a total of 83,407 interactions, 37% of which correspond to the sustainability cluster, 55% to the waste management cluster, and 8% to the methodology code. Considering the absolute percentage, on one hand, the codes with the highest interactions are processes (21%) and materials (17%), both from the waste management cluster. The sum of these two codes (38%) exceeds the total codes of the sustainability cluster. On the other hand, the codes with the lowest citations with respect to the total are sustainability (4%) and infrastructure (4%), followed by waste management (6%). The analysis of the relative percentages for each cluster reveals that in the waste Management cluster the highest number of interactions are processes and materials, and the one with the least amount is infrastructure. In the case of the sustainability cluster, the ones with the highest interactions are economic and environmental and the one with the least is sustainability. Considering these data, it can be argued that within the 418 articles analyzed, the most relevant topics (with the highest number of interactions) are processes and materials, while the least relevant (with the lowest number of interactions) are sustainability and infrastructure.

4.2. Relations of Semantic Concepts (Semantic Network)

Atlas.ti defines a semantic network that establishes the relationships of meaning between codes. The linking of the codes allows to identify sentences with related thematic contents. The types of relationship used in our analysis were “is property of”, “is part of”, “is cause of”, and “is associated with”. The creation of the semantic network was performed manually considering the origin of the codes, their plausible clustering and/or relationship according to the narrative of the 418 papers analyzed and the congruence with the concepts used.

The semantic network, that shows the relationship between the 11 codes (Figure 3), was structured around two thematic clusters: sustainability and waste management. Sustainability (Ss) is composed of environmental (En), political (Pl), social (Sc) and economic (Ec) codes. Waste management (Wm) is comprise processes (Pr), technologies (Tc), infrastructure (In) and materials (Ma). In addition, causal relationships are established between them. For example, Tc is the cause of Pl and En, while it is associated with Am; in turn, Am is caused by Pr, Sc, P, and En, and associated with In. The relationships presented give an idea of the structure of the waste management system and the existing links with sustainability, which will used again in the approach of the Multidimensional Model for Sustainable Waste Management for Cities (m-SWM4Cities).

4.3. Interactions between Codes and Clusters (Co-Occurrences)

Once the semantic network was created, the co-occurrence tables were generated using the “Co-occurrence table” analytical function of Atlas.ti. The first co-occurrence table shows the results considering all the codes (

Table 4. General co-occurrences, codes vs. codes (one to one).

	En_	Ec_	Wm_	In_	Ma_	Pl_	Pr_	Sc_	Ss_	Tc_
En_	0	1441	632	321	1726	600	1969	733	770	549
Ec_	1441	0	677	467	1824	679	2255	1125	711	561
Wm_	632	677	0	279	726	712	1056	490	550	170
In_	321	467	279	0	524	318	1154	252	186	223
Ma_	1726	1824	726	524	0	545	3912	828	748	946
Pl_	600	679	712	318	545	0	1055	571	419	204
Pr_	1969	2255	1056	1154	3912	1055	0	1148	870	1434
Sc_	733	1125	490	252	828	571	1148	0	406	138
Ss_	770	711	550	186	748	419	870	406	0	191
Tc_	549	561	170	223	946	204	1434	138	191	0

Source: authors’ own elaboration.

). The second one presents the co-occurrences between the codes of the sustainability group (Ss) against those of the waste management group (Wm), that is, it relates the co-occurrences of the group conformed by En, Ec, Pl and Sc against those resulting from the group conformed by In, Ma, Pr and Tc (

Table 5. Percentages of co-occurrences relating Ss cluster codes vs. Wm cluster codes.

		Sustainability Cluster Codes
		En_	Ec_	Pl_	Sc_	Summation
Waste management cluster codes	In_	2.27%	3.30%	2.25%	1.78%	9.59%
	Ma_	12.19%	12.88%	3.85%	5.85%	34.77%
	Pr_	13.91%	15.93%	7.45%	8.11%	45.39%
	Tc_	3.88%	3.96%	1.44%	0.97%	10.25%
	Summation	32.24%	36.07%	14.99%	16.71%	100.00%

Source: authors’ own elaboration.

