Case Study · Indian Banking Sector

Dynamic Climate Risk Assessment in the Indian Banking Sector

A forward-looking framework for quantifying climate-related financial risks using NGFS scenarios, TCFD principles, and PCAF standards.

NGFS ScenariosTCFD FrameworkPCAF Standards Climate Risk Resilience ScoreCredit RiskCapital Adequacy
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Executive Summary

This case study presents a dynamic, forward-looking framework for assessing climate-related financial risks in the Indian banking sector. Building upon NGFS, TCFD, and PCAF principles, it introduces an enhanced, interactive model for real-time scenario analysis and dynamic parameter adjustments.

The model quantifies the impact of various climate pathways on key banking metrics — credit risk, capital adequacy, and liquidity — culminating in a composite Climate Risk Resilience Score (CRRS).

Through a comparative analysis of a large public sector bank, a large private sector bank, and a mid-sized private bank, this study demonstrates the heterogeneous impact of climate risk across the sector.

The study highlights that public sector banks often face higher risks due to fossil-fuel intensive portfolios. Through the CRRS, the study provides a quantifiable metric for assessing how well financial institutions can withstand economic shocks triggered by the green transition.

Ultimately, the research advocates for proactive portfolio rebalancing and enhanced regulatory oversight to safeguard long-term financial system stability.

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The Enhanced Climate Risk Framework

The framework enhances traditional climate risk approaches by incorporating five key innovations:

⚡

Dynamic Scenario Generation

Real-time updates and customization of climate scenarios.

🔗

Macro-Financial Linkages

Explicitly models transmission of climate shocks to the broader economy.

📊

Granular Risk Modules

Detailed assessments of credit and liquidity risks by sector.

🔍

Dynamic Climate VaR

Forward-looking, probabilistic measure of potential losses.

🌎

Double Materiality

Assesses both climate impact on the bank and the bank's impact on climate.

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The Working Model: A Demonstration

A Python-based working model was developed to simulate the impact of various NGFS scenarios on sample bank portfolios, with dynamic adjustments to carbon prices and physical risk multipliers.

3.1 Interactive Web Application

Users can select bank archetypes, choose NGFS climate scenarios, dynamically adjust parameters, and view real-time impact on risk and financial metrics.

Interactive Web Application
Interactive Climate Risk Assessment Web Application
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Analysis & Results

4.1 Portfolio Risk Profile

The radar chart illustrates the risk profile of a large public sector bank across four NGFS scenarios, highlighting trade-offs between risk dimensions. Orderly transition scenarios present higher transition risk but result in lower physical risk and higher overall resilience.

Portfolio Metrics Radar Chart
Figure 1: Portfolio Risk Profile of a Public Sector Bank

4.2 Cross-Bank and Cross-Scenario Comparison

The heatmap compares the CRRS of the three bank archetypes across all NGFS scenarios. The private sector bank is significantly more resilient, while “Hot House” scenarios pose the greatest threat to all banks.

Scenario Comparison Heatmap
Figure 2: CRRS Comparison Across Banks and Scenarios

4.3 Sector-Level Risk Analysis

Charts breaking down the climate risk impact by sector for the public sector bank under a “Disorderly Delayed Transition” scenario. Thermal Power and Steel & Metals are the primary drivers of increased credit risk.

Sector Risk Analysis
Figure 3: Sector-Level Risk Analysis for a Public Sector Bank

4.4 Capital Adequacy Impact

CET1 capital ratio erosion for the three bank archetypes. The public sector bank experiences the most significant erosion, particularly under disorderly and hot house scenarios.

Capital Impact Comparison
Figure 4: CET1 Capital Impact Under Climate Stress

4.5 Sensitivity Analysis

Heatmaps showing the impact of varying carbon price and physical risk multiplier on CRRS and CET1 erosion. Higher carbon prices and greater physical risk lead to lower resilience and greater capital erosion.

Sensitivity Analysis
Figure 5: Sensitivity of CRRS and CET1 Erosion to Dynamic Parameters

4.6 CRRS Breakdown

The CRRS is a composite score from six components. The chart breaks down CRRS for the public sector bank under a disorderly transition — low resilience is primarily driven by high transition risk and low green alignment.

CRRS Breakdown
Figure 6: CRRS Component Breakdown for a Public Sector Bank

4.7 Expected Loss Waterfall

The waterfall chart illustrates drivers of the increase in Expected Loss (EL). Thermal Power is the largest contributing sector to increased credit losses.

Expected Loss Waterfall
Figure 7: Expected Loss Waterfall Analysis
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Conclusion & Recommendations

Key Takeaways

  • ⚠️Climate risk is a material threat to the Indian banking sector, with significant potential impact on credit risk, capital adequacy, and liquidity.
  • 📊Impact is highly heterogeneous — portfolio composition and transition readiness are key determinants of resilience.
  • 🔮A dynamic, scenario-based approach is essential for capturing the forward-looking and uncertain nature of climate risks.
  • 🏛️Robust governance and strategic planning are critical for navigating the transition to a low-carbon economy.

Recommendations

  1. 1

    Adopt a dynamic, integrated approach to climate risk assessment — incorporating it into core risk management frameworks and strategic planning processes.

  2. 2

    Proactively rebalance portfolios to reduce exposure to carbon-intensive sectors and increase financing of green and transition assets.

  3. 3

    Regulators should provide clear, consistent guidance on climate risk management and disclosure, and consider incorporating climate stress testing into supervisory frameworks.

  4. 4

    Industry should collaborate to improve data availability and modeling capabilities, and develop common best practices in climate risk management.

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References