# Diagnostic of Households' Knowledge, Perceptions, and Practices in Biowaste Management and Identification of Management Levers

Célestine Guillou

► To cite this version:

Célestine Guillou. Diagnostic of Households' Knowledge, Perceptions, and Practices in Biowaste Management and Identification of Management Levers. Agriculture, economy and politics. 2025. hal-05262134

HAL Id: hal-05262134

https://hal.inrae.fr/hal-05262134v1

Submitted on 15 Sep 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Université de Bordeaux - INRAE UR ASTRO

Mémoire de fin d'étude : Mars - Août 2025

Célestine GUILLOU

Master II Économiste-Analyste de données de développement (EADD) /
Magistère de sciences économiques de Bordeaux parcours économie du
développement et évaluation de politiques publiques

“Diagnostic of Households’ Knowledge, Perceptions, and
Practices in Biowaste Management and Identification of
Management Levers”

Encadrée par BARLAGNE Carla, chercheuse, UR ASTRO (INRAE), BLAZY
Jean-Marc, directeur de recherches, UR ASTRO (INRAE) et GARNIER
Suzon, doctorante, UR ASTRO (INRAE)
Abstract

We aim to identify the determinants of household biowaste sorting in Guadeloupe in order to inform
the design of effective strategies to promote this behaviour. Drawing on a dataset of 244 households
and using the COM-B model (Capability-Opportunity-Motivation-Behaviour) as a theoretical
framework, we combine survey data, comprising a discrete choice experiment, with semi-structured
interviews, to analyse both the current drivers of sorting and composting and model household
preferences for alternative biowaste management scenarios. Our results show that type of
accommodation and age are strong predictors of sorting behaviour, while the main barrier to wider
adoption remains physical opportunity, particularly access to sorting infrastructures. While
participants consistently report already high motivation to sort, they also exhibit very low awareness
of sorting and composting practices, paradoxically with the little demand for training or guidance
when selecting between favored management scenarios. The preferred scenarios emphasize
individual management solutions, especially individual composting that allows households to reuse
compost in their gardens. Based on these findings, we recommend improving the biowaste
management system by providing compost containers to households and expanding sorting
infrastructures ensuring that these efforts are implemented homogeneously across municipalities.
Complementary, clear and consistent information on biowaste sorting is essential to support the
long-term adoption of sorting behaviours, particularly by reinforcing confidence in the global waste
management system and improving knowledge on the available sorting options and the practical
management of compost.
Remerciements

Ce travail représente l'aboutissement de six mois de réflexion, discussions et rencontres.

Je tiens tout d'abord à remercier Carla Barlagne en tant qu'encadrante principale, pour son
accompagnement tout le long de ce stage, ses enseignements et la confiance accordée. Je remercie
Jean Marc Blazy en tant que co-encadrant et directeur de l'unité ASTRO, pour son accueil et sa
réactivité lorsque la recherche devient un peu trop une aventure. Enfin, en tant que co-encadrante
également, je remercie Suzon Garnier pour son accompagnement sur le terrain, son efficacité lors de
nos rencontres et sa bienveillance.

Je remercie également François Causeret pour son engagement dans le projet et les nombreux
conseils apportés, ainsi que Juliette Perez et Killian Etonno-Jacques pour leur accompagnement lors
des journées d'enquêtes.

Ce travail de recherche n'aurait bien sûr pas pu voir le jour sans la participation de nombreux acteurs : les répondants au questionnaire, les ménages rencontrés lors des premiers entretiens et des phases pilotes, ainsi que les associations rencontrées. Les échanges ont toujours été d'une grande richesse, et je remercie grandement tous les participants pour le temps et l'attention qu'ils ont consacrés à cette étude.

Pour l'aide logistique et organisationnelle apportée lors de la phase de terrain, leur participation à différentes étapes de l'étude, et les échanges toujours très formateurs, je remercie l'ensemble de l'étage ASTRO, chercheurs, ingénieurs et thésards. Pareillement, je remercie l'ensemble des gestionnaires ASTRO, pour avoir rendu les missions possibles et leur patience au quotidien...

Après six mois de vie sur l'île, j'ai bien sûr une pensée pour mes (nombreux) colocataires, les stagiaires et rencontres diverses qui ont marqué ce stage et avec qui j'ai pu découvrir cette belle île qu'est la Guadeloupe, que cela soit au travers d'un sentier de trail, sur un bateau ou en haut de la montagne.

Finalement, je remercie sincèrement ma famille, élargie lors de mon absence, qui à défaut de
toujours comprendre mon travail, continue de m'encourager quotidiennement dans tous mes projets.
# Table of contents

List of figures.................. 6

List of tables.................. 7

List of acronyms and abbreviations.................. 8

I. Introduction.................. 9

I.1 General introduction.................. 9

I.2 Project rationale and research questions.................. 10

I.3 Literature review.................. 10

I.3.1 Determinants, perceptions, and barriers to biowaste sorting.................. 10

I.3.2 Theoretical framework: COM-B model.................. 11

II. Methods.................. 13

II.1 Design.................. 13

II.2 Qualitative phase.................. 13

II.2.1 Participants and recruitment.................. 13

II.2.2 Semi-structured interviews with key informants.................. 14

II.2.3 Focus Group.................. 14

II.2.4 Semi-structured interviews of households.................. 14

II.3 Questionnaire.................. 14

II.3.1 Participants and fieldwork.................. 14

II.3.2 Conception of the questionnaire.................. 15

II.3.3 Choice experiment.................. 15

II.4 Ethical considerations.................. 18

II.5 Data Analysis.................. 18

III. Results.................. 20

III.1 Characterisation of sample.................. 20

III.1.1 Socio-demographic profile of the sample.................. 20

III.1.2 Sorting practices of the sample.................. 21

III.1.3 Awareness level of the population.................. 22

III.2 Behavioural factors associated with sorting/composting.................. 22

III.3 Identification of covariates for composting and sorting behaviour.................. 23

III.3.1 Probit regression models on sorting behaviour.................. 23

III.3.2 Probit regression models on composting.................. 24

III.4 Choice experiment treatment.................. 25

III.4.1 Results from the food waste (participants with a garden) choice experiment.................. 25

III.4.2 Results from the green waste (participants with a garden) choice experiment.................. 28

III.5 Qualitative results.................. 31

IV. Discussion.................. 33

IV.1 Discussion on findings.................. 33
IV.1.1 Structural and socio-demographic determinants: housing types and age as main drivers, education effects nuanced..................33

IV.1.2 Motivational and perceptual aspects: confidence in the system matters, but motivations play a more secondary role..................33

IV.1.3 Insights from the choice experiment: strong preferences for individual solutions and control over outcomes..................34

IV.1.4 Accompaniment and incentives: limited attractiveness of training and weak impact of financial incentives..................35

IV.2 Political implications and recommendations..................35

IV.3 Limitations and future research..................36

V. Conclusion..................38

Bibliography..................39

Appendices..................44
List of figures

Fig 1: The COM-B model (Michie et al., 2011)

Fig 2: The behaviour change wheel (Michie et al, 2011)

Fig 3: Example of the choice cards used in the choice experiment section of the questionnaire.
List of tables

Tab 1: Summary of participants' socio-demographic characteristics

Tab 2: Index of comparison criteria for probit regression on sorting

Tab 3: Determinants of sorting behaviour (probit regression)

Tab 4: Index of comparison criteria for probit regression on composting

Tab 5: Determinants of compost (probit regression)

Tab 6: Index of comparison criteria for CE food waste/ participants with a garden

Tab 7: M10' results : interactions between alternatives, ages, study levels and gender (food waste)

Tab 8: M16' results : interactions between outlets, ages, study levels and gender (food waste)

Tab 9: Index of comparison criteria for CE green waste / participants with a garden

Tab 10: M11' results : interactions between alternatives, ages, study levels and gender (green waste)

Tab 11: M16's results : interactions between outlets, ages and study levels (green waste)
List of acronyms and abbreviations

AIC : Akaike Information Criterion

BAU : Business as usual

BAV : Borne d'apport volontaire

BCW : Behaviour change wheel

BIC : Bayesian Information Criterion

CAGSC : Communauté d'agglomérations de Grand Sud Caraïbes

CANBT : Communauté d'agglomérations de Nord Basse-Terre

CANGT : Communauté d'agglomération de Nord Grande-Terre

CARL : Communauté d'agglomération de la Riviera du Levant

CCMG : Communauté de communes de Marie-Galante

DCE : Discrete Choice Experiment

EPCI : Établissement Public de Coopération Intercommunale

IIA : Independence of Irrelevant Alternatives

IRIS : Ilots Regroupés pour l'Information Statistique

MNL : Multinomial Logit

MXL : Mixed Logit

SDG : Sustainable Development Goal

VIF : Variance Inflation Factors
# I. Introduction

## 1.1 General introduction

Over the last decade, the management of biowaste has become a critical and ongoing public policy issue in Europe (European Commission, 2010; Interreg Europe, 2021). Traditional disposal methods of biowaste management, such as landfilling and incineration, while widespread, generate significant environmental impacts (Audrey et al., 2024). Developing and promoting alternative solutions for biowaste recovery, such as composting, shared composting, anaerobic digestion, or other reuse processes, has been proven as able to reduce these negative effects, notably reduction of carbon emissions, while supporting value creation from this resource (Ortner et al., 2013; Pavlas et al., 2020).

In France, sorting biowaste sources became mandatory for all individuals and professionals on January 1, 2024, in accordance with European regulations and the 2020 Anti-Waste Law for a Circular Economy (Ministère de la Transition écologique, 2024). This policy direction is aligned with the Sustainable Development Goal (SDG) 12: "Ensure sustainable consumption and production patterns." In fact, biowaste accounts for nearly one-third of unsorted household waste (Ministère de la Transition écologique, 2024). In order to place communities in accordance with AGEC law, diverse tools have been promoted: separate guides according to needs (events, protected sites, etc.) emphasizing the need for communication and training dedicated staff. (Ministère de la Transition écologique, 2024).

Biowaste comprise both food waste (leftovers, peelings, rotten fruits and vegetables, etc) and green waste (clippings, leaves, branches, etc), two streams that vary in their properties, which in turn influence their collection, transport, and treatment methods (Tremier et al., 2024). In fact, because of the vegetation cycles, its lower density and higher fibrosity, green waste are well-suited for composting, while dense and moist food waste requires frequent collection and is better fitted to anaerobic digestion for the production of biogas (Tremier et al., 2024).

In Guadeloupe, a small island with a tropical climate, dispersed housings, and a congested road network, significant logistical and technical challenges arise in organizing waste collection and treatment. Biowaste represents almost 30% of household waste, striking to 100.6 kg per inhabitant, a level that exceeds both the national average and the levels observed in other French overseas departments and territories (Jourda & Malet, 2022; Routier & Marcoux, 2023). Despite this consequent share, landfilling remains the dominant waste management method, with 77% of household waste being buried, while sorting rates remain low and sorting refusals reach 27% (ADEME, 2023). These low recycling rates, combined with the saturation of landfill sites, place the island in a situation of non-compliance with the 2024 AGEC law. In a nutshell, these structural difficulties represent major challenges both for urban collection services and for the island's broader sustainable development.

An exploratory survey conducted in 2024 with 156 households (Kernevez, 2024) revealed that only 36% of respondents systematically sort their biowaste. However, 84% declared they would sort their waste if a separate collection system were available, and 54% expressed interest in using a shared composting facility, which is engaging. Nevertheless, 60% reported insufficient information on biowaste recycling practices.
This research is part of the BEST SCHOOL project, funded by the INRAE metaprogram BETTER (BioÉconomie des TERRitoires urbains), which aims to establish a diagnostic of household biowaste management practices in Guadeloupe and assess the feasibility and effectiveness of targeted interventions.

Our work is particularly relevant, as few research initiated the employment of experimental economics methods to investigate biowaste management. More precisely, we applied the COM-B model (Michie et al., 2011) in tropical and insular settings. Initially developed for public health and increasingly applied in waste management, its application in this specific context is unprecedented, making our study a novel contribution. To the best of our knowledge, studies addressing garden waste management remain particularly rare, this study aims to fill this gap.

## 1.2 Project rationale and research questions.

In this study, we used a mixed-method approach to identify determinants of biowaste sorting among Guadeloupe households. In fact, despite the potential value represented by biowaste, Guadeloupe households face multiple barriers that limit effective sorting. Low participation rates, heterogeneous accessible infrastructures, and limited awareness of sorting and composting hinder the development of sustainable biowaste management in Guadeloupe. Hence, understanding household perceptions, preferences and constraints is crucial to design adapted interventions and public policies that can effectively increase sorting and valorization of biowaste.

To this end, our research questions are:

* How do Guadeloupe households sort their biowaste? What are their current practices and perceptions?

* What are the main barriers to sorting biowaste? What reasons do households give for not sorting biowaste ?

* What are households’ preferences for sorting biowaste?

* What potential levers can be used to develop appropriate biowaste sorting practices: interventions and public policies ?

## 1.2 Literature review

### 1.2.1 Determinants, perceptions, and barriers to biowaste sorting

Household engagements in biowaste sorting are strongly influenced by perceptions of individuals on the practices, their social environment (family, neighbours, friends), their type of accommodations and whether they live in rural or urban areas. Several studies revealed that perceptions of biowaste sorting are formed by a combination of practical, psychological, and social factors, with many households considering it an inconvenient or unpleasant task due to concerns about odors, pests, and hygiene (Allison et al., 2022 ; Touveneau Petersen, 2018). Among the principal determinants of sorting behaviour, housing characteristics such as the type of dwelling, access to outdoor space (like a garden), and availability of correct deposit units are highlighted (Allison et al., 2022 ; Rogowska et al., 2024). Knowledge and awareness also emerge as levers: households with better access to information and greater familiarity with composting or sorting systems are more able to adopt sustainable biowaste practices (Montero-Vega et al., 2024 ; Rastegari Kopaei et al., 2021). Logistical
constraints such as the absence of nearby collection infrastructure, limited support from local authorities, or a lack of dedicated bins often hinder durable adoption (Slavík et al., 2019; Temmerman & Veeckman, 2024). While time, financial constraints, and socio-demographic criteria, except for age (Yuan et al, 2015), are sometimes mentioned in literature, they appear to have a more secondary place as determinants of biowaste sorting.

### 1.2.3 Theoretical framework: COM-B model

As a theoretical framework, we used the COM-B model presented by Michie et al. (2011) (Fig 1). First used for public health, it's now more and more used in the field of recycling, waste management, as it can be seen within various studies (Allison et al., 2022a, Allison et al, 2022b). This meta-model states that for a behaviour to happen, it needs 3 different factors combined together: capacities (psychological/physical), opportunities (social and physical), and motivations (automatic and reflexive).

<figure><img src="image_2.png" /><figcaption>Fig 1: The COM-B model (Michie et al, 2011)</figcaption></figure>

One of the key strengths of this model in the context of our study lies in its direct connection with policy design, as structured within the broader framework of the “Behaviour Change Wheel” (BCW) developed by Michie et al. (2011) (Fig 2). This framework not only links behavioural determinants to appropriate types of interventions but also establishes connections between these interventions and the broader policy environment. For example, to address physical capability barriers, interventions such as training or enablement are recommended. In a second stage, the behaviour change wheel also guides policymakers by suggesting which types of public policy tools (e.g., guidelines, fiscal measures, service provision) are best suited to support these interventions.
<figure><img src="image_3.png" /><figcaption>Fig 2: The behaviour change wheel (Michie et al, 2011)</figcaption></figure>

In our study, this model guided us to identify the barriers and levers to biowaste sorting, considered as the desired behaviour. More specifically, it allowed us, in the different phases of our study, to highlight and measure factors related to capacities, opportunities, and motivations and in the sense they can either hinder or spread biowaste sorting for Guadeloupe households. By linking these findings to the interventions and policies presented in the BCW, it indicated how it would be possible to address the barriers and reinforce biowaste sorting in occurrence.
## II. Methods

### II.1 Design

We conducted a mixed sequential exploratory study. It consisted in two distinct and chronological phases, starting first with a collect and analysis of qualitative data followed by a quantitative phase. The third phase consists of the integration of data from the 2 strands of data in order to achieve “triangulation”, i.e., increasing validity of findings by seeking validation between qualitative and quantitative results (Creswell and Plano Clark, 2011; Fetters et al., 2013). The study aimed to administer a questionnaire structured in four parts, including a choice experiment, to explore practices, preferences, attitudes and knowledge of Guadeloupe households regarding biowaste sorting. The initial qualitative phase highlighted the first findings and determinants that the questionnaire allowed to test and generalize.

