Industrial sector - IMACLIM
|Model link||http://dina.centre-cired.fr/IMACLIM/Description-des-modeles-IMACLIM/?lang=en; https://github.com/GaelleLeTreut/IMACLIM-Country/tree/V1.1; https://hal.archives-ouvertes.fr/hal-02949396/document|
|Institution||Centre international de recherche sur l'environnement et le développement (CIRED), France, http://www.centre-cired.fr., Societe de Mathematiques Appliquees et de Sciences Humaines (SMASH), France, http://www.smash.fr.|
|Solution concept||General equilibrium (closed economy)|
|Solution method||SimulationImaclim-R is implemented in Scilab, and uses the fonction fsolve from a shared C++ library to solve the static equilibrium system of non-linear equations.|
|Anticipation||Recursive dynamics: each year the equilibrium is solved (system of non-linear equations), in between two years parameters to the equilibrium evolve according to specified functions.|
Energy use by productive sectors
Induced technical change in productive sectors of the economy is modelled in Imaclim-R according to two assumptions. First, energy efficiency improvements are induced by devolopments in energy prices. Second, energy substitution occurs driven by learning-by-doing processes. At the aggregate level, energy efficiency improvements and energy substitution may result from structural changes in economic activity.
Energy efficiency improvements in productive sectors
For each productive sector (industry, construction, services, agriculture), the region with the lowest final energy use per unit of production at base year is identified as the most energy efficient region, thus dividing the world into one leader region and eleven followers for each sector. The energy efficiency of the leader evolves as a function of an energy price index, and an exogenous trend in energy efficiency improvements at constant energy prices. The energy price index is determined endogenously, and the energy efficiency growth rate of the leader will increase (resp. decrease) in response to increases (resp. decreases) in energy prices. For each sector, the energy intensity of the followers is assumed to converge towards the performance of the leader. The speed of convergence also depends on the level of energy prices. Some emerging economies appear to be more energy efficient in some sectors at the year of calibration. From combining IEA energy matrices and GTAP input-output tables, agriculture in Africa appears to be 12% more efficient than the leader (Japan). This can be due to missing data, or difference in the structures of the sectors, and thus suggests precaution with the use of the data. Conforti and Giampietro (1997) 1 also reports that some African countries display a very high energy output to input ratio (Uganda is 380 times more 'efficient' than Japan). In more efficient regions, the energy intensity of the relevant sectors is allowed to start with lower levels of energy intensity then the leader, before converging towards the leader. Energy efficiency improvements are assumed to be in part free, and in part linked to higher cost of capital. Energy efficiency improvements in productive sectors are not biased towards low carbon energy sources meaning that the use of fossil and non-fossil energy decreases uniformly. A shift from carbon intensive to low carbon energy use in these sectors may be induced by an increase in fossil fuel energy prices brought about by the introduction of a carbon price. In general, substitutions between energy carriers (coal, oil, gas, electricity, refined fuel) and transportation modes (road, rail, air, water) are driven by relative prices given explicit constraints on energy production and end-use equipment.
Energy efficiency improvements induce lower energy consumption per unit of output (ICuener) in each productive sector. This may result in higher or lower aggregated energy consumption (ICener), depending on the relative effects of lower unit consumption and higher sectoral production (Q) induced by lower prices. Lower overall energy consumption affects energy prices in two ways: a decrease in wholesale energy prices because of lower energy use (ICener) and lower emissions lead to a relaxation of the carbon tax required to reach a set climate objective. Overall, lower energy consumption thus results in lower tax-inclusive energy prices. As energy efficiency improvements are driven by the energy price index, lower energy prices may in turn counterbalance energy efficiency improvements. On the production side, lower unitary energy requirements (ICuener) decrease production costs and prices (p), driving up demand and production (Q).
Substitution and structural change
Substitution between energy carriers (i.e. coal, oil, natural gas, electricity, refined liquid fuels) and substitution between transportation modes (i.e. by road, rail, air or water) are driven by relative prices, given explicit constraints on energy production and end-use infrastructure e.g. energy production and conversion capacities and available end-use equipment. These substitutions occur at the end-use sector level.
At the micro level, learning-by-doing may induce substitution between technologies, which may in turn induce energy carrier substitution e.g. from coal to gas for electricity production. Technology substitution is also explicitly modelled at the end-use level for transport, e.g. between conventional and electric cars. Energy efficiency improvements are not biased towards low or high carbon energy carriers, as the consumption of all types of energy decreases uniformly. However, for the sectors using fossil fuels, carbon pricing will increase the energy price index. The substitution between energy carriers however depends on relative prices and relies on a logit decision function for new vintages of productive capacities and equipment (the sectoral energy mix being the sum of energy demands of all vintages). Technical change may occur at the level of specific technologies through learning-by-doing processes. The cost of technologies is assumed to decrease with cumulative investment and production through learning-by-doing, using learning curves for all explicit technologies. The pace of cost reductions down the learning curve depends on the initial installed capacity, the learning rate and the cost floor. This approach has been used to characterise energy technologies, see for instance (McDonald and Schrattenholzer, 2001 2; Neij, 2008 3). It is used in Imaclim-R to model electricity and oil production technologies, or for demand technologies (such as cars). In energy production sectors, learning-by-doing for low-carbon electricity production technologies (triggered by carbon prices) may improve the carbon efficiency of energy transformation through the substitution from fossil energy towards low carbon-alternatives. At the macro level, carbon pricing policies may induce a change in the structure of demand both at the household and firm levels by altering energy prices. This may in turn change the nature of the goods produced, and hence the structure of each sector and in the relative weight of each sector in total economic output.
- Piero Conforti, Mario Giampietro (1997). Fossil energy use in agriculture: an international comparison. Agriculture, ecosystems \& environment, 65 (3), 231--243. | |
- Alan McDonald, Leo Schrattenholzer (2001). Learning rates for energy technologies. Energy policy, 29 (4), 255--261. | |
- Lena Neij (2008). Cost development of future technologies for power generation—A study based on experience curves and complementary bottom-up assessments. Energy policy, 36 (6), 2200--2211. | |