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To what extent can behavioural change be the answer to emissions reductions emanating form UK transport sector oil consumption? A Semi-parametric structural time series analysis.
David C. Broadstock,
Surrey Energy Economics Centre,
Department of Economics,
University of Surrey,
Guildford
GU2 7XH
d.broadstock@surrey.ac.uk
This work presents new evidence on the price and income elasticities of road transport fuel in the UK (1960-2005), and indirectly also upon the maximum potential range that travel demand can feasibly be changed by using behavioural type measures. The evidence is derived from an econometric study applying the structural time series model and using the Kalman filter (see for example Hunt et al, 2003) to measure changes in travel behaviour as a result of non-price factors/policies. This is based on the premise that the stochastic trend represents systematic behavioural responses by the market to (i) changes in energy efficiency resulting from technical progress, and/or (ii) attitudinal responses to increased knowledge/awareness of the environmental impact of certain types of transport.
The general model is specified as;
EMBED Equation.3 (1)
EMBED Equation.3 (2)
EMBED Equation.3 (3)
Where EMBED Equation.3 and EMBED Equation.3 . A(L) is the polynomial lag operator 1 - f1L - f 2L2 - f 3L3 - f 4L4, B(L) the polynomial lag operator p0 + p1L + p2L2 + p3L3 + p4L4, C(L) the polynomial lag operator j0 + j1L + j2L2 + j3L3 + j4L4. et is the natural logarithm of energy consumption (treating gasoline and diesel independently of each other) while yt is the natural logarithm of income/output, pt the natural logarithm of real energy and et the error term.
Stoffer and Wall (1991, 2004) discuss how time series analyses using state space methods (such as the structural time series model) often fail to completely satisfy assumptions of normality unless sample sizes are fairly large and suggest the use of bootstrap methods to support inference. Further, Li and Madalla (1999) conclude in an analysis of US energy demand that bootstrap methods are a preferable approach to hypothesis testing (in the context of long-run demand elasticities). This paper therefore carefully evaluates the findings of the econometric analysis by comparing standard maximum likelihood based inference to that derived from a bootstrap analysis. This approach to modelling and inference is broadly consistent with a philosophy of allowing the data to tell the story by imposing fewer restrictions on the analytical approach, while conforming to economic theory.
Given increasing concerns surrounding attitudes toward travel behaviour, and the way it is so heavily linked with environmental concerns (see for example Eddington, 2006), this work provides information on: (i) The potential reliance upon softer approaches to manage the demand for fossil-fuel based road travel and (ii) The scope for Smarter Choice (see Cairns et al 2004) style travel interventions/policies to support UK carbon reduction targets. Early results indicate firstly that the income elasticities of gasoline and diesel are not identical and hence to model them together as an aggregate fuel may result in the loss of valuable policy relevant information. Secondly the underlying energy demand trends seem to reveal several interesting features including an overall attitude which suggests that environmentally focussed policy initiatives are working, but are seemingly being counteracted by persistent growth in national income.
References
Cairns, S., Sloman, L., Newson, C., Anable, J., Kirkbride, A. & Goodwin, P. (2004b), Smarter Choices - Changing the Way We Travel, Final report of the research project: 'The influence of soft factor interventions on travel demand', Report published by the Department for Transport, London.
Eddington, R. (2006), The Eddington Transport Study. The case for action: Sir Rod Eddingtons advice to Government, HM Treasury/Department for Transport, UK.
Hunt. L, Judge. G, and Ninomiya. Y, (2003), Underlying Trends and Seasonality in UK Energy Demand: A Sectoral Analysis, Energy Economics, 25(1), pp. 93-118.
Li. H, and Maddala. G, (1999), Bootstrap variance estimation of nonlinear functions of parameters: An application to long-run elasticities of energy demand, The Review of Economics and Statistics, 81(4), 728-733.
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Stoffer. D, and Wall. K, (2004), Resampling in state space models, Chapter 9 of State Space and Unobserved Component Models: Theory and Applications, Harvey. A, Koopman. S, and Shephard. N, (Editors), Cambridge University Press.
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