### II.2 Qualitative phase

The common way of proceeding in qualitative research is to combine collective interviews with individual perspectives (Baribeau et al., 2010). During the qualitative phase, we process by combining semi-directed interviews, with associations and households, and focus groups in a multi methodological perspective.

#### II.2.1 Participants and recruitment

For the qualitative phase, we began by conducting focus groups which were complemented by individual semi-structured interviews. Three distinct recruitment strategies were applied depending on the method. The key informants were identified and contacted through their social networks and internet websites. For the focus group, specific selection criteria were used to ensure the relevance and homogeneity of participants. Following established guidelines for focus group composition (Kitzinger, 2004; Morgan, 1998), participants were selected based on two criteria: having access to a garden or outdoor space and already sorting biowaste. The first ensured the relevance of spatial conditions for composting and storage practices, while the second served as a proxy for participants’ knowledge of sorting rules. These criteria allowed the creation of a relatively homogeneous group, fostering interaction and comfort during discussions. In contrast, for the individual interviews conducted with households without a garden, recruitment followed a more flexible approach. These participants were identified primarily through snowball sampling within personal networks.

#### II.2.2 Semi-structured interviews with key informants

We first contacted associations engaged in the topic of waste management in order to conduct semi-structured interviews. Indeed, given their interactions with households, three interviews allowed us to capture the overall perception and attitude of Guadeloupe households toward biowaste management.

We elaborated interview guides (see Appendix 1) based on the hypothesis emerging from the literature and from the previous study conducted in 2024 among Guadeloupe households (Kernevez, 2024). A first part aimed to discuss the practices, the perceptions, and representations of households
regarding biowaste. A second part, aimed to question the concrete curbs and levers to biowaste sorting, in order to elaborate future recommendations, and highlighting key issues to be addressed through the questionnaire. We only prepared open questions to expand discussions, and relaunches were anticipated.

### II.2.3 Focus Group

To complete these first interviews. We conducted a focus group with ten Guadeloupe households. It permits exploration of the motivations, agreements, and disagreements on the biowaste sorting system in a collective procedure (Kitizinger, 1995; Morgan, 1998). In fact, following the first interviews with key-informants, the collective perception of managing biowaste, especially the social acceptability of biowaste, of compost, but also the collective management of shared gardens of shared compost appear as specific drivers of the recycling. Organising a focus group was a way of answering several economics imperatives, such as lack of time, money but also consistent with scientist rigour (Baribeau et al., 2011).

The guide (see Appendix 2) was constructed as a set of open questions, starting with ice breaker questions to further dive into the main subject. The number of questions was limited to 8 as recommended, with relaunches in order to clarify how, why, and where (Baribeau, 2011; Morgan et al., 1998). During the discussion, special care was paid ensuring that people communicate with each other and not just with the person leading the discussion (Kitzinger et al., 2004) and that speaking time is equally distributed.

### II.2.4 Semi-structured interviews of households

We conducted seven short semi-structured interviews (approximately fifteen minutes each), by phone or in person, with households that do not sort their biowaste and who were recruited outside the workplace. These interviews aimed to explore the determinants and factors underlying non-sorting practices in order to complement our sample, and refine our analysis of determinants of biowaste sorting. Particular attention was given to participants living in “Ensembles,” that is, in urban residential areas. The structure and thematics of this guide was similar as those previously.

### II.3 Questionnaire

The questionnaire (see Appendix 3) was designed to provide a comprehensive overview of current biowaste sorting practices, levels of awareness, and attitudes toward biowaste management. The preceding qualitative phase allowed us to refine the measurement tools by identifying the most relevant elements to measure households’ propensity of sorting as well as the most common attitudes towards sorting. More specifically, it allowed selection of the most relevant attributes and levels for biowaste management alternatives, while alleviating the length of the questionnaire to make it accessible to all potential respondents.

#### II.3.1 Participants and fieldwork

For the quantitative phase, a non-probability sampling strategy combining face-to-face administration of the questionnaire and online dissemination was implemented, given the absence of
an exhaustive list of email addresses of all households. Although the questionnaire was shared on social networks, pre-test results indicated an average completion time of around twenty minutes, which led us to prioritize in-person administration to limit abandonment rates and ensure high-quality responses. In fact, previous research tends to support that online surveys have significantly lower response rates compared to other data collection modes (Daikeler et al., 2020; Shih & Fan, 2008).

Fieldwork was conducted over one month (three days per week on average), covering key locations across Guadeloupe and Marie-Galante to fulfill territorial quotas. Locations were strategically selected and daily adapted to reach diverse populations, including individuals without access to a car. During the second half of the fieldwork, special care was paid to respondents from EPCI Communauté d'agglomérations de grand sud Caraïbes (CAGSC), Communauté d'agglomération de la riviera du levant (CARL), and Communauté d'agglomération de nord grande terre (CANGT), and to working-age adults (30–50 years).

The objective was to achieve proportional representation by EPCI and meet the sample size requirements for the choice experiment, as defined by Orme's (1998) rule of thumb:

$$N \geq 500 \frac{L^{max}}{JS},$$

Where L is the maximum number of levels across attributes, J is the number of alternatives per choice set (excluding the BAU option if applicable), and S is the number of choice tasks presented to each respondent as determined through the Ngene design.

This implied a minimum of 111 respondents without a garden and 133 with a garden to obtain precise estimations of parameters and ensure minimum detectable effect (Assele et al., 2023).

All four interviewers followed standardized experimental conditions: each question and choice set was read in the same manner to all participants to avoid interviewer-induced bias, particularly during the choice experiment. The main focus during administration was to ensure understanding of the choice cards so that scenarios selected reflected options participants could realistically adopt in their daily lives.

### II.3.2 Conception of the questionnaire

The survey was developed and administered using the SurveyEngine software (SurveyEngine, 2025), which was developed for the implementation of choice experiments. The questionnaire is divided into five sections, following recommendations on survey construction in stated choice studies (Liebe et al., 2021). Following the filter questions, the first part included preliminary questions on participants’ current practices. The second part consisted of the choice experiment (see dedicated part II.4.2). The third part focused on attitudes towards bio-waste sorting, measured using Likert scales; it aimed to assess different affirmations, each one, relative to a part of the COM-B framework. The fourth part consisted of questions on respondents’ knowledge and awareness of bio-waste management. Finally, the last section demanded socio-demographic information of the
respondent in order to characterize our sample and account for heterogeneity in preferences during
the analysis phase.

Prior to the main data collection, two preparatory stages were implemented: a pre-test and a pilot study, consistent with standard survey deployment practices (Louviere et al., 2000; Rose & Bliemer, 2009). The pre-test was conducted with twelve respondents, in paper format. It first evaluated the clarity and comprehensibility of the questionnaire and above all the survey length. The subsequent pilot study, carried out with about more than 10% of the intended sample (i.e. 29 people), provided a quantitative assessment of the survey's feasibility and allowed collection of priors to ensure robustness of the choice experiment design (Rose & Bliemer, 2013).

II.2.3 *Choice experiment*

The discrete choice experiment (DCE) constituted the pillar of the questionnaire, designed to reveal preferences for diverse biowaste management strategies. Following the framework proposed by Adamowicz et al. (1998) and consistent with Random Utility Theory (McFadden, 1974), the DCE was developed as a labelled experiment, since alternatives represent qualitatively distinct policy options that respondents may evaluate differently with expected heterogeneity in willingness to accept (WTA) across them, and because the study's broader objective is to derive policy-relevant insights for designing integrated biowaste management tools in Guadeloupe (Hensher, Rose, & Greene, 2015).

Concretely, three choice experiments were created (fig 3 for example of choice card): one for the food and green waste management of participants with access to a garden, and a specific one for food waste management only for participants without a garden. Waste management alternatives included door-to-door collection, shared composting, and biowaste collection points (BAV) for households without access to a garden, and an additional individual composting option for households with garden space, replacing the door-to-door alternative. The three alternatives of the green waste management choice experiment were individual composting, recycling center, and door-to-door collection. A business as usual (BAU) alternative : “je conserve ma pratique actuelle” reflecting the current waste management practice of respondents, was included in each experiment.
<table><caption>Fig 3: Example of the choice cards used in the choice experiment section of the questionnaire</caption><thead><tr><th></th><th>Compostage individuel: en tas ou avec composteur</th><th>Collecte à domicile</th><th>Apport en déchetterie</th><th>Je préfère conserver ma pratique actuelle</th></tr></thead><tbody><tr><th scope="row">Débouché</th><td>Mise à disposition de compost pour les agriculteurs</td><td>Production d'énergie</td><td>Production d'énergie</td><td></td></tr><tr><th scope="row">Accompagnement et formation</th><td>Livret explicatif</td><td>Atelier collectif (2h00)</td><td>Aucun</td><td></td></tr><tr><th scope="row">Bon d'achat</th><td>100 €/an</td><td>50 €/an</td><td>75 €/an</td><td></td></tr><tr><th scope="row">Quelle option choisiriez-vous?</th><td>●</td><td>●</td><td>●</td><td>●</td></tr></tbody></table>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

In fact, significant differences in results of choice experiments have been reported in the literature depending on the presence or absence of this status quo alternative (Dhar, 1997), with the general recommendation that status quo alternatives should be incorporated when possible (Adamowicz et al., 1998; Bateman et al., 2002; Bennett & Blamey, 2001). After the choice experiment, a dedicated section was added to examine the importance of each attribute, allowing for a better understanding of their influence on participants' decisions. Additionally, respondents who consistently selected the BAU (business-as-usual) option were asked to indicate their reasons, such as satisfaction with current practices, perceiving alternatives as too difficult to adopt, or considerations related to the overall waste management system

Each alternative was described by three attributes: (i) financial incentives, expressed as a willingness to accept (WTA) of participants in the form of vouchers; (ii) technical accompaniment, capturing the form of institutional support offered (e.g., none, booklet, collective workshop); (iii) final use of the compost (e.g., returned to participants, used in local agriculture, transformation in energy, or not valorised). Attribute levels were defined based on focus group discussions and expert consultation to ensure realism and relevance for the target population (Liebe, Glenk, & Meyerhoff, 2021).

The design financial attribute we followed recommendations from Carlsson, Ferrini, and Scarpa (2007), Hensher, Shore, and Train (2005), and Bennett (2001): various monetary instruments were considered, like program costs, tax reductions, financial bonuses, and initial equipment costs. For reasons of social acceptance and political feasibility, based on the previous qualitative phase, fines and per-kilogram rewards were excluded. The attribute was calibrated using local data (Jourda & Malet, 2022): the cost of public waste management services (~€179 per inhabitant), the share attributable to biowaste (~30%), and the treatment costs of biowaste (1.5–2× those of residual waste) (ODG, 2017; ADEME, 2024). In this context, the financial attribute was designed to reward
participants in the form of vouchers usable in local shops. These ones would be allocated for concrete engagement waste sorting practices.

To reduce cognitive burden and ensure statistical efficiency, a D-efficient design was generated in Ngene using priors obtained from the pilot study (Rose & Bliemer, 2009, 2013). The final design consists of five choice cards per respondent for the two choice experiments addressed to participants with a garden, and six choice cards per respondent for the ones without a garden.

## II.4 Ethical considerations

Ethical clearance was obtained for the project from the Data Protection Officer of INRAE and the study registered as (february 21th 2025). All participants provided informed consent before taking part in the study, in line with ethical research standards (Bryman, 2016; Babbie, 2020). Participation was entirely voluntary, and confidentiality of responses was guaranteed to ensure anonymity and respect for privacy. For participants interested in receiving the results of the study, contact details have been collected and distinctly stored.

## II.5 Data Analysis

The qualitative data from both group discussions and semi-structured interviews were analyzed using a structured approach based on the COM-B framework. Transcriptions of the discussions and interviews were then coded according to COM-B categories. Explicit statements as well as supporting elements such as facts, maxims, anecdotes, or examples were included. Frequencies of themes were counted to identify the most recurrent elements. This categorical coding approach is consistent with classical content analysis methodology in qualitative research, which allows systematic categorization of textual data to identify patterns and themes (Krippendorff, 2013).

Statistical analyses were performed using Stata (StataCorp, 2025) and RStudio with the Apollo package (Hess & Palma, 2019). Quantitative data from our questionnaire were methodologically analyzed. As data cleaning, we first excluded respondents who were not permanent residents of Guadeloupe (initial screening criterion) as well as the “test-lines.” Questionnaires that had been initiated but were incomplete at various steps were also excluded. Finally, we checked the completion time and removed surveys filled out in less than six minutes after controller consistency of answers.

A set of derived variables was constructed, including household composition, type of housing, food waste-sorting practices (code as a dummy), and composting behaviour (code as a dummy). An index of awareness of biowaste management (ranging from 0 to 5) has been created with the variables related to household knowledge, receipt of information on compost, access to a composter, and awareness of national and local movement, excluding the variable on children's education. Attitudinal measures were recoded for the negative one, and grouped according to the COM-B framework into three composite indicators: motivation, opportunities, and capabilities, calculated as mean scores, according to the methodology presented by Allison et al. (2022).

Descriptive statistics were produced to characterize the sample (sociodemographic profile, housing conditions, waste management practices), followed by bivariate analyses (chi-square tests and t-test) to explore associations between sorting/composting and key explanatory variables according to our study assumptions.
Finally, hierarchical multivariate models were estimated using probit regressions to identify the determinants of sorting and composting behaviour, see Woolridge (2016), methodology for binary response analysis. As the outcome variables in our probit regressions were food waste sorting behaviour and composting behaviour, the predictors variables used were the index relative to the components of the COM-B model, the index of awareness, the age, type of accommodation, and the level of study (as a proxy for the current occupation). Prior to estimating the regressions, potential multicollinearity among the explanatory variables was assessed, notably through the examination of Variance Inflation Factors (VIF). In addition, heteroskedasticity-robust standard errors were employed using the *vce(robust)* option on Stata, in order to obtain consistent inference even in the presence of heteroskedasticity (White, 1980). Competing model specifications were compared using the Akaike Information Criterion (AIC) (Findly & Parzen, 1998) and Bayesian Information Criterion (BIC) (Schwartz, 1978) criteria to retain the most explanatory models.

We analyzed the discrete choice experiment (DCE) data following a specific econometric strategy. Because of time constraints, we restricted the analysis to the results from the two choice experiments (food and green waste) attributed to participants owning a garden, and we performed the same procedure for both. For the food waste choice experiment, we first estimated a basic Multinomial Logit (MNL) model, which assumes that the coefficients associated with each attribute are fixed across individuals and that the Independence of Irrelevant Alternatives (IIA) property holds (Train, 2009). This provided a baseline of average preferences across the sample. We then gradually introduced interactions between alternative-specific constants and socio-demographic variables, focusing on age, gender, and level of education, according to our study assumptions. This allowed us to examine whether baseline preferences for the different waste management alternatives varied across these characteristics. In a second step, we relaxed the IIA assumption by estimating Mixed Logit (MXL) models, which allow for random taste variation and provide a more precise view of preferences (Hess & Bliemer, 2020). We progressively randomized the following coefficients: financial incentive, accompaniment, and compost outlets and observed evolution of AIC and BIC criteria. In a third step, we randomized the interaction coefficients between alternative-specific constants and socio-demographic variables, until obtaining the final specification. Finally, in distinct model specifications, we added heterogeneity by interacting socio-demographic characteristics with the attribute related to compost outlets. This choice was motivated by the qualitative phase which underlined the importance of outlet for households and the fact that coefficients related to accompaniment levels showed limited influence on participants' preferences in the initial MNL estimations, whereas outlets appeared as more significant drivers of choice. For the garden waste choice experiment we followed the same methodology: first estimating MNL models with gradually added interactions for age, education, and gender, and then moving to MXL specifications with progressively randomized interaction coefficients.
# III. Results

## III.1 Characterisation of sample

### III.1.1 Socio-demographic profile of the sample

The final sample comprised 244 respondents, for whom socio-demographic characteristics are summarized in tab 1. Participants (M (age) = 45.63, SD (age) = 17.51) were mostly females (58%) and were in the majority educated to an undergraduate level (35.25%). The majority of participants were employees (26.23%). In the majority, participants lived in detached homes (70.90%) and managed a garden (58.61%). The size of households ranges from 1 to 14 (M = 2.86, SD = 1.80). Participants are proportionally divided according to EPCI demography : CapExcellence (n = 54), CAGSC (n = 45), Communauté d'agglomérations de nord basse terre (CANBT) (n = 52), CARL (n =30), CANGT (n =38), and Communauté de communes de Marie Galante (CCMG) (n =25).

