Climate Finance Architecture: Disaster Loss, Policy Uncertainty and Adaptation Investment Across the Global South

Abstract

Climate-related disasters are escalating in frequency and severity, yet global adaptation finance remains critically insufficiently structured to respond after disasters occur rather than before. This study empirically examines disaster loss data, climate finance flows, and financial instrument evidence to test two hypotheses: whether climate finance is disaster-reactive, and whether policy uncertainty constrains it. We integrate data from the Emergency Events Database (EM-DAT), covering seven climate-induced hazard types (droughts, extreme temperatures, floods, glacial lake outburst floods, wet mass movements, storms, and wildfires), in addition to the OECD Creditor Reporting System (CRS), the World Uncertainty Index (WUI), the ND-GAIN vulnerability index, and the World Governance Indicators, the Green Climate Fund Open Data Library, and the Artemis Deal Directory across 131 countries (2011–2024) for Hypothesis 1 and 100 countries (2012–2024) for Hypothesis 2. Fixed-effects panel regressions with Driscoll–Kraay standard errors confirm that prior-year disaster losses significantly predict subsequent climate finance flows (β = 0.040, p = 0.009; N = 1769 country-year observations), establishing a reactive financing pattern. Policy uncertainty interacting with high vulnerability is found to suppress adaptation finance flows (β = −2.587, p = 0.080, N = 878 country-year observations), with the effect concentrated among the most climate-exposed economies. We propose a risk-layered climate finance architecture aligning instruments with distinct hazard tiers across the Global South. Credible policy signals, strategic public investment, and systematic integration of insurance mechanisms are essential preconditions for unlocking scalable, forward-looking resilience finance.

1. Introduction

Climate-related hazards caused hundreds of billions of dollars in economic losses, disproportionately affecting the most vulnerable communities and threatening to reverse decades of sustainable development progress (Bangalore et al., 2017; CPI, 2024). Developing economies bear a inconsistent burden of these losses relative to their capacity to absorb them, with disaster impacts frequently undermining progress toward sustainable development goals (United Nations Office for Disaster Risk Reduction, 2023). Globally, 2024 disaster-related losses exceeded USD 320 billion, reflecting an alarming escalation in climate-induced economic impacts (Munich Re, 2025; Ranger et al., 2021). These figures highlight not only the physical consequences of a changing climate but also its direct, compounding and growing implications for corporate and sovereign financial stability, a dimension that has attracted increasing scholarly attention (Dietz et al., 2016; Monasterolo, 2020; Ranger et al., 2021) yet remains incompletely translated into the design of climate finance flows for disaster-affected developing economies.

Climate-related financial risks operate through two structurally distinct channels: physical risks, arising from climate-induced damages to productive capital, natural assets, and sovereign fiscal revenues, and transition risks, arising from the sudden revaluation of carbon-intensive assets in response to unanticipated policy, regulatory, or technological shocks (Monasterolo, 2020). These risks are characterised by deep uncertainty, non-linearity, and endogeneity. These properties render traditional financial valuation models inadequate (Battiston et al., 2017). Critically, both channels remain largely unpriced in financial markets, and financial interconnectedness can amplify losses through second-round network effects, with systemic implications for financial stability (Battiston et al., 2021). Dietz et al. estimated that unmitigated climate change could reduce global financial assets by 1.8% (approximately USD 2.5 trillion) by 2100, with tail-risk scenarios reaching 16.9% (over USD 20 trillion) (Dietz et al., 2016). Traditional insurance options are increasingly inadequate in the Global South: coverage frequently excludes critical climate threats such as droughts and heatwaves, and insured losses represent only a fraction of total economic damage (Surminski & Oramas-Dorta, 2014; Swiss Re, 2024). Despite this growing financing demand, the least developed and most climate-vulnerable economies remain systematically underserved: adaptation finance flows to the Global South have covered only a fraction of estimated needs as of 2024 (Bangalore et al., 2017; CPI, 2024).

Despite the growing volume of theoretical work on climate financial risks, systematic cross-country empirical evidence on the determinants of public climate finance allocation in developing economies remains limited. Existing studies predominantly examine individual instruments such as catastrophe bonds (Cummins & Weiss, 2009) or parametric insurance (Surminski & Oramas-Dorta, 2014) or analyse aggregate climate finance flows without isolating the role of disaster experience or policy conditions as drivers (Nor, 2025; Prasad & Singhania, 2026). Building financial resilience to disasters is as critical as building physical resilience (Bangalore et al., 2017; Battiston et al., 2021; Surminski & Oramas-Dorta, 2014), and it is jointly estimated whether past disaster losses trigger reactive public finance flows and whether policy uncertainty suppresses them particularly in the most climate-vulnerable developing economies.

This study addresses the following research questions:

RQ1: Does prior disaster loss experience predict subsequent bilateral climate finance commitments in developing economies, and, if so, what does this reactive pattern imply for the architecture of climate finance allocation?

RQ2: Does macroeconomic policy uncertainty suppress private climate finance mobilisation in developing economies, and is this effect amplified in countries with high climate vulnerability?

RQ3: Do blended public finance instruments generate systematically different leverage ratios for private capital mobilisation, and which instrument types offer the greatest catalytic efficiency in climate-vulnerable developing economies?

The primary contributions of this study are threefold. First, it provides systematic cross-country panel evidence on the determinants of public climate finance allocation across 131 developing economies over 2011–2024 (H1). Second, it furnishes empirical evidence that public climate finance flows are disaster-reactive to the prior-year disaster losses and significantly predict subsequent bilateral climate finance commitments of the current climate finance architecture across the Global South (H2). Third, it quantifies the role of policy uncertainty as a structural moderator of public climate finance mobilisation, showing that uncertainty interacting with high vulnerability significantly suppresses adaptation finance, providing evidence for the importance of credible, long-term climate policy commitments in unlocking finance for the most climate-exposed economies (H3).

