Gabrial Anandarajah

Towards a low-carbon economy: scenarios and policies for the UK

This article analyses the implications of long-term low-carbon scenarios for the UK, and against these it assesses both the current status and the required scope of the UK energy policy. The scenarios are generated using the well-established MARKAL (acronym for MARKet ALlocation) UK energy systems model, which has already been extensively used for UK policy analysis and support. The scenarios incorporate different levels of ambition for carbon reduction, ranging from 40% to 90% cuts from 1990s level by the year 2050, to shed insights into the options for achieving the UKs current legally binding target of an 80% cut by the same date. The scenarios achieve their carbon reductions through very different combinations of demand reduction (implying behaviour change) and implementation of low-carbon and energy efficiency technologies on both the supply and demand sides. In all cases, however, the costs of achieving the reductions are relatively modest. The ensuing policy analysis suggests that while the cuts are feasible both technically and economically and while a number of new policies have been introduced in order to achieve them, it is not yet clear whether these policies will deliver the required combination of both short- and long-term technology deployment, and behaviour change for the UK Governments targets to be achieved.

Exploring national decarbonization pathways and global energy trade flows: a multi-scale analysis

The role of fossils fuels in national economies will change radically over the next 40 years under a strong climate regime. However, capturing this changing role through national-based analyses is challenging due to the global nature of fossil fuel demand and resulting trade patterns. This article sets out the limitations of existing national-scale decarbonization analyses in adequately capturing global conditions and explores how the introduction of a global modelling framework could provide vital insights, particularly for those countries that are dependent on fossil fuel exports or imports. The article shows that fossil fuel use will significantly decline by 2050, although gas will have an important transition role. This leaves large fossil fuel exporters exposed, the extent of which is determined by mitigation action in different regions and especially by the pathways adopted by the larger Asian economies. We find that global-scale models provide critical insights that complement the more detailed national analyses and should play a stronger role in informing deep decarbonization pathways (DDPs). They also provide an important basis for exploring key uncertainties around technology uptake, mitigation rates and how this plays out in the demand for fossil fuels. However, use of global models also calls for improved representation of country specifics in global models, which can oversimplify national economic and political realities. Using both model scales provides important insights that are complementary but that can challenge the other’s orthodoxy. However, neither can replace the other’s strengths. Policy relevance: In recent years, how global fossil fuel markets will evolve under different climate regimes has been subject to much debate and analysis. This debate includes whether investments in fossil fuel production still make sense or will be exposed in the future to liabilities associated with high carbon prices. This is important for governments who need to develop coherent policy in relation to fossil fuel sectors and their role as drivers of economic growth and in providing for domestic energy needs. This article argues that national analyses need to be fully cognizant of the global-scale transition, which can be informed by using a multi-scale modelling approach.

Multi-cluster Technology Learning in TIMES: A Transport Sector Case Study with TIAM-UCL

The costs of technologies often fall over time due to a range of processes including learning-by-doing. This is a well-characterized concept in the economics of innovation, in which learning about a particular technology, and hence cost reduction, is related to cumulative investments in that technology. This chapter provides a case study applying technology learning endogenously in a TIMES model. It describes many of the key challenges in modelling technology learning endogenously, both in terms of the interpretation and policy relevance of the results, and in terms of methodological challenges. The chapter then presents a case study, exploring a multi-cluster learning approach where many key technologies (fuel cells, automotive batteries, and electric drivetrains) are shared across a set of transport modes (cars, buses and LGVs) and technologies (hybrid and plug-in hybrid fuel cell vehicles, battery electric vehicles, hybrid and plug-in hybrid petrol and diesel vehicles). The multi-region TIAM-UCL Global energy system model has been used to model the multi-cluster approach. The analysis is used to explore the competitive and/or complementary relationship between hydrogen and electricity as low-carbon transport fuels.

Modelling socio-environmental sensitivities: how public responses to low carbon energy technologies could shape the UK energy system

Low carbon energy technologies are not deployed in a social vacuum; there are a variety of complex ways in which people understand and engage with these technologies and the changing energy system overall. However, the role of the publics socio-environmental sensitivities to low carbon energy technologies and their responses to energy deployments does not receive much serious attention in planning decarbonisation pathways to 2050. Resistance to certain resources and technologies based on particular socio-environmental sensitivities would alter the portfolio of options available which could shape how the energy system achieves decarbonisation (the decarbonisation pathway) as well as affecting the cost and achievability of decarbonisation. Thus, this paper presents a series of three modelled scenarios which illustrate the way that a variety of socio-environmental sensitivities could impact the development of the energy system and the decarbonisation pathway. The scenarios represent risk aversion (DREAD) which avoids deployment of potentially unsafe large-scale technology, local protectionism (NIMBY) that constrains systems to their existing spatial footprint, and environmental awareness (ECO) where protection of natural resources is paramount. Very different solutions for all three sets of constraints are identified; some seem slightly implausible (DREAD) and all show increased cost (especially in ECO).

Climate change impacts on the energy system: a review of trends and gaps

Major transformation of the global energy system is required for climate change mitigation. However, energy demand patterns and supply systems are themselves subject to climate change impacts. These impacts will variously help and hinder mitigation and adaptation efforts, so it is vital they are well understood and incorporated into models used to study energy system decarbonisation pathways. To assess the current state of understanding of this topic and identify research priorities, this paper critically reviews the literature on the impacts of climate change on the energy supply system, summarising the regional coverage of studies, trends in their results and sources of disagreement. We then examine the ways in which these impacts have been represented in integrated assessment models of the electricity or energy system.Studies tend to agree broadly on impacts for wind, solar and thermal power stations. Projections for impacts on hydropower and bioenergy resources are more varied. Key uncertainties and gaps remain due to the variation between climate projections, modelling limitations and the regional bias of research interests. Priorities for future research include the following: further regional impact studies for developing countries; studies examining impacts of the changing variability of renewable resources, extreme weather events and combined hazards; inclusion of multiple climate feedback mechanisms in IAMs, accounting for adaptation options and climate model uncertainty.