Introduction

The aim of this study is to provide insight as to why the projections and outcomes for carbon capture and storage (CCS) might differ among a selection of the more influential integrated assessment models (IAMs). By exploring the assumptions, background calculations and input data, the intention is to provide a transparent approach to understanding model results. It is not the intention of the study to advocate particular scenarios. CCS is represented in most IAMs and plays a key role in a large number of energy and emissions scenarios. While IAMs often align on high level messaging about the value and need for CCS, the actual role, impact and applications (e.g. power vs industrial, coal vs gas, CCS vs BECCS) vary considerably. This can result in confusion and a lack of appreciation of the value of CCS within the energy sector by manufacturers, policy makers, regulators and the general public. Inaction or inappropriate action is often the result.

Summary

Integrated assessment models (IAMs) quantify the interactions and trade-offs between societal demands for energy, economic, and environmental services, using a systems approach. These systems are typically the energy system, the economy, the earth-land system, the water system and atmospheric climate system, although every IAM does not necessarily include all these systems and have varying degrees of completeness or complexity.

The mathematical approach underpinning each IAM can vary across the models. Classifications include whether a model’s equations finds a partial equilibrium or general equilibrium between supply and demand, whether or not the model is attempting to optimise or simulate, the range of sectors included in the model, the treatment of discounting of costs, the temporal resolution and treatment of foresight; all of these influence the model dynamics and responsiveness in differing ways. Each IAM has its own strengths and weaknesses. Some industry medium-term models based on econometric simulation techniques describe their analysis as outlooks, implying a level of forecasting accuracy, while most research long-term IAMs do not claim to have forecasting capabilities as the future is too uncertain, and instead gain insights by describing sets of potential futures under scenario analysis covering a broad range of uncertainty in input assumptions.

CCS is represented in most IAMs and plays a key role in a large number of energy and emissions scenarios. While IAMs often align on high level messaging about the value and need for CCS, the actual role, impact and applications (e.g. power vs industrial, coal vs gas, CCS vs BECCS) vary considerably. Due to the nature of scenario making, the input data, background calculations and assumptions are not always presented in a clear and transparent way together with the results. This can result in confusion and a lack of appreciation of the value of CCS (in both general and specific applications) within the energy sector, e.g. with manufacturers, policy makers, regulators and the general public. Inaction or inappropriate action is often the result.

It is also important to note that, while global results are often presented, for most policy makers it is the projections for countries and regions that are most meaningful. Thus the geographical granularity that underpins any particular IAM is of crucial importance. In many IAMs, this is not adequately addressed.

The aim of this study, undertaken by a consortium comprising University College Cork (study lead), Imperial College London and the University of Oxford, is to provide insight as to why the projections and outcomes for carbon capture and storage might differ among a selection of the more influential IAMs, by exploring the assumptions, background calculations and input data. The purpose of the study is to provide a transparent approach to understanding model results. It is not the intention of the study to advocate particular scenarios.