2. Literature Review: The Architecture of Climate Finance: Instruments, Mechanisms, and Theoretical Foundations

2.1. Blended Finance as a Foundational Mechanism

At institutions such as the Green Climate Fund (GCF), blended finance approaches combine grants, concessional loans, guarantees, equity, and first-loss capital to make resilience projects financially viable for the private sector (Broccolini et al., 2020; Green Climate Fund [GCF], 2026; OECD & UNCDF, 2020). The fit-for-purpose blended finance approach is designed specifically to de-risk markets and unlock funds for disaster preparedness (OECD, 2025a). Brandon et al. document 162 cases of climate adaptation financing between 2015 and 2025 (Brandon et al., 2025), finding that blended finance is the most frequently deployed instrument across income levels, encompassing disaster risk financing, bonds, debt swaps, insurance and risk transfer, and guarantees. This breadth reflects the versatility of blended structures in bridging public mandates and private return requirements. Empirical evidence confirms that multilateral development bank lending generates significant positive mobilisation effects on private capital flows, increasing both the volume and maturity of syndicated lending to developing economies (Broccolini et al., 2020).

2.2. Parametric Insurance and Catastrophe Bonds

Parametric insurance contracts pay out rapidly following a disaster based on predetermined trigger parameters, eliminating the need for lengthy damage assessments (Ocampo & Moreira, 2024; Pleterski, 2025). Following Hurricane Beryl in 2024, CCRIF made payouts totaling US$84.5 million to seven members within 14 days of the event, enabling immediate evacuation and shelter deployment (CCRIF SPC, 2025). CCRIF, established in 2007 as the first multi-country catastrophe risk pool of its kind, offers parametric insurance policies for tropical cyclones, earthquakes, excess rainfall, and the fisheries and electric utility sectors (CCRIF SPC, 2025). Similarly, the Pacific Catastrophe Risk Insurance Company (PCRIC) provides parametric climate and disaster insurance to Pacific Island Countries, equipping them with financial tools and knowledge to enhance resilience (Pacific Catastrophe Risk Insurance Company [PCRIC], 2024; Bonazzi et al., 2024).

Catastrophe bonds tap global capital markets to transfer large-scale disaster risk to investors (Cummins & Weiss, 2009). The outstanding catastrophe bond market reached 49.4 billion by the end of 2024, setting a new record (AON, 2024; Artemis, 2025). Investors are attracted to equity-like returns that are uncorrelated with broader financial markets (Cummins & Weiss, 2009), making catastrophe bonds a structurally distinct asset class that complements mainstream portfolios. Cat bond pricing dynamics are shaped primarily by expected loss and prevailing market conditions, with implications for how developing-country sovereigns can access the market (Morana & Sbrana, 2019). The Artemis Deal Directory documents structural deepening across an increasingly diverse set of peril types and sponsor jurisdictions (Artemis, n.d.).

Despite this growth, parametric instruments carry inherent limitations that constrain their scalability in the most vulnerable contexts. Basis risk, the divergence between the parametric trigger and actual losses experienced, remains a persistent structural challenge, particularly where trigger design relies on coarse spatial or meteorological indices that fail to capture localised damage patterns (Pleterski, 2025; Surminski & Oramas-Dorta, 2014). Premium affordability and limited actuarial data in low-income countries further restrict sovereign uptake. These constraints underscore why parametric and catastrophe bond instruments, while valuable as liquidity mechanisms, remain insufficient substitutes for the concessional public finance flows examined in this study.

2.3. Insurance-Backed Debt and Debt-for-Nature Mechanisms

Insurance-backed debt mechanisms represent an innovation at the intersection of climate finance and sovereign risk management. Shock-resilient loans bundle climate finance with insurance so that when disasters strike, insurers temporarily cover debt payments, freeing government resources for emergency response (Clarke & Dercon, 2016). Debt-for-nature swaps replace government debt with cheaper loans and invest the interest savings in environmental projects (Nedopil et al., 2024; Steele & Patel, 2020). A prominent recent example is Barbados’s debt-for-climate-resilience conversion supported by the Green Climate Fund and partner institutions, which refinanced sovereign debt while redirecting fiscal savings toward climate resilience and marine conservation investments (Green Climate Fund [GCF], 2026). Analysis of 67 debt-distressed countries shows that they collectively hold over 22% of global biodiversity priority areas, most of which remain unprotected, underscoring the dual fiscal and conservation rationale for debt-for-nature structures in climate-vulnerable economies (Nedopil et al., 2024). Despite growing interest, these hybrid instruments remain nascent in the OECD CRS bilateral commitments data that form the empirical basis of this study, and their contribution to tracked public climate finance flows is not yet fully separable from conventional concessional lending.

2.4. The Reactive Finance Problem and the Case for Risk Layering

A recurring finding in the disaster risk finance literature is that adaptation and resilience finance tends to be triggered by realised losses rather than anticipated risk (Kellett & Caravani, 2013). Reactive allocation delays protective investments, increases recovery costs, and perpetuates exposure, particularly in low-income and climate-vulnerable regions (Bangalore et al., 2017). The World Bank argues that shifting from reactive crisis response to proactive risk management requires equipping countries with innovative financial tools to absorb and recover from shocks before they occur (Atamuratova et al., 2014). The concept of risk layering in disaster finance has foundational roots in Linnerooth-Bayer and Mechler, who demonstrated that a tiered instrument approach combining retention, insurance, and international assistance improves fiscal efficiency and reduces sovereign exposure to catastrophic losses (Linnerooth-Bayer & Mechler, 2006).

Risk-layering frameworks operationalize this logic by aligning financial instruments with different tiers of hazard frequency and severity: national budget reserves for high-frequency, low-severity events; insurance and pooled risk-sharing for moderate risks; and catastrophe bonds or international assistance for rare but catastrophic events (Clarke & Dercon, 2016). Taylor (2023) identifies the evolving policy landscape for disaster risk financing as one of the defining governance challenges of the current decade, noting that institutional barriers and data limitations continue to constrain the adoption of proactive frameworks even where instruments are technically available. This tiered approach, while conceptually well-established, has not previously been empirically validated against a comprehensive cross-country dataset of climate finance commitments across a fifteen-year horizon.

