**2. Methods**

#### *2.1. Analytical Hierarchy Process (AHP)*

The present study applied the Analytical Hierarchy Process (AHP). Although AHP is one of the oldest MCDA methods, developed by Saaty [13], it is still widely used today. AHP allows the problem to be broken down into its constitutive elements, listed in relation to the main goal [14]. AHP 'is a multicriteria decision-making technique, which can concurrently consider qualitative and quantitative comparison criteria and where a lot of baseline research literature is available. Therefore, AHP is ideally suited to a project such as this, which needs to do comparative research involving many stakeholders having various interests [15]. AHP is applicable in a wide range of fields, including management, business, and policy [16,17], and is also often used to solve complex problems in environmental management. AHP application in land managemen<sup>t</sup> is discussed by Schmoldt et al. [18].

The AHP method is widely applied in emerging countries, where waste managemen<sup>t</sup> decisions need to be made in the absence of established sound environmental solutions. It has been used to compare solid waste treatment scenarios for cities [19] and university campuses [2] or to select the recycling strategy for specific waste categories like WEEE [20]. Taboada-González et al. [21] used AHP to select the best waste treatment with energy recovery for Ensenada, Baja California (Mexico). Araiza Aguilar et al. [22] looked for the zones suitable for the emplacement of waste managemen<sup>t</sup> infrastructure in Mexico with the help of geographical information systems (GIS) and AHP. Martínez-Morales et al. [23] applied AHP to identify the municipalities of Mexico State with major waste managemen<sup>t</sup> problems. Gomez Jauregui Abdo [24] discussed development of domestic water supply in Guadalajara with the help of AHP. A wide range of applications of the AHP method shows that it is a powerful decision tool for assisting decision makers in the selection of sustainable waste managemen<sup>t</sup> strategies.

The AHP hierarchical structure allows decision makers to prioritize solutions in terms of relevant criteria. Additional criteria can be added later in the hierarchical structure after the first results are obtained. The decision procedure using the AHP is made up of four steps, as described by Saaty [25]:


## *2.2. Study Area*

Mexico is a diverse country with 125 million residents and abundant natural resources. It is a member of the Organization for Economic Co-operation and Development and simultaneously a developing country with a GDP per capita of 8201.3 US\$ [26]. Mexico City, the capital and the most populated city, is considered in this case study. The city is located in the Valley of Mexico in the center of the country. It consists of 16 boroughs and is spread over an area of 1485 km2. The estimated population of 9 million [27] generates 12,920 mega grams (Mg) of MSW per day with per capita production of 1.43 kg per day. However, approximately 4 million people travel for work to Mexico City from the nearby states. The composition of collected MSW is presented in Figure 1, based on the analysis made in 2 boroughs of the city, Coyoacán and Venustiano Carranza. These districts can be taken to represent a broad middle of society. Therefore, the waste composition in these areas is assumed to be representative for the whole city. However, it is to be mentioned that the drivers of waste trucks, being part of the informal recycling sector, separate the major part of cardboard, PET, and metals for further sale. The sample thus represents the composition of waste arriving at the transfer station, but not the waste coming directly from the households. The results of the waste composition analysis correspond to the outcomes of the study by the Polytechnic University in 2013 [28]. The waste composition in Mexico City is not typical for developing countries, which tend to have a higher organic fraction (more than 50%). This shows that Mexico is an emerging economy, in transition from developing to an industrialized country. However, the biggest fraction is represented by food waste (27.77%), while plastics and paper/cardboard constitute 15.79% and 10.55%, respectively. The interesting fact here is that around 6% of the MSW is represented by toilet paper, explained by cultural habits and the capacity of the sewage system.

**Figure 1.** MSW waste composition in Mexico City 2017.

The MSW managemen<sup>t</sup> system of the city consists of 12 transfer stations, 2 sorting facilities, and 8 composting plants. 14% of the generated waste is recycled with the help of the informal sector [29]. Almost all the collected waste is sent to the five landfills located outside of the city in the nearby state. This is a big challenge because it enormously increases transportation costs. Hence, a new waste legal norm, Norma NADF-024-AMBT-2013, was introduced in July 2017. This aims to increase the amount of collected recyclable materials and thereby decrease the quantity of landfilled material. At the same time, the city is planning to construct an incineration plant, which will treat almost half the collected residues.

