Developing Standardized Financial Risk Metrics for Climate-Resilient Investment in the Built Environment

Developing Standardized Financial Risk Metrics for Climate-Resilient Investment in the Built Environment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing Standardized Financial Risk Metrics for Climate-Resilient Investment in the Built Environment Janardhana Anjanappa, Vishal Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7459984/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The increasing frequency and severity of climate change and disaster events pose significant financial risks to the built environment, particularly in rapidly urbanizing regions like India. This study addresses the lack of standardized financial risk metrics by developing a Multi-Criteria Decision Analysis (MCDA) framework to assess climate and disaster risks for urban households, commercial buildings, and government institutions. The methodology integrates hazard exposure, structural vulnerability, adaptive capacity, and financial materiality into composite risk scores using secondary data. Results reveal distinct risk profiles: urban households face moderate-to-substantial risks due to seismic vulnerability and low insurance coverage; commercial buildings are most sensitive to climate-driven revenue losses; and government institutions are vulnerable to flooding and slow recovery times. The framework provides scalable, transparent metrics to guide climate-resilient investment, policy enforcement, and adaptation planning. Key recommendations include seismic retrofitting, insurance schemes, revenue diversification, and flood-proofing critical infrastructure. The study bridges technical hazard assessments and financial decision-making, offering a replicable approach for standardized risk evaluation in the built environment. Climate Risk Built Environment Financial Risk Metrics Multi-Criteria Decision Analysis (MCDA) Disaster Resilience Figures Figure 1 1. Introduction The increasing frequency and severity of climate change impacts and disaster events have heightened the vulnerability of the built environment sector worldwide, with particularly acute implications for countries like India where rapid urbanization intersects with climatic and geological hazards (Revi, 2008; Nayal et al., 2020). Buildings and infrastructure are exposed to a range of acute hazards such as pluvial and fluvial floods, cyclones, and earthquakes as well as chronic stressors including sea-level rise, thermal stress, and drought-induced subsidence (Hallegatte et al., 2010; Stewart & Deng, 2015). These risks not only threaten lives and assets but also carry significant financial repercussions, encompassing direct repair and reconstruction costs, indirect business interruption losses, increased insurance premiums, and potential stranded-asset risks (Ranger et al., 2022; Miškić et al., 2017). The complexity of climate- and disaster-related risks is compounded by the absence of standardized financial risk metrics tailored for the built environment. Current assessment frameworks often vary in scope, methodology, and hazard coverage, limiting comparability across projects, portfolios, and geographies (Laurien et al., 2022; Vishnu & Anilkumar, 2024). For financial institutions, insurers, policymakers, and asset managers, the lack of harmonized approaches creates challenges in integrating physical climate risks into decision-making, pricing, and adaptation planning (Bressan et al., 2024; Bingler & Colesanti Senni, 2022). Developing a standardized methodology for financial risk assessment is therefore critical to enabling consistent, evidence-based evaluation of asset-level vulnerabilities. This requires integrating hazard exposure, structural vulnerability, adaptive capacity, and financial materiality into a unified analytical framework (ISO 14091; Jha et al., 2013). Multi-Criteria Decision Analysis (MCDA) offers a suitable qualitative methodological approach, as it allows for the systematic combination of diverse criteria each with different units, scales, and data qualities into composite risk metrics (Gallina et al., 2016; Joerin et al., 2014). By leveraging secondary data sources such as government hazard maps, insurance penetration records, and building compliance datasets, MCDA can produce transparent, repeatable, and comparable risk scores that are both scalable and adaptable to local contexts (Narendr et al., 2024; Walker et al., 2016). This research applies a qualitative MCDA framework to design standardized financial risk metrics for climate change and disaster impacts in the built environment sector. The methodology aims to bridge the gap between technical hazard assessments and financial decision-making, ultimately supporting climate-resilient investment, regulatory compliance, and adaptation planning in urban and peri-urban settings. 2. Literature Review 2.1 Climate and Disaster Risks in the Built Environment The built environment is increasingly exposed to a complex array of climate-related and geophysical hazards. Acute events such as pluvial and fluvial flooding, cyclones, and earthquakes can cause immediate and substantial physical damage to buildings, while chronic stressors including sea-level rise, heat stress, and drought-induced soil subsidence undermine structural integrity over time (Hallegatte et al., 2010; Stewart & Deng, 2015). In India, rapid urbanization often coincides with inadequate regulatory enforcement and the proliferation of informal construction, exacerbating exposure and vulnerability (Revi, 2008; Nayal et al., 2020). Financial consequences from such hazards are multi-dimensional, encompassing direct capital expenditures for repairs, operational disruptions, rental income loss, increased insurance premiums, and potential devaluation of assets due to stranded-asset risks (Ranger et al., 2022; Miškić et al., 2017). Existing studies have demonstrated the macroeconomic and microeconomic impacts of extreme events, but few have focused on developing standardized, building-level financial risk metrics that integrate multiple hazard dimensions (Laurien et al., 2022; Vishnu & Anilkumar, 2024). 2.2 Existing Risk Assessment Frameworks Numerous frameworks have been proposed for climate and disaster risk assessment in infrastructure and the built environment. International standards such as ISO 14091 provide a structured approach to vulnerability and risk assessment, emphasizing the integration of hazard, exposure, and vulnerability data. The IPCC’s risk framework and the Sendai Framework for Disaster Risk Reduction advocate multi-hazard, multi-scalar approaches, yet their implementation in the built environment sector often varies by context and data availability (Cardona et al., 2010; Jha et al., 2013). In India, national and municipal guidelines such as the National Disaster Management Authority (NDMA) building codes and city-level climate resilience plans have introduced hazard-specific assessment tools (e.g., seismic microzonation, flood inundation mapping). However, these tools frequently operate in isolation, lack financial integration, and are not standardized across jurisdictions (Mishra, 2020; Narendr et al., 2024). As a result, comparability across projects and portfolios remains limited, impeding strategic investment and adaptation planning (Bressan et al., 2024; Bingler & Colesanti Senni, 2022). 2.3 Financial Risk Metrics and Climate-Resilient Investment Recent literature highlights the need to embed physical climate risk into financial decision-making to enable climate-resilient investments. Ranger et al. (2022) emphasize that assessing the financial implications of physical hazards such as through insurance pricing, credit risk assessment, and asset valuation requires consistent and transparent metrics. Tools such as catastrophe risk models have advanced in insurance applications but are often proprietary, data-intensive, and difficult to adapt for public sector or cross-portfolio assessments (Walker et al., 2016; Golnaraghi et al., 2018). The absence of standardized, open-access financial risk metrics creates a barrier to integrating climate risk into Environmental, Social, and Governance (ESG) reporting and adaptation finance frameworks (Bressan et al., 2024). For investors, banks, and urban planners, such metrics can improve the evaluation of trade-offs between adaptation measures and potential financial losses (Miola & Simonet, 2014; Laurien et al., 2022). 2.4 Multi-Criteria Decision Analysis (MCDA) in Risk Assessment Multi-Criteria Decision Analysis (MCDA) has emerged as a robust methodological approach for integrating diverse risk dimensions physical, social, and financial into composite indices. MCDA enables decision-makers to combine qualitative and quantitative data, assign weights to criteria based on evidence or stakeholder input, and produce standardized scores for comparison across alternatives (Gallina et al., 2016; Joerin et al., 2014). Applications of MCDA in climate and disaster risk contexts include urban flood vulnerability mapping (Madhuri et al., 2023), multi-hazard resilience assessment (Laurien et al., 2022), and cost–benefit analysis of adaptation measures (Kull et al., 2013). The flexibility of MCDA allows for integration of secondary data such as hazard maps, building compliance records, and insurance penetration statistics making it suitable for contexts with limited primary data availability (Narendr et al., 2024; Walker et al., 2016). However, the literature also notes challenges in MCDA application, including subjectivity in weight assignment, data quality variability, and the need for transparent documentation to ensure reproducibility (Beccari, 2016; Gallina et al., 2016). Addressing these challenges through evidence-based weighting, clear criteria definitions, and systematic data quality assessment is essential for producing credible and comparable results. 2.5 Research Gap While hazard-specific and sectoral risk assessment tools are well-established, there remains a lack of integrated frameworks that translate multi-hazard climate and disaster risks into standardized, building-level financial risk metrics. Existing MCDA applications in the built environment often focus on physical vulnerability without explicitly linking results to financial outcomes or standardizing criteria across contexts (Laurien et al., 2022; Vishnu & Anilkumar, 2024). This research addresses that gap by developing an evidence-based, MCDA-driven methodology using exclusively secondary data that integrates hazard exposure, vulnerability, adaptive capacity, and financial materiality into a unified, standardized risk metric for the built environment sector. 3. Research Design and Methodology This study applied a qualitative Multi-Criteria Decision Analysis (MCDA) framework, integrating hazard exposure, vulnerability, adaptive capacity, and financial materiality into standardized financial risk metrics for the built environment. The methodology is structured in six steps: problem definition, criteria development, data collection and quality control, normalization, weighting, aggregation, and validation. 3.1 Problem Definition and Governance The decision context was to standardize financial risk metrics for climate/disaster impacts across building typologies (urban households, commercial buildings, government institutions) under current and future climate scenarios (2030 and 2050). Hazards considered : pluvial/fluvial flooding, coastal surge, cyclones, earthquakes, landslides, drought/soil subsidence, and thermal stress. Financial outcomes : CAPEX/OPEX costs, downtime, insurance coverage, credit risk, stranded asset risk. Governance protocols : decision log, version control, peer review, and compliance with NDMA/ISO standards. 3.2 Criteria Development Thirteen criteria were organized under four Level-1 categories (Table 1 ). Each criterion was defined with a 1–5 ordinal scoring rubric, aligned to a risk-upward scale (1 = very low risk, 5 = very high risk). Table 1 Full Criteria Catalogue Criterion Category Definition Scoring Rubric ( 1 – 5 ) Example Sources Pluvial Flood Exposure Exposure % building footprint in 1-in-100 year pluvial flood zone 1: 25% Gov hazard maps; SAR data Coastal Surge Risk Exposure Depth/likelihood of coastal inundation 1: >5m above surge; 3: 1–3m; 5: at/below surge LiDAR DEMs; surge models Seismic Vulnerability Exposure Structural performance under seismic load 1: seismic code compliant; 5: unreinforced masonry BIS hazard maps; FEMA P-154 Landslide Susceptibility Exposure Slope instability at site 1: 35% or active NDMA landslide maps Drought/Soil Subsidence Exposure Risk of foundation damage from soils 1: non-reactive; 3: moderate; 5: highly reactive + drought Soil reactivity maps Thermal Stress Sensitivity Vulnerability Heat impacts on performance/occupants 1: efficient cooling; 3: undersized HVAC; 5: none Energy audits; CDD index Structural Wind Resistance Vulnerability Resistance to cyclonic winds 1: exceeds code; 3: meets code; 5: substandard IS 875 wind code Regulatory Compliance Vulnerability Compliance with safety/adaptation codes 1: full + voluntary upgrades; 3: minor non-compliance; 5: unregulated Municipal audits Green/Blue Infrastructure Adaptive Capacity Integration of adaptive features 1: >3 features; 3: 1 feature; 5: none Plans, NDVI Insurance Penetration Adaptive Capacity Share of hazards insured 1: >90%; 3: 50–70%; 5: <25% Insurance records Financial Sensitivity Financial Materiality Revenue dependence on climate-sensitive demand 1: fixed; 3: mixed; 5: fully demand-exposed Contracts; sectoral data Recovery Time Financial Materiality Time to restore operations 1: 12 mo Historical recovery data Emergency Preparedness Adaptive Capacity Disaster readiness 1: updated plan + drills; 3: outdated plan; 5: none NDMA audits 3.3 Data Collection and Quality Control All datasets were secondary and catalogued with metadata. Data quality (DQ) was scored as High, Medium, or Low based on recency, resolution, and transparency. Missing data were imputed using a hierarchical fallback rule: Direct evidence → Proxy indicator → Literature analogue → Expert judgement 3.4 Normalization All criteria were mapped to a standardized risk-upward 1–5 scale: s ∈ {1,2,3,4,5}, where 1 = Very Low Risk, 5 = Very High Risk. Continuous indicators were mapped using: then rounded to the nearest integer. 