2.5. Policy Uncertainty and Climate Finance Allocation

Ahir et al. (2022) construct the World Uncertainty Index (WUI) for 143 countries from 1996 onwards, counting occurrences of the root “uncertain*” in the Economist Intelligence Unit’s (EIU) quarterly country reports and normalising by total word count. The index is counter-cyclical and higher on average in developing and low-income countries than in advanced economies. In a vector autoregression estimated on 46 countries, WUI innovations account for approximately three per cent of the variance in per capita GDP growth after eight quarters, and the index correlates are 0.705 with Baker, Bloom and Davis’s Economic Policy Uncertainty index and 0.430 with equity market volatility, offering complementary coverage in countries where financial markets are thin or absent (Ahir et al., 2022).

The empirical literature on climate finance allocation has established physical exposure and governance quality as the principal determinants of bilateral flows. Using OECD CRS adaptation aid data for 144 developing countries over 2011 to 2014, the study finds that countries more exposed to extreme weather events and sea-level rise receive significantly more adaptation aid, with the most vulnerable recipients receiving approximately USD 3 more per capita annually than the least vulnerable (Betzold & Weiler, 2017). Extending the window to 2015, Weiler et al. (2018) confirm the physical exposure effect but find that low adaptive capacity does not attract additional flows. Donors instead reward well-governed recipients and direct aid in ways consistent with their own trade and economic interests. Another study applies system GMM to 133 countries over 2000 to 2018 across mitigation, adaptation, and cross-cutting categories, finding that higher climate vulnerability consistently predicts larger allocations after controlling for governance and income (Islam, 2022).

All existing studies restrict attention to adaptation aid or a subset of OECD CRS categories and use estimation windows of four to six years, precluding within-country fixed-effects identification. Most importantly, none incorporates policy uncertainty as a determinant of bilateral climate finance commitments, despite evidence that uncertainty is systematically elevated in the developing-country recipients that the architecture is designed to serve. This study addresses these gaps through a fifteen-year fixed-effects panel with Driscoll–Kraay standard errors, as described in Section 3.

3. Materials and Methods

3.1. Data Sources

This study integrates multiple authoritative databases, each providing a distinct set of variables for the panel analysis. Disaster losses and exposure data are drawn from the Emergency Events Database (EM-DAT), maintained by the Centre for Research on the Epidemiology of Disasters at UCLouvain (Delforge et al., 2025; EM-DAT, 2024). Only climate-induced hydrometeorological and climatological events are retained. Geological hazards such as earthquakes and volcanic activity are excluded as they fall outside the scope of climate finance and adaptation investment for this study. Total economic damage is aggregated to the country-year level in constant 2023 million USD; missing damage figures are assigned zero, consistent with the EM-DAT protocol whereby absence of a damage record indicates no estimate was reported rather than unknown damage. EM-DAT is subject to known methodological limitations including time and geographic reporting biases, a ten-deaths inclusion threshold that may systematically undercount small-scale frequent events, and unequal reporting across impact variables; these are acknowledged in the sensitivity discussion (Delforge et al., 2025).

Climate finance flows are sourced from the Climate Policy Initiative (CPI) Global Landscape of Climate Finance (CPI, 2024), the OECD Development Assistance Committee (DAC) Creditor Reporting System (OECD, 2025b), and the GCF Open Data Library (CPI, 2024; Green Climate Fund [GCF], 2026). The CPI (2024) edition provides comprehensive coverage for 2018–2022, while pre-2018 estimates (2010–2017) are supplemented from earlier CPI annual landscape reports and the OECD DAC series, with the two periods harmonised to 2023 constant USD to ensure comparability across the full 2010–2024 raw data window. From the OECD CRS, only bilateral aid commitments carrying the adaptation Rio marker at the principal (score = 2) or significant (score = 1) level are retained.

Risk-transfer instrument data are drawn from public summaries in the Artemis Deal Directory (Artemis, n.d.) and from regional risk pool reports including CCRIF (CCRIF SPC, 2025), and PCRIC (Pacific Catastrophe Risk Insurance Company [PCRIC], 2024). Macroeconomic and policy context data are obtained from the World Bank World Development Indicators static snapshot (World Bank, 2025a), the World Bank Worldwide Governance Indicators (Kaufmann et al., 2010; World Bank, 2025b), the Notre Dame Global Adaptation Initiative (ND-GAIN) vulnerability index (University of Notre Dame, n.d.), and the World Uncertainty Index (WUI), T6 smoothed series (Ahir et al., 2022). All monetary values were converted to constant 2023 US dollars using the U.S. Bureau of Labor Statistics Consumer Price Index for All Urban Consumers (CPI-U), Series CUUR0000SA0 with 2023 as the base year (U.S. Bureau of Labor Statistics, 2025).

3.2. Data Harmonisation

All monetary series are expressed in constant 2023 USD. OECD CRS commitment values are already reported in constant prices by the OECD; a scalar base-year adjustment dividing by the BLS CPI-U index for 2023 (U.S. Bureau of Labor Statistics, 2025) is applied uniformly to convert all remaining current-price series to the same base. No cross-source aggregation of public bilateral flows is performed: the study uses OECD CRS as the sole source for bilateral adaptation commitments (H1), avoiding double-counting with CPI aggregates that draw on the same DAC reporting pool. Climate finance definitions follow the OECD DAC Statistical Reporting Directives (OECD, 2025a) for official bilateral flows, and the UNFCCC Standing Committee on Finance biennial assessment framework (United Nations Framework Convention on Climate Change [UNFCCC], 2024) for the broader mobilisation envelope used in H2. The analysis uses commitments throughout, and the disbursements are excluded to avoid double-counting across reporting years. Disaster losses are lagged by one year (t − 1) to respect the temporal ordering between climate shocks and financing responses. This lag structure is applied before panel construction so that the regression sample begins in 2011 for H1. Missing values in log GDP per capita (H1: 13.1%; H2: 9.5%) and the WGI composite (H1: 4.6%; H2: 1.4%) are addressed through Multiple Imputation by Chained Equations (MICE), implemented via scikit-learn’s IterativeImputer with an ExtraTreesRegressor estimator (50 trees, 10 iterations, seed = 42) (Buuren & Groothuis-Oudshoorn, 2011). Variables with more than 45% missing values are dropped rather than imputed. Country-years with a zero EM-DAT loss value are retained and coded as zero (no damage events recorded), consistent with the database’s reporting structure; only country-years where the country is entirely absent from EM-DAT are treated as structurally missing. The H1 panel comprises 1769 country-year observations across 131 recipient countries over 2011–2024. The 65-observation shortfall reflects the exclusion of countries absent from both the OECD CRS recipient registry and the ND-GAIN index. The H2 panel comprises 918 observations across 100 developing-country recipients over 2012–2024. The sample is restricted to recipient countries by design, and the within-sample shortfall from 1300 reflects residual gaps in GDP and ND-GAIN coverage addressed partially by imputation.

