Project: UK ENERGY REVIEW - Transport Sector Projections to 2050

Reference: SRT 13/2/2

Last update: 17/02/2010 10:33:16

Objectives

The Department for Transport and the Energy Review Team are looking for a report to guide the Energy Review's task of investigating the Government's options for:

- Reducing carbon emissions
- improving security of supply in the UK transport sector to 2050

Description

One of the key elements of the transport analysis in the Energy Review will be to understand how the sector's long term energy needs to 2050 can be met by different technology mixes. The projections of the technology mix under different conditions will help the Energy Review to understand prospects for policy in the transport sector to meet the goals set out in the Energy White Paper

Contractor(s)

AEA Technology Environment
Harwell Business Centre, Didcot, Oxfordshire, OX11 0QJ
+44 (0)1235 432201

Contract details

Cost to the Department: £18,690.00

Actual start date: 18 April 2006

Actual completion date: 19 May 2006

Publication(s)

UK ENERGY REVIEW: Transport Sector Projections to 2050
Author: AEA Technology - Heather Haydock
Publication date: 02/06/2006
Source: AEA Technology - Nikolas Hill. Copy located with S&R Unit, DfT ERS@dft.gov.uk

Summary of results

  1. Updated transport technology data, transport demand data and fuel price data for all sectors have successfully been incorporated into the version of the UK MARKAL model used previously for analysis for the 2003 Energy White Paper. The model appears to be working well with credible baseline and scenario run results both with and without CO2 constraints. It remains to be seen whether the new version of MARKAL, currently under development by PSI and FES, which incorporates a macro model, other model enhancements and revised data for non-transport sectors will give similar results. We will do what we can to promote compatibility by ensuring the same transport data are used in the PSI/FES project. Some further work may be required once PSI has produced its first constrained baseline run to interpret and explain any significant differences.

    In interpreting results from MARKAL modelling, it is important to bear in mind the strengths and limitations of the model. MARKAL is designed to investigate possible future outcomes for the whole economy based on bottom-up, technology-rich data and the implicit assumption that all technologies that are cost effective will be taken up. In practice there are non-price barriers to be overcome and a case for policy intervention to encourage even cost-effective technologies to be deployed. MARKAL is not a forecasting tool and in particular should not be used to project in detail the future uptake of technologies and fuels in a single sector or sub-sector. While it may give useful insights into potentially cost-effective technologies for cars, for example, it cannot predict which technology will be the long-term winner or how quickly that technology will enter the mix.

    Under baseline conditions without constraints on CO2 emissions, energy demand and CO2 emissions are expected to fall by 18% and 22% respectively over the period 2000 to 2050. This is mainly a result of significant improvements in energy efficiency in all sectors of the economy, with cost effective demand-side measures deployed much more rapidly than has been achieved in recent years. These reductions in energy use and CO2 emissions are greater than those expected from DTI forecasts because the DTI forecasts are implicitly based on historical trends in technology improvement.

    Without CO2 constraints, fossil fuels and conventional technologies continue to dominate in all sectors including transport. In the transport sector this means internal combustion engine technology with significant improvements in efficiency through hybridisation from 2010/2020. Hydrogen is also used from 2020 in buses and 2040 in vans, but is not taken up as a fuel for cars. The hydrogen taken up by the model is derived from natural gas steam reforming as this is the most cost-effective source. Low carbon fuels such as biofuels and hydrogen from renewable sources do not penetrate significantly as they are not cost effective except in some cases at very low rates of fuel duty.

    All sectors have a role in reducing CO2 emissions under a constraint that reduces CO2 emissions from the whole economy by 60% from 1990 levels by 2050. Initially this is achieved through further demand-side energy efficiency improvements and fuel switching in the electricity supply industry. The latter includes new nuclear build that is not taken up under baseline conditions. The transport sector reduces emissions more slowly than other sectors from 2010 to 2040 and then decarbonises rapidly in 2050 through fuel switching to hydrogen. These results assume a linearly increasing CO2 constraint over time. This profile does not represent the least cost route for reducing cumulative CO2 emissions over the period to 2050. It could be interesting to use MARKAL to explore the least cost CO2 reduction profile for saving a certain amount of cumulative CO2 to 2050, and the fuel and technology mixes that correspond to this profile.

    If the model is constrained by preventing new nuclear capacity from being built this has no impact on the baseline results but it does change the picture under a -60% CO2 constraint. The most significant changes are in the electricity and transport sectors, with greater deployment of CCGT, some clean coal technologies and renewables for electricity generation and earlier and greater deployment of hydrogen fuel cell technology for transport.

    The baseline scenario for cars includes a large increase in the deployment of petrol hybrids between 2020 and 2030. Under a -60% CO2 constraint, this sudden uptake of petrol hybrids comes instead between 2010 and 2020 and there is also a step change in the deployment of hydrogen fuel cell cars between 2040 and 2050. These step changes do not reflect the transition time actually required to introduce a new technology or fuel. It may be worthwhile to undertake additional work to explore whether and how such transition times could be incorporated into MARKAL. Off-model spreadsheet calculations or information from DfT's National Transport Model (NTM) could help with this. For example, the NTM could be used to estimate the maximum increase in vehicle-km for a new technology from one time period to the next, i.e. over a decade. This is likely to vary by technology as the transition from
    hybrids to plug-in hybrids would require less drastic infrastructure and industry changes than the introduction of hydrogen fuel cell vehicles, for example.

    The results for cars are sensitive to cost assumptions for hydrogen fuel cells and plug-in hybrids, which are currently quite uncertain. Even relatively small changes in the costs of fuel cell systems, hybridisation technologies, hydrogen storage and batteries result in the model choosing plug-in hybrids in preference to hydrogen fuel cells in some cases. The plug-in hybrid is an interesting new technology option that was not considered at the time of the Energy White Paper, and it may merit further investigation. Further sensitivity runs on the capital costs of hydrogen fuel cell and plug-in hybrid vehicles could be undertaken to explore the cost reductions in these technologies that would be required to make them cost effective earlier, say in 2030, with and without CO2 constraints. This could provide insights into the scope for bringing forward the deployment of lower carbon vehicle technologies and contribute information to the process of setting R&D targets and priorities.