Module 4: Green Finance Risk Management#

UT (University of Twente)

Interactive Module Page: Explore Module 4 with slides, formulas, and case studies

Module Overview#

This module addresses climate-related financial risks and their management within financial institutions and investment portfolios. Students learn to classify, measure, and manage climate risks using TCFD/TNFD frameworks, NGFS scenarios, carbon metrics, Climate Value-at-Risk (CVaR), and stress testing. All concepts are applied to the ASEAN context with equal coverage of Vietnam, Thailand, and the Philippines.

M4/M5 Boundary: Module 4 covers risk management (identify, measure, manage). Module 5 covers risk reporting (disclose, communicate). TCFD and TNFD appear here as risk assessment frameworks. Reporting and disclosure compliance belongs in Module 5.

Lessons:

1. Climate-Related Financial Risks: Taxonomy, Transmission, and Materiality
2. Climate and Nature Risk Frameworks: TCFD and TNFD for Risk Assessment
3. Scenario Analysis and Climate Modeling: NGFS Pathways to IAMs
4. Carbon Metrics and Climate Value-at-Risk: Quantifying Portfolio Exposure
5. Climate Stress Testing: Design, Execution, and Supervisory Practice
6. ASEAN Climate Vulnerability: Integrated Risk Assessment and Adaptation Finance

Learning Outcomes#

Upon completing this module, students will be able to:

1. Classify climate-related financial risks using physical/transition/liability taxonomy and map transmission channels to financial institutions
2. Apply TCFD and TNFD frameworks for internal climate and nature risk assessment
3. Model climate scenarios using NGFS pathways and Integrated Assessment Models
4. Calculate financed emissions, carbon metrics, and Climate Value-at-Risk (CVaR) for portfolios
5. Design climate stress tests using NGFS scenarios and evaluate supervisory approaches
6. Evaluate ASEAN-specific climate vulnerabilities and adaptation finance mechanisms

Topics Covered#

1. Climate-Related Financial Risks: Taxonomy, Transmission, and Materiality#

View detailed lesson page with slides, formulas, and case study

Pedagogical arc: “Before you can manage risk, you must classify it.”

Foundation#

• Physical risks – acute (typhoons, floods, droughts) and chronic (sea-level rise, temperature increase, precipitation shifts)
• Transition risks – policy (carbon taxes, emission caps), technology (renewable disruption), market (consumer preference shifts), reputation (divestment campaigns), legal (climate litigation)
• Liability risks – litigation exposure, failure to adapt, greenwashing liability
• Risk transmission channels – credit risk (PD increase), market risk (asset repricing), operational risk (supply chain disruption), liquidity risk (stranded asset fire sales)
• Time horizons – short (1-3 years), medium (3-10 years), long (10-30+ years)
• Materiality assessment – financial materiality vs. impact materiality (double materiality concept)

Intermediate#

• Sectoral exposure mapping: high-exposure sectors (fossil fuels, cement, steel, aviation, agriculture) vs. low-exposure sectors (technology, healthcare)
• Compound risk analysis: physical + transition interaction effects (e.g., flood damages compounded by carbon tax on reconstruction materials)
• Risk heatmap construction: sector x geography x time horizon matrix
• Worked example: Classify risks for a diversified ASEAN bank portfolio across 10 sectors and 3 countries

PhD Research#

• Risk taxonomy evolution: from Carney (2015) “tragedy of the horizon” speech to NGFS risk categories
• Endogenous vs. exogenous risk framing (Battiston et al. 2017) – climate risk as systemic, not idiosyncratic
• Network propagation models: how climate shocks cascade through interbank networks and supply chains
• Climate litigation as emerging systematic risk (Setzer & Higham 2023) – 2,500+ cases globally, liability exposure quantification

Quantitative Lab#

• Dataset: NGFS IIASA Scenario Explorer + World Bank Climate Portal (both free, open access)
• Code: Python notebook – construct a sector x risk-type heatmap for a synthetic ASEAN bank portfolio using matplotlib + seaborn
• Output: 10-sector x 6-risk-type heatmap with severity scores (1-5 scale), color-coded by exposure level

