Why climate policy scenarios are important, how to use them, and what has been learned
summary
In an ideal world without uncertainty, policymakers should take a range of measures to reduce greenhouse gas emissions, but the core policy should be to keep carbon emissions at the level of the marginal cost of carbon emissions, or equal to the social cost of carbon emissions to price . However, the real world is highly uncertain. Uncertainties related to climate science, the economic impact of climate change, and appropriate discount rates across generations complicate estimates of the social cost of carbon emissions. There is a wide range of estimates, ranging from negative figures to thousands of US$ per tonne of CO2 (Wang et al. 2019), and policymakers are unlikely to agree even vaguely on the social costs of achieve carbon. There is also a strong case for moving beyond carbon pricing and adopting a mix of policies that reduce economic costs and increase political acceptance of alternative climate policies.
In response to climate uncertainty, researchers, companies, and policymakers are turning to scenario analysis. The use of scenarios is crucial for policy making due to the level of uncertainty and the highly dynamic nature of the system. Policies must adapt to new information about climate change, new technologies and economic responses. However, to use scenarios effectively, policy makers need to understand how scenarios are developed and the strengths and weaknesses of different modeling approaches.
This policy brief has two purposes. The first is to inform policymakers about existing scenario approaches and how scenarios applied to large-scale models should be used first to understand the nature and magnitude of potential climate shocks and then alternative policy approaches to respond to the develop and assess climate change. A key message for policymakers, who increasingly use financial system stress testing scenarios, is not to force convergence of results across different types of models. The differences in model forecasts help policymakers understand the nature of the uncertainty and what actions might help minimize those uncertainties. For example, integrated assessment models (IAMs) focus on technologies needed to reduce emissions, while economic models focus more on changing household and corporate behavior and endogenous structural change in economies to reduce emissions.
The second objective of the paper is to draw some policy conclusions for climate policy-making that have emerged from recent scenario exercises. There are significant climate risks with potentially large economic costs, such as B. Physical risks from chronic climate change and extreme climate events as well as shocks to economies from changes in climate policy (transition risk).
We also summarize the different types of scenarios that have been considered and outline the types of models that are commonly used to develop long-term and short-term scenarios. Carbon pricing is important to change household and business behavior to reduce greenhouse gas emissions. However, carbon prices may not be sufficient, as infrastructure investments by governments and other policies due to market failures play an important role in reducing the adjustment costs of the transition to a low-carbon world.
In addition to the lessons already learned from model-based scenarios, another key policy lesson from this briefing is that policymakers should be careful when using scenarios to design robust strategies for a wide range of economic viewpoints, rather than optimal strategies to look for in a specific model.
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