Helmut Yabar

The recent proliferation of electronic waste (E-waste) in developing countries has become a pressing environmental and socio-economic issue, particularly in urban areas where informal waste management practices dominate. The current E-waste collection system in Yaoundé comprises three streams: informal, formal, and municipal solid waste collection. However, transitioning to a prospective, integrated system requires optimizing E-waste collection. Given that the current formal collection (CFC) scenario has only 3 formal collection points, this study employs a survey-based approach and GIS network analysis to allocate 8 additional collection points to maximize formal collection coverage and quantity in Yaoundé. The applied methodologies included the consumer and use model and GIS-based location-allocation, service-area, and route-optimization analyses. The results indicate a 52.81% increase in formal collection quantity for the maximized formal collection (MFC) scenario. Furthermore, Route 1 proved to be the most cost-effective, with a fuel consumption cost of 806,472.25 FCFA/year. Additionally, Route 1 yielded the lowest GHG emissions, at 2610.32 kg CO₂ eq/year, compared with Routes 2 and 3. Finally, transitioning from the current business-as-usual (BAU) to a prospective integrated E-waste management (IEM) system resulted in a 13.83% potential reduction in emissions. This emission reduction contributed 3.04% to Cameroon’s nationally determined contributions (NDCs) 2030 target for greenhouse gas (GHG) emission reduction in the waste sector. The study’s outcome proves informative for decision-making in optimizing E-waste management systems in developing economies.

The recent proliferation of electronic waste (E-waste) in developing countries has become a pressing environmental and socio-economic issue, particularly in urban areas dominated by informal waste management practices. The current E-waste collection system in Yaoundé comprises three collection streams: informal, formal, and municipal solid waste collection. However, transitioning to a prospective, integrated system requires optimizing E-waste collection. Given that the current formal collection (CFC) scenario has only 3 formal collection points, this study employs a survey-based and GIS network analyses to allocate 8 additional collection points to maximize formal collection coverage in Yaoundé. The results indicate a 52.81% increase in formal collection coverage for the maximized formal collection (MFC) scenario. Furthermore, Route 1 proved to be the most cost-effective, with a fuel consumption cost of 2,074,553.15 FCFA/year. Additionally, Route 1 yielded the lowest GHG emissions, at 6,714.74 kg CO₂ eq/year, compared with Routes 2 and 3. Finally, transitioning from the current business-as-usual (BAU) to a prospective integrated E-waste management (IEM) system resulted in a 13.83% reduction in emissions. This contributed 3.04% to Cameroon’s 2030 greenhouse gas (GHG) emission reduction target, as defined in its nationally determined contributions (NDC). The study’s findings are informative for decision-making in optimizing E-waste management systems in developing economies.

The rapid growth and seasonal availability of agricultural materials, such as straws, stalks, husks, shells, and processing wastes, present both a disposal challenge and an opportunity for renewable fuel production. Solar-assisted thermochemical conversion, such as solar-driven pyrolysis, gasification, and hydrothermal routes, provides a pathway to produce bio-oils, syngas, and upgraded chars with substantially reduced fossil energy inputs compared to conventional thermal systems. Recent experimental research and plant-level techno-economic studies suggest that integrating concentrated solar thermal (CSP) collectors, falling particle receivers, or solar microwave hybrid heating with thermochemical reactors can reduce fossil auxiliary energy demand and enhance life-cycle greenhouse gas (GHG) performance. The primary challenges are operational intermittency and the capital costs of solar collectors. Alongside, machine learning (ML) and AI tools (surrogate models, Bayesian optimization, physics-informed neural networks) are accelerating feedstock screening, process control, and multi-objective optimization, significantly reducing experimental burden and improving the predictability of yields and emissions. This review presents recent experimental, modeling, and techno-economic literature to propose a unified classification of feedstocks, solar-integration modes, and AI roles. It reveals urgent research needs for standardized AI-ready datasets, long-term field demonstrations with thermal storage (e.g., integrating PCM), hybrid physics-ML models for interpretability, and region-specific TEA/LCA frameworks, which are most strongly recommended. Data’s reporting metrics and a reproducible dataset template are provided to accelerate translation from laboratory research to farm-level deployment.