).

The general co-occurrence analysis (Table 4) presents information on the intensity of the relationship between codes. For example, when a sentence of an article was about processes (Pr), it related to materials (Ma) in 3912 occasions. Similarly, social issues (Sc) were related to technology aspects (Tc) in 138 occasions. In other words, Table 4 shows the intensity (frequency) with which one code is related to the others.

Comparing the results for sustainability (Ss) against waste management (Gr), in the sum of the co-occurrences, sustainability has the lowest number with 4851 against 5292 of waste management. Analyzing the comparison for the group codes, that is, for the eight codes (En, Ec, Pl, Sc, In, Ma, Pr and Tc), there are five codes in which Wm is greater than Ss, and in three the opposite occurs. The codes in which Ss is predominant are environmental, economic and technology. This finding would allow us to establish that the concept of sustainability has a lower relationship with issues of infrastructure, materials, policy, processes and social, and is related more to environmental, economic and technology issues. For waste management, the opposite occurs. The results on this behavior will be considered to propose a balance between the relationships of the elements and interactions of the Multidimensional Model for Sustainable Waste Management in cities (m-SWM4Cities).

The selective analysis of co-occurrences synthesizes the findings (Table 5). The process code (45.39%) is the one with the highest percentage of co-occurrences, particularly linked with economic (15.93%) and environmental (13.91%) issues. Infrastructure aspects (9.59%) have the least number of co-occurrences; their relationship was the most with social aspects (1.78%) and with politics (2.25%) was the weakest. The waste management cluster presents process and material codes as the most relevant and infrastructure and technology codes as the less important ones. In the case of the sustainability cluster, the economic and environmental aspects are the most important, and the political and social aspects are the least important aspects.

4.4. Delineating a First Approach to a Multidimensional Sustainable Waste Management Model for Cities

Finally, the analysis reveals that some links between the analyzed concepts are not at all consistent and fully integrated for waste management. That is why the authors propose of a Multidimensional Sustainable Waste Management Model integrating the following aspects:

• Strengthening the relationship between sustainability and waste management, in particular the links between processes and materials of waste and the economic and environmental issues of the socio-ecological system;
• Generate better relationship between social and technological concepts that tend to be mostly dissociated with other elements of the socio-ecological system of waste management;
• Create stronger links between the waste management cluster and the social and political aspects;
• Improve the relationships between the sustainability cluster and the technology and infrastructure aspects.

Considering the above, we show that a Multidimensional Model for Sustainable Waste Management (m-SWM4Cities) understands sustainability as a dynamic balance of the relevant elements and actors that considers the interrelationships and the interactions with the socio-ecological system, thereby enabling a more socio-efficient and circular (urban) metabolism (including aspects of health, atmosphere and productivity). In other words, waste contains both natural and human work, called exergy, which must be efficient in reducing the natural entropy of the socio-ecological system, thereby decreasing the negative impacts on the environment and society [19,52].

Due to the diversity of actors related to waste management, a move towards a transdisciplinary approach is mandated [24]. A transdisciplinary approach in sustainability sciences refers to a work in which the diversity of actors are taken into account in the different phases of analyzing management scenarios, thus co-producing knowledge and empowering the actors in the implementation of interventions. Transdisciplinary approaches can be succinctly described as the work between disciplines, across different disciplines, and beyond all disciplines [53,54,55,56,57], allowing to reframe the guiding research questions if needed [58].

Coordination and involvement in waste management across different levels, dependencies and actors are essential to guarantee adequate operation of the system. The best management of public services in a city, as argued by Delgado [59], must be the product of a broad exercise of knowledge exchange and thus of a complex, transdisciplinary and reflective coproduction of knowledge.