ASEAN Case Study: Vietnam – Mekong Delta Physical Risk Exposure#

• The Mekong Delta produces 50% of Vietnam’s rice output and 90% of aquaculture exports
• Accelerating saltwater intrusion (projected 40-50km inland by 2050) and seasonal flooding
• Task: Classify all climate risks facing a bank with 30% agricultural loan exposure in the Delta region, identify transmission channels (credit, market, operational), and assess financial materiality using a 3-horizon framework

Key References#

• Carney, M. (2015). Breaking the tragedy of the horizon – climate change and financial stability. Bank of England.
• Battiston, S., Mandel, A., Monasterolo, I., et al. (2017). A climate stress-test of the financial system. Nature Climate Change, 7(4), 283-288.
• NGFS (2024). Guide to Climate-Related Financial Risks for Supervisors.
• ADB (2021). Climate Risk Country Profiles: Southeast Asia.
• Setzer, J. & Higham, C. (2023). Global Trends in Climate Change Litigation: 2023 Snapshot. Grantham Research Institute, LSE.

2. Climate and Nature Risk Frameworks: TCFD and TNFD for Risk Assessment#

View detailed lesson page with slides, formulas, and case study

Pedagogical arc: “Now that you can classify risks, learn the global framework for assessing them.”

M4/M5 boundary: This lesson teaches TCFD/TNFD as risk assessment tools (the “Risk Management” pillar). Reporting and disclosure aspects belong in Module 5.

Foundation#

• TCFD four pillars: Governance, Strategy, Risk Management, Metrics & Targets
• Risk Management pillar focus: processes for identifying, assessing, and managing climate-related risks
• Enterprise Risk Management (ERM) integration: embedding climate risk into existing risk frameworks
• TNFD and the LEAP approach: Locate, Evaluate, Assess, Prepare – extending risk assessment to nature and biodiversity
• Nature-finance link: deforestation risk, water stress, pollinator loss as financial risk drivers

Intermediate#

• TCFD implementation for internal risk assessment: translating recommendations into operational risk processes
• TNFD sector guidance: financial institutions, agriculture, extractives
• Scenario analysis requirements under TCFD Risk Management pillar
• Basel III/IV integration: how climate risk fits into Pillar 1 (capital), Pillar 2 (supervisory review), Pillar 3 (disclosure)
• Worked example: Score a Thai commercial bank’s TCFD Risk Management implementation against best practice

PhD Research#

• TCFD effectiveness critique: Krueger, Sautner & Starks (2020) – do institutional investors act on climate risk?
• TNFD data challenges: spatial resolution, biodiversity metrics, dependency mapping
• Double materiality in practice: reconciling financial and impact materiality in risk assessment
• Regulatory landscape evolution: from voluntary TCFD to mandatory ISSB S2

Quantitative Lab#

• Task: Score TCFD Risk Management pillar disclosures for 10 ASEAN financial institutions (5 banks, 3 insurers, 2 asset managers)
• Method: Rubric-based scoring (0-3 per sub-criterion), weighted by implementation quality
• Output: Radar chart comparing institutions across TCFD Risk Management sub-categories (identification, assessment, management, integration)

ASEAN Case Study: Thailand – Bank of Thailand TCFD Implementation#

• Bank of Thailand (BOT) mandated TCFD-aligned climate risk assessment for all commercial banks from 2024
• Task: Compare BOT’s supervisory approach to TCFD risk management with MAS (Singapore) and SBV (Vietnam), evaluate implementation maturity across the three jurisdictions, and identify gaps in ASEAN supervisory practice

Key References#

• TCFD (2023). Final Report: Recommendations of the Task Force on Climate-related Financial Disclosures.
• TNFD (2023). Recommendations of the Taskforce on Nature-related Financial Disclosures.
• Krueger, P., Sautner, Z., & Starks, L.T. (2020). The importance of climate risks for institutional investors. Review of Financial Studies, 33(3), 1067-1111.
• Bank of Thailand (2023). Sustainable Finance Framework and TCFD Guidelines.
• MAS (2022). Guidelines on Environmental Risk Management for Banks.