Our sample is quite representative of the Guadeloupean population in terms of household size, constituted, on average, of 2.13 people in Guadeloupe in 2022 (INSEE, 2025), age with an average of 43 years old in 2022 (INSEE, 2025), and type of accommodation, with houses representing 74.3% of the type of principal residence (INSEE, 2025). The employment distribution of our sample shows a majority of employees, which is quite consistent with the Guadeloupean labor market, where employees form the biggest socio-professional group (INSEE, 2025). However, our sample comprised more females and is more educated than the regional average (INSEE, 2025). Though exact figures on garden possession are unknown, an ongoing work produced an estimation of the share of households with a garden and their average surface by "Ilots Regroupés pour l'Information Statistique" (IRIS). The resulting maps confirm that our survey sample is representative.
<table><caption>TABLE 1 - Summary of participants' socio-demographic characteristics</caption><thead><tr><th>Characteristics</th><th>N (Missing)</th><th>%</th><th>Mean</th><th>SD</th></tr></thead><tbody><tr><td><strong>Gender</strong></td><td>244 (0)</td><td></td><td></td><td></td></tr><tr><td>Male</td><td>100</td><td>40.98</td><td></td><td></td></tr><tr><td>Female</td><td>141</td><td>57.79</td><td></td><td></td></tr><tr><td>Prefer not to say</td><td>3</td><td>1.23</td><td></td><td></td></tr><tr><td><strong>Age (years)</strong></td><td>244 (0)</td><td></td><td>45.65</td><td>17.48</td></tr><tr><td><strong>Highest level of education</strong></td><td>244 (0)</td><td></td><td></td><td></td></tr><tr><td>Middle school</td><td>26</td><td>10.66</td><td></td><td></td></tr><tr><td>High school</td><td>76</td><td>31.15</td><td></td><td></td></tr><tr><td>Undergraduate level</td><td>86</td><td>35.25</td><td></td><td></td></tr><tr><td>Master degree or higher</td><td>56</td><td>22.95</td><td></td><td></td></tr><tr><td><strong>Occupations</strong></td><td>244 (0)</td><td></td><td></td><td></td></tr><tr><td>Farmer / Agricultural worker</td><td>10</td><td>4.10</td><td></td><td></td></tr><tr><td>Artisan, Trader or Business Owner</td><td>27</td><td>11.07</td><td></td><td></td></tr><tr><td>Executive or Professional</td><td>41</td><td>16.80</td><td></td><td></td></tr><tr><td>Intermediate profession</td><td>8</td><td>3.28</td><td></td><td></td></tr><tr><td>Employee</td><td>64</td><td>26.23</td><td></td><td></td></tr><tr><td>Retired</td><td>32</td><td>13.11</td><td></td><td></td></tr><tr><td>Student</td><td>27</td><td>11.07</td><td></td><td></td></tr><tr><td>Homemaker</td><td>6</td><td>2.46</td><td></td><td></td></tr><tr><td>Unemployed</td><td>18</td><td>7.38</td><td></td><td></td></tr><tr><td>Other</td><td>11</td><td>4.10</td><td></td><td></td></tr><tr><td><strong>Type of housing</strong></td><td>244 (0)</td><td></td><td></td><td></td></tr><tr><td>Apartment in a building or residence</td><td>52</td><td>21.31</td><td></td><td></td></tr><tr><td>Detached house</td><td>173</td><td>70.90</td><td></td><td></td></tr><tr><td>Top or bottom unit of a villa</td><td>17</td><td>6.97</td><td></td><td></td></tr><tr><td>Other</td><td>2</td><td>0.82</td><td></td><td></td></tr><tr><td><strong>Garden ownership</strong></td><td>244 (0)</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>143</td><td>58.61</td><td></td><td></td></tr><tr><td>No</td><td>101</td><td>41.39</td><td></td><td></td></tr><tr><td><strong>EPCI</strong></td><td>244 (0)</td><td></td><td></td><td></td></tr><tr><td>CAPEX</td><td>54</td><td>22.13</td><td></td><td></td></tr><tr><td>CAGSC</td><td>45</td><td>18.44</td><td></td><td></td></tr><tr><td>CANBT</td><td>52</td><td>21.31</td><td></td><td></td></tr><tr><td>CARL</td><td>30</td><td>12.30</td><td></td><td></td></tr><tr><td>CANGT</td><td>38</td><td>15.57</td><td></td><td></td></tr><tr><td>CCMG</td><td>25</td><td>10.25</td><td></td><td></td></tr><tr><td><strong>Household size</strong></td><td>244 (0)</td><td></td><td>2.88</td><td>1.80</td></tr></tbody></table>

### III.1.2 Sorting practices of the sample

Survey's results reveal heterogeneous practices in terms of biowaste sorting. Only 56.9% of participants engage in any form of food waste sorting, that is, other than household trash. This predominant practice for food waste management is followed by individual composting (23.4%) and
feeding to animals (20.9%). Green waste practices vary depending on the type of waste: small green waste is often left "as it is" on private property (25.9%) or composted (21%), whereas large green waste is more frequently taken to recycling centers (24.5%) or burned (19.6%).

Statistical analyses (t-test and chi2 test) indicate that age is significantly associated with food waste sorting behaviour, with individuals who sort being older on average (p-value < 0.01). Moreover, sorting practices are strongly associated with the type of housing (collective or independent), EPCI, and existing sorting habits for glass and packaging (all p-values < 0.01). On the contrary, no significant gender effect was observed for food waste sorting (p-value = 0.49).

### III.1.3 Awareness level of the population

The results reveal relatively low levels of information and exposure to awareness campaigns regarding biowaste management. While the majority of respondents (63.9%) report knowing where their closest recycling center is, only 23.4% have received an informational booklet on sorting or composting, and just 16% have been provided with a composter. Awareness of local or national campaigns is limited: only 15.6% have ever heard about "Quinzaine Tous au compost" and 32% of the "Semaine européenne de réduction des déchets". The composite awareness score reveals that a significant portion of the population has low exposure to awareness on biowaste management: 59% of respondents have a score of 0 or 1.

Further, the mean awareness score is significantly higher among those who sort their biowaste (M = 1.86) than those who do not (M = 1.04), with a statistically significant difference (p-value < 0.001). A related tendency is observed for composting: individuals who compost food waste have significantly higher awareness scores (M = 2.15) than those who do not (M = 1.30).

### III.2. Behavioural factors associated with sorting/composting

Following this descriptive assessment of current practices and information levels, we enlarged the analysis by mobilizing the COM-B model to evaluate the behavioural determinants of sorting.

We assessed the three constructed composite scores derived from the COM-B model: capacities' score (M = 2.93), opportunities' score (M= 2.63), and motivations' score (M = 3.92). It indicates commonly high motivation among participants, but more moderate levels of their perceived capacities and opportunities.

**Capacities** - Participants answer 4 items to measure capacities, such as "I find it difficult to sort biowaste".

**Opportunities** - Participants answer 4 items to measure opportunities, such as "I don't have an adapted container for biowaste".

**Motivations** - Participants answer 6 items to measure motivations, such as "I don't perceive interest in sorting or composting biowaste".

Independent t-tests revealed significant differences in these scores between individuals who declared sorting or composting their biowaste and those who did not. Participants who sorted their biowaste had significantly higher capacities (M = 3.21 vs. 2.56, p-value < 0.001), motivations (M = 4.06 vs. 3.74, p-value < 0.001), and opportunities (M = 2.97 vs. 2.19, p-value < 0.001) than those who did not. Similarly, individuals who practiced composting also reported significantly higher capacities (M
= 3.37 vs. 2.78, p-value < 0.001), motivations (M = 4.11 vs. 3.86, p-value = 0.002), and opportunities (M = 3.21 vs. 2.44, p-value < 0.001) scores.

### III.3 Identification of covariates for composting and sorting behaviour

This section aimed to identify covariates associated with composting and sorting behaviour, in order to better apprehend determinants of biowaste sorting and composting. To this end, we ran several probit regression models to measure the association between socio-demographic characteristics, the COM-B relative scores, the awareness composite score, and the probability of engaging in sorting or composting.

Given that some levels of the variable relative to accommodation types (q25) contained a small number of participants, some of the groups were combined to obtain only two categories: individual and collective accommodations. We admitted it for the rest of the analysis.

#### III.3.1 Probit regression models on sorting behaviour

The baseline model (M0) showed no explanatory power. Adding age and education level (M1) significantly improved the model fit. The following models incorporate the opportunity score (M2), with opportunity positively affecting the probability of sorting. The addition of a capacity score further improved model fit (M3). Building one by one on M2, with the motivation score (M4) and awareness scores (M5) also showed positive improvements of the fitting, but adding EPCI related variables (M6) worsened the AIC and BIC criteria.

According to all criteria (see tab 2), M5 estimation was retained as the best fitted model (see tab 3). Opportunity (coefficient = 0.40, p-value < 0.01), capacity (coefficient = 0.28, p-value < 0.05), and awareness scores (coefficient = 0.26, p-value < 0.05) appear to all be positively and significantly associated with the probability of sorting. Living in collective housing is negatively associated with sorting (coefficient = -0.85, p-value < 0.001) compared to living in individual accommodation. Higher education levels, compared to middle school level, tended to reduce the probability of sorting, with negative and significant coefficients.

<table><caption>TABLE 2 - Index of comparison criteria for probit regression on sorting</caption><thead><tr><td>Model</td><td>LL(model)</td><td>AIC</td><td>BIC</td></tr></thead><tbody><tr><td>M0 (baseline)</td><td>-166.7513</td><td>335.5027</td><td>338.9998</td></tr><tr><td>M1</td><td>-148.176</td><td>308.352</td><td>329.335</td></tr><tr><td>M2</td><td>-131.6691</td><td>277.3383</td><td>301.8185</td></tr><tr><td>M3</td><td>-128.2918</td><td>272.5836</td><td>300.5609</td></tr><tr><td>M4</td><td>-128.3988</td><td>272.7975</td><td>300.7749</td></tr><tr><td>M5</td><td>-124.0592</td><td>266.1184</td><td>297.5929</td></tr><tr><td>M6</td><td>-122.7917</td><td>271.5833</td><td>317.0465</td></tr></tbody></table>
<table><caption>TABLE 3 - Determinants of sorting behaviour (probit regression)</caption><tr><td>sorting</td></tr><tr><td>Age (years) -0.00106<br/>(0.00552)</td></tr><tr><td>Individual accommodation 0<br/>(.)</td></tr><tr><td>Collective housing -0.845***<br/>(0.235)</td></tr><tr><td>Middle school's level 0<br/>(.)</td></tr><tr><td>High school's level -0.294<br/>(0.349)</td></tr><tr><td>Undergraduate's degree -0.595*<br/>(0.351)</td></tr><tr><td>Master's degree or higher -0.622*<br/>(0.350)</td></tr><tr><td>Opportunity score 0.396***<br/>(0.132)</td></tr><tr><td>Capacities score 0.283**<br/>(0.118)</td></tr><tr><td>Awareness score 0.260***<br/>(0.0929)</td></tr><tr><td>Constant -1.372**<br/>(0.563)</td></tr><tr><td>Observations 244</td></tr></table>

Notes: Robust standard errors are reported. Significance levels: * p<0.10, ** p<0.05, *** p<0.01.

### III.3.2 Probit regression models on composting

The baseline model (M0) showed no explanatory power. Adding age and education (M1) improved fit. Including the opportunity score (M2) further improved the AIC and BIC criteria (see tab 4). The distinct addition of a capacity score (M3), motivation score (M4), awareness score (M5), EPCI related variable (M6) don't significantly improve model fit, integration with the opportunity score either. To select the best-fitting model among the closed estimated specifications we obtained, we focus on the BIC criteria, which penalizes model complexity and is usually favored for model selection. According to the BIC, the M2 specification was retained as the best-fitted model (see tab 5). In this model, age was positively associated with the probability of composting (coefficient = 0.014, p-value = 0.012), with individuals holding an undergraduate degree significantly more likely to compost (coefficient = 0.803, p-value = 0.022) than people with a middle school educational level, while other education categories were not significant. The opportunity score was strongly and positively associated with composting (coefficient = 0.663, p-value < 0.001).
<table><caption>TABLE 4 - Index of comparison criteria for probit regression on composting</caption><thead><tr><th>Model</th><th>LL(model)</th><th>AIC</th><th>BIC</th></tr></thead><tbody><tr><td>M0 (baseline)</td><td>-137.2098</td><td>276.4196</td><td>279.9167</td></tr><tr><td>M1</td><td>-129.1802</td><td>268.3604</td><td>285.8463</td></tr><tr><td>M2</td><td>-112.1254</td><td>236.2508</td><td>257.2338</td></tr><tr><td>M3</td><td>-110.3264</td><td>234.6527</td><td>259.1329</td></tr><tr><td>M4</td><td>-111.8561</td><td>237.7121</td><td>262.1923</td></tr><tr><td>M5</td><td>-110.2784</td><td>234.5568</td><td>259.0369</td></tr><tr><td>M6</td><td>-110.5176</td><td>243.0353</td><td>281.5041</td></tr></tbody></table>

<table><caption>TABLE 5- Determinants of compost (probit regression)</caption><tr><td>compost</td></tr><tr><td>Age (years) 0.0144**</td></tr><tr><td>(0.00577)</td></tr><tr><td>Middle school's level 0</td></tr><tr><td>(.)</td></tr><tr><td>High school's level 0.195</td></tr><tr><td>(0.365)</td></tr><tr><td>Undergraduate's degree 0.803**</td></tr><tr><td>(0.350)</td></tr><tr><td>Master's degree or higher 0.466</td></tr><tr><td>(0.359)</td></tr><tr><td>Opportunity score 0.663***</td></tr><tr><td>(0.120)</td></tr><tr><td>Constant -3.682***</td></tr><tr><td>(0.526)</td></tr><tr><td>Observations 244</td></tr></table>

Notes: Robust standard errors are reported. Significance levels: * p<0.10, ** p<0.05, *** p<0.01.

### III.4 Choice experiment treatment

#### III.4.1 Results from the food waste (participants with a garden) choice experiment

In order to analyze households' preferences for food waste sorting scenarios, we estimate diverse models (see tab). The experiment included 143 respondents. Among them, 45 (31%) consistently chose the BAU option, mainly because they were satisfied with their current practice (32 respondents, 22%), and to a lesser extent because they believed that people are not ready to adopt new solutions (7 respondents, 5%).

We first estimated a basic Multinomial Logit (MNL) model, M0, as a baseline of average preferences. Interactions with socio-demographic variables were then added: M1 (age), M2 (gender), and M3 (education). Mixed Logit (MXL) models were estimated next to capture unobserved heterogeneity, randomizing coefficients for financial incentives (M4), accompaniment (M5), and compost outlets (M6, M7). Interaction coefficients were gradually randomized in M8, M9, and M10. M10 was selected for analysis of the primary model results, presenting the lowest AIC (see tab 6).
Finally, heterogeneity related to compost outlets and socio-demographic characteristics was added: M11–M13 are MNL models with interactions for age, education, and gender, while M14–M16 are Mixed Logit models with progressively randomized interaction coefficients. We retained model M16 (Mixed Logit with random age/outlets and education/outlets interactions) for analysis, as it provides the best trade-off between underfitting and overfitting with the lowest AIC (1842.1) compared to alternative specifications.

