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page 26 of 188 RIVM report 461502024 machines etc. In the next stage, often less energy-intensive activities start to dominate sectoral energy consumption at the margin. As a result, the activities within the sector grow faster than energy use, and thus intensity declines. There are still large uncertainties about what actually happens and further research is needed. In )LJXUH, the demand for useful energy is shown in a stylised form as a function of the driving force DFpc. If energy-intensity is expressed per unit of DFpc, the hyperboles represent constant useful energy demand per capita isolines 13 . As 7DEOH shows, this is the case for all sectors but the industrial sector. This is because at higher GDP/cap levels the share of VA industry tends to decline and one has to introduce some form of irreversibility to avoid energy-intensity going up again as income increases. Therefore, for this sector we use GDP per capita as driver of change. Using this curve assumes that certain phenomena are universal in nature, such as the transition from energy- and materials-intensive bulk products towards knowledge-intensive processed goods within industry, the increase of the size of dwellings and of office spaces and the add-on luxuries in cars if people become materially more affluent. To be sure: these are lifestyle related developments, not natural laws. The present-day emergence of a global consumer culture tends to affirm these trends, but other courses of (political and consumer) action might lead to quite different trends. 7DEOH6HFWRUDOLQGLFDWRUVXVHGLQWKHVWUXFWXUDOFKDQJHIRUPXODWLRQ 6HFWRU ,QWHQVLW\LQGLFDWRU8(, IUR]HQ 'ULYHURIFKDQJH') SF Industry Transport Residential sector Services Other UE frozen, industry / VA industry UE frozen, transport / GDP UE frozen, residential / Priv. Cons UE frozen, services / VA services UE frozen, other / GDP GDP per capita GDP per capita Private consumption per capita Value added services per capita GDP per capita We express the stylised curve of )LJXUH for the intensity UEI frozen (see also (TQas a function of PPP-corrected values for sectoral activity indicators: 8(, = 8(,EDVH + 1/( α + β * ')SF + γ * ')SF ) GJ/$ (3.4) UVL UVL UVL UVL By choosing certain values of the parameters α, β, γ, and δ (based on historical trends) (TQ can take a form in which the intensity initially rises, goes through a maximum for a value of DFpc = (-1/γδ) 1/(δ-1) , approaching asymptotically a fixed per capita level, UEIbase rsi + 1/α r,s,i which is region- and sector-dependent. Hence, at high activity levels the UED-intensity follows an isoline of constant GJ/cap of the magnitude 1/α r,s,i . In &KDSWHUthe dynamics of (TQ are analysed in more detail. Note that this curve is for UEI frozen , that is, for the 1971-level of technology and prices. An important aspect of this formulation is the possibility to introduce physical data into the energy demand simulation. Regions differ in climate, in the stage of their techno-economic development etc. Such information can be introduced in the assessment of the regional saturation levels by gauging the regional curves to account for differences in climate (residential and services, but also industry), population density (transport, but also residential), primary sector self-sufficiency (industry) and traditional fuel use for cooking (residential). In UV UV δ 13 All converted to PPP 1995 $, also indicated as international dollars, I$.
RIVM report 461502024 page 27 of 188 general, the differences can be given a sensible interpretation and can be linked to similar analyses done by others (E.g. Sorensen, 1998). 0.25 Isolines (UE/cap) ,QWHQVLW\8(SHU*'3 0.20 0.15 0.10 0.05 0.00 0 5000 10000 15000 20000 25000 'ULYHU*'3SHUFDSLWD )LJXUH*HQHUDOVKDSHRIWKHLQWUDVHFWRUDOVWUXFWXUDOFKDQJH(TQ A more advanced approach would be to use intermediate explaining variables such as office floor space, number and size of trucks etc. We hope to do this in future work, bridging monetary top-down with process-based bottom-up approaches (Price, 1999; Groenenberg, 1999). Still, one may miss important explaining variables in this way, for instance a supply push in the case of electric power overcapacity or heavily subsidised pricing. On top of this is the (un)reliability of the sectoral data, including changes in sectoral definition. It should be reiterated that UED frozen is a non-observable quantity. The parameterisation is done by gauging the curve to the 1971-1995 historical data, entering reasonable estimates from the literature on conversion efficiencies and the role of autonomous and price-induced efficiency improvements. We have constructed a new and consistent database from IEA and other sources to this purpose (see Appendix A). For the scenario part, we assume regional per capita saturation level trying to account for differences in: • industry: the product/process mix and the state of technology; • transport: population density, mobility patterns, and the state of infrastructure and technology; • residences: climate, building practices and cooking and heating/cooling habits; • services: climate, building practices, heating/cooling habits and the nature of the service/commercial sector; • other: no special considerations have been applied; the main activity in this category is agriculture. This category is often small and/or a statistical artefact which tends to diminish as the energy statistics are improving. An additional consideration is that the demand for useful energy is not always met - there may be an unmet, or latent, demand that cannot be satisfied due to lack of purchasing power or supply capacity. For the calibration, this phenomenon is not accounted for.
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