Erasmus+ Capacity Building in Higher Education
Green Finance Risk Management
Module 4 · Micro-Credential · Developed by UT (University of Twente)
6Lessons
0.8ECTS
20-24Hours
3Tiers
6Labs
M4
Lessons
Foundation: Physical risks (acute events such as floods, storms; chronic shifts such as sea-level rise, temperature change). Transition risks across five channels: policy, technology, market sentiment, reputation, and legal/litigation. Liability risks as an emerging category. Risk transmission channels through credit, market, operational, and liquidity pathways. Time horizons from short-term to long-term structural shifts. Materiality assessment frameworks for financial institutions.
Intermediate: Sectoral exposure mapping across carbon-intensive industries. Compound risk analysis where multiple climate drivers interact. Construction of risk heatmaps combining probability, severity, and time horizon. Double materiality perspective linking financial and impact materiality.
PhD: Carney (2015) “Tragedy of the Horizon” framework. Battiston et al. (2017) network propagation models for climate risk. Systemic risk transmission through interbank and supply-chain networks. Climate litigation trends and liability risk quantification (Setzer & Higham 2023).
🇻🇳 Case: Vietnam — Mekong Delta Physical Risk Exposure
M4/M5 Boundary: This lesson treats TCFD/TNFD as risk assessment tools (identification, measurement, management). Disclosure and reporting obligations are covered in Module 5.
Foundation: TCFD four pillars: Governance, Strategy, Risk Management, Metrics & Targets. Focus on the Risk Management pillar as a structured approach to identifying, assessing, and managing climate-related risks. Integration with enterprise risk management (ERM) frameworks. TNFD LEAP approach: Locate, Evaluate, Assess, Prepare. The nature-finance nexus connecting biodiversity loss to financial risk.
Intermediate: Implementation challenges across different institution types. Sector-specific TCFD guidance for banks, insurers, and asset managers. Basel III/IV integration of climate risk into prudential frameworks. Quantitative scoring of TCFD disclosure quality.
PhD: Effectiveness critique of TCFD: does disclosure change behavior? Double materiality debate in TCFD vs. ISSB vs. CSRD frameworks. Regulatory landscape evolution from voluntary TCFD to mandatory disclosure regimes across jurisdictions.
🇹🇭 Case: Thailand — Bank of Thailand TCFD Implementation
Foundation: Why scenarios instead of forecasts: deep uncertainty and path dependency. NGFS scenario framework with three families: Orderly (early, smooth transition), Disorderly (late, abrupt transition), and Hot House World (no transition, severe physical risk). IEA scenarios as energy-sector complement. The scenario-to-impact transmission chain from macro pathways to portfolio-level outcomes.
Intermediate: Hands-on use of the NGFS Scenario Explorer. Calibrating global scenarios to ASEAN country conditions. Monte Carlo simulation methods for uncertainty quantification. Constructing fan charts and tornado diagrams to visualize scenario dispersion.
PhD: Integrated Assessment Models (IAMs): DICE (Nordhaus), REMIND, GCAM, and MESSAGE. Damage function specification and critique (Weitzman 2009 fat tails). Model uncertainty and ensemble approaches.
IAM Damage Function
$$\text{Damage} = \frac{\alpha T^2}{1 + \beta T^2}$$🇵🇭 Case: Philippines — Typhoon Exposure Under Climate Scenarios
Foundation: GHG Protocol: Scope 1 (direct emissions), Scope 2 (purchased energy), Scope 3 (value chain). Financed emissions concept: attributing borrower/investee emissions to the financial institution. Weighted Average Carbon Intensity (WACI). PCAF data quality tiers (1-5) for emissions attribution.
Intermediate: PCAF methodology for calculating financed emissions across asset classes (loans, bonds, equity, real estate, project finance). Implied Temperature Rise (ITR) metrics. Portfolio alignment approaches.
PhD: Climate Value-at-Risk (CVaR) decomposed into physical and transition components. Carbon beta estimation via factor models. Event study and difference-in-differences approaches to measuring climate risk pricing in financial markets.
