International building sustainability assessment tools (BSATs) offer a comprehensive framework for assessing environmental, economic, and social sustainability. However, these tools cannot fill the gap between their standards and the regional needs of developing countries such as Morocco. This paper presents a new framework to assess the sustainability of buildings in Morocco. The methodology proposed is the Fuzzy Delphi method to minimize the list of indicators with the help of 14 local experts and give an appropriate weight to the indicators and sub-indicators. The two-round analysis found a balanced weighting for the environmental, economic, and social dimensions, with the social pillar ranked highest in importance. A hierarchical framework of six consensus-based categories and 63 sub-indicators was developed. Consensus was measured using the dispersion threshold approach ≤ 0.2. The results show that waste and pollution (0.80), adaptability and resilience (0.78), and resources (0.75) are prioritized over the innovation category. Notably, sewage management, water reuse, and public infrastructure emerged as critical sub-indicators. A comparative evaluation against local BSATs from the region—Ethiopia, Sub-Saharan Africa, Saudi Arabia, and Oman—revealed convergence in core indicators like energy and water, yet divergence in economic and resilience criteria, reflecting regional specificities. This work contributes to the literature by presenting a validated, expert-driven assessment tool that aligns with local needs, offering a practical basis for national green certification and sustainable housing policy in Morocco and similar contexts.

Sheep manure and beet waste (the uneatable leaf part of the beet) are promising feedstock for biogas production due to their abundance and organic richness. However, their high lignocellulosic content reduces anaerobic digestibility and controls methane yield. This study investigates the effect of various pretreatment strategies, namely physical, thermal, and combined physical–thermal methods, on the Biochemical Methane Potential (BMP) of sheep manure and beet waste. Batch anaerobic digestion experiments were conducted under mesophilic conditions, with BMP values recorded for each treatment. The highest BMP for sheep manure, 125 Nml CH₄/g VS, was achieved using combined physical and thermal pretreatment. This approach enhanced methane production by 16%, 25%, and 60% compared to physical pretreatment (PP) alone, thermal pretreatment (TP) alone, and no pretreatment, respectively, while the one BMP for beet waste is 80 Nml CH₄/g VS and obtained with thermal pretreatment. To predict BMP outcomes, three machine learning approaches are applied, namely Linear Regression (LM), Random Forest Regression (RFR), and Gradient Boosting Machine (GBM), using digestion time (N days), total solids (Ts), volatile solids (Vs), pretreatment type, and biomass type. The variance analysis confirmed that the interaction between pretreatment and biomass type significantly improved model performance. While diagnostic checks revealed non-linear patterns limiting the linear model, ensemble methods achieved stronger results. The RFR model explained 79.5% of the variance with a Root Mean Square Error (RMSE) of about 15.7, whereas the GBM model achieved the lowest RMSE of 5.05. GBM captures complex non-linear interactions. In addition, variable importance analyses identified digestion time, solid content, and pretreatment as the most influential factors for methane yield, with the combined chemical and physical pretreatment producing the highest biogas outputs. These findings underscore the potential of advanced machine learning models, particularly GBM (Gradient Boosting Machine), for optimizing anaerobic digestion strategies and maximizing biogas recovery from sheep manure and beet waste.