Facilitating interdisciplinary learning among the Realising Transition Pathways models

Six quantitative energy models and two appraisal techniques are being developed in the Realising Transition Pathways (RTP) project. All these models and techniques address the UK power system transition until 2050, but differ in their disciplinary perspective, objectives, methodological approaches and parts of the power system addressed. This working paper aims to compare these models to each other in order to facilitate interdisciplinary learning among the models and their developers. First, the RTP models are mapped out in order to understand their overlays and differences. Second, by means of running the models with harmonised assumptions of the “Central Co-ordination” transition pathway, converging and diverging insights of these models are identified. In this way, areas for further development of the models are suggested. This report describes the process and outcomes of this multi-model analysis

Energy scenario choices: insights from a retrospective review of UK energy futures

Since the 1980s, there has been a shift in energy research. It has shifted from approaches that forecast or project the future to approaches which make more tentative claims and which explore several plausible scenarios. Due to multiple uncertainties in energy systems, there is an infinite amount of plausible scenarios that could be constructed and scenario developers therefore choose smaller, more tangible sets of scenarios to analyse. Yet, it is often unclear how and why this scenario choice is made and how such choices might be improved. This paper presents a retrospective analysis of twelve UK energy scenarios developed between 1978 and 2002. It investigates how specific scenarios were chosen and whether these choices captured the actual UK energy system transition. It finds that scenario choice reflected contemporary debates, leading to a focus on certain issues and limiting the insights gleaned from these exercises. The paper argues for multi-organisation and multi-method approaches to the development of energy scenarios to capture the wide range of insights on offer. Rather than focus on uncertainty in model parameters, greater reflection on structural uncertainties, such as shifts in energy governance, is also required

Synergies and trade-offs between governance and costs in electricity system transition

Affordability and costs of an energy transition are often viewed as the most influential drivers. Conversely, multi-level transitions theory argues that governance and the choices of key actors, such as energy companies, government and civil society, drive the transition, not only on the basis of costs. This paper combines the two approaches and presents a cost appraisal of the UK transition to a low-carbon electricity system under alternate governance logics. A novel approach is used that links qualitative governance narratives with quantitative transition pathways (electricity system scenarios) and their appraisal. The results contrast the dominant market-led transition pathway (Market Rules) with alternate pathways that have either stronger governmental control elements (Central Co-ordination), or bottom-up proactive engagement of civil society (Thousand Flowers). Market Rules has the lowest investment costs by 2050. Central Co-ordination is more likely to deliver the energy policy goals and possibly even a synergistic reduction in the total system costs, if policies can be enacted and maintained. Thousand Flowers, which envisions wider participation of the society, comes at the expense of higher investment and total system costs. The paper closes with a discussion of the policy implications from cost drivers and the roles of market, government and society

Revealing effective regional decarbonisation measures to limit global temperature increase in uncertain transition scenarios with machine learning techniques

Climate change mitigation scenarios generated by integrated assessment models have been extensively used to support climate change negotiations on the global stage. To date, most studies exploring ensembles of these scenarios focus on the global picture, with more limited attention to regional metrics. A systematic approach is still lacking to improve the understanding of regional heterogeneity, highlighting key regional decarbonisation measures and their relative importance for meeting global climate goals under deep uncertainty. This study proposes a novel approach to gaining robust insights into regional decarbonisation strategies using machine learning techniques based on the IPCC SR1.5 scenario database. Random forest analysis first reveals crucial metrics to limit global temperature increases. Logistic regression modelling and the patient rule induction method are then used to identify which of these metrics and their combinations are most influential in meeting climate goals below 2 °C or below 1.5 °C. Solar power and sectoral electrification across all regions have been found to be the most effective measures to limit temperature increases. To further limit increase below 1.5 °C and not only 2 °C, decommissioning of unabated gas plants should be prioritised along with energy efficiency improvements. Bioenergy and wind power show higher regional heterogeneity in limiting temperature increases, with lower influences than aforementioned measures, and are especially relevant in Latin America (bioenergy) and countries of the Organisation for Economic Co-operation and Development and the Former Soviet Union (bioenergy and wind). In the future, a larger scenario ensemble can be applied to reveal more robust and comprehensive insights

Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of 'IAMs'. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results