Policy Insights from a Single-Operator Model for Municipal Solid Waste Management

Abstract

Driven by the path of ecological transition, municipal solid waste management is now more than ever at the center of debates on the most efficient delivery methods. Although competition policy advocates subdivision into lots to facilitate medium-sized enterprise participation, in some cases—notably when substantial investments are required to achieve circular economy and sustainable development goals—a single-operator model may prove more efficient. Using a mixed research approach that integrates empirical evidence and market analysis, this study examines the relevance of cost curves, transaction costs, and market structure in determining the optimal service delivery model. The findings indicate that for large cities, consolidating MSW management services under a single contract yields significant cost advantages due to economies of scale and scope and is better suited to supporting the investments necessary for circular economy objectives. Practical implications for local policymakers highlight the need to assess utility sector policies carefully. Decisions at the local level should account for the interplay between the economic environment and the role of industrialization and economies of scale in fostering sustainable development. We suggest policymakers design policies that balance market efficiency with equitable access to services while also considering the scale of service provision, as it influences sustainability and economic resilience.

1. Introduction

In today’s public management environment, public administrators face increasing demands for accountability and resource optimization. Given that the efficient provision of public environmental services enhances social well-being [1], it is important to adhere to two fundamental criteria: economic and environmental efficiency. Economic efficiency is influenced by various factors, including morphology, business models, and market structure. Many of these factors also affect environmental efficiency and are associated with policies such as circular economy (CE) targets and sustainable development goals [2,3].

In Europe, especially since the nineties, many services provided by public monopolists have been partially liberalized to improve efficiency, as competition theory suggests that overly concentrated markets can lead to efficiency losses, typically in the medium to long term. This approach has opened many public services to competitive tendering processes. However, public authorities face a complex dilemma in structuring these ten

ders: whether to consolidate services into a comprehensive lot or divide them into multiple lots. While dividing contracts into lots is generally seen as a tool to promote competition, this approach may be overridden when economic and technical improvements justify a monopolistic setting for unitary service management.

The literature on the quality of public procurement yields heterogeneous results [4,5,6]. This aligns with evidence from a previous study [5], which did not support the hypothesis that dividing contracts into lots increases the likelihood of success for medium-sized enterprises (SMEs). Conversely, another recent study reported that splitting a contract into multiple lots is associated with a higher probability of success for SMEs [7]. Additionally, dividing contracts can effectively lower entry barriers for SMEs, particularly regarding their technical and financial capabilities [8].

These works are thought-provoking as they provide robust evidence supporting alternative theories. A gap in the literature remains, which can be addressed by analyzing unusual and significant cases. We contribute to this discussion by comparing the economic performance achievable through unitary management versus subdivision into multiple lots.

This study is driven by the following research question: What is the optimal contract size for the integrated municipal solid waste (MSW) cycle? We hypothesize that the optimal size should align with the geographical and functional dimensions of the service. The starting premise is that dividing cities into multiple service areas promotes SME participation, enhances competition, and mitigates the risk of monopolistic rents. Conversely, a monopolistic structure enables centralized coordination, maximizes operational synergies, and improves responsiveness to community needs while leveraging economies of scale. As a novel contribution to the literature, this paper presents a case study based on an empirical analysis conducted in Milan, offering concrete evidence on the impact of contract size in the integrated municipal waste management cycle.

Despite the increasing complexity of public intervention mechanisms, this analysis sharpens its focus on the economic trade-offs between centralization and supply fragmentation, supported by empirical evidence. It integrates key economic dimensions—such as economies of scale, business models, organizational synergies, and production costs—with considerations of contract award frameworks and competitive market design principles. Transaction costs, scale efficiencies, and market dynamics are pivotal in assessing a sector’s operational sustainability and economic performance. This framework acknowledges the need for contextualization, as MSW management is shaped by a complex interplay of factors, including: (i) Geographical context—territorial extension, topographical features, and road infrastructure; (ii) Socio-economic context—industrial composition, demographic trends, and tourism levels; and (iii) Infrastructural context—waste treatment facilities, technical infrastructure, and essential facility provisions.

