A flood can interrupt production at a factory. Changes in energy prices can affect a company’s operating costs. Investment in cleaner technology can alter its funding requirements. For a financial analyst, the task is to investigate how these developments could affect cash flow, asset values and repayment capacity.
A climate risk modelling course should help learners make these connections. It should explain how to move from a climate-related assumption to a financial result, while making the data requirements, modelling choices and uncertainty visible.
Understand Physical and Transition Risk
Physical climate risk includes the effects of events such as floods and storms, alongside longer-term changes such as rising temperatures and sea levels. Transition risk concerns the adjustment towards a lower-carbon economy, including changes in policy, technology and market behaviour. Network for Greening the Financial System
These risks require different analytical approaches. A physical risk exercise might examine the location and vulnerability of a borrower’s operating assets. A transition risk exercise might investigate how changes in energy costs or product demand affect its business model.
Learning both areas helps students avoid treating climate exposure as a single number with the same meaning for every company.
Connect Climate Drivers to Financial Risk
Climate-related developments can feed into familiar financial risk categories, including credit, market, liquidity and operational risk. Their effects can differ across locations, sectors and financial systems. The Basel Committee describes these connections as transmission channels. bis.org
A useful classroom example begins with a hypothetical manufacturer facing temporary production disruption. Learners can consider reduced sales, repair expenses and delayed customer deliveries, then examine the implications for cash availability.
The model should explain each connection. An exposure to a hazard does not, by itself, establish the size of a financial loss or the likelihood of borrower default.
Learn Scenario Analysis Before Building Complex Models
Scenario analysis examines how outcomes could change under different assumptions. The NGFS describes a process that begins with defining the exercise, selecting scenarios, assessing impacts and communicating the results. ngfs.net
For a learning project, start with a clear question: how might a company’s operating cash flow respond to a specified change in energy costs over a chosen period?
State the scenario, time horizon and business assumptions before calculating the result. A scenario describes a conditional pathway; it should not be presented as a prediction of exactly what will happen. NGFS documentation explicitly distinguishes its scenarios from forecasts. ngfs.net
Work Carefully with Climate and Financial Data
A practical climate risk modelling course should teach learners to inspect data before using it. Record the source, publication version, units, geographic coverage, time horizon and assumptions behind each variable.
NGFS resources provide access to transition scenario data and physical climate impact data through separate tools and supporting documentation. Translating these inputs into company-level financial effects requires additional assumptions. ngfs.net
An exercise could involve matching a hypothetical loan portfolio to borrower sectors and operating locations. Missing locations or unclear sector classifications should remain visible as data gaps. They should not silently become zero-risk observations.
Build a Transparent Transition Risk Exercise
Consider an illustrative company with 5,000 tonnes of annual emissions subject to a hypothetical charge of ₹2,000 per tonne. The direct annual cost would be ₹1 crore, assuming all those emissions are covered and no allowances or exemptions apply.
This is an arithmetic training example, not a statement about an actual Indian carbon price or policy. Its purpose is to show how assumptions enter a financial model.
Extend the exercise by allowing the company to reduce emissions, pass some costs to customers or invest in new equipment. Compare the resulting cash flows and document which assumptions explain the differences. This produces a more useful analysis than applying the same cost shock to every borrower.
Design a Physical Risk Case Study
A suggested physical risk project could compare two hypothetical facilities exposed to the same flood scenario. Give them different building characteristics, protection measures, recovery times and insurance assumptions.
Ask learners to estimate the financial effects using clearly stated exercise inputs. Separate property damage from business interruption and explain the timing of any assumed insurance recovery.
The purpose is to understand the model’s structure. A classroom case does not establish that its assumed damage rates are suitable for real assets. Professional use requires appropriate evidence and review.
Use Excel and Python to Make the Analysis Reviewable
Excel can help learners build a small model with visible inputs, calculations and outputs. A workbook might contain borrower information, scenario assumptions, projected cash flows and a summary of sensitivity results.
Python can extend the exercise across a larger synthetic portfolio and support repeatable data preparation. Learners should compare selected results against independently checked calculations.
Both approaches need documentation. A reviewer should understand how the data was transformed, which assumptions were applied and how the outputs were produced. More code does not automatically create a more reliable model.
Treat Uncertainty as Part of the Result
A credible project should explain where its conclusions are sensitive to uncertain inputs. Test alternative assumptions about cost pass-through, adaptation spending, disruption duration and recovery.
Also review whether separate calculations overlap. For example, a revenue adjustment and a business-interruption estimate may partly describe the same loss.
Present results with enough context for someone to understand their limits. A useful report identifies the exposures that deserve further investigation, the assumptions driving the findings and the data needed to improve the analysis.
Explore Climate Risk Learning with Peaks2Tails
Peaks2Tails lists Climate Risk among its specialised learning tracks. Its website also describes Excel and Python implementation, model validation, interpretation and foundation refreshers in mathematics, statistics and coding. peaks2tails.com
Learners interested in a climate risk modelling course can discuss the current offering with the team. Confirm the detailed syllabus, coverage of physical and transition risk, datasets, practical assignments, assessment requirements and access to feedback.
The examples in this article are suggested learning exercises. They should not be assumed to form part of a particular Peaks2Tails course unless confirmed in its current syllabus.
Develop the Ability to Explain Every Modelling Decision
The most valuable outcome of climate risk training is the ability to connect evidence, assumptions and financial consequences in a way another person can examine. That requires financial understanding, careful data handling and a willingness to explain uncertainty.
Choose a course that gives you practice building and reviewing complete analyses. By the end, you should be able to explain the scenario you selected, how it affects the business, what your model calculates and where its conclusions remain limited.