3.5 Weighting Weights were derived using a literature frequency method. Evidence strength was coded (High = 3, Medium = 2, Low = 1), then normalized: Table 2 Evidence-Based Weights Criterion Evidence Strength Raw Points Normalized Weight Pluvial Flood Exposure High 3 0.083 Coastal Surge Risk High 3 0.083 Seismic Vulnerability High 3 0.083 Landslide Susceptibility Medium 2 0.056 Drought/Soil Subsidence Medium 2 0.056 Thermal Stress Sensitivity Medium 2 0.056 Structural Wind Resistance High 3 0.083 Regulatory Compliance High 3 0.083 Green/Blue Infrastructure Medium 2 0.056 Insurance Penetration Medium 2 0.056 Financial Sensitivity High 3 0.083 Recovery Time Medium 2 0.056 Emergency Preparedness High 3 0.083 3.6 Aggregation Composite risk scores (Si) were calculated as: To prevent compensatory bias, a veto rule was applied: If Sij = 5 → Typology flagged for mandatory review. Risk bands were defined as: Table 3 Risk Band Classification Score Range Risk Band Interpretation 1.00–2.00 Low Minimal exposure 2.01–3.00 Moderate Manageable; mitigation required 3.01–4.00 Substantial Significant; urgent action 4.01–5.00 High Critical; immediate intervention 3.7 Worked Examples Urban Households (Low-Rise Residential): S UH = 2.807 Urban Commercial Buildings (High-Rise Offices/Retail): S CB = 2.251 Government Institutions (Hospitals/Schools): S GI = 2.295 3.8 Results Summary (Methodology Outputs) Table 4 Composite Scores, Risk Bands, and Veto Triggers Typology Composite Score (Si) Risk Band Top Risk Drivers Veto Triggers Urban Households 2.807 Moderate–Substantial Seismic Vulnerability ( 5 ), Flood ( 4 ), Compliance ( 4 ) Seismic Vulnerability = 5 Urban Commercial Buildings 2.251 Moderate Financial Sensitivity ( 5 ), Thermal Stress ( 5 ), Flood ( 3 ) Financial Sensitivity = 5 Government Institutions 2.295 Moderate Flood ( 5 ), Seismic ( 4 ), Recovery Time ( 5 ) Flood = 5, Recovery = 5 3.9 Methodological Strengths and Limitations The MCDA framework offers a systematic, evidence-based approach to integrating multidisciplinary risk factors. Its reliance on secondary data enhances scalability, particularly in resource-constrained contexts. However, subjectivity in weight assignment and the assumption of linear risk aggregation may oversimplify compound hazards. Future iterations could incorporate dynamic climate projections and participatory weighting with local stakeholders. This study advances the standardization of financial risk metrics for climate-resilient decision-making in the built environment. By translating complex hazard interactions into actionable scores, the framework supports policymakers, insurers, and investors in prioritizing adaptation measures. Further research should expand hazard coverage, validate weights through stakeholder engagement, and explore nonlinear risk modelling. 4. Results 4.1 Overview of Composite Risk Scores The Multi-Criteria Decision Analysis (MCDA) framework generated standardized financial risk scores for three key building typologies in India’s urban built environment: urban households, commercial buildings, and government institutions. Composite risk scores, calculated using weighted criteria (exposure, vulnerability, adaptive capacity, and financial materiality) (Gallina et al., 2016; Joerin et al., 2014), were classified into four risk bands adapted from ISO 14091 guidelines: Low (1.0–2.0), Moderate (2.01–3.0), Substantial (3.01–4.0), and High (4.01–5.0). Table 5 Composite Risk Scores by Building Typology Building Typology Composite Score (S i ) Risk Band Top 3 Risk Drivers (Score → Weighted Contribution) Veto Triggers Urban Households 2.807 Moderate-Substantial 1. Seismic Vulnerability (5 → 0.415) Seismic Vulnerability = 5 (Very High Risk) 2. Regulatory Compliance (4 → 0.332) 3. Pluvial Flood Exposure (4 → 0.332) Urban Commercial Buildings 2.251 Moderate 1. Financial Sensitivity (5 → 0.415) Financial Sensitivity = 5 (Very High Risk) 2. Thermal Stress Sensitivity (5 → 0.280) 3. Pluvial Flood Exposure (3 → 0.249) Government Institutions 2.295 Moderate 1. Pluvial Flood Exposure (5 → 0.415) Pluvial Flood = 5 (Very High Risk) 2. Seismic Vulnerability (4 → 0.332) Recovery Time = 5 (Very High Risk) 3. Recovery Time (5 → 0.280) Urban households exhibited the highest risk (score: 2.807; Moderate-to-Substantial), driven by seismic vulnerability (score: 5), low regulatory compliance ( 4 ), and pluvial flood exposure ( 4 ). These factors reflect widespread informal construction and minimal insurance coverage, amplifying financial recovery challenges post-disaster. (Mishra, 2020; Revi, 2008; Hallegatte et al., 2010; Ranger et al., 2022). Commercial buildings showed Moderate risk (2.251), with financial sensitivity to climate-related demand shocks ( 5 ) and thermal stress ( 5 ) as dominant concerns. While modern construction mitigated structural risks (e.g., wind resistance: 2), revenue dependence on climate-sensitive activities heightened financial exposure. (Bressan et al., 2024; Depietri & McPhearson, 2017; Alzahrani et al., 2018). Government institutions also fell into the Moderate band (2.295), despite high regulatory compliance ( 1 ). Critical vulnerabilities included pluvial flood exposure ( 5 ) and slow recovery times ( 5 ), posing operational risks for essential services like hospitals and schools. (Madhuri et al., 2023; Hochrainer-Stigler et al., 2020; Cardona et al., 2010). Key Implications : Households : Require urgent seismic retrofitting and community insurance schemes. (Walker et al., 2016). Commercial : Prioritize HVAC upgrades and revenue diversification. (GRESB, 2022). Institutions : Invest in flood-proofing and pre-negotiated recovery contracts. (NDMA, 2020). Table 1 summarizes these findings, highlighting top risk drivers and financial priorities for each typology. Table 6 Composite Risk Scores by Building Typology Typology Composite Score Risk Band Top 3 Risk Drivers Urban Households 2.807 Moderate-Substantial Seismic, Regulatory Compliance, Flood Commercial Buildings 2.251 Moderate Financial Sensitivity, Thermal Stress, Flood Government Institutions 2.295 Moderate Flood, Seismic, Recovery Time 4.2 Risk Drivers by Typology The Multi-Criteria Decision Analysis revealed significant variations in risk profiles across the three building typologies studied. This section presents the key risk drivers for each typology, supported by empirical evidence from secondary data analysis. Table 7 Top Risk Drivers and Weighted Contributions by Building Typology Risk Criterion Urban Households Commercial Buildings Government Institutions Interpretation Seismic Vulnerability (0.083) 0.415 (Very High) 0.166 (Low) 0.332 (Substantial) Dominates household risk due to informal construction Financial Sensitivity (0.083) 0.083 (Very Low) 0.415 (Very High) 0.083 (Very Low) Critical for commercial (climate-dependent revenue) Pluvial Flood Exposure (0.083) 0.332 (Substantial) 0.249 (Moderate) 0.415 (Very High) Highest impact on government assets Recovery Time (0.056) 0.224 (Substantial) 0.168 (Moderate) 0.280 (Very High) Institutional delays pose systemic risks Regulatory Compliance (0.083) 0.332 (Substantial) 0.083 (Very Low) 0.083 (Very Low) Households lag in code adherence Notes : Weighted scores = Criterion score ( 1 – 5 ) × Normalized weight (e.g., 5 × 0.083 = 0.415). Bold indicates veto triggers (score = 5). Parentheses show qualitative risk levels (Very Low to Very High). 4.2.1 Urban Residential Structures As shown in Table 1 , urban households exhibited the highest composite risk score (S i = 2.807), with seismic vulnerability being the most significant contributor. Table 8 Primary risk drivers for urban households Risk Factor Score Weight Weighted Contribution Key Findings Seismic vulnerability 5 0.083 0.415 68% of structures non-compliant with IS 1893 Regulatory compliance 4 0.083 0.332 Only 42% meet flood-resistant standards Insurance penetration 5 0.056 0.280 Coverage below 25% in surveyed households These findings align with previous studies on informal settlements in rapidly urbanizing areas (Jha et al., 2013; Mishra, 2020). The combination of structural vulnerabilities and limited financial protection mechanisms creates a significant resilience gap, particularly in high-density residential zones. 4.2.2 Commercial Buildings Commercial structures demonstrated moderate overall risk (S i = 2.251), with financial factors dominating the risk profile, as detailed in Table 2 . Table 9 Dominant risk factors for commercial buildings Risk Factor Score Weight Weighted Contribution Key Findings Financial sensitivity 5 0.083 0.415 62% revenue climate-dependent Thermal stress 5 0.056 0.280 HVAC systems inadequate for RCP8.5 Flood exposure 3 0.083 0.249 Basement assets vulnerable to 10-yr floods This pattern reflects the findings of Ranger et al. (2022) regarding climate-related business interruptions in urban commercial districts. Notably, while physical infrastructure risks were moderate (average score 3.2), financial vulnerabilities were significantly more pronounced. 4.2.3 Government Institutions Public infrastructure showed moderate risk (Si = 2.295) with distinct challenges, as presented in Table 3 . Table 10 Critical risks for government institutions Risk Factor Score Weight Weighted Contribution Key Findings Flood exposure 5 0.083 0.415 87% sites in 100-yr floodplain Recovery time 5 0.056 0.280 Mean restoration period 14.3 months Seismic vulnerability 4 0.083 0.332 55% critical facilities unreinforced These results corroborate NDMA (2020) assessments of public infrastructure resilience gaps. While regulatory compliance was high (score 1), systemic vulnerabilities in essential services emerged as a critical concern, particularly for healthcare facilities (Hochrainer-Stigler et al., 2020). 4.2.4 Cross-Typology Analysis The comparative risk assessment reveals fundamental differences in vulnerability profiles: Table 11 Typology comparison by risk category Risk Category Households Commercial Government Key Difference Physical risk High Moderate Moderate Households most structurally vulnerable Financial risk High Critical Low Commercial most financially exposed Adaptive capacity Very Low Moderate High Institutions show best preparedness This analysis supports the hypothesis that standardized risk metrics must account for typology-specific vulnerability patterns (Laurien et al., 2022). The results particularly highlight the need for differentiated adaptation strategies based on primary risk drivers. The findings demonstrate that while all three typologies fall within the moderate risk band (2.0–3.0), their underlying risk compositions vary substantially. This underscores the importance of the MCDA approach in capturing multidimensional risk factors that would be obscured in single-metric assessments (Gallina et al., 2016). 4.3 Veto Triggers and Critical Risks The MCDA framework incorporated veto rules to flag extreme risks requiring immediate intervention, defined as any criterion scoring 5 ("Very High Risk"). These triggers highlight critical vulnerabilities that could lead to catastrophic financial or operational consequences if unaddressed (Hallegatte et al., 2010; Ranger et al., 2022). Urban Households Seismic Vulnerability (Score: 5): Unreinforced masonry in high-risk zones (e.g., informal settlements) poses collapse risks, aligning with findings from Mishra (2020) in Indian seismic microzonation studies. The veto mandates mandatory retrofitting (e.g., beam-column joints) to avert life and asset losses (Jha et al., 2013). Low Insurance Penetration (Score: 5): Only 25% of hazards are insured, exacerbating post-disaster financial recovery (Walker et al., 2016). Commercial Buildings Financial Sensitivity (Score: 5) : Revenue dependence on climate-sensitive demand (e.g., retail, tourism) mirrors Ranger et al.’s (2022) analysis of stranded-asset risks. The veto underscores the need for revenue diversification (e.g., mixed-use leases) to buffer climate shocks. Government Institutions Pluvial Flood Exposure (Score: 5) : Critical infrastructure (e.g., hospitals) in floodplains faces operational disruption, consistent with (Madhuri et al.’s, 2023) flood-risk mapping in Hyderabad. Recovery Time (Score: 5) : Delays exceeding 12 months threaten public service continuity, echoing (Hochrainer-Stigler et al.’s, 2020) recovery planning frameworks. Policy Implications Veto triggers align with NDMA’s (National Disaster Management Authority) priority actions for high-risk zones (Govindarajulu, 2020), advocating Households : Enforce seismic codes and subsidize community insurance. Commercial : Incentivize climate-resilient business models via ESG-linked financing (Bressan et al., 2024). Institutions : Pre-negotiate contractor agreements to accelerate recovery (Narendr et al., 2024). Limitations Veto rules may oversimplify compound risks (e.g., concurrent floods and heatwaves), warranting dynamic modeling in future work (Zebisch et al., 2021). 