Posted in | News | Energy

Achieving Energy Transition Goals with New Economic Modeling

Researchers from the University of Oxford’s Oxford Smith School and the Institute for New Economic Thinking have outlined the difficulties faced by policymakers using traditional economic modeling in the public and private sectors in a featured comment publication for Nature Energy.

Statistics on stock market info, which including of many economic statistics such as bar chart and pie diagram on digital information screen

Image Credit: Vintage Tone/Shutterstock.com

The study urges a move away from limited cost-benefit analysis and modeling predicated on economic equilibrium and toward models that accurately depict policy concepts and reflect the dynamics of the transition. These skills are necessary to align with the policies that governments are developing and putting into effect right now, such as the US Inflation Reduction Act, the UK’s offshore wind auctions, and China’s ETS.

The lead author of the study, Dr. Pete Barbrook-Johnson, is a Senior Research Associate at the Smith School for Enterprise and the Environment at Oxford and the Institute for New Economic Thinking. He noted that the discourse surrounding global policy has changed, resulting in new demands for economic modelers.

We have been working with partners in China, India, Brazil, the UK, and Europe to explore what kind of modelling support they need to understand the energy transition. What they tell us is that they really need models that allow them to capture the detail of policies to understand what their impacts might be and how the energy transition might unfold. We have been developing these capabilities for a few years, and this cohort of new models is now maturing.

Dr. Pete Barbrook-Johnson, Study Lead Author and Senior Research Associate, Smith School for Enterprise and the Environment, University of Oxford

He added, “But, we must acknowledge what is required is a new type of modelling that is not been established and used in lots of places, so we need to do more to expand and learn from the promising work that is already out there. In this paper, we explore what we what we need to do, things like invest in new teams, work with partners in more countries, and collect more detailed economic data to match the details of the model.

Doyne Farmer, the director of the INET Oxford Complexity Economics Programme and the Baillie Gifford Professor of Complex Systems Science at the Oxford Martin School’s Smith School, warned against the risk that the energy revolution will leave traditional economic modelers behind.

Traditional economics has failed us by providing, so far, mostly bad advice. We’re trying to remedy that by developing a whole new style of models based on a different set of principles that does a better job of explaining empirical facts and that can better guide us through the transition. This paper lays out a kind of manifesto for what can be done, including talking about the successes we have had in the EEIST project, where we gave examples of models that we think are doing a significantly better job than others.

Doyne Farmer, Baillie Gifford Professor of Complex Systems Science, University of Oxford

He concluded, “Among other things we have shown that the energy transition is going to happen much faster than people have been realizing as the costs of renewable energy are going to drop lower than fossil fuels on a purely economic basis. We need new economic modelling to support this. We still need some government support for technologies like green hydrogen that are needed to provide storage.

Journal Reference:

Barbrook-Johnson, P., et. al. (2024) Economic modelling fit for the demands of energy decision makers. Nature Energy. doi:10.1038/s41560-024-01452-7

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.