# Assessing the eco-efficiency of inter-municipal waste management services in France

Aissatou Ndimblane, Kassoum Ayouba, Olivier Aznar

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Aissatou Ndimblane, Kassoum Ayouba, Olivier Aznar. Assessing the eco-efficiency of inter-municipal waste management services in France. Journal of Environmental Management, 2025, 393, pp.126941. 10.1016/j.jenvman.2025.126941. hal-05232549

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Research article

# Assessing the eco-efficiency of inter-municipal waste management services in France

Aissatou Ndimblane<sup>a,b,*</sup> <sup> ID </sup>, Kassoum Ayouba<sup>c</sup>, Olivier Aznar<sup>b,D</sup>

## ARTICLE INFO

JEL classification:

C14

C61

H21

Q53

Keywords:

Eco-efficiency

Municipal waste management

Conditional approach

Contextual variables

## ABSTRACT

We assess the eco-efficiency of French inter-municipal cooperation entities in charge of waste management. We employ a conditional order-m approach to (i) estimate their eco-efficiency considering variables characterizing their environmental context, (ii) evaluate the effect of these contextual variables on the eco-efficiency. Our results demonstrate that the population size, the type of area (e.g., tourist, rural), and the waste pricing systems significantly influence eco-efficiency. These findings underscore the importance of tailoring local waste management policies to the specific characteristics and available resources of each area. They provide valuable insights for local authorities seeking to enhance eco-efficiency and optimize waste management practices.

## 1. Introduction

Waste management accounts for the largest share of public expenditure on environmental protection in France (Eurostat, 2023). French authorities enforce laws and regulations that promote waste prevention and that aim to reduce the economic and environmental costs of waste management. At national level, several regulations have been adopted. For example, the 2015 law on energy transition for green growth<fnref n="1" /> mandates a 65 % recycling rate for biowaste by 2025. This law also promotes the implementation of incentive-based waste pricing and aims to ensure that at least 25 % of the population is covered by this pricing system by 2025. These regulations share a common objective: they encourage waste prevention and sorting, while prioritizing material and organic recovery over incineration and landfill. However, stricter legislation adds new requirements for waste collection and treatment, which increase management costs. According to the Statistical Data and Studies Department (SDES), current expenditure on waste management in France increased by an average of 4.4 % per year (in current euros) between 2000 and 2019 (SDES, 2021). Controlling waste management costs is therefore a major challenge for local authorities. In France, inter-municipal cooperation entities (IMCs) are responsible for

providing household waste management services for inhabitants of their areas. Some of these IMCs are implementing solutions such as local biowaste management or incentive-based pricing to limit the quantities of waste to be collected and treated. In this context, it is crucial to assess the eco-efficiency of IMCs, taking into account local contexts and the waste management policy instruments adopted. This is the main objective of our analysis. Specifically, we first evaluate the eco-efficiency of IMCs in charge of waste management, then we examine the effect of socio-demographic factors, such as population size and IMCs typology (rural, tourist, urban, or mixed), as well as the effect of waste management policies on eco-efficiency in the French context.

A significant portion of the literature on public service performance consists of comparative studies that identify and describe best practices among decision-making units (DMUs) based on efficiency calculations (Sarra et al., 2017). These studies distinguish between parametric and non-parametric methods, depending on whether or not the functional form of the technology is explicitly specified (Simoes and Marques, 2012). The non-parametric approach is the most widely used in municipal waste management service provision (Sarra et al., 2017), due to its flexibility, which makes it easy to handle multi-input and multi-output analyses (Ruggiero, 2007). It is based on envelopment

<footnote><sup>a</sup> ADEME, 20, avenue du Grésillé - BP 90406, Cedex 01, 49004, Angers, France</footnote>

<footnote><sup>b</sup> UMR Territoires, Université Clermont Auvergne, AgroParisTech, INRAE, VetAgro Sup, 63370, Lempdes, France</footnote>

<footnote><sup>c</sup> CESAER, Institut Agro, INRAE, F-21000, Dijon, France</footnote>

<footnote>* Corresponding author. VetAgro Sup, campus agronomique, 89 Avenue de l'Europe, 63370, Lempdes, France.</footnote>

<footnote>E-mail addresses: Aissatou.NDIMBLANE@doctorant.uca.fr (A. Ndimblane), kassoum.ayouba@inrae.fr (K. Ayouba), olivier.aznar@vetagro-sup.fr (O. Aznar).</footnote>

<footnote n="1">¹ Law no. 2015-992 on Energy Transition for Green Growth (Energy Transition Law).</footnote>
estimators such as Data Envelopment Analysis (DEA, see Charnes et al., 1978) or Free Disposal Hull (FDH, see Deprins et al., 1984). DEA assumes both convexity and free disposability of the production set, whereas FDH relies only on free disposability. FDH requires fewer assumptions and is therefore the less restrictive of the two methods, that is why we considered it for this study.