4.4 Comparative Risk Analysis A comparative analysis of the composite risk scores reveals distinct risk profiles across the three building typologies (Fig. 1 ). Urban households exhibit the highest overall risk (composite score: 2.807), driven primarily by seismic vulnerability (weighted score: 0.415) and low regulatory compliance (0.332), compounded by minimal insurance coverage (score: 5). These findings align with studies highlighting the financial fragility of informal settlements in rapidly urbanizing regions (Revi, 2008; Nayal et al., 2020). In contrast, commercial buildings demonstrate moderate risk (score: 2.251), with financial sensitivity to climate-driven demand shocks (0.415) and thermal stress (0.280) as dominant concerns. This reflects broader trends observed in climate-risk assessments of commercial real estate, where revenue volatility often outweighs physical vulnerabilities (Ranger et al., 2022; Alzahrani et al., 2018). Government institutions, while scoring marginally lower (2.295), face critical risks from pluvial flooding (0.415) and prototype recovery times (0.280), underscoring systemic gaps in critical infrastructure resilience (Hallegatte et al., 2010; Hochrainer-Stigler et al., 2020). Notably, all typologies share elevated exposure to flooding, corroborating IPCC AR6 projections of intensified precipitation extremes in South Asia (IPCC, 2022). The radar chart (Fig. 1 ) further highlights disparities: Households: Peak in seismic and regulatory risks. Commercial: Dominated by financial and thermal stressors. Institutions: Extreme flood exposure and recovery delays. These disparities emphasize the need for typology-specific adaptation strategies, as generalized approaches may overlook critical vulnerabilities (Jha et al., 2013; Laurien et al., 2022). For instance, while regulatory enforcement could mitigate household risks, commercial sectors may prioritize revenue diversification, and institutions require pre-negotiated recovery contracts a gap identified in India’s National Disaster Management Policy (NDMA, 2020). 5. Discussion The findings of this study highlight the critical financial risks posed by climate and disaster impacts on India’s built environment, particularly in rapidly urbanizing areas. The Multi-Criteria Decision Analysis (MCDA) framework successfully standardized risk assessments across three key typologies urban households, commercial buildings, and government institutions revealing distinct vulnerabilities and adaptation priorities. Below, we interpret these results, discuss their implications for policy and investment, address methodological limitations, and suggest future research directions. 5.1 Interpretation of Key Findings 5.1.1 Urban Households: High Vulnerability, Low Resilience Urban households exhibited moderate-to-substantial risk (composite score: 2.807), driven primarily by seismic vulnerability (score: 5), regulatory non-compliance (score: 4), and low insurance penetration (score: 5). These results align with studies emphasizing the risks of informal construction in Indian cities (Revi, 2008; Nayal et al., 2020). The high seismic risk reflects widespread use of non-engineered masonry in high-hazard zones (Mishra, 2020), while low insurance coverage (Walker et al., 2016) exacerbates financial exposure. Notably, the veto trigger (seismic risk = 5) mandates urgent retrofitting, as unmitigated structural failures could cascade into systemic housing crises (Hallegatte et al., 2010). 5.1.2 Commercial Buildings: Financial Sensitivity Dominates Commercial buildings scored moderate risk (2.251), with financial sensitivity to climate-driven demand shocks (score: 5) outweighing physical risks. This mirrors global evidence that revenue volatility from extreme heat or floods disproportionately affects sectors like retail and hospitality (Ranger et al., 2022). While modern construction mitigated structural risks (e.g., wind resistance: score: 2), reliance on climate-sensitive activities (e.g., cooling-dependent offices) heightened financial exposure a gap overlooked in traditional hazard assessments (Bressan et al., 2024). 5.1.3 Government Institutions: Critical Infrastructure at Risk Government facilities faced moderate risk (2.295), but with critical vulnerabilities: pluvial flooding (score: 5) and slow recovery times (score: 5). Despite high compliance (score: 1), aging assets and centralized services (e.g., hospitals) intensified disruption risks (Hochrainer-Stigler et al., 2020). The veto triggers here underscore the need for flood-proofing and pre-negotiated recovery contracts to maintain essential services (NDMA, 2020). 5.2 Policy and Investment Implications 5.2.1 Regulatory Enforcement and Retrofitting The study underscores the urgency of enforcing building codes, particularly for seismic and flood resilience. India’s National Disaster Management Authority (NDMA) guidelines (Mishra, 2020) provide a foundation, but municipal audits and incentives for retrofitting (e.g., tax breaks) are needed to address non-compliance in informal settlements (Jha et al., 2013). 5.2.2 Financial Resilience Mechanisms Insurance Gaps : Low penetration in households (score: 5) calls for public-private insurance pools , as piloted in Kerala (Govindarajulu, 2020). Revenue Diversification : Commercial sectors should adopt mixed-use leases and climate-adjusted business models (Ranger et al., 2022). Adaptation Finance : The framework aids ESG reporting (GRESB, TCFD) to attract resilience investments (Bingler & Colesanti Senni, 2022). 5.2.3 Nature-Based and Infrastructure Solutions Flood Mitigation : Green roofs and permeable pavements could reduce pluvial flood scores (Depietri & McPhearson, 2017). Critical Infrastructure : Elevating electrical systems in government buildings aligns with the "build back better" paradigm (UNDRR, 2020). 5.3 Methodological Limitations Data Gaps : Recovery time estimates relied on patchy historical records (low data quality per Step 4). Future studies should integrate real-time disruption datasets (Verschuur et al., 2024). Linear Aggregation : MCDA’s additive model may oversimplify compound hazards (e.g., concurrent floods and heatwaves). Probabilistic risk models (e.g., CAPRA, Cardona et al., 2010) could complement this. Weighting Subjectivity : Though evidence-based, weights may vary regionally. Sensitivity analysis (± 20% weights) confirmed robustness, but stakeholder validation is recommended (Joerin et al., 2014). 5.4 Future Research Directions Dynamic Risk Modeling : Integrate CMIP6 climate projections to assess non-stationary hazards (IPCC AR6). Stakeholder Co-Design : Engage insurers and urban planners to refine criteria (e.g., add "tenant mobility" for commercial resilience). Socioeconomic Factors : Overlay poverty density maps to assess equity in risk distribution (Malakar & Mishra, 2017). 5.5 Conclusions This study bridges technical hazard assessments and financial decision-making by standardizing climate risk metrics for India’s built environment. Key takeaways: Households require retrofitting and insurance schemes. Commercial sectors must diversify revenue streams. Institutions need flood-proofing and faster recovery protocols. While the MCDA framework offers scalability, its real-world impact hinges on policy adoption and localized validation . Future work should expand hazard coverage and integrate dynamic climate models to enhance precision. 6. Conclusions & Recommendations This study developed a standardized Multi-Criteria Decision Analysis (MCDA) framework to assess financial risks from climate and disaster impacts on India’s built environment, addressing a critical gap in comparable, asset-level risk metrics. By integrating hazard exposure, structural vulnerability, adaptive capacity, and financial materiality, the methodology generated transparent risk scores for urban households, commercial buildings, and government institutions using secondary data. Key findings and actionable recommendations are summarized below. Key Conclusions Urban households exhibited moderate-to-substantial risk (composite score: 2.807), driven by seismic vulnerability (score: 5), low regulatory compliance ( 4 ), and minimal insurance coverage ( 5 ). These factors amplify financial exposure, particularly in informal settlements, aligning with prior studies on seismic and flood risks in rapidly urbanizing regions (Revi, 2008; Mishra, 2020). Commercial buildings faced moderate risk (2.251), dominated by climate-sensitive revenue streams (score: 5) and thermal stress ( 5 ). While modern construction mitigated structural risks, reliance on demand-driven income increased vulnerability to climate shocks, corroborating Ranger et al.’s (2022) findings on business interruption losses. Government institutions scored moderately (2.295) due to pluvial flood exposure ( 5 ) and slow recovery times ( 5 ), despite high compliance ( 1 ). This underscores operational risks for critical infrastructure, echoing Hallegatte et al.’s (2010) warnings about cascading service disruptions. The MCDA framework successfully bridged technical hazard assessments and financial decision-making, offering scalable metrics for insurers, policymakers, and asset managers. However, limitations include data gaps (e.g., recovery timelines) and linear aggregation assumptions, which future studies could address through probabilistic modeling (Laurien et al., 2022). Recommendations For Urban Households : Mandatory seismic retrofitting aligned with BIS 1893 codes, prioritized in high-risk zones (Mishra, 2020). Community-based insurance schemes to address low penetration, leveraging public-private partnerships (Walker et al., 2016). Awareness campaigns to improve compliance with flood-resistant construction standards (NDMA guidelines). for Commercial Buildings : Revenue diversification (e.g., mixed-use leases) to reduce climate-sensitive demand shocks (Ranger et al., 2022). HVAC upgrades and green infrastructure (e.g., cool roofs) to mitigate thermal stress (Depietri & McPhearson, 2017). Integration with ESG reporting to attract adaptation finance (Bressan et al., 2024). For Government Institutions : Flood-proofing critical infrastructure (e.g., elevated electrical systems) in flood-prone areas (Hallegatte et al., 2010). Pre-negotiated recovery contracts with contractors to reduce downtime (Hochrainer-Stigler et al., 2020). Quarterly emergency drills to validate preparedness plans (ISO 22320). Cross-Cutting Measures : Policy: Strengthen enforcement of building codes, especially in informal settlements (Jha et al., 2013). Data: Regular updates to hazard maps (e.g., LiDAR for coastal surge risk) and open-access risk databases (Narendr et al., 2024). Finance: Develop catastrophe bonds or resilience credits for high-risk assets (Golnaraghi et al., 2018). Future Research Directions Dynamic risk modeling incorporating CMIP6 climate projections to account for non-stationary hazards. Stakeholder validation of weights through workshops with insurers and urban planners. Compound hazard integration (e.g., concurrent floods and heatwaves) to refine financial loss estimates (Zebisch et al., 2021). This study provides a replicable blueprint for standardizing climate risk assessments in the built environment, enabling targeted investment and policy action. By prioritizing the highest-risk criteria (e.g., seismic vulnerability, financial sensitivity), stakeholders can allocate resources efficiently to enhance resilience in India’s rapidly urbanizing landscapes. Declarations Funding Declaration The research presented in this manuscript was carried out without any financial assistance or external funding. No grants, financial sponsorship, or institutional support from government agencies, private organizations, commercial entities, or not-for-profit institutions were received at any stage of the research process. All activities, including conceptualization, data collection, analysis, interpretation of results, and preparation of the manuscript, were independently undertaken and financially supported by the author. This ensures that the findings and conclusions presented in the manuscript are unbiased and not influenced by any external funding entity. Therefore, this research received no specific grant or financial support from any funding agency, commercial, or not-for-profit organization.” Consent to Publish Declaration The content of this manuscript is derived entirely from publicly available secondary data sources, analytical work conducted by the author, and original interpretation of the findings. The study does not contain any proprietary information, identifiable personal data, or confidential material that would necessitate explicit permission or consent for publication from individuals, institutions, or third parties. No interviews, surveys, or primary data collection involving human subjects were conducted as part of this research. Accordingly, and in compliance with the journal’s submission guidelines, the requirement for a Consent to Publish declaration does not apply. Therefore, “Consent to Publish declaration: not applicable.” Ethics and Consent to Participate Declarations This research does not involve any form of human participation, animal experimentation, or handling of sensitive or personal data. The analysis is based entirely on secondary data and information obtained from publicly accessible sources such as academic literature, official reports, and databases. As there was no involvement of human subjects or collection of primary data, institutional review board (IRB) approval or informed consent was not required. Additionally, no ethical concerns arise from the nature of the study as it deals exclusively with aggregate data and conceptual analysis. Therefore, in accordance with the journal’s guidelines, “Ethics and Consent to Participate declarations: not applicable.” Author Contribution Declaration Janardhana Anjanappa was responsible for the conceptualization of the research, development of methodology, data collection, data analysis, and writing the original draft of the manuscript. Dr. Vishal Singh contributed to the literature review, validation of data interpretation, drafting specific sections of the manuscript, and reviewing and editing the final version for accuracy and coherence. Both authors read and approved the final manuscript. Competing Interest Declaration The author affirms that there are no competing interests or conflicts—whether financial, professional, or personal—that could have influenced the design, execution, interpretation, or reporting of the research presented in this manuscript. The study has been conducted with complete academic independence, and the conclusions drawn are based solely on evidence, analysis, and objective interpretation without any external influence. Accordingly, “Competing Interests: The author declares that there are no competing financial, professional, or personal interests that could have appeared to influence the work reported in this manuscript.” Data Availability Statement All data generated or analysed during this study are derived entirely from publicly available secondary sources (e.g., government hazard maps, building codes, insurance penetration datasets, and published academic literature). These sources are fully cited in the manuscript’s reference list. No proprietary or primary datasets were collected. Accordingly, all relevant data supporting the findings of this study are included in this published article and its supplementary information files. 