A Value-Cost Optimization Framework for Green Construction Projects

Thesis Abstract

This study addresses the persistent misalignment between lifecycle value and upfront cost in green construction projects, where traditional cost-focused frameworks often overlook long-term value drivers such as energy efficiency, maintenance, and environmental impact. The aim is to develop and validate a Value-Cost Optimization Framework (VCOF) that integrates lifecycle value assessment with cost optimization to support decision-making in sustainable construction. Specific objectives are (i) to map the components of value in green projects including energy performance, water efficiency, material circularity, resilience, and stakeholder value; (ii) to specify a multi-criteria optimization model that interlinks lifecycle cost with quantified value outcomes; (iii) to test the framework using a case-based approach in residential and commercial building projects; (iv) to examine the role of risk and uncertainty in value realization; and (v) to provide actionable guidelines for practitioners and policymakers. The study adopts a mixed-methods design combining quantitative optimization modeling with qualitative insights to enhance model validity. The population comprises green-certified construction projects undertaken in Europe and North America from 2015 to 2025, with a purposive sample of 18 case projects and 60 project teams for survey data. Data collection employs (a) project documents (bidding records, energy simulations, lifecycle assessments, maintenance logs) and (b) structured questionnaires administered to project managers, cost engineers, and facilities managers, yielding 180 usable responses. Instrument validity is established through content validity by a panel of five experts in value engineering, sustainable construction, and lifecycle assessment, and reliability is assessed via Cronbach’s alpha (? = 0.82 for the survey scales). The core analytical approach comprises (i) a systems dynamics model to capture interdependencies between capital expenditure, operating costs, and value drivers over a 25-year horizon, (ii) multi-objective optimization using a non-dominated sorting genetic algorithm II (NSGA-II) to generate Pareto-efficient solutions balancing lifecycle cost and quantified value metrics, and (iii) regression analyses (stepwise and robust standard errors) to identify determinants of value realization and sensitivity analyses to assess parameter uncertainty. The conceptual backbone includes the Theory of Value and the Real Options theory to model strategic investment in green technologies under uncertainty, complemented by the Stakeholder Theory to interpret value distribution among occupants, investors, and society. Expected findings indicate that incorporating value-focused criteria significantly shifts preferred design choices toward high-performance envelope solutions, advanced building management systems, and materials with high circularity scores, with projected 15–25% reductions in 25-year net present value of lifecycle costs and substantial improvements in energy use intensity and indoor environmental quality. The framework is anticipated to reveal critical thresholds where incremental up-front costs yield disproportionate lifecycle value, and to identify robust strategies under input variability, such as probabilistic energy price scenarios and reliability of renewables integration. The study contributes to knowledge by operationalizing a concrete Value-Cost Optimization Framework that integrates lifecycle value with cost considerations in green construction, expanding the methodological toolkit for value engineering in sustainable projects, and advancing the use of multi-objective optimization in procurement and design decisions. Practical implications include a Decision Support Tool (DST) prototype for practitioners, a set of value-oriented design guidelines, and policy recommendations to incentivize investments that maximize long-term value rather than mere cost minimization. The research concludes that a value-centric, probabilistic optimization approach yields superior long-run performance and stakeholder satisfaction compared with conventional cost-only frameworks, and it recommends embedding the VCOF in early design-phase decision workflows, expanding data collection on value outcomes, and conducting longitudinal post-occupancy evaluations to continuously refine value estimations. Limitations relate to case-study generalizability given regional regulatory differences and data availability, suggesting future work to test the framework in varied regulatory contexts and with larger samples across continents.