Therefore, the proposed multidimensional model m-SWM4Cities in this paper differs from other models as it integrates several main characteristics: (a) interrelationships between elements of waste management through modeling with flows and stocks (waste, resources, infrastructure, users, suppliers and decision-makers); (b) interaction relationships between key elements of the waste “sector” and components of the socio-ecological system (participation, institutions, processes and technology, pollutants, and the flow of matter and energy); and (c) interrelationships between decision-makers, suppliers and users for advancing an integral shared collective action for sustainable waste management.

Figure 4 shows an approximation of the general Multidimensional Model for Sustainable Waste Management (m-SWM4Cities). The first level of complexity is the socio-ecological system, composed of both the ecological and social subsystems. The second level of complexity establishes the coupling relationships between the socio-ecological system and the waste sector. The proposed relationships are the following: participation, education, institutions, processes, technology, and materials and energy. The third level of complexity is defined by the elements of waste management: waste, natural resources (within urban space), infrastructure, users, suppliers, and decision-makers. The interactions defined between the elements are the following: public health, policies, (waste) generation, (waste) management, usable nutrients, pollutants, operations, competitiveness, transparency, taxes, materials used, services, cooperation, financing, environmental services, and regulation.

The m-SWM4Cities model moves beyond the premise of a simple urban socio-ecological system for sustainable management of waste. A system, as we understand, is the combination of elements that act together to achieve a specific objective [60]; in our case the objective is sustainable management of waste. An element is a particular unit with a function in the system; in our case, it could be processes, actors, activities or resources related to the operation of waste management.

We do recognize that a model is a simplified representation of reality, which in this case is framed for an urban socio-ecological system, showing its influence on the waste sector. This m-SWM4Cities model considers the amounts of waste in tons per day as known inputs and the result of the outputs to different processes is consequently obtained in terms of materials recycling, treatment of organic waste, generation of energy from residuals and final disposal; this is what is known as experimental mathematical modeling [60].

After defining the m-SWM4Cities model, it has been applied to the case of Mexico City, from now on called mD-SWM4CDMX. For this model, we adapted a general model of the cities’ conditions to Mexico City, delineating it as a dynamic system. The proposed mD-SWM4CDMX dynamic model considers the elements, processes, and actors related to sustainable waste management in Mexico City, including technical, economic, social and environmental aspects, all of which are presented in Appendix A. For this reason, the this system is applied from an analytical integrative approach, that is, the proposed model aims to comprehensively analyze the sustainable management of waste observing the effects of the relationships present in the analyzed urban socio-ecological system. Figure 6 shows a graphical representation of this model.

Dynamic systems are widely used to analyze and predict the behavior of physical, ecological, economic and social phenomena. Therefore, they are a viable approach for application in sustainability sciences. In a nutshell, a dynamic system changes over time and its behaviors are complex because it contains a large number of elements and functional interrelationships [51]. Dynamic systems have been found to be useful in biology, economics, physics, among other fields [51,61,62], where they are represented, or modeled, using differential equations [63]. In our case, the mD-SWM4CDMX model uses flows and stocks that consider, on one hand, the temporal variation in the amount of waste as it passes through different elements/processes, and, on the other hand, the intervention of actors in the system.

Seadon [24] has proposed a structure for understanding the dynamics of waste management, in which the elements of the waste management system are interrelated and thus influenced by each other. He proposes an arrangement of the elements that constitute a sustainable waste management system without simulating it. Other authors who have applied dynamics systems theory in waste include Chen [64] (MSW) and Yuan [61], who offer a limited analysis for construction and demolition of waste and for specific cases of waste energy technologies.