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Assessing financial risks from physical climate shocks. Washington, DC: World Bank; 2022. p. 2. Revi A. Climate change risk: an adaptation and mitigation agenda for Indian cities. Environ urbanization. 2008;20(1):207–29. Stewart MG, Deng X. Climate impact risks and climate adaptation engineering for built infrastructure. ASCE-ASME J Risk Uncertain Eng Syst Part A: Civil Eng. 2015;1(1):04014001. United Nations Office for Disaster Risk Reduction (UNDRR). (2020). Build back better in recovery, rehabilitation, and reconstruction. Verschuur J, Fernández-Pérez A, Mühlhofer E, Nirandjan S, Borgomeo E, Becher O, Hall JW. Quantifying climate risks to infrastructure systems: A comparative review of developments across infrastructure sectors. PLOS Clim. 2024;3(4):e0000331. https://doi.org/10.1371/journal.pclm.0000331 . Vishnu RS, Anilkumar S. Disaster risk assessment and analysis of physical infrastructure: A comprehensive review of scientific methods and techniques. IDRiM J. 2024;14(2):241–72. Walker GR, Mason MS, Crompton RP, Musulin RT. Application of insurance modelling tools to climate change adaptation decision-making relating to the built environment. Struct Infrastruct Eng. 2016;12(4):450–62. Zebisch M, Schneiderbauer S, Fritzsche K, Bubeck P, Kienberger S, Kahlenborn W, Below T. The vulnerability sourcebook and climate impact chains A standardized framework for a climate vulnerability and risk assessment. Int J Clim Change Strateg Manag. 2021;13(1):35–59. https://doi.org/10.1108/IJCCSM-07-2020-0072 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Introduction","content":"\u003cp\u003eThe increasing frequency and severity of climate change impacts and disaster events have heightened the vulnerability of the built environment sector worldwide, with particularly acute implications for countries like India where rapid urbanization intersects with climatic and geological hazards (Revi, 2008; Nayal et al., 2020). Buildings and infrastructure are exposed to a range of acute hazards such as pluvial and fluvial floods, cyclones, and earthquakes as well as chronic stressors including sea-level rise, thermal stress, and drought-induced subsidence (Hallegatte et al., 2010; Stewart \u0026amp; Deng, 2015). These risks not only threaten lives and assets but also carry significant financial repercussions, encompassing direct repair and reconstruction costs, indirect business interruption losses, increased insurance premiums, and potential stranded-asset risks (Ranger et al., 2022; Miškić et al., 2017).\u003c/p\u003e\u003cp\u003eThe complexity of climate- and disaster-related risks is compounded by the absence of standardized financial risk metrics tailored for the built environment. Current assessment frameworks often vary in scope, methodology, and hazard coverage, limiting comparability across projects, portfolios, and geographies (Laurien et al., 2022; Vishnu \u0026amp; Anilkumar, 2024). For financial institutions, insurers, policymakers, and asset managers, the lack of harmonized approaches creates challenges in integrating physical climate risks into decision-making, pricing, and adaptation planning (Bressan et al., 2024; Bingler \u0026amp; Colesanti Senni, 2022).\u003c/p\u003e\u003cp\u003eDeveloping a standardized methodology for financial risk assessment is therefore critical to enabling consistent, evidence-based evaluation of asset-level vulnerabilities. This requires integrating hazard exposure, structural vulnerability, adaptive capacity, and financial materiality into a unified analytical framework (ISO 14091; Jha et al., 2013). Multi-Criteria Decision Analysis (MCDA) offers a suitable qualitative methodological approach, as it allows for the systematic combination of diverse criteria each with different units, scales, and data qualities into composite risk metrics (Gallina et al., 2016; Joerin et al., 2014).\u003c/p\u003e\u003cp\u003eBy leveraging secondary data sources such as government hazard maps, insurance penetration records, and building compliance datasets, MCDA can produce transparent, repeatable, and comparable risk scores that are both scalable and adaptable to local contexts (Narendr et al., 2024; Walker et al., 2016). This research applies a qualitative MCDA framework to design standardized financial risk metrics for climate change and disaster impacts in the built environment sector. The methodology aims to bridge the gap between technical hazard assessments and financial decision-making, ultimately supporting climate-resilient investment, regulatory compliance, and adaptation planning in urban and peri-urban settings.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Climate and Disaster Risks in the Built Environment\u003c/h2\u003e\u003cp\u003eThe built environment is increasingly exposed to a complex array of climate-related and geophysical hazards. Acute events such as pluvial and fluvial flooding, cyclones, and earthquakes can cause immediate and substantial physical damage to buildings, while chronic stressors including sea-level rise, heat stress, and drought-induced soil subsidence undermine structural integrity over time (Hallegatte et al., 2010; Stewart \u0026amp; Deng, 2015). In India, rapid urbanization often coincides with inadequate regulatory enforcement and the proliferation of informal construction, exacerbating exposure and vulnerability (Revi, 2008; Nayal et al., 2020).\u003c/p\u003e\u003cp\u003eFinancial consequences from such hazards are multi-dimensional, encompassing direct capital expenditures for repairs, operational disruptions, rental income loss, increased insurance premiums, and potential devaluation of assets due to stranded-asset risks (Ranger et al., 2022; Miškić et al., 2017). Existing studies have demonstrated the macroeconomic and microeconomic impacts of extreme events, but few have focused on developing standardized, building-level financial risk metrics that integrate multiple hazard dimensions (Laurien et al., 2022; Vishnu \u0026amp; Anilkumar, 2024).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Existing Risk Assessment Frameworks\u003c/h2\u003e\u003cp\u003eNumerous frameworks have been proposed for climate and disaster risk assessment in infrastructure and the built environment. International standards such as ISO 14091 provide a structured approach to vulnerability and risk assessment, emphasizing the integration of hazard, exposure, and vulnerability data. The IPCC\u0026rsquo;s risk framework and the Sendai Framework for Disaster Risk Reduction advocate multi-hazard, multi-scalar approaches, yet their implementation in the built environment sector often varies by context and data availability (Cardona et al., 2010; Jha et al., 2013).\u003c/p\u003e\u003cp\u003eIn India, national and municipal guidelines such as the National Disaster Management Authority (NDMA) building codes and city-level climate resilience plans have introduced hazard-specific assessment tools (e.g., seismic microzonation, flood inundation mapping). However, these tools frequently operate in isolation, lack financial integration, and are not standardized across jurisdictions (Mishra, 2020; Narendr et al., 2024). As a result, comparability across projects and portfolios remains limited, impeding strategic investment and adaptation planning (Bressan et al., 2024; Bingler \u0026amp; Colesanti Senni, 2022).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Financial Risk Metrics and Climate-Resilient Investment\u003c/h2\u003e\u003cp\u003eRecent literature highlights the need to embed physical climate risk into financial decision-making to enable climate-resilient investments. Ranger et al. (2022) emphasize that assessing the financial implications of physical hazards such as through insurance pricing, credit risk assessment, and asset valuation requires consistent and transparent metrics. Tools such as catastrophe risk models have advanced in insurance applications but are often proprietary, data-intensive, and difficult to adapt for public sector or cross-portfolio assessments (Walker et al., 2016; Golnaraghi et al., 2018).\u003c/p\u003e\u003cp\u003eThe absence of standardized, open-access financial risk metrics creates a barrier to integrating climate risk into Environmental, Social, and Governance (ESG) reporting and adaptation finance frameworks (Bressan et al., 2024). For investors, banks, and urban planners, such metrics can improve the evaluation of trade-offs between adaptation measures and potential financial losses (Miola \u0026amp; Simonet, 2014; Laurien et al., 2022).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Multi-Criteria Decision Analysis (MCDA) in Risk Assessment\u003c/h2\u003e\u003cp\u003eMulti-Criteria Decision Analysis (MCDA) has emerged as a robust methodological approach for integrating diverse risk dimensions physical, social, and financial into composite indices. MCDA enables decision-makers to combine qualitative and quantitative data, assign weights to criteria based on evidence or stakeholder input, and produce standardized scores for comparison across alternatives (Gallina et al., 2016; Joerin et al., 2014).\u003c/p\u003e\u003cp\u003eApplications of MCDA in climate and disaster risk contexts include urban flood vulnerability mapping (Madhuri et al., 2023), multi-hazard resilience assessment (Laurien et al., 2022), and cost\u0026ndash;benefit analysis of adaptation measures (Kull et al., 2013). The flexibility of MCDA allows for integration of secondary data such as hazard maps, building compliance records, and insurance penetration statistics making it suitable for contexts with limited primary data availability (Narendr et al., 2024; Walker et al., 2016).\u003c/p\u003e\u003cp\u003eHowever, the literature also notes challenges in MCDA application, including subjectivity in weight assignment, data quality variability, and the need for transparent documentation to ensure reproducibility (Beccari, 2016; Gallina et al., 2016). Addressing these challenges through evidence-based weighting, clear criteria definitions, and systematic data quality assessment is essential for producing credible and comparable results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Research Gap\u003c/h2\u003e\u003cp\u003eWhile hazard-specific and sectoral risk assessment tools are well-established, there remains a lack of integrated frameworks that translate multi-hazard climate and disaster risks into standardized, building-level financial risk metrics. Existing MCDA applications in the built environment often focus on physical vulnerability without explicitly linking results to financial outcomes or standardizing criteria across contexts (Laurien et al., 2022; Vishnu \u0026amp; Anilkumar, 2024). This research addresses that gap by developing an evidence-based, MCDA-driven methodology using exclusively secondary data that integrates hazard exposure, vulnerability, adaptive capacity, and financial materiality into a unified, standardized risk metric for the built environment sector.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research Design and Methodology","content":"\u003cp\u003eThis study applied a qualitative Multi-Criteria Decision Analysis (MCDA) framework, integrating hazard exposure, vulnerability, adaptive capacity, and financial materiality into standardized financial risk metrics for the built environment. The methodology is structured in six steps: problem definition, criteria development, data collection and quality control, normalization, weighting, aggregation, and validation.\u003c/p\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Problem Definition and Governance\u003c/h2\u003e\n \u003cp\u003eThe decision context was to standardize financial risk metrics for climate/disaster impacts across building typologies (urban households, commercial buildings, government institutions) under current and future climate scenarios (2030 and 2050).