3. Scenario Analysis and Climate Modeling: NGFS Pathways to IAMs#

View detailed lesson page with slides, formulas, and case study

Pedagogical arc: “With risk identified and frameworks established, now model the future.”

Foundation#

• Why scenarios, not predictions: deep uncertainty in climate-economy interactions requires exploring multiple futures
• NGFS framework: Orderly (early, smooth transition), Disorderly (late, abrupt transition), Hot House World (limited action, severe physical risks)
• Key scenario variables: carbon price trajectories, energy mix, GDP impact, temperature pathways
• IEA scenarios: Net Zero Emissions, Announced Pledges, Stated Policies – complementary energy-sector detail
• Scenario-to-financial-impact chain: climate pathway –> macro variables –> sector shocks –> portfolio losses

Intermediate#

• NGFS Scenario Explorer: navigating the online tool, downloading data, selecting variables
• Scenario selection for ASEAN: which NGFS scenarios are most relevant for emerging market portfolios
• Calibrating sectoral shocks: translating macro scenario variables into sector-specific PD/LGD adjustments
• Monte Carlo extensions: adding stochastic variation around central scenario paths
• Worked example: Map NGFS Disorderly transition scenario to a Philippine commercial bank’s sector exposures

PhD Research#

• Integrated Assessment Models (IAMs): DICE (Nordhaus), REMIND-MAgPIE (PIK), GCAM (PNNL), MESSAGE (IIASA)
• Damage function specification: Damage = alpha * T^2 / (1 + beta * T^2) – sensitivity to functional form
• Sensitivity analysis: discount rate choice (Stern 1.4% vs. Nordhaus 4.5%), climate sensitivity parameter, damage function curvature
• IAM critiques: Weitzman (2009) fat-tailed catastrophic risk, Pindyck (2013) “models of doom,” Stern-Nordhaus debate on discounting

Quantitative Lab#

• Task: Download NGFS Scenario Explorer data for Vietnam, Thailand, and Philippines under 3 scenarios (Net Zero 2050, Delayed Transition, Current Policies)
• Code: Python notebook – extract GDP, carbon price, energy mix, and temperature variables; generate Monte Carlo fan charts with confidence bands; produce sensitivity tornado diagrams for key parameters
• Output: 3-country x 3-scenario comparison dashboard with fan charts and tornado plots

ASEAN Case Study: Philippines – Typhoon Exposure Under Climate Scenarios#

• The Philippines experiences 20+ typhoons annually; climate models project intensity increases of 10-30% by 2050
• Super Typhoon Haiyan (2013) caused $12.9 billion in damages
• Task: Model agricultural loan default rates under NGFS Hot House and Disorderly scenarios, incorporating projected typhoon intensity increases and cropping pattern shifts

Key References#

• NGFS (2024). NGFS Climate Scenarios for Central Banks and Supervisors (Phase IV).
• Nordhaus, W. (2017). Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 114(7), 1518-1523.
• Weitzman, M. (2009). On modeling and interpreting the economics of catastrophic climate change. Review of Economics and Statistics, 91(1), 1-19.
• IEA (2024). World Energy Outlook 2024.
• ADB (2023). Climate Risk and Adaptation in the Philippines Financial Sector.

4. Carbon Metrics and Climate Value-at-Risk: Quantifying Portfolio Exposure#

View detailed lesson page with slides, formulas, and case study

Pedagogical arc: “Now measure the risk. Put numbers on the exposure.”

Foundation#

• GHG Protocol: Scope 1 (direct emissions), Scope 2 (purchased energy), Scope 3 (value chain – 15 categories)
• Financed emissions formula: FE_i = (Outstanding_i / TotalAssets_i) x Borrower_Emissions_i
• Weighted Average Carbon Intensity (WACI): portfolio-weighted sum of revenue-normalized emissions

WACI = Sum(w_i x Emissions_i / Revenue_i)

where w_i is the portfolio weight of holding i, Emissions_i is the absolute greenhouse gas emissions, and Revenue_i is the total revenue of company i.