#### *2.3. Selection of Technical Alternatives*

This research considers the following scenarios: (1) landfilling and composting, (2) anaerobic digestion, (3) MBT (mechanical–biological treatment) with composting as biological stage, and (4) incineration. The first scenario was chosen as a base scenario, which presents a business-as-usual scenario. It involves windrow open composting and engineered landfills. These composting piles are turned to improve porosity and oxygen content. Incineration and MBT plants were selected because these options are the most discussed options of sustainable waste managemen<sup>t</sup> in developing countries [30–32]. The second scenario is considered to be the alternative for the others. Due to the high percentage of organics, wet anaerobic digestion can be very beneficial through energy and fertilizer supply, and is, according to Badri et al. [16], the most favorable treatment option for organic waste through energy and fertilizer supply. However, this option requires the source separation scheme of waste at households.

#### *2.4. Waste Management Scenarios*

This research assesses the sustainability of several alternative scenarios for waste managemen<sup>t</sup> for Mexico City. Since 2003, municipal solid waste in Mexico City has been separated at source into two fractions: organics and inorganics. However, the new regulation NADF-024-AMBT-2013 mandates the new segregation of residual waste into five fractions: organic, recyclables, nonrecyclables, hazardous waste, and bulky waste [33]. This work compares the proposed system with three alternatives: a baseline scenario with composting of organics, a scenario which involves anaerobic digestion (AD) of organics, and a mechanical–biological treatment (MBT) scenario with no source separation. MBT is a collective term, mainly used in Europe, which incorporates several variations of MSW treatment, based on a combination of mechanical processing and biological treatment (in most cases aerobic or anaerobic decomposition) [34,35]. According to the new regulation Norma 024, the organics should be composted, recyclable materials should be sorted and recycled, while nonrecyclables, hazardous waste, and bulky waste would be incinerated with energy recovery. The mass flows of each scenario are presented in Figures 2 and 3. The charts were made and flows calculated with the program STAN, developed by TU Vienna [36]. The mass flows were assessed based on the following assumptions: 14% of generated MSW is recycled through the informal sector; the sorting plants receive daily 1725 Mg of recyclables from the State of Mexico. The source separation efficiency of the baseline corresponds with the official data from the Environmental Ministry of Mexico City [33]. The separation in AD and Norma 024 scenarios is determined by the waste composition from Figure 1 and corresponds to the ideal efficiency for comparison. As well, the sorting efficiency of the MBT is based on Navarotto and Llauro [37]. Detailed studies of process efficiency for MBT plants, in terms of sorting efficiencies and quality of recovered materials, are scarce in published literature. According to Cipman et al. [38], the study of Navarotto and Llauro [37] is one of the most detailed descriptions available. During their tests, the MBT Ecoparc 4 was subjected to a three-month-long campaign, and materials flows were recorded, sampled, and analyzed, including waste input, products, plant residue, and some intermediary process flows. The detailed process description of the material recovery section in the Ecoparc 4 MBT plant in Barcelona, Spain, is given in the Supplementary Material.

Hospital and hazardous waste is assumed to be gathered separately and burnt at special incineration plants, however, it is not considered in the mass flow analysis. The sources and methods used for each estimation in the mass flow diagram (MFD) are presented in Table 1. Each MFD is discussed in detail in the Supplementary Material.

 **Figure 2.** *Cont*.

**Figure 2.** Mass flows of waste scenarios: (**a**) baseline scenario; (**b**) scenario with MBT.

**Figure 3.** *Cont*.

(**b**) 

**Figure 3.** Mass flows of waste scenarios: (**a**) scenario with wet AD; (**b**) scenario in compliance with Norma 024.


