The construction sector is a major contributor to global waste output, with construction and demolition waste (CDW) producing substantial environmental, economic, and logistical challenges. Traditional methods for handling waste in developing countries have failed to implement sustainability concepts successfully, resulting in inefficient resource consumption and increasing landfill reliance. This study develops an Analytic Hierarchy Process (AHP) framework to integrate circular economy (CE) principles into construction waste management (CWM). The framework evaluates four criteria under economic, environmental, social, and technological categorization and applies expert-based pairwise comparisons to prioritize alternative strategies. To ensure reliability, the results were further validated through sensitivity analysis and cross-validation using complementary MCDM methods, including the TOPSIS, WSM, and WPM. The research attempted to determine the most successful waste management approach by examining critical economic, social, technical, and environmental issues in the setting of Sri Lanka as a case study. A hierarchical model was built, and expert views were gathered using pairwise comparisons to assess the relative importance of each criterion. The results showed that environmental considerations had the greatest relative importance (41.6%), followed by economic (38.4%), technical (12.6%), and social aspects (7.4%). On-site waste segregation appeared as the most suitable method owing to its immediate contribution to sustainability, while off-site treatment, prefabrication, modular construction, and waste-to-energy conversion followed. The research underlines the significance of organized decision-making in waste management and advises incorporating real-time data analytics and artificial intelligence to boost adaptable and sustainable construction practices.

Nowadays the planet is facing emerging global issues related to climate change, pollution, deforestation, desertification and the number of challenges is expected to grow as the global population is forecasted to reach 10 billion margin by 2050. A concept of circular economy can have a positive contribution to the current development trajectories. In order to implement it, preferably all the energy should be produced by using renewable energy sources, but there has always been a challenge for storage of renewable energy. Therefore, considering technical and economical parameters, construction options for a pumped storage hydropower plant in Latvia have been evaluated using the desk research methodology. Results have shown that Daugavpils PSHP is the most attractive project from the technological point of view, but it requires the greatest amount of investment and construction of Daugavpils HPP. At present all the construction options for PSHP in Latvia are economically disadvantageous and would not be viable without co-financing from European or national funds. Considering both technical and economical parameters, the authors emphasise Plavinas PSHP construction option.

The tendencies of the development of real estate market could affect the tendencies within the real estate development. The development of the real estate market could be influenced by the variety of factors, depending on the economic system of the country and other factors. The aim of the research is to analyse the tendencies in the Latvian real estate market, by paying particular attention to its influencing factors and sustainable real estate development. The literature review, comparative, historical, statistical analysis, observation and logical access methods have been used in the research. The research provides an interdisciplinary view on the topic. Particular attention is paid to the case of Latvia. The research can be used to promote the importance of sustainability and sustainable development using different forms, mostly economic and environmental ones. Changes within the economic dimension, including changes of real estate market prices, and other significant aspects of real estate development can influence the sustainability of the real estate market in general and may affect the financial results of market participants. Thus, the research can also be useful for real estate developers for a planned, balanced and financially successful entrepreneurship in the changing environment.

The growing urgency of resource-efficient construction in water-stressed and rapidly urbanizing regions necessitates integrated decision support frameworks that move beyond isolated sustainability metrics. This study operationalizes the water-energy nexus within building design evaluation by developing a structured hybrid multi-criteria decision-making (MCDM) framework tailored to the Indian construction context. Unlike conventional sustainability assessments that treat water and energy independently, the proposed approach integrates life cycle-based water consumption, operational and embodied energy demand, environmental impacts, economic feasibility, and project constraints within a unified analytical hierarchy. A Delphi-validated criterion structure comprising five main criteria and twenty sub-criteria is weighted using the Analytic Hierarchy Process (AHP), and ranked using the VIKOR compromise solution method. To strengthen methodological robustness, ranking outcomes are validated across three independent MCDM logics including TOPSIS, PROMETHEE, and COPRAS. The framework evaluates four representative building strategies aligned with Indian regulatory and certification systems (NBC, ECBC, IGBC/GRIHA, and net-zero water-energy design). Using expert-informed weights derived from a Delphi–AHP involving a panel of experienced practitioners, the VIKOR compromise ranking consistently identifies the net-zero alternative as the most favorable option within the evaluated framework. The results are therefore interpreted as an expert-informed assessment demonstrating the applicability of the proposed decision support methodology rather than as statistically generalizable priorities for the entire Indian construction sector. The study contributes by (i) embedding nexus-based resource interdependence into building-level MCDM modeling, (ii) enhancing transparency through explicit benefit-cost classification and decision matrix disclosure, and (iii) demonstrating ranking stability across multiple validation techniques. The proposed framework provides a transferable methodological approach that can be adapted to different regional contexts through locally derived expert inputs.

The success of a construction project is significantly influenced by the reliability of its material suppliers, particularly when operational efficiency and sustainability factors are considered. Traditionally, project managers have relied on conventional scorecards and heuristic decision-making methods, which are often subjective and limited in scope. To provide a more objective and data-driven approach, this study introduces a Machine Learning (ML)-based pipeline for evaluating supplier reliability. Four ML algorithms, Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), were assessed, while Shapley Additive Explanations (SHAP) were employed to interpret model outputs and identify the supplier characteristics most influential to each algorithm. Additionally, a proprietary Sustainability Score (SC) was integrated to account for the sustainability aspect provided by each supplier. The dataset used consisted of 105 records from five major suppliers involved in two large-scale residential construction projects in Sri Lanka, incorporating key features such as waste generation, lead time, material cost, and delivery accuracy. Among the evaluated models, XGBoost demonstrated the best performance, achieving an F1-score of 0.89 and an Area Under the Curve (AUC) of 0.97, followed by RF. Notably, the SC analysis revealed that some medium-reliability suppliers achieved higher sustainability scores than high-reliability ones, highlighting the importance of multi-criteria evaluation frameworks that balance operational reliability with environmental responsibility in supplier selection. By facilitating precise, open, and ecologically responsible supplier evaluations, this integrated ML framework aids in sustainable procurement decisions for construction projects.