Although the distinction between public and private management is not the focus of this article, it is worth noting the expected benefits of competitive tendering procedures that allow private firms to participate rather than relying on in-house awards. Conducting economic evaluations to demonstrate the cost-effectiveness of in-house provision compared to outsourcing can be particularly challenging due to the scale of operations, the complexity of a service composed of multiple sub-services, and the coexistence of different regulatory frameworks.

We tested our hypothesis using insights from the literature, empirical evidence, and field data. This approach enabled us to analyze reliable data and support technicians, managers, and policymakers. Our mixed research methodology strengthens data robustness by triangulating sources and methods, ensuring reliability. This triangulation allowed us to examine data from multiple perspectives and methodologies, offering more solid support for scholars, managers, and policymakers.

The remainder of this paper is organized as follows: Section 1 introduces the topic; Section 2 provides contextual information for interpreting the subsequent sections; Section 3 outlines our approach to testing the hypotheses; Section 4 presents the research results; and Section 5 discusses these findings. Finally, Section 6 concludes the study.

2. Background

The discussion on dividing contracts into lots highlights the need to balance improving SMEs’ accessibility with maintaining economic and operational efficiency. In Europe, Directive 2014/24 promotes the division of large public procurement contracts into lots to enhance SME participation but allows exceptions for efficiency considerations. Several studies have analyzed the effectiveness of this directive [4,9], identifying key factors. Research on the impact of regulatory quality on SME participation in public procurement has also found a positive relationship [10]. However, evidence on the effectiveness of contract division into lots remains inconclusive, as results vary depending on threshold levels, with significant impacts observed only in relatively small lots. While dividing contracts into lots is generally seen as a tool to foster competition, its application may be overridden when economic and technical benefits justify single-operator setting.

Various business models exist for awarding contracts, including in-house provision, outsourcing, commissioning, joint management, coproduction, and third-party certification [11,12], with possible combinations. A service contract is established between the contracting authority and an economic operator to provide services, with performance-based payment guaranteed. In this model, business risk falls on the operator; however, there is no service management risk. The administration retains direct control over service quality and execution [13].

In the service concession model, the contracting authority entrusts service management to an economic operator who assumes operational and management risks, with remuneration obtained through user fees. Service concessions can be implemented through public initiatives or private project financing proposals [14]. In contrast, in-house provision means the public administration manages the service directly, without delegating management or risks to external parties [15]. Public-private partnerships involve collaboration between a public entity and one or more private partners to implement and manage infrastructure projects or public services [16]. In the outsourcing model, service providers—usually private firms—administer the service [17]; however, this model is not prominent in large MSW contracts. Finally, cooperative management involves consortia or cooperatives handling service management and provision [18]. The choice of model depends on the contracting authority’s needs, the nature of the service, available resources, and the strategic objectives of the public entity.

Empirically, this study is motivated by a specific case. In 2020, with the impending expiration of the MSW management contract, the City Council set environmental goals in its annual planning document, aiming to open the service to market competition while ensuring uniform quality standards and economic conditions across the municipality. A European tender was launched and the contract was awarded based on the most economically advantageous offer.

3. Methods

This study employs a mixed methodology to enhance the robustness of the analysis. This approach provides a structured framework for integrating multiple methods and is widely used in the social sciences [19,20]. To ensure triangulation, we combined empirical analysis, literature review, and primary data analysis. Triangulation involves applying and integrating different research methodologies, theories, data sources, and empirical analyses to examine the same phenomenon [21,22]. Our methodological approach is based on three complementary analyses.