• PCAF data quality tiers: Tier 1 (verified borrower data) through Tier 5 (sector average estimates)
• Portfolio alignment tools: PACTA (sector decarbonization pathways), TPI (management quality + carbon performance), SBTi (science-based target validation)

Intermediate#

• PCAF methodology per asset class: corporate loans, project finance, mortgages, sovereign debt
• Calculating WACI for a sample ASEAN portfolio: data sourcing, gap-filling, normalization
• Implied Temperature Rise (ITR) calculation: mapping portfolio trajectory to temperature outcome
• ASEAN data challenges: low Scope 1/2 coverage in emerging markets, sector-proxy approaches, PCAF Tier 4-5 reliance

PhD Research#

• Climate Value-at-Risk (CVaR): CVaR = Portfolio_t0 - E[Portfolio_tT | Scenario]
• Physical CVaR: Sum over i of (Exposure_i x PD_climate_i x LGD_i) – acute + chronic physical risk losses
• Transition CVaR: Sum over i of (Exposure_i x Delta_PD_carbontax_i x LGD_i) – policy-driven repricing losses
• Carbon beta: R_i = alpha + beta_mkt * R_m + beta_carbon * CarbonIntensity – cross-sectional asset pricing factor
• Bolton & Kacperczyk (2021): carbon premium in equity returns, higher returns for high-emission firms
• Event study and difference-in-differences designs for carbon policy impact estimation

Quantitative Lab#

• Task: Build a complete carbon metrics pipeline for a synthetic 50-borrower ASEAN portfolio
• Code: Python notebook – (1) calculate financed emissions per borrower using PCAF formula, (2) compute portfolio WACI and ITR, (3) calculate Physical CVaR and Transition CVaR under two NGFS scenarios, (4) estimate carbon beta via OLS regression on historical return data
• Output: Portfolio carbon dashboard with emissions waterfall, CVaR decomposition chart, and regression summary table

ASEAN Case Study: Vietnam – Financed Emissions of the Banking Sector#

• Vietnamese banking sector: ~18% agricultural loan exposure + ~3% coal/fossil fuel exposure
• Counterintuitive finding: agriculture Physical CVaR often exceeds coal Transition CVaR due to extreme flood/drought exposure
• Task: Calculate financed emissions for a representative Vietnamese bank, decompose by sector and scope, compute Physical vs. Transition CVaR, and explain the agriculture-coal CVaR inversion

Key References#

• PCAF (2024). The Global GHG Accounting and Reporting Standard for the Financial Industry (3rd edition).
• Bolton, P. & Kacperczyk, M. (2021). Do investors care about carbon risk? Journal of Financial Economics, 142(2), 517-549.
• Battiston, S., et al. (2017). A climate stress-test of the financial system. Nature Climate Change, 7, 283-288.
• 2DII (2024). PACTA Methodology Documentation.
• Sautner, Z., van Lent, L., Vilkov, G., & Zhang, R. (2023). Firm-level climate change exposure. Journal of Finance, 78(3), 1449-1498.

5. Climate Stress Testing: Design, Execution, and Supervisory Practice#

View detailed lesson page with slides, formulas, and case study

Pedagogical arc: “You have measured the risk. Now stress-test the institution’s resilience.”

Foundation#

• What is a climate stress test: assessing financial institution resilience to climate-related shocks
• Key differences from traditional stress tests: longer time horizons (30+ years vs. 3-5), scenario uncertainty (deep vs. parametric), data gaps (novel vs. historical), systemic scope (economy-wide vs. institution-specific)
• Design elements: scenario selection, transmission channels, portfolio mapping, loss estimation, capital impact
• Supervisory approaches: ECB Climate Stress Test (2022), BoE Climate Biennial Exploratory Scenario (CBES 2021), HKMA pilot, MAS industry-wide stress test

