Community Participation, Informal Sector Dynamics, and Smart Solutions in Urban Solid Waste Management: Evidence from Jimma, Ethiopia

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This study investigates the synergistic potential of community participation, informal waste sector dynamics, and technology-driven smart solutions in Jimma, Ethiopia, a medium-sized city generating approximately 150 tons of waste daily, with only 60% collected. Employing a mixed-methods sequential explanatory design, the research integrated quantitative data from 400 randomly selected households with qualitative insights from 15 key informant interviews and 4 focus group discussions. Results reveal a significant gap between household willingness to participate in SWM (78%) and actual participation (32%), primarily driven by inadequate infrastructure (63%), low awareness (57%), and weak feedback mechanisms (45%). The informal sector, comprising 500–700 individuals, diverts approximately 28% of recyclable materials from landfills yet operates under precarious conditions. A strong positive correlation (r = 0.72, p < 0.01) exists between awareness and participation, with regression analysis identifying awareness and service reliability as key predictors (R² = 0.59, p < 0.01). Furthermore, 85% of households expressed willingness to use mobile apps for SWM notifications, though only 35% supported digital payments due to trust issues. The study concludes that sustainable SWM requires a tripartite strategy. It proposes an Integrated Participatory Smart Waste Management (IPSWM) model, advocating for enhanced community engagement through targeted campaigns, formalization of the informal sector, and the implementation of low-cost smart solutions like SMS alerts. This framework offers a resilient, equitable, and scalable blueprint for Jimma and similar secondary African cities, directly contributing to SDG 11 (Sustainable Cities) and SDG 12 (Responsible Consumption). Solid Waste Management Community Participation Informal Sector Smart Cities Recycling Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction 1.1. Background of the Study Rapid urbanization and population growth exert immense pressure on urban infrastructure in developing nations, with solid waste management (SWM) representing one of the most visible and persistent challenges (Guerrero, Maas, & Hogland, 2013). Cities in Sub-Saharan Africa grapple with inefficient collection, transportation, and disposal systems, leading to severe environmental degradation and public health risks (Marshall & Farahbakhsh, 2013). Ethiopia, with an urban growth rate exceeding 4% per annum (World Bank, 2023), epitomizes this struggle. Jimma, a major economic hub in southwestern Ethiopia with a population over 200,000, generates roughly 150 tons of waste daily, of which only an estimated 60% is collected (Jimma City Administration, 2024). The current system, characterized by top-down approach and limited resources, is overwhelmed. However, within this challenge lie two underutilized assets: a willing community and a vibrant informal recycling sector, whose potential, especially when integrated with appropriate technology, remains largely unexplored. 1.2. Statement of the Problem The existing SWM system in Jimma is unsustainable and inefficient. Overstretched municipal capacity leads to inconsistent collection, resulting in widespread illegal dumping, open burning, and the proliferation of indiscriminate dump sites. These practices pose severe environmental and public health hazards (Miezah et al., 2015). While community participation is advocated as a cornerstone of sustainable SWM (Bandara et al., 2007), its implementation in Jimma is fragmented. Similarly, the informal waste pickers, who play a crucial role in resource recovery, operate on the margins, facing social stigma and economic exclusion (Wilson et al., 2006). Concurrently, the potential of digital "smart solutions" to optimize SWM remains largely untapped. A significant research gap exists in understanding the synergies between these three elements—community, informal sector, and smart technology—in the specific socio-economic context of Jimma. This study seeks to fill this gap. While the individual components of community participation, the informal waste sector, and smart technology have been studied in isolation, a significant research gap exists in understanding their synergistic potential. There is a lack of empirical evidence on how these three pillars can be effectively integrated within the specific socio-economic, infrastructural, and cultural context of medium-sized cities in Ethiopia. This study seeks to fill this critical gap by investigating these interlinkages in Jimma, with the aim of developing a holistic and sustainable waste management model. 1.3. Research Questions This study is guided by the following questions: What is the current level and nature of community participation in SWM practices in Jimma, and what are the key factors that hinder or foster it? What is the structure, scale, and economic dynamics of the informal waste sector in Jimma, and how does it contribute to the city's overall waste management system? What is the level of receptivity among residents and municipal authorities towards adopting smart technology solutions for SWM in Jimma? What integrated strategies, based on empirical evidence, can synergize community participation, informal sector integration, and smart solutions for sustainable SWM in Jimma? 1.4. Objectives of the Study General Objective To analyze the dynamics of community participation and the informal waste sector and assess the feasibility of smart solutions for developing a sustainable integrated solid waste management model for Jimma City. Specific Objectives : To assess the awareness, attitudes, and practices of Jimma households regarding waste separation, storage, disposal, and willingness to participate in SWM programs. To map and analyze the operations, challenges, and contributions of the informal waste sector to material recovery and recycling in Jimma. To evaluate the potential and prerequisites for implementing smart technology solutions (e.g., GIS-based collection, mobile apps) for improving SWM efficiency and citizen engagement. To propose a holistic and context-specific model for integrated SWM that synergizes community action, informal sector integration, and smart technologies. 2. Literature Review 2.1. The Global Challenge of Urban Solid Waste Management Global waste generation is projected to rise to 3.88 billion tonnes by 2050, with Sub-Saharan Africa seeing the fastest growth (Kaza et al., 2018). While high-income nations have advanced integrated systems, low and middle-income countries often spend 20–50% of municipal budgets on SWM yet collect only 50–80% of generated waste, with the remainder mismanaged (UN-Habitat, 2020; Ferronato & Torretta, 2019). 2.2. Community Participation in SWM A global shift from top-down to participatory solid waste management (SWM) recognizes communities as key stakeholders, moving beyond passive compliance to active roles in source separation, composting, and co-management (Zurbrugg, 2002). Enabling factors include education, income, trust in local authorities, and accessible infrastructure like bins and regular collection services (Owusu et al., 2013; Mugambi & M’Ikanga, 2022). Conversely, barriers such as low awareness, economic constraints, and the perception of SWM as a governmental duty hinder engagement (Mukui, 2013). While these factors are well-documented, their interplay in medium-sized Ethiopian cities like Jimma, particularly alongside technological interventions, remains underexplored. This study examines how awareness campaigns and infrastructure can bridge the gap between willingness and actual participation in Jimma’s unique socio-cultural context. 2.3. The Role of the Informal Sector in SWM The informal waste sector (IWS) serves as the backbone of recycling in many Global South cities, recovering 50–80% of recyclable materials like plastics and metals, thus reducing landfill burdens and conserving resources (Medina, 2010; WIEGO, 2021). Despite their contributions, waste pickers face health risks, social stigma, and economic precarity (Samson, 2020). Integration strategies, such as legal recognition, cooperatives, and Material Recovery Facilities (MRFs), have proven effective in contexts like Latin America but require adaptation to Ethiopia’s socio-political environment (Oteng-Ababio et al., 2023; Nzeadibe & Agu, 2021). This study investigates the structure and challenges of Jimma’s IWS to propose locally relevant integration models, emphasizing cooperative formation and safe recovery practices. 2.4. Emergence and Critiques of Smart Solutions in SWM The "smart city" paradigm promotes ICT for urban services, including sensor-based bins, mobile apps, and GIS for route optimization (Esmaeilian et al., 2018; Singh, 2019). However, significant challenges exist in transplanting these technologies to resource-constrained cities, including high costs, low digital literacy, and unreliable infrastructure (Cobbinah & Niminga-Beka, 2021; Smith & Johnson, 2024). Therefore, this study argues that for cities like Jimma, 'smart' must be redefined as 'appropriate-tech'—focusing on low-cost, high-impact solutions like SMS and GIS rather than sensor-based networks. This research evaluates the feasibility of this approach. 2.5. Synthesis and Conceptual Framework Sustainable SWM requires a multi-stakeholder approach. This study's framework (Fig. 1) integrates three pillars: community (source reduction/separation, enhanced by "guzo" for cultural buy-in), informal sector (agile recycling), and smart technology (enabling platform), within policy support. Unlike generic UN-Habitat models, IPSWM incorporates Jimma-specific elements, such as kebele-level (smallest administrative unit) coordination inspired by Hawassa (Tadesse & Worku, 2023) vs. centralized Accra systems (Oteng-Ababio et al., 2023), emphasizing bidirectional flows for equity. Framework Explanation The model illustrates a dynamic, bidirectional relationship between the three core pillars, all operating within a supportive policy environment. The community engages in source separation, whose success is enabled by clear information from smart platforms and a reliable market provided by the formalized informal sector. Smart solutions optimize collection routes for the municipality and provide a feedback channel for citizens, while also providing data that can help organize the informal sector. The formalized informal sector efficiently recovers materials, which reduces the waste burden on the formal system and provides economic incentives back to the community. The entire system is overseen and facilitated by a supportive policy framework that encourages integration, funds infrastructure, and protects workers' rights. 3. Research Methodology 3.1. Research Design This study employed a mixed-methods sequential explanatory design. This design was chosen as the quantitative data from households provided a broad, generalizable understanding of patterns and relationships, which was then explained, contextualized, and deepened through rich qualitative data from KIIs and FGDs (Creswell & Plano Clark, 2017). 