The first part of the analysis examines the relationship between service size and average cost, a topic extensively explored in the literature. This analysis highlights economies of scale. Specifically, we observe a limited number of units with unique characteristics at the far right of the population distribution axis. In these large cities, waste management services exhibit structurally distinct features, while the left extreme is characterized by a concentration of numerous small municipalities. Therefore, a more precise analysis requires considering sample subsets for a more representative evaluation.

The primary data source for MSW generation and management was the waste cadaster managed by the Italian Institute for Environmental Protection and Research (ISPRA). This dataset allowed us to analyze the relationship between the scale of MSW management services and service costs by testing the U-shaped curve formalized in Equation (1), which models the relationship between public service costs and operational scale.

$$C\left(x\right)=a{x}^2+bx+c$$

(1)

$C\left(x\right)$ is the cost of service as a function of scale $x$, $a$ is a positive coefficient that determines the U-shape of the curve (if $a>0$), $b$ is the marginal effect of scale on efficiency, and $c$ is a constant, e.g., fixed costs. The U-shaped curve assumes that at low levels of x (small operational size), costs are high, then decrease to a minimum point, and finally begin to increase again at higher values of x (large operational size). We discussed that it is misleading to assume the existence of economies of scale up to a certain point, beyond which costs increase due to inefficiencies in managing extensive operations. Many studies reaching this conclusion overlook that the cost structure varies significantly between large cities and small municipalities. Therefore, to conduct a targeted analysis, it is necessary to extrapolate some sub- and ancillary services not provided in small or medium-sized municipalities.

Table 1. Statistical summary of MSW cost.

Stats	Cost Per Capita (€)	Population	Cost Per Ton (€)
Min	43.44	37	7.1
1st percentile	71	110	158.8
Median	152.18	2839	347.9
99th percentile	545.07	88,737	865.9
Max	1484.25	2,748,109	1982.9
Mean	172.0	8729.6	375.8
SD	89.7	47,376.2	144.9
Kurtosis	32.2	2142.0	11.2

Source: ISPRA.

summarizes key descriptive statistics on the cost per capita, per ton of waste, and population.

In scenarios where MSW management is delegated to $n$ separate operators, all operators are assumed to operate with equivalent efficiency. We use ${\widehat{C}}_n$ to represent the costs of multiple operators without considering the coordination and integration factor $\gamma$. The cost for $n$ operators without $\gamma$ can be expressed as in Equation (2).

$${\widehat{C}}_n=n\left(\alpha +\beta \right)$$

(2)

In scenarios involving one operator, the total cost $C_1$ is given by $C_1=\alpha +\beta$. The various operators are deemed equally efficient; thus, the cost with $n$ operators, without the factor $\gamma$, can be represented as follows: ${\widehat{C}}_n=C_1$. However, when dealing with multiple operators, an additional cost, $\gamma$, necessary for coordinating and integrating diverse business plans, is added. The total cost with n operators, including this factor, is presented in Equation (3).

$$C_n={\widehat{C}}_n+\gamma =n\left(\alpha +\beta \right)+\gamma$$

(3)

Equation (4) illustrates the difference between the two alternatives, emphasizing the coordination factor $\gamma$ where ${\widehat{C}}_n$ is the total cost without the coordination cost, while $C_n$ includes the $\gamma$ factor.

$$C_n-C_1=\left(n-1\right)\left(\alpha +\beta \right)+\gamma$$

(4)

The second part of the analysis compares the total costs of MSW management under the two scenarios. Considering, for example, a group of operators that each independently produces a fraction of the service, the total production cost equals the sum of the costs each operator incurs, denoted by $C_i=q_i\times p_i$ where $q$ is the output amount, and $i$ identifies one of the potential $n$ firms in the sector. The cost of producing the total quantity $Q$ is the sum of the costs each operator incurs in producing its share $q_i$. Thus, $C_1=q_1\times p_1$. Similarly, the total cost $T{C}_n$, borne by $n$ operators, each managing a portion of the service, is expressed in Equation (5).

$$C_n=\sum _{i=1}^n\left(q_i\times p_i\right)$$

(5)

Equation (6) represents the subadditivity hypothesis, which compares the total cost incurred by a monopolist with that of $n$ firms operating in the market:

$$q_1\times p_1\le \sum _{i=1}^n\left(q_i\times p_i\right)$$

(6)

In other words, the theory of subadditivity indicates that the cost for a single firm managing the entire service $q_1$ is less than or equal to the sum of the costs incurred by multiple firms $q_i$ for each $i$.