Intermediate#

• Top-down vs. bottom-up approaches: macro stress (regulator-driven aggregate) vs. micro stress (institution-level granular)
• Transmission channel modeling: PD/LGD adjustments for credit risk, asset repricing for market risk, business disruption for operational risk
• Capital adequacy impact: Delta_CAR = (RegulatoryCapital - ClimateLosses) / RWA – pre-stress vs. post-stress comparison
• Data proxies and gap-filling: using sector averages, geographic proxies, and expert judgment where borrower-level data is unavailable
• Worked example: Top-down climate stress test for a Thai mid-size bank under NGFS Delayed Transition scenario

PhD Research#

• Full bottom-up stress testing methodology: loan-level PD models with climate covariates, LGD estimation under physical damage scenarios
• Dynamic balance sheet assumptions: management actions, portfolio rebalancing, new lending under stress
• Second-round effects: contagion through interbank markets, fire-sale externalities, feedback loops between real economy and financial system
• Comparing supervisory approaches: ECB vs. BoE vs. ASEAN – scope, methodology, data requirements, and disclosure of results
• Research frontier: Acharya, V., Berner, R., et al. (2023) on climate stress testing design and calibration challenges

Quantitative Lab#

• Task: Execute a simplified climate stress test on a synthetic 1,000-loan ASEAN bank portfolio
• Code: Python notebook – (1) load portfolio with sector/geography/maturity attributes, (2) apply NGFS-calibrated PD and LGD shocks by sector and scenario, (3) calculate stressed provisions and expected losses, (4) compute pre-stress and post-stress Capital Adequacy Ratio (CAR)
• Visualization: QGIS map overlay – Thai bank branch locations vs. flood risk zones (using open flood data)
• Output: Stress test results dashboard with loss waterfall, CAR comparison bar chart, and geographic risk map

ASEAN Case Study: Thailand – BOT Climate Stress Test Pilot#

• Thailand’s 2011 floods caused $46.5 billion in damages – a natural calibration event for climate stress models
• BOT launched its climate stress test pilot program in 2023 using NGFS scenarios with 2050 projections
• Task: Use the 2011 flood as a historical calibration point, project forward under NGFS Hot House scenario with intensified flood frequency, estimate capital impact on a representative Thai bank, and evaluate the BOT supervisory framework

Key References#

• ECB (2022). 2022 Climate Risk Stress Test: Results and Key Findings.
• Bank of England (2022). Results of the 2021 Climate Biennial Exploratory Scenario (CBES).
• UNEP FI (2024). Good Practice Guide to Climate Stress Testing for Financial Institutions.
• Acharya, V., Berner, R., Engle, R., et al. (2023). Climate stress testing. Annual Review of Financial Economics, 15, 291-326.
• Bank of Thailand (2024). Climate Risk Stress Testing Framework for Thai Financial Institutions.

6. ASEAN Climate Vulnerability: Integrated Risk Assessment and Adaptation Finance#

View detailed lesson page with slides, formulas, and case study

Pedagogical arc: “Apply everything you learned to your own region. Make it real.”

Foundation#

• ASEAN’s unique climate risk profile: tropical location, long coastlines, agriculture-dependent economies, rapid urbanization in flood-prone areas
• Vietnam: Mekong Delta sea-level rise, Hanoi/HCMC urban flooding, agricultural transition pressure
• Thailand: Bangkok subsidence + flood risk, drought in the Northeast, coral reef loss affecting tourism
• Philippines: typhoon belt exposure (20+ storms/year), volcanic/seismic compound risks, remittance-dependent coastal communities
• Adaptation finance: why mitigation alone is insufficient for climate-vulnerable developing countries
• Adaptation instruments: catastrophe bonds (cat bonds), resilience bonds, Green Climate Fund (GCF), Adaptation Fund, parametric insurance
• Just transition: ensuring climate policies do not disproportionately burden vulnerable populations

