Table 1 presents an overview of the 20 papers reviewed using different types of information and categorization. After the author’s name, the main purpose of the paper and decision focus are briefly presented. The type of settings (marine or terrestrial) addressed by each paper, geographic information (continent, region, country, or city), the type of plastic dealt with and type of company or place involved in the case study are then mentioned. The next two columns are about the MCDA methods applied to solve problem proposed and the weighting strategy used. The last column summarizes the main conclusions of each paper.

Most studies are concerned with terrestrial settings (80%). Bachér et al. (2018) evaluated bottlenecks in the recycling value chains and waste collection methods is the subject of Balwada et al. (2021). A large part of terrestrial environment studies deals with end-of-life options (Gomes et al., 2008; Cardoso et al., 2009; Rochat et al., 2013; Vinodh et al., 2014; Bhagat et al., 2016; Husáková et al., 2016; Nirmala and Uthra, 2017; Delvere et al., 2019; Mikusová et al., 2019; Vo Dong et al., 2019; Geetha et al., 2021). Different disposal options (including recycling options) are considered in the MCDA frameworks built for each of these papers. Reverse logistics is examined by Mavi et al. (2017); Senthil et al. (2018), and Jimenez et al. (2019) (see Table 1).

Meanwhile, the MCDA case studies in a marine setting deal with plastic litter management, namely, Chrissley et al. (2017) present cleaning options for marine debris removal systems and Deshpande et al. (2020) look at end-of-life issues. Risks associated with human activities are assessed by Lieske et al. (2019), and the effectiveness of plastic reduction consumer-based actions is studied by Marazzi et al. (2020) (see Table 1).

Case studies reported in the reviewed articles span three continents and more than 12 countries, with some studies covering larger regions (please see column Country/Region of Table 1, where 3 regions are mentioned). For seven of these studies, the location of the first author was used to define the place of the study (Cardoso et al., 2009; Husáková et al., 2016; Chrissley et al., 2017; Nirmala and Uthra, 2017; Delvere et al., 2019; Mikusová et al., 2019; Geetha et al., 2021).

Europe and Asia are viewed as the continents with most studies in MCDA papers in the environmental field (Huang et al., 2011) and this review corroborates this (see Figure 2). By country, India leads with six studies. However, the analysis by country is debatable because some studies are focused on more extensive regions rather than a country, for example, the European Union; the Western North Atlantic Ocean; and Europe. The oldest study in this review concerns Brazil and was published in 2008 in Omega-International Journal of Management Science, and few articles appeared between then and 2015 (Figure 2). Since 2016, on average three papers have been published per year.

Many types of alternatives to manage plastic waste have been considered. It is important to group the alternatives so that the studies can be compared. Supplementary Table 1 depicts an organized presentation of different types of alternatives. There are three levels of categorization: Aim, Alternative type, and Alternatives (detailed for each paper).

Figure 3 shows the number of distinct alternatives (some options are repeated through the studies, such as recycling, for example) considering the choices proposed in the studies when the type of plastic (using classification assigned by the author) dealt with is the issue (combined information from Table 1 and Supplementary Table 1).

Alternatives concerning the selection of cleaning methods (e.g., waste collection systems and cleaning plastics from the seas) and disposal alternatives were found in almost all types of plastic categorized in this review. The most common purpose is to make a choice from the end-of-life disposal options, such as different forms of recycling, incineration, landfilling, etc. (Supplementary Table 1). This also involves choosing technological devices.

Actions and processes group of alternatives include the work of Bachér et al. (2018) who proposed the assessment and prioritization of five bottlenecks in the recycling value chains to improve the co-operation between stakeholders and the circular economy. Also the work of Marazzi et al. (2020) evaluated 27 consumer-based plastic reduction actions. In fact, people’s individual behavior in consuming single-use plastics is an active focus of concern for the management of plastics pollution in rivers, since in far too many cases this plastic waste ends up in coastal areas and seas, where it impacts aquatic wildlife and ecosystems (both freshwater and marine). Alternatives about selecting methods of cleaning and disposal options are numerous (34 alternatives are mentioned in Supplementary Table 1) and are those used by the highest number of authors (15 authors are mentioned in the same table).

Regarding removing plastics from a marine environment or freshwater systems, seven cleaning alternatives have been considered for plastic cleaning by Chrissley et al. (2017). They assessed options such as vacuum, vessel, barge (two types) and artificial floating island (three types). Although they found that an autonomous vacuum had the most favorable cost, the artificial floating island (types with motor and a sail) are too close to the first option to make a clear-cut decision possible about which is the best solution to this problem. They also added that the development (by increasing its rate of removal or capacity) of the artificial floating island (with motor) concept could alter the values of the scores achieved by the alternatives and new better positioned alternatives could emerge.

