Trend projections for energy use

The procedure for estimating non-renewable-energy resources and their corresponding emissions as shown in Chapters 3.3 and 3.4 relates to the CAPRI base period (three-year average of 2001-2003). Consequently, all underlying statistical parameters and expert know-ledge are also extracted for the base period. Because alternative policy scenarios refer to the CAPRI baseline (established for the year 2013) for this analysis, trend estimates for energy -use parameters and their corresponding emissions are required to systematically follow the approach shown in Chapter 3.2.7. Since this implies the consideration of trend projections conducted for CAPRI production activities and their corresponding input coeffi-cients, trend projection for energy use is a two-sided approach.

When considering plant-production activities, trend projection for two parameters – mineral-fertiliser use and machinery use, including fuel use – appears to be crucial for the display of future energy use. The actual trend projections for mineral-fertiliser use comprise two parameters: shifts in the quantity of mineral fertiliser applied, and shifts in energy use per unit of mineral fertiliser. Consequently, an adapted trend curve taking both compo-nents into account may be created as shown in Equation 36.

)

Equation 36 Adapted trend-estimation curve for mineral-fertiliser energy use

Represents the data subject to trend estimation [five-dimensional array spanning i, j, r, t, data status]

Product

Items [e.g. fertiliser use]

Nutrient component of mineral-fertiliser use Region

Points in time

Data status [Trend/Observation]

Source: based on Britz et al. (2007), adapted.

Trend estimation for energy use based on machinery use and its associated fuel con-sumption consists of three basic parameters: changes in machinery stock per component (tractor/harvester/trailed machinery), changes in the composition of the component (trac-tor engine-power class) and category-specific fuel consumption. The current analysis co-vers all of these parameters using specific methodological approaches. Changes in overall machinery stock are estimated by trend analysis of existing machinery-stock statistics and the respective forecasts. Here, a distinction is made between tractors and harvesters. The underlying data sources enable a country-specific (NUTS-0) forecast for tractor stock in the EU-15. No data were applicable for the EU-10 countries owing to the abnormalities in ma-chinery stock observable for the period 1990–2000. As regards harvesters, the data per-mit a comprehensive forecast for the EU-15 and a liper-mited forecast (based on a short refer-ence period) for the EU-10 countries. The relevant data for tractors show a slight decrease in stock for most of the EU-15 countries, and a slight increase for Greece, Spain, Italy and Portugal. The engine-power-class-specific analysis shows an above-average decrease for smaller tractors (<40 kW) and a below-average decrease or current increase for bigger trac-tors (>40 kW). For harvesters, a negligible-to-small decrease in stocks can be observed for most of the EU-15 countries, whilst a small-to-significant increase was calculated for most EU-10 countries. The engine-specific fuel-requirement forecasts are calculated on the ba-sis of indices given in FOEN (2007). This Internet database enables fuel use to be calculated per engine category, with trend projections being extracted whilst taking account of Landis (2007). Consequently, trend projection for machinery use and fuel consumption is calcu-lated as shown in Equation 37 and Equation 38, respectively.

A more simplified approach is chosen to calculate the energy use of other input com-ponents of plant-production activities. Owing to the lack of statistical data required for trend projection of seeds and crop protection, expert knowledge is applied to set up sim-ple adjustment coefficients and integrate these according to the procedure shown in Equa-tion 37. For irrigaEqua-tion and drying processes, simple adjustments in energy use per produc-tion unit are applied based on expert knowledge (2 per cent annual reducproduc-tion in energy use for irrigation processes; 1 per cent annual reduction in energy use for drying). Because of the rather unspecific nature of the base-year set-up for drying and irrigation processes in the different technical systems, the trend estimation covers basic estimates of technical progress in terms of the required equipment. As regards the application of diversified pro-duction technologies such as conservation soil tillage, a simple trend estimation of the ex-tensivisation rate of the years preceding the base period (plus 0.5 per cent of

conservation-j n

tillage share based on the base-period values) is carried out to adjust their respective appli-cation rates.

Equation 37 Trend-estimation curve for machinery use

X

Represents the data subject to trend estimation [five-dimensional array spanning p, j, r, t, data status]

CAPRI production activity Items [tractors/harvester]

Machinery type as a function of engine-power class [<40 / 40-60 / 61-100 /

>100 kW]

Region Points in time

Data status [Trend/Observation]

Source: based on Britz et al.( 2007), adapted.

Equation 38 Trend-estimation curve for fuel consumption

X j mt t

Represents the data subject to trend estimation [three-dimensional array spanning j, t, mt]

Items [tractors/harvester]

Machinery type as a function of engine-power class [<40 / 40-60 / 61-100 /

>100 kW]

Points in time

Source: based on Britz et al. ( 2007), adapted.

For animal-production activities, trend estimation must be divided into two basic pa-rameters. Firstly, feedstuff components account for a range of adjustments carried out for plant-production activities, and as such constitute a major part of overall energy use in ani-mal-production activities. Secondly, the range of animal-specific input components such as electricity, heating gas and buildings is subject to trend estimation. As for irrigation and drying, owing to the set-up chosen for the CAPRI approach and the rather meagre data-base for parameters such as buildings, expert knowledge is applied to cover basic assump-tions on technical progress, and is incorporated in the baseline modelling as shown in 3.2.7.