The Urban Sustainability Assessment System is developed for the project: "Development of an AI-Based Smart Sustainability Assessment System for Urban Infrastructure Management."
This system provides an intelligent, data-driven approach to evaluating the sustainability of urban infrastructure by analysing key environmental, social, governance, and economic parameters and generating actionable planning recommendations.
The ESGEc Sustainability Assessment model evaluates urban sustainability using four key dimensions: Environmental, Social, Governance, and Economic. Each dimension contains specific indicators representing the physical, social, institutional, and financial conditions of urban infrastructure systems.
Evaluates ecological and physical conditions affecting environmental resilience and urban flood performance, including precipitation exposure, surface permeability, drainage capacity, and land use patterns.
Evaluates how infrastructure supports people, access, and community wellbeing. Reflects the degree to which infrastructure design serves social equity, inclusivity, and public livability.
Evaluates management quality and institutional capacity for sustainable infrastructure delivery. Strong governance enables effective planning, regulation, and long-term maintenance.
Evaluates financial sustainability and infrastructure cost efficiency, including resource utilisation during development and the long-term affordability of operational maintenance.
The system employs an AI-driven scoring model that analyses each indicator against established sustainability benchmarks. Individual indicator scores are normalised, weighted, and aggregated into dimension scores, which are then combined into a comprehensive Sustainability Index classified into five sustainability levels.
Based on the assessment results, the AI recommendation engine generates targeted, context-aware suggestions for improving urban infrastructure sustainability across all four ESGEc dimensions.
This prototype demonstrates how AI-powered assessment tools can support:
Each indicator in the ESGEc model is classified as either a Benefit Indicator or a Cost Indicator, reflecting the direction in which changes affect sustainability performance.
A benefit indicator is a variable where higher values improve sustainability performance. Increasing a benefit indicator contributes positively to the overall Sustainability Index.
Examples in this model:
A cost indicator is a variable where higher values reduce sustainability performance. Increasing a cost indicator negatively affects the overall Sustainability Index.
Examples in this model:
Because the indicators use different units and scales, the system converts each raw indicator value into a common scale between 0 and 1 before aggregation. This ensures fair and consistent comparison across all indicators regardless of their original measurement units.
Higher raw values → higher normalised score → better sustainability.
Higher raw values → lower normalised score → weaker sustainability.
This normalisation approach ensures fair comparison across indicators with different units and directions, producing dimension scores that are directly comparable and combinable into the overall Sustainability Index.
The Sustainability Index (SI) is a composite score representing the overall sustainability performance of the assessed urban system. It integrates normalised, weighted indicator scores from the four ESGEc dimensions into a single interpretable value ranging from 0 to 1.
Each dimension is calculated from its respective indicators using a weighted aggregation formula. The four dimension scores are then combined into the final Sustainability Index using dimension-level weights derived from the ESGEc research framework:
Higher values indicate stronger sustainability performance. Lower values indicate weaker sustainability conditions requiring targeted planning intervention.
Interpretation Categories:
This framework supports urban planners, researchers, and decision-makers in evaluating infrastructure sustainability and testing improvement scenarios through interventions such as drainage upgrades, policy reform, and green infrastructure investment.
AI-Based Smart Sustainability Assessment
Created by Nur Fitriah Isa