Table 1. Descriptive statistics for panel variables (2011–2024, constant 2023 USD).

Variable	H	N	Mean	SD	Min	Max
log(CF commitment)	H1	1769	5.568	2.097	0.003	10.136
log(Disaster loss, t − 1)		1769	1.068	2.319	0	11.225
log(GDP per capita)		1769	8.118	0.997	5.263	10.086
WGI governance composite		1769	−0.46	0.63	−2.19	1.198
ND-GAIN vulnerability		1736	0.473	0.08	0.307	0.655
ND-GAIN readiness		1739	0.353	0.088	0.115	0.631
WUI (annual)	H2	918	0.076	0.054	0	0.343
log(Private mob. % GDP + eps)		918	−3.8	2.515	−12.904	2.807
WUI × Vuln-high (interaction)		918	0.035	0.053	0	0.327

Note: H1 panel: 1769 observations, 131 countries. H2 panel: 918 observations, 100 countries. All monetary variables log-transformed in estimation. WUI × Vuln-high is the interaction term used in the preferred H2 specification (Column 4, Table 1). Descriptive statistics are computed on untransformed panel values prior to NA-dropping.

reports descriptive statistics for all variables entering the H1 and H2 panels. The difference in N reflects the narrower country coverage of OECD private-finance mobilisation data relative to OECD CRS adaptation commitments.

3.3. Analytical Methods

Three complementary analytical approaches were applied. First, pairwise Pearson correlations were computed across all key panel variables, and were then used to assess co-movement between disaster losses and climate finance flows, serving as a descriptive pre-analysis diagnostic.

Second, fixed-effects panel regressions were estimated to isolate the impact of disaster losses and policy uncertainty on climate finance flows and private investment, controlling for country and year fixed effects; this provides the primary test of H2. Two-way fixed-effects panel regressions are estimated for both H1 and H2, absorbing all time-invariant country characteristics via entity fixed effects and common annual shocks via year fixed effects. Standard errors follow (Driscoll & Kraay, 1998), computed with a Bartlett kernel and a bandwidth of four periods; a conservative floor is recommended for panels of T ≈ 13–15, derived as floor(T^2/9) per (Hoechle, 2007). All models are estimated in Python 3.10.12 using the linearmodels package (PanelOLS). For H1, the estimating equation is

log(CF_commit + 1)_it = α + β₁·log(DisasterLoss_{t−1})_it + β₂·log(GDP_pc)_it + β₃·WGI_it + γ_i + δ_t + ε_it

For H2, the preferred specification augments the base model with the WUI × ndgain_vuln_high interaction term to test whether policy uncertainty depresses private mobilisation more severely in high-vulnerability countries. Policy uncertainty is measured using the World Uncertainty Index (WUI) T6 smoothed series, a three-quarter weighted moving average of the raw quarterly EIU country-report measure (Ahir et al., 2022).

Third, instrument-specific leverage analysis quantifies private capital mobilisation per unit of public finance, following the OECD DAC statistical framework for amounts mobilised from the private sector by official development finance interventions (OECD & UNCDF, 2020). Leverage ratios are computed empirically from the OECD CRS Mobilisation database and reported by instrument type, providing the primary evidence base for H3.

Three nested H2 specifications are estimated to assess robustness (

Table 2. Two-way fixed-effects panel regression results.

Variable	H1	H2 Column 1	H2 Column 2	H2 Column 3 (Preferred)
Dependent variable	log(CF commit + 1)	log(Priv mob % GDP)	log(Priv mob % GDP)	log(Priv mob % GDP)
log(Disaster loss, t − 1)	0.040 ***	—	—	—
	(0.015)
WUI annual	—	−0.409	−0.444	0.785
		(1.580)	(1.608)	(1.496)
WUI × Vuln-high	—	—	—	−2.587 *
				(1.475)
log(GDP per capita)	−0.643 ***	1.045 ***	1.064 ***	1.068 ***
	(0.155)	(0.349)	(0.334)	(0.337)
WGI composite	0.870 ***	1.620 *	1.638 *	1.685 *
	(0.273)	(0.908)	(0.904)	(0.911)
ND-GAIN vulnerability	—	—	4.779	5.494
			(5.592)	(5.706)
log(CF commitment)	—	0.044	0.043	0.044
		(0.059)	(0.060)	(0.059)
Country FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes
N	1769	878	878	878
Countries	131	97	97	97
Period	2011–2024	2012–2024	2012–2024	2012–2024
R2 (within)	0.025	0.016	0.011	0.012

Note: Driscoll–Kraay standard errors in parentheses, Bartlett kernel, bandwidth = 4 (Hoechle, 2007). *** p < 0.01, * p < 0.10. H2 Columns 1–3 are nested robustness specifications: Column 1 is the base model; Column 2 adds ND-GAIN vulnerability; Column 3 adds the WUI × vulnerability-high interaction term (preferred specification). All monetary values in constant 2023 USD.

, Columns 2–4). Column 1 includes WUI and macroeconomic controls only; Column 2 adds ND-GAIN vulnerability as a continuous control; and in Column 3 the preferred specification adds the WUI × vulnerability-high interaction term. The WUI main-effect coefficient remains consistently non-significant across all three specifications (β = −0.409, −0.444, and +0.785 respectively), confirming that the null main effect is not an artefact of model choice. The significant interaction term (β = −2.587, p = 0.080) emerges only in Column 3, identifying high-vulnerability countries as the subgroup for which uncertainty suppresses private mobilisation. The Driscoll–Kraay bandwidth of four years follows (Hoechle, 2007) for panels with T ≈ 13–15 periods. Two-way entity and year fixed effects absorb time-invariant country characteristics and global annual shocks.