<table><caption>TABLE 6 - Index of comparison criteria for CE food waste/ participants with a garden</caption><thead><tr><td>Model</td><td>LL(model)</td><td>AIC</td><td>BIC</td></tr></thead><tbody><tr><td>M0</td><td>-938.705</td><td>1903.413</td><td>1962.853</td></tr><tr><td>M1</td><td>-916.97</td><td>1865.931</td><td>1939.088</td></tr><tr><td>M2</td><td>-893.17</td><td>1836.342</td><td>1950.649</td></tr><tr><td>M3</td><td>-881.96</td><td>1819.927</td><td>1947.754</td></tr><tr><td>M4</td><td>-884.969</td><td>1797.938</td><td>1861.95</td></tr><tr><td>M5</td><td>-873.9394</td><td>1781.879</td><td>1859.608</td></tr><tr><td>M6</td><td>-868.2681</td><td>1772.536</td><td>1854.837</td></tr><tr><td>M7</td><td>-857.79</td><td>1753.576</td><td>1840.45</td></tr><tr><td>M8</td><td>-812.5551</td><td>1663.11</td><td>1749.983</td></tr><tr><td>M9</td><td>-767.7438</td><td>1609.488</td><td>1778.662</td></tr><tr><td>M10</td><td>-753.423</td><td>1592.846</td><td>1789.152</td></tr><tr><td>M11</td><td>-935.67</td><td>1897.344</td><td>1956.783</td></tr><tr><td>M12</td><td>-919.04</td><td>1876.077</td><td>1962.817</td></tr><tr><td>M13</td><td>-918.34</td><td>1878.673</td><td>1974.543</td></tr><tr><td>M14</td><td>-915.3</td><td>1860.597</td><td>1929.076</td></tr><tr><td>M15</td><td>-894.0674</td><td>1842.135</td><td>1965.397</td></tr><tr><td>M16</td><td>-893.471</td><td>1848.942</td><td>1990.465</td></tr></tbody></table>

The results from the M10 model (see tab 7) disclose that households prefer maintaining their current food waste management practices rather than switching to shared composting or using drop-off points near glass collection sites. The alternative-specific constants indicate a lower utility for shared composting and drop-off alternatives than for keeping their BAU (asc_2 = -3.54, p-value = 0.001; asc_3 = -2.95, p-value = 0.004), while individual composting (asc_1 = -1.20, p-value = 0.104) is perceived as not significantly different than the BAU option regarding households' preferences. Households significantly prefer keeping compost for personal use rather than giving it to farmers when choosing individual composting (deb_compind_agri = -0.76, p-value = 0.002). For the other alternatives, participants do not show a significant preference between converting compost into energy or providing it to farmers. For the accompaniment attribute, participants prefer no accompaniment over receiving a booklet (acc_livret = -0.37, p-value = 0.046), while workshops or training by a master composter do not significantly affect choices and differ in the accompaniment preferences. Financial incentives also have no significant impact. When asked to rank the importance of the different attributes, alternatives were considered the most important criterion by 55 respondents (38%) and very important by 49 (34%). The outlet of compost was judged very important by 58 respondents (41%) and the most important by 31 (22%). Accompaniment was seen as somewhat important by 51 respondents (36%), not important at all by 55 (38%), and very important by 29 (20%). Finally, financial incentives were judged not important at all by 62
respondents (43%), somewhat important by 50 (35%), very important by 23 (16%), and the most important criterion by only 8 (6%).

Introducing random coefficients in interactions provides precious insights into preference heterogeneity. Most of the standard deviation coefficients are significant, highlighting intra-individual variation by age, education level, and gender, and demonstrating the precision obtained by moving from MNL to MXL. The model shows that older participants are less likely to choose shared composting or drop-off options (mu_asc2_age = -0.061, p-value < 0.001; mu_asc3_age = -0.053, p-value = 0.001). Participants with master's degrees are more likely to select individual or shared composting compared to those with lower education levels, indicating that a higher educational level increases the probability of adopting alternative sorting practices. Gender, by contrast, does not significantly influence the choice of an alternative, except in interaction terms where some variation is observed.

<table><caption>TABLE 7 - M10' results : interactions between alternatives, ages, study levels and gender (food waste)</caption><thead><tr><th>Parameter</th><th>Estimate</th><th>Std. Error</th><th>t-ratio</th><th>p (1-sided)</th></tr></thead><tbody><tr><td>asc_1</td><td>-0.62564</td><td>0.75026</td><td>-0.8</td><td>0.202</td></tr><tr><td>asc_2</td><td>-2.10579</td><td>1.03754</td><td>-2.0</td><td>0.021 *</td></tr><tr><td>asc_3</td><td>-0.87583</td><td>1.50548</td><td>-0.6</td><td>0.280</td></tr><tr><td>mu_asc1_age</td><td>-0.05333</td><td>0.01911</td><td>-2.8</td><td>0.003 **</td></tr><tr><td>sigma_asc1_age</td><td>-0.05858</td><td>0.06033</td><td>-1.0</td><td>0.166</td></tr><tr><td>mu_asc2_age</td><td>-0.06691</td><td>0.02832</td><td>-2.4</td><td>0.009 **</td></tr><tr><td>sigma_asc2_age</td><td>-0.09258</td><td>0.03191</td><td>-2.9</td><td>0.002 **</td></tr><tr><td>mu_asc3_age</td><td>-0.05680</td><td>0.02607</td><td>-2.2</td><td>0.015 *</td></tr><tr><td>sigma_asc3_age</td><td>0.08217</td><td>0.05396</td><td>1.5</td><td>0.064 .</td></tr><tr><td>mu_asc1_edu_lycee</td><td>-0.16235</td><td>0.98624</td><td>-0.2</td><td>0.435</td></tr><tr><td>sigma_asc1_edu_lycee</td><td>2.52727</td><td>0.54893</td><td>4.6</td><td>0.000002 ***</td></tr><tr><td>mu_asc1_edu_bac2_3</td><td>-0.26014</td><td>0.76977</td><td>-0.3</td><td>0.368</td></tr><tr><td>sigma_asc1_edu_bac2_3</td><td>1.71023</td><td>0.60173</td><td>2.8</td><td>0.002 **</td></tr><tr><td>mu_asc1_edu_bac5</td><td>1.02861</td><td>1.08577</td><td>0.9</td><td>0.172</td></tr><tr><td>sigma_asc1_edu_bac5</td><td>-2.54125</td><td>1.22250</td><td>-2.1</td><td>0.019 *</td></tr><tr><td>mu_asc2_edu_lycee</td><td>0.82751</td><td>1.19179</td><td>0.7</td><td>0.244</td></tr><tr><td>sigma_asc2_edu_lycee</td><td>-3.16507</td><td>0.75893</td><td>-4.2</td><td>0.00002 ***</td></tr><tr><td>mu_asc2_edu_bac2_3</td><td>2.01895</td><td>1.08842</td><td>1.9</td><td>0.032 *</td></tr><tr><td>sigma_asc2_edu_bac2_3</td><td>-1.05186</td><td>0.58551</td><td>-1.8</td><td>0.036 *</td></tr><tr><td>mu_asc2_edu_bac5</td><td>2.52260</td><td>1.31058</td><td>1.9</td><td>0.027 *</td></tr><tr><td>sigma_asc2_edu_bac5</td><td>-2.59994</td><td>0.76524</td><td>-3.4</td><td>0.0003 ***</td></tr><tr><td>mu_asc3_edu_lycee</td><td>-2.26840</td><td>1.86203</td><td>-1.2</td><td>0.112</td></tr><tr><td>sigma_asc3_edu_lycee</td><td>3.09103</td><td>0.91564</td><td>3.4</td><td>0.0004 ***</td></tr><tr><td>mu_asc3_edu_bac2_3</td><td>-0.07498</td><td>1.54156</td><td>0.0</td><td>0.481</td></tr><tr><td>sigma_asc3_edu_bac2_3</td><td>-0.29306</td><td>0.44697</td><td>-0.7</td><td>0.256</td></tr><tr><td>mu_asc3_edu_bac5</td><td>-0.66864</td><td>2.34926</td><td>-0.3</td><td>0.388</td></tr><tr><td>sigma_asc3_edu_bac5</td><td>-2.67056</td><td>1.38095</td><td>-1.9</td><td>0.027 *</td></tr><tr><td>mu_asc1_sexe</td><td>1.70127</td><td>0.62288</td><td>2.7</td><td>0.003 **</td></tr><tr><td>sigma_asc1_sexe</td><td>-1.17417</td><td>0.54390</td><td>-2.2</td><td>0.015 *</td></tr><tr><td>mu_asc2_sexe</td><td>0.98735</td><td>0.65744</td><td>1.5</td><td>0.067 .</td></tr><tr><td>sigma_asc2_sexe</td><td>0.11306</td><td>0.37943</td><td>0.3</td><td>0.383</td></tr><tr><td>mu_asc3_sexe</td><td>0.31148</td><td>1.15511</td><td>0.3</td><td>0.394</td></tr><tr><td>sigma_asc3_sexe</td><td>-1.96724</td><td>1.20898</td><td>-1.6</td><td>0.052 .</td></tr><tr><td>deb_compind_j</td><td>0.65655</td><td>0.28859</td><td>2.3</td><td>0.011 *</td></tr><tr><td>deb_ener</td><td>-0.06734</td><td>0.24381</td><td>-0.3</td><td>0.391</td></tr><tr><td>deb_dechcomp_j</td><td>-0.15851</td><td>0.39949</td><td>-0.4</td><td>0.346</td></tr><tr><td>acc_livret</td><td>-0.45345</td><td>0.24067</td><td>-1.9</td><td>0.030 *</td></tr><tr><td>acc_atelier</td><td>-0.16147</td><td>0.22261</td><td>-0.7</td><td>0.234</td></tr><tr><td>acc_mcomp</td><td>-0.93677</td><td>0.32676</td><td>-2.9</td><td>0.002 **</td></tr><tr><td>b_x3</td><td>0.00088</td><td>0.00262</td><td>0.3</td><td>0.369</td></tr></tbody></table>

Significance codes: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’, 0.1 ‘.’, 1

The results from the M16 model (see tab 8) are consistent with these insights. In this specification, individual composting is significantly preferred over the BAU option (asc_1 = 0.402, p-value =
0.034), whereas participants maintain a stronger preference for keeping their BAU rather than switching to shared composting or drop-off alternatives (asc_2 = -0.626, p = 0.006; asc_3 = -0.604, p-value = 0.006). Coefficients for accompaniment levels (acc_livret = -0.172, p-value = 0.134; acc_atelier = 0.053, p-value = 0.358; acc_mcomp = 0.198, p-value = 0.123) and financial incentives (b_x3 = 0.00015, p-value = 0.467) remain non-significant. Approximately half of the standard deviation coefficients are significant, confirming the benefit of the MXL model in capturing preference heterogeneity. Looking at interactions reveals that younger participants significantly prefer converting compost into energy compared to older individuals (mu_deb_ener_age = -0.028, p-value = 0.006), while participants with a high school education exhibit an aversion to giving compost to farmers (mu_deb_compind_agri_edu_lycee = -1.269, p-value = 0.035).

<table><caption>TABLE 8 -M16' results : interactions between outlets, ages, study levels and gender (food waste)</caption><thead><tr><th>Parameter</th><th>Estimate</th><th>Std. Error</th><th>t-ratio</th><th>p (1-sided)</th></tr></thead><tbody><tr><td>asc_1</td><td>0.40249</td><td>0.22034</td><td>1.8</td><td>0.034 *</td></tr><tr><td>asc_2</td><td>-0.62570</td><td>0.25130</td><td>-2.5</td><td>0.006 **</td></tr><tr><td>asc_3</td><td>-0.60404</td><td>0.23839</td><td>-2.5</td><td>0.006 **</td></tr><tr><td>mu_deb_ener_age</td><td>-0.02826</td><td>0.01130</td><td>-2.5</td><td>0.006 **</td></tr><tr><td>sigma_deb_ener_age</td><td>-0.00242</td><td>0.00520</td><td>-0.5</td><td>0.321</td></tr><tr><td>mu_deb_compind_agri_age</td><td>-0.02304</td><td>0.01518</td><td>-1.5</td><td>0.065 .</td></tr><tr><td>sigma_deb_compind_agri_age</td><td>0.08153</td><td>0.02293</td><td>3.6</td><td>0.0002 ***</td></tr><tr><td>mu_deb_ener_edu_lycee</td><td>-0.37399</td><td>0.40632</td><td>-0.9</td><td>0.179</td></tr><tr><td>sigma_deb_ener_edu_lycee</td><td>-0.76330</td><td>0.62545</td><td>-1.2</td><td>0.111</td></tr><tr><td>mu_deb_ener_edu_bac2_3</td><td>-0.27631</td><td>0.47714</td><td>-0.6</td><td>0.281</td></tr><tr><td>sigma_deb_ener_edu_bac2_3</td><td>-1.63900</td><td>0.42694</td><td>-3.8</td><td>0.00006 ***</td></tr><tr><td>mu_deb_ener_edu_bac5</td><td>0.33703</td><td>0.51809</td><td>0.7</td><td>0.258</td></tr><tr><td>sigma_deb_ener_edu_bac5</td><td>1.55247</td><td>0.81048</td><td>1.9</td><td>0.028 *</td></tr><tr><td>mu_deb_compind_agri_edu_lycee</td><td>-1.26884</td><td>0.69976</td><td>-1.8</td><td>0.035 *</td></tr><tr><td>sigma_deb_compind_agri_edu_lycee</td><td>1.39180</td><td>0.82878</td><td>1.7</td><td>0.047 *</td></tr><tr><td>mu_deb_compind_agri_edu_bac2_3</td><td>-0.57884</td><td>0.39879</td><td>-1.5</td><td>0.073 .</td></tr><tr><td>sigma_deb_compind_agri_edu_bac2_3</td><td>0.42216</td><td>0.71819</td><td>0.6</td><td>0.278</td></tr><tr><td>mu_deb_compind_agri_edu_bac5</td><td>0.49038</td><td>0.60857</td><td>0.8</td><td>0.210</td></tr><tr><td>sigma_deb_compind_agri_edu_bac5</td><td>-2.21075</td><td>0.81878</td><td>-2.7</td><td>0.003 **</td></tr><tr><td>mu_deb_ener_sexe</td><td>-0.31666</td><td>0.43351</td><td>-0.7</td><td>0.233</td></tr><tr><td>sigma_deb_ener_sexe</td><td>-1.09659</td><td>0.65668</td><td>-1.7</td><td>0.047 *</td></tr><tr><td>mu_deb_compind_agri_sexe</td><td>-0.28717</td><td>0.40270</td><td>-0.7</td><td>0.238</td></tr><tr><td>sigma_deb_compind_agri_sexe</td><td>-0.11975</td><td>0.99660</td><td>-0.1</td><td>0.452</td></tr><tr><td>acc_livret</td><td>-0.17239</td><td>0.15582</td><td>-1.1</td><td>0.134</td></tr><tr><td>acc_atelier</td><td>0.05260</td><td>0.14409</td><td>0.4</td><td>0.358</td></tr><tr><td>acc_mcomp</td><td>0.19836</td><td>0.17119</td><td>1.2</td><td>0.123</td></tr><tr><td>b_x3</td><td>0.00015</td><td>0.00185</td><td>0.1</td><td>0.467</td></tr></tbody></table>

Significance codes: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’, 0.1 ‘.’, 1

### III.4.2 Results from the green waste (participants with a garden) choice experiment

A total of 143 respondents participated in the green waste choice experiment. Similar gradual modelisation (see tab 9) as described in the previous section for the food waste choice experiment treatment (III.4.1) was applied here. Consistent with this approach, we selected the M11 and M16 models for detailed analysis, considering AIC and BIC criteria. An earlier specification, referred to as M3, was estimated, including interactions between household size and the alternatives to explore potential demographic effects. However, these interactions did not bring conclusive results, and we
didn't keep this interaction for the next estimations. The M16 model, on the other hand, represents a more advanced Mixed Logit specification, explicitly incorporating interactions between participants' age, education level, and the alternative outlets, but not randomizing coefficients associated to gender and outlet interactions because of outliers.

<table><caption>TABLE 9 - Index of comparison criteria for CE green waste / participants with a garden</caption><thead><tr><th>Model</th><th>LL(model)</th><th>AIC</th><th>BIC</th></tr></thead><tbody><tr><td>M0</td><td>-933.85</td><td>1895.706</td><td>1959.718</td></tr><tr><td>M1</td><td>-916.88</td><td>1867.756</td><td>1945.485</td></tr><tr><td>M2</td><td>-900.39</td><td>1852.786</td><td>1971.665</td></tr><tr><td>M3</td><td>-885.76</td><td>1829.517</td><td>1961.91</td></tr><tr><td>M4</td><td>-888.16</td><td>1836.32</td><td>1973.278</td></tr><tr><td>M5</td><td>-873.8504</td><td>1777.701</td><td>1846.285</td></tr><tr><td>M6</td><td>-844.83</td><td>1725.652</td><td>1807.953</td></tr><tr><td>M7</td><td>-827.9985</td><td>1693.997</td><td>1780.87</td></tr><tr><td>M8</td><td>-810.0445</td><td>1660.089</td><td>1751.535</td></tr><tr><td>M9</td><td>-786.2831</td><td>1614.634</td><td>1703.872</td></tr><tr><td>M10</td><td>-767.7438</td><td>1609.488</td><td>1778.662</td></tr><tr><td>M11</td><td>-744.98</td><td>1577.963</td><td>1778.835</td></tr><tr><td>M12</td><td>-926.57</td><td>1881.132</td><td>1945.046</td></tr><tr><td>M13</td><td>-928.19</td><td>1896.373</td><td>1987.819</td></tr><tr><td>M14</td><td>-923.89</td><td>1891.771</td><td>1992.361</td></tr><tr><td>M15</td><td>-881.28</td><td>1794.569</td><td>1867.613</td></tr><tr><td>M16</td><td>-860.76</td><td>1777.523</td><td>1905.35</td></tr></tbody></table>

The results from the M11 model (see tab 10) reveal that participants significantly prefer maintaining their current green waste practices rather than switching to door-to-door collection (asc_2 = -2.11, p-value = 0.021). On the contrary, preferences for individual composting (asc_1 = -0.63, p-value = 0.202) and drop-off at a recycling center (asc_3 = -0.88, p-value = 0.280) are not significantly different from the BAU option. Regarding the compost destination attribute, participants significantly prefer keeping compost for their own garden rather than giving it to farmers (deb_compind_j = 0.66, p-value = 0.011). For the accompaniment attribute, receiving a booklet (acc_livret = -0.45, p-value = 0.030) or support from a compost master (acc_mcomp = -0.94, p-value = 0.002) significantly reduces the utility of the participants, while workshops and financial incentives have no significant effect.

