Biochar has gained attention for its role in soil improvement and environmental remediation. Predicting its physicochemical properties from various biomass sources is essential for optimising its applications. Existing linear regression models have investigated the effects of biomass type and pyrolysis conditions on biochar physicochemical properties and soil quality. The models are highly specific and lack predictive accuracy with limited biomass classifications. This study develops a multiple linear regression model, integrating Principal Component Analysis (PCA) to reduce dependent variables and enhance predictions of key biochar properties: pH, Cation Exchange Capacity (CEC), and Electrical Conductivity (EC). Three classification approaches—uncategorised (Combination UC), lignocellulosic analysis (Combination LA), and elemental analysis (Combination EA)—were compared. Classification significantly improved prediction accuracy, with Combination EA outperforming Combination LA. Applying PCA to Combination EA (EAPCA) further enhanced model efficiency, achieving high adjusted R² values for pH, EC, and CEC in woody (0.817, 0.537, 0.875), herbaceous (0.795, 0.759, 0.732), and wet biomass (0.76, 0.787, 0.607) categories. The woody biomass case exhibited the strongest predictive performance for CEC. Key parameters identified through PCA included residence time, heating rate, nitrogen, hydrogen, and H/C ratio. The model's RMSE (15.4778) and R² (0.875) indicate strong predictive capability, explaining 87.5 % of the variance in CEC. This study highlights the effectiveness of classification and PCA in improving biochar property predictions.

Mono-crystalline silicon (mono-Si) PV modules account for around 70 % of global c-Si production, making robust environmental assessments essential. Life Cycle Assessment (LCA) is widely used for this purpose, yet inconsistent data reporting limits transparency and comparability. While prior reviews have explored environmental impacts and parameter trends, none have systematically evaluated reporting quality in mono-Si PV LCAs. To address this, the current review conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided review of 32 mono-Si PV LCA studies published from 2019 to 2024. Each was assessed using the ISO 14040/14044 framework and IEA guidelines, with a focus on key elements such as functional unit, system boundary, data sources, software, characterization models, and data quality analysis. The review applied both a descriptive summary of common practices and a grading rubric to evaluate reporting transparency. Results reveal major inconsistencies, with fewer than half of the studies meeting international standards. This underscores the urgent need for harmonized reporting protocols in mono-Si PV LCA literature.