4. Results

4.1. Reactive Financing Pattern (H1)

The Pearson correlation between lagged log disaster losses and log bilateral adaptation commitments across the H1 panel is r = 0.21 (p < 0.001; Figure 1A), providing preliminary descriptive support for H1. This correlation is computed on log-transformed variables and reflects pooled co-movement prior to any fixed-effects adjustment; it should be interpreted as a descriptive diagnostic rather than a causal estimate. Notably, 78.7% of country-years (1392 of 1769) record zero EM-DAT disaster losses in the lagged year, reflecting both the episodic nature of major climate events and known EM-DAT reporting thresholds; the raw scatter for non-zero observations is shown in Figure 1A. Although the one-year lag between disaster loss and climate finance commitments reduces the risk of contemporaneous reverse causality, the association remains observational: omitted country-level factors such as institutional capacity or donor relationships could simultaneously drive both disaster exposure and financing inflows. The result is therefore interpreted as a robust conditional correlation rather than a causal estimate.

The two-way fixed-effects regression provides the primary test of H1 (Table 2). The coefficient for log lagged disaster loss is β = 0.040 (Driscoll–Kraay SE = 0.015, p = 0.009, 95% CI [0.010, 0.069]), statistically significant at the 1% level and robust to serial correlation and cross-sectional dependence. In a log–log specification, this coefficient is elastic: a doubling of prior-year disaster losses is associated with an approximately 2.8% increase in bilateral adaptation commitments in the following year, holding country and year fixed effects constant. While the direction is consistent with H1, finance does respond to realised losses and the magnitude is economically modest, suggesting that the reactive signal is present but weak. The system responds at the margin rather than proportionally to shock severity.

Among the control variables, the coefficient for log GDP per capita is β = −0.643 (SE = 0.155, p < 0.001), indicating that within-country income growth is associated with reduced adaptation finance receipts. This is consistent with aid graduation dynamics documented in the bilateral aid literature (Betzold & Weiler, 2017); as recipient-country income rises over time, donor governments reallocate commitments toward lower-income countries. The governance composite (WGI) carries a positive and significant coefficient of β = 0.870 (SE = 0.273, p = 0.002), confirming that institutional quality is a consistent predictor of adaptation finance receipt, a finding aligned with (Islam, 2022; Weiler et al., 2018), who show that absorptive capacity and governance quality shape donor allocation decisions.

The within-R² of 0.025 indicates that the three time-varying regressors explain approximately 2.5% of the within-country variation in adaptation commitments after absorbing country and year fixed effects. This is typical of aid allocation panels where the bulk of cross-country variation is captured by the country fixed effects themselves. It also underscores that year-to-year changes in adaptation finance are driven primarily by donor-side budgetary and political cycles absorbed by δ_t rather than by recipient-country disaster exposure alone, reinforcing the reactive rather than needs-based characterisation of the system.

Figure 2 illustrates the structural mismatch between risk-transfer market growth and insured loss exposure over 2010–2024. Catastrophe bond outstanding expanded at a real CAGR of 8.1%, rising from USD 16.2 billion to USD 48.1 billion in constant 2023 prices. Insured losses grew at 5.1% but with pronounced volatility peaking at USD 180 billion in 2017 following Hurricanes Harvey, Irma, and Maria and reached USD 133.3 billion in 2024. Despite sustained market growth, the protection gap averaged 61% across 2015–2024, ranging from 52% to 73%, meaning that more than half of total catastrophe losses remained uninsured throughout the period.

4.2. Geographic Distribution

Figure 3 shows the regional distribution of GCF-approved adaptation financing across the full portfolio (USD 20.3bn, 354 projects, 2015–2026/March). Africa accounts for the largest share (38%), consistent with its concentration of Least Developed Countries (32 of 54 recipient countries). Asia and the Pacific received 28%, driven by the highest SIDS count of any region (17 SIDS). Latin America and the Caribbean account for 23% across 16 SIDS. Eastern Europe, Central Asia, and the Middle East receives substantially less 10% of the total portfolio. Together, these three development-focused regions absorb 90% of GCF adaptation finance, reflecting the fund’s mandate to prioritise the most climate-vulnerable economies.

Figure 4 plots the estimated coefficients and 95% Driscoll–Kraay confidence intervals for both hypotheses side by side. In Panel A, WGI governance quality is the dominant predictor of bilateral adaptation commitments (β = 0.870, p < 0.01), while lagged disaster loss exerts a small but statistically significant positive effect (β = 0.040, p < 0.01) and income level a significant negative one (β = −0.643, p < 0.01). In Panel B, income level is again the strongest predictor of private finance mobilisation (β = 1.068, p < 0.01), while the WUI main effect is positive but not significant (β = 0.785, p = 0.60). The WUI × vulnerability-high interaction is negative and marginally significant (β = −2.587, p = 0.08), indicating that high policy uncertainty suppresses private finance in the most climate-exposed economies, which is the opposite of the catalytic relationship hypothesised under H2.

4.3. Policy Uncertainty and Private Climate Finance Mobilisation (H2)

Figure 5 presents the Pearson correlation matrix for all variables entering the H1 and H2 panels (N = 870 country-year observations; 95 countries; 2012–2024). The five countries in H2 that drop out of the merge are China (CHN), Côte d’Ivoire (CIV), Eritrea (ERI), Laos (LAO), and Turkmenistan (TKM). They have private mobilisation data in H2 but are missing from H1 due to OECD CRS or ND-GAIN gaps.

First, the disaster loss–CF commitment correlation is positive and modest (r = 0.18), consistent with the small but statistically significant panel estimate for H1 (β = 0.040, p < 0.01), and confirming that pooled correlations understate the within-country signal recovered by two-way fixed effects.

Second, CF commitments flow disproportionately to lower-income, more vulnerable recipients (r = −0.38 with GDP per capita; r = +0.32 with ND-GAIN vulnerability), consistent with needs-based donor targeting. This pattern also rules out a simple crowding-in story whereby public finance follows private capital into high-capacity markets—the direction of targeting runs the other way.

