Uncertainty is a common challenge to any long-term planning. This working paper takes stock of how long-term low-emission development strategies submitted to UNFCCC perceive and handle uncertainties, and how scenarios are used to illustrate different pathways that countries may take under uncertainty. Based on the stock-take and literature review, the paper suggests application of a model-assisted quantitative approach to systematically exploring uncertainty and identifying policy-relevant scenarios to improve the practice. To experiment with the applicability and usefulness of the approach, the paper demonstrates a model-assisted scenario analysis. The results illustrate the benefits and applicability of the approach along with some limitations. This publication is intended for researchers and policy analysts who are tasked with or interested in analyses for developing long-term climate strategies.
This paper takes stock of how the long-term strategies submitted to the United Nations Framework Convention on Climate Change (UNFCCC) perceive and handle uncertainties.
The most common sources of uncertainty involve future climate impacts, technological innovation and deployment, the availability of large-scale carbon removal solutions, and the reliability of current GHG emission data.
Approaches to handling uncertainties include deferring full analysis of an uncertainty until more is known through research and data collection, making assumptions about uncertainty factors, and conducting sensitivity analysis or scenario analysis. Scenario analysis is the most diverse in its approaches to framing uncertainties.
The use of scenario analysis in the submitted long-term strategies was reviewed and a model-assisted quantitative approach to improve scenario analysis was suggested. The paper examines the suggested approach through a quantitative model analysis and illustrates its benefits and applicability along with some limitations.
Identifying and addressing material uncertainties can mitigate the vulnerability of long-term strategies. Scenario analysis is useful for that purpose and it can be strengthened with the model-assisted quantitative approach.