The stocks represent the quantity that needs to be analyzed in the applied dynamic model mD-SWM4CDMX; it is a cumulative quantity of residues. Flows are considered as inputs and outputs of stocks. Therefore, the value of the analyzed “items” increases or decreases in accordance to the changes experienced in waste flows [65]. The item analyzed in the proposed model is the amount of urban solid waste (MSW) in units of tonnes per day (tonne/day). Variables that influence the change in flows and stocks quantity are also considered. Figure 6 shows a representation of the proposed dynamic model, created via the Macintosh educational VensimPLE^® V6.4b computer program, applied to mD-SWM4CDMX. This dynamic model, in a nutshell, performs a calculation of the variation in the amount of waste through different elements (influence variables, flows and storage) and the final effect in terms of GHG emissions, leachates and final disposal due to unused waste.

The mD-SWM4CDMX dynamic model considers the following as stock variables: generated waste, usable waste, natural resources, materials and available energy. Flow variables include the following: flow from generators, flow in infrastructure, flow of nutrients, and flow of M&E (materials and energy). The variables with influence on some stock or flow variables are those considered as interactions in the multidimensional model m-SWM4Cities; see Figure 5. Those variables include public service, cooperation, regulation, management, operation, financing, polluting waste, taxes, public health, transparency, and politics. The actors, decision-makers, and suppliers have also been modeled as variables of influence. It is important to clarify that the interactions, namely competitiveness and environmental services, shown in the m-SWM4Cities multidimensional model, have been left out for the applied dynamic model mD-SWM4CDMX, since they themselves imply greater complexity and detail in their functions of value, which for the moment are outside the scope of this paper.

At this stage of development of the applied mD-SWM4CDMX dynamic model, the existing information was considered to calibrate the model in VensimPLE^®. The information is obtained from the annual inventories of solid waste in Mexico City from 2006 to 2021 [48,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80]. The variables and equations of the applied dynamic model mD-SWM4CDMX are presented in Appendix A.

5. Discussion

5.1. Scenarios Analyzed with the mD-SWM4CDMX Dynamic Model

Figure 6, Figure 7, Figure 8, Figure 9 and Figure 10 show the results of the analyzed scenarios. The first scenario, business as usual (BAU) without feedback (Figure 6), shows how current practices in Mexico City have negative effects on the environment, namely increases in GHG emissions, leachate, and unused solid waste streams (waste not being recovered, reused or recycled). It also shows that despite the limited stream of waste being “used” (21% in average), as it is not connected to the system (since the city practically does not have a circular system), the result within the model remains isolated. Also, Figure 6 shows the same results of the scenario without feedbacks and with 50–50 improvements, that is, equal percentages of polluting and usable waste.

The second linear scenario, with improvements, involves carrying out actions to gradually increase the amount of waste being used and to gradually reduce the amount of polluting waste for final disposition. Different model runs were carried out for this second linear scenario, adjusting the values of the proportions between polluting and usable waste. Figure 7 shows a scenario without feedbacks, in which 60% of waste use has been reached and 40% continue to be polluting waste. Between these two scenarios, there are changes in the socioecological system that show the boundaries of SWM for Mexico City; this is discussed in detail in Section 5.2.

The mD-SWM4CDMX’s sustainable management scenarios are shown in Figure 8, Figure 9 and Figure 10. The main difference between the model without feedback and the sustainable model is that, in the latter the amount of production of materials and energy from waste is connected to the waste generated therefore consolidating a circular system. This means that the benefits derived from various waste processes are thus integrated into the system. Figure 8 shows the sustainable management scenario with a proportion of 20% usable waste and 80% of polluting waste, similar to the data of Mexico City’s current operation. The scenarios in Figure 9 and Figure 10 show a variation in the proportions of usable and polluting waste: in Figure 9, the proportion is 50-50 and in Figure 10, there is 70% of usable waste and 30% of polluting waste. The analysis of the results obtained is presented in Section 5.2.

5.2. Discussion of mD-SWM4CDMX Scenarios

One of the important findings obtained by applying the mD-SWM4CDMX model is that by not linking the efforts being made between the use of waste and the generation of waste, the same socio-ecological system for waste management can be maintained, varying the results depending on the proportions of waste that are used or that generate pollution. A second finding is that in the sustainable waste management scenario, by incorporating the materials and energy used in the initial waste generation, an integrated effect on the behavior of the socio-ecological system is observed, in which changes occur in the system due to the effect of the interactions of all the elements.

