Summary:
In a perfect world, carbon emissions would be priced at a level equivalent to their marginal or societal cost. Estimates of the social cost of carbon emissions are complicated by climate science uncertainties. New information on the changing climate, emerging technologies, and economic responses must be incorporated into policies. The Brief aims to educate decision-makers on the various scenario approaches currently in use and how techniques applied to large-scale models should be used. Policymakers can better grasp the nature of uncertainty and potential benefits of various policies by comparing the projections from different models, says OA.
Governments should employ various measures to lower greenhouse gas emissions in a perfect world without uncertainty. Still, the main one should be to price carbon emissions at a level equivalent to their marginal or societal cost. The real world, however, is incredibly unpredictable. Estimates of the social cost of carbon emissions are complicated by climate science uncertainties, climate change’s economic effects, and appropriate discount rates over generations. Given the vast range of estimates on the social cost of carbon, ranging from negative figures to thousands of dollars per tonne of CO2 (Wang et al. 2019), it seems unlikely that policymakers will ever come to a loose agreement. There is also a compelling case for going beyond carbon pricing and implementing a combination of measures that reduce economic costs and increase public support for alternative climate policies.
Researchers, companies, and governments use scenario analysis to deal with climate uncertainty. The degree of uncertainty and the system’s extremely dynamic character make using scenarios for policy planning essential. New information on the changing climate, emerging technologies, and economic responses must be incorporated into policies. Policymakers must, however, comprehend how scenarios are created and the advantages and disadvantages of the various modelling methodologies to use systems effectively.
Two objectives drive this policy brief
The first is to educate decision-makers on the various scenario approaches currently in use and how techniques applied to large-scale models should be used to comprehend the nature and magnitude of potential climate shocks before developing and assessing alternative policy approaches to address climate change. OA’s critical advice for policymakers increasingly adopting scenarios to stress financial test systems is to avoid forcing outcomes from various models to converge. Policymakers can better grasp the nature of uncertainty and the potential benefits of various policies by comparing the projections from different models. For instance, integrated assessment models (IAMs) concentrate on the technology needed to cut emissions; in contrast, economic models focus more on altering consumer and business behaviour and endogenous structural change in economies.
The paper’s second objective is to present some policy recommendations for designing climate policies that have resulted from recent scenario exercises. Significant climate hazards include physical threats from long-term climate change and extreme weather occurrences, as well as shocks to economies from changes in climate policies (transition risk).
We also list the many scenarios considered and describe the models typically employed for creating long-term and short-term plans. Carbon pricing is crucial for altering household and business behavior to lower greenhouse gas emissions. Due to market inefficiencies, infrastructure investment by governments and other measures plays a significant role in reducing the costs associated with the transition to a low-carbon future. Carbon pricing alone, however, may not be sufficient to achieve this goal.
Another critical policy lesson from this brief is that policymakers should be cautious when using scenarios to design robust policies across a wide range of economic viewpoints rather than seeking optimal policies in a particular model. This is in addition to the insights already gained using model-based scenarios.
Analysis by: Advocacy Unified Network
