![\"Nina](\"https:\/\/www.newsbeep.com\/us\/wp-content\/uploads\/2025\/09\/Nina-Chen-headshot-239x300.png\")
Dr Yue (Nina) Chen<\/p>\n Dr Yue (Nina) Chen<\/p>\n
Dr Yue (Nina) Chen was chief climate risk officer at US federal banking regulator the Office of the Comptroller of the Currency until March 2025 and is currently a senior visiting fellow at the Centre for Economic Transition Expertise at LSE<\/p>\n
We all know the fable of the blind men and an elephant<\/a>, and probably see it as a charming parable. Yet do bankers and investors truly grasp the limitations of a critical tool for assessing climate-related financial risks \u2013 climate scenario analysis (CSA) \u2013 as it is commonly practised?<\/p>\n Too often, CSA is treated as a compliance formality. I frequently hear from institutions that their CSA showed only mild losses or negligible impact on strategic asset allocation. The implicit \u2013 and sometimes explicit \u2013 conclusion? Their portfolios must face limited climate risk, so no further action is necessary.<\/p>\n That is a dangerous misread.<\/p>\n The problem with off-the-shelf models<\/p>\n For transition risk, most institutions use the scenarios produced by the Network for Greening the Financial System (NGFS) because they are \u201cwhat regulators ask us to use\u201d or \u201cthe norm\u201d.<\/p>\n There is no question that NGFS has elevated the quality and visibility of climate risk assessments. Yet the integrated assessment models (IAM) underpinning its scenarios have known limitations, acknowledged in academic literature<\/a> and NGFS documentation<\/a>.<\/p>\n NGFS notes<\/a> that its outputs may underestimate economic and financial system impacts (albeit in an easily overlooked disclaimer) and highlights two gaps: the exclusion of tail risks and tipping points. People tend to view \u201ctail risks\u201d and \u201ctipping points\u201d as highly unlikely and decades out (eg a climate tipping point like Amazon forest dieback), hence immaterial for risk assessments whose timeframe is often within several years.<\/p>\n However, even NGFS\u2019s near-term outputs can underrepresent reality given structural model limitations. Process-based IAMs and other energy-environment-economy models currently used by NGFS use a general equilibrium framework and have difficulty in fully capturing nonlinear changes like rapid, S-curve technology adoption.<\/p>\n NGFS also uses a carbon price to represent all transition risk drivers, ignoring technological disruptions already enabled by past R&D and policy.<\/p>\n Take battery cost: NGFS\u2019s short-term highly ambitious transition risk scenario \u2013 Highway to Paris \u2013 projects a 40 percent drop from 2024 to 2030. But reality is moving faster, with a 30 percent drop between 2022 and 2024<\/a>, and another 15 percent drop by early 2025<\/a>.<\/p>\n Cheaper batteries accelerate wider EV adoption, reshaping not only transportation but also the global value chain of oil with disparate geographical impacts. Without further policy support, battery costs<\/a> (also solar and wind<\/a>) are expected to drop further.<\/p>\n When NGFS\u2019s most aggressive transition scenario underestimates historical trends, how can institutions rely on it to prepare for a more disruptive future?<\/p>\n Misjudging physical risks<\/p>\n For physical risk, institutions often model only the direct impact of one or several natural disasters on residential mortgages. This is a reasonable start, but far from sufficient.<\/p>\n If climate change only caused several more disasters, we wouldn\u2019t need to worry.<\/p>\n Climate change is dangerous because it is systemic, global and persistent. Limiting analysis to property damages overlooks critical indirect impacts \u2013 housing price declines, employment shocks, weakened tax bases, and interactions between climate and non-climate risk such as an economic downturn (ie compound risk<\/a>).<\/p>\n Past US experience \u2013 where rebuilding enabled by federal disaster aid cushioned financial impacts \u2013 no longer guarantees the same outcome in today\u2019s political environment. Without rebuilding, property values may fall and local economies may falter, affecting credit cards, commercial real estate and small business lending.<\/p>\n Commercial mortgages and commercial and industrial (C&I) loans are similarly exposed. A property may remain intact, but if surrounding infrastructure fails, businesses without sufficient business interruption (BI) insurance may not operate. Only about 40 percent of small businesses<\/a> carry BI insurance today.<\/p>\n Other often overlooked transmission channels with present-day effects include:<\/p>\n Supply chain disruptions from extreme weather; Imperfect tools, false precision<\/p>\n Banks often use regulatory stress testing apparatus for CSA, which models short-term, one-time macrofinancial shocks. Climate change produces compounding, persistent impacts with strong regional and sectoral disparities. These impacts are missed when institutions implement CSA using the same tools and assumptions without careful adjustment.<\/p>\n Granted, some institutions recognise the limitations. But once a result is produced \u2013 a tidy number, a graph \u2013 our cognitive bias towards numerical certainty overrides our knowledge of model limitations, leading us to complacency.<\/p>\n We need a warning label on such CSA outputs: \u201cPartial estimate. Likely underestimates. Use for education, not risk quantification.\u201d<\/p>\n A shift in the wind<\/p>\n Two cross-Atlantic forces may change CSA practices. In the US, with federal regulators rolling back climate risk supervision, climate risk teams need to prove their value by showing CSA\u2019s business relevance.<\/p>\n In Europe, consultations by the European Banking Authority<\/a> and the Bank of England<\/a>\u00a0 call on firms to justify scenario selection and acknowledge limitations and uncertainties.<\/p>\n These developments will rightly raise the bar.<\/p>\n Better practices are emerging<\/p>\n
\nReduced labour productivity and power generation from heat waves;
\nLower demand due to weaker growth and economic performance;
\nDeteriorating insurance availability and affordability, ultimately affecting banks\u2019 mortgage underwriting in the absence of a robust public backstop.<\/p>\n