Third, WUI shows near-zero pooled correlation with private mobilisation (r = 0.02). This is expected: the uncertainty–investment relationship is a within-country, time-varying dynamic that only becomes visible once entity fixed effects absorb cross-sectional heterogeneity, as identified by the WUI × vulnerability-high interaction term in Table 2.

The near-zero pooled WUI private mobilisation correlation (r = 0.02; Figure 5) holds consistently across all income and regional subgroups. Among income groups, the correlation is effectively zero for both low-income countries (r = 0.000, p = 0.996; N = 479) and emerging-market countries (r = 0.069, p = 0.147; N = 439). Across the five regions in the H2 sample, correlations range from −0.107 to +0.115, with no group approaching conventional significance (all p > 0.10). The absence of subgroup heterogeneity reinforces that the suppression effect identified in Column 4 operates through the vulnerability dimension rather than through income level or geography.

Fourth, GDP per capita, WGI governance, and the two ND-GAIN indicators are strongly intercorrelated. The strongest association in the matrix is between income and climate vulnerability (r = −0.80), reflecting that wealthier countries are systematically less exposed. Income and institutional quality also move closely together (r = 0.61), as the governance and adaptive readiness (r = 0.75), and vulnerability and readiness run in opposite directions (r = −0.65). This co-movement confirms that these indicators capture overlapping dimensions of development and motivates their joint inclusion as controls rather than as independent explanatory variables.

4.4. Instrument Leverage and Private Finance Mobilisation (H3)

The instrument-level analysis in Figure 6 provides empirical support for H3. Panel A shows average leverage ratios and uncertainty ranges for six instrument types drawn from OECD mobilisation records (2015–2024). Guarantees exhibit the highest average leverage at 4.1× (upper bound 6.5×), followed by syndicated loans at 3.2× (upper bound 5.0×), direct investment in companies and SPVs at 2.8× (upper bound 4.0×), shares in collective investment vehicles at 2.3×, credit lines at 2.0×, and simple co-financing at 1.4×. These estimates are broadly consistent with OECD blended finance evaluation benchmarks, which report average leverage of 3.6× for climate mitigation and 2.1× for climate adaptation transactions (Brandon et al., 2025), reflecting the less mature market for adaptation-focused blended structures. The upper-bound estimates for guarantees reflect best-in-class transactions where risk-transfer structures are specifically designed to maximise private mobilisation.

Panel B situates each instrument in a two-dimensional space defined by average leverage ratio and total private capital mobilised over the study period. Direct investment in companies and SPVs mobilised the largest absolute volume of USD 53.0 billion across 2015–2024 despite its mid-range leverage ratio of 2.8×, reflecting its broad reach across approximately 43 recipient countries per year. Guarantees mobilised USD 34.1 billion and syndicated loans USD 32.4 billion, concentrated across a narrower set of approximately 30 recipient countries. Simple co-financing reaches the widest recipient base (approximately 81 countries per year) but at the lowest leverage, confirming that breadth and efficiency trade-off across instrument types. The colour scale in Panel B proxying average protection gaps is uniform across instruments at 61%, reflecting the panel-level average for 2015–2024 rather than instrument-specific exposure, and should be interpreted accordingly.

Taken together, these patterns confirm that H3 instrument choice is a first-order determinant of private finance leverage, with a four-fold difference in average mobilisation efficiency between the most and least effective instrument types. The results support the policy implication that shifting the instrument mix within the existing public finance envelope from simple co-financing toward guarantees and syndicated structures can substantially increase private capital mobilisation without requiring additional public expenditure.

5. Discussion

5.1. Reactive Finance, Fiscal Exposure, and the Corporate Risk Nexus

The fixed-effects panel estimates provide evidence consistent with a reactive pattern in bilateral adaptation finance. A one-unit increase in log lagged disaster loss is associated with a 0.040-unit increase in log bilateral adaptation commitment in the following year (β = 0.040, p < 0.01, Driscoll-Kraay SE), after controlling for income level, governance quality, and country and year fixed effects. This association is statistically robust but modest in magnitude (R²_within = 0.025). Results are consistent with the hypothesis that bilateral donors respond to observable disaster events in prior periods, a pattern documented in the broader aid allocation literature. From a fiscal policy perspective, a reactive allocation pattern has significant implications for sovereign budget management. When public adaptation finance is mobilised primarily in response to realised losses rather than anticipated risk, governments face compounding fiscal pressures: emergency expenditure is drawn from the same budget envelope that would otherwise fund structural resilience investment. This dynamic is particularly acute in lower-income, high-vulnerability countries, the same countries that receive disproportionately more CF commitments (r = +0.32 with ND-GAIN vulnerability; r = −0.38 with GDP per capita, Figure 5) where fiscal buffers are thinnest and recovery timelines are longest. The implication for fiscal policy is that pre-arranged financing instruments—contingent credit lines, catastrophe reserve funds, or parametric insurance—offer a structurally superior alternative to ex-post appropriations, reducing both the cost and timing lag of disaster response.

From a corporate finance perspective, the reactive pattern implies that firms operating in climate-exposed sectors such as agriculture, coastal real estate, and energy infrastructure bear residual physical risk that is not offset by anticipatory public finance. The translation of this risk into financial flows depends on the enabling conditions identified in the instrument-level analysis: governance quality (β = 0.870, p < 0.01 in H1) is the dominant predictor of bilateral commitment levels, suggesting that institutional capacity, not disaster severity alone, determines whether finance reaches exposed economies. Embedding ex-ante risk management into corporate planning—through climate stress testing, contingent liability disclosure, and alignment with national adaptation finance frameworks—is therefore a precondition for private capital mobilisation rather than a downstream consequence of public finance flows.