Some studies have focused on the impact of contextual factors, such as the economic and sociodemographic characteristics of DMUs, on efficiency measured using non-parametric methods. In the context of waste management services, the two-stage approach is often used (e.g., Sarra et al., 2017; Delgado-Antequera et al., 2021; Lo Storto, 2021). This method involves estimating efficiency scores in the first stage, and then regressing them on contextual variables. However, this approach relies on the separability assumption, which postulates that contextual variables only influence the distribution of efficiency scores and not the technology frontier itself<fnref n="2" /> (Simar and Wilson, 2007). This hypothesis is often invalidated in the case of public service provision, where economic and sociodemographic characteristics influence both the quantities of resources used and the levels of outcomes achieved (Cordero et al., 2017). This is the case in the waste sector, where it has been shown that the quantities of waste collected are strongly linked to sociodemographic factors such as population, density, tourism, urbanization (e.g., Romano et al., 2019). Simar and Wilson (2007) highlight that the two-step approach introduces bias when the separability condition is not met. A solution is to consider the conditional approach, proposed by Daraio and Simar (2005) that integrates contextual factors directly into the analysis, avoiding the restrictive separability assumption.

Although several contextual variables (e.g., population, density, tourism) have been identified as relevant, their influence on the efficiency of waste management services remains contradictory in several studies (Sarra et al., 2017; Lo Storto, 2021). For example, Guerrini et al. (2017), who studied a sample of Italian municipalities, concluded that small municipalities tend to benefit from a more favorable operating environment than larger ones. Conversely, Expósito and Velasco (2018) found that population size does not explain the efficiency of waste management services in Spanish regions. These discrepancies may be explained by different contexts, since as pointed out by Daraio et al. (2024), country-specific studies are required due to the complexity of waste management. The contrasting findings in the literature underscore the importance of examining how these contextual variables influence the eco-efficiency of waste management services in different contexts. Also, few studies (e.g., De Jaeger et al., 2011; Carvalho and Marques, 2014) have considered local waste management policies in their assessments. These studies specifically evaluated the impact of policies such as incentive-based pricing for waste (De Jaeger et al., 2011) and industrial composting of biowaste (Carvalho and Marques, 2014), but only in terms of economic efficiency. Furthermore, as these studies were carried out in various countries, the literature still lacks specific insights into the French context.

To the best of our knowledge, our study is the first in the French context, to assess the eco-efficiency in waste management and to explore the influence of contextual variables on it. Moreover, it also contributes to the literature by examining the impact of local waste management policies, specifically incentive-based pricing and separate biowaste collection, on the eco-efficiency of waste management. These policies are increasingly implemented in France due to the regulations mentioned earlier, yet their effects on eco-efficiency remain largely unexplored. To achieve this, we use a conditional order-m approach (see Cazals et al. (2002) and Daraio and Simar (2005, 2007a, 2007b)), which allows us to assess the eco-efficiency of IMCs by directly integrating

contextual variables into the analysis to identify their effect.

The article is structured as follows. Section 2 presents the methodology. Section 3 describes the sample and data used for the empirical analysis. Section 4 presents and discusses the results. Finally, the last section provides the study's conclusions and suggests directions to improve local waste management policies.

## 2. Methodology

### 2.1. Selection of technology variables

Several studies have assessed the economic efficiency of municipal waste management, primarily using non-parametric methods that incorporate waste management costs as a variable to be minimized (e.g., Guerrini et al., 2017). In addition to cost considerations, other researchers have evaluated eco-efficiency by incorporating residual waste as an undesirable output to be reduced in their models (e.g., Sarra et al., 2017; Romano and Molinos-Senante, 2020; Daraio et al., 2024). These studies highlight the importance of an integrated approach that accounts for both economic and environmental aspects when assessing waste management performance. Building on this, our study evaluates eco-efficiency by considering total cost, sorted waste<fnref n="3" /> and unsorted waste. Specifically, our model is specified as follows: (i) Total cost is included as an input, as IMCs aim to minimize it. (ii) Sorted waste is treated as a desirable output, as it is collected separately for recycling and contributes to the circular economy. However, we acknowledge that managing sorted waste entails costs for IMCs. (iii) Unsorted waste is considered as an undesirable output, as its treatment (e.g., landfilling, incineration) entails significant economic and environmental costs (Rios and Picazo-Tadeo, 2021).

An extensive literature addresses the handling of undesirable outputs in the evaluation of DMUs' efficiency (e.g., Färe et al., 1989; Yang and Pollitt, 2010). A common approach involves assuming weak disposability of undesirable outputs and null-jointness of desirable and undesirable outputs (Färe et al., 1989). The weak disposability assumption implies that undesirable outputs cannot be freely reduced but must decrease proportionally with desirable outputs, while the null-jointness assumption states that the good output cannot be produced without the production of the bad output. However, in the specific case of waste management, a reduction in unsorted waste does not necessarily lead to a proportional reduction in sorted waste. For instance, some policies, such as local composting of biowaste, can decrease unsorted waste volumes without affecting sorted and collected waste quantities. Consequently, the proportional relationship between undesirable and desirable outputs is broken, making the weak disposability assumption inappropriate<fnref n="4" /> for undesirable outputs in this context (Delgado-Antequera et al., 2021). Another way to model undesirable outputs, particularly in the case of waste, is to treat them as inputs and assume strong disposability, meaning that the technology set allows undesirable outputs to be reduced without necessarily decreasing desirable outputs (e.g., Sarra et al., 2017; Daraio et al., 2024). These authors argue that since IMCs seek to reduce rather than increase undesirable outputs, they can be modeled as inputs. We adopt this approach by modeling unsorted waste as an input, as it aligns with the objectives of sustainable waste management: reducing residual waste and minimizing waste management costs. These objectives are central to

<footnote n="2">² This means that contextual variables influence the relative performance of DMUs within a given technology but do not directly alter the shape or position of the technology frontier. Instead, shifts in this frontier may reflect technological change or transformations in production possibilities.</footnote>

<footnote n="3">³ This is waste subject to selective collection. It includes glass, packaging, newspapers, magazines and biowaste.</footnote>

<footnote n="4">⁴ We have the same observation for the null-jointness assumption. With local composting of biowaste, one can have strictly positive quantity of good output without the generation of a bad output.</footnote>
IMCs' strategies, making this modeling approach particularly relevant.