**Table 1.** Description of the discussed scenarios.

#### *2.5. Selection of Indicators*

To examine the indicators for the sustainability of a waste treatment scenario a literature review was performed. Hokkanen and Salminen [39] identified a set of 24 indicators for waste managemen<sup>t</sup> and divided them into six groups: economic, technical, environmental, political, employment and resource recovery. Greene and Tonjes [40] applied 12 indicators: MSW recycled, MSW landfilled, MSW diverted from the landfill, diversion rate, recycling rate, curbside recycling rate, landfilling rate, recycling per capita, landfilling per capita, diversion per capita, GHG reductions, energy savings. The present study applied the sustainable indicators used in the AHP model of Milutinovi´c et al. [41]:


The set of indicators was selected according to the following criteria: relevance of the indicator for local sustainability of waste management, potential measurability at the local level and power of the local authority to change the outcomes measured by the indicator [19]. No extra indicators were added during the AHP process.

#### *2.6. Evaluation of Indicators*

#### 2.6.1. Overall Waste Management Performance

The amount of waste that remains after treatment for landfill disposal was estimated based on the mass flow modelled with the help of the STAN software. The software was used for both the representation and calculations. The baseline scenario is provided by the data presented by the Environmental Ministry of the city [33]. Information for other scenarios was based on data from the literature, since the technologies considered are not presently available in Mexico City; the data for MBT was based on Navarotto and Llauro [37], AD and incineration data on the report for the Austrian ministry of Ecology [42], and composting on Andersen et al. [43]. These sources have proven reliable when used in previous studies from Rodi´c and Wilson and Masood et al. [3,44]. Due to the absence of the technologies discussed in Mexico and overall in the region of Latin America, no real data could be used for the research. Therefore, the study could only consider values from the literature.

#### 2.6.2. Environmental Indicators

Nitrogen oxides emissions to the atmosphere, BOD in water, and mercury concentration in soil were estimated within the life-cycle inventory (LCI) using EASETECH software [45] and its last available database version from July 2017. These indicators are assumed to give an overall environmental assessment, considering contamination in the atmosphere, water, and soil. The same criteria were applied in the AHP analysis in Multinovic [41].

This study has been carried out using the EASETECH model. In assessing emissions, this model calculates the emissions from the point at which a material is discarded into the waste stream to the point at which it is either converted into a useful material or finally disposed of (Kirkeby [46]). The EASEWASTE models were elaborated including waste treatment options and external processes, which can appear both upstream and downstream of a waste managemen<sup>t</sup> system. The program also evaluates emissions associated with the fuel consumption for collection and transportation of waste. However, emissions from transportation to recycling facilities have not been included due to the lack of data on distances. Recycled materials and energy derived from the waste managemen<sup>t</sup> system are regarded as substitutes for virgin materials or energy. Emissions into water, air, and soil alongside resource consumptions, which are avoided as a result, are subtracted from the other emissions and resource consumptions in the waste system. The model calculates emissions into water, air, and soil, along with the consumption of resources. The model applies life-cycle impact assessment (LCIA) methods for conversion of these exchanges into environmental impacts [46,47].

Climate change impact was estimated based on the International Reference Life Cycle Data System (ILCD) method. It contains the impact categories recommended by the European Commission and described by Hauschild et al. [48] (2013). The source for characterization factors for climate change at midpoint was the IPCC report for a 100-year period [49]. The calculations were made using the database of EASETECH from July 2017. All the processes were based on the premodelled technologies existing in the database, except the MBT. The MBT plant was presented as a combination of a composting and

sorting plant. Among others, the emissions from the incineration process were calculated using the data from the Danish plant described by Møller et al. [50]. Emission profiles were not available for some external processes, but most impacts are covered.

#### 2.6.3. Economic Indicators

The economic indicators include investment and operational costs for the prevailing treatment method for each scenario: AD, MBT, and incineration. The assessment was performed based on data from IADB [51], Münnich et al. [52], and Tsilemou and Panagiotakopoulos [53]. The costs of the current scenario are given in IADB [51]. The calculations were based on € Mg MSW−1. For evaluation of investment costs, the expenditures for design and construction of landfills and waste treatment facilities were considered. However, taxation, amortization, and inflation were not considered.