Financed Emissions
$$FE_i = \frac{\text{Outstanding}_i}{\text{TotalAssets}_i} \times \text{Borrower Emissions}_i$$Climate Value-at-Risk
$$\text{CVaR} = \text{Portfolio}_{t=0} - \mathbb{E}[\text{Portfolio}_{t=T} \mid \text{Scenario}]$$Climate Beta
$$R_i = \alpha + \beta_{\text{mkt}} R_m + \beta_{\text{climate}} \text{ClimateF}_i$$🇻🇳 Case: Vietnam — Banking Sector Financed Emissions
Foundation: How climate stress tests extend traditional financial stress testing. Key differences: longer time horizons (30+ years vs. 3 years), novel risk drivers, scenario uncertainty, data gaps. Design elements: scope, scenario selection, transmission models, balance sheet projection, capital impact. Overview of supervisory approaches across jurisdictions.
Intermediate: Top-down vs. bottom-up stress testing methodologies. Modeling PD/LGD shifts under climate scenarios. Calculating stressed Capital Adequacy Ratio (CAR). Incorporating physical risk via geospatial overlays (flood zones, typhoon corridors).
PhD: Dynamic balance sheet assumptions vs. static. Second-round effects and contagion through interbank markets. Comparative analysis of ECB (2022) vs. BoE (2022) stress test methodologies, assumptions, and findings.
Capital Adequacy Impact
$$\Delta\text{CAR} = \frac{\text{Regulatory Capital} - \text{Climate Losses}}{\text{RWA}}$$🇹🇭 Case: Thailand — BOT Climate Stress Test Pilot
Foundation: ASEAN’s unique risk profile: high physical vulnerability, rapid economic growth, emerging financial systems, large agricultural sectors. Country-specific exposures: Vietnam (sea-level rise, Mekong Delta), Thailand (floods, droughts, agricultural dependence), Philippines (typhoon belt, volcanic and seismic co-risks). Adaptation finance instruments: green resilience bonds, parametric insurance, climate adaptation funds. Just transition considerations for coal-dependent communities.
Intermediate: Risk = Hazard × Exposure × Vulnerability framework applied to ASEAN financial systems. Quantifying the adaptation finance gap. ND-GAIN Country Index for comparative vulnerability assessment. Designing financial instruments for adaptation.
PhD: GIS-based spatial risk modeling for financial portfolios. Panel regression of climate events on NPL ratios across ASEAN banking systems. Difference-in-differences estimation of adaptation investment effectiveness.
Panel Regression
$$\text{NPL}_{it} = \alpha + \beta_1 \text{ClimateEvent}_{it} + \beta_2 \text{Controls}_{it} + \gamma_i + \delta_t + \varepsilon_{it}$$Carbon Intensity
$$CI_{\text{portfolio}} = \frac{\sum \text{Financed Emissions}}{\sum \text{Revenue}}$$🇵🇭 Case: Philippines — Typhoon Belt Banking Sector Resilience
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Assessment Framework
| Component | Weight | Description |
|---|---|---|
| Risk Assessment Report | 30% | Classify and map climate-related financial risks for an ASEAN financial institution using the taxonomy from Lessons 4.1–4.2 |
| Quantitative Portfolio Analysis | 40% | Calculate financed emissions, WACI, and Climate Value-at-Risk; conduct scenario analysis using NGFS pathways (Lessons 4.3–4.4) |
| Climate Stress Test Design | 30% | Design and present a climate stress test for an ASEAN bank, incorporating physical and transition risk channels (Lessons 4.5–4.6) |
A
ASEAN Case Study Distribution
🇻🇳
4.1
Vietnam
🇹🇭
4.2
Thailand
🇵🇭
4.3
Philippines
🇻🇳
4.4
Vietnam
🇹🇭
4.5
Thailand
🇵🇭
4.6
Philippines
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Module Connections
M1Fundamentals → Provides the ESG context and green finance foundations that M4 risk methods build upon
M2Contexts → International policy frameworks and Paris Agreement targets create the scenario pathways used in M4
M3Products → M4 risk assessment methods are applied to evaluate green bonds, SLBs, and other products from M3
M5Reporting → M4 risk metrics (CVaR, financed emissions, stress test results) become the quantitative inputs for M5 disclosure