Harnessing the Power of Science-Based and Context-Based Analysis to Augment ESG Underwriting

As environmental, social, and governance (ESG) underwriting takes on increasing importance in (re)insurance and finance, there is a pressing need to bolster this process with more robust risk analysis. Two approaches have emerged as instrumental: Science-Based and Context-Based analysis, both of which should be integrated into ESG underwriting to optimize its efficacy and relevance.

Science-Based ESG Risk Analysis is used in the context of climate-related or environmental risks. These metrics are typically grounded in established scientific principles, providing an objective standard against which ESG performance can be measured and are aligned with climate science to keep global warming below 2°C, in line with the Paris Agreement. However, while SBTs provide valuable comparability, they often overlook the unique characteristics of a (re)insured's operations or the specific social and governance issues that could be critical to a (re)insured's ESG performance.

Context-Based ESG Risk Analysis refers to understanding a (re)insured's ESG risks within the specific context of its operating environment, industry sector, geographical location, and other situational variables. It acknowledges that ESG risks are not the same for every organization; for instance, an oil company operating in a region with high political instability might have different social and governance risks compared to a technology company based in a politically stable country. Similarly, a manufacturing company located in a region prone to water scarcity will face different environmental risks compared to a company operating in a water-rich area. Context-based analysis is flexible and customizable. However, its weakness lies in the potential lack of consistency and comparability across companies due to differences in context.

For an optimal understanding of a (re)insured's ESG risks, both context-based and science-based approaches should be employed in tandem. This dual methodology combines the global, objective standard of the science-based approach with the specifics and nuances provided by the context-based perspective. 

When creating models to manage ESG risks, brokers and (re)insurers should consider adding the following to ESG models:

1. Science-Based Targets (SBTs) Development and Benchmarking: Begin by defining SBTs, adhering to globally recognized standards like those recommended by the Task Force on Climate-related Financial Disclosures (TCFD) and the Science Based Targets initiative (SBTi). This includes conducting a greenhouse gas (GHG) inventory to gauge your (re)insured's carbon footprint and set reduction targets.
2. Life Cycle Assessment (LCA): Perform a LCA to quantify the environmental impacts of a (re)insured's products, services, or operations. This helps identify environmental hotspots and facilitates informed decision-making.
3. Context-Based Assessment: Identify the (re)insured´s context-specific ESG risks by conducting a thorough materiality assessment. This typically involves stakeholder engagement, peer benchmarking, and risk analysis within the frame of the (re)insured´s operational context, sector, and geographical location.
4. Scenario Analysis and Stress Testing: Apply context-specific scenario analysis and stress tests, as recommended by the TCFD and highlighted in the article "The benefits of scenario analysis to quantify the impact of climate change on (re)insurance portfolios" to understand how different plausible future scenarios, including physical and transition risks associated with climate change, can affect your (re)insured. This could be a rise in sea level for a coastal plant or stricter regulations for a high-emitting industry.
5. Integrating Science-Based and Context-Based Risks: Use the insights from the above steps to integrate science-based and context-based risks into your ESG underwriting strategy, including a GHG Inventory to quantify your portfolio´s carbon emissions across its entire value chain (Scope 1, Scope 2, and Scope 3 emissions). Science-based targets provide a quantitative foundation, and context-based risks provide a qualitative layer on top of this foundation, thus offering a more nuanced and comprehensive ESG risk profile.
6. ESG Metrics and Reporting: Adopt a combination of standard ESG metrics and customized metrics relevant to your specific context. Measure your progress in line with these metrics, showcasing how your actions align with both science-based targets and context-specific priorities.
7. Continuous Improvement: Regularly review and revise your ESG broking or underwriting strategy based on new scientific insights, changes in operational context, stakeholder feedback, and progress against set targets.

The stages of this model are interconnected and should be viewed as part of a continuous process, requiring updates and adjustments based on new data, changes in the (re)insured's context, stakeholder feedback, and evolving scientific knowledge. Building this model requires a multi-disciplinary team that includes expertise in sustainability science, data analysis, risk management, and stakeholder engagement.

If constructed correctly, the model represents the future of ESG risk management and underwriting, merging science and context to create a more sustainable and responsible business world, while supporting your company's ESG strategy to be both globally responsible and locally relevant.

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