The information for the case study in Milan was obtained from the city’s official website, as cited in [23]. The simulation designed to test our hypotheses compared the total costs of MSW management under two scenarios: the business-as-usual scenario, which envisions a one operator managing the service for the city, and the alternative scenario, which projects the costs of MSW management under the assumption that four operators manage the service in this case. Figure 1 shows the scope of our analysis.

The business-as-usual scenario foresees the incumbent company that has managed the service for decades: initially as a municipality’s special undertaking, then as an in-house joint-stock company, and from 2001 to 2021 as a joint-stock company. The alternative scenario envisions the city’s division into four areas to allow multiple operators to run the service simultaneously, considering EU legislation on competition to encourage SME participation in public procurement.

The third part of the analysis addresses the market structure, a frequently overlooked municipal waste management efficiency dimension. This aspect is particularly significant because a substantial portion of the total cost is associated with waste treatment and disposal activities—a segment of the supply chain characterized by technical monopolies, limited price elasticity, and the necessity for facility regulation within the essential infrastructure framework. In this context concentration measures such as the Herfindahl-Hirschman Index (HHI) and the concentration ratio (CR) are widely used. The HHI is the sum of the squares of the market shares $S_i$ of all $n$ players in the market, as shown in Equation (7).

$$\mathrm{H}\mathrm{H}\mathrm{I}=\sum _{i=1}^n{S}_i^2$$

(7)

The HHI provides widely used information about competitive pressure in the market, ranging from 0 (in perfect competition) to 10,000 (in monopoly).

The CR of $k$ firms is presented in Equation (8), where $k$ is the number of major players considered, $S_i$ is the market share of operator $i$, with $i$ ranging from 1 to $k=4$ in our analysis, and $0<CR<100$, where 0 corresponds to perfect competition, and 100 reflects a monopoly:

$$\mathrm{C}{\mathrm{R}}_{\mathrm{k}}=\sum _{i=1}^k{S}_i$$

(8)

Data on waste treatment and disposal facility ownership, which were already collected in a previous study [24], were updated and integrated into this analysis to compute concentration indices. Specifically, we cross-referenced data from (i) the ISPRA waste management cadaster, (ii) the Bureau Van Dijk AIDA database, and (iii) municipalities in the case of in-house provision.

Table 2. Market share of Waste to Energy (WtE) owners versus plants.

Variable	Owner	Plant
Min	0.0733	0.0734
p50	1.048	1.1631
Max	37.209	12.2401
Mean	3.4483	2.0408
SD	7.7266	2.5343
Kurtosis	14.334	8.6031

Source: The authors, based on [24].

lists the key statistics.

This methodology, however, has certain limitations. The simulation relies on data derived from the industrial structure of the incumbent operator in the city, without previous cases from which to draw direct information. This must be considered, as the calculations were conducted based on the current functional organization. The cost curve analysis indicates that its shape and parameters may vary depending on the reference sample used. Finally, in terms of market structure, it is essential to account for the diverse regulatory frameworks that significantly influence the organization of waste management services.

4. Results

This section presents key evidence from our research about the relationship between MSW management scale and cost for the community, alternative business models from a governance perspective, and the implications of market structure.

