The construction industry plays a significant role in global warming, accounting for 42% of primary energy use and 39% of greenhouse gas (GHG) emissions worldwide. Life Cycle Assessment (LCA) has emerged as a key methodology for evaluating environmental impacts throughout a building’s life cycle, yet its integration in the early design phase remains limited. This review aims to examine strategies and tools for incorporating LCA in the early design phase to enhance sustainability in building construction. The objectives of this study are: (1) to identify the main challenges in integrating LCA into early design workflows, (2) to analyze and compare LCA tools suitable for early-stage assessments, and (3) to explore emerging trends and technological advancements. A systematic literature review was employed using the Scopus database to analyze existing literature, identifying current practices, challenges, and technological advancements in early-stage LCA implementation. A total of 56 studies were identified for the review. The results highlight the growing adoption of Building Information Modeling (BIM), Artificial Intelligence (AI), and parametric modeling in streamlining LCA integration. Despite these advancements, barriers such as data scarcity, lack of standardization, and interoperability issues persist. Key findings suggest that simplified and computational LCA tools can improve accessibility and real-time decision-making during early-stage design. The study concludes that enhancing data availability, refining methodologies, and fostering collaboration between architects, engineers, and policymakers are crucial for mainstreaming LCA in sustainable building design. This review provides actionable insights to bridge the gap between sustainability goals and early-stage design decisions and framework, ultimately supporting a more environmentally responsible construction industry.

As the population increases, the growing demand for residential housing escalates construction activities, significantly impacting global warming by contributing 42% of primary energy use and 39% of global greenhouse gas (GHG) emissions. This study addresses a gap in research on lifecycle assessment (LCA) for Indian residential buildings by evaluating the full cradle-to-grave carbon footprint of a typical single-family house in Northern India. A BIM-based LCA framework was applied to a 110 m² single-family dwelling over a 60-year life span. Operational use performance and climate analysis was evaluated via cove tool. The total carbon footprint over a 60-year lifespan was approximately 5884 kg CO₂e, with operational energy use accounting for about 87% and embodied carbon approximately 11%. Additional impacts came from maintenance and replacements. Energy usage was calculated as 71.76 kWh/m²/year and water usage as 232.2 m³/year. Energy consumption was the biggest driver of emissions, but substantial impacts also stemmed from material production. Cement-based components and steel were the largest embodied carbon contributors. Under the business-as-usual (BAU) scenario, the operational emissions reach approximately 668,000 kg CO₂e with HVAC and 482,000 kg CO₂e without HVAC. The findings highlight the necessity of integrating embodied carbon considerations alongside operational energy efficiency in India’s building codes, emphasizing reductions in energy consumption and the adoption of low-carbon materials to mitigate the environmental impact of residential buildings. Future work should focus on the dynamic modeling of electricity decarbonization, improved regional datasets, and scenario-based LCA to better support India’s transition to net-zero emissions by 2070.

Soil moisture regulates infiltration, evapotranspiration, runoff, vegetation dynamics and drought response, but its spatial and seasonal variability is difficult to characterise from conventional observations alone. This study evaluates the seasonal predictability of soil moisture across Bihar, India, using multi-source Earth observation and environmental variables within a machine-learning framework. A final analytical dataset comprising 12,897 observations from 1,279 sampling locations was developed for June 2025–May 2026. The predictor set included latitude, longitude, elevation, slope, aspect, land use/land cover, clay, sand, silt, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), land surface temperature (LST), and rainfall. Monthly mean ERA5-Land volumetric soil water content for layer 1 was used as the target variable. Separate Random Forest (RF) models were developed for the Monsoon, Post-Monsoon, Winter and Pre-Monsoon seasons. To minimise spatial information leakage caused by repeated observations at the same locations, point-aware train–test validation was applied. RF performance varied substantially among seasons, with R² values of 0.8984, 0.9693, 0.6681 and 0.9773 for Monsoon, Post-Monsoon, Winter and Pre-Monsoon, respectively. Corresponding RMSE values were 0.0267, 0.0107, 0.0050 and 0.0073 m³/m³, while MAE values were 0.0158, 0.0049, 0.0027 and 0.0044 m³/m³. Rainfall was the dominant predictor during Monsoon, Post-Monsoon and Pre-Monsoon, whereas LST was most important during Winter. The annual RF model achieved an R² of 0.9272, compared with 0.6028 for the Multiple Linear Regression baseline. The results demonstrate that soil-moisture predictability and the relative importance of environmental controls vary markedly across seasons. Season-specific machine-learning modelling therefore provides a useful framework for characterising regional soil-moisture variability across Bihar using multi-source environmental observations.