Based on this specification, we can i) estimate eco-efficiency scores measuring to what extent a given IMC could radially<fnref n="5" /> reduce total costs and unsorted wastes, given its level of sorted wastes and ii) analyze the impact of contextual variables on estimated eco-efficiency using the standard conditional order-m approach, introduced by Cazals et al. (2002). This non-parametric approach is particularly well suited to our study because it i) does not rely on the separability assumption and, ii) does not impose strong disposability assumption on the functional form of the technology. This flexibility makes it highly adaptable to the heterogeneous nature of local waste management practices. The methodology is further detailed in the next subsection.

## 2.2. Eco-efficiency estimation model

The methodology proposed by Cazals et al. (2002) and extended by Daraio and Simar (2005, 2007a, 2007b) involves estimating unconditional and conditional efficiency scores. The ratio of these efficiency scores is then regressed on contextual variables to examine their effect on efficiency, using a non-parametric smoothed regression. Following this approach, we assume that the production process can be represented by a set of two inputs x, corresponding to total management costs and unsorted waste (x ∈ R<sub>+</sub><sup>2</sup>) and one output y, corresponding to sorted waste (y ∈ R<sub>+</sub><sup>1</sup>). The production technology Ψ representing the possible combinations of inputs and output, is defined in equation (1) below:

$$ \Psi = \{(x, y) \in R_{+}^{2} \times R_{+}^{1} | x \text{ can produce } y\} \quad (1) $$

Under the assumption of free disposability of the inputs and the output, the unconditional realizable set Ψ can be characterized by:

$$ \Psi = \{(x, y) \in R_{+}^{2} \times R_{+}^{1} | H_{X,Y}(x, y) > 0\} \quad (2) $$

In equation (2), H<sub>X,Y</sub>(x, y) = Prob(X ≤ x, Y ≥ y) represents the probability of a DMU reaching or exceeding the level (x, y).

This methodological approach facilitates the integration of operational context information by constraining the production process to a specific value of the contextual variable Z (Daraio and Simar, 2007a): H(x, y) = Prob(X ≤ x, Y ≥ y | Z = z).

This conditional distribution function represents the probability of a DMU reaching the (x, y) level under the condition that all DMUs have the same z condition. From these distribution functions, unconditional and conditional efficiency scores can be estimated (Daraio and Simar, 2007a). However, like other non-parametric frontier methods, FDH is highly sensitive to outliers and extreme values because it constructs the efficiency frontier using all observations in the sample. This approach can sometimes lead to comparisons between DMUs with atypical data, potentially resulting in overestimation or underestimation of efficiency (Daraio and Simar, 2007b). To mitigate this issue, we apply the "partial frontier" approach developed by Cazals et al. (2002), which enhances robustness by reducing the influence of outliers and extreme values. This approach involves constructing the FDH frontier from a random subsample of size m (m < n), where n is the size of the full sample. Since atypical observations are not systematically included in the subsamples, their impact on the FDH frontier is mitigated. The unconditional efficiency score of order-m for a DMU consuming the inputs x and producing the outputs y, in an input-oriented context, is estimated by the formula presented in equation (3) (see Daraio and Simar, 2007b for details):

$$ \theta_{\text{FDH}}^{m}(\mathbf{x}, \mathbf{y}) = \int_{0}^{\infty} (1 - \hat{F}_{X|Y}(\mathbf{u}\mathbf{x}|\mathbf{y}))^{m} \mathrm{d}\mathbf{u} \quad (3) $$

$$ \text{with } \hat{F}_{X|Y}(\mathbf{u}\mathbf{x}|\mathbf{y}) = \frac{\sum_{i=1}^{n} I(\mathbf{X}_i \le \mathbf{u}\mathbf{x}, \mathbf{Y}_i \ge \mathbf{y})}{\sum_{i=1}^{n} I(\mathbf{Y}_i \ge \mathbf{y})} $$

$\theta(\mathbf{x}, \mathbf{y}) \ge 1$ indicates that the evaluated production plan is fully efficient, while $\theta(\mathbf{x}, \mathbf{y}) < 1$ indicates that the evaluated production plan is inefficient.

I is an indicator function.