In terrestrial settings, regarding capturing and removing, Balwada et al. (2021) assess plastic waste collection methods to support the circular economy. Their results state that the deposit and refund method is the best option compared with vehicular or curbside collection, drop-off recycling or buy-back center options.

Several disposal options are evaluated as alternatives in 12 papers. Five types of recycling were examined in the selected studies: mechanical, chemical, feedstock, thermal, and bottle-to-bottle. These options were evaluated individually or in combination with other strategies.

In some studies, the traditional (or mechanical) recycling is the leading alternative for general plastic waste, marine debris, and reinforced plastic: either individually (Vinodh et al., 2014; Delvere et al., 2019; Deshpande et al., 2020), or associated with incineration (Bhagat et al., 2016). However, Nirmala and Uthra (2017) found thermal recycling the best option, with the possibility of recovering embodied energy in plastics by incineration or by using it in industry as a substitute fuel.

Pyrolysis alternatives did not perform well for Bhagat et al. (2016). This was because the technique was associated with increased incidence of air pollution and long-term illness, although techniques like plasma pyrolysis technology (PPT) could be cleaner and more efficient. Microwave pyrolysis was performed in laboratory conditions on small material samples of FRP (fiber reinforced plastics) by Delvere et al. (2019). For them, this technique is very similar to pyrolysis where waste is subjected to very high temperatures in the absence of oxygen. The outcome is a recycled fiber with almost the same tensile strength as that of the virgin fiber.

In the case of polyethylene terephthalate (PET) waste management, Rochat et al. (2013) have chosen bottle-to-bottle recycling as the best option. They assert that bottle-to-bottle recycling is a combination of conventional mechanical recycling and chemical recycling. Furthermore, this leads to a product that can be reused straightaway for food and drinks because the quality does not infringe any legal requirements stipulated for such use. For fiber reinforced plastics (FRP), Delvere et al. (2019) reports that studies suggest that chemical recycling can recover high quality fibers.

Gomes et al. (2008) and Cardoso et al. (2009) observed that there are many different kinds of plastic with specific properties (chemical and mechanical), so they studied just one type, high-density polyethylene—HDPE. These two studies were the only ones that considered a reuse alternative, and both concluded it was the best option (in Gomes et al., 2008 mechanical recycling is also in joint first place together with reuse). Geetha et al. (2021), meanwhile, compared recycling methods for six plastic types: the preferred methods are chemical recycling for HDPE; mechanical recycling for PET (polyethylene terephthalates) and PP (polypropylene); feedstock recycling for PVC (polyvinyl chloride) and PS (polystyrene); and incineration with energy recovery for LDPE (low-density polyethylene).

Landfilling is addressed for general waste, marine debris, and polyethylene. That option was in general ranked as the least favored (Gomes et al., 2008; Cardoso et al., 2009; Rochat et al., 2013; Bhagat et al., 2016). That option was also evaluated by Cardoso et al. (2009), who noted it could be the alternative that requires less energy but it does not generate income with selling products. Rochat et al. (2013) assert that landfilling plastics can be an economic loss since plastic is a reusable resource.

Bhagat et al. (2016) refer to landfilling as being a significant source of contamination, and they used mixing landfill alternatives along with recycling or incineration. However, this mixing was not enough to put alternatives of that kind in the top places. The study by Deshpande et al. (2020) identified with stakeholders two main factors (transport and the processing cost of fishing tackle and rope waste) that could result in landfilling being preferred over recycling or incineration.

For Gomes et al. (2008), landfilling was considered to have the lowest CO2 emission. However, Rochat et al. (2013) asserted that in theory the amounts of carbon dioxide (CO2) emitted to the atmosphere over a very long time horizon should be the same for landfilling and incineration. Rochat et al. (2013) were the only researchers that used a landfill with extract and burning gases as an alternative. Still, even that was not enough to be better than recycling, which they consider could provide employment and profits.

Referring to the choice of a location for recycling, Deshpande et al. (2020) highlight the importance of recycling operations within the region because the possible positive effects for the environment and society could lead to improved economic benefits from resource conservation and energy recovery. These situations depend on how the authors designed their studies. For example, Deshpande et al. (2020) did not consider the energy recovered, plastic resources conserved, and the revenue generated from recycling (occurring outside the system boundaries) for exported litter, and so they considered only the transport costs of exports. Two studies analyzed technological devices for recycling, both in Slovakia (Husáková et al., 2016; Mikusová et al., 2019). They evaluated variants of knife mill devices and both chose an option with similar characteristics and price.

Vo Dong et al. (2019) developed a “multi-period approach for the deployment of new recycling sites (Grinding, Pyrolysis, Supercritical Water, Microwave)” in France, relating to aerospace carbon fiber reinforced polymer waste. They perform their alternative evaluation through scenarios pondering the amount of waste generated per 20 years: “business as usual,” “strong/light increase,” and “strong/light decrease.” They stated that “the compromise strategy for both economic and environmental objectives lead to centralized configurations at the regions close to significant waste sources.”

