Yves here. The effort to engage in estimating financial risk of climate change is a fool’s errand. First, climate change is subject to positive feedback loops, which means that really dire outcomes can come faster than anticipated. But second, financial risk approaches all use discounted cash flows. Having done lots of modeling, anything more than 10 or 15 years out is worth pretty much nothing in NPV terms. That effect is made worse by the fact, as Andrew Haldane at the Bank of England and others have documented, investors employ discount rates that are unduly high, which has the effect of reducing long-term costs and payoffs.
Requiring a stab at risk metrics is analogous to a very very weak form of taxation: investors are supposed to take note and punish companies that are behaving badly, and/or the companies themselves are supposed to self-tax by spending more on less climate-ravaging approaches.
Taxation is the wrong approach when the future of the planet is at stake. As Haldane explained in his paper, The $100 Billion Question:
The car industry is a pollutant. Exhaust fumes are a noxious by-product. Motoring benefits those producing and consuming car travel services – the private benefits of motoring. But it also endangers innocent bystanders within the wider community – the social costs of exhaust pollution…
The banking industry is also a pollutant. Systemic risk is a noxious by-product. Banking benefits those producing and consuming financial services – the private benefits for bank employees, depositors, borrowers and investors. But it also risks endangering innocent bystanders within the wider economy – the social costs to the general public from banking crises.
Stop for a minute. This paper was written in 2010. As people are more willing to say now, the big risk of auto exhaust is not pollution, as in damage to lungs, but greenhouse gas emission, which is on track to produce such large scale climate change as to threaten agriculture, lower the level of ocean life, flood out major world cities, in other words, damage human life and potentially undermine production to a degree that Life on the Prairie level lifestyles will look like not too terrible downside.
Back to the paper:
In making these choices, economists have often drawn on Martin Weitzman’s classic public goods framework from the early 1970s.13 Under this framework, the optimal amount of pollution control is found by equating the marginal social benefits of pollution-control and the marginal private costs of this control. With no uncertainty about either costs or benefits, a policymaker would be indifferent between taxation and restrictions when striking this cost/benefit balance.
In the real world, there is considerable uncertainty about both costs and benefits. Weitzman’s framework tells us how to choose between pollution-control instruments in this setting. If the marginal social benefits foregone of the wrong choice are large, relative to the private costs incurred, then quantitative restrictions are optimal. Why? Because fixing quantities to achieve pollution control, while letting prices vary, does not have large private costs. When the marginal social benefit curve is steeper than the marginal private cost curve, restrictions dominate.
The results flip when the marginal cost/benefit trade-offs are reversed. If the private costs of the wrong choice are high, relative to the social benefits foregone, fixing these costs through taxation is likely to deliver the better welfare outcome. When the marginal social benefit curve is flatter than the marginal private cost curve, taxation dominates. So the choice of taxation versus prohibition in controlling pollution is ultimately an empirical issue.
Translated out of econospeak: if the choice is between oil industry and all their dependents’ profits versus danger to civilization, it’s a no-brainer that governments should be engaging in wide-ranging prohibition, not angels-on-the-head-of-a-pin level debates about what proper financial risk metrics should be.
But no one believe in muscular government any more…outside literal muscle, as in policing.
By Julie Vano, PhD, is Research Director at the Aspen Global Change Institute, and Lana Vali is a Philanthropy Senior Associate at Energy Innovation. Originally published at Yale Climate Connections
Weather and climate disasters have cost the United States more than $600 billionover the past five years, with impacts to individuals, businesses, and public sector coffers. As just one example, the climate-fueled, record-breaking rainfall of Hurricane Maria in 2017 significantly disrupted pharmaceutical manufacturing in Puerto Rico, which accounted for 25% of total U.S. pharmaceutical exports and 72% of Puerto Rico’s 2016 exports, harming investors and people who needed critical medical supplies like I.V. bags.
In response to the increasingly extreme weather patterns, global policymakers and investors are keen to predict and better prepare for future climate-change risks. Yet, to reduce damages caused by climate impacts and make fully informed policy and financial decisions, they need to accurately value those risks.
Investors, companies, and policymakers especially need this type of climate risk information. But despite large sets of publicly available climate data, the connective tissue required to apply that data to financial risk analyses is not fully developed and requires better coordination among climate scientists and those trying to assess financial risk. Researchers are now increasingly considering how climate science can better inform business risk analysis.