Similarly, the conditional order-m efficiency score for a given value of the contextual variable Z = z is estimated using the following equation (4) (Cazals et al., 2002; Daraio and Simar, 2007a):

$$ \theta_{\text{FDH}}^{m}(\mathbf{x}, \mathbf{y}|\mathbf{z}) = \int_{0}^{\infty} (1 - \hat{F}_{X|Y,Z}(\mathbf{u}\mathbf{x}|\mathbf{y}, \mathbf{z}))^{m} \mathrm{d}\mathbf{u} \quad (4) $$

$$ \text{With } \hat{F}_{X|Y}(\mathbf{u}\mathbf{x}|\mathbf{y}, \mathbf{z}) = \frac{\sum_{i=1}^{n} I(\mathbf{X}_i \le \mathbf{u}\mathbf{x}, \mathbf{Y}_i \ge \mathbf{y}) K((\mathbf{Z} - \mathbf{Z}_i)/\mathbf{h})}{\sum_{i=1}^{n} I(\mathbf{Y}_i \ge \mathbf{y}) K((\mathbf{Z} - \mathbf{Z}_i)/\mathbf{h})} $$

I is an indicator function.

K(.) is the kernel function. As the contextual variables z can be both discrete and continuous, we follow De Witte and Kortelainen (2013) by using both discrete and continuous kernel functions.

h is the appropriate bandwidth. The bandwidth is optimized using the least squares cross-validation (LSCV) procedure (Bädin et al., 2012). Descriptive statistics of the optimal bandwidth estimated for each contextual variable are reported in Appendix 1.

## 2.3. Estimating the effect of contextual variables

By comparing the ratios of conditional to unconditional efficiency measures, we can analyze how contextual variables affect frontier shifts (technological change) and the distribution of efficiency scores. The ratio is given by:

$$ R_m(\mathbf{x}, \mathbf{y}|\mathbf{z}) = \frac{\theta_{\text{FDH}}^{m}(\mathbf{x}, \mathbf{y}|\mathbf{z})}{\theta_{\text{FDH}}^{m}(\mathbf{x}, \mathbf{y})} \quad (5) $$

According to Daraio and Simar (2005), in this input-oriented framework, a decreasing (respectively increasing) trend in R<sub>m</sub>(x, y|z) with contextual variables indicates a favorable (respectively unfavorable) effect on efficiency. Analysis of the ratio presented in equation (5) with the total frontier gives the effect of contextual variables on shifting the efficiency frontier, while analysis of the ratio for small values of m (e.g., m = 1) allows us to examine the influence of contextual variables on the distribution of efficiency scores.

## 3. Data and descriptive statistics

Data on waste management costs and quantities of waste collected by IMCs were obtained from the SINOE® database, managed by the French Agency for Ecological Transition (ADEME). The quantities of waste collected, reported in this database, are from surveys conducted by ADEME on all IMCs with waste management responsibilities. The cost data are provided by the IMCs themselves following guidelines designed by ADEME. They could be assisted by ADEME throughout the process (free training, financial aid, etc.).<fnref n="6" /> From the SINOE® database, we identified an unbalanced panel of 727 IMCs (272 for 2019, 232 for 2021, and 223 for 2023), for which we have data on the total cost of waste

<footnote n="5">⁵ This means that total costs and unsorted wastes are proportionally reduced, unlike in non-radial models, which allow for inputs to be reduced in different proportions.</footnote>

<footnote n="6">⁶ There are missing values regarding this variable, meaning that, even with the support proposed by ADEME, some IMCs were unable to compute and report their waste management costs. This can potentially introduce a selection bias in our analysis.</footnote>
management and the quantities of waste collected.

### 3.1. Input-output variables

The total cost of waste management represents total expenditure on waste collection, transport, and treatment, expressed in euros. Table 1 presents the descriptive statistics for the input and output variables, highlighting substantial variability across IMCs. The average waste management cost is significantly higher than the median, indicating a considerable disparity in costs. A similar trend is observed for waste quantities, with a significant difference between the mean and median values, potentially reflecting highly heterogeneous waste production. These differences can be attributed to multiple factors, including sociodemographic characteristics (e.g., population, density) and variations in waste management infrastructure and policies (Oliveira et al., 2018; Romano et al., 2019).

### 3.2. Contextual variables

To identify the key factors influencing the eco-efficiency of French IMCs, we rely on insights from the literature (e.g., Sarra et al., 2017; Romano and Molinos-Senante, 2020; Daraio et al., 2024) and data availability. The contextual variables used in our study are POPULATION, TYPOLOGY, PRICING, SORTING_BIOWASTE, and YEAR. All of these variables are taken from the SINOE® database.<fnref n="7" />

The POPULATION variable represents the total number of residents served by waste collection services and is commonly used in efficiency studies to analyze the impact of IMC size and potential economies of scale (e.g., De Jaeger et al., 2011; Guerrini et al., 2017; Lo Storto, 2021; Daraio et al., 2024). Our sample shows significant variation in population size, as evidenced by the large gap between the mean and median values, highlighting substantial heterogeneity.

The TYPOLOGY variable classifies IMCs as urban, tourist, rural, mixed, or very touristy. As noted by Passarini et al. (2011), area characteristics such as urbanization, rurality, density, and tourism influence waste management performance. The distribution in our sample is unbalanced: 41 % of IMCs are rural, 39.2 % are mixed, 6 % are urban, 6 % are tourist, and 7.8 % are highly tourist-oriented. Details on the classification method used in SINOE® are provided in Appendix 2.

