Stockholm (NordSIP) – Given the increased impact of climate change as well as the struggles to combat it and transition to a net-zero carbon emissions system, it is increasingly relevant for policymakers and researchers to understand how these dynamics affect the economy. To this end, the Network for the Greening of Financial Services (NGFS), an organisation gathering central banks that seek to understand the relationship between monetary policy and climate change, has recently published a climate macroeconomic modelling handbook.
The document is divided into two broad sections. The first focuses on the physical impact modelling of climate change, such as the effect of changes in the probability of weather events along several interconnected dimensions. The second section of the handbook reviews advances in the work on transition impact modelling, such as the macroeconomic effects that policies such as carbon pricing and environmental regulation can have on the phasing out of fossil fuels and the adoption of more energy-efficient and less polluting technologies.
The report has three key takeaways for modellers at central banks and financial regulators: First, modelling climate change is paramount to understanding both the coming physical damages and the economic disruptions inherent in transition policies tailored to mitigate them. Second, modelling climate change is difficult. Models have improved, but there is no silver bullet. Last but not least, modelling climate change requires addressing several dimensions of uncertainty that differ between physical and transition impacts.
Physical Impacts and Preferred Models
According to the NGFS report, most physical effects of climate change can be categorised into “chronic” and “acute” impacts. Chronic impacts refer to changes in the average values of climate variables, such as average temperature or sea level rise, which can be thought of as affecting the economy in more predictable ways. Acute impacts are changes at the ends (or tails) of the probability distribution of climate variables, such as extreme weather events (i.e.: droughts, floods, wildfires and hurricanes).
The NGFS report notes that the approaches to understanding these physical impacts fall under the heading of Integrated Assessment Models (IAMs), which combine economic and climate modules to understand the effects of climate change in the economy. The report notes that “different questions benefit from different methodological approaches, whose merits and limitations should be understood so as to make them work as effective complements.”
As a result, models of chronic impacts tend to favour IAMs based on a computable general equilibrium (CGE) structure, which assumes perfect foresight, and thus, no uncertainty, which is an assumption users should be mindful of. To model higher frequency, acute climate events, IAMs based on a Dynamic Stochastic General Equilibrium (DSGE) structure which are better equipped to deal with stochastic events, which are also useful to evaluate policy scenarios, particularly with regards to business cycle policies, such as monetary and fiscal policy.
The report also considers the effect of uncertainty for these models, both in terms of the parameters underlying the model and the lack of perfect foresight by economic agents. “For physical impacts, the key uncertainty is about how a process interacts with the economies. Here the challenge is improving the modelling of climate change and its impacts, which are highly multidimensional, non-linear, and subject to tipping points.
Transition Impacts
The rest of the handbook focuses on the modelling of the macroeconomic effects of transitioning to a more climate-friendly system. The model considers phasing out of fossil fuels and the adoption of more energy efficient and less polluting technologies, often motivated policies such as carbon pricing and regulatory requirements will have on the economy.
Much of the modelling considerations in this section focus on the supply side and consider the substitutability of inputs and short- as well as long-term dynamics. To this effect, the handbook also presents examples on how to model technological change – including technology functions that are relevant for speeding up the transition.
As was the case with the modelling of the effects of climate change, the modelling of the transition also includes considerations about the role of uncertainty – about the model and agent expectations, but also about the uncertainty of transition policies themselves.