This study does not include the collection and transportation costs, even though they may reach up to 50–80% of the entire costs of a waste managemen<sup>t</sup> system, as in industrialized countries [54]. The expenditures for education campaigns for citizens regarding source separation are also not included due to the lack of data.

#### 2.6.4. Social Indicators

The number of new jobs in waste treatment is calculated depending on the amount of processed waste (Mg MSW). The evaluation was done based on the data from the Environmental Ministry of Mexico City SEDEMA [33], Friends of the Earth [55], and European Commission [56]. Public acceptance is a qualitative criterion which cannot be measured, therefore, the scale established in the AHP (1-worst, 9-best) was used for the assessment of this criterion. These results were obtained during interviews with experts, where they expressed their opinion about different waste treatment options.

#### 2.6.5. Ranking of Indicators

Indicators (not the alternatives) are compared pairwise applying a scale from 1 to 9 for making a ranking. An example questionnaire given to the experts can be found in Table 2. When "7" is chosen in the first row, the landfill elimination rate is much more important than the recycling rate for the sustainability of waste management, according to the personal opinion of the consulted experts, while "5" chosen in the second row means that the emissions of greenhouse gases are considerably more important than the rate of elimination of landfills.


**Table 2.** Example of ranking questionnaire for the experts.

Use the scale from 1 to 9 (where 9 is extreme importance and 1 of no importance) to indicate the relative importance of indicator in the left column to the indicator in the right column. Between 1 and 9, all situations are intermediate. Only one entry can be made in each row.

Pairwise comparisons were used to determine the relative importance of each alternative in terms of each criterion. To make a pairwise comparison and subjective priority weightings for the criteria, experts working in the waste managemen<sup>t</sup> sector in Mexico City were consulted. This includes

scientists working at the Universities UAM, UNAM, and Instituto Politécnico Nacional (the three main public universities in the City) in environmental and economic science and in the field of waste treatment, as well as experts from the governmen<sup>t</sup> of Mexico dealing with the problem of waste managemen<sup>t</sup> and environmental consultants. These Mexico-based scientists and experts were asked to rank the importance of the criteria with respect to the goal, selection of the most sustainable waste managemen<sup>t</sup> scenario. The pairwise comparison was made by 5 experts. The process of filling out the questionnaire is time-consuming and the authors received only 5 filled out ones out of 14 distributed. The experts were not informed of the results of the indicators presented in Table 3. The filled-out questionnaires received by the authors are presented in Supplementary Material.

#### *2.7. Limitations of Methodology*

The methodology has some limitations. The mass flow analysis for the scenarios b, c, and d was based on Navarotto and Llauro [37], the report for the Austrian Ministry of Ecology [42], and Andersen et al. [43], due to the absence of real data in the regional context. The calculations made in the EASETECH model were based on the default data, because no chemical analysis of the waste composition was made. For the social indicators, the number of the workplaces was estimated through reports which were applied in other, previous studies. The costs of waste treatment were based on data from IADB [51], Münnich et al. [52], and Tsilemou and Panagiotakopoulos [53]. These limitations may affect the outcome of the study. The evaluation of indicators directly influences the ranking, which may lead to biased results. Nevertheless, the assumptions should to be made to implement the research.

#### **3. Results and Discussion**

#### *3.1. Assessment of Indicators*

The evaluation of the indicators (overall waste managemen<sup>t</sup> performance, environmental, economic, and social criteria) is presented in Table 3.


**Table 3.** Evaluation of indicators.

Figure 4 shows the ranking of indicators made based on the pairwise comparison of the experts. According to the preferences of the experts, environmental criteria are most crucial for integrated sustainable waste management, while the economic indicators play the least significant role. The calculation of ranking of indicators is available in Supplementary Material.