The PRICING variable indicates the type of waste pricing system. We consider three types of waste pricing systems: classic pricing, where the price paid by residents is independent of the amount of waste produced; incentive pricing, which is calculated on the basis of the amount of waste produced; and mixed pricing. An IMC is considered to have mixed pricing when part of its population is subject to classic pricing and the other part to incentive pricing. The distribution is highly skewed: 79.2 % of IMCs use classic pricing, while only 17.2 % have adopted incentive pricing, and 3.6 % apply a mixed system. The effects of pricing systems on efficiency have received limited attention in the literature. De Jaeger et al. (2011), who studied this variable, were unable to conclude on its effect on economic efficiency.

The SORTING_BIOWASTE variable indicates whether an IMC has implemented separate biowaste collection. Only 27.5 % of IMCs have adopted this practice, while 72.5 % have not. It is important to note that the binary nature (yes/no) of this variable prevents us from measuring the extent of separate biowaste collection. An IMC may answer 'yes' even if the policy is only implemented for a small portion of its population. Moreover, this variable does not fully capture the biowaste sorting policies implemented by IMCs. In fact, the practice of biowaste composting, which constitutes a complementary policy, is not reflected in our data, although it may have a significant impact on the waste streams to be collected.

Lastly, the YEAR variable is included to assess whether eco-efficiency has evolved over time. The dataset covers three years: 2019 (37.4 % of observations), 2021 (31.9 %), and 2023 (30.7 %). A balanced distribution across years allows us to account for potential changes over time.

Descriptive statistics reveal significant heterogeneity among IMCs in terms of size, typology, and waste management policies (see Table 2). From a methodological perspective, this heterogeneity calls for caution in interpreting unconditional eco-efficiency scores and highlights the importance of using a conditional approach, which accounts for differences in operating environments.

## 4. Results and discussion

This section is divided into two parts. In the first, we present the calculated eco-efficiency scores. In the second part, we analyze the effect of contextual variables on the distribution of eco-efficiency scores.

### 4.1. Unconditional and conditional eco-efficiency scores

Based on our sample and following Daraio and Simar (2005), we determined the value of m (i.e., the size of the partial frontier) as the value for which the variation in the percentage of super-efficient observations<fnref n="8" /> decreases progressively with m. In our case, we choose m = 300 (see Appendix 3). The number of bootstrap replicates (B) to obtain robust scores is set at 1000. The estimated unconditional and conditional eco-efficiency scores are shown in Table 3.

The average unconditional eco-efficiency score is 0.73. This suggests that, on average, the IMCs studied could reduce total waste management costs and residual waste collected by 27 %, while keeping sorted waste quantities constant. In the sample, 111 IMCs out of the 727 evaluated (i. e., 15.3 %) have an eco-efficiency score greater than or equal to 1. They are therefore eco-efficient. This means that they would not be able to reduce total waste management costs and residual waste collected compared to all IMCs, without any change in the quantity of waste sorted.

Conditional eco-efficiency scores are higher than unconditional scores. This suggests that IMCs appear more efficient when contextual factors are taken into account. The gap between conditional and unconditional scores highlights the importance of accounting for these contextual factors to ensure a fairer assessment of eco-efficiency. The average conditional eco-efficiency score is 0.83, indicating that, on average, in a given context and all else being equal, the IMCs studied could reduce total waste management costs and the amount of residual waste collected by 17 % given sorted waste tonnage. Furthermore, 195 out of 727 IMCs (i.e., 26.82 %) achieve the highest level of eco-efficiency within their respective contexts. This suggests that a majority (73 %) of IMCs still have room for improvement.

The minimum unconditional and conditional eco-efficiency scores (0.11 and 0.26, respectively) suggest that some IMCs may need guidance to improve their waste management services. Conversely, some IMCs exhibit eco-efficiency scores greater than 1, meaning they are super-efficient. This occurs because, in the robust model, the evaluated DMU may not be included in the reference sample used to construct the partial frontier. As a result, its eco-efficiency score can exceed 1, indicating that it outperforms the average of the IMCs to which it was compared (De Witte and Kortelainen, 2013).

### 4.2. Effects of contextual variables on eco-efficiency

This section examines the influence of contextual variables on the distribution of eco-efficiency scores. The objective is to identify levers that can be activated by local authorities to target eco-efficiency

<footnote n="7">⁷ To avoid potential multicollinearity issues, population density was deliberately excluded, as it is highly correlated with population served.</footnote>

<footnote n="8">⁸ A DMU is defined as super-efficient if it gets a value higher than 1 in the robust analysis.</footnote>
<table><caption>**Table 1**

Descriptive statistics for input-output variables included in the model.</caption><thead><tr><th></th><th>Minimum</th><th>1st quartile</th><th>Median</th><th>Mean</th><th>3rd quartile</th><th>Maximum</th></tr></thead><tbody><tr><td><strong>Input</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>COST (euros)</td><td>342,021</td><td>1,970,935</td><td>3,487,832</td><td>7,404,385</td><td>7,332,569</td><td>145,211,697</td></tr><tr><td><strong>Desirable output</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SORTED_WASTE (tonnes)</td><td>121.9</td><td>1283.5</td><td>2638.1</td><td>5363.7</td><td>5851.5</td><td>100,057.4</td></tr><tr><td><strong>Undesirable output</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>UNSORTED_WASTE (tonnes)</td><td>473</td><td>2538</td><td>5217</td><td>11,432</td><td>11,107</td><td>316,445</td></tr></tbody></table>

