From Narrative to Algorithm: A Risk Mechanism Theory Index for Urban Infrastructure Investment in Sub-Saharan Africa
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Abstract
Rapidly expanding metropolitan areas across Sub-Saharan Africa face systemic risk from hazard-induced disruptions, exacerbated by infrastructure deficits and socioeconomic vulnerabilities. Traditional risk assessment frameworks frequently fail decision-makers by offering static, qualitative narratives that cannot prioritize capital allocation or track resilience over dynamic budget cycles. This paper presents the Risk Mechanism Theory Index (RMTI) framework, a single, decision-ready mathematical model, grounded in Risk Mechanism Design and Theory, and built to assess, quantify, and treat systemic risk across organizational, health, infrastructure, and urban contexts. Structurally, the RMTI reduces every risk environment to three correctly signed dimensions drawn from Table 9.4: RMTre (risk exposure, L · E), RMTseen (socio-economic and environmental vulnerability, V), and RMTrl (resilience capacity, ρ), combined into a single bounded 0–1 composite residual score R = L · Esel · V · (1−ρ). The RMTI is operationalized through a purpose-built calculator that automates the full computational pipeline and is complemented by a Python reference implementation (RMTI-Py) for registry-scale and pipeline use. Both modalities are validated end-to-end against three World Bank-profiled urban infrastructure cases in Sub-Saharan Africa (Bamako, Mali; Lagos, Nigeria; Lilongwe, Malawi) and benchmarked against three comparator cities (Nairobi, Kenya; Dar es Salaam, Tanzania; Lusaka, Zambia). Empirical results demonstrate that targeted $50 million capital investments reduce composite risk scores by 30.7% to 47.5%, systematically transitioning all three primary environments from Tier 4 (Severe) to Tier 3 (High). The framework provides a transparent, algorithmic pipeline for embedding risk-informed governance into capital-budgeting workflows across sectors, and is offered as a management decision-support tool for institutional investors, development banks, and municipal finance officers seeking a reproducible, data-analytic alternative to narrative-based risk reporting.
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