Introducing random coefficients in interactions provides valuable insights into preference heterogeneity. Most of the standard deviation parameters are significant. Results indicate that older participants are less likely to adopt alternatives compared to the BAU option (mu_asc1_age = -0.053, p-value = 0.003; mu_asc2_age = -0.067, p-value = 0.009; mu_asc3_age = -0.057, p-value = 0.015). Participants with undergraduate or master's degrees are significantly more likely to prefer door-to-door collection compared to those with middle school education (mu_asc2_edubac2_3 = 2.02, p-value = 0.032; mu_asc2_edubac5 = 2.52, p-value = 0.027). Women significantly prefer individual composting over men (mu_asc1_sexe = 1.70, p-value = 0.003).
<table><caption>TABLE 10 - M11' results : interactions between alternatives, ages, study levels and gender (green waste)</caption><thead><tr><th>Parameter</th><th>Estimate</th><th>Std. Error</th><th>t-ratio</th><th>p (1-sided)</th></tr></thead><tbody><tr><td>asc_1</td><td>-0.62564</td><td>0.75026</td><td>-0.8</td><td>0.202</td></tr><tr><td>asc_2</td><td>-2.10579</td><td>1.03754</td><td>-2.0</td><td>0.021 *</td></tr><tr><td>asc_3</td><td>-0.87583</td><td>1.50548</td><td>-0.6</td><td>0.280</td></tr><tr><td>mu_asc1_age</td><td>-0.05333</td><td>0.01911</td><td>-2.8</td><td>0.003 **</td></tr><tr><td>sigma_asc1_age</td><td>-0.05858</td><td>0.06033</td><td>-1.0</td><td>0.166</td></tr><tr><td>mu_asc2_age</td><td>-0.06691</td><td>0.02832</td><td>-2.4</td><td>0.009 **</td></tr><tr><td>sigma_asc2_age</td><td>-0.09258</td><td>0.03191</td><td>-2.9</td><td>0.002 **</td></tr><tr><td>mu_asc3_age</td><td>-0.05680</td><td>0.02607</td><td>-2.2</td><td>0.015 *</td></tr><tr><td>sigma_asc3_age</td><td>0.08217</td><td>0.05396</td><td>1.5</td><td>0.064 .</td></tr><tr><td>mu_asc1_edu_lycee</td><td>-0.16235</td><td>0.98624</td><td>-0.2</td><td>0.435</td></tr><tr><td>sigma_asc1_edu_lycee</td><td>2.52727</td><td>0.54893</td><td>4.6</td><td>0.000002 ***</td></tr><tr><td>mu_asc1_edu_bac2_3</td><td>-0.26014</td><td>0.76977</td><td>-0.3</td><td>0.368</td></tr><tr><td>sigma_asc1_edu_bac2_3</td><td>1.71023</td><td>0.60173</td><td>2.8</td><td>0.002 **</td></tr><tr><td>mu_asc1_edu_bac5</td><td>1.02861</td><td>1.08577</td><td>0.9</td><td>0.172</td></tr><tr><td>sigma_asc1_edu_bac5</td><td>-2.54125</td><td>1.22250</td><td>-2.1</td><td>0.019 *</td></tr><tr><td>mu_asc2_edu_lycee</td><td>0.82751</td><td>1.19179</td><td>0.7</td><td>0.244</td></tr><tr><td>sigma_asc2_edu_lycee</td><td>-3.16507</td><td>0.75893</td><td>-4.2</td><td>0.00002 ***</td></tr><tr><td>mu_asc2_edu_bac2_3</td><td>2.01895</td><td>1.08842</td><td>1.9</td><td>0.032 *</td></tr><tr><td>sigma_asc2_edu_bac2_3</td><td>-1.05186</td><td>0.58551</td><td>-1.8</td><td>0.036 *</td></tr><tr><td>mu_asc2_edu_bac5</td><td>2.52260</td><td>1.31058</td><td>1.9</td><td>0.027 *</td></tr><tr><td>sigma_asc2_edu_bac5</td><td>-2.59994</td><td>0.76524</td><td>-3.4</td><td>0.0003 ***</td></tr><tr><td>mu_asc3_edu_lycee</td><td>-2.26840</td><td>1.86203</td><td>-1.2</td><td>0.112</td></tr><tr><td>sigma_asc3_edu_lycee</td><td>3.09103</td><td>0.91564</td><td>3.4</td><td>0.0004 ***</td></tr><tr><td>mu_asc3_edu_bac2_3</td><td>-0.07498</td><td>1.54156</td><td>0.0</td><td>0.481</td></tr><tr><td>sigma_asc3_edu_bac2_3</td><td>-0.29306</td><td>0.44697</td><td>-0.7</td><td>0.256</td></tr><tr><td>mu_asc3_edu_bac5</td><td>-0.66864</td><td>2.34926</td><td>-0.3</td><td>0.388</td></tr><tr><td>sigma_asc3_edu_bac5</td><td>-2.67056</td><td>1.38095</td><td>-1.9</td><td>0.027 *</td></tr><tr><td>mu_asc1_sexe</td><td>1.70127</td><td>0.62288</td><td>2.7</td><td>0.003 **</td></tr><tr><td>sigma_asc1_sexe</td><td>-1.17417</td><td>0.54390</td><td>-2.2</td><td>0.015 *</td></tr><tr><td>mu_asc2_sexe</td><td>0.98735</td><td>0.65744</td><td>1.5</td><td>0.067 .</td></tr><tr><td>sigma_asc2_sexe</td><td>0.11306</td><td>0.37943</td><td>0.3</td><td>0.383</td></tr><tr><td>mu_asc3_sexe</td><td>0.31148</td><td>1.15511</td><td>0.3</td><td>0.394</td></tr><tr><td>sigma_asc3_sexe</td><td>-1.96724</td><td>1.20898</td><td>-1.6</td><td>0.052 .</td></tr><tr><td>deb_compind_j</td><td>0.65655</td><td>0.28859</td><td>2.3</td><td>0.011 *</td></tr><tr><td>deb_ener</td><td>-0.06734</td><td>0.24381</td><td>-0.3</td><td>0.391</td></tr><tr><td>deb_dechcomp_j</td><td>-0.15851</td><td>0.39949</td><td>-0.4</td><td>0.346</td></tr><tr><td>acc_livret</td><td>-0.45345</td><td>0.24067</td><td>-1.9</td><td>0.030 *</td></tr><tr><td>acc_atelier</td><td>-0.16147</td><td>0.22261</td><td>-0.7</td><td>0.234</td></tr><tr><td>acc_mcomp</td><td>-0.93677</td><td>0.32676</td><td>-2.9</td><td>0.002 **</td></tr><tr><td>b_x3</td><td>0.00088</td><td>0.00262</td><td>0.3</td><td>0.369</td></tr></tbody></table>

Significance codes: 0 ‘***’, 0.001 ‘**’, 0.01 ‘*’, 0.05 ‘.’, 0.1 ‘.’, 1

The results from the M16 model (see tab 11) are consistent with the M11 estimation. Participants significantly prefer individual composting over the BAU option (asc_1 = 0.46, p-value = 0.033) and express a net aversion to bringing waste to the recycling center (asc_3 = -0.76, p-value = 0.007). Preferences between door-to-door collection and keeping BAU remain not significantly different (asc_2 = 0.28, p-value = 0.144). As in M11, accompaniment measures are generally disliked: receiving a booklet (acc_livret = -0.36, p-value = 0.032) or support from a compost master (acc_mcomp = -0.84, p-value = 0.006) significantly reduce utility, while workshops (acc_atelier = -0.14, p-value = 0.222) are not significant. Financial incentives (b_x3 = -0.0003, p-value = 0.445) again show no impact.

Approximately half of the standard deviation coefficients are significant. Interaction terms further highlight heterogeneity: participants with a high school degree significantly dislike the transformation of compost into biogas compared to middle school participants
(mu_deb_ener_edu_lycee = -1.24, p-value = 0.026). Age also influences preferences for compost use, with older participants significantly less likely to choose energy transformation compared to younger ones (mu_deb_ener_age = -0.041, p-value < 0.001). In addition, education-related heterogeneity emerges for compost destinations, with significant dispersion coefficients observed for several schooling levels.

<table><caption>TABLE 11 - M16's results : interactions between outlets, ages and study levels (green waste)</caption><thead><tr><th>Parameter</th><th>Estimate</th><th>Std. Error</th><th>t-ratio</th><th>p (1-sided)</th></tr></thead><tbody><tr><td>asc_1</td><td>0.45782</td><td>0.24990</td><td>1.8</td><td>0.033 *</td></tr><tr><td>asc_2</td><td>0.27727</td><td>0.26092</td><td>1.1</td><td>0.144</td></tr><tr><td>asc_3</td><td>-0.75933</td><td>0.31081</td><td>-2.4</td><td>0.007 **</td></tr><tr><td>mu_deb_compind_j_edu_lycee</td><td>-0.60669</td><td>0.87851</td><td>-0.7</td><td>0.245</td></tr><tr><td>sigma_deb_compind_j_edu_lycee</td><td>-4.35334</td><td>1.49819</td><td>-2.9</td><td>0.002 **</td></tr><tr><td>mu_deb_compind_j_edu_bac2_3</td><td>0.13784</td><td>0.44236</td><td>0.3</td><td>0.378</td></tr><tr><td>sigma_deb_compind_j_edu_bac2_3</td><td>-1.68430</td><td>0.57426</td><td>-2.9</td><td>0.002 **</td></tr><tr><td>mu_deb_compind_j_edu_bac5</td><td>0.26931</td><td>0.49770</td><td>0.5</td><td>0.294</td></tr><tr><td>sigma_deb_compind_j_edu_bac5</td><td>1.85965</td><td>0.77783</td><td>2.4</td><td>0.008 **</td></tr><tr><td>mu_deb_ener_edu_lycee</td><td>-1.23975</td><td>0.63837</td><td>-1.9</td><td>0.026 *</td></tr><tr><td>sigma_deb_ener_edu_lycee</td><td>1.93973</td><td>0.67178</td><td>2.9</td><td>0.002 **</td></tr><tr><td>mu_deb_ener_edu_bac2_3</td><td>-0.27809</td><td>0.38936</td><td>-0.7</td><td>0.238</td></tr><tr><td>sigma_deb_ener_edu_bac2_3</td><td>1.99764</td><td>0.51404</td><td>3.9</td><td>0.00005 ***</td></tr><tr><td>mu_deb_ener_edu_bac5</td><td>-0.16530</td><td>0.43137</td><td>-0.4</td><td>0.351</td></tr><tr><td>sigma_deb_ener_edu_bac5</td><td>-1.61159</td><td>0.56117</td><td>-2.9</td><td>0.002 **</td></tr><tr><td>mu_deb_compind_j_age</td><td>0.00355</td><td>0.02017</td><td>0.2</td><td>0.430</td></tr><tr><td>sigma_deb_compind_j_age</td><td>-0.11817</td><td>0.03539</td><td>-3.3</td><td>0.0004 ***</td></tr><tr><td>mu_deb_ener_age</td><td>-0.04055</td><td>0.01278</td><td>-3.2</td><td>0.0008 ***</td></tr><tr><td>sigma_deb_ener_age</td><td>-0.00725</td><td>0.01156</td><td>-0.6</td><td>0.265</td></tr><tr><td>deb_dechcomp_j</td><td>0.33204</td><td>0.32420</td><td>1.0</td><td>0.153</td></tr><tr><td>acc_livret</td><td>-0.35755</td><td>0.19255</td><td>-1.9</td><td>0.032 *</td></tr><tr><td>acc_atelier</td><td>-0.13778</td><td>0.17964</td><td>-0.8</td><td>0.222</td></tr><tr><td>acc_mcomp</td><td>-0.83513</td><td>0.33144</td><td>-2.5</td><td>0.006 **</td></tr><tr><td>b_x3</td><td>-0.00027</td><td>0.00195</td><td>-0.1</td><td>0.445</td></tr></tbody></table>

Significance codes: 0 '**', 0.001 '**', 0.01 '**', 0.05 ' ', 0.1 ' ', 1

### III.5 Qualitative results

Interviews reveal that individuals know and are aware that sorting biowaste is a good thing to do. There's a general sense that it's the "right" gesture to make. Among households involved in awareness sessions run by associations, it was said that "they're ready, they've understood that they can and should valorize their biowaste." This suggests an intuitive understanding that sorting is the right thing to do. Associations emphasized that people are usually interested when they speak in the market or at public events, and this was confirmed in household interviews. Children and teenagers are especially interested during school interventions.

One of the major points highlighted during the qualitative phase is a widespread lack of trust in the waste sorting system overall. This uncertainty serves as an "excuse" or "pretext" not to sort biowaste, believing that everything will end up being mixed together anyway. As one interviewee said: "If residual waste and cardboard are already being mixed together, why should I bother improving things by sorting my biowaste?" People are reluctant to invest time in a system they believe doesn't care. Moreover, residents express disposition to sort biowaste but report a lack of space for participants without gardens, adequate infrastructure, and clear, accessible information on how to sort.
From the perspective of EPCIs, public misunderstanding and fear of pests are seen as main obstacles, magnified by funding shortages for large-scale solutions. EPCIs often lack both financial resources and time in their agenda to prioritize biowaste management projects. Moreover, practices (like bins' colours, number of recycle centers and distribution of compost containers), communication (frequency of awareness campaigns), and investments in biowaste management are heterogeneous across the territory.

Associations note that frequent staff turnover further impedes the construction of long-term projects, and also consider that awareness campaigns should be intensified and that local, proximity support is essential. While current efforts, such as compost distribution, are valuable, they remain unequal across the territory. Some EPCIs complement compost distribution with training and educational sessions, providing stronger support for households, whereas others distribute compost containers with little or no follow-up training. Associations particularly emphasize the persistent lack of accessible information and guidance across the territory, which limits the effectiveness of biowaste management initiatives.
# IV. Discussion

## IV.1 Discussion on findings

### IV.1.1 Structural and socio-demographic determinants: housing types and age as main drivers, education effects nuanced

Both quantitative and qualitative results highlight the importance of structural and contextual factors in shaping biowaste management practices. The age, the type of accommodation (i.e., living in a detached house versus a collective residence) and the perceived opportunities (i.e., presence and accessibility of sorting infrastructures) emerge as key predictors of both sorting and composting behaviour. Our results are consistent with previous research that identifies physical opportunities as the main barriers to biowaste sorting (Allison et al., 2022; Davan Wetton et al., 2025; Roy et al., 2022). The findings also reinforce the established importance of housing type (Rogowska et al., 2024).

Socio-demographic characteristics play a more ambivalent role. Our results highlight the importance of age as a driver of sorting behaviour; it aligns with evidence from literature (Rousta et al., 2020; Yuan et al., 2015), but we do not find statistical evidence on the play of gender or households' size. Our results also reveal that a higher level of education appears to reduce the probability of sorting; however, this effect is nuanced, as education is positively associated with composting behaviour. The contradictory effect of education level may be explained by differences in the nature of the two practices. More educated households are more likely to live in urban areas, in collective housing, or to belong to younger age groups, according to the general rise of education levels which makes younger participants overrepresented in master's degree studies. It suggests that the apparent "negative" effect of education may partly capture the influence of age. Since age itself emerges as a strong driver of sorting behaviour, the education effect should be interpreted with caution. At the same time, higher education is associated with greater openness to changing practices, which explains why these households are more likely to adopt composting. Finally, our results align with Rousta et al. (2020), who show that socio-demographic factors, including education, often have a weak or inconsistent influence on sorting in developing contexts, as higher education may coincide with other priorities or structural constraints.

