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page 158 of 188 RIVM report 461502024 It is easily seen that for λ = 1/(1+β), a=b and F i0 =0 for i=1,2, eqn. 9.15 changes into eqn. 9.11, the basic equation for the multinomial logit equation (γ 21 = p 1 /p 2 ). The two approaches are equivalent and the multinomial logit parameter λ is directly related to the substitution elasticity β of the underlying production function. The condition that a=b implies that at equal prices each fuel takes half of the market. Alternatively, one can view the prices bp 1 and ap 2 as shadow (or perceived) prices. If we redefine γ = (bp 1 / ap 2 ) and introduce φ=(γ λ F 10 -F 20 ), it can also be derived that: λ γ ) 1 −φ ,062 = (9.17) λ ( 1+ γ )) − φ 1 which shows again the equivalence with eqn. 9.11 but also that the calculation of the IMSvalues is recursive unless φ=0 i.e. for F i0 =0 for i=1,2. If only F 20 =0 and we introduce α= F 10 /(F 1 + F 2 ) as the fraction of fuel c.q. option 1 which cannot be substituted away, then eqn. 9.13 can be rewritten as: ,061 −α ,062 = (9.18) λ 1+ γ ( ),06 −α λ 1 γ This has been used to account for parts of the market of fuel c.q. option 1 inaccessible for substitution by fuel c.q. option 2. &DWFKLQJ8S In explaining regional differences of economic growth, differences in technological development are a crucial factor. In the energy model, differences in the state of (energy) technology are reflected in different energy conservation and supply cost curves among regions. For policy analysis, it is interesting question how to speed up technological development in order to meet climate change targets at the lowest possible cost. Although in neo-classical theory technology is assumed to spread immediately, there is a substantial amount of more thoughtful analyses on technology transfers between economies. Some emphasise traded good as carriers of spillover (eg. Giliches, 1979; Silverberg and Soete, 1994; CPB, 1995); others point out that knowledge can be transmitted by channels such as conferences, scientific literature, labour mobility, patent information, or pure imitation. We follow the notion that technology diffusion is related to activities and abilities of the agents. Abromovitz (1986) argues that the catching-up process is conditional upon some specific factors, referred to as social capability and technological congruence. Social capability refers to all factors that facilitate the imitation of a technology, or the implementation of technology spillovers. This relates to factors like education, financial conditions and labour market relations. Technological congruence concerns the extent to which the country is technologically near to the leader country, i.e. to which extent it is able to apply the technical features near or at the production frontier.
RIVM report 461502024 page 159 of 188 In this section we describe a simple, transparent formulation of the complex dynamics of technology diffusion in order to be of use in the energy policy model <strong>TIMER</strong>. The focus is on the AEEI and PIEEI multipliers and the learning curves for fossil fuels and non-fossil alternatives. 'HVFULSWLRQ We implement the catching-up process in line with the Worldscan model (CPB, 1995), such that we can compare and use scenarios of Worldscan more easily. However, we also want to have the flexibility of a stand-alone model and to apply scenarios in order to assess the impact of increasing knowledge transfers on the energy supply and demand technology. Define the technological front (TF) as the minimum of the technology level parameter (TL) over all the regions (that is, if a decrease of the parameter reflex an improvement of technology, otherwise take the maximum): 7) ( 1−τ ) * 0,1( W 7/W −1, (9.19) U where τ is the mark-up of the notional technological frontier. It is unclear which determinants determine catching up. CPB (1995) includes, among other determinants, the capital-labour ratio and price competitiveness. However, the conditional factors as described in Abromovitz (1986) are not included adequately in both Worldscan and <strong>TIMER</strong>. We therefore want to have the freedom to define scenarios, which represent the complex qualitative developments in social capability and technological congruence. For example we may assume that political changes may lead to increasing social capability and technological congruence and are therefore stimulating catching-up. An example are the political changes in China, the former Soviet-Union and India, which have caused various kinds of economic and technological catching-up. So, by assuming a time-path for a variable we call transformation elasticity, γ[r], it is possible to mimic such developments. If it is low (γ=0) there is no catching-up to the technological frontier; at the other extreme is the situation that a region experiences an immediate technology transfer to the level of the frontier region (γ[r]=1). Hence, γ[r] is a scenario variable representing the catch up which in principle can be related to scenarios of the WorldScan model (CPB, 1995) in which the transformation elasticity is estimated for different sectors. In the simulation the catching-up dynamics is formulated as: such that [ ] γ U 7/ / 7/ 7) W 7/ − 1, / 7/ U W, = U W−1, (9.20) U W [ ] γ U 7/ / W, U 7/ W−1, U / W−1, U 7) W = (9.21) All regions will experience learning-by-doing through cumulated production and RD&D programs, as has been set forth in the previous paragraphs. However, inclusion of the technology transfer mechanism speeds up the learning in less advanced regions as a result of
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