Understanding whether economies of scale exist can help optimize operations and inform policy decisions in MSW management contexts. The efficiency level is related to the optimal service size, resource use, organization, and planning [25]. Literature reviews yielded varied results on the relationship between cost efficiency and optimal service size. Some studies align with the so-called U-shaped cost curve [26], indicating an optimal size for cost efficiency regarding population [26] and concerning environmental performance [27]. In contrast, other studies did not find this relationship [28]. According to recent studies, smaller municipalities can benefit from cooperating with neighboring municipalities to reduce costs [29,30] and enhance cost efficiency, whereas cities or larger areas can consider splitting services. In addition to cost efficiency, numerous factors significantly influence MSW management efficiency. Various drivers impact MSW management efficiency [31], such as population, density, size, morphology, type of service, and environmental performance [32]. For example, higher population densities can strengthen economies of scale advantage [33]. Urban sprawl can increase costs in some medium-sized and large cities [34], depending on population changes [35]. Several studies have emphasized the role of economies of scale in specific aspects of MSW management, such as separate collection and recycling. These studies demonstrate that as the rate of separate collection increases, the total cost rises less than proportionally [30,36]. Intermunicipal cooperation is critical for achieving cost savings and enhancing service quality in MSW management, especially in rural and small municipalities. Collaboration between municipalities can help to harness economies of scale that can be unattainable independently. Thus, many municipalities can cooperate to improve MSW management efficiency [6]. This is supported by the spatial interdependencies in MSW management performance [37]. Given these insights, the changing global economic landscape and the need for substantial investments in CE goals further underscore the growing importance of economies of scale. The analyses of economies of scale in medium-to-large cities should be distinct from those regarding small municipalities and medium-sized towns because of the noncomparability of MSW management services. If MSW management includes similar sub-services in most municipalities and medium-sized cities, it encompasses many more services in large cities. In conclusion, research on economies of scale in MSW management presents a nuanced picture. Although evidence supports economies of scale, particularly in separate MSW collections and smaller municipalities, the benefits are not universally observed across all contexts [38]. Figure 2 presents some evidence.

To better interpret the arguments on the service scale, it is worth noting that most waste management operators serve more municipalities. However, this analysis is not focused on the economies of scale on the company side but on the cost that citizens pay through the waste tax, which is computed at the municipal level. This is a significant difference; otherwise, it would be necessary to aggregate data by operator and not by municipality. It is necessary to consider that the supply chain includes both the collection and treatment phases; studies aimed at analyzing companies’ economies of scale should consider part of the cost.

Figure 3 illustrates a theoretical approximation of how different business models can lead to different transaction costs within the MSW chain, divisible into two stages. The transaction cost approach is employed across various contexts, ranging from simple scenarios to broader concepts, including different resource allocation methods and economic activity coordination [39]. These costs reduce social welfare due to allocative inefficiency. Complex transactions, such as multilateral contracts involving multiple parties, increase transaction costs. Figure 3 depicts an integrated supplier operating at both levels, displaying varying market settings based on the number and scope of firms involved.

Previous studies have demonstrated that providing services under a single contract is more efficient under specific conditions [23].

MSW collection can be characterized as a natural monopoly due to economies of scale and density, which reduce the costs of the service when managed by a single company rather than multiple companies. However, the presence of multiple companies can prove more efficient beyond certain production levels or in different morphological contexts. Based on these insights, a reasonable approximation of the total industrial cost for various activities relating to waste management services is developed. The total cost includes collection, treatment, and disposal activities.

Table 3. Production cost.

Cost	HR	Vehicles	Production Cost
One contract	2444	761	314.88
Four lots	2555	795	332.26

Source: based on [23]. The table shows the simulation results to estimate the cost and production inputs needed to provide the service, considering the incumbent operator’s current organizational structure. The latter is divided into four central business units, and this structure was used as a reference to determine the resources needed to provide the same service under current conditions.

outlines the distribution of total costs based on a simulation designed to test the subadditivity hypothesis.

Table 3 indicates that the output produced by one firm incurs lower costs than that produced by multiple firms. This differential is evident when comparing the labor force and production resources: the results indicate that the monopolist achieved the same output level as the four firms with fewer employees and resources.