### IV.1.2 Motivational and perceptual aspects: confidence in the system matters, but motivations play a more secondary role

Qualitative results provide additional nuance by stressing the role of motivations, particularly confidence in the waste management system in Guadeloupe and the desire for a clear purpose behind sorting. Households expressed that if they invest time and effort into sorting, they expect a tangible benefit from their action. However, these motivational aspects did not emerge significantly in our modelling, contrary to previous studies that emphasized a more central role of motivations as behaviours' drivers (Allison et al., 2022; Cook, 2023; Davan Wetton, 2025; Roy et al., 2022). This divergence may be explained by the fact that households in Guadeloupe are already generally motivated to engage in sorting, which reduces the explanatory power of motivation. Moreover, in contrast to other studies (Allison et al., 2022; Rastegari Kopaei et al., 2021), Guadeloupe households
did not report concerns related to pests, and motivations played a smaller role overall, which is
hopeful for the spread of compost alternatives, and explain the mitigated role of motivation as main
driver of biowaste sorting.

IV.1.3 Insights from the choice experiment : strong preferences for individual solutions and control over outcomes

The choice experiment provides valuable insights into Guadeloupean households’ preferences regarding biowaste management. Our models reveal that participants display strong preferences for individual management solutions, particularly for food waste, where household composting, and especially the possibility of keeping the compost for personal use, emerges as the most attractive option. It is consistent with previous studies showing that preferences for home composting were also observed in other island contexts where individual composting emerged as the most favoured solution, although collective schemes were relatively more accepted than in our case (I Suryawan et al., 2025). In Poland, too, individual composting remains a preferred practice among households in detached housing (Rogowska et al., 2024).

This strong preference for individual solutions can be interpreted in several ways. First, compost produced at home can directly serve gardening needs, reducing the necessity to buy soil amendments in a context of economic constraints. Second, the profile of respondents who consistently selected the BAU option is mainly composed of households that already compost or older participants, groups generally more inclined to adopt compost, with more available time for instauring and managing it (Nsimbe, Mendoza, Wafula, & Ndejjo, 2018). This highlights an elemental mistrust towards collective waste management systems in Guadeloupe, echoing qualitative findings about households’ desire to see the tangible use of their sorted waste. The preference for individual composting can also show an aversion to displacement for disposal, which is consistent with findings from Slavik et al. (2019). Together, these results align with the qualitative insights that emphasize mistrust in the local waste system and uncertainty over the final use of biowaste, making individual management solutions more tempting.

The centrality of the outlet's compost attribute in the models confirms that the perceived purpose and outcome of sorting is the main driver of preferences, as quantitative results emphasized. Younger and higher-educated participants appear more open to alternative solutions, including technological options such as energy recovery, which may correspond better to their lifestyle constraints. Preferences regarding garden waste follow a similar trend, with households favouring individual management over collective or regional options. This is a significant finding considering that composting practices are most effective when combining kitchen and garden waste (Bartelings, 1999).

The predominance of individual strategies for managing both food and green waste not only reflects households' desire to remain in control of the process, but also constitutes a positive factor for composting efficiency, since the common management of green and food waste is required to achieve the right balance of carbon and nitrogen inputs.
IV.1.4 Accompaniment and incentives : limited attractiveness of training and weak impact of financial incentives

By contrast, the attribute related to awareness-raising and accompaniment ranked very low in
importance for the choice of participants. This finding is paradoxical given the very low levels of
awareness and knowledge about biowaste management reported by Guadeloupean households, and
in light of the literature, which consistently highlights knowledge as a key determinant of
composting practices (Rastegari Kopaei et al., 2021; Sulewski et al., 2021). Yet, very few studies
have examined households' acceptance of training or accompaniment specifically, despite the fact
that support measures are often assumed to foster knowledge acquisition. The lack of interest
observed here may thus be explained by time constraints, reluctance to have external actors intervene
in the household, or a perception that such support would not provide added value.

Moreover, the lack of significance of financial incentives for encouraging biowaste sorting aligns
with prior research showing that monetary rewards play only a marginal and ambiguous role in
shaping recycle behaviors, notably because people don't believe in truly obtaining the rewards
(Shaws & Maynard, 2008; Zhao & Van de Walle, 2024).

IV.2 Political implications and recommendations

The lack of sorting outlets and infrastructures emerges as the main determinants of household biowaste sorting for Guadeloupean households. These factors represent key points of attention, as well as potential levers to support, in order to encourage the wider adoption of sorting practices.

By using a discrete choice experiment, our study has provided valuable insights into household
preferences, which represent promising levers to promote biowaste sorting on the island. In
particular, composting appears to be the preferred alternative for households, in line with the
objectives announced by the EPCIs to reinforce this practice. Strengthening campaigns for the
distribution of compost bins across the island, in a more homogeneous way among EPCIs, would
represent an important first step, as limited access to physical infrastructures remains the main
barrier to sorting practices. In parallel with the distribution of composters and bio-buckets,
households could be trained to compost in heaps, which requires fewer physical resources.

However, as highlighted in the literature, maintaining long-term composting practices requires
adequate knowledge, both to avoid nuisances (pests, odors) and to ensure the production of quality
soil material, which depends on knowing what to compost and in what proportions. Evidence shows
that knowledge and awareness are crucial for successful home composting initiatives (Sulewski et
al., 2021 ; Oke & Kruijsen, 2016). Even if our results suggest that most households express
reluctance to engage in direct or intensive training, some form of support remains necessary. More
accessible forms of communication, such as public campaigns, community role models, or radio
broadcasts, can help sustain engagement over time (Abila & Kantola, 2019 ; Shaw et al., 2008).
Combining these strategies with small incentives and pledges has also been shown to enhance
long-term behavioral change in waste sorting (Alonso-Paulí et al., 2025).

Beyond physical infrastructures and education, nudging strategies, popularized by Thaler and Sunstein (2008), can play a complementary role in fostering biowaste sorting behaviour. Empirical
evidence shows that peer-based nudges and role models are often more effective than simple information provision for pro-environmental behaviours (Lakshmi et al., 2022). Barker et al. (2021), in a systematic review on food waste, emphasize the effectiveness of nudges based on social norms, reminders for developing the recycling behaviours. Nevertheless, the literature also highlights the limitations of relying on nudges alone and the need for combining with other interventions (Saulitis et al., 2024; Chapman et al., 2019; Wyse et al., 2019). In the context of Guadeloupe, these insights suggest the combined use of role-model campaigns, social norm messaging, for instance with neighbours challenges at sorting, and increasing the transparency regarding the treatment and transformation of sorted biowaste could further develop biowaste sorting behaviours. Actors engaged in waste management in Guadeloupe : EPCIs, the regional government, and associations, could benefit from closer coordination to combine infrastructure improvements, awareness on compost and carefully designed nudging strategies. This approach could help overcome current barriers and align the island with the principles of the AGEC law.

A second major point relates to the outlets for biowaste treatment. Households expressed a strong demand to know how their waste is treated and transformed. The organization of an energy recovery chain, which emerged as an attractive option for households, especially younger ones, could enhance sorting practices, provided that trust in the collection, transport, and treatment system is reinforced. Biogas initiatives from food and biodegradable waste are increasingly being implemented on islands. Hidalgo-Sánchez et al. (2025) show that Tenerife uses kitchen and restaurant waste in anaerobic digestion to reduce landfill and replace fossil-fuel energy. Similar procedures have been introduced on Porto Santo Island (Pereira & Silva, 2023) and in the Maldives (Wang et al., 2024). Supporting the development of an energy recovery sector is consistent with the first local initiatives already in place, particularly small-scale biogas projects. By contrast, we do not discuss here the option of developing a compost supply chain for farmers, as this was found to negatively influence household sorting practices.

IV.3 Limitations and Future Research

Our study brings new insights by quantifying households’ concrete preferences for food and green waste management scenarios adapted to an insular context. The focus on garden and green waste is particularly rare, and our work is grounded in a solid theoretical framework and a careful methodological design that can be used again in similar experiments.

That said, our study has several limitations. First, our sample size was limited; we only satisfied the minimum required for the choice experiment for choice experiments addressed to households owning a garden, and didn’t fulfill the minimum required size for the choice experiment proposed to households without a garden. Allowing only for the minimum sample size, still constrains the precision of the parameter estimates and the power for hypothesis testing (Assele et al., 2023). Second, time constraints restricted the scope of the analysis, particularly the full implementation of a Hybrid Choice Model (HCM). HCM allows accounting for attitudinal latent constructs into individual choices as the field has recognised the limitations of the hypothesis of full rationality of the individuals when making choice. New modelling approaches such as HCM recognise that not all decisions are fully rational and aim to help include a broader set of determinants of choice therefore improving understanding of those choices, it has been used in diverse works like Zemo & Termansen (2022). Further limitations relate to the COM-B analysis. While we explored three broad categories
of behavioural determinants, a more detailed approach using all six COM-B subcategories could provide deeper insights. In particular, testing additional links, especially regarding the effects of accompaniment, would enrich our understanding of potential levers for household participation in biowaste management.

Future research could expand sample sizes, notably in the municipalities we were not able to investigate, examine additional socio-demographic heterogeneity, and integrate richer attitudinal measures into advanced discrete choice models. Other levers for developing biowaste sorting could be assessed: the effect of social norm social or the influence of role models as we discussed previously. Integrating impact evaluation design on a longest period could also be considered, in order to test the influence of interventions likely to work on the sorting and composting habits of Guadeloupe households. These extensions would improve the understanding of household preferences and help design more effective, context-specific biowaste management interventions in insular territories.
# V. Conclusion

Developing source separation of biowaste in Guadeloupe, in line with the AGEC law, requires a nuanced understanding of the determinants shaping this behaviour. To this end, our mixed-methods study examined households' current practices and the barriers they face in terms of capability, opportunity, and motivation to biowaste sorting. To design sustainable and long-lasting management strategies, we employed a discrete choice experiment aimed at eliciting households' preferences regarding different sorting options. Age and housing type appear as main drivers of biowaste sorting, older households representatives, and those owning a garden being more likely to sort and compost. Our results also point to the need to address structural barriers, particularly those related to physical opportunities (e.g., access to sorting infrastructures and composting tools). Individual biowaste management solutions, particularly individual composting, emerge as the most preferred option and the one to prioritise for promoting biowaste sorting. However, expanding compost containers distributions alone will not guarantee the long-term adoption of sorting practices; such efforts must be complemented by sustained and reinforced awareness-raising campaigns, fostering Guadeloupean households' trust in sorting system, and proximity support initiatives, where neighbourhood role models and associations can effectively complement the work of EPCIs. Although focused on Guadeloupe, these findings offer broader insights for waste management policies in similar contexts and show the value of discrete choice experiments in informing recycling intervention design.
Bibliography

1) Abila, B., & Kantola, J. (2019). The Perceived Role of Financial Incentives in Promoting Waste Recycling—Empirical Evidence from Finland. *Recycling, 4*(1), 4.

2) Adamowicz, W., Boxall, P., Williams, M., & Louviere, J. (1998). Stated preference approaches for measuring passive use values: Choice experiments and contingent valuation. *American Journal of Agricultural Economics, 80*(1), 64–75.

3) ADEME. (2024). *Rapport sur la gestion des biodéchets en France*. Agence de l'environnement et de la maîtrise de l'énergie.

4) Allison, A.L.; Lorencatto, F.; Michie, S.; Miodownik, M. Barriers and Enablers to Food Waste Recycling: A Mixed Methods Study amongst UK Citizens. *Int. J. Environ. Res. Public Health* 2022, 19, 2729.

5) Allison AL, Purkiss D, Lorencatto F, Miodownik M and Michie S (2022) Improving compostable plastic disposal: An application of the Behaviour Change Wheel intervention development method. *Front. Sustain.*

6) Alonso-Paulí, E., Balart, P., & Hernandez-Arenaz, I. (2025). Incentives and intrinsic motivation for pro-environmental behavior: Field evidence from waste sorting. [*Working paper*]. Universitat de les Illes Balears & Universidad Pública de Navarra.

7) Andris Saulītis, Vineta Silkāne, Gerda Ajanta Gaile, Nudging pro-environmental behaviour in a subsidized waste recycling system: A field experimental study, *Journal of Environmental Psychology*, Volume 99, 2024, 102416, ISSN 0272-4944

8) Assele, S. Y., Meulders, M., & Vandebroek, M. (2023). Sample size selection for discrete choice experiments using design features. *Journal of Choice Modelling, 49*, 100436.

9) Audrey, B. (2024). Environmental consequences of waste disposal: Landfilling vs. incineration. *Journal of Environmental Hazards, 8*(4).

10) Babbie, E. R. (2020). *The Practice of Social Research* (15th ed.). Cengage Learning.

11) Ballinger, A., Shanks, W., Hogg, D., Sherrington, C., & Duffield, L. (2020, December 4). *Greenhouse gas and air quality impacts of incineration and landfill: Report to ClientEarth*. Eunomia Research & Consulting Ltd.

12) Bardus M, Massoud MA. Predicting the Intention to Sort Waste at Home in Rural Communities in Lebanon: An Application of the Theory of Planned Behaviour. *International Journal of Environmental Research and Public Health*. 2022

13) Baribeau, C., & Germain, M. (2010). L'entretien de groupe : considérations théoriques et méthodologiques. *Recherches qualitatives*, 29(1), 28-49.

14) Barker, H., Shaw, P. J., Richards, B., Clegg, Z., & Smith, D. (2021). What Nudge Techniques Work for Food Waste Behaviour Change at the Consumer Level? A Systematic Review. *Sustainability*, 13(19), 11099.

15) Bartelings, H. (1999). Household waste management in a Swedish municipality: Determinants of waste disposal, recycling and composting. *Environmental and Resource Economics*, 13(4), 473–491.

16) Bateman, I. J., Carson, R. T., Day, B., Hanemann, M., Hanley, N., Hett, T., ... & Swanson, J. (2002). *Economic valuation with stated preference techniques: A manual*. Edward Elgar Publishing.
17) Bennett, J., & Blamey, R. (2001). *The choice modelling approach to environmental valuation*. Edward Elgar Publishing.

18) Birol, E., Karousakis, K., & Koundouri, P. (2006). Using a choice experiment to account for preference heterogeneity in wetland attributes: The case of Cheimaditida wetland in Greece. *Ecological Economics*, 60(1), 145–156.

19) Bryman, A. (2016). *Social Research Methods* (5th ed.). Oxford University Press.

20) Caussade, S., Ortúzar, J. D., Rizzi, L. I., & Hensher, D. A. (2005). Assessing the influence of design dimensions on stated choice experiment estimates. *Transportation Research Part B: Methodological*, 39(7), 621–640.

21) Carlsson, F., Kataria, M., & Lampi, E. (2003). Using choice experiments to estimate the value of improving forest recreation sites. *Scandinavian Journal of Forest Research*, 18(4), 306–312.

22) Cook, N., Collins, J., Porter, J., & Goodwin, D. (2023). Applying the theoretical domains framework and behavior change wheel to inform interventions for food and food-related waste audits in hospital foodservices.Frontiers in Nutrition, 10, 1204980.

23) Creswell, John W., and Vicki L. Plano Clark. 2011. Designing and Conducting Mixed Methods Research. SAGE Publications. 2nd ed. Thousand Oaks, CA: SAGE Publications.

24) Daikeler, J., Bosnjak, M., & Lozar Manfreda, K. (2020). Web versus other survey modes: An updated and extended meta-analysis comparing response rates. *Journal of Survey Statistics and Methodology*, 8(3), 513–539.

25) Davan Wetton, J., Santilli, M., Gitau, H., Muindi, K., Zimmermann, N., Michie, S., & Davies, M. (2025). Behavioural Systems Mapping of Solid Waste Management in Kisumu, Kenya, to Understand the Role of Behaviour in a Health and Sustainability Problem. *Behavioral Sciences*, 15(2), 133.

26) Dhar, R. (1997). Consumer preference for a no-choice option. *Journal of Consumer Research*, 24(2), 215–231.

27) European Commission, DG Environment. (2010). *Final report: Assessment of the options to improve the management of bio-waste in the European Union* (Study Contract No. 07.0307/2008/517621/ETU/G4; ARCADIS Project No. 11/004759, Version C).

