Research Information System
in: World Sustainability Series, Springer International Publishing Ag, pp.73-102, 2026
What if the key to smarter, faster, and more sustainable infrastructure finance decisions lies in the power of machine learning? Infrastructure projects lock in capital for multiple decades while contending with volatile policies, climate‑driven hazards, and intensifying stakeholder scrutiny. Deterministic discounted‑cash‑flow approaches struggle to value such assets because they overlook non‑linear feedbacks and data fragmentation. This chapter surveys how machine‑learning (ML) techniques can close that gap. First, we map the rapidly expanding data landscape CAPEX/OPEX ledgers, IoT sensor streams, satellite imagery, and ESG disclosures—and show where inconsistent formats still erode model fidelity. Second, we benchmark supervised, unsupervised, and reinforcement‑learning pipelines across four recurring tasks: credit‑scoring, cost‑overrun prediction, adaptive portfolio rebalancing, and policy‑scenario simulation. Case studies demonstrate that multi‑output SVR and DQN allocators lift forecasting accuracy by 18–32% over traditional baselines while preserving temporal dependencies critical to long‑horizon assets. Third, we outline a governance toolkit that couples explainable‑AI dashboards with bias‑mitigation procedures and emergent regulations such as GDPR and algorithm‑audit clauses in public‑private‑partnership contracts. Together, these elements form a decision framework that allows public agencies, institutional investors, and project sponsors to convert heterogeneous data into resilient, low‑carbon investment strategies aligning financial returns with long‑term societal and environmental objectives.