3.2. Study Area The study was conducted in Jimma City, Oromia Regional State, Ethiopia. Three sub-cities of Jimma Town were selected purposively to ensure representation of the city's core urban, peri-urban, and mixed commercial-residential areas, capturing a wide range of socio-economic and waste management scenarios. 3.3. Data Sources and Collection Techniques Household Survey A structured questionnaire was administered to 400 randomly selected household heads. The survey questionnaire was pre-tested in a pilot study involving 30 households (not included in the main sample) from a similar neighborhood. The pilot aimed to assess the clarity, relevance, and timing of the survey. Based on feedback, minor adjustments were made to the wording of three questions related to 'willingness to pay' to enhance comprehension, and the average completion time was confirmed to be 15–20 minutes. The sample was drawn from an estimated total population of 50,000 households in the selected sub-cities, using the Cochran formula for a finite population at a 95% confidence level (Z-score = 1.96) and a 5% margin of error. The response rate was 95%, with non-respondents primarily due to absence; no significant bias was detected as demographics matched the sample frame. A stratified random sampling technique was used to ensure proportional representation from the three selected sub-cities. The questionnaire demonstrated high internal consistency (Cronbach's α = 0.85 for awareness scale; α = 0.82 for participation attitudes). The semi-structured Key Informant Interviews (KIIs) had an average duration of 50 minutes, while the Focus Group Discussions (FGDs) were longer, averaging 100 minutes, to allow for deep exploration of community and waste-picker perspectives. Qualitative data collection continued until thematic saturation was reached. Necessary adjustments for clarity and contextual relevance were made after the pilot. Key Informant Interviews (KIIs) 15 semi-structured interviews were conducted with officials from the Jimma City Solid Waste Management Office (n = 4), heads of kebele administrations (n = 4), leaders of private micro-enterprises (n = 3), and known leaders of informal waste picker groups (n = 4). Focus Group Discussions (FGDs) Four FGDs were held, each with 8–10 participants. This included two with mixed groups of residents (one male-dominated, one female-dominated) and two with groups of informal waste pickers (separated by gender to encourage open discussion of gender-specific issues). FGDs continued until data saturation was reached, where no new substantive themes emerged from the fourth session. Direct Observation Researchers conducted systematic observational walks in selected neighborhoods and around the main landfill site to physically verify practices like waste storage, collection points, and informal sector activities, thereby triangulating self-reported data. 3.4. Data Analysis Quantitative data from questionnaires were cleaned, coded, and analyzed using SPSS version 27. Descriptive statistics (frequencies, percentages, means, and standard deviations) summarized the data. Inferential statistics, including Chi-square tests and Pearson correlation analysis, were employed to examine relationships between variables (e.g., education level and waste separation practices). A linear regression analysis was conducted to identify key predictors of community participation. All assumptions for linear regression (linearity, homoscedasticity, normality of residuals, and absence of multicollinearity) were checked and met. A post-hoc power analysis using G*Power, based on the observed effect size (Cohen's f² = 0.85), confirmed adequate statistical power (0.92) for the regression model given the sample size. Qualitative data from KIIs and FGDs were audio-recorded, transcribed verbatim. Qualitative data from KIIs and FGDs were audio-recorded, transcribed verbatim, translated from Afan Oromo/Amharic to English. Two independent coders analyzed the qualitative data using NVivo 12. An inter-rater reliability coefficient of 0.89 was achieved. The few coding discrepancies that arose were resolved through discussion and consensus-building between the coders, referring back to the original transcripts to ensure the final themes accurately reflected the participants' meanings. The analysis followed the six-phase approach by Braun and Clarke (2006): (1) familiarization, (2) generating initial codes, (3) searching for themes, (4) reviewing themes, (5) defining themes, and (6) producing the report. Two independent coders were used to enhance reliability, and an inter-rater reliability coefficient of 0.89 was achieved, indicating a high level of agreement. Themes were then interpreted and used to explain the quantitative findings. Methodological triangulation was a key strategy, whereby findings from the surveys, interviews, FGDs, and observations were compared and contrasted to validate and provide a more comprehensive understanding of the research problems. 3.5. Ethical Approval and Consent to Participate This study was performed in line with the principles of the Declaration of Helsinki. Formal ethical approval was granted by the Research Ethics Committee of the Ethiopian Civil Service University (Approval No. ECSU/REC/2024/045) prior to the commencement of the study. Informed consent was obtained from all individual participants included in the study. For the household survey, written consent was obtained from each household head after the purpose of the study, procedures, risks, benefits, and the rights to withdraw were explained verbally and via a written information sheet. For key informant interviews and focus group discussions, written consent was also obtained prior to recording. For participants who were illiterate, the information sheet and consent form were read aloud in the presence of an impartial witness, and oral consent was documented and thumb-printed. All data were anonymized during analysis to ensure participant confidentiality. For vulnerable groups, such as informal waste pickers, interviews were conducted in a safe and private environment, and the principle of do-no-harm was paramount throughout the research process. 4. Results and Discussion 4.1. Socio-Demographic Characteristics of Respondents Of the 400 household respondents, 62% were male and 38% were female. The average age was 38.7 years. In terms of education, 25% had no formal education, 40% had primary education, 28% had secondary education, and 7% had a diploma or degree. The average household size was 4.8 persons (SD = 1.6). Table 1 Socio-Demographic Profile of Household Respondents (N = 400) Characteristic Category Frequency Percentage Gender Male 248 62.0% Female 152 38.0% Age Group 18–30 112 28.0% 31–45 178 44.5% 46–60 82 20.5% > 60 28 7.0% Education Level No Formal Education 100 25.0% Primary 160 40.0% Secondary 112 28.0% Tertiary 28 7.0% Average Household Size 4.8 persons (SD: 1.6) - - Adapted from household survey data. 4.2. Awareness, Attitudes, and Practices on SWM A high level of awareness (82%) regarding the negative health and environmental impacts of poor SWM was recorded. However, a significant attitude-behavior gap was observed. While 78% of respondents expressed a willingness to separate their waste at home if provided with separate bins, only 22% actually practiced any form of separation, primarily segregating high-value materials like plastic bottles and metals for sale to informal collectors. The primary method of disposal was waiting for the municipal truck (45%), though 25% relied on shared containers which were often overflowing. A significant minority (15%) resorted to burning or burying waste within their compounds, and 5% admitted to illegal dumping in open spaces. A Chi-square test revealed a statistically significant relationship (χ² = 25.8, p < 0.05) between higher education levels and the adoption of proper disposal practices. 4.3. Level and Drivers of Community Participation Community participation was measured through a composite index of four indicators: consistent payment of service fees, participation in organized cleaning campaigns, proper waste storage, and practicing waste reduction/separation. Only 32% of households were ranked as "active participants" based on these criteria. The main drivers of participation were a sense of civic duty (65%) and a desire for a clean and healthy neighborhood (60%). This disconnect between willingness and action underscores a critical system failure. As one KII from the city administration noted: "We expect residents to store waste properly and even separate it, but we have not been able to provide them with the basic tools like reliable bins or consistent collection schedules. Our public awareness efforts are also sporadic. It's an unfair expectation." (KII_04, Municipal Official). 4.4. Dynamics and Contributions of the Informal Waste Sector The sector comprises 500–700 individuals who divert approximately 28% of the city's recyclable waste from the landfill, representing a massive hidden subsidy to the formal system (WIEGO, 2021). They operate a sophisticated economy around materials like PET, cardboard, and metals (Table 2 ). However, they face acute health risks, social stigma, and economic instability. Table 2 Economics of Key Recyclable Materials in Jimma's Informal Sector (Data from FGDs and KIIs) Material Average Collection Rate (kg/day/picker) Buying Price from Households (ETB/kg) Selling Price to Wholesaler (ETB/kg) Main End Market PET Bottles 15–20 3–4 6–8 Addis Ababa Cardboard 20–30 2–3 4–5 Jimma/Addis Ababa Aluminum Cans 3–5 25–30 35–40 Addis Ababa Scrap Metal Varies 4–6 8–10 Jimma Glass Bottles 10–15 0.5–1.0 (per bottle) 1.5–2.0 (per bottle) Jimma Adapted from FGDs and KIIs. By cross-referencing the estimated daily collection per picker with the estimated number of pickers and comparing it to municipal data on total waste generation and composition, we estimate that the informal sector diverts approximately 28% of the total recyclable waste generated in the city from the landfill. This estimate aligns with similar rates in Addis Ababa (Assefa & Glawe, 2022), representing a significant saving in disposal costs and environmental footprint for the municipality. However, these workers operate under profound challenges: acute health risks (cuts, infections, respiratory problems from burning), social stigma, harassment by police and the public, and extreme economic instability tied to global commodity prices. A female waste picker shared in an FGD: "We are seen as dirty people. People shout at us and chase us away from their neighborhoods. But we are the ones cleaning the city, and we are feeding our children with this work. No one sees that." (FGD_03, Female Waste Picker). 4.5. Potential for Smart Solutions The study found a high level of mobile phone penetration (92% of households) and a growing use of smartphones (48% of households), providing a solid foundation for digital solutions. When asked about their willingness to use a mobile application for SWM: 85% were willing to receive SMS alerts on collection schedules. 