5.2. Policy Uncertainty as a Conditional Barrier

The H2 results reveal that the relationship between climate policy uncertainty and private finance mobilisation is conditional on country vulnerability rather than universal. The World Uncertainty Index (Ahir et al., 2022) main effect on private mobilisation as a share of GDP is positive and statistically insignificant (β = +0.785, p = 0.600), indicating that policy uncertainty does not uniformly suppress private investment across all developing economies. However, the interaction term between WUI and high climate vulnerability (WUI × vuln_high) is negative and marginally significant (β = −2.587, p = 0.080), providing partial support for H2, whereby elevated policy uncertainty is associated with lower private mobilisation, specifically in the most climate-exposed economies, where adaptation investment is most urgently needed. This constitutes a form of country-group disaggregation in that the estimated uncertainty penalty is concentrated among high-vulnerability recipients, while lower-vulnerability economies show no statistically discernible effect. The policy implication is that instruments specifically designed to transfer or absorb climate risk guarantees, syndicated loans, catastrophe bonds, and blended credit facilities are most constrained precisely where the protection gap is largest. Foundational work by (Huang & Sun, 2024) establishes that policy uncertainty reduces corporate investment through real-options channels; our interaction result suggests that this mechanism operates with particular force in high-vulnerability contexts where investor uncertainty about policy continuity compounds underlying physical risk. National adaptation plans that include legislated climate risk disclosure requirements and multi-year budget commitments to insurance premiums and guarantee funds can directly address this barrier by reducing the policy risk premium that currently deters private capital in high-exposure markets.

5.3. Risk-Layering as an Organising Framework

The leverage ratio findings (Figure 6) confirm that the choice of financial instrument is not neutral: risk-transfer mechanisms generate substantially more private capital per public dollar than concessional instruments. This finding, combined with the reactive financing pattern identified under H1, points toward the need for a structured risk-layering framework that aligns instrument selection with hazard tier.

In the proposed architecture (Figure 7), high-frequency, low-severity events such as seasonal flooding are managed through national budget reserves and contingency funds. Medium-severity risks such as regional droughts are addressed through insurance pools and parametric triggers. Rare but catastrophic events such as major hurricanes, large-scale earthquakes are handled through catastrophe bonds, international concessional assistance, and debt-for-nature mechanisms. This tiered structure ensures that resources are deployed efficiently, that funding is available at the moment of need, and that private capital is engaged at the tier where its leverage is highest.

Capital flows from funding sources (multilateral funds, bilateral donors, development finance institutions, institutional and private investors) through financial instruments (grants, concessional loans, guarantees, equity, parametric insurance, catastrophe bonds) to adaptation end-uses (resilient infrastructure, adaptation projects, disaster risk reduction investments, early warning systems). Ribbon widths are illustrative, informed by aggregate flow data. Sources: (Artemis, n.d.; Convergence Blended Finance, 2024; CPI, 2024; Green Climate Fund [GCF], 2026; OECD, 2025b).

Early warning systems (EWSs) occupy a cross-cutting role in this architecture, providing the data and trigger infrastructure that underpin parametric instruments across all tiers. Despite consistently high cost–benefit ratios, EWSs remain chronically underfunded (UNDRR, 2022). Increasing public investment in EWSs and structuring parametric triggers around EWS outputs represents one of the highest-return interventions available within the risk-layered framework.

5.4. Geographic Equity and the Deployment Gap in Risk-Finance Instruments

The GCF regional distribution (Figure 3) shows Africa receiving the largest share of adaptation finance (38%, USD 7.6bn), followed by Asia–Pacific (28.5%), Latin America and the Caribbean (23.2%), and Eastern Europe and Central Asia (10.3%). While this broadly tracks vulnerability need, the correlation between CF commitments and ND-GAIN vulnerability in the H1 panel (r = +0.32, Figure 5) confirms that needs-based targeting operates imperfectly at the country level: governance quality (β = 0.870, p < 0.01) remains the dominant predictor of bilateral commitment levels, meaning the most institutionally constrained and often most exposed countries receive systematically less finance per unit of vulnerability. This gap is not confined to developing economies. High-income markets in Europe and North America also face significant uninsured climate losses the global protection gap averaged 61% over 2015–2024 (Figure 2)—reflecting structural underinvestment in resilience finance across vulnerability contexts (Ocampo & Moreira, 2024). Risk-transfer instruments, catastrophe bonds, guarantees, and parametric insurance are well-established in high-income markets but remain significantly underdeployed in the climate-vulnerable developing economies, where H2 results show the uncertainty penalty is concentrated (WUI × vuln_high β = −2.587, p = 0.080). The barrier is not instrument novelty but institutional readiness: lower governance scores and thinner financial systems raise transaction costs and reduce investor confidence, limiting access to structures that are routine in European and North American markets (OECD & UNCDF, 2020; Pleterski, 2025; Robinson, 2020).

Addressing this deployment gap requires targeted public and concessional support to extend resilience finance to contexts with weaker institutions or higher perceived risk. Regional risk-pooling models such as CCRIF and PCRIC demonstrate that sovereign catastrophe risk transfer can be made affordable through collective risk diversification, even in highly exposed, low-income contexts (CCRIF SPC, 2025; Pacific Catastrophe Risk Insurance Company [PCRIC], 2024). Scaling these models to other regions including West Africa, the Indian Ocean basin, and Central Asia would require upfront capitalization support from multilateral development banks, but the leverage ratios identified in this study suggest that the subsequent private capital mobilisation would justify the investment.

5.5. Policy Recommendations for a Risk-Layered Adaptation Finance Architecture

Based on the empirical findings, this study offers the following recommendations, grounded in three core results: the reactive financing pattern identified under H1, the vulnerability-conditional uncertainty barrier under H2, and the instrument leverage gradient under H3.

5.5.1. Governments and Multilateral Donors

The H1 findings state that bilateral finance responds to prior-year disaster losses rather than anticipated risk, implying that reactive budgeting is the prevailing norm. Governments, particularly in high-vulnerability developing economies, should formalise ex-ante risk finance strategies that pre-allocate resources across hazard tiers, such as contingency reserves for high-frequency events, insurance pools for moderate risks, and contingent credit or catastrophe bonds for low-frequency catastrophes (Clarke & Dercon, 2016; World Bank Group, 2014). Such frameworks already exist in several European and OECD economies; the priority is to extend coverage requirements to climate adaptation specifically and to mandate multi-year budget commitments to insurance premiums and guarantee funds (Kellett & Caravani, 2013; Taylor, 2023). The H2 interaction result (WUI × vuln_high β = −2.587, p = 0.080) confirms that policy uncertainty suppresses private mobilisation most severely in climate-exposed economies. Mandatory climate risk disclosure requirements embedded in national adaptation plans and sovereign debt frameworks directly address this barrier by providing investors with the stable policy signals needed to price adaptation instruments (Bhandary et al., 2021; Huang & Sun, 2024). Multi-year budget commitments to guarantee funds and insurance premiums should be treated as non-discretionary fiscal infrastructure, not subject to annual appropriation cycles (Hochrainer-Stigler et al., 2014).

