Scenario models too narrow to accurately predict climate risk

Assessing the macroeconomic risks of climate change requires more short-term data and a better grasp of model uncertainty, explains Sebastian Werner, head of climate risk scenario design, Citi.

The risks of climate change to the economy and society have been researched, evaluated and discussed for at least half a century. However, the macroeconomic risks of climate change have only recently become a key interest for academics, policy-makers as well as the private sector. These risks are commonly classified as transition risk, acute physical risk and chronic physical risk.

A natural starting point to assess these risks is gross domestic product. Common estimates of reduced labour productivity and lower economic activity are significant when taking a long-term perspective and comparing GDP levels with and without climate change. Yet, this does not reflect the short-term impact on GDP growth, which is usually the key metric for financial market participants and observers.

Natural disasters that have been aggravated by climate change do not perturb the economic growth path of large countries. The impact is local and rebuilding efforts add positively to GDP. Reducing the output of carbon-intensive sectors in a short period would be significant for a sector such as fossil fuel mining (oil, gas and coal) but the direct contribution of these sectors is less than 2% in most developed countries and the loss is offset by a boom in the renewable sector.

Weather-related events constrain the supply of various commodities either through decreased agricultural yields or their impact on supply chains. These negative supply shocks should increase prices and ultimately raise inflation. Markets may then perceive these shocks as either transitory or permanent and possibly shift long-term inflation expectations. Transition policies such as carbon pricing will pass through to higher energy prices and increase the price of carbon-intensive products until the economy has been sufficiently decarbonised.

However, the case can also be made for deflationary pressures as the marginal cost of renewable energy is assumed to be significantly cheaper than fossil fuel. Moreover, on the back of the energy transition, headline inflation (which includes more volatile energy prices) dynamics may dampen as it depends less on price swings in global crude oil and natural gas markets and more on local weather variability (wind and sunshine). Needless to say, a regime change of inflation dynamics, inflation expectations and inflation risk premia will impact a wide range of asset classes, particularly the term structure of interest rates.

Just as the climate risks of GDP are specific to location and industry, so is unemployment. Municipalities will be more prone to chronic climate outcomes that will reduce labour productivity, lower wages and eventually cause loss of employment. Mitigation of climate change is likely to cause a higher unemployment rate in areas that are heavily dependent on fossil fuel extraction or manufacturing of carbon-intensive goods.

Geographic heterogeneity will affect the housing market. Real estate prices will drop in locations where a large share of the housing stock is at risk of becoming a stranded asset due to sea level rise and uninsurability. Naturally, this should increase the price of houses in non-affected areas and the aggregate impact would even out. Policy-makers have also focused on the energy efficiency of dwellings. The price wedge will become greater between well-insulated and inefficient homes.

It seems therefore that macroeconomic risks are large if the economy is small and exposed to severe climate outcomes (an island nation that is vulnerable to severe storms) or is heavily dependent on fossil fuel extraction. In turn, we could then conclude that the macroeconomic risks of climate change are mild if the economy is large and well-diversified.

However, this ignores model uncertainty, nonlinearities and mispricing of risks. Uncertainty of model outcomes is primarily driven by the physical climate models themselves. The severity of future hurricanes based on increased warming varies greatly across models and assumptions. Even if there was greater certainty about climate outcomes, the economic impact is often estimated using historical data, which belies the true impact of greater physical damage.

Tipping points (inherent nonlinearities in the global climate) may suddenly cause irreversible damage to the economy. For example, the agricultural and power utility sectors contribute less than 5% to GDP in developed countries, yet the systemic importance is much greater. In the case of severe destruction in both sectors, the whole economy would tumble down.

Finally, given all these uncertainties and nonlinearities, but also public beliefs about climate change, financial markets may not price assets correctly and in periods of great awakening re-price these risks substantially.

The views expressed in this article are solely those of the author and not those of CITIGROUP.

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