70% were interested in an app to report overflowing bins or illegal dumping. Only 35% were willing to use an app for digital payment of fees, citing trust issues and a preference for cash. Municipal officials were also receptive but concerned about startup costs, technical expertise, and the critical need for a behavior change campaign to accompany any technological rollout to ensure adoption. 4.6. Correlation and Regression Analysis A strong positive correlation was found between education and awareness (r = 0.68, p < 0.01) and, more importantly, between awareness and participation (r = 0.72, p < 0.01). Regression analysis confirmed that awareness level (β = 0.45, p < 0.01) and perceived service reliability (β = 0.38, p < 0.01) were the strongest predictors of participation, together explaining 59% of the variance (R² = 0.59, p < 0.01). The remaining 41% of unexplained variance suggests other factors, such as cultural norms and social influence—constructs from behavioral economics—play a significant role. Future research should employ Structural Equation Modeling (SEM) to incorporate these latent variables. Table 3 Multivariate Regression Results for Predictors of Participation (N = 400) Predictor Beta (95% CI) SE t p Awareness 0.45 (0.29–0.61) 0.08 5.63 < 0.01 Service Reliability 0.38 (0.24–0.52) 0.07 5.43 < 0.01 Education 0.15 (-0.03-0.33) 0.09 1.67 0.10 Constant 1.20 (0.71–1.69) 0.25 4.80 < 0.01 R²=0.59; Adjusted R²=0.58; F(3,396) = 192.3, p < 0.01. Unexplained 41% suggests cultural norms; SEM could test: Cultural norms → Awareness → Participation (moderated by social influence). Adapted from household survey data. 5. Discussion This study set out to analyze the dynamics of community participation, the informal sector, and smart solutions in Jimma's SWM system. The results reveal a system characterized by significant potential that is constrained by critical systemic failures. The most striking finding is the profound gap between household willingness to participate (78%) and actual participation (32%). This dissonance is not primarily a failure of public will but of public provision, driven by inadequate infrastructure, sporadic awareness campaigns, and a lack of reliable feedback mechanisms. This aligns with findings from Accra, where institutional weaknesses similarly stifle community engagement despite high willingness (Oteng-Ababio et al., 2023). In contrast, Hawassa’s higher participation rate (45%) stems from decentralized kebele-level coordination, suggesting Jimma could adopt similar models (Tadesse & Worku, 2023). The informal sector’s 28% diversion rate is a critical, unpaid subsidy to the municipal system, consistent with Global South trends (WIEGO, 2021). However, its efficiency operates despite systemic exclusion, echoing Kampala’s precarious IWS conditions (Nzeadibe & Agu, 2021). The proposed Ethiopian Informal Integration Index (EIII) scores Jimma’s integration potential at 4.5/10, with low scores for legal recognition (2/10, due to absent policies), health support (3/10, limited PPE access), and medium scores for cooperative potential (5/10, based on FGDs showing interest but no structure). This indicates urgent reform needs. High receptivity to low-cost smart solutions (e.g., 85% for SMS alerts) aligns with post-COVID digital adoption in Africa (Cobbinah & Niminga-Beka, 2021). However, reluctance toward digital payments (35%) and official concerns about costs echo critiques of inappropriate tech transfers (Smith & Johnson, 2024). An SMS-based system is thus prioritized over sensor-based networks for feasibility. The regression analysis (R² = 0.59, p < 0.01) confirms awareness (β = 0.45) and service reliability (β = 0.38) as key participation drivers, aligning with global findings (Mugambi & M’Ikanga, 2022). However, the 41% unexplained variance suggests unmeasured factors. In Jimma’s context, cultural norms like communal responsibility (guzo in Oromo culture) and social influence (peer pressure within kebeles) likely play roles. For instance, FGDs highlighted residents’ deference to community leaders, suggesting social capital as a latent variable. Future research should employ Structural Equation Modeling (SEM) to test these constructs, modeling paths like: cultural norms → awareness → participation, or social influence → trust → compliance. This would clarify how Ethiopian socio-cultural dynamics shape SWM behavior, addressing the variance gap. 6. Conclusions and Recommendations 6.1. Conclusion This study concludes that achieving sustainable solid waste management in Jimma requires a fundamental paradigm shift from a disjointed, top-down model to an Integrated Participatory Smart Waste Management (IPSWM) model. The primary contribution of this research is the empirical validation and detailed proposal of this tripartite framework. The model's novelty lies in its synergistic integration of three underutilized assets: activating community agency through enabling infrastructure and awareness; formally recognizing and integrating the informal sector to harness its efficiency while ensuring justice; and leveraging appropriate, low-cost digital technologies as an enabling platform. This framework offers a resilient, equitable, and contextually appropriate blueprint for Jimma and similar secondary African cities, providing a practical pathway towards achieving national SDG targets. 6.2. Limitations of the Study While this study provides valuable insights, several limitations should be acknowledged. First, the cross-sectional design captures data at a single point in time, revealing correlations but not definitive causal relationships. For instance, while a strong correlation exists between awareness and participation, longitudinal studies are needed to confirm that increased awareness directly causes higher participation rates. Second, the reliance on self-reported data, particularly for sensitive topics like payment of fees and illegal dumping, is susceptible to social desirability bias, where respondents may provide answers they believe are socially acceptable rather than reflecting their actual practices. We attempted to mitigate this through triangulation with direct observation and key informant interviews. Finally, while the sampling strategy ensured representation from three diverse sub-cities, the findings may not be fully generalizable to all neighborhoods of Jimma or to other Ethiopian cities with different cultural and governance structures. The proposed model should be adapted to local contexts following similar diagnostic studies. 6.3. Scalability to Other Contexts The IPSWM model, tailored to Jimma’s context, is adaptable to other Ethiopian cities (e.g., Dire Dawa, Mekelle) and Global South settings. Its low-cost focus (SMS, GIS) enhances portability, potentially reducing landfill use by 20–30% based on Hawassa pilots (Tadesse & Worku, 2023). However, barriers exist: Table 4 Barriers to Scalability and Mitigation Strategies (Compared Across Cities) Barrier Jimma Example Comparison (e.g., Hawassa/Accra) Mitigation Strategy Municipal Funding Shortages Limited budgets restrict bins/MRFs Hawassa: 25% landfill reduction via PPPs (Tadesse & Worku, 2023); Accra: Donor-funded (Oteng-Ababio et al., 2023) Leverage World Bank grants; PPPs for valorization Digital Infrastructure Gaps Uneven mobile coverage in peri-urban areas Ghana: Post-COVID adoption but rural gaps (Osei et al., 2024) Phased pilots in urban cores; offline alternatives (radio) Political Will Stigma hinders informal integration Johannesburg: ARO organization success (Frontiers in Sustainable Cities, 2025) Workshops; MRF pilots for proof-of-concept Cultural Variations "Guzo" norms vary regionally Accra: Community-led but less communal (Oteng-Ababio et al., 2023) Local diagnostics via EIII tool Adapted from literature and study findings. 6.4. Recommendations A. For the Jimma City Administration and SWM Office : Immediate Infrastructure and Service Improvement: Prioritize the procurement and distribution of standardised household/communal waste bins in partnership with kebeles. Publish and adhere to a reliable, publicly available collection schedule. Launch a Sustained Awareness Campaign: Partner with local media, schools, and religious institutions to launch a multi-lingual campaign focused on practical waste separation and composting techniques, highlighting the economic benefits of selling recyclables. Initiate Informal Sector Integration: Begin a formal dialogue with waste picker representatives. A pilot project could include: issuing official identification cards; providing basic personal protective equipment (PPE); and designating a pilot Material Recovery Facility (MRF) site. Pilot a Low-Cost Smart Solution: Within the next 6–12 months, pilot an SMS-based collection alert system for 2–3 sub-cities. The estimated cost of 200,000 ETB for SMS credits and management could be sought from development partners or reallocated from efficiency savings. Simultaneously, develop a simple mobile app for reporting issues, to be tested in a tech-savvy demographic. B. For Community Leaders and Residents : Take Collective Ownership: Kebele administrations should facilitate the formation of neighborhood waste committees to monitor practices, organize clean-ups, and serve as a liaison with the city SWM office. Practice Source Separation: Begin separating recyclables at home into separate sacks for itinerant buyers, turning waste into a source of income. C. For the Informal Waste Sector : Move towards Organization: With support from NGOs or the city, explore forming an association or cooperative to collectively bargain for better prices and advocate for rights. D. For Future Research : Conduct a detailed waste audit to precisely quantify the waste stream and recycling potential. Implement and assess the impact of the proposed SMS pilot on participation rates and collection efficiency. Conduct a comprehensive study on the health impacts and vulnerability of waste pickers to inform social protection policies. 6.5. Proposed Integrated Model for Jimma The Integrated Participatory Smart Waste Management (IPSWM) model visualizes a synergistic SWM ecosystem (Fig. 5 ). It integrates three pillars within a supportive policy framework: Description: Diagram of the IPSWM model with three pillars (Community, Informal Sector, Smart Solutions) in a policy framework, using high-contrast colors (e.g., blue, green, orange) and patterns for accessibility. Arrows show circular flows. Source: Study model. Note Submit figures as separate TIFF/EPS files if > 10 MB; embed in .docx for review. ENABLING POLICY & INSTITUTIONAL FRAMEWORK • Supportive Regulations (e.g., legal recognition for IWS) • Financial Allocation (e.g., donor-funded bins, MRFs) • Cross-Departmental Coordination (e.g., health, urban planning) • Monitoring & Evaluation (e.g., EIII tracking) COMMUNITY (The Source) • Separation (household sorting of recyclables) • Reduction (composting, reuse) • Reporting (via SMS/apps) • Fee Payment (cash-based, building trust for digital) SMART SOLUTIONS (The Digital Enabler) • SMS Alerts (collection schedules) • Reporting App (overflow/illegal dumping) • GIS Routing (optimized collection) INFORMAL SECTOR (The Recycler) • Cooperatives (organized IWS groups) • MRF Access (designated sorting facilities) • Safe Recovery (PPE, health support) • Fair Markets (stable prices via cooperatives) Interactions • Community provides materials and legitimacy (fees, compliance), supported by smart solution feedback (SMS alerts). • Smart solutions enhance efficiency (GIS routing) and citizen engagement (reporting apps), providing data to organize IWS. • Informal sector recycles materials, reducing landfill loads and offering economic incentives to households. Outcomes Cleaner city, enhanced resource recovery, social inclusion, reduced costs, improved public health. Ethiopian Informal Integration Index (EIII) A scoring tool to assess IWS integration (1–10 scale) • Legal Recognition: 2/10 (no formal policies in Jimma). • Health Support: 3/10 (minimal PPE, health risks prevalent). • Cooperative Potential: 5/10 (interest shown in FGDs, no structure). • Market Access: 4/10 (reliance on volatile wholesalers). • Overall Score: 4.5/10, indicating urgent need for policy reform, starting with ID cards and MRF pilots. This model ensures a circular flow of information, materials, and support, fostering a sustainable, equitable SWM system. Statements and Declarations Competing Interests : Authors are required to disclose financial or non-financial interests that are directly or indirectly related to the work submitted for publication. The authors have no relevant financial or non-financial interests to disclose. Declarations Acknowledgements The authors thank the Jimma City Administration for all-rounded support during the data collection process. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics Approval This study was approved by the Research Ethics Committee of the Ethiopian Civil Service University (Approval No. ECSU/REC/2024/045). Consent to Participate Informed consent was obtained from all individual participants included in the study. Consent for Publication Participants consented to the publication of the anonymized data collected for this study. Authorship Contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by the lead researcher. 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07:22:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHousehold Waste Disposal Practices (N=400)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescription: Diagram illustrating three pillars (Community, Informal Sector, Smart Solutions) within a policy framework, using high-contrast colors (e.g., blue for Community, green for Informal Sector, orange for Smart Solutions, black for Policy) and patterns (e.g., stripes, dots) for colorblind accessibility. Arrows indicate bidirectional interactions. Source: Study framework.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7652455/v1/8baedb7cf6ca2f874cc2c314.png"},{"id":93657085,"identity":"8bfb0f31-6e43-48dd-9381-d6c1f3a8c46c","added_by":"auto","created_at":"2025-10-16 07:22:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":502315,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: Household Waste Disposal Practices (N=400)\u003c/p\u003e\n\u003cp\u003eDescription: Bar chart with high-contrast colors (e.g., blue for Municipal truck at 45%, green for Shared containers at 25%, red for Burning/burying at 15%, orange for Illegal dumping at 5%, gray for Other at 10%) and patterns (e.g., stripes) for accessibility. Source: Household survey data.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7652455/v1/77f9c764f2f4daaa79c38d62.png"},{"id":93657059,"identity":"77513d60-819f-47cc-824c-8f57eaa63a75","added_by":"auto","created_at":"2025-10-16 07:22:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":488378,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3: Perceived Barriers to Community Participation (Multiple Responses Allowed, N=400)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescription: Bar chart showing percentages with high-contrast colors (e.g., blue for Inadequate infrastructure at 63%, green for Lack of awareness at 57%, red for Weak feedback mechanisms at 45%, orange for Cost concerns at 30%, gray for Other at 5%) and patterns for accessibility. Source: Household survey data.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7652455/v1/16a39975c575737a88a88c1b.png"},{"id":93658001,"identity":"afebad02-d587-4e26-a0ca-c29242349a68","added_by":"auto","created_at":"2025-10-16 07:30:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":337195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4: Willingness to Adopt Smart SWM Services (N=400)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescription: Bar chart with high-contrast colors (e.g., blue for SMS alerts at 85%, green for Reporting app at 70%, red for Digital payments at 35%, gray for Other at 10%) and patterns for accessibility. Source: Household survey data.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7652455/v1/5a47f6d85dc2a39b0665e83f.png"},{"id":93658000,"identity":"9e330764-2ccb-4378-b586-e5e26f88c3b8","added_by":"auto","created_at":"2025-10-16 07:30:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":402413,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Research Methodology section.\u003c/p\u003e","description":"","filename":"Uf1.png","url":"https://assets-eu.researchsquare.com/files/rs-7652455/v1/cf1fbed670ddb6b0d007b52d.png"},{"id":97896629,"identity":"a398b42d-5cd9-44e7-9898-bd4b5772c95b","added_by":"auto","created_at":"2025-12-10 15:36:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2870910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7652455/v1/064f026e-7804-4882-9781-b1874b8dd3a6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCommunity Participation, Informal Sector Dynamics, and Smart Solutions in Urban Solid Waste Management: Evidence from Jimma, Ethiopia\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003e1.1. Background of the Study\u003c/h2\u003e\u003cp\u003eRapid urbanization and population growth exert immense pressure on urban infrastructure in developing nations, with solid waste management (SWM) representing one of the most visible and persistent challenges (Guerrero, Maas, \u0026amp; Hogland, 2013). Cities in Sub-Saharan Africa grapple with inefficient collection, transportation, and disposal systems, leading to severe environmental degradation and public health risks (Marshall \u0026amp; Farahbakhsh, 2013). Ethiopia, with an urban growth rate exceeding 4% per annum (World Bank, 2023), epitomizes this struggle. Jimma, a major economic hub in southwestern Ethiopia with a population over 200,000, generates roughly 150 tons of waste daily, of which only an estimated 60% is collected (Jimma City Administration, 2024). The current system, characterized by top-down approach and limited resources, is overwhelmed. However, within this challenge lie two underutilized assets: a willing community and a vibrant informal recycling sector, whose potential, especially when integrated with appropriate technology, remains largely unexplored.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.2. Statement of the Problem\u003c/h2\u003e\u003cp\u003eThe existing SWM system in Jimma is unsustainable and inefficient. Overstretched municipal capacity leads to inconsistent collection, resulting in widespread illegal dumping, open burning, and the proliferation of indiscriminate dump sites. These practices pose severe environmental and public health hazards (Miezah et al., 2015). While community participation is advocated as a cornerstone of sustainable SWM (Bandara et al., 2007), its implementation in Jimma is fragmented. Similarly, the informal waste pickers, who play a crucial role in resource recovery, operate on the margins, facing social stigma and economic exclusion (Wilson et al., 2006). Concurrently, the potential of digital \"smart solutions\" to optimize SWM remains largely untapped. A significant research gap exists in understanding the synergies between these three elements\u0026mdash;community, informal sector, and smart technology\u0026mdash;in the specific socio-economic context of Jimma. This study seeks to fill this gap. While the individual components of community participation, the informal waste sector, and smart technology have been studied in isolation, a significant research gap exists in understanding their synergistic potential. There is a lack of empirical evidence on how these three pillars can be effectively integrated within the specific socio-economic, infrastructural, and cultural context of medium-sized cities in Ethiopia. This study seeks to fill this critical gap by investigating these interlinkages in Jimma, with the aim of developing a holistic and sustainable waste management model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.3. Research Questions\u003c/h2\u003e\u003cp\u003eThis study is guided by the following questions:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the current level and nature of community participation in SWM practices in Jimma, and what are the key factors that hinder or foster it?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the structure, scale, and economic dynamics of the informal waste sector in Jimma, and how does it contribute to the city's overall waste management system?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the level of receptivity among residents and municipal authorities towards adopting smart technology solutions for SWM in Jimma?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat integrated strategies, based on empirical evidence, can synergize community participation, informal sector integration, and smart solutions for sustainable SWM in Jimma?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e1.4. Objectives of the Study General Objective\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo analyze the dynamics of community participation and the informal waste sector and assess the feasibility of smart solutions for developing a sustainable integrated solid waste management model for Jimma City.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecific Objectives\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo assess the awareness, attitudes, and practices of Jimma households regarding waste separation, storage, disposal, and willingness to participate in SWM programs.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo map and analyze the operations, challenges, and contributions of the informal waste sector to material recovery and recycling in Jimma.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo evaluate the potential and prerequisites for implementing smart technology solutions (e.g., GIS-based collection, mobile apps) for improving SWM efficiency and citizen engagement.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo propose a holistic and context-specific model for integrated SWM that synergizes community action, informal sector integration, and smart technologies.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. The Global Challenge of Urban Solid Waste Management\u003c/h2\u003e\n \u003cp\u003eGlobal waste generation is projected to rise to 3.88\u0026nbsp;billion tonnes by 2050, with Sub-Saharan Africa seeing the fastest growth (Kaza et al., 2018). While high-income nations have advanced integrated systems, low and middle-income countries often spend 20\u0026ndash;50% of municipal budgets on SWM yet collect only 50\u0026ndash;80% of generated waste, with the remainder mismanaged (UN-Habitat, 2020; Ferronato \u0026amp; Torretta, 2019).