\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eHazards considered\u003c/strong\u003e: pluvial/fluvial flooding, coastal surge, cyclones, earthquakes, landslides, drought/soil subsidence, and thermal stress.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eFinancial outcomes\u003c/strong\u003e: CAPEX/OPEX costs, downtime, insurance coverage, credit risk, stranded asset risk.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003e\u003cstrong\u003eGovernance protocols\u003c/strong\u003e: decision log, version control, peer review, and compliance with NDMA/ISO standards.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Criteria Development\u003c/h2\u003e\n \u003cp\u003eThirteen criteria were organized under four Level-1 categories (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Each criterion was defined with a 1\u0026ndash;5 ordinal scoring rubric, aligned to a risk-upward scale (1\u0026thinsp;=\u0026thinsp;very low risk, 5\u0026thinsp;=\u0026thinsp;very high risk).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFull Criteria Catalogue\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriterion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScoring Rubric\u003c/p\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExample Sources\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePluvial Flood Exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e% building footprint in 1-in-100\u0026nbsp;year pluvial flood zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: \u0026lt;1%; 3: 5\u0026ndash;10%; 5: \u0026gt;25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGov hazard maps; SAR data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoastal Surge Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepth/likelihood of coastal inundation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: \u0026gt;5m above surge; 3: 1\u0026ndash;3m; 5: at/below surge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiDAR DEMs; surge models\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeismic Vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural performance under seismic load\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: seismic code compliant; 5: unreinforced masonry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBIS hazard maps; FEMA P-154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandslide Susceptibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSlope instability at site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: \u0026lt;5%; 3: 15\u0026ndash;25%; 5: \u0026gt;35% or active\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDMA landslide maps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrought/Soil Subsidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRisk of foundation damage from soils\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: non-reactive; 3: moderate; 5: highly reactive\u0026thinsp;+\u0026thinsp;drought\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoil reactivity maps\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThermal Stress Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeat impacts on performance/occupants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: efficient cooling; 3: undersized HVAC; 5: none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy audits; CDD index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural Wind Resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResistance to cyclonic winds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: exceeds code; 3: meets code; 5: substandard\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIS 875 wind code\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulatory Compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompliance with safety/adaptation codes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: full\u0026thinsp;+\u0026thinsp;voluntary upgrades; 3: minor non-compliance; 5: unregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMunicipal audits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen/Blue Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdaptive Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegration of adaptive features\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: \u0026gt;3 features; 3: 1 feature; 5: none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlans, NDVI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance Penetration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdaptive Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShare of hazards insured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: \u0026gt;90%; 3: 50\u0026ndash;70%; 5: \u0026lt;25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance records\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Materiality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRevenue dependence on climate-sensitive demand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: fixed; 3: mixed; 5: fully demand-exposed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContracts; sectoral data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Materiality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTime to restore operations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: \u0026lt;1 mo; 3: 3\u0026ndash;6 mo; 5: \u0026gt;12 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistorical recovery data\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency Preparedness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdaptive Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDisaster readiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1: updated plan\u0026thinsp;+\u0026thinsp;drills; 3: outdated plan; 5: none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNDMA audits\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Data Collection and Quality Control\u003c/h2\u003e\n \u003cp\u003eAll datasets were secondary and catalogued with metadata. Data quality (DQ) was scored as High, Medium, or Low based on recency, resolution, and transparency. Missing data were imputed using a \u003cstrong\u003ehierarchical fallback\u003c/strong\u003e rule:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDirect evidence \u0026rarr; Proxy indicator \u0026rarr; Literature analogue \u0026rarr; Expert judgement\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Normalization\u003c/h2\u003e\n \u003cp\u003eAll criteria were mapped to a standardized risk-upward 1\u0026ndash;5 scale:\u003c/p\u003e\n \u003cp\u003es \u0026isin; {1,2,3,4,5}, where 1\u0026thinsp;=\u0026thinsp;Very Low Risk, 5\u0026thinsp;=\u0026thinsp;Very High Risk.\u003c/p\u003e\n \u003cp\u003eContinuous indicators were mapped using:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003ethen rounded to the nearest integer.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Weighting\u003c/h2\u003e\n \u003cp\u003eWeights were derived using a literature frequency method. Evidence strength was coded (High\u0026thinsp;=\u0026thinsp;3, Medium\u0026thinsp;=\u0026thinsp;2, Low\u0026thinsp;=\u0026thinsp;1), then normalized:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEvidence-Based Weights\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriterion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEvidence Strength\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRaw Points\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNormalized Weight\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePluvial Flood Exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoastal Surge Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeismic Vulnerability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandslide Susceptibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrought/Soil Subsidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThermal Stress Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStructural Wind Resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegulatory Compliance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen/Blue Infrastructure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsurance Penetration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecovery Time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmergency Preparedness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Aggregation\u003c/h2\u003e\n \u003cp\u003eComposite risk scores (Si) were calculated as:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cp\u003eTo prevent compensatory bias, a veto rule was applied:\u003c/p\u003e\n \u003cp\u003eIf Sij\u0026thinsp;=\u0026thinsp;5 \u0026rarr; Typology flagged for mandatory review.\u003c/p\u003e\n \u003cp\u003eRisk bands were defined as:\u003c/p\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk Band Classification\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScore Range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk Band\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u0026ndash;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimal exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.01\u0026ndash;3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManageable; mitigation required\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01\u0026ndash;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubstantial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificant; urgent action\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.01\u0026ndash;5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical; immediate intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.7 Worked Examples\u003c/strong\u003e\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eUrban Households (Low-Rise Residential): S\u003csub\u003eUH\u003c/sub\u003e = 2.807\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eUrban Commercial Buildings (High-Rise Offices/Retail): S\u003csub\u003eCB\u003c/sub\u003e = 2.251\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eGovernment Institutions (Hospitals/Schools): S\u003csub\u003eGI\u003c/sub\u003e = 2.295\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Results Summary (Methodology Outputs)\u003c/h2\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComposite Scores, Risk Bands, and Veto Triggers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTypology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComposite Score (Si)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRisk Band\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop Risk Drivers\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVeto Triggers\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u0026ndash;Substantial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeismic Vulnerability (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e), Flood (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e), Compliance (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeismic Vulnerability\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Commercial Buildings\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Sensitivity (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e), Thermal Stress (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e), Flood (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Sensitivity\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment Institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlood (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e), Seismic (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e), Recovery Time (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFlood\u0026thinsp;=\u0026thinsp;5, Recovery\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.9 Methodological Strengths and Limitations\u003c/h2\u003e\n \u003cp\u003eThe MCDA framework offers a systematic, evidence-based approach to integrating multidisciplinary risk factors. Its reliance on secondary data enhances scalability, particularly in resource-constrained contexts. However, subjectivity in weight assignment and the assumption of linear risk aggregation may oversimplify compound hazards. Future iterations could incorporate dynamic climate projections and participatory weighting with local stakeholders.\u003c/p\u003e\n \u003cp\u003eThis study advances the standardization of financial risk metrics for climate-resilient decision-making in the built environment. By translating complex hazard interactions into actionable scores, the framework supports policymakers, insurers, and investors in prioritizing adaptation measures. Further research should expand hazard coverage, validate weights through stakeholder engagement, and explore nonlinear risk modelling.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Overview of Composite Risk Scores\u003c/h2\u003e\u003cp\u003eThe Multi-Criteria Decision Analysis (MCDA) framework generated standardized financial risk scores for three key building typologies in India\u0026rsquo;s urban built environment: urban households, commercial buildings, and government institutions. Composite risk scores, calculated using weighted criteria (exposure, vulnerability, adaptive capacity, and financial materiality) (Gallina et al., 2016; Joerin et al., 2014), were classified into four risk bands adapted from ISO 14091 guidelines: Low (1.0\u0026ndash;2.0), Moderate (2.01\u0026ndash;3.0), Substantial (3.01\u0026ndash;4.0), and High (4.01\u0026ndash;5.0).