Intermediate#

• Country-level integrated risk framework: Hazard x Exposure x Vulnerability = Risk
• Deep dives for each country: Vietnam (Mekong Delta salinity + HCMC flooding), Thailand (Chao Phraya Basin + Eastern Seaboard industrial zone), Philippines (Visayas typhoon corridor + Metro Manila flood basin)
• Adaptation finance gap: estimated $40 billion/year needed for ASEAN adaptation, only ~$5 billion currently mobilized
• Blended finance for adaptation: structuring concessional + commercial capital for resilience infrastructure
• Comparative analysis of national adaptation plans and NDC commitments across all three countries

PhD Research#

• GIS spatial risk modeling: overlaying climate hazard layers with financial exposure data (bank branch networks, loan portfolio locations)
• Panel regression: NPL_it = alpha + beta_1 * ClimateEvent_it + beta_2 * Controls_it + gamma_i + delta_t + epsilon_it (country and time fixed effects)
• Difference-in-differences: estimating causal effect of climate disclosure mandates on bank lending behavior
• Adaptation finance effectiveness: do GCF and Adaptation Fund disbursements reduce climate-related NPLs? (instrumental variable approaches)
• Research gaps: ASEAN-specific damage functions, cross-border climate risk transmission, adaptation finance additionality measurement

Quantitative Lab#

• Task: Build an integrated ASEAN climate vulnerability dashboard combining spatial, financial, and adaptation data
• Code: (1) QGIS – flood zone mapping for Vietnam, Thailand, Philippines using open hazard data, (2) Python – ND-GAIN vulnerability index visualization and decomposition, (3) Python – panel regression of disaster frequency on NPL ratios (EM-DAT + national banking data), (4) Python – adaptation finance gap charts (committed vs. needed by country and sector)
• Output: Multi-panel dashboard with maps, vulnerability decomposition, regression results, and finance gap visualization

ASEAN Case Study: Philippines – Typhoon Belt Banking Sector Resilience#

• The Philippines loses an estimated 3.2% of GDP annually to natural disasters, among the highest globally
• Banking sector exposure concentrated in typhoon-prone Visayas and Mindanao regions
• Task: Conduct an integrated risk assessment combining (1) typhoon frequency/intensity projections, (2) bank portfolio geographic mapping, (3) historical NPL analysis post-disaster, and (4) evaluation of the Philippine Catastrophe Insurance Pool (PCIP) effectiveness

Key References#

• ADB (2023). Southeast Asia Climate Finance: Needs, Flows, and Gaps.
• Cevik, S. & Jalles, J.T. (2022). This changes everything: climate shocks and sovereign bonds. Energy Economics, 107, 105856.
• EM-DAT (2025). International Disaster Database. Centre for Research on the Epidemiology of Disasters.
• Green Climate Fund (2024). GCF Portfolio Performance and Impact Report.
• World Bank (2024). Climate and Development Reports: East Asia and Pacific.

Teaching Methods#

Assessment#

Key References#

Required Reading#

• TCFD (2023). Final Report: Recommendations of the Task Force on Climate-related Financial Disclosures.
• NGFS (2024). NGFS Climate Scenarios for Central Banks and Supervisors (Phase IV).
• PCAF (2024). The Global GHG Accounting and Reporting Standard for the Financial Industry.

Recommended Reading#

• Battiston, S., et al. (2017). A climate stress-test of the financial system. Nature Climate Change, 7, 283-288.
• Bolton, P. & Kacperczyk, M. (2021). Do investors care about carbon risk? Journal of Financial Economics, 142(2), 517-549.
• UNEP FI (2024). Good Practice Guide to Climate Stress Testing.
• ECB (2022). 2022 Climate Risk Stress Test Results.
• ADB (2023). Southeast Asia Climate Finance: Needs, Flows, and Gaps.

Connection to Other Modules#

ASEAN Adaptation Notes#

Local Customization Required

Each ASEAN partner should supplement core content with:

• Country-specific physical risk assessments and hazard maps
• National climate commitments (NDCs) and adaptation plans
• Local disclosure requirements and supervisory expectations
• Regional case studies from partner institutions

(c) GREEN FINANCE Consortium 2026