Bangladesh generates approximately 3000 tons of plastic waste daily, and high mismanagement leads to substantial discharge into soils, rivers, and oceans. Limited research exists on plastic pollution along Cox’s Bazar in southeastern Bangladesh, with no studies spanning the entire coast; this study provides the first comprehensive assessment of the full coastline. This study investigates the abundance, types, and distribution of macro-, meso-, and microplastics in sediments from 23 stations covering Tourism, Active, and Less Active areas. Plastics were classified by size, shape, color, and polymer composition using stereomicroscopy and Fourier Transform Infrared Spectroscopy (FTIR), while spatial patterns of microplastic polymers were analyzed using Inverse Distance Weighted (IDW) interpolation. A total of 11,558 plastic particles were identified, with microplastics dominating (409.04 particles/m²), followed by mesoplastics (60.7 particles/m²) and macroplastics (32.8 particles/m²). Expanded polystyrene (EPS) and fragments were the most prevalent shapes, while transparent-white particles dominated in color. Polystyrene (PS), polypropylene (PP), and polyethylene (PE) comprised over 95% of polymers. IDW mapping highlighted Tourism, urban, and industrial zones as microplastic hotspots, with higher abundances in tourism areas. These findings provide a baseline for monitoring coastal plastic pollution and emphasize improved plastic management and recycling, contributing globally to understanding contamination in rapidly urbanizing, tourism-driven developing regions.

For sustainable livestock manure management, composting is a common practice for supplying nutrients to crops. Therefore, optimizing plant locations for composting from livestock manure is essential in Bangladesh. This study performed a land suitability analysis using Geographic Information System (GIS) spatial modeling to identify suitable sites for composting plants, which was optimized through network analysis. After spatial analysis, 15, 42, and 147 locations were identified for large-scale, medium-scale, and small-scale manure-based compost production, respectively, across different scenarios. As a result, approximately 1537.74 kilotons/year of compost can be generated from 2703.86 kilotons of livestock manure, replacing about 44.31% of synthetic fertilizer use in Bangladesh in 2024. The potential reduction in greenhouse gas (GHG) emissions was assessed at 1986.76 gigagrams CO_2eq/year, with nutrient leaching reduction potentials of 15.11 and 10.98 kilotons/year for nitrogen and phosphorus, respectively. Additionally, around 4.51 million tons of livestock manure can be disposed of annually by establishing composting plants. However, assessing the potential environmental benefits by optimizing composting plant locations can support the development of strategies to produce organic fertilizer by utilizing natural resources in Bangladesh.

As global pressure to reduce emissions intensifies, China is increasingly turning to digital technologies to drive sustainable industrial development, aiming to boost production while keeping carbon emissions in check. This study takes a micro-level approach by dividing the industry into 17 sectors and applying an environmentally-extended input–output (EEIO) model combined with structural decomposition analysis (SDA) to quantify the impact of digital transformation on carbon emissions across sectors. This study used input–output data from 2012 and 2017. The results indicate that (1) technological improvements driven by digitalization play a key role in reducing industrial carbon emissions, and (2) while high-carbon sectors show substantial emission reductions due to digital transformation, industries such as textiles—where digital adoption is more challenging—exhibit only limited improvements. These findings underscore the need to further advance technological upgrading and transformation in less digitally integrated sectors.

Floods are among the most devastating hydrometeorological natural disasters worldwide, causing massive infrastructure and economic loss in low-lying, flood-prone developing countries like Bangladesh. Effective disaster mitigation relies on organized and detailed flood damage information to facilitate emergency evacuation, coordinate relief distribution, and formulate an effective disaster management policy. Nevertheless, the nation confronts considerable obstacles due to insufficient historical flood damage data and the underdevelopment of near-real-time (NRT) flood monitoring systems. This study addresses this issue by developing a replicable methodology for flood damage assessment and NRT monitoring systems. Using the Google Earth Engine (GEE) platform, we analyzed flood events from 2019 to 2023, integrating geospatial layers such as roads, cropland, etc. Analysis of flood events over the five-year period revealed substantial impacts, with 21.60% of the total area experiencing inundation. This flooding affected 6.92% of cropland and 4.16% of the population. Furthermore, 18.10% of the road network, spanning over 21,000 km within the study area, was also affected. This system has the potential to enhance emergency response capabilities during flood events and inform more effective disaster mitigation policies.

This study anticipates cooperative manure management as a process for generating bioenergy from livestock manure, thereby reducing greenhouse gas (GHG) emissions in Bangladesh. Therefore, this study’s main objective was to identify clusters for cooperative society development and optimize suitable locations for biogas plant establishment within a cooperative system. Scenarios were explored based on manure types using cluster and network analyses of geographic information systems (GIS). The study observed 13 clusters, which have the potential to produce 6045 million m³ of biogas that can be converted to 9068.64 GWh of electricity yearly. Biogas plants additionally produced 5491.04 kilotons of biofertilizer by reducing GHG emissions estimated to be 10.16 million tons of CO_2eq in 2024. This study also optimized 10, 6, and 8 optimum locations for biogas plants according to the scenarios. To implement the findings, this study recommended a coordinated action plan based on the circular economy, which helps to obtain both environmental and economic benefits for a cooperative society. These cooperatives can be implemented for renewable energy production from livestock manure at the community level for sustainable energy generation in Bangladesh.