A mixed-effect model approach for assessing land-based mitigation in integrated assessment models: A regional...

Area of Science:

• Climate Science
• Environmental Modeling
• Energy Systems Analysis

Background:

• Integrated Assessment Models (IAMs) project significant reliance on Carbon Dioxide Removals (CDRs) for ambitious climate mitigation.
• Bioenergy with Carbon Capture and Storage (BECCS) and afforestation/reforestation are key CDR strategies in these models.
• Understanding the characteristics and regional deployment of these CDRs is vital for effective climate policy.

Purpose of the Study:

• To analyze the deployment characteristics of BECCS and afforestation/reforestation within the IAMC 1.5℃ database.
• To investigate the regional and inter-model variations in CDR implementation.
• To identify the driving factors behind the selection of BECCS versus afforestation in mitigation scenarios.

Main Methods:

• Utilized the IAMC 1.5℃ database containing various climate mitigation scenarios.
• Applied a linear mixed-effect model to analyze regional and cross-model effects on CDR deployment.
• Compared the deployment patterns of BECCS and afforestation/reforestation across different Integrated Assessment Models (IAMs).

Main Results:

• Significant differences in BECCS and afforestation deployment were observed across IAMs, regions, and to a lesser extent, time.
• BECCS is often selected as an alternative to afforestation, rather than for its energy-expanding capabilities.
• Afforestation deployment is associated with a decrease in energy use in the scenarios.
• No consistent regional pattern for CDR deployment was found across scenarios and IAMs.

Conclusions:

• The varied and inconsistent regional deployment of CDRs highlights the need for cautious interpretation of IAM outputs for policy.
• Further comprehensive research is required to understand the nuances of CDR deployment before supporting specific policy proposals.
• The findings underscore the complexity of CDR integration into climate mitigation strategies and the importance of model-specific and regional analyses.