28) Ferrini, S., & Scarpa, R. (2007). Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study. *Journal of Environmental Economics and Management*, 53(3), 342–363.

29) Fetters, Michael D., Leslie A. Curry, and John W. Creswell. 2013. “Achieving Integration in Mixed Methods Designs - Principles and Practices.” Health Services Research 48(6pt2): 2134-2156.

30) Findley, D. F., & Parzen, E. (1998). A conversation with Hirotugu Akaike. *Statistical Science*, 13(1), 52–69.

31) French Ministry for Ecological Transition (2024). *Sorting biowaste: an obligation as of January 1, 2024*.

32) Geeta Lakshmi, Kim Nguyen, Arman Mazhikeyev, Dieu Hack-Polay, Zhibek Anafiyayeva, Nudging student recycling behaviour: An experimental study in Kazakhstan and UK higher education, *Journal of Cleaner Production*, Volume 377, 2022, 134164, ISSN 0959-6526,

33) Hensher, D.A., Rose, J.M., Rose, J.M., Greene, W.H., 2015. Applied Choice Analysis: A Primer. Cambridge university press, Cambridge.
34) Hensher, D. A., Shore, N., & Train, K. (2005). Household willingness to pay for water service attributes. *Environmental and Resource Economics*, 32(3), 509–531.

35) Hess S, Palma D (2019). "Apollo: a flexible, powerful and customisable freeware package for choice model estimation and application." *Journal of Choice Modelling*, 32.

36) Hidalgo-Sánchez, V., Borges, M. E., Hofmann, J., Cuñarro, D., Schneider, S., & Finsterwalder, T. (2025). Biogas from Food Waste on the Island of Tenerife: Potential from Kitchens and Restaurants, Stabilisation and Conversion in a Biogas Plant Made of Textile Materials. *Applied Sciences*, 15(12), 6922.

37) I Wayan Koko Suryawan, Sapta Suhardono, Ari Rahman, Thi Thanh Thuy Phan, Chun-Hung Lee, Hypothetical scenarios for circular bioeconomy preferences in the Bali metropolitan area, *Waste Management Bulletin*, Volume 3, Issue 3, 2025, 100197, ISSN 2949-7507,

38) Insee (2025). *Dossier complet – Département de la Guadeloupe (971)*. Chiffres détaillés, géographie au 01/01/2025. Paru le 10 juillet 2025.

39) Interreg Europe, Policy Learning Platform on Environment and Resource Efficiency. (2021, December). *Biowaste challenge: Policy brief*.

40) Jamaludin, Nurul Nadiah and Ramlie, Mohd Khairulnizam and Md. Nazri, Haslinda (2023) *Rethinking recycling: understanding audience perception for designing effective campaigns/Nurul Nadiah Jamaludin, Mohd Khairulnizam Ramlie and Haslinda Md. Nazri. Idealogy Journal*, 8 (2). pp. 267-276.

41) Jourda, G., & Malet, V. (2022). *Rapport d'information n° 195 : Gestion des déchets dans les outre-mer* [Rapport]. Sénat, Délégation sénatoriale aux outre-mer.

42) Kernevez, E. (2024). *Diagnostic de la situation actuelle des biomasses résiduelles du territoire guadeloupéen* [Rapport de stage de césure, Institut Agro Montpellier]. INRAE Antilles-Guyane.

43) Jenny Kitzinger, Ivana Markova, Nikos Kalampalikis. Qu’est-ce que les focus groups ?. *Bulletin de psychologie*, 2004, 57 ((3)), pp.237-243.

44) Krippendorff, K. (2013). *Content analysis: An introduction to its methodology* (3rd ed.). Sage Publications.

45) Liebe, U., Glenk, K., & Meyerhoff, J. (2021). *Choice experiments in energy and environmental research: State-of-the-art and future challenges*. *Energy Policy*, 156, 112–414.

46) Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). *Stated choice methods: Analysis and applications*. Cambridge University Press.

47) McFadden, D. (1974). Conditional logit analysis of qualitative choice behavior. In P. Zarembka (Ed.), *Frontiers in econometrics* (pp. 105–142). Academic Press.

48) Michie, S., van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method for characterising and designing behaviour change interventions. *Implementation Science*, 6(1), 42.

49) Milford, A. B., Øvrum, A., & Helgesen, H. (2015). Nudges to increase recycling and reduce waste (Discussion Paper No. 2015-01). Norwegian Agricultural Economics Research Institute.

50) Ministère de la Transition écologique. (2023, 15 septembre). *Tri à la source des biodéchets : une obligation, de nombreuses solutions*. Ministère de la Transition écologique.
51) Montero-Vega, M., Brenes-Peralta, L. P., Baltodano-Zúñiga, D., & García-Barquero, M. E. (2024). Which factors determine food waste-related behavior? *Perspectives from households for local policymaking in developing countries*. Cogent Food & Agriculture, 10(1).

52) Morgan, D. L. (1998). *Planning focus groups*. Thousand Oaks, CA: Sage Publications.

53) Nsimbe, P., Mendoza, H., Wafula, S. T., & Ndejjo, R. (2018). Factors associated with composting of solid waste at household level in Masaka Municipality, Central Uganda. *Journal of Environmental and Public Health*, 2018, 1–10.

54) ODG (Observatoire des Déchets de Guadeloupe). (2017). *Rapport annuel sur la gestion des déchets en Guadeloupe*.

55) Oke, A., & Kruijsen, J. (2016). The Importance of Specific Recycling Information in Designing a Waste Management Scheme. Recycling, 1(2), 271-285.

56) Orme, B., 1998. Sawtooth Software Sample Size Issues for Conjoint Analysis Studies. Research Paper Series 98382.

57) Ortner, M. E., Müller, W., & Bockreis, A. (2013). The greenhouse gas and energy balance of different treatment concepts for bio-waste. *Waste Management & Research, 31*(10 Suppl), 46–55.

58) Pavlas, M., Dvořáček, J., Pitschke, T., & Peche, R. (2020). Biowaste treatment and waste-to-energy—Environmental benefits. *Energies*, 13(8), 1994.

59) Pereira, F., & Silva, C. (2023). Energetic Valorization of Bio-Waste from Municipal Solid Waste in Porto Santo Island. Clean Technologies, 5(1), 233-258.

60) Rastegari Kopaei, H.; Nooripoor, M.; Karami, A.; Petrescu-Mag, R.M.; Petrescu, D.C. Drivers of Residents’ Home Composting Intention: Integrating the Theory of Planned Behavior, the Norm Activation Model, and the Moderating Role of Composting Knowledge. Sustainability 2021, 13, 6826.

61) Rogowska, J.; Pia łkowska, K.; Głowacz, Z. Societal Involvement in Household Waste Sorting Behavior in the Context of the Circular Economy: A Case Study of Poland. Sustainability 2024, 16, 1841.

62) ROUTIER Gautier, MARCOUX Marie-Amélie, Ecogeos. 2023. Caractérisation des DMA sur le territoire de la Guadeloupe. 93 pages.

63) Rose, J. M., & Bliemer, M. C. J. (2009). Constructing efficient stated choice experimental designs. *Transport Reviews*, 29(5), 587–617.

64) Rousta, K., Zisen, L., & Hellwig, C. (2020). Household Waste Sorting Participation in Developing Countries—A Meta-Analysis. Recycling, 5(1), 6.

65) Roy D, Berry E, Dempster M (2022) "If it is not made easy for me, I will just not bother". A qualitative exploration of the barriers and facilitators to recycling plastics. PLoS ONE 17(5): e0267284.

66) Scarpa, R., & Rose, J. M. (2008). Design efficiency for non-market valuation with choice modelling: How to measure it, what to report and why. *Australian Journal of Agricultural and Resource Economics*, 52(3), 253–282.

67) Schwarz, G. (1978). Estimating the dimension of a model. *The Annals of Statistics*, 6(2), 461–464.

68) Shaw P.J., Maynard S.J., The potential of financial incentives to enhance householders’ kerbside recycling behaviour, *Waste Management, Volume 28, Issue 10, 2008, Pages 1732-1741*,ISSN 0956-053X.
69) Shih, T.-H., & Fan, X. (2008). Comparing response rates from web and mail surveys: A meta-analysis. *Field Methods, 20*(3), 249–271.

70) Slavík, J., Rybová, K., & Dolejš, M. (2019). Biowaste separation at source and its limitations based on spatial conditions. *Detritus*

71) StataCorp. 2025. *Stata Statistical Software: Release 19*. College Station, TX: StataCorp LLC.

72) Sulewski, P., Kais, K., Gołaś, M., Rawa, G., Urbańska, K., & Wąs, A. (2021). Home Bio-Waste Composting for the Circular Economy. *Energies, 14*(19), 6164.

73) SurveyEngine. (2025). *SurveyEngine* [Computer software]. https://www.surveyengine.com

74) Temmerman, L., & Veeckman, C. (2024). Developing a Local Behaviour Change Intervention for Increased Biowaste Sorting: A Social Marketing Formative Study. *Journal of Social Marketing*

75) Thaler, R. H., & Sunstein, C. R. (2008). *Nudge: Improving decisions about health, wealth, and happiness*. Yale University Press.

76) Touveneau Petersen, S. (2018). Biowaste Non-Sorters: who cares. Examining arguments and factors from citizens not sorting biowaste in Copenhagen and recommendations for a way forward. *Master Thesis Series in Environmental Studies and Sustainability Science*.

77) Train, K. (2009). *Discrete choice methods with simulation* (2nd ed.). Cambridge University Press.

78) Tremier Anne, Julia Barrault, Rémy Bayard, Sylvaine Berger, Mathilde Besson, et al.. *Bio-waste: A Unique and Valuable Resource with Significant Recovery, Prevention and Management Challenges*. *Waste and Biomass Valorization*, 2024,

79) Waliczek, T., McFarland, A., & Holmes, M. (2016). The Relationship between a Campus Composting Program and Environmental Attitudes, Environmental Locus of Control, Compost Knowledge, and Compost Attitudes of College Students. *HortTechnology, 26*(5), 592–598.

80) Wang, Y., Ruiz-Acevedo, A., Rameez, E., Raghavan, V., Hussain, A., & Fei, X. (2024). Toward sustainable waste management in small island developing states: Integrated waste-to-energy solutions in the Maldives context. *Frontiers of Environmental Science & Engineering, 18*(2), 24.

81) White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. *Econometrica, 48*(4), 817–838.

82) Wooldridge, J. M. (2016). *Introductory econometrics: A modern approach* (6th ed.). Cengage Learning.

83) Yuan, Y., & Yabe, M. (2015). Residents' Preferences for Household Kitchen Waste Source Separation Services in Beijing: A Choice Experiment Approach. *International Journal of Environmental Research and Public Health, 12*(1), 176-190.

84) Zemo, K. H., & Termansen, M. (2022). Environmental identity economics: An application to farmers' pro-environmental investment behaviour. *European Review of Agricultural Economics, 49*(2), 331–358.

85) Zhao, Y., & Van de Walle, S. (2024). The effect of incentives and procedure simplification in policy implementation: evidence from a survey experiment on waste sorting. *Journal of Asian Public Policy, 1*–20.
# Appendices

**Appendix 1:** Interviews guide for key informant

**Appendix 2:** Focus group guide

**Appendix 3:** Questionnaire
# Appendix 1: Interviews guide for key informant

**Présentation du sujet:** L'objectif de notre projet de recherche, mené au sein de l'UR ASTRO sous tutelle de l'INRAE, est de réaliser le diagnostic des connaissances, perceptions et pratiques actuelles des foyers en matière de tri des biodéchets et d'identifier des leviers de promotion des pratiques de tri et recyclage. Les données récoltées serviront à créer un outil d'aide à la décision pour optimiser la valorisation de ces ressources.

Une première partie de l'enquête consiste donc en la réalisation d'entretiens semi-dirigés auprès d'acteurs agissant autour des pratiques de tri, et des ménages, afin de faire un premier état des lieux des pratiques de tri des biodéchets des ménages et de soulever des premiers questionnements sur les freins aux pratiques de tri et des recommandations éventuelles.

**Pour l'enquêteur:**

Les biodéchets sont définis dans l'article L541-161 du Code de l'Environnement comme « les déchets non dangereux biodégradables de jardin ou de parc, les déchets alimentaires ou de cuisine provenant des ménages, des bureaux, des restaurants, du commerce de gros, des cantines, des traiteurs ou des magasins de vente au détail, ainsi que les déchets comparables provenant des usines de transformation de denrées alimentaires »

PRGPD (2020) : Plan prévention et gestion des déchets Guadeloupe, associés au déploiement du tri à la source des biodéchets :

→ Développer le tri à la source des biodéchets et le généraliser pour tous les producteurs de déchets avant 2025, en développant des solutions de compostage de proximité

→ Valoriser 68 % en 2026 et 73% en 2032 des déchets non dangereux non inertes sous forme matière et organique

## Normes RGPD / possibilité de prendre des notes et/ou d'enregistrer l'entretien/anonymat

**Informations personne entretenue:**

**Nom et prénom:**

**Secteur de l'île (urbain/rural) :**

**Rôle et fonction au sein de l'organisme public/de l'association:**

**Pouvez-vous nous décrire les missions de votre structure, notamment celles en lien avec les pratiques de tri des biodéchets ?**

**Contact direct avec les ménages sur leurs pratiques en matière de tri des biodéchets ?**
<table><thead><tr><th>Thèmes</th><th>Questions</th><th>Relances</th></tr></thead><tbody><tr><td>Pratiques de tri des biodéchets par les ménages guadeloupéens</td><td><ul><li>Comment percevez-vous les pratiques de tri et gestion des biodéchets par les ménages Guadeloupéens ?</li><li>Une estimation de la part des ménages guadeloupéens qui pratiquent le tri des biodéchets? Et à quelle régularité?</li><li>Comment se fait généralement l'élimination des biodéchets par les ménages (poubelle classique, compostage, collecte spécifique, autres méthodes) au sein des foyers ?</li><li>Relevez-vous une distinction dans les pratiques intra et extra foyers des individus? (plus/moins d'attention porté au tri en dehors des foyers, sur le lieu professionnel?)</li><li>Avez-vous remarqué des différences entre les ménages ruraux et urbains dans les pratiques et connaissances autour du tri des biodéchets ?</li><li>Évolution notable des pratiques de tri dans le temps?</li></ul></td><td><ul><li>Des spécificités dans les pratiques de tri pour certains biodéchets (un fruit, légume particulier par exemple? DA/DV)</li><li>Hommes/femmes ?</li><li>Enfants/adultes ?</li><li>Selon les communes?</li><li>Si oui, comment l'expliquer, quel événement marquant (loi, projet, campagne...)?</li></ul></td></tr><tr><td>Perceptions et représentations du tri des biodéchets par les ménages</td><td><ul><li>Comment les ménages perçoivent-ils le tri des biodéchets ? Y a-t-il des croyances ou des idées reçues qui influencent leur comportement ?</li><li>Comment expliquerez-vous le</li></ul></td><td><ul><li>Lesquelles?</li></ul></td></tr></tbody></table>
<table><tr><td></td><td>rapport des ménages à l'usage du compost?</td><td>- Perception de la propreté? Craintes?<br/>- D'autres retours sur d'autres pratiques ?</td></tr><tr><td>Freins relevés</td><td>- Pensez-vous que les individus sont suffisamment informés sur la définition de "biodéchet" et sur leurs spécificités/ possibilités de tri?<br/>- Quels sont les principaux freins au tri des biodéchets selon vous ? (manque d'information, contraintes logistiques, manque d'intérêt, infrastructures insuffisantes, etc)</td><td>- Quelles confusions pouvez-vous éventuellement relever?<br/>- Freins économiques (achat du compost...) / freins sociaux (pression sociale, conformité à la norme)?/ temps consacré?</td></tr><tr><td>Leviers et recommandations éventuelles</td><td>- Quels sont les facteurs concrets qui, aujourd'hui, encouragent les ménages à trier leurs biodéchets ? (proximité d'une solution de compostage, incitations financières, accompagnement éducatif, etc)<br/>- Quelles initiatives ou actions vous semblent efficaces pour encourager le tri des biodéchets ?<br/>- Quels messages ou moyens de communication seraient les plus adaptés pour sensibiliser les ménages guadeloupéens?<br/>- Quels facteurs qui n'existent pas nécessairement dans le contexte local mais qui pourraient être mis en oeuvre</td><td>- Des exemples, retours d'expériences ?<br/>- Quelle échelle?</td></tr></table>

Y a-t-il un point que vous souhaitez ajouter ou approfondir sur ce sujet ?