Data source: SINOE®

<table><caption>**Table 2**

Descriptive statistics for contextual variables.</caption><thead><tr><th>Contextual variables</th><th>Minimum</th><th>1st quartile</th><th>Median</th><th>Mean</th><th>3rd quartile</th><th>Maximum</th></tr></thead><tbody><tr><td>Quantitative</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>POPULATION (inhabitants)</td><td>2559</td><td>14,228</td><td>25,349</td><td>51,936</td><td>53,883</td><td>1,424,677</td></tr><tr><td>Qualitative</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>TYPOLOGY</td><td>rural (41.0 %)</td><td>tourist (6.0 %)</td><td>urban (6.0 %)</td><td></td><td>very touristy (7.8 %)</td><td>mixed territory (39.2 %)</td></tr><tr><td>PRICING</td><td>Incentive (17.2 %)</td><td></td><td>classic (79.2 %)</td><td></td><td>mixed (3.6 %)</td><td></td></tr><tr><td>BIOWASTE_COLLISION</td><td></td><td>yes (27.5 %)</td><td></td><td></td><td>no (72.5 %)</td><td></td></tr><tr><td>YEAR</td><td>2019 (37.4 %)</td><td></td><td>2021 (31.9 %)</td><td></td><td>2023 (30.7 %)</td><td></td></tr></tbody></table>

Data source: SINOE®

<table><caption>**Table 3**

Unconditional and conditional eco-efficiency scores.</caption><thead><tr><th></th><th>UNCOND_EFF</th><th>COND_EFF</th></tr></thead><tbody><tr><td>Minimum</td><td>0.11</td><td>0.26</td></tr><tr><td>1st quartile</td><td>0.56</td><td>0.73</td></tr><tr><td>Median</td><td>0.71</td><td>0.87</td></tr><tr><td>Mean</td><td>0.73</td><td>0.83</td></tr><tr><td>3rd quartile</td><td>0.91</td><td>0.99</td></tr><tr><td>Maximum</td><td>2.52</td><td>1.01</td></tr></tbody></table>

improvement. As explained in Section 3, we non-parametrically regress the ratio of conditional to unconditional eco-efficiency scores on contextual variables. Fig. 1 presents the results of this analysis for the partial frontier, illustrating the effects of contextual variables on eco-efficiency.

Our results indicate a negative effect of population size on eco-efficiency, suggesting the presence of diseconomies of scale in waste management. This may be explained by the fact that IMCs with smaller populations generate less waste, may have more homogeneous environments, and may not need to invest in large-scale infrastructure, which could make waste management less complex in these IMCs. The literature on this issue presents mixed findings. Similar to our study, Guerrini et al. (2017) found that smaller Italian municipalities tend to operate in more favorable conditions. In contrast, Carvalho and Marques (2014) argue that there exists a critical population threshold beyond which efficiency improves, estimating this threshold to be between 400, 000 and 550,000 inhabitants in Portugal. Expósito and Velasco (2018) found that population size does not explain the efficiency of waste management services in Spanish regions. Conversely, Bohm et al. (2010) report economies of scale in waste management, highlighting that larger populations can help optimize overall waste management costs, as fixed costs are spread over a greater number of inhabitants, thus reducing the cost per capita. This heterogeneity in the results reported in the literature is likely due to differences in the size of structures in charge of waste management between countries as well as institutional or logistical specificities that could be explored in future research. For example, in France, IMCs responsible for providing waste management services are relatively large in terms of population served (they serve on average 51, 936 inhabitants in our sample), and this can make the logistic more complex and increase organization and coordination cost.

Our results also highlight disparities in eco-efficiency across different

IMCs typologies. Rural IMCs demonstrate, on average, higher eco-efficiency than mixed or very touristy IMCs. This could be explained by several factors. Rural areas are less populated, and as noted by Gómez-Sanabria et al. (2022), they generally produce less waste. Additionally, due to the availability of space and the presence of gardens, waste management systems in rural areas can rely more on local solutions, such as home composting, thereby reducing both residual waste and transportation costs. As shown by Timlett and Williams (2009), the lack of space for temporary waste storage can be one of the main barriers to waste sorting practices. However, this result does not imply that rural IMCs are free from challenges. Sparse populations may still lead to higher per capita collection costs, even if the overall system remains more efficient. In contrast, our study finds that IMCs with high levels of tourism exhibit lower eco-efficiency, consistent with previous findings (Romano and Molinos-Senante, 2020; Delgado-Antequera et al., 2021). Tourism introduces seasonal fluctuations in waste generation, which creates operational difficulties such as the need for temporary collection reinforcements, additional sorting infrastructure, and efforts to inform tourists about local sorting practices (Caponi, 2022). The challenge of managing waste from non-permanent residents, who may be unfamiliar with local waste management practices, contributes to inefficiencies. This issue is particularly pronounced in very touristy areas, where waste composition can vary due to the seasonal influx of visitors. Addressing these inefficiencies requires innovative solutions, such as deploying temporary waste collection systems during peak seasons and enhancing communication campaigns targeting tourists. As demonstrated by Avrami et al. (2024), targeted communications on the circular economy can increase sustainable behavior in terms of waste recycling. For mixed IMCs, those that are neither strictly rural nor highly urbanized or tourist, interpreting the results requires further analysis. These IMCs, which often encompass suburban or peri-urban areas, face unique challenges: they lack the logistical simplicity of rural IMCs and do not benefit from the extensive waste management infrastructure of large urban areas. Their intermediate status can lead to inefficiencies in coordinating waste management strategies. Additionally, as shown in Appendix 4, IMCs with a mixed typology generally have larger populations than rural IMCs. This larger population size often comes with greater heterogeneity in lifestyles (e.g., urban and rural residents), complicating waste management efforts. For instance, the diversity of lifestyles and behaviors across urban, suburban, and rural areas can create challenges in effectively informing residents about waste sorting and recycling practices, making the standardization of solutions more
<figure><img src="image_3.png" /><figcaption>Fig. 1. Marginal effects of contextual variables on the conditional/unconditional eco-efficiency ratio (partial frontier, m = 1).</figcaption></figure>
difficult. A more detailed investigation of these areas, including a more
thorough study of their characteristics, could provide additional insights
and refine the interpretation of their eco-efficiency.