Policymakers Require transparency … Easier Said Than Done
Policymakers now are starting to take the risks climate impacts pose to the economy much more seriously. Last year, the Biden Administration released an Executive Order on Climate-Related Financial Risk, directing federal agencies to analyze and mitigate risks climate change poses to homeowners, consumers, businesses, workers, the financial system, and the federal government. The U.S. Securities and Exchange Commission (SEC) recently proposed a mandate, which, if adopted, would require that all publicly traded companies for the first time disclose climate-related impacts to their business and report their greenhouse gas emissions in a standardized way. And the Financial Stability Board’s Task Force on Climate-related Financial Disclosures (TCFD) is charged with developing consistent climate-related financial risk disclosures for use by companies, banks, and investors in providing information to stakeholders.
The Task Force developed a set of recommendations for disclosing information about climate risks and opportunities, and as of October 2021, 1,069 financial institutions(responsible for $194 trillion in assets) have pledged to support these recommendations. Broad public and bipartisan support backs these corporate climate risk disclosure mandates: 87 percent of Americans are in favor of companies reporting their climate-related risks, including nearly three-quarters of Republicans.
But assessing climate risks to businesses is easier said than done, as researchers point out. Individuals preparing climate-related financial disclosures encounter significant obstacles. In a recent survey, they reported finding relevant data and applying appropriate risk assessment methodologies as their two greatest challenges, both of which are central to their work. Furthermore, despite the wide availability of public climate data, the data does not match the geographies or timelines used in most financial risk analyses.
Climate Models Don’t Readily Predict Local, Specific Climate Risks
Existing climate models help climate scientists better understand how greenhouse gases increase surface temperatures, thereby warping weather patterns across large swaths of the globe. Most model outputs are relevant for evaluating climate-change conditions at a global scale and in 50 to 100 years, so model outputs cannot easily answer the top question on investors’ minds: What are the potential impacts to a specific project in the next few years or couple of decades?
Attempts to use current climate data to inform financial decisions vary considerably and can be fraught. Squeezing long-term, global-scale climate data into a model that outputs projections of short-term, region-specific financial risks without adequate interpretation leads to artificial information at best. These misguided attempts can actually lead to maladaptation and heightened vulnerability to climate change, an overconfidence in risk assessments, and a misrepresentation or “greenwashing” of a company’s climate response, the authors of this study point out.
While the direct use of climate-change data is no silver bullet to assess climate risk for business, an interdisciplinary team of experts in Australia has charted a path forward for how climate science can better help businesses and their investors, lenders, and insurance underwriters make informed economic decisions. Recent research led by Tanya Fiedler, of the University of Sydney Business School, and Andy Pitman, of the Climate Change Research Centre, UNSW, Sydney, explains the core problem with current attempts to assess risk: Climate research usually flows in only one direction, from researchers to businesses attempting to make well-informed decisions. This limitation does not allow businesses to communicate their needs or questions to climate researchers, nor does it allow climate researchers to adapt models and outputs to meet business needs.
A New Design for Better Risk Assessments – and Helping Make the Business Case for Action
As an alternative, Fielder and her colleagues propose strengthening intermediary groups of professionals focused on operational prediction and climate service to encourage more engagement among climate scientists and businesses and to bring more transparency to the value and limits of climate model information. They emphasize that current barriers will not be solved simply by open access data or by climate service providers repackaging information. Instead, they call for a redesign of the information flow so appropriate climate projections are developed, refined, and communicated in consultation with key decision makers.
The weather forecasting industry has shown that a more engaged approach is possible, as outlined by Fielder and her colleagues. Weather forecasts use complex numerical models that are 1) continuously updated and improved and 2) supported by major national and international investments in science and data systems. Additionally, weather forecasters translate complex weather simulations into information useful to non-experts. Fielder and her colleagues say a similar level of investment in climate risk modeling is needed to understand and communicate climate risks to decision makers beyond just climate specialists, such as those in the financial sector.
With the help of these “climate translators” and dedicated modeling, businesses can build more resilience so they can continue to operate safely and profitably in an evolving and warming world. Without better understanding of and preparation for the new uncertainties of climate change, assets, supply chains, jobs, and livelihoods will be disrupted more severely and more frequently as climate feedback loops accelerate. Weather will become more extreme, but with the right support, the financial system on which we rely can be better prepared.
Increased and improved awareness of climate risks has already led some businesses to commit to reducing emissions in their own operations to help slow unmanageable climate change. As more companies come up to speed on the threat climate poses to their business, increased climate leadership from the corporate sector—leadership desperately needed to lower global greenhouse gas emissions.