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Community Participation in SWM\u003c/h2\u003e\n \u003cp\u003eA global shift from top-down to participatory solid waste management (SWM) recognizes communities as key stakeholders, moving beyond passive compliance to active roles in source separation, composting, and co-management (Zurbrugg, 2002). Enabling factors include education, income, trust in local authorities, and accessible infrastructure like bins and regular collection services (Owusu et al., 2013; Mugambi \u0026amp; M\u0026rsquo;Ikanga, 2022). Conversely, barriers such as low awareness, economic constraints, and the perception of SWM as a governmental duty hinder engagement (Mukui, 2013). While these factors are well-documented, their interplay in medium-sized Ethiopian cities like Jimma, particularly alongside technological interventions, remains underexplored. This study examines how awareness campaigns and infrastructure can bridge the gap between willingness and actual participation in Jimma\u0026rsquo;s unique socio-cultural context.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. The Role of the Informal Sector in SWM\u003c/h2\u003e\n \u003cp\u003eThe informal waste sector (IWS) serves as the backbone of recycling in many Global South cities, recovering 50\u0026ndash;80% of recyclable materials like plastics and metals, thus reducing landfill burdens and conserving resources (Medina, 2010; WIEGO, 2021). Despite their contributions, waste pickers face health risks, social stigma, and economic precarity (Samson, 2020). Integration strategies, such as legal recognition, cooperatives, and Material Recovery Facilities (MRFs), have proven effective in contexts like Latin America but require adaptation to Ethiopia\u0026rsquo;s socio-political environment (Oteng-Ababio et al., 2023; Nzeadibe \u0026amp; Agu, 2021). This study investigates the structure and challenges of Jimma\u0026rsquo;s IWS to propose locally relevant integration models, emphasizing cooperative formation and safe recovery practices.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Emergence and Critiques of Smart Solutions in SWM\u003c/h2\u003e\n \u003cp\u003eThe \u0026quot;smart city\u0026quot; paradigm promotes ICT for urban services, including sensor-based bins, mobile apps, and GIS for route optimization (Esmaeilian et al., 2018; Singh, 2019). However, significant challenges exist in transplanting these technologies to resource-constrained cities, including high costs, low digital literacy, and unreliable infrastructure (Cobbinah \u0026amp; Niminga-Beka, 2021; Smith \u0026amp; Johnson, 2024). Therefore, this study argues that for cities like Jimma, \u0026apos;smart\u0026apos; must be redefined as \u0026apos;appropriate-tech\u0026apos;\u0026mdash;focusing on low-cost, high-impact solutions like SMS and GIS rather than sensor-based networks. This research evaluates the feasibility of this approach.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Synthesis and Conceptual Framework\u003c/h2\u003e\n \u003cp\u003eSustainable SWM requires a multi-stakeholder approach. This study\u0026apos;s framework (Fig. 1) integrates three pillars: community (source reduction/separation, enhanced by \u0026quot;guzo\u0026quot; for cultural buy-in), informal sector (agile recycling), and smart technology (enabling platform), within policy support. Unlike generic UN-Habitat models, IPSWM incorporates Jimma-specific elements, such as kebele-level (smallest administrative unit) coordination inspired by Hawassa (Tadesse \u0026amp; Worku, 2023) vs. centralized Accra systems (Oteng-Ababio et al., 2023), emphasizing bidirectional flows for equity.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFramework Explanation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe model illustrates a dynamic, bidirectional relationship between the three core pillars, all operating within a supportive policy environment. The community engages in source separation, whose success is enabled by clear information from smart platforms and a reliable market provided by the formalized informal sector. Smart solutions optimize collection routes for the municipality and provide a feedback channel for citizens, while also providing data that can help organize the informal sector. The formalized informal sector efficiently recovers materials, which reduces the waste burden on the formal system and provides economic incentives back to the community. The entire system is overseen and facilitated by a supportive policy framework that encourages integration, funds infrastructure, and protects workers\u0026apos; rights.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Research Methodology","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Research Design\u003c/h2\u003e\u003cp\u003eThis study employed a mixed-methods sequential explanatory design. This design was chosen as the quantitative data from households provided a broad, generalizable understanding of patterns and relationships, which was then explained, contextualized, and deepened through rich qualitative data from KIIs and FGDs (Creswell \u0026amp; Plano Clark, 2017).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Study Area\u003c/h2\u003e\u003cp\u003eThe study was conducted in Jimma City, Oromia Regional State, Ethiopia. Three sub-cities of Jimma Town were selected purposively to ensure representation of the city's core urban, peri-urban, and mixed commercial-residential areas, capturing a wide range of socio-economic and waste management scenarios.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Data Sources and Collection Techniques Household Survey\u003c/h2\u003e\u003cp\u003eA structured questionnaire was administered to 400 randomly selected household heads. The survey questionnaire was pre-tested in a pilot study involving 30 households (not included in the main sample) from a similar neighborhood. The pilot aimed to assess the clarity, relevance, and timing of the survey. Based on feedback, minor adjustments were made to the wording of three questions related to 'willingness to pay' to enhance comprehension, and the average completion time was confirmed to be 15\u0026ndash;20 minutes. The sample was drawn from an estimated total population of 50,000 households in the selected sub-cities, using the Cochran formula for a finite population at a 95% confidence level (Z-score\u0026thinsp;=\u0026thinsp;1.96) and a 5% margin of error. The response rate was 95%, with non-respondents primarily due to absence; no significant bias was detected as demographics matched the sample frame. A stratified random sampling technique was used to ensure proportional representation from the three selected sub-cities. The questionnaire demonstrated high internal consistency (Cronbach's α\u0026thinsp;=\u0026thinsp;0.85 for awareness scale; α\u0026thinsp;=\u0026thinsp;0.82 for participation attitudes). The semi-structured Key Informant Interviews (KIIs) had an average duration of 50 minutes, while the Focus Group Discussions (FGDs) were longer, averaging 100 minutes, to allow for deep exploration of community and waste-picker perspectives. Qualitative data collection continued until thematic saturation was reached. Necessary adjustments for clarity and contextual relevance were made after the pilot.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eKey Informant Interviews (KIIs)\u003c/strong\u003e\u003cp\u003e15 semi-structured interviews were conducted with officials from the Jimma City Solid Waste Management Office (n\u0026thinsp;=\u0026thinsp;4), heads of kebele administrations (n\u0026thinsp;=\u0026thinsp;4), leaders of private micro-enterprises (n\u0026thinsp;=\u0026thinsp;3), and known leaders of informal waste picker groups (n\u0026thinsp;=\u0026thinsp;4).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFocus Group Discussions (FGDs)\u003c/strong\u003e\u003cp\u003eFour FGDs were held, each with 8\u0026ndash;10 participants. This included two with mixed groups of residents (one male-dominated, one female-dominated) and two with groups of informal waste pickers (separated by gender to encourage open discussion of gender-specific issues). FGDs continued until data saturation was reached, where no new substantive themes emerged from the fourth session.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDirect Observation\u003c/strong\u003e\u003cp\u003eResearchers conducted systematic observational walks in selected neighborhoods and around the main landfill site to physically verify practices like waste storage, collection points, and informal sector activities, thereby triangulating self-reported data.\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Data Analysis\u003c/h2\u003e\u003cp\u003eQuantitative data from questionnaires were cleaned, coded, and analyzed using SPSS version 27. Descriptive statistics (frequencies, percentages, means, and standard deviations) summarized the data. Inferential statistics, including Chi-square tests and Pearson correlation analysis, were employed to examine relationships between variables (e.g., education level and waste separation practices). A linear regression analysis was conducted to identify key predictors of community participation. All assumptions for linear regression (linearity, homoscedasticity, normality of residuals, and absence of multicollinearity) were checked and met. A post-hoc power analysis using G*Power, based on the observed effect size (Cohen's f\u0026sup2; = 0.85), confirmed adequate statistical power (0.92) for the regression model given the sample size. Qualitative data from KIIs and FGDs were audio-recorded, transcribed verbatim.\u003c/p\u003e\u003cp\u003eQualitative data from KIIs and FGDs were audio-recorded, transcribed verbatim, translated from Afan Oromo/Amharic to English. Two independent coders analyzed the qualitative data using NVivo 12. An inter-rater reliability coefficient of 0.89 was achieved. The few coding discrepancies that arose were resolved through discussion and consensus-building between the coders, referring back to the original transcripts to ensure the final themes accurately reflected the participants' meanings. The analysis followed the six-phase approach by Braun and Clarke (2006): (1) familiarization, (2) generating initial codes, (3) searching for themes, (4) reviewing themes, (5) defining themes, and (6) producing the report. Two independent coders were used to enhance reliability, and an inter-rater reliability coefficient of 0.89 was achieved, indicating a high level of agreement. Themes were then interpreted and used to explain the quantitative findings. Methodological triangulation was a key strategy, whereby findings from the surveys, interviews, FGDs, and observations were compared and contrasted to validate and provide a more comprehensive understanding of the research problems.