Dr. James Glynn is a Senior Research Scholar at the Center on Global Energy Policy at Columbia University SIPA, focused on energy systems modelling. He has over 15 years of experience within energy systems analysis and energy technology research, development and deployment, collaborating with governments, technologists and energy analysts in the United States, Europe, and Asia.
Dr. Glynn’s research interests focus on developing and applying integrated energy systems models and their interactions with the climate, economy, and society to find resilient pathways to future sustainable development goals. He is an expert developer and user of the International Energy Agency’s Energy Technology Systems Analysis Programmes’ (IEA-ETSAP) TIMES source code, developing global and national energy systems models. These model applications have provided insights into Irish, European and International energy policy in collaboration with a broad range of stakeholders.
Dr. Glynn’s talk concerned energy systems modelling, and how newer models can better inform investors and policy-makers about potential futures. Energy system modelling is required to plan for managing and adapting to changes in water, energy, climate, etc. and is essential to international agreements like the Paris Agreement. Energy system refers to every aspect of the system from mining through to end use.
Past models typically use historical trends of energy demand and correlate them with socio-economic and demographic drivers to predict future energy demand. There is immense amounts of uncertainty in this because socio-economic and demographic changes are impossible to predict with complete accuracy. Especially given that these models predict as far out as 2100 there is always a wide range of possibilities.
This uncertainty makes decision-making challenging because the potential technologies and policy options are so vast and it is unclear how many or what caliber of interventions we need. This leads to a lack of action as investors and policy-makers are unwilling to take on such high risk with this level of uncertainty. There is no one pathway, there exists a wide range of pathways.
Typical IAM Analysis only alters one or a handful of variables. It creates a small amount of pathways with a lot of large gaps or ‘white space’ between pathways. This means a lot of potential pathways are not represented and it becomes difficult to place where we are in a pathway once we are part way down it.
The ETSAP-TIMES integrated assessment model is a long term model including several thousand technologies that can be deployed at a variety of scales from local to global. It gives a wide array of possible futures. By running the model thousands of times changing a variety of variables each time (18 are presented) then a wider range of temperature possibilities and pathways are generated. These can be used to make probabilistic estimates of future cost and emissions of different technologies and regions. These estimates show investors and policy-makers where investments are reasonable and at what point they become unreasonable
A few key takeaways from the modelling. 1) The probability of 1.5 degree temperature overshoot is ~70%. 2) The annual energy system cost to achieve Paris goals are $28-180 trillion, with the range due to climate sensitivity, carbon budgets, the start year, and the severity of climate related damages. 3) Delayed mitigation action of 10 years may result in 2 degree celsius mitigation costs being similar with the 1.5 scenario, and with a larger medium-term uncertainty.