Residential and commercial sectors - IMAGE
|Institution||Utrecht University (UU), Netherlands, https://www.uu.nl/en., PBL Netherlands Environmental Assessment Agency (PBL), Netherlands, https://www.pbl.nl/en.|
|Solution concept||Partial equilibrium (price elastic demand)|
|Anticipation||Simulation modelling framework, without foresight. However, a simplified version of the energy/climate part of the model (called FAIR) can be run prior to running the framework to obtain data for climate policy simulations.|
The residential submodule describes the energy demand from household energy functions of cooking, appliances, space heating and cooling, water heating and lighting. These functions are described in detail in 1 and 2.
Structural change in energy demand is presented by modelling end-use household functions:
- Energy service demand for space heating is modelled using correlations with floor area, heating degree days and energy intensity, the last including building efficiency improvements.
- Hot water demand is modelled as a function of household income and heating degree days.
- Energy service demand for cooking is determined on the basis of an average constant consumption of 3 MJUE/capita/day.
- Energy use related to appliances is based on ownership, household income, efficiency reference values, and autonomous and price-induced improvements. Space cooling follows a similar approach, but also includes cooling degree days (Isaac and Van Vuuren, 2009).
- Electricity use for lighting is determined on the basis of floor area, wattage and lighting hours based on geographic location.
Efficiency improvements are included in different ways. Exogenously driven energy efficiency improvement over time is used for appliances, light bulbs, air conditioning, building insulation and heating equipment, Price-induced energy efficiency improvements (PIEEI) occur by explicitly describing the investments in appliances with a similar performance level but with different energy and investment costs. For example, competition between incandescent light bulbs and more energy-efficient lighting is determined by changes in energy prices.
The model distinguishes five income quintiles for both the urban and rural population. After determining the energy demand per function for each population quintile, the choice of fuel type is determined on the basis of relative costs. This is based on a multinomial logit formulation for energy functions that can involve multiple fuels, such as cooking and space heating. In the calculations, consumer discount rates are assumed to decrease along with household income levels, and there will be increasing appreciation of clean and convenient fuels 1. For developing countries, this endogenously results in the substitution processes described by the energy ladder. This refers to the progressive use of modern energy types as incomes grow, from traditional bioenergy to coal and kerosene, to energy carriers such as natural gas, heating oil and electricity.
The residential submodule also includes access to electricity and the associated investments 3. Projections for access to electricity are based on an econometric analysis that found a relation between level of access, and GDP per capita and population density. The investment model is based on population density on a 0.5x0.5 degree grid, from which a stylised power grid is derived and analysed to determine investments in low-, medium- and high-voltage lines and transformers.
- Bas J. van Ruijven, Detlef P. van Vuuren, Bert J.M. de Vries, Morna Isaac, Jeroen P. van der Sluijs, Paul L. Lucas, P. Balachandra (2011). Model projections for household energy use in India. Energy Policy, 39 (), 7747-7761. http://dx.doi.org/10.1016/j.enpol.2011.09.021 | | |
- Vassilis Daioglou, Bas J. van Ruijven, Detlef P. van Vuuren (2012). Model projections for household energy use in developing countries. Energy, 37 (), 601-615. http://dx.doi.org/10.1016/j.energy.2011.10.044 | | |
- Bas J. van Ruijven, Jules Schers, Detlef P. van Vuuren (2012). Model-based scenarios for rural electrification in developing countries. Energy, 38 (), 386-397. http://dx.doi.org/10.1016/j.energy.2011.11.037 | | |