Volume 1 Issue 1 (2025): In Progress

The environmental burden of cement production, responsible for nearly 7% of global CO₂ emissions, has intensified the search for low-carbon, resource-efficient alternatives in the construction sector. Biochar, a carbon-rich byproduct derived from the thermochemical conversion of agricultural and urban biomass, has emerged as a multifunctional additive in both cementitious and non-cementitious systems. Its high porosity, alkaline pH, and stable carbon content enable improvements in hydration, mechanical strength, thermal insulation, and durability, while simultaneously offering long-term carbon sequestration. This review critically evaluates the morphological, physicochemical, and functional characteristics of biochar and its effects on cement-based materials, drawing from over 127 published studies. It also highlights the potential of hydrochar, produced through hydrothermal carbonization, as a complementary material in low-carbon construction systems, although research in this area remains limited. Key parameters such as feedstock type, pyrolysis conditions, particle size, and dosage are identified as major factors influencing performance. Beyond technical performance, the use of biochar aligns with circular economy principles by valorizing organic waste streams, reducing reliance on virgin cement and aggregate resources, and enabling industrial symbiosis. Emerging applications in thermal and acoustic panels, multifunctional coatings, and lightweight composites further reinforce its versatility. However, challenges remain regarding workability, performance variability, scalability, and the lack of standardized production and application protocols. Future directions include the standardization of biochar characteristics, large-scale durability validation, integration with life cycle assessment (LCA), development of technical guidelines, and cost–benefit analyses. Overall, biochar and hydrochar represent viable strategies to decarbonize the construction sector and promote sustainable material flows in alignment with global climate and resource-efficiency goals.

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The study develops a data-driven framework for predicting household CO₂ emissions within a developing economic setting using Talba Estate in Minna, Niger State, Nigeria, as a case study. Hourly data were collected from 10 households for the whole of 2023, encapsulating electricity consumption, income, household size, and climatic parameters. Four machine learning models were benchmarked and evaluated within a prediction-uncertainty risk assessment framework, which quantifies the likelihood and impact of model-based prediction errors rather than policy or environmental risks. The models were trained on a 70/30 train-test split and evaluated within a novel prediction-error risk assessment framework that quantifies model uncertainty. The XGBoost achieved the highest in predictive accuracy among the four, with minimum error rates: MAPE = 0.0073, RMSE = 0.1463, and MAE = 0.0340, an R² of 0.9999, almost a perfect fit. The robustness of the model was also tested by prediction-error risk scoring, with values averaging around zero and stability values at about 0.100 across households. The key innovation is the integration of machine learning forecasting with a structured prediction-error risk assessment framework, applied to high-resolution household data in a resource-constrained setting, a combination rarely addressed in existing literature. The results point toward a promising outlook for hybridizing an advanced machine-learning toolkit with prediction-uncertainty risk quantification toward accurate carbon forecasting in resource constraint context. The findings offer actionable insights for policymakers supporting Sustainable Development Goal 13 and Nigeria’s net-zero emissions targets, advancing scalable carbon monitoring frameworks for developing regions.

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