Reverse logistics is a process for moving materials and raw equipment back through the supply chain, i.e., from customers to suppliers or manufacturers. Returns are managed efficiently and economically to extract as much value as possible and they become a competitive advantage (Panigrahi et al., 2018). Reverse logistics is turning out to be a promising activity of interest for many practitioners (Panigrahi et al., 2018). Three papers were analyzed in this context: Jimenez et al. (2019), to identify “good practices and trends” in the plastic industry companies; Mavi et al. (2017), to choose the most suitable third-party reverse logistic provider for a plastics factory; and Senthil et al. (2018), to analyze and prioritize the different potential risks in reverse logistics for providing useful insight to the supply chain managers and researchers for decision making.

A rather different type of study is a specific assessment (hierarchization) of different seabird species sensitivity to the risk associated with human activities in the marine environment (Lieske et al., 2019). In fact, the alternatives (i.e., the object of the evaluation) dealt with in this case study are living organisms (seabird species) as mentioned in Supplementary Table 1 and not end-of-life options or reverse logistics issues. Fisheries bycatch (particularly when involving suspended gill nets) was “identified as the greatest risk across a wide range of species.”

It is challenging to find the best options when they have very different (often conflicting) impacts. Multicriteria decision analysis (MCDA) is a way to embrace different dimensions and values to inform decision making.

Many MCDA methodologies and techniques were used in the studies reviewed. Five MCDA approaches were used by the authors of the studies to address plastic waste:

In these five approaches (Table 2 and Figure 4), the most frequent are the analytic hierarchy process (AHP) followed by TOPSIS and outranking approaches (ELECTRE III, PROMETHEE, and THOR). This trend and popularity of AHP is also mentioned in the studies by Huang et al. (2011) and Cegan et al. (2017). They suggest it is because of the accessibility and user-friendliness of the AHP method. Some studies used different MCDA methods to address the same case or plastic type (Table 1).

The AHP is also the method most often combined with other methods: MAVT/MAUT, TOPSIS, or Outranking (Vinodh et al., 2014; Bachér et al., 2018; Senthil et al., 2018; Delvere et al., 2019). Vo Dong et al. (2019) used Outranking (PROMETHEE) and TOPSIS in their study, while Geetha et al. (2021) used Outranking (ELECTRE III) to choose their alternatives and TOPSIS for a comparative analysis.

The criteria (Supplementary Table 2 and Figure 5) are grouped into a small number of dimensions (economic, environmental, operational, other managerial, political, and social). The operational dimension is the most frequent, being found in 43 studies, followed by the environmental dimension that is casted in 37 studies and still the economic dimension appearing also in a large number of studies (22). The political criteria were not present in marine environment studies, although international border issues do emerge in these cases.

Some patterns were found in the criteria used by the authors through the dimensions. These were grouped in categories and subcategories (Supplementary Table 2). Comparing the criteria from the studies showed that “Performance” is an intersectional area since diverse criteria are carrying it out in different dimensions (e.g., financial performance in the “Economic” dimension; production, resources consumption, and waste reduction performance in the “Environmental” dimension; and managerial performance in “Other managerial” dimension). “Organizational” is another intersectional category that can be found in different dimension (e.g. leadership and planning in the “Environmental” dimension; reverse logistics and service in the “Operational” dimension; feasibility, stakeholder in “Other managerial” dimension; and labor and workers safety in the “Social” dimension). It can also be noticed that “risk” is an important subcategory of criteria.

MCDA typically requires setting the value of the method’s parameters that reflect preferences, namely, the criteria weights. These parameters are elicited from the decision makers, experts, or other actors involved in the construction of the MCDA evaluation model. The actors most often consulted were identified as experts and followed those identified as decision makers (see Figure 6). It is important to mention that some authors distinguish between experts and decision makers, and that some professionals can be experts as well as decision makers. The authors of a study may be experts themselves and in some studies the criteria weights were chosen by them.

Some of these weights were assigned by panels in the context of workshops (Rochat et al., 2013; Bhagat et al., 2016; Bachér et al., 2018) or inquired about through questionnaires or surveys (Mavi et al., 2017; Jimenez et al., 2019; Deshpande et al., 2020).

The literature reviewed includes several recommendations stemming from the analysis of the alternatives that were assessed and the MCDA method’s results. Supplementary Tables 1, 2 present an extensive list of alternatives and criteria used in the studies reviewed here, which can serve either as a checklist or as a source of inspiration when using MCDA to address marine and terrestrial plastic waste management. In the following discussion we also mention the studies of Andreoni et al. (2015) and Rodrigues et al. (2018), which computed impacts on multiple criteria but did not aggregate the results using an MCDA method.