\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.5. Ethical Approval and Consent to Participate\u003c/b\u003e\u003c/p\u003e\u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Formal ethical approval was granted by the Research Ethics Committee of the Ethiopian Civil Service University (Approval No. ECSU/REC/2024/045) prior to the commencement of the study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003cp\u003ewas obtained from all individual participants included in the study. For the household survey, written consent was obtained from each household head after the purpose of the study, procedures, risks, benefits, and the rights to withdraw were explained verbally and via a written information sheet. For key informant interviews and focus group discussions, written consent was also obtained prior to recording. For participants who were illiterate, the information sheet and consent form were read aloud in the presence of an impartial witness, and oral consent was documented and thumb-printed.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eAll data were anonymized during analysis to ensure participant confidentiality. For vulnerable groups, such as informal waste pickers, interviews were conducted in a safe and private environment, and the principle of do-no-harm was paramount throughout the research process.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Socio-Demographic Characteristics of Respondents\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eOf the 400 household respondents, 62% were male and 38% were female. The average age was 38.7 years. In terms of education, 25% had no formal education, 40% had primary education, 28% had secondary education, and 7% had a diploma or degree. The average household size was 4.8 persons (SD\u0026thinsp;=\u0026thinsp;1.6).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocio-Demographic Profile of Household Respondents (N\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u0026ndash;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo Formal Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrimary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSecondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTertiary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage Household Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.8 persons (SD: 1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdapted from household survey data.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Awareness, Attitudes, and Practices on SWM\u003c/h2\u003e\u003cp\u003eA high level of awareness (82%) regarding the negative health and environmental impacts of poor SWM was recorded. However, a significant attitude-behavior gap was observed. While 78% of respondents expressed a willingness to separate their waste at home if provided with separate bins, only 22% actually practiced any form of separation, primarily segregating high-value materials like plastic bottles and metals for sale to informal collectors. The primary method of disposal was waiting for the municipal truck (45%), though 25% relied on shared containers which were often overflowing. A significant minority (15%) resorted to burning or burying waste within their compounds, and 5% admitted to illegal dumping in open spaces. A Chi-square test revealed a statistically significant relationship (χ\u0026sup2; = 25.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between higher education levels and the adoption of proper disposal practices.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Level and Drivers of Community Participation\u003c/h2\u003e\u003cp\u003e Community participation was measured through a composite index of four indicators: consistent payment of service fees, participation in organized cleaning campaigns, proper waste storage, and practicing waste reduction/separation. Only 32% of households were ranked as \"active participants\" based on these criteria. The main drivers of participation were a sense of civic duty (65%) and a desire for a clean and healthy neighborhood (60%).\u003c/p\u003e\u003cp\u003eThis disconnect between willingness and action underscores a critical system failure. As one KII from the city administration noted: \"We expect residents to store waste properly and even separate it, but we have not been able to provide them with the basic tools like reliable bins or consistent collection schedules. Our public awareness efforts are also sporadic. It's an unfair expectation.\" (KII_04, Municipal Official).\u003c/p\u003e\u003cp\u003e\u003cb\u003e4.4. Dynamics and Contributions of the Informal Waste Sector\u003c/b\u003e The sector comprises 500\u0026ndash;700 individuals who divert approximately 28% of the city's recyclable waste from the landfill, representing a massive hidden subsidy to the formal system (WIEGO, 2021). They operate a sophisticated economy around materials like PET, cardboard, and metals (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, they face acute health risks, social stigma, and economic instability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEconomics of Key Recyclable Materials in Jimma's Informal Sector (Data from FGDs and KIIs)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaterial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eAverage Collection Rate (kg/day/picker)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBuying Price from Households (ETB/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSelling Price to Wholesaler (ETB/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMain End Market\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePET Bottles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e15\u0026ndash;20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAddis Ababa\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardboard\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e20\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJimma/Addis Ababa\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAluminum Cans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25\u0026ndash;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e35\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAddis Ababa\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScrap Metal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eVaries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJimma\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlass Bottles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e10\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u0026ndash;1.0 (per bottle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5\u0026ndash;2.0 (per bottle)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJimma\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAdapted from FGDs and KIIs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBy cross-referencing the estimated daily collection per picker with the estimated number of pickers and comparing it to municipal data on total waste generation and composition, we estimate that the informal sector diverts approximately 28% of the total recyclable waste generated in the city from the landfill. This estimate aligns with similar rates in Addis Ababa (Assefa \u0026amp; Glawe, 2022), representing a significant saving in disposal costs and environmental footprint for the municipality. However, these workers operate under profound challenges: acute health risks (cuts, infections, respiratory problems from burning), social stigma, harassment by police and the public, and extreme economic instability tied to global commodity prices. A female waste picker shared in an FGD: \"We are seen as dirty people. People shout at us and chase us away from their neighborhoods. But we are the ones cleaning the city, and we are feeding our children with this work. No one sees that.\" (FGD_03, Female Waste Picker).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Potential for Smart Solutions\u003c/h2\u003e\u003cp\u003eThe study found a high level of mobile phone penetration (92% of households) and a growing use of smartphones (48% of households), providing a solid foundation for digital solutions. When asked about their willingness to use a mobile application for SWM: 85% were willing to receive SMS alerts on collection schedules. 70% were interested in an app to report overflowing bins or illegal dumping. Only 35% were willing to use an app for digital payment of fees, citing trust issues and a preference for cash.\u003c/p\u003e\u003cp\u003eMunicipal officials were also receptive but concerned about startup costs, technical expertise, and the critical need for a behavior change campaign to accompany any technological rollout to ensure adoption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.6. Correlation and Regression Analysis\u003c/h2\u003e\u003cp\u003eA strong positive correlation was found between education and awareness (r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and, more importantly, between awareness and participation (r\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Regression analysis confirmed that awareness level (β\u0026thinsp;=\u0026thinsp;0.45, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and perceived service reliability (β\u0026thinsp;=\u0026thinsp;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were the strongest predictors of participation, together explaining 59% of the variance (R\u0026sup2; = 0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The remaining 41% of unexplained variance suggests other factors, such as cultural norms and social influence\u0026mdash;constructs from behavioral economics\u0026mdash;play a significant role. Future research should employ Structural Equation Modeling (SEM) to incorporate these latent variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Regression Results for Predictors of Participation (N\u0026thinsp;=\u0026thinsp;400)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePredictor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeta (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAwareness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45 (0.29\u0026ndash;0.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eService Reliability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.38 (0.24\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15 (-0.03-0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20 (0.71\u0026ndash;1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u0026sup2;=0.59; Adjusted R\u0026sup2;=0.58; F(3,396)\u0026thinsp;=\u0026thinsp;192.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Unexplained 41% suggests cultural norms; SEM could test: Cultural norms \u0026rarr; Awareness \u0026rarr; Participation (moderated by social influence). Adapted from household survey data.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003e This study set out to analyze the dynamics of community participation, the informal sector, and smart solutions in Jimma's SWM system. The results reveal a system characterized by significant potential that is constrained by critical systemic failures. The most striking finding is the profound gap between household willingness to participate (78%) and actual participation (32%). This dissonance is not primarily a failure of public will but of public provision, driven by inadequate infrastructure, sporadic awareness campaigns, and a lack of reliable feedback mechanisms. This aligns with findings from Accra, where institutional weaknesses similarly stifle community engagement despite high willingness (Oteng-Ababio et al., 2023). In contrast, Hawassa\u0026rsquo;s higher participation rate (45%) stems from decentralized kebele-level coordination, suggesting Jimma could adopt similar models (Tadesse \u0026amp; Worku, 2023).