The BUA scenario without feedback and the scenario without feedback that only incorporates engineering solutions also show variations among themselves which are relevant to highlight. It is evident that if things remain as they are, the environment will continue to degrade; consequently, GHG, leachate and solid waste disposed on the ground will increase, see Figure 6. The scenario without feedback, that only incorporates engineering solutions, maintains the trend established in the BAU scenario until reaching a proportion of 50-50 (Figure 6), a situation that is modified if the proportion changes to 60% of usable waste and 40% of polluting waste (Figure 7). This means that there is a change in the threshold between the scenario without feedback with 50-50 improvements and the scenario without feedback with 60-40 improvements. In the latter scenario, after half of the analyzed period, or the fifth year, a recovery of the environment is observed in terms of a reduction in GHG-associated emissions, leachate generation and solid waste final disposition on the ground. The behavior of the system continues to be favorable to the environment for scenarios with 70-30, 80-20 and 90-10 improvements.

The sustainable management scenario has a more complex behavior. Figure 8 shows a behavior that varies over time around an equilibrium value, with moments in which the environment is improved finally followed by an abrupt change to its detriment. On one hand, the 50-50 sustainability scenario remains quite stable during the first 6 years of the analyzed period; later, a detriment to the environment is identified and towards the last quarter of the period, the recovery of the environment is accelerated. This can be explained by the cumulative effect on the production of materials and energy from waste and its impact on the waste generated. On the other hand, the scenario of sustainable waste management with 70% of usable waste and 30% of pollutants, shows variations in the socio-ecological system with an inverse effect to the 50-50 scenario, as it is observed that after five years, the environment is slightly degraded but towards the year 7, a recovery with a high acceleration is observed, reaching a maximum point from which a slight fall is verified in the last year of the analyzed period. Although not as evident as in the 60-40 linear scenario, in the 70-30 sustainable management scenario, there is a threshold for the behavior of the socio-ecological system as well. It is also clear that in the 70-30 sustainable management scenario the area under the behavior curve of the environmental aspect is positive for the accumulated period of analysis, which is in contrast with the 20-80 and the 50-50 sustainable management scenarios.

6. Conclusions

This paper introduces the m-SWM4Cities model, focusing on its methodological aspects while also applying it to the case of Mexico City (mD-SWM4CDMX model). Through a comprehensive qualitative and quantitative analysis of the existing literature on waste, our aim was to explore the key concepts, identify areas for improvement, and propose enhanced solutions and plausible sustainable waste management scenarios.

The presented models incorporate the key elements, actors, and interactions from a systemic perspective, providing a holistic understanding of sustainable waste management. While the concept of sustainable waste management has been previously defined, the integration of various aspects was lacking. The proposed multidimensional model is a novel qualitative approach aimed at better understanding the complexities associated with sustainable waste management. This model serves as the foundation for the dynamic model applied to Mexico City, where quantitative value relationships between elements, actors, and interactions have been established to estimate variations in waste generation under different intervention scenarios.

Our findings demonstrate that m-SWM4Cities facilitates the identification of essential waste management indicators that warrant monitoring within a comprehensive and sustainable waste management strategy. The applied case (mD-SWM4CDMX) provides a more robust understanding of Mexico City’s waste system scope and complexity, offering a valuable model to be used for developing more secure and dynamic interventions, expected to be implemented within the context of the recently approved Zero Waste and Circular Economy Act of Mexico City on February 2023.

Finally, it is important to highlight the replicability of the presented Multidimensional Sustainable Urban Waste Management Model (m-SWM4Cities), as it can be tailored to address context-specific features of different cities. In this sense, we argue that this model serves as a valuable tool for comprehensively assessing current and alternative waste urban management models, facilitating informed decision-making while promoting more sustainable and inclusive practices.