5.5.2. Financial Institutions and Banks

The H3 results identify a four-fold difference in private mobilisation efficiency between guarantees (4.1×) and simple co-financing (1.4×). Banks and development finance institutions should shift their instrument mix toward high-leverage structure guarantees, syndicated loans, and blended credit facilities, which generate substantially more private capital per unit of public finance deployed (Boston Consulting Group [BCG], 2026; Brandon et al., 2025). Building internal climate physical risk assessment capacity is a prerequisite for pricing these structures appropriately (Battiston et al., 2017, 2021; Campiglio et al., 2023) Dedicated adaptation finance windows with streamlined approval would reduce transaction costs that currently deter deployment in governance-constrained markets, the same markets where the governance coefficient (β = 0.870, p < 0.01) identifies institutional capacity as the binding constraint on bilateral flows. The USD 100–130 billion annual financing opportunity identified by Boston Consulting Group (BCG, 2026) represents a fully incremental market for institutions that develop early-mover expertise in adaptation-linked structures.

5.5.3. Institutional Investors

Institutional investor pension funds, sovereign wealth funds, and insurance companies should mainstream climate physical risk into asset allocation frameworks as a fiduciary obligation, consistent with evidence that climate risk exposures represent material to long-term portfolio returns (Krueger et al., 2020; Ranger et al., 2021). Allocations to resilience-linked green bonds, climate-resilient infrastructure funds, and catastrophe bond portfolios which have demonstrated real CAGR of 8.1% over 2010–2024 (Figure 2) are consistent with this objective. Investor engagement with portfolio companies on physical climate risk management can further accelerate corporate-level adaptation investment (Krueger et al., 2020; Mandel et al., 2025).

5.5.4. Insurance and Reinsurance Sector

The protection gap averaged 61% over 2015–2024 (Figure 2), confirming that the majority of climate-related losses remain uninsured globally. The insurance and reinsurance sector should treat closing this gap as a core business priority, scaling parametric coverage for agriculture, urban infrastructure, and sovereign risk in currently underserved markets (Biffis et al., 2022; Ocampo & Moreira, 2024; Pleterski, 2025). Product innovation through hybrid instruments combining parametric triggers with indemnity components can extend coverage to markets where data scarcity currently prevents pure parametric pricing (Bonazzi et al., 2024; Surminski & Oramas-Dorta, 2014). Regional risk-pooling models such as CCRIF and PCRIC provide proven templates for extending affordable sovereign coverage through collective diversification (CCRIF SPC, 2025; Pacific Catastrophe Risk Insurance Company [PCRIC], 2024). Collaboration with governments and MDBs on public reinsurance backstops for uninsurable tail risks, particularly in the high-vulnerability economies identified in H2, is essential for maintaining sector solvency as physical risk concentrations intensify (Mallucci, 2022; Ranger et al., 2021).

6. Conclusions

This study set out to address the question of how innovative financial instruments and a risk-layered architecture can close the adaptation finance gap and shift climate resilience finance from reactive to anticipatory. The empirical findings provide clear answers on each of the three hypotheses tested.

First, adaptation finance is demonstrably reactive: bilateral flows track realised disaster losses with a one-year lag (β = 0.040, p < 0.01; pooled r = 0.18), confirming H1. The lag perpetuates exposure, inflates recovery costs, and delays the protective investments that would reduce future losses. The governance quality of recipient countries further conditions allocation: The WGI composite coefficient (β = 0.870, p < 0.01) indicates that institutional capacity is a prerequisite for absorbing bilateral adaptation finance, a finding with direct implications for SIDS and LDC programming (Bhandary et al., 2021; Robinson, 2020). Second, climate policy uncertainty does not exert a statistically significant main effect on private climate finance mobilisation (β = 0.785, p = 0.600); however, the vulnerability-conditioned interaction (WUI × Vuln-high: β = −2.587, p = 0.080) provides partial support for H2, identifying regulatory instability as a disproportionate barrier to scaling resilience finance. Third, risk-transfer instrument guarantees mobilise between 2.9 and 4.1 times more private capital per public dollar than concessional instruments (with the upper Driscoll–Kraay confidence interval reaching 6.5× for guarantees, with average blended finance leverage documented at 2.1–3.6× across the broader market (Convergence Blended Finance, 2024)), confirming H3 and establishing the instrument hierarchy that should guide a risk-layered architecture.

The proposed risk-layered climate finance architecture operationalizes these findings by aligning instrument selection with hazard tier, engaging private capital where its leverage is highest, and reserving concessional public finance for contexts and tiers where market-based instruments are insufficient. Closing the substantial gap between current adaptation flows and estimated 2030 needs (CPI, 2024; United Nations Framework Convention on Climate Change [UNFCCC], 2024) will require this architecture to become standard practice rather than a niche experiment embedded in national climate strategies, multilateral lending programmes, and corporate risk management frameworks alike. This is especially urgent for Sub-Saharan Africa and SIDS, which together account for the majority of GCF-approved adaptation allocations (38% and a disproportionate per-country share, respectively) yet remain most constrained by the institutional capacity barriers identified in H1.

A more resilient financial architecture will not prevent hurricanes, floods, or droughts. It can, however, ensure that governments, corporations, and communities are financially prepared to respond when they occur, reducing recovery time, limiting economic setbacks, and unlocking the proactive investments that shift the trajectory from reactive crisis response to anticipatory risk management. Realising this shift will require not only instrument innovation but commensurate investment in the governance and institutional capacity of recipient countries, the factor this study identifies as the single strongest correlate of bilateral adaptation finance allocation. In an era of converging climate and economic uncertainties, building this proactive finance framework is not optional: it is an imperative for global financial stability and sustainable development.