\u003c/p\u003e\u003cp\u003eThe informal sector\u0026rsquo;s 28% diversion rate is a critical, unpaid subsidy to the municipal system, consistent with Global South trends (WIEGO, 2021). However, its efficiency operates despite systemic exclusion, echoing Kampala\u0026rsquo;s precarious IWS conditions (Nzeadibe \u0026amp; Agu, 2021). The proposed Ethiopian Informal Integration Index (EIII) scores Jimma\u0026rsquo;s integration potential at 4.5/10, with low scores for legal recognition (2/10, due to absent policies), health support (3/10, limited PPE access), and medium scores for cooperative potential (5/10, based on FGDs showing interest but no structure). This indicates urgent reform needs.\u003c/p\u003e\u003cp\u003eHigh receptivity to low-cost smart solutions (e.g., 85% for SMS alerts) aligns with post-COVID digital adoption in Africa (Cobbinah \u0026amp; Niminga-Beka, 2021). However, reluctance toward digital payments (35%) and official concerns about costs echo critiques of inappropriate tech transfers (Smith \u0026amp; Johnson, 2024). An SMS-based system is thus prioritized over sensor-based networks for feasibility.\u003c/p\u003e\u003cp\u003eThe regression analysis (R\u0026sup2; = 0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) confirms awareness (β\u0026thinsp;=\u0026thinsp;0.45) and service reliability (β\u0026thinsp;=\u0026thinsp;0.38) as key participation drivers, aligning with global findings (Mugambi \u0026amp; M\u0026rsquo;Ikanga, 2022). However, the 41% unexplained variance suggests unmeasured factors. In Jimma\u0026rsquo;s context, cultural norms like communal responsibility (guzo in Oromo culture) and social influence (peer pressure within kebeles) likely play roles. For instance, FGDs highlighted residents\u0026rsquo; deference to community leaders, suggesting social capital as a latent variable. Future research should employ Structural Equation Modeling (SEM) to test these constructs, modeling paths like: cultural norms \u0026rarr; awareness \u0026rarr; participation, or social influence \u0026rarr; trust \u0026rarr; compliance. This would clarify how Ethiopian socio-cultural dynamics shape SWM behavior, addressing the variance gap.\u003c/p\u003e"},{"header":"6. Conclusions and Recommendations","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e6.1. Conclusion\u003c/h2\u003e\u003cp\u003eThis study concludes that achieving sustainable solid waste management in Jimma requires a fundamental paradigm shift from a disjointed, top-down model to an Integrated Participatory Smart Waste Management (IPSWM) model. The primary contribution of this research is the empirical validation and detailed proposal of this tripartite framework. The model's novelty lies in its synergistic integration of three underutilized assets: activating community agency through enabling infrastructure and awareness; formally recognizing and integrating the informal sector to harness its efficiency while ensuring justice; and leveraging appropriate, low-cost digital technologies as an enabling platform. This framework offers a resilient, equitable, and contextually appropriate blueprint for Jimma and similar secondary African cities, providing a practical pathway towards achieving national SDG targets.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e6.2. Limitations of the Study\u003c/h2\u003e\u003cp\u003eWhile this study provides valuable insights, several limitations should be acknowledged. First, the cross-sectional design captures data at a single point in time, revealing correlations but not definitive causal relationships. For instance, while a strong correlation exists between awareness and participation, longitudinal studies are needed to confirm that increased awareness directly causes higher participation rates. Second, the reliance on self-reported data, particularly for sensitive topics like payment of fees and illegal dumping, is susceptible to social desirability bias, where respondents may provide answers they believe are socially acceptable rather than reflecting their actual practices. We attempted to mitigate this through triangulation with direct observation and key informant interviews. Finally, while the sampling strategy ensured representation from three diverse sub-cities, the findings may not be fully generalizable to all neighborhoods of Jimma or to other Ethiopian cities with different cultural and governance structures. The proposed model should be adapted to local contexts following similar diagnostic studies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e6.3. Scalability to Other Contexts\u003c/h2\u003e\u003cp\u003eThe IPSWM model, tailored to Jimma\u0026rsquo;s context, is adaptable to other Ethiopian cities (e.g., Dire Dawa, Mekelle) and Global South settings. Its low-cost focus (SMS, GIS) enhances portability, potentially reducing landfill use by 20\u0026ndash;30% based on Hawassa pilots (Tadesse \u0026amp; Worku, 2023). However, barriers exist:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBarriers to Scalability and Mitigation Strategies (Compared Across Cities)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBarrier\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJimma Example\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eComparison (e.g., Hawassa/Accra)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMitigation Strategy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMunicipal Funding Shortages\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLimited budgets restrict bins/MRFs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eHawassa: 25% landfill reduction via PPPs (Tadesse \u0026amp; Worku, 2023); Accra: Donor-funded (Oteng-Ababio et al., 2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLeverage World Bank grants; PPPs for valorization\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Infrastructure Gaps\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUneven mobile coverage in peri-urban areas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eGhana: Post-COVID adoption but rural gaps (Osei et al., 2024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhased pilots in urban cores; offline alternatives (radio)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical Will\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStigma hinders informal integration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eJohannesburg: ARO organization success (Frontiers in Sustainable Cities, 2025)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWorkshops; MRF pilots for proof-of-concept\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCultural Variations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\"Guzo\" norms vary regionally\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eAccra: Community-led but less communal (Oteng-Ababio et al., 2023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLocal diagnostics via EIII tool\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAdapted from literature and study findings.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e6.4. Recommendations A. For the Jimma City Administration and SWM Office\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eImmediate Infrastructure and Service Improvement: Prioritize the procurement and distribution of standardised household/communal waste bins in partnership with kebeles. Publish and adhere to a reliable, publicly available collection schedule.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eLaunch a Sustained Awareness Campaign: Partner with local media, schools, and religious institutions to launch a multi-lingual campaign focused on practical waste separation and composting techniques, highlighting the economic benefits of selling recyclables.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eInitiate Informal Sector Integration: Begin a formal dialogue with waste picker representatives. A pilot project could include: issuing official identification cards; providing basic personal protective equipment (PPE); and designating a pilot Material Recovery Facility (MRF) site.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePilot a Low-Cost Smart Solution: Within the next 6\u0026ndash;12 months, pilot an SMS-based collection alert system for 2\u0026ndash;3 sub-cities. The estimated cost of 200,000 ETB for SMS credits and management could be sought from development partners or reallocated from efficiency savings. Simultaneously, develop a simple mobile app for reporting issues, to be tested in a tech-savvy demographic.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eB. For Community Leaders and Residents\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTake Collective Ownership: Kebele administrations should facilitate the formation of neighborhood waste committees to monitor practices, organize clean-ups, and serve as a liaison with the city SWM office.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003ePractice Source Separation: Begin separating recyclables at home into separate sacks for itinerant buyers, turning waste into a source of income.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eC. For the Informal Waste Sector\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eMove towards Organization: With support from NGOs or the city, explore forming an association or cooperative to collectively bargain for better prices and advocate for rights.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eD. For Future Research\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConduct a detailed waste audit to precisely quantify the waste stream and recycling potential.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eImplement and assess the impact of the proposed SMS pilot on participation rates and collection efficiency.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eConduct a comprehensive study on the health impacts and vulnerability of waste pickers to inform social protection policies.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e6.5. Proposed Integrated Model for Jimma\u003c/h2\u003e\u003cp\u003eThe Integrated Participatory Smart Waste Management (IPSWM) model visualizes a synergistic SWM ecosystem (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e). It integrates three pillars within a supportive policy framework:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDescription: Diagram of the IPSWM model with three pillars (Community, Informal Sector, Smart Solutions) in a policy framework, using high-contrast colors (e.g., blue, green, orange) and patterns for accessibility. Arrows show circular flows. Source: Study model.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003cp\u003eSubmit figures as separate TIFF/EPS files if\u0026thinsp;\u0026gt;\u0026thinsp;10 MB; embed in .docx for review.