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComposite Risk Scores by Building Typology\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuilding Typology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComposite Score (S\u003cem\u003ei\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRisk Band\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTop 3 Risk Drivers (Score \u0026rarr; Weighted Contribution)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVeto Triggers\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate-Substantial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1. Seismic Vulnerability (5 \u0026rarr; 0.415)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSeismic Vulnerability\u0026thinsp;=\u0026thinsp;5 (Very High Risk)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2. Regulatory Compliance (4 \u0026rarr; 0.332)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3. Pluvial Flood Exposure (4 \u0026rarr; 0.332)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban Commercial Buildings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1. Financial Sensitivity (5 \u0026rarr; 0.415)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFinancial Sensitivity\u0026thinsp;=\u0026thinsp;5 (Very High Risk)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2. Thermal Stress Sensitivity (5 \u0026rarr; 0.280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3. Pluvial Flood Exposure (3 \u0026rarr; 0.249)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment Institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1. Pluvial Flood Exposure (5 \u0026rarr; 0.415)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePluvial Flood\u0026thinsp;=\u0026thinsp;5 (Very High Risk)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2. Seismic Vulnerability (4 \u0026rarr; 0.332)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecovery Time\u0026thinsp;=\u0026thinsp;5 (Very High Risk)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3. Recovery Time (5 \u0026rarr; 0.280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUrban households exhibited the highest risk (score: 2.807; Moderate-to-Substantial), driven by seismic vulnerability (score: 5), low regulatory compliance (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), and pluvial flood exposure (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). These factors reflect widespread informal construction and minimal insurance coverage, amplifying financial recovery challenges post-disaster. (Mishra, 2020; Revi, 2008; Hallegatte et al., 2010; Ranger et al., 2022).\u003c/p\u003e\u003cp\u003eCommercial buildings showed Moderate risk (2.251), with financial sensitivity to climate-related demand shocks (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and thermal stress (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) as dominant concerns. While modern construction mitigated structural risks (e.g., wind resistance: 2), revenue dependence on climate-sensitive activities heightened financial exposure. (Bressan et al., 2024; Depietri \u0026amp; McPhearson, 2017; Alzahrani et al., 2018).\u003c/p\u003e\u003cp\u003eGovernment institutions also fell into the Moderate band (2.295), despite high regulatory compliance (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Critical vulnerabilities included pluvial flood exposure (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and slow recovery times (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), posing operational risks for essential services like hospitals and schools. (Madhuri et al., 2023; Hochrainer-Stigler et al., 2020; Cardona et al., 2010).\u003c/p\u003e\u003cp\u003e\u003cb\u003eKey Implications\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHouseholds\u003c/b\u003e: Require urgent seismic retrofitting and community insurance schemes. (Walker et al., 2016).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCommercial\u003c/b\u003e: Prioritize HVAC upgrades and revenue diversification. (GRESB, 2022).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInstitutions\u003c/b\u003e: Invest in flood-proofing and pre-negotiated recovery contracts. (NDMA, 2020).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes these findings, highlighting top risk drivers and financial priorities for each typology.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComposite Risk Scores by Building Typology\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTypology\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eComposite Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRisk Band\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTop 3 Risk Drivers\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban Households\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate-Substantial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSeismic, Regulatory Compliance, Flood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommercial Buildings\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFinancial Sensitivity, Thermal Stress, Flood\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment Institutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFlood, Seismic, Recovery Time\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Risk Drivers by Typology\u003c/h2\u003e\u003cp\u003eThe Multi-Criteria Decision Analysis revealed significant variations in risk profiles across the three building typologies studied. This section presents the key risk drivers for each typology, supported by empirical evidence from secondary data analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop Risk Drivers and Weighted Contributions by Building Typology\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Criterion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban Households\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCommercial Buildings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGovernment Institutions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeismic Vulnerability\u0026nbsp;(0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.415 (Very High)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.166 (Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.332 (Substantial)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDominates household risk due to informal construction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial Sensitivity\u0026nbsp;(0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.083 (Very Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.415 (Very High)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.083 (Very Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCritical for commercial (climate-dependent revenue)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePluvial Flood Exposure\u0026nbsp;(0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.332 (Substantial)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.249 (Moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.415 (Very High)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHighest impact on government assets\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecovery Time\u0026nbsp;(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.224 (Substantial)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.168 (Moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.280 (Very High)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInstitutional delays pose systemic risks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegulatory Compliance\u0026nbsp;(0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.332 (Substantial)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083 (Very Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.083 (Very Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHouseholds lag in code adherence\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eWeighted scores\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Criterion score (\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) \u0026times; Normalized weight (e.g., 5 \u0026times; 0.083\u0026thinsp;=\u0026thinsp;0.415).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eBold\u003c/b\u003e indicates veto triggers (score\u0026thinsp;=\u0026thinsp;5).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eParentheses show qualitative risk levels (Very Low to Very High).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Urban Residential Structures\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, urban households exhibited the highest composite risk score (S\u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.807), with seismic vulnerability being the most significant contributor.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePrimary risk drivers for urban households\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeighted Contribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeismic vulnerability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e68% of structures non-compliant with IS 1893\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegulatory compliance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOnly 42% meet flood-resistant standards\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsurance penetration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoverage below 25% in surveyed households\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese findings align with previous studies on informal settlements in rapidly urbanizing areas (Jha et al., 2013; Mishra, 2020). The combination of structural vulnerabilities and limited financial protection mechanisms creates a significant resilience gap, particularly in high-density residential zones.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Commercial Buildings\u003c/h2\u003e\u003cp\u003eCommercial structures demonstrated moderate overall risk (S\u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.251), with financial factors dominating the risk profile, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDominant risk factors for commercial buildings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeighted Contribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial sensitivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62% revenue climate-dependent\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThermal stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHVAC systems inadequate for RCP8.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBasement assets vulnerable to 10-yr floods\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis pattern reflects the findings of Ranger et al. (2022) regarding climate-related business interruptions in urban commercial districts. Notably, while physical infrastructure risks were moderate (average score 3.2), financial vulnerabilities were significantly more pronounced.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Government Institutions\u003c/h2\u003e\u003cp\u003ePublic infrastructure showed moderate risk (Si\u0026thinsp;=\u0026thinsp;2.295) with distinct challenges, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCritical risks for government institutions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWeighted Contribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlood exposure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87% sites in 100-yr floodplain\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecovery time\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean restoration period 14.3 months\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeismic vulnerability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55% critical facilities unreinforced\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThese results corroborate NDMA (2020) assessments of public infrastructure resilience gaps. While regulatory compliance was high (score 1), systemic vulnerabilities in essential services emerged as a critical concern, particularly for healthcare facilities (Hochrainer-Stigler et al., 2020).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e4.2.4 Cross-Typology Analysis\u003c/h2\u003e\u003cp\u003eThe comparative risk assessment reveals fundamental differences in vulnerability profiles:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTypology comparison by risk category\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHouseholds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCommercial\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGovernment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Difference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHouseholds most structurally vulnerable\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinancial risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCritical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCommercial most financially exposed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdaptive capacity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInstitutions show best preparedness\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis analysis supports the hypothesis that standardized risk metrics must account for typology-specific vulnerability patterns (Laurien et al., 2022). The results particularly highlight the need for differentiated adaptation strategies based on primary risk drivers.\u003c/p\u003e\u003cp\u003eThe findings demonstrate that while all three typologies fall within the moderate risk band (2.0\u0026ndash;3.0), their underlying risk compositions vary substantially. This underscores the importance of the MCDA approach in capturing multidimensional risk factors that would be obscured in single-metric assessments (Gallina et al., 2016).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Veto Triggers and Critical Risks\u003c/h2\u003e\u003cp\u003eThe MCDA framework incorporated veto rules to flag extreme risks requiring immediate intervention, defined as any criterion scoring 5 (\"Very High Risk\"). These triggers highlight critical vulnerabilities that could lead to catastrophic financial or operational consequences if unaddressed (Hallegatte et al., 2010; Ranger et al., 2022).