The concept of multiple service managers operating within the same area is ineffective and inefficient because of the duplication of fixed costs and the investments required to organize services in smaller operational zones. Such fragmentation does not result in a proportionate reduction in costs.

The role of transaction costs is also worth noting. Understood as contexts in decision-making where changing commitments result in economic losses, such costs have been extensively studied in economics [23,40]. From a broader perspective, these costs can also include coordination issues. Transaction costs include those related to the coordination and management of multiple operators, the risk of conflicts and inefficiencies in dividing responsibilities, and the uncertainty regarding how different operators can interact in managing different parts of the same service. However, few studies have measured the impact of transaction costs on the overall cost-effectiveness of public service provision. Generalizing this impact is challenging because of the many influencing factors [41].

Graphically, potential additional costs driven by the need to integrate different business plans for tariff computation are depicted in Figure 4.

We examined how a single-operator model unifies business planning, fostering operational synergies through coordinated management, optimized tariff structures, and cost efficiencies. Conversely, fragmenting the municipal service area may introduce technical and regulatory cost and tariff determination complexities. Disparities in pricing structures can emerge when multiple providers operate within distinct budgetary frameworks. In geographically homogeneous cities, tariff differentiation within the same municipality may be inappropriate, necessitating an equalization mechanism. This mechanism ensures that firms facing cost-recovery shortfalls are compensated using surplus revenues from overcompensated providers. Thus, if different tariffs are adopted within the same municipality, a detailed analysis of various business plans will be essential, considering specific data on waste production, distribution, and user characteristics. In addition, the costs of cleaning services in the city are heterogeneous and not evenly distributed, necessitating careful evaluation for effective business planning. These findings underscore the importance of comprehensive information in developing accurate cost simulations, which would complicate the analytical comparison of operating costs. Finally, regarding the market structure, it is worth noting that the empirical evidence on the role of market structure in MSW management costs is sparse [42,43,44]. Although cost formation in the first phase is elastic for the typically analyzed context variables, such as sociodemographic characteristics of the population and morphological and socioeconomic variables, the cost of the treatment phase is inelastic concerning these variables, being instead sensitive to technological aspects, tariff structures, financing mechanisms, and ownership forms. Some treatment plants are natural or technical monopolies with inelastic tariffs. Hence, economic regulation of essential infrastructure is necessary to ensure the healthy functioning of the market and guarantee a level playing field and access to nondiscriminatory tariffs. The relevant market is the smallest setting where it is possible to exercise a significant degree of market power, i.e., the ability to set prices above the competitive level [45]. A relevant product market includes substitutable products and services. This study’s relevant product market relates to treating residual MSW and related treatment options. For example, three relevant markets can be identified: WtE plants and landfills, WtE plants, and landfills. Similarly, the relevant geographic market includes areas with homogeneous competitive conditions. In this study, the relevant geographic markets are Italy and its regions. As explained in the Methods section, many studies analyzing market structure rely on two well-known indicators: the HHI [46] and the CR [47]. While these measures share some similarities, they differ in their ability to assess market concentration and power. Figure 5 (see [24] for additional details) illustrates the primary information considering Italy as a geographical dimension and residual MSW as the product dimension, which is consistent with [24]. Considering landfills and WtE plants, a relatively competitive market emerges for the national market, but this can be misleading given sectoral regulations.

Limiting the product dimension to landfills at the national level reveals significant competition. This disposal method remains the only option in six of the 20 regions. The scenario shifts when considering waste processed in waste-to-energy (WtE) plants as a product dimension. An HHI near 2000 indicates moderate market concentration. However, treatment and disposal costs tend to decrease as the C1 index increases. In contrast, an increase in the HHI index corresponds to a slight cost increase. The literature should focus more on market structure because anti-competitive practices can emerge in this sector, particularly in the treatment phase of the waste management chain. These practices can influence overall service costs, often analyzed primarily in the collection phase.

