An Innovative Methodology for Estimating the Hidden Economic Costs of Conflict: A Space Borne Decision Support Framework

Abstract

Conflict zones routinely suffer from severe statistical blackouts, compromising the capacity of international organizations to evaluate economic destruction and plan post-conflict reconstruction. This study addresses this challenge by developing a generalized, Python-based Predictive Decision Support System (PDSS) deployed via an interactive Streamlit framework. The platform architecture integrates a dual-engine predictive pipeline: an empirical spatial Nowcasting Engine fueled by satellite Nighttime Lights (NTL) radiance grids (DMSP/VIIRS), and a retrospective Counterfactual Prediction Engine (backcasting). To ensure cross-country econometric consistency, the simulation establishes a standardized baseline parameter anchoring the target annual GDP growth rate in a peace scenario strictly at 4.00%. The framework is empirically validated using two highly volatile regional conflict theaters: Libya and the Syrian Arab Republic across the 2011–2021 timeline. Computational results reveal stark variations in macroeconomic scarring. Following the 2011 structural shock, Libya’s actual economic trajectory diverged sharply from its potential, yielding a terminal Cumulative Growth Gap (CGG) of 18.62%. Conversely, Syria exhibited a continuous, non-linear collapse, sacrificing over three-fifths of its economic capacity to record a devastating terminal CGG of 64.28%. The remote sensing verification model establishes an extreme statistical convergence (R2 = 0.84) between orbital radiance dimming—driven by localized infrastructure de-electrification—and ground-level aggregate output contraction. By executing vectorized array programming, the system operates efficiently in offline deployment, delivering data-independent, manipulation-resistant analytical assets. This study demonstrates that shifting the analytical paradigm toward space-borne predictive intelligence offers a robust, scalable methodology for objective damage assessment and structural fiscal realignment in fractured states.