Digital service innovation for climate-resilient urban pavements: Decision-as-a-service linking UHI-aware ML to environmental-economic performance.

Cities increasingly require digital services that convert urban climate signals into maintenance decisions with measurable environmental and economic value. This study develops a Decision-as-a-Service (DaaS) framework for climate-resilient urban pavement management in Jordanian cities over the 2025-2060 planning horizon. The novel contribution of the framework is its transformation of climate-aware pavement analysis from a stand-alone prediction task into an operational digital service that links data ingestion, deterioration prediction, life-cycle assessment, optimization, and corridor-level decision support. The proposed DaaS platform integrates downscaled urban climate projections, urban heat island (UHI) indicators, IoT/telemetry data, geographic information systems (GIS)-based corridor attributes, traffic loading, pavement condition records, procurement information, and material alternatives. These inputs are processed through machine learning (ML) deterioration models, life-cycle cost and embodied-emissions accounting, and budget-constrained optimization to generate decision-ready maintenance plans. Results show that the neural-network predictor achieved approximately 92% prediction accuracy, outperforming random forest and support vector machine benchmarks. Under projected warming of 2.5-4.0 °C and precipitation reductions of up to 30% by 2060, high-exposure urban corridors exhibited accelerated cracking and rutting, particularly where UHI intensity and imperviousness increased surface temperature and runoff-related moisture stress. Climate-adaptive strategies, including polymer-modified mixes, moisture-resilient additives, targeted drainage improvements, and preventive scheduling, reduced 30-year maintenance costs by up to 35%, lowered deterioration by approximately 20% in high-risk corridors, and improved crack resistance by about 25%. Beyond prediction and optimization, the DaaS framework supports environmental management by converting sensor-level evidence into procurement guidance, EMS/ISO 14001-aligned monitoring indicators, SLA-style performance clauses, and policy incentives for greener road-works supply chains. The study demonstrates how digital service innovation can operate climate-resilient pavement management while advancing environmental performance, fiscal efficiency, and infrastructure sustainability.