However, the evidence presented needs to be interpreted cautiously because of the limited number of specific studies. Further analysis is required to confirm the relationships identified, even if the integrated approach and triangulation were consistent with the literature.

5. Discussion

The international context necessitates more efficient operations within the CE framework to enhance resilience, stimulate growth and reduce resource dependence [48,49]. The analysis presented in this study underscores the benefits of a single contract for municipal waste management in cities characterized by substantial investment requirements to achieve CE goals in MSW. Consistent with the literature, firm size emerges as a significant driver for CE initiatives, especially compared with smaller firms, which are often financially constrained [50,51]. Larger firms are typically seen as better positioned to invest in the technological and structural adjustments required for a successful transition to a CE. The availability of financial resources is pivotal to facilitating CE investments [52,53]. Although firm size influences the scope and nature of investments, firms’ financial landscape, strategies, and structural capabilities are equally critical in shaping their commitment to CE. To this end, as previous literature suggests [54,55], performance assessment is needed given that there is room for improvement and that performance is also driven by tariff regulation. However, the choice between dividing into lots or adopting a single contract hinges on a comprehensive assessment of the pros and cons of management efficiency, competition promotion, risk management and economic considerations. Dividing the data into lots can support SMEs but requires careful management and coordination. In contrast, a single contract can streamline management and use economies of scale, but at the risk of reduced competition. Thus, decisions should be based on a thorough analysis of the specific context, evaluating how to optimally balance these factors to achieve the best possible outcome regarding value for money and public satisfaction. We propose a strengths, weaknesses, opportunities, and threats (SWOT) analysis in

Table 4. SWOT analysis of alternative models.

	Division into Lots	Single Contract
Strength	Facilitating the participation of SMEs promotes diversification while enhancing competition. In addition, adapting procurement to specific categories or specializations increases flexibility, leading to higher-quality offers and greater alignment with the market’s needs.	Simplifying administrative management reduces monitoring and coordination costs. Using economies of scale lowers production costs while minimizing transition costs, which ensures service continuity with fewer operational disruptions.
Weakness	Fragmentation can limit economies of scale and diminish overall economic efficiency, whereas managing multiple contracts increases administrative complexity and associated costs. Coordinating multiple suppliers introduces the risk of inefficiencies due to potential synchronization difficulties.	Dependence on a single operator can lead to delays or increased costs if the operator underperforms. Furthermore, limiting participation to large companies reduces competition, potentially leading to monopolistic rents.
Opportunity	Specialized bidding can enhance the quality of services by attracting more designed expertise, while increased competition among bidders potentially reduces costs for the contracting authority.	A single operator can reduce costs through economies of scale, whereas a large, long-term contract facilitates significant investments in infrastructure, promoting sustainable development and operational efficiency.
Threats	Fragmentation can compromise operational efficiency. Excessive fragmentation introduces operational inefficiencies and coordination challenges. If not carefully planned, fragmentation can paradoxically limit competition.	Relying on a single supplier concentrates on risks. This approach can also limit competition, potentially increasing costs.

Source: The authors.

.

The policy implications are as follows: while dividing contracts into lots to promote SME participation in public procurement is a viable approach, in MSW management—where substantial investments are required and economies of scale can be significant—this strategy may not always be the most efficient solution. Our findings indicate that a single contract can be more cost-effective, but it is essential to carefully consider a territory’s complexity, orography, geographical extent, and existing infrastructure.

Dividing a city into multiple geographic areas, each managed by a different company for MSW management, fosters a competitive environment that can lead to inefficiencies and inconsistencies in service delivery. This decentralized approach often lacks coordination, complicating operational synergies and effective community response maximization. Therefore, this model can result in higher operating costs and complicated service quality uniformity across the city, negating potential economies of scale benefits and posing challenges to equity among residents. A single operator allows for more coordinated management and resource optimization, thus mitigating inefficiencies due to fragmentation. It also supports adopting long-term strategies and facilitating infrastructure investments necessary to meet CE goals. Therefore, policies should carefully balance the promotion of competition with the enhancement of management efficiency, considering the unique characteristics of the MSW sector.