Our results also indicate that IMCs with incentive-based pricing
systems are more eco-efficient than those using mixed or classical
pricing systems. Furthermore, mixed pricing systems perform better
than classical ones, confirming the positive role of economic instruments
in optimizing waste management. To our knowledge, the effects of
pricing systems on eco-efficiency have not been extensively studied in
the literature. De Jaeger et al. (2011), who examined the effect of
incentive-based pricing on economic efficiency, were unable to draw
any clear conclusions. However, our results underline the importance of
adopting this waste pricing model. Our finding aligns with the studies of
Allers and Hoeben (2010) and Romano and Masserini (2023), who
demonstrated that incentive-based pricing helps reduce residual waste
production. The mechanism behind this improvement likely stems from
behavioral changes: households subject to variable pricing tend to adopt
more sustainable waste management practices, such as increased sorting
and waste reduction at the source (e.g., composting biowaste). The
impact of incentive-based pricing is not limited to waste reduction, it
also has structural consequences for waste collection operations. By
reducing the volume of residual waste, IMCs can lower collection fre-
quency, resulting in cost savings in transportation and labor. However,
implementing incentive pricing may face several challenges. As Kinna-
man (2006) and Allers and Hoeben (2010) have pointed out, such sys-
tems can lead to illegal dumping and, consequently, may be costly for
municipalities to implement. Public awareness campaigns and comple-
mentary policies, such as biowaste composting solutions that facilitate
the reduction of unsorted waste, may be necessary to foster the accep-
tance of an incentive pricing system.

Regarding the separate collection of biowaste, our results do not
indicate a significant effect on eco-efficiency. In fact, the proportion of
IMCs implementing this policy may still be too low in our sample to
detect a statistically significant effect. Therefore, we cannot draw firm
conclusions about its impact on eco-efficiency. With a richer database, it
could be interesting in future research to investigate the effect of a
separate collection of biowaste on eco-efficiency or on cost-efficiency (i.
e., economic efficiency). Indeed, as the separate collection of biowaste
should normally lower the quantity of unsorted waste, analyzing for
example whether the benefit in terms of cost from the decrease of un-
sorted waste outweighs the cost from the implementation of a separate
collection of biowaste is important. On related variables, there are also
relatively few studies. Carvalho and Marques (2014), have shown that
industrial composting increases costs for municipalities. Based on this, it
would be interesting to investigate the effect of home composting
(which does not require transporting waste to industrial composting
sites).

Finally, our analysis shows that time does not have a significant ef-
fect on eco-efficiency within our study period (2019, 2021, 2023). This
result suggests that, with the available data, we are unable to capture the
impact of major events, such as the COVID-19 pandemic, on waste
management eco-efficiency. The lack of a significant time effect could be
due to several reasons. First, the four-year timeframe might be too short
to observe structural shifts in waste management performance. Second,
the absence of data for 2020 prevents us from directly analyzing the
pandemic's immediate effects. Future studies covering a longer period
or using more granular data at the monthly level could provide a clearer
assessment of the pandemic's influence on waste management eco-
efficiency.

Appendix 5 presents the marginal effects of contextual variables on

the ratio of conditional to unconditional eco-efficiency scores, based on
the full frontier. These results provide insights into how contextual
factors influence the position of the technology frontier. They show that
the contextual variables assessed, notably population and pricing sys-
tem, also influence the position of the technology frontier. These find-
ings confirm the choice of our methodological approach, demonstrating
that standard two-stage models would have been inadequate for
capturing the relationships between contextual factors and eco-
efficiency in our case.

5. Conclusion

In this article, we assessed the eco-efficiency of French IMCs
responsible for public waste management services and examined the
contextual factors influencing this eco-efficiency using a conditional
order-m approach. Our results reveal that the type of area (e.g., tourist
or rural) and the pricing system significantly influence eco-efficiency in
waste management. These results highlight the challenges IMCs face in
optimizing waste management, depending on their operating context.

To address these challenges and enhance eco-efficiency, local au-
thorities need to develop strategies tailored to the specific characteris-
tics of their territories. For example, in highly tourist areas, waste
collection must adapt to seasonal fluctuations in waste generation.
Targeted solutions, such as improved logistical planning, flexible waste
collection schedules, and effective communication campaigns to raise
tourists' awareness of sorting instructions, could help mitigate opera-
tional difficulties. Similarly, mixed territories, which are neither fully
rural nor highly urbanized, require special attention, as they may face
logistical inefficiencies. Our study also underscores the importance of
expanding incentive-based pricing systems to reduce residual waste and
optimize costs. However, successful implementation depends on over-
coming potential barriers, including administrative complexity and
public acceptance.

