A Review of Criticisms of Integrated Assessment Models and Proposed Approaches to Address These, through the Lens of BECCS
This paper reviews the many criticisms that Integrated Assessment Models (IAMs)—the bedrock of mitigation analysis—have received in recent years. Critics have asserted that there is a lack of transparency around model structures and input assumptions, a lack of credibility in those input assumptions...
Saved in:
Published in: | Energies (Basel) Vol. 12; no. 9; p. 1747 |
---|---|
Main Authors: | , , , , |
Format: | Journal Article |
Language: | English |
Published: |
Basel
MDPI AG
08-05-2019
|
Subjects: | |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | This paper reviews the many criticisms that Integrated Assessment Models (IAMs)—the bedrock of mitigation analysis—have received in recent years. Critics have asserted that there is a lack of transparency around model structures and input assumptions, a lack of credibility in those input assumptions that are made visible, an over-reliance on particular technologies and an inadequate representation of real-world policies and processes such as innovation and behaviour change. The paper then reviews the proposals and actions that follow from these criticisms, which fall into three broad categories: scrap the models and use other techniques to set out low-carbon futures; transform them by improving their representation of real-world processes and their transparency; and supplement them with other models and approaches. The article considers the implications of each proposal, through the particular lens of how it would explore the role of a key low-carbon technology—bioenergy with carbon capture and storage (BECCS), to produce net negative emissions. The paper concludes that IAMs remain critically important in mitigation pathways analysis, because they can encompass a large number of technologies and policies in a consistent framework, but that they should increasingly be supplemented with other models and analytical approaches. |
---|---|
ISSN: | 1996-1073 1996-1073 |
DOI: | 10.3390/en12091747 |