**Figure 4.** Ranking of indicators with respect to the goal.

## *3.2. Scenario Ranking*

Following the pairwise criteria, the criteria weight of each scenario with respect to the goal was obtained as shown in Figure 5, presented in Supplementary Material Criteria's evaluation. The results show that Scenario 4, involving recycling of recyclable waste, composting of organic waste, and the thermal treatment of the remaining mixed waste for energy recovery (WTE), has the highest-ranking priority of 30.78%. The results show that the more separately collected categories of waste are involved in the plan, the more sustainable the scenario is considered to be by the experts.

**Figure 5.** Ranking of scenarios.

Figure 4 indicates that in assessing the sustainability of waste treatment, the most relevant indicator is the GHG emissions. Therefore, the WTE scenario was ranked as the highest. However, it should be considered that the evaluation of indicators for this scenario was based on data from the literature, under the assumption that source separation rate is 100%. In order to achieve that result, the new incineration plant would have to comply with European standards, including emissions standards [57].

## *3.3. Sensitivity Analysis*

The last step of the decision process using the AHP method is sensitivity analysis, where the input data of criteria weighting are modified to observe their impact on the results. If the ranking of scenarios does not change, the results are said to be robust [58]. The sensitivity analysis was performed to assess the influence of individual sustainability indicators on the proposed waste treatment scenarios. The following cases were examined following the procedure of Milutinovi´c et al. [41]:

Case 1: All indicators had an equal weighting factor (10%). In this case, scenario ranking was changed. Baseline Scenario was top-ranked and most sustainable in terms of all indicators with a priority ranking of 29.9%. Scenario 4, which corresponds to the new waste managemen<sup>t</sup> regulation in Mexico City and involves the incineration process and composting, was then in 2nd place.

Case 2: The group of environmental indicators was assigned a weighting factor of 100% in total (each of them had a weighting factor of 25%), while all the others had a weighting factor of 0%. In this case, Scenario 4 was ranked as the best with priority ranking of 34.62%, due to smaller CO2 and nitrogen oxides emissions, as well as the fact that recycling greatly reduces the disposed waste volume.

Case 3: One waste indicator had a weighting factor of 100%, while all others had a weighting factor of 0%. There are 10 options, and the results showed that when economic and social waste managemen<sup>t</sup> indicators had a weighting factor of 100%, while others had a weighting factor of 0%, Scenario 1 (baseline) go<sup>t</sup> the first place. Scenario 4 (WTE) ranked best in cases where indicators of landfill disposal, CO2 emission, and content of heavy metals in soil indicators had a weighting factor of 100%, while others had a weighting factor of 0%.

Case 4: The group of economic indicators were weighted at 100% in total (each at 50%), while all others had a weighting factor of 0%. Here, Scenario 1 is mostly preferred with a priority ranking of 54.57%. Under these conditions, the ranking changed because of the lower investment and operation costs of the existing waste managemen<sup>t</sup> system.

Case 5: The group of social indicators was given a weighting factor of 100% in total (each 50%), while all others had a weighting factor of 0%. In this case, Scenario 3 was ranked in first place with a priority ranking of 40.68%. Scenario 4 had the lowest place in the ranking due to the small number of jobs created and public disquiet about thermal waste treatment.

All the results of the sensitivity analysis are presented in Figures 6 and 7. The calculations are presented in Supplementary Material.

**Figure 6.** Results of sensitivity analysis: Case 1, Case 2, Case 3.

**Figure 7.** Results of sensitivity analysis: Case 4, Case 5.

The main conclusions from sensitivity analysis are the following:

Scenario 4 (WTE) ranked first when priority was given to the environmental indicators. However, this result is not stable due to fluctuations in the weighting of criteria. When priority was given to the economic and social impact of waste managemen<sup>t</sup> strategies, Scenario 4 was not ranked first. In Case 4, the baseline scenario was ranked the highest, and in Case 5, the scheme involving MBT.

Scenario 2 (AD) is the most stable solution. With different indicators rankings, it is ranked third in Cases 1, 2, and 5 and never ranked first, however other scenarios change their rating so much that Scenario 2 emerges as the most stable.