This study is the first to analyze eco-efficiency in the context of
municipal waste management in France while also investigating the
influence of contextual variables. Our findings regarding the positive
effect of incentive-based pricing are particularly novel, as this aspect has
been relatively underexplored in existing literature. In terms of tourism,
our results align with previous studies, confirming its negative impact on
eco-efficiency. In contrast, we found evidence of diseconomies of scale
in the French context, an outcome that diverges from part of the liter-
ature, which identifies scale economies in different national contexts.

Finally, by demonstrating that improving waste management per-
formance requires policies adapted to local specificities, our study offers
valuable insights for local authorities. Future research should further
explore other policy levers, such as source separation of biowaste.
Adopting a more dynamic approach, by analyzing longer time series,
could also provide deeper insights into how eco-efficiency evolves in
response to regulatory changes and local waste policy developments.

CRediT authorship contribution statement

**Aissatou Ndimblane:** Writing – review & editing, Writing – original draft, Validation, Methodology, Formal analysis, Data curation, Conceptualization. **Kassoum Ayouba:** Writing – review & editing, Validation, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. **Olivier Aznar:** Writing – review & editing, Validation, Supervision, Conceptualization.
Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work was supported by the French Agency for Ecological Transition (ADEME) and VetAgro Sup.

Appendix

Appendix 1. Descriptive statistics of the optimal bandwidth estimated for each variable

<table><thead><tr><th></th><th>Minimum</th><th>1st quartile</th><th>Median</th><th>Mean</th><th>3rd quartile</th><th>Maximum</th></tr></thead><tbody><tr><td>POPULATION</td><td>760.20</td><td>1014.20</td><td>1258.70</td><td>2163.10</td><td>1310.80</td><td>9235.70</td></tr><tr><td>TYPOLOGY</td><td>0.00</td><td>0.24</td><td>0.30</td><td>0.29</td><td>0.35</td><td>0.75</td></tr><tr><td>PRICING</td><td>0.00</td><td>0.05</td><td>0.06</td><td>0.12</td><td>0.11</td><td>0.67</td></tr><tr><td>SORTING_BIOWASTE</td><td>0.08</td><td>0.22</td><td>0.29</td><td>0.29</td><td>0.33</td><td>0.50</td></tr><tr><td>YEAR</td><td>0.15</td><td>0.61</td><td>0.67</td><td>0.63</td><td>0.67</td><td>0.67</td></tr></tbody></table>

Appendix 2. IMCs typologies in SINOE®

The typology of an IMC is determined based on reference data provided by the French National Institute of Statistics and Economic Studies (INSEE) and the French Institute for the Environment (IFEN). These data are collected at the municipal level and are available at the following address: http://www.sinoe.org/documents/consult-doc/idDoc/1131. The reference data are then used to calculate variables related to trade, tourism, housing, and population at the IMC for each year. The calculation is based on the list of municipalities within the respective IMC. Finally, the typology of each IMC is determined based on the criteria outlined in the following table.

<table><thead><tr><th>Typology</th><th>Definition</th></tr></thead><tbody><tr><td>TOURIST</td><td>This variable groups together three types of IMCs:<br/>i) IMC is classified as "TOURIST and/or COMMERCIAL" if it meets at least one of the following three criteria: number of tourist beds per capita >1.5, OR percentage of secondary residence >50 %, OR at least 10 shops per 1000 inhabitants.<br/>ii) IMC is classified as "URBAN TOURIST" if the number of tourist beds per capita is ≤2.5 AND housing density is > 100 dwellings/km²<br/>iii) IMC is classified as "OTHER TOURIST" if the number of tourist beds per capita is <2.5 AND housing density is < 100 dwellings/km².</td></tr><tr><td>VERY TOURISTY</td><td>IMC is classified as "VERY TOURISTY" if the number of tourist beds/capita is > 2.5.</td></tr><tr><td>URBAN</td><td>IMC is classified as "URBAN" if it is not "TOURIST or COMMERCIAL" AND if its housing density >200 dwellings/km² OR if its housing density >35 dwellings/km² and its collective housing rate >45 % AND if its housing density ≤1800 dwellings/km² built.</td></tr><tr><td>RURAL</td><td>IMC is classified as "RURAL" if it is not "TOURIST or COMMERCIAL" AND if its housing density is ≤ 35 dwellings/km² and its collective housing rate ≤ 20 %.</td></tr><tr><td>MIXED</td><td>IMC is classified as "MIXED" if it does not fall into any of the above categories. In other words, it is neither "TOURIST", nor "VERY TOURISTY", nor "URBAN", nor "RURAL".</td></tr></tbody></table>

Source: SINOE®, rules for calculating indicators in the Actor sheet.

<figure><img src="image_4.png" /><figcaption>Appendix 3. Graph for selecting the size of the partial frontier (m)</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Appendix 4.** Population Distribution by Rural and Mixed Typologies</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Appendix 5.** Marginal effects of contextual variables on the conditional/unconditional eco-efficiency ratio (full frontier)</figcaption></figure>

Data availability

Requests for access to waste-related data from the SINOE® database should be addressed to ADEME.
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