Remerciements
# Appendix 2: Focus group guide

## 1- Accueil

* Remerciements pour la participation et le temps accordé

* Explication du déroulement de la discussion

## 2- Rappel du contexte, de l'objectif de l'étude de ce focus group

L'objectif de cette discussion est d'explorer les habitudes et perceptions des foyers possédant un jardin et pratiquant déjà le tri des biodéchets. Nous souhaitons mieux comprendre vos pratiques et identifier des pistes d'amélioration pour faciliter et encourager ces gestes à plus grande échelle. Ces échanges serviront à construire un questionnaire doté d'une expérience de choix et formuler des recommandations pour optimiser la gestion et la valorisation des biodéchets.

## 3- Brise glace

Tour de table en disant le prénom + les différents types de déchets générés

* RELANCE : Quels sont les principaux types de biodéchets que vous générez ?

* RELANCE : Comment définiriez-vous un biodéchet ?

### Eclaircir définitions biodéchets:

"Les déchets non dangereux biodégradables de jardin ou de parc, les déchets alimentaires ou de cuisine provenant des ménages, des bureaux, des restaurants, du commerce de gros, des cantines, des traiteurs ou des magasins de vente au détail, ainsi que les déchets comparables provenant des usines de transformation de denrées alimentaires." (article L. 541-1-1 du code de l'environnement)

### Bien insister sur le fait que les biodéchets concernent les déchets alimentaires/déchets verts

## 4- Guide d'entretiens FG

* Pouvez-vous nous partager vos habitudes actuelles en matière de tri et de gestion des biodéchets ?

RELANCE : Quels dispositifs utilisez-vous (composteur, bac de collecte, autre) ?

RELANCE: Les déchets verts, des spécificités?

RELANCE : Tous les membres du foyer participent-ils de la même manière ?

* Que pensez-vous de l'utilité et de l'efficacité du tri des biodéchets ?

RELANCE : Quels avantages et inconvénients ?

* Comment est perçue l'utilisation du compost autour de vous? (voisinage?)
RELANCE : Des craintes particulières (odeurs, nuisibles, contraintes) sont-elles revenues?

* Quels obstacles rencontrez-vous dans la gestion de vos biodéchets ?

RELANCE : Est-ce une question de temps, de logistique, d'informations insuffisantes ?

* Quelles initiatives ou améliorations vous sembleraient utiles pour faciliter la gestion des biodéchets ?

RELANCE : Communication plus ciblée, meilleure organisation, soutien technique ?

## 5- Discussion sur les options de gestion des biodéchets

Quels sont vos avis sur les interventions suivantes ?

* **Modalités de gestion :**

* Compostage collectif entre voisins (aspect social)

* Dépôt dans une borne de collecte

* **Incitations financières :**

* Bonus pour le tri (bon d'achat mensuel pour des produits locaux)

* Réductions pour l'accès à des infrastructures publiques (piscine, transports...)

* **Incitations sociales :**

* Autocollant et t-shirt « Je trie mes biodéchets et je composte »/ label "foyer vert"

* **Accompagnement technique :**

* Don d'un composteur et formation de 2 heures

* Formation + suivi (une visite tous les 2 mois pendant 6 mois)

* Accès à un numéro vert/plateforme/forum de discussions sur l'utilisation du compost

* **Valorisation du compost :**

* Accès à un jardin partagé avec distribution de production des individus impliqués

* Don du compost aux agriculteurs

## 5- Clôture et synthèse du FG.

* Synthèse rapides des points abordés

* Demande de remarques ou suggestions finales

* Remerciements

* Explication des prochaines étapes de l'étude et possibilité d'un suivi
# Appendix 3: Questionnaire

## I. Etude sur les biodéchets - fiche d'information

Nous vous proposons de participer à une étude sur les biodéchets. Cette enquête est menée par l'Unité de Recherche ASTRO dans le cadre d'un projet de recherche piloté par INRAE. Cette enquête a pour objectif d'en savoir davantage sur les connaissances, perceptions et pratiques actuelles des foyers en matière de tri des biodéchets. Vos réponses nous permettront d'élaborer des recommandations pour une meilleure gestion de ces ressources localement.

On entend par “biodéchets” :

* Les épluchures de fruits et légumes, fruits et légumes passés, restes de repas, marc de café, etc.

* Les déchets de jardin : tontes de pelouse et fauchage, feuilles mortes, tailles d'arbustes, haies et brindilles, déchets ligneux, etc.

Votre participation à cette enquête est entièrement volontaire. Vos réponses resteront strictement anonymes, conformément au Règlement Général sur la Protection des Données (RGPD), et seront uniquement utilisées à des fins de recherche.

Le questionnaire dure environ 15 minutes. Vous pouvez arrêter de répondre à tout moment, sans aucunes conséquences, vos données ne seront alors pas collectées.

Si vous désirez plus d'information, n'hésitez pas à contacter :

Carla BARLAGNE, chargée de Recherche à INRAE : carla.barlagne@inrae.fr ou Célestine GUILLOU stagiaire à INRAE : celestine.guillou@inrae.fr

### Comment visualiser au mieux cette enquête?

Si vous répondez à cette enquête à partir de votre téléphone, veuillez utiliser le mode "version pour ordinateur" de votre téléphone afin de réaliser l'enquête dans les meilleures conditions possibles d'affichage.

### Déclaration de consentement

Veuillez sélectionner votre choix ci-dessous. Cliquer sur le bouton « accepter » indique que :

J'atteste avoir pris connaissance des informations sur l'étude sur les "biodéchets".

J'ai eu le temps nécessaire pour réfléchir à mon implication dans cette recherche et je suis conscient.e que ma participation au projet de recherche est entièrement volontaire

J'accepte que les informations qui seront collectées lors ma participation à cette étude soient traitées et analysées par INRAE.
☐ Je suis d'accord pour participer à cette étude

☐ Je ne suis pas d'accord pour participer à cette étude

## Questions filtres

1. Habitez-vous en Guadeloupe de manière durable ?

☐ Oui

☐ Non

2. Avez-vous un jardin ou vous occupez-vous d'un jardin là où vous habitez ?

☐ Oui

☐ Non

## II. Vos habitudes en matière de gestion des déchets et biodéchets

1. Triez-vous régulièrement les déchets suivants ?

• Verre : ☐ Oui ☐ Non

• Emballages recyclables (cartons, papiers, journaux, etc.) : ☐ Oui ☐ Non

2. Disposez-vous d'un contenant spécifique pour trier vos biodéchets ? (ex. : composteur, bio-seau, poubelle ou sac distinct)

☐ Oui

☐ Non

3. Que faites-vous le plus fréquemment de vos déchets alimentaires ?

☐ Je les jette avec les ordures ménagères

☐ Je les composte en tas ou dans un composteur

☐ Je les mets dans un composteur partagé (voisins, collectivité)

☐ Je les donne aux animaux

☐ Je les jette à l'extérieur (terrain, nature)

☐ Autre (précisez) : ...

4. Que faites-vous le plus fréquemment de vos petits déchets verts (feuilles, tonte) ?

☐ Je les amène à la déchetterie

☐ Je les donne aux animaux

☐ Je les mets dans un composteur individuel

☐ Je les mets dans un compost partagé

☐ Je les dépose au bord de la route

☐ Je les brûle

☐ Je les mets en tas sur mon terrain

☐ Je les mets dans la nature (hors de chez moi)

☐ Autre (précisez) : ...
5. Que faites-vous le plus fréquemment de vos gros déchets verts (branches, troncs) ?

☐ Je les amène à la déchetterie

☐ Je les mets dans un composteur individuel

☐ Je les mets dans un compost partagé

☐ Je les dépose au bord de la route

☐ Je les brûle

☐ Je les mets en tas sur mon terrain

☐ Je les mets dans la nature (hors de chez moi)

☐ Autre (précisez) : ...

### III. Expériences de choix et préférences

#### Scénario 1 : Déchets alimentaires

Imaginons qu'un nouveau programme public vous propose trois solutions pour mieux gérer **vos déchets alimentaires**. Ces solutions pourraient être les suivantes :

* **Compostage individuel**, avec remise gratuite d'un composteur.

* **Compostage partagé de proximité**, avec jardin partagé géré collectivement à moins de 5 minutes de chez vous.

* **Bacs de tri près du point de collecte du verre**, avec sacs biodégradables fournis et collecte régulière.

Chaque solution est décrite selon trois éléments :

* **Débouché des biodéchets** : compost pour votre jardin, compost pour un jardin partagé, mise à disposition pour les agriculteurs, ou production d'énergie.

* **Type d'accompagnement** : atelier collectif, livret explicatif.

* **Bon d'achat utilisable** dans des commerces locaux, attribué en cas d'engagement réel dans la pratique du tri (attestation et contrôles possibles).

Vous pouvez choisir l'une de ces trois solutions, ou conserver vos pratiques actuelles.

Certaines propositions peuvent sembler proches, mais elles diffèrent sur des points importants. Prenez le temps de les comparer comme si vous faisiez un vrai choix dans votre vie quotidienne (temps, effort, habitudes...).

#### EXPÉRIENCE DE CHOIX DÉCHETS ALIMENTAIRES

##### Scénario 2 : Déchets verts

Dans un second temps, imaginons qu'on vous propose trois options pour gérer vos **déchets verts** :

* **Compostage individuel**, avec composteur gratuit.

* **Collecte en bord de route**, avec sacs biodégradables et collecte régulière.
* **Apport en déchetterie**, à proximité de votre domicile.

Ces solutions reprennent les mêmes caractéristiques que précédemment, à l'exception du **débouché** :

* Débouché des biodéchets : compost pour votre jardin (pouvant être remis en échange d'un apport à la déchetterie), mise à disposition pour les agriculteurs, ou production d'énergie.

## EXPÉRIENCE DE CHOIX DÉCHETS VERTS

Ces différents éléments ont-ils compté dans vos choix ?

<table><thead><tr><th>Critère</th><th>Pas du tout</th><th>Un peu</th><th>Beaucoup</th><th>C'est le critère le plus important</th></tr></thead><tbody><tr><td>Mode de gestion</td><td>☐</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Débouché</td><td>☐</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Accompagnement et formation</td><td>☐</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Bon d'achat</td><td>☐</td><td>☐</td><td>☐</td><td>☐</td></tr></tbody></table>

Si vous avez toujours choisi "je préfère conserver ma pratique actuelle":

☐ Je n'ai pas toujours choisi cette option (je passe à la suite)

☐ Je suis satisfait.e de ma pratique actuelle

☐ Les options proposées sont trop compliquées à mettre en œuvre

☐ Les choix étaient trop difficiles à départager

☐ Trier/composter mes biodéchets ne m'intéresse pas

☐ Je préfère conserver ma liberté

☐ Les habitants ne sont pas encore prêts en Guadeloupe

☐ Autre (précisez) : ...

## IV. Votre opinion générale sur le tri des biodéchets

Échelle : 0 = Pas du tout d'accord, 5 = Tout à fait d'accord

* Trier les biodéchets préserve la qualité de vie locale

* Je suis en désaccord avec mes voisins sur la gestion des biodéchets
* J'ai peur que le compost attire des nuisibles

* Trier n'est pas contraignant physiquement

* J'oublie souvent de séparer mes biodéchets

* Je n'ai pas le temps de trier/composter

* Je ne vois pas l'intérêt de trier/composter

* Je soutiens les initiatives locales de tri/compostage

* Composter réduit les déchets et les odeurs de la poubelle

* Je suis prêt.e à faire plus de 10 min de route pour un point de collecte

* Trier/composter a un impact positif sur l'environnement

* Composter fait partie des habitudes de mon entourage

* Je n'ai pas la place / pas de lieu de dépôt adapté

* Il est trop compliqué de m'organiser pour trier

1. Utiliseriez-vous un broyeur de végétaux itinérant (2x par mois dans votre quartier) ?

☐ Oui

☐ Non

2. Utiliseriez-vous un numéro vert avec maître composteur (conseils via photos/vidéos) ?

☐ Oui

☐ Non

## V. Connaissances sur le tri des biodéchets

Selon vous, quelle part les biodéchets représentent-ils ?

☐ 10 %

☐ 30 %

☐ 50 %

☐ Je ne sais pas

Savez-vous où se trouvent les équipements permettant de trier ou de déposer vos biodéchets ?

☐ Oui

☐ Non

Avez-vous déjà reçu un livret expliquant le tri ou le compostage des biodéchets ?

☐ Oui

☐ Non

Avez-vous déjà assisté à un atelier ou formation sur le tri ou le compostage des biodéchets ?

☐ Oui

☐ Non

Vos enfants ont-ils déjà participé à une activité pédagogique sur le tri ou compostage à l'école ?

☐ Oui

☐ Non
☐ Je ne sais pas

☐ Je n'ai pas d'enfants

### Avez-vous entendu parler de :

<table><thead><tr><th>Événement</th><th>Oui</th><th>Non</th></tr></thead><tbody><tr><td>Quinzaine « Tous au compost »</td><td>☐</td><td>☐</td></tr><tr><td>Semaine européenne de réduction des déchets</td><td>☐</td><td>☐</td></tr></tbody></table>

### Avez-vous reçu un composteur (mairie, association, etc.) ?

☐ Oui

☐ Non

### Quels déchets pouvez-vous mettre dans un compost ?

<table><thead><tr><th>Déchet</th><th>Oui</th><th>Non</th><th>Je ne sais pas</th></tr></thead><tbody><tr><td>Restes de viande ou poisson</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Carton/papier non imprimé (essuie-tout, boîte à œufs)</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Feuilles et tontes de pelouse</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Litière ou déjections animales</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Huiles alimentaires</td><td>☐</td><td>☐</td><td>☐</td></tr><tr><td>Déchets plastiques</td><td>☐</td><td>☐</td><td>☐</td></tr></tbody></table>
Épluchures de légumes/fruits

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

Coquilles d'œufs

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

# VI. Pouvez-vous nous en dire un peu plus à propos de vous...

## Quel âge avez-vous ?

Entrez votre âge : ...

## Êtes-vous :

<figure><img src="image_15.png" /></figure>

Homme

<figure><img src="image_16.png" /></figure>

Femme

<figure><img src="image_17.png" /></figure>

Préfère ne pas répondre

## Niveau d'études le plus élevé atteint ?

<figure><img src="image_18.png" /></figure>

Collège (brevet)

<figure><img src="image_19.png" /></figure>

Lycée (bac)

<figure><img src="image_20.png" /></figure>

Bac +2 / Bac +3

<figure><img src="image_21.png" /></figure>

Bac +5 et +

## Quelle est votre situation professionnelle actuelle ?

<figure><img src="image_22.png" /></figure>

Agriculteur·rice exploitant·e

<figure><img src="image_23.png" /></figure>

Artisan·e, commerçant·e ou chef·fe d'entreprise

<figure><img src="image_24.png" /></figure>

Cadre ou profession intellectuelle supérieure

<figure><img src="image_25.png" /></figure>

Profession intermédiaire

<figure><img src="image_26.png" /></figure>

Employé·e

<figure><img src="image_27.png" /></figure>

Ouvrier·ère

<figure><img src="image_28.png" /></figure>

Retraité·e

<figure><img src="image_29.png" /></figure>

Étudiant·e / élève

<figure><img src="image_30.png" /></figure>

Personne au foyer

<figure><img src="image_31.png" /></figure>

Sans emploi

<figure><img src="image_32.png" /></figure>

Autre : ...

## Dans quelle commune résidez-vous ?

(liste déroulante des communes de Guadeloupe)

## Habitez-vous ... ?

<figure><img src="image_33.png" /></figure>

Un appartement dans une immeuble ou une résidence?

<figure><img src="image_34.png" /></figure>

Une maison individuelle

<figure><img src="image_35.png" /></figure>

Haut ou bas de villa

<figure><img src="image_36.png" /></figure>

Autre : ...
Quelle est la taille de votre foyer ?

Nombre d'adultes : ...

Nombre d'enfants ( moins de 18 ans) : ...

Merci de votre participation !

Souhaitez-vous être informé-e des résultats de l'enquête ?

☐ Oui, je laisse mes coordonnées (nom, téléphone, e-mail)

☐ Non, je ne souhaite pas être recontacté-e

Souhaitez-vous être contacté-e pour d'autres études INRAE ?

☐ Oui

☐ Non

Ces informations seront stockées de manière sécurisée sur le serveur INRAE et supprimées ou anonymisées après usage.

Commentaire libre (optionnel) :

...