\u003c/p\u003e\u003cp\u003e\u003cb\u003eUrban Households\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSeismic Vulnerability (Score: 5): Unreinforced masonry in high-risk zones (e.g., informal settlements) poses collapse risks, aligning with findings from Mishra (2020) in Indian seismic microzonation studies. The veto mandates mandatory retrofitting (e.g., beam-column joints) to avert life and asset losses (Jha et al., 2013).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLow Insurance Penetration (Score: 5): Only 25% of hazards are insured, exacerbating post-disaster financial recovery (Walker et al., 2016).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCommercial Buildings\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFinancial Sensitivity (Score: 5)\u003c/b\u003e: Revenue dependence on climate-sensitive demand (e.g., retail, tourism) mirrors Ranger et al.\u0026rsquo;s (2022) analysis of stranded-asset risks. The veto underscores the need for revenue diversification (e.g., mixed-use leases) to buffer climate shocks.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eGovernment Institutions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePluvial Flood Exposure (Score: 5)\u003c/b\u003e: Critical infrastructure (e.g., hospitals) in floodplains faces operational disruption, consistent with (Madhuri et al.\u0026rsquo;s, 2023) flood-risk mapping in Hyderabad.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRecovery Time (Score: 5)\u003c/b\u003e: Delays exceeding 12 months threaten public service continuity, echoing (Hochrainer-Stigler et al.\u0026rsquo;s, 2020) recovery planning frameworks.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePolicy Implications\u003c/strong\u003e\u003cp\u003eVeto triggers align with NDMA\u0026rsquo;s (National Disaster Management Authority) priority actions for high-risk zones (Govindarajulu, 2020), advocating\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHouseholds\u003c/b\u003e: Enforce seismic codes and subsidize community insurance.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCommercial\u003c/b\u003e: Incentivize climate-resilient business models via ESG-linked financing (Bressan et al., 2024).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInstitutions\u003c/b\u003e: Pre-negotiate contractor agreements to accelerate recovery (Narendr et al., 2024).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003cp\u003eVeto rules may oversimplify compound risks (e.g., concurrent floods and heatwaves), warranting dynamic modeling in future work (Zebisch et al., 2021).\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Comparative Risk Analysis\u003c/h2\u003e\u003cp\u003eA comparative analysis of the composite risk scores reveals distinct risk profiles across the three building typologies (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Urban households exhibit the highest overall risk (composite score: 2.807), driven primarily by seismic vulnerability (weighted score: 0.415) and low regulatory compliance (0.332), compounded by minimal insurance coverage (score: 5). These findings align with studies highlighting the financial fragility of informal settlements in rapidly urbanizing regions (Revi, 2008; Nayal et al., 2020). In contrast, commercial buildings demonstrate moderate risk (score: 2.251), with financial sensitivity to climate-driven demand shocks (0.415) and thermal stress (0.280) as dominant concerns. This reflects broader trends observed in climate-risk assessments of commercial real estate, where revenue volatility often outweighs physical vulnerabilities (Ranger et al., 2022; Alzahrani et al., 2018).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGovernment institutions, while scoring marginally lower (2.295), face critical risks from pluvial flooding (0.415) and prototype recovery times (0.280), underscoring systemic gaps in critical infrastructure resilience (Hallegatte et al., 2010; Hochrainer-Stigler et al., 2020). Notably, all typologies share elevated exposure to flooding, corroborating IPCC AR6 projections of intensified precipitation extremes in South Asia (IPCC, 2022).\u003c/p\u003e\u003cp\u003eThe radar chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) further highlights disparities:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHouseholds: Peak in seismic and regulatory risks.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCommercial: Dominated by financial and thermal stressors.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eInstitutions: Extreme flood exposure and recovery delays.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThese disparities emphasize the need for typology-specific adaptation strategies, as generalized approaches may overlook critical vulnerabilities (Jha et al., 2013; Laurien et al., 2022). For instance, while regulatory enforcement could mitigate household risks, commercial sectors may prioritize revenue diversification, and institutions require pre-negotiated recovery contracts a gap identified in India\u0026rsquo;s National Disaster Management Policy (NDMA, 2020).\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe findings of this study highlight the critical financial risks posed by climate and disaster impacts on India\u0026rsquo;s built environment, particularly in rapidly urbanizing areas. The Multi-Criteria Decision Analysis (MCDA) framework successfully standardized risk assessments across three key typologies urban households, commercial buildings, and government institutions revealing distinct vulnerabilities and adaptation priorities. Below, we interpret these results, discuss their implications for policy and investment, address methodological limitations, and suggest future research directions.\u003c/p\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Interpretation of Key Findings\u003c/h2\u003e\u003cdiv id=\"Sec28\" class=\"Section3\"\u003e\u003ch2\u003e5.1.1 Urban Households: High Vulnerability, Low Resilience\u003c/h2\u003e\u003cp\u003eUrban households exhibited moderate-to-substantial risk (composite score: 2.807), driven primarily by seismic vulnerability (score: 5), regulatory non-compliance (score: 4), and low insurance penetration (score: 5). These results align with studies emphasizing the risks of informal construction in Indian cities (Revi, 2008; Nayal et al., 2020). The high seismic risk reflects widespread use of non-engineered masonry in high-hazard zones (Mishra, 2020), while low insurance coverage (Walker et al., 2016) exacerbates financial exposure. Notably, the veto trigger (seismic risk\u0026thinsp;=\u0026thinsp;5) mandates urgent retrofitting, as unmitigated structural failures could cascade into systemic housing crises (Hallegatte et al., 2010).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section3\"\u003e\u003ch2\u003e5.1.2 Commercial Buildings: Financial Sensitivity Dominates\u003c/h2\u003e\u003cp\u003eCommercial buildings scored moderate risk (2.251), with financial sensitivity to climate-driven demand shocks (score: 5) outweighing physical risks. This mirrors global evidence that revenue volatility from extreme heat or floods disproportionately affects sectors like retail and hospitality (Ranger et al., 2022). While modern construction mitigated structural risks (e.g., wind resistance: score: 2), reliance on climate-sensitive activities (e.g., cooling-dependent offices) heightened financial exposure a gap overlooked in traditional hazard assessments (Bressan et al., 2024).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003ch2\u003e5.1.3 Government Institutions: Critical Infrastructure at Risk\u003c/h2\u003e\u003cp\u003eGovernment facilities faced moderate risk (2.295), but with critical vulnerabilities: pluvial flooding (score: 5) and slow recovery times (score: 5). Despite high compliance (score: 1), aging assets and centralized services (e.g., hospitals) intensified disruption risks (Hochrainer-Stigler et al., 2020). The veto triggers here underscore the need for flood-proofing and pre-negotiated recovery contracts to maintain essential services (NDMA, 2020).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Policy and Investment Implications\u003c/h2\u003e\u003cdiv id=\"Sec32\" class=\"Section3\"\u003e\u003ch2\u003e5.2.1 Regulatory Enforcement and Retrofitting\u003c/h2\u003e\u003cp\u003eThe study underscores the urgency of enforcing building codes, particularly for seismic and flood resilience. India\u0026rsquo;s National Disaster Management Authority (NDMA) guidelines (Mishra, 2020) provide a foundation, but municipal audits and incentives for retrofitting (e.g., tax breaks) are needed to address non-compliance in informal settlements (Jha et al., 2013).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\u003ch2\u003e5.2.2 Financial Resilience Mechanisms\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInsurance Gaps\u003c/b\u003e: Low penetration in households (score: 5) calls for \u003cb\u003epublic-private insurance pools\u003c/b\u003e, as piloted in Kerala (Govindarajulu, 2020).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eRevenue Diversification\u003c/b\u003e: Commercial sectors should adopt \u003cb\u003emixed-use leases\u003c/b\u003e and \u003cb\u003eclimate-adjusted business models\u003c/b\u003e (Ranger et al., 2022).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eAdaptation Finance\u003c/b\u003e: The framework aids ESG reporting (GRESB, TCFD) to attract resilience investments (Bingler \u0026amp; Colesanti Senni, 2022).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec34\" class=\"Section3\"\u003e\u003ch2\u003e5.2.3 Nature-Based and Infrastructure Solutions\u003c/h2\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFlood Mitigation\u003c/b\u003e: Green roofs and permeable pavements could reduce pluvial flood scores (Depietri \u0026amp; McPhearson, 2017).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCritical Infrastructure\u003c/b\u003e: Elevating electrical systems in government buildings aligns with the \"build back better\" paradigm (UNDRR, 2020).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e5.3 Methodological Limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eData Gaps\u003c/b\u003e: Recovery time estimates relied on patchy historical records (low data quality per Step 4). Future studies should integrate real-time disruption datasets (Verschuur et al., 2024).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLinear Aggregation\u003c/b\u003e: MCDA\u0026rsquo;s additive model may oversimplify \u003cb\u003ecompound hazards\u003c/b\u003e (e.g., concurrent floods and heatwaves). Probabilistic risk models (e.g., CAPRA, Cardona et al., 2010) could complement this.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eWeighting Subjectivity\u003c/b\u003e: Though evidence-based, weights may vary regionally. Sensitivity analysis (\u0026plusmn;\u0026thinsp;20% weights) confirmed robustness, but stakeholder validation is recommended (Joerin et al., 2014).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e5.4 Future Research Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDynamic Risk Modeling\u003c/b\u003e: Integrate CMIP6 climate projections to assess non-stationary hazards (IPCC AR6).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStakeholder Co-Design\u003c/b\u003e: Engage insurers and urban planners to refine criteria (e.g., add \"tenant mobility\" for commercial resilience).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eSocioeconomic Factors\u003c/b\u003e: Overlay poverty density maps to assess equity in risk distribution (Malakar \u0026amp; Mishra, 2017).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec35\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Conclusions\u003c/h2\u003e\u003cp\u003eThis study bridges technical hazard assessments and financial decision-making by standardizing climate risk metrics for India\u0026rsquo;s built environment. Key takeaways:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eHouseholds\u003c/b\u003e require retrofitting and insurance schemes.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCommercial\u003c/b\u003e sectors must diversify revenue streams.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eInstitutions\u003c/b\u003e need flood-proofing and faster recovery protocols.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eWhile the MCDA framework offers scalability, its real-world impact hinges on \u003cb\u003epolicy adoption\u003c/b\u003e and \u003cb\u003elocalized validation\u003c/b\u003e. Future work should expand hazard coverage and integrate dynamic climate models to enhance precision.\u003c/p\u003e\u003c/div\u003e"},{"header":"6. Conclusions \u0026 Recommendations","content":"\u003cp\u003eThis study developed a standardized Multi-Criteria Decision Analysis (MCDA) framework to assess financial risks from climate and disaster impacts on India\u0026rsquo;s built environment, addressing a critical gap in comparable, asset-level risk metrics. By integrating hazard exposure, structural vulnerability, adaptive capacity, and financial materiality, the methodology generated transparent risk scores for urban households, commercial buildings, and government institutions using secondary data. Key findings and actionable recommendations are summarized below.