The recommendations of a study depend on the alternatives and criteria considered, and can also be contingent on specific aspects, such as the resources and culture of each company in business decisions (Jimenez et al., 2019). In some of the studies a clear winning solution emerged. For instance, Andreoni et al. (2015) found that socio-economic and environmental benefits across the EU would be significant if their recommended alternative was implemented, e.g., reducing approximately 1.5 × 10⁶ tons of CO_{2 eq} emissions and saving tens of millions of euro in waste management costs. The same authors suggest these benefits might be even larger, if technological developments or faster adoption paths were considered. The study by Marazzi et al. (2020) found several of their top-ranked solutions to reduce plastic pollution in rivers were actually actions already implemented in several countries (e.g., banning plastic straws). Other results reflected the conflicting nature of different criteria, e.g., economic viability vs. other qualities (Delvere et al., 2019), leading those authors to conclude that much work remains to be done to develop better solutions.

The best alternative is often a combination of multiple strategies, e.g., combining recycling and incineration (Bhagat et al., 2016), combining alternatives to benefit from different types of litter removal (Chrissley et al., 2017), or combining technologies in a waste supply chain (Vo Dong et al., 2019). Yet, in their analysis of recycling alternatives, Rochat et al. (2013) point out that the interest of recycling options depends on wider factors, such as collection rate or the availability of other traditional outlets for recycled plastic. Similarly, Gomes et al. (2008) remark that solutions such as recycling methods often cannot be fully appraised without considering the logistics system in which these recycling solutions will be included. Therefore, if the best alternative is a combination of multiple elements, it must be kept in mind that the number of potential combinations when considering a broader system can be even larger.

As policy recommendations, Andreoni et al. (2015) suggest that more challenging targets could be set by the EU or its Member States, while taking into account the associated costs and the feasibility of meeting such targets, alongside the potential benefits. Bachér et al. (2018) call for the drafting of detailed statistics on waste across the EU and technological solutions to enable the flow of materials to be tracked. This would include both quantitative and qualitative data encompassing economic, social, and environmental aspects. Improving data collection methods is also important to determine the relevant material flows (Rochat et al., 2013). Marazzi et al. (2020) call for more data on impacts such as water consumption and carbon emission impacts, and the use of life cycle assessment in combination with MCDA (on this topic, see also Rochat et al., 2013; Dias et al., 2019). Delvere et al. (2019) point out that MCDA results might depend on just a few criteria in cases where data is available only for those criteria, but not for other ones. Also concerning data, specifically on microplastics, Rodrigues et al. (2018) call for improved monitoring and the adoption of more stringent size limits, in order to not underestimate the problem and also to allow comparing the results of different research works.

For Deshpande et al. (2020), it is important to engage communities, and MCDA can be instrumental in this regard. Focusing on end of life alternatives, they recommend clearer regulations on recycling and more work on the assessment of environmental and economic impacts of different alternatives so that choices can be made with less uncertainty in the context of the EU’s circular economy strategy for plastics. Clear incentives for consumers to change their behavior and favor pro-environmental behaviors have also been suggested (Marazzi et al., 2020).

Complementing these analyses, Khandelwal and Barua (2020) evaluated several policy-related barriers to implementing a circular economy in the plastics industry. Per their results, more active measures from governments are needed. In particular, economic incentives and tax benefits are needed for organizations to build technology and innovation. Training and awareness programs to enhance the knowledge and skills of stakeholders are also a priority.

In general, the authors of the studies reviewed recommended MCDA as a tool for choosing the best solutions for clearing marine litter or, more generally, in environmental management (e.g., Bhagat et al., 2016). MCDA proved to be useful in that it enabled environmental, socioeconomic, and other criteria to be considered simultaneously. The possibility of considering qualitative criteria has also been praised, along with the possibility of involving different stakeholders (Bhagat et al., 2016; Deshpande et al., 2020). In participatory decision making, participants have the opportunity to learn from each other. Feedback from the stakeholders and experts involved in MCDA has been reported to be positive (Bachér et al., 2018). Another reported advantage of MCDA is its transparency (Bhagat et al., 2016; Deshpande et al., 2020).

However, most authors point out that MCDA is limited by the availability of comprehensive, updated and sufficiently detailed data. Also, results are often based on inputs provided by experts, reflecting their interpretation of the available information, and thus being dependent on the situation and possibly varying in time (Bachér et al., 2018). In contexts involving value chains, they recommend involving experts in the analysis at all stages of the chain and having a clearly defined goal for MCDA. Some authors also remark that MCDA requires resources such as time and money to gather all the information required (Bachér et al., 2018). Finally, MCDA results can also depend on a number of modeling choices, including choice of variables and normalization operations (Rochat et al., 2013).