The integrated city utility model represents an effective and sustainable system for managing complex public services. This approach ensures central coordination that maximizes service synergies, providing a more adaptable response to community needs. In addition, this model facilitates the provision of public services in a coordinated manner, thereby generating economies of scale that foster the adoption of advanced technologies and reduce operating costs.

Economies of scale and scope emerge as key factors in improving the efficiency of municipal waste management, albeit with variations related to the specific context and governance of the service. The U-shaped pattern of costs in relation to population observed across the entire sample confirms the size thresholds beyond which the benefits of scale are attenuated or reversed. However, when narrowing the analysis to larger municipalities and making the sample more homogeneous, this relationship tends to change and is no longer robust. The reason is that the services’ scope influences the average cost per ton. As city size increases, waste management services tend to encompass a broader range of sub-services than smaller municipalities, affecting cost structure and altering its relationship with operational scale. Similarly, business models and market structures significantly influence costs, highlighting the importance of appropriate economic regulation to ensure competitive conditions and fair access to services. In this regard, waste treatment capacity is directly related to the probability of achieving the European CE goals: maximizing the percentage of recycling in line with the CE package and reducing the share of waste landfilled to within 10% of the total waste. In this context, both SMEs and large multi-utilities can significantly contribute. In particular, through investments in the recycling chain of sorted waste and the development of plants to treat residual waste. Considering the context of MSW, the Italian peculiarity lies in the fact that there is a significant network of companies involved in the recycling of sorted waste, particularly packaging waste.

In contrast, there are significant disparities in treating residual MSW between regions. In this scenario, developing plants for treatment and disposal requires significant investments, and multi-utilities play an increasingly important role.

While this work provides insights into the economics of MSW management, it is important to acknowledge some limitations. It is primarily based on a simulation using data from the industrial structure of an incumbent operator, with no previous direct cases for comparison. Additionally, examining the cost curve is intended to illustrate how its shape and parameters may vary depending on the reference sample. Further studies are needed to confirm the relationships identified and explore the robustness of the results. Finally, different regulatory frameworks impact waste management governance and should be considered in future research.

6. Conclusions

This study analyzed the optimal contract size for MSW management from the governance perspectives, focusing on economics and policy implications. Based on a literature review, field data, and empirical simulations, the results indicate that a single operator can be preferable due to economies of scale, reduced transaction costs, improved coordination, and the additional benefits of business synergies. These advantages are particularly relevant in geographically uniform cities, where fragmented service provision can lead to inefficiencies, complicate management, and compromise service quality and timeliness. However, the decision to adopt a single-operator model must be contextualized and adapted to local conditions to maximize collective well-being.

This model is based on a comprehensive analysis of several crucial factors. First, economies of scale are significant, and the results further demonstrate that, on average, increasing the service scale reduces costs. Second, the emergence of synergies represents an important benefit. These include the seamless integration of services, improved internal coordination, overcoming the complexities of managing multiple entities, and streamlined contract management, allowing municipalities to interact with a single operator rather than multiple ones. Finally, as the capital-intensive phase of the waste management chain, market concentration in the treatment and disposal segment also significantly impacts price formation, thereby influencing overall service costs.

However, evaluating the optimal setting requires robust and detailed information and data, often available to operators only due to information asymmetries. This data scarcity complicates the comparative analysis of different configurations, making it challenging to assess their relative efficiency and effectiveness. Consequently, adopting a single-operator model is a valid discretionary decision when adequately justified and contextualized. It balances efficiency and competition, mitigates structural inefficiencies, and enhances service quality. Additionally, the benefits of unitary management—such as improved service integration, streamlined coordination, and simplified contract management for municipalities—further support this approach. However, a detailed assessment requires a methodology based on reliable data and a thorough market-specific analysis.