\u003c/p\u003e\u003cp\u003e\u003cb\u003eKey Conclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eUrban households\u003c/b\u003e exhibited moderate-to-substantial risk (composite score: 2.807), driven by seismic vulnerability (score: 5), low regulatory compliance (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), and minimal insurance coverage (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). These factors amplify financial exposure, particularly in informal settlements, aligning with prior studies on seismic and flood risks in rapidly urbanizing regions (Revi, 2008; Mishra, 2020).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCommercial buildings\u003c/b\u003e faced moderate risk (2.251), dominated by climate-sensitive revenue streams (score: 5) and thermal stress (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). While modern construction mitigated structural risks, reliance on demand-driven income increased vulnerability to climate shocks, corroborating Ranger et al.\u0026rsquo;s (2022) findings on business interruption losses.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eGovernment institutions\u003c/b\u003e scored moderately (2.295) due to pluvial flood exposure (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and slow recovery times (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), despite high compliance (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This underscores operational risks for critical infrastructure, echoing Hallegatte et al.\u0026rsquo;s (2010) warnings about cascading service disruptions.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe MCDA framework successfully bridged technical hazard assessments and financial decision-making, offering scalable metrics for insurers, policymakers, and asset managers. However, limitations include data gaps (e.g., recovery timelines) and linear aggregation assumptions, which future studies could address through probabilistic modeling (Laurien et al., 2022).\u003c/p\u003e\u003cp\u003e\u003cb\u003eRecommendations\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFor Urban Households\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eMandatory seismic retrofitting aligned with BIS 1893 codes, prioritized in high-risk zones (Mishra, 2020).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCommunity-based insurance schemes to address low penetration, leveraging public-private partnerships (Walker et al., 2016).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAwareness campaigns to improve compliance with flood-resistant construction standards (NDMA guidelines).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003efor Commercial Buildings\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eRevenue diversification (e.g., mixed-use leases) to reduce climate-sensitive demand shocks (Ranger et al., 2022).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHVAC upgrades and green infrastructure (e.g., cool roofs) to mitigate thermal stress (Depietri \u0026amp; McPhearson, 2017).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIntegration with ESG reporting to attract adaptation finance (Bressan et al., 2024).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFor Government Institutions\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFlood-proofing critical infrastructure (e.g., elevated electrical systems) in flood-prone areas (Hallegatte et al., 2010).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePre-negotiated recovery contracts with contractors to reduce downtime (Hochrainer-Stigler et al., 2020).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eQuarterly emergency drills to validate preparedness plans (ISO 22320).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCross-Cutting Measures\u003c/b\u003e:\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePolicy: Strengthen enforcement of building codes, especially in informal settlements (Jha et al., 2013).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eData: Regular updates to hazard maps (e.g., LiDAR for coastal surge risk) and open-access risk databases (Narendr et al., 2024).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFinance: Develop catastrophe bonds or resilience credits for high-risk assets (Golnaraghi et al., 2018).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFuture Research Directions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDynamic risk modeling incorporating CMIP6 climate projections to account for non-stationary hazards.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eStakeholder validation of weights through workshops with insurers and urban planners.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCompound hazard integration (e.g., concurrent floods and heatwaves) to refine financial loss estimates (Zebisch et al., 2021).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis study provides a replicable blueprint for standardizing climate risk assessments in the built environment, enabling targeted investment and policy action. By prioritizing the highest-risk criteria (e.g., seismic vulnerability, financial sensitivity), stakeholders can allocate resources efficiently to enhance resilience in India\u0026rsquo;s rapidly urbanizing landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe research presented in this manuscript was carried out without any financial assistance or external funding. No grants, financial sponsorship, or institutional support from government agencies, private organizations, commercial entities, or not-for-profit institutions were received at any stage of the research process. All activities, including conceptualization, data collection, analysis, interpretation of results, and preparation of the manuscript, were independently undertaken and financially supported by the author. This ensures that the findings and conclusions presented in the manuscript are unbiased and not influenced by any external funding entity. Therefore, this research received no specific grant or financial support from any funding agency, commercial, or not-for-profit organization.\u0026rdquo;\u003c/p\u003e\n\u003col start=\"2\"\u003e\n\u003cli\u003e\u003cstrong\u003eConsent to Publish Declaration\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe content of this manuscript is derived entirely from publicly available secondary data sources, analytical work conducted by the author, and original interpretation of the findings. The study does not contain any proprietary information, identifiable personal data, or confidential material that would necessitate explicit permission or consent for publication from individuals, institutions, or third parties. No interviews, surveys, or primary data collection involving human subjects were conducted as part of this research. Accordingly, and in compliance with the journal\u0026rsquo;s submission guidelines, the requirement for a Consent to Publish declaration does not apply. Therefore, \u0026ldquo;Consent to Publish declaration: not applicable.\u0026rdquo;\u003c/p\u003e\n\u003col start=\"3\"\u003e\n\u003cli\u003e\u003cstrong\u003eEthics and Consent to Participate Declarations\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis research does not involve any form of human participation, animal experimentation, or handling of sensitive or personal data. The analysis is based entirely on secondary data and information obtained from publicly accessible sources such as academic literature, official reports, and databases. As there was no involvement of human subjects or collection of primary data, institutional review board (IRB) approval or informed consent was not required. Additionally, no ethical concerns arise from the nature of the study as it deals exclusively with aggregate data and conceptual analysis. Therefore, in accordance with the journal\u0026rsquo;s guidelines, \u0026ldquo;Ethics and Consent to Participate declarations: not applicable.\u0026rdquo;\u003c/p\u003e\n\u003col start=\"4\"\u003e\n\u003cli\u003e\u003cstrong\u003eAuthor Contribution Declaration\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eJanardhana Anjanappa was responsible for the conceptualization of the research, development of methodology, data collection, data analysis, and writing the original draft of the manuscript.\u003c/p\u003e\n\u003cp\u003eDr. Vishal Singh contributed to the literature review, validation of data interpretation, drafting specific sections of the manuscript, and reviewing and editing the final version for accuracy and coherence.\u003c/p\u003e\n\u003cp\u003eBoth authors read and approved the final manuscript.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n\u003cli\u003e\u003cstrong\u003eCompeting Interest Declaration\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe author affirms that there are no competing interests or conflicts\u0026mdash;whether financial, professional, or personal\u0026mdash;that could have influenced the design, execution, interpretation, or reporting of the research presented in this manuscript. The study has been conducted with complete academic independence, and the conclusions drawn are based solely on evidence, analysis, and objective interpretation without any external influence. Accordingly, \u0026ldquo;Competing Interests: The author declares that there are no competing financial, professional, or personal interests that could have appeared to influence the work reported in this manuscript.\u0026rdquo;\u003c/p\u003e\n\u003col start=\"6\"\u003e\n\u003cli\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll data generated or analysed during this study are derived entirely from publicly available secondary sources (e.g., government hazard maps, building codes, insurance penetration datasets, and published academic literature). These sources are fully cited in the manuscript\u0026rsquo;s reference list. No proprietary or primary datasets were collected. Accordingly, all relevant data supporting the findings of this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdrian MT, Grippa P, Gross MM, Haksar MV, Krznar MI, Lepore C, Panagiotopoulos MA. Approaches to climate risk analysis in FSAPs. International Monetary Fund; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgrawal M. Multisector exposure and vulnerability to climate change in India: Case of National Capital Territory of Delhi, India. Disaster Prev Management: Int J. 2020;29(5):761\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlzahrani A, Boussabaine H, Almarri K. Emerging financial risks from climate changes on building assets in the UK. Facilities. 2018;36(9/10):460\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAssab A. 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Disaster risk assessment and analysis of physical infrastructure: A comprehensive review of scientific methods and techniques. IDRiM J. 2024;14(2):241\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker GR, Mason MS, Crompton RP, Musulin RT. Application of insurance modelling tools to climate change adaptation decision-making relating to the built environment. Struct Infrastruct Eng. 2016;12(4):450\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZebisch M, Schneiderbauer S, Fritzsche K, Bubeck P, Kienberger S, Kahlenborn W, Below T. The vulnerability sourcebook and climate impact chains A standardized framework for a climate vulnerability and risk assessment. Int J Clim Change Strateg Manag. 2021;13(1):35\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1108/IJCCSM-07-2020-0072\u003c/span\u003e\u003cspan address=\"10.1108/IJCCSM-07-2020-0072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate Risk, Built Environment, Financial Risk Metrics, Multi-Criteria Decision Analysis (MCDA), Disaster Resilience","lastPublishedDoi":"10.21203/rs.3.rs-7459984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7459984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing frequency and severity of climate change and disaster events pose significant financial risks to the built environment, particularly in rapidly urbanizing regions like India. This study addresses the lack of standardized financial risk metrics by developing a Multi-Criteria Decision Analysis (MCDA) framework to assess climate and disaster risks for urban households, commercial buildings, and government institutions. The methodology integrates hazard exposure, structural vulnerability, adaptive capacity, and financial materiality into composite risk scores using secondary data. Results reveal distinct risk profiles: urban households face moderate-to-substantial risks due to seismic vulnerability and low insurance coverage; commercial buildings are most sensitive to climate-driven revenue losses; and government institutions are vulnerable to flooding and slow recovery times. The framework provides scalable, transparent metrics to guide climate-resilient investment, policy enforcement, and adaptation planning. Key recommendations include seismic retrofitting, insurance schemes, revenue diversification, and flood-proofing critical infrastructure. The study bridges technical hazard assessments and financial decision-making, offering a replicable approach for standardized risk evaluation in the built environment.\u003c/p\u003e","manuscriptTitle":"Developing Standardized Financial Risk Metrics for Climate-Resilient Investment in the Built Environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 17:51:31","doi":"10.21203/rs.3.rs-7459984/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4327b5c-8c8f-4e6e-9f1a-ceee467c89ca","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T11:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 17:51:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7459984","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7459984","identity":"rs-7459984","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}