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eENABLING POLICY \u0026amp; INSTITUTIONAL FRAMEWORK\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Supportive Regulations (e.g., legal recognition for IWS)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Financial Allocation (e.g., donor-funded bins, MRFs)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Cross-Departmental Coordination (e.g., health, urban planning)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Monitoring \u0026amp; Evaluation (e.g., EIII tracking)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eCOMMUNITY (The Source)\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Separation (household sorting of recyclables)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Reduction (composting, reuse)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Reporting (via SMS/apps)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Fee Payment (cash-based, building trust for digital)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eSMART SOLUTIONS (The Digital Enabler)\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; SMS Alerts (collection schedules)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Reporting App (overflow/illegal dumping)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; GIS Routing (optimized collection)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eINFORMAL SECTOR (The Recycler)\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Cooperatives (organized IWS groups)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; MRF Access (designated sorting facilities)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Safe Recovery (PPE, health support)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Fair Markets (stable prices via cooperatives)\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eInteractions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Community provides materials and legitimacy (fees, compliance), supported by smart solution feedback (SMS alerts).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Smart solutions enhance efficiency (GIS routing) and citizen engagement (reporting apps), providing data to organize IWS.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Informal sector recycles materials, reducing landfill loads and offering economic incentives to households.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003cp\u003eCleaner city, enhanced resource recovery, social inclusion, reduced costs, improved public health.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthiopian Informal Integration Index (EIII)\u003c/strong\u003e\u003cp\u003eA scoring tool to assess IWS integration (1\u0026ndash;10 scale)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Legal Recognition: 2/10 (no formal policies in Jimma).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Health Support: 3/10 (minimal PPE, health risks prevalent).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Cooperative Potential: 5/10 (interest shown in FGDs, no structure).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Market Access: 4/10 (reliance on volatile wholesalers).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u0026bull; Overall Score: 4.5/10, indicating urgent need for policy reform, starting with ID cards and MRF pilots.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis model ensures a circular flow of information, materials, and support, fostering a sustainable, equitable SWM system.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatements and Declarations Competing Interests\u003c/b\u003e: Authors are required to disclose financial or non-financial interests that are directly or indirectly related to the work submitted for publication. The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e The authors thank the Jimma City Administration for all-rounded support during the data collection process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e This study was approved by the Research Ethics Committee of the Ethiopian Civil Service University (Approval No. ECSU/REC/2024/045).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e Participants consented to the publication of the anonymized data collected for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship Contribution\u003c/strong\u003e All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by the lead researcher. The first draft of the manuscript was written by one co-author, and all authors commented on previous versions. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgyei-Mensah, S., et al. (2025). Sustainable smart waste management. Sustainable Cities and Society, https://doi.org/10.1016/j.scs.2025.105678 Allam, Z., \u0026amp; Dhunny, Z. A. (2019). On big data, artificial intelligence and smart cities. Cities, 89, 80\u0026ndash;91. https://doi.org/10.1016/j.cities.2019.01.032 Assefa, T., \u0026amp; Glawe, U. (2022). Informal waste recycling in Addis Ababa: Opportunities and challenges. Journal of Cleaner Production, 345, 131123. https://doi.org/10.1016/j.jclepro.2022.131123 Bandara, N. J., Hettiaratchi, J. P., Wirasinghe, S. C., \u0026amp; Pilapiiya, S. (2007). Relation of waste generation and composition to socio-economic factors: A case study. Environmental Monitoring and Assessment, 135(1\u0026ndash;3), 31\u0026ndash;39. https://doi.org/10.1007/s10661-007-9705-3 Braun, V., \u0026amp; Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77\u0026ndash;101. https://doi.org/10.1191/1478088706qp063oa Cobbinah, P. B., \u0026amp; Niminga-Beka, R. (2021). Urbanisation and the paradox of sustainable development in Africa. Cities, 113, 103143. https://doi.org/10.1016/j.cities.2021.103143 Creswell, J. W., \u0026amp; Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications. Esmaeilian, B., Wang, B., Lewis, K., Duarte, F., Ratti, C., \u0026amp; Behdad, S. (2018). The future of waste management in smart and sustainable cities: A review and concept paper. Waste Management, 81, 177\u0026ndash;195. https://doi.org/10.1016/j.wasman.2018.09.047 Ferronato, N., \u0026amp; Torretta, V. (2019). Waste mismanagement in developing countries: A review of global issues. International Journal of Environmental Research and Public Health, 16(6), 1060. https://doi.org/10.3390/ijerph16061060 Guerrero, L. A., Maas, G., \u0026amp; Hogland, W. (2013). Solid waste management challenges for cities in developing countries. Waste Management, 33(1), 220\u0026ndash;232. https://doi.org/10.1016/j.wasman.2012.09.008 Jimma City Administration. (2024). Annual municipal service report [Unpublished raw data]. Kaza, S., Yao, L., Bhada-Tata, P., \u0026amp; Van Woerden, F. (2018). What a waste 2.0: A global snapshot of solid waste management to 2050. World Bank. https://doi.org/10.1596/978-1-4648-1329-0 Marshall, R. E., \u0026amp; Farahbakhsh, K. (2013). Systems approaches to integrated solid waste management in developing countries. Waste Management, 33(4), 988\u0026ndash;1003. https://doi.org/10.1016/j.wasman.2012.12.023 Medina, M. (2010). Solid wastes, poverty and the environment in developing country cities: Challenges and opportunities. Journal of Environmental Management, 91(11), 2187\u0026ndash;2192. https://doi.org/10.1016/j.jenvman.2010.06.002 Miezah, K., Obiri-Danso, K., K\u0026aacute;d\u0026aacute;r, Z., Fei-Baffoe, B., \u0026amp; Mensah, M. Y. (2015). Municipal solid waste characterization and quantification as a measure towards effective waste management in Ghana. Waste Management, 46, 15\u0026ndash;27. https://doi.org/10.1016/j.wasman.2015.09.009 Mugambi, F., \u0026amp; M\u0026rsquo;Ikanga, H. (2022). Determinants of household participation in solid waste management in urban areas of developing countries: A systematic review. 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Sustainable Cities and Society, 98, 104812. https://doi.org/10.1016/j.scs.2023.104812 UN-Habitat. (2020). Solid waste management in the world\u0026rsquo;s cities. United Nations Human Settlements Programme. WIEGO. (2021). Informal workers in solid waste management: A statistical snapshot (WIEGO Statistical Brief No. 32). Wilson, D. C., Velis, C., \u0026amp; Cheeseman, C. (2006). Role of informal sector recycling in waste management in developing countries. Habitat International, 30(4), 797\u0026ndash;808. https://doi.org/10.1016/j.habitatint.2005.09.005 World Bank. (2024). Urban population growth (annual %) - Ethiopia. https://data.worldbank.org/indicator/SP.URB.GROW?locations=ET (for urban growth; replaced 2023). Zurbrugg, C. (2002). Urban solid waste management in low-income countries of Asia: How to cope with the garbage crisis. EAWAG News, 53, 1\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Solid Waste Management, Community Participation, Informal Sector, Smart Cities, Recycling, Sustainability","lastPublishedDoi":"10.21203/rs.3.rs-7652455/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7652455/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban solid waste management (SWM) is a pervasive challenge for rapidly growing cities in the Global South, where resource constraints collide with escalating waste volumes. This study investigates the synergistic potential of community participation, informal waste sector dynamics, and technology-driven smart solutions in Jimma, Ethiopia, a medium-sized city generating approximately 150 tons of waste daily, with only 60% collected. Employing a mixed-methods sequential explanatory design, the research integrated quantitative data from 400 randomly selected households with qualitative insights from 15 key informant interviews and 4 focus group discussions. Results reveal a significant gap between household willingness to participate in SWM (78%) and actual participation (32%), primarily driven by inadequate infrastructure (63%), low awareness (57%), and weak feedback mechanisms (45%). The informal sector, comprising 500\u0026ndash;700 individuals, diverts approximately 28% of recyclable materials from landfills yet operates under precarious conditions. A strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) exists between awareness and participation, with regression analysis identifying awareness and service reliability as key predictors (R\u0026sup2; = 0.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Furthermore, 85% of households expressed willingness to use mobile apps for SWM notifications, though only 35% supported digital payments due to trust issues. The study concludes that sustainable SWM requires a tripartite strategy. It proposes an Integrated Participatory Smart Waste Management (IPSWM) model, advocating for enhanced community engagement through targeted campaigns, formalization of the informal sector, and the implementation of low-cost smart solutions like SMS alerts. This framework offers a resilient, equitable, and scalable blueprint for Jimma and similar secondary African cities, directly contributing to SDG 11 (Sustainable Cities) and SDG 12 (Responsible Consumption).\u003c/p\u003e","manuscriptTitle":"Community Participation, Informal Sector Dynamics, and Smart Solutions in Urban Solid Waste Management: Evidence from Jimma, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 07:22:01","doi":"10.21203/rs.3.rs-7652455/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"673aa146-a112-47ea-b1a6-2b6fd0064f1e","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-09T08:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 07:22:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7652455","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7652455","identity":"rs-7652455","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}