Spatial Vulnerability Index Modeling for Climate Change Risk Assessment in Indonesia

This study develops a Fuzzy Logic-based Intelligent Decision Support Model (IDSM) to map climate change vulnerability across 34 Indonesian provinces, using 20 variables grouped into four parameters: greenhouse gases, geological factors, anthropological factors, and weather conditions. Data from 2015–2020 were sourced from the Global Atmosphere Watch, LAPAN, BASINS-CAT, BPS, and the DigComp 2.0 framework at the provincial level. The methodology involved data normalization, trend analysis via linear regression, relative value calculation using Euclidean distance, and a two-stage aggregation through a fuzzy inference system to produce a Vulnerability Score. Results indicate Jakarta as the most vulnerable (0.6145), Bali as the least vulnerable (0.498), and West Kalimantan (0.502), Maluku (0.5215), and Papua (0.500) as moderately vulnerable. These variations stem from differing environmental, social, and economic conditions, highlighting the need for location-specific adaptation and mitigation policies. The model offers a valuable tool for prioritizing climate action interventions.