Energy demand/Policy issues: Difference between revisions

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{{ComponentSubPolicyTemplate
{{ComponentPolicyIssueTemplate
|Application=Resource Efficiency
|Reference=Van den Berg et al., 2011; Van Ruijven et al., 2007;
|Description=<h2>Baseline development</h2>
}}
[[File:FinalEnergyDemand.png|left|thumb|240px|alt=Development of final energy demand by sector and energy carriers, in the baseline|Development of final energy demand by sector and energy carriers, in the baseline]]  
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In the baseline scenario, energy demand is projection to grow significantly during the 21st century. Most of the demand growth is driven by a growth in energy use in developing countries. In fact, per capita use in developed countries is project to remain more-or-less constant (consistent with historical trends). The increase in energy demand, is mostly met by electricity (in the first half of the century), fossil fuels and in the long-run also hydrogen (in transport).  
==Baseline developments==
<br clear=all>
The model shows that under a typical baseline scenario such as the one of the [[Roads from Rio+20 (2012) project|Rio+20]] study, energy demand is projected to grow significantly during the 21st century. Most growth will be driven by an increase in energy use in low-income countries. Per capita use in high-income countries is projected to remain more or less constant, consistent with recent historical trends. The increase in energy demand in the first half of the century will be mostly met by fossil fuels and electricity. In this model simulation, hydrogen becomes competitive in the transport sector in the second half of the century, as a result of increasing oil prices and the assumed progress in hydrogen technologies. An alternative assumption could result in a similar role for electricity.
 
{{DisplayPolicyInterventionFigureTemplate|{{#titleparts: {{PAGENAME}}|1}}|Baseline figure}}
 
==Policy interventions==
==Policy interventions==
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{{#ask:[[Category:PolicyIntervention]][[HasComponent::{{#titleparts: {{BASEPAGENAME}} | 1 }}]]
Various policy interventions can be implemented in the energy demand submodules in different ways (see also the Policy interventions Table below):
|mainlabel=Policy intervention
* Energy tax and carbon tax. This changes the prices for the energy carriers influences the choice of technology.
|?HasDescription=Description
* Discount rate/payback time. In the residential submodule , the perceived costs of capital (discount rate) influence the extent of energy efficiency improvement (PIEEI) and the choice of fuel and/or technology in the residential submodule.
|?HasReference=References
* Preferences. Fuel choice can be influenced by correction factors, representing aspects that influence fuel choice but are not incorporated in the price, such as fuel characteristics (e.g., cleanliness, availability), comfort and speed considerations, and infrastructure.
|format=table
* Efficiency standards. Such improvements can be introduced for the submodules that focus on specific technologies, for example, in transport, heavy industry and households.
|template=PolicyInterventionDisplayTemplate
* Enforced market shares of fuel types. Such an analysis could, for instance, provide insight into the implications in the model of increasing the use of biofuels, electricity or hydrogen ([[Van Ruijven et al., 2007]]).
}}
 
==Example of Policy Interventions==
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An example of several of these interventions forms the study “Resource Efficiency”. Here, the TIMER model was used to explore the impact of radically improving energy efficiency. This, for instance, included the information of best-available technologies in iron and steel production and other industries, the implementation of the most efficient cars and aircraft is assumed, a moderate shift is assumed from aircraft to high speed trains,and building of highly efficient housing (mostly insulation measures). It was also assumed that new power plants would be based on best-available technologies. New plants in all regions are assumed to be built on the basis of efficient technologies. The measures assumed in this global energy efficiency scenario are able to considerably reduce energy use. Primary energy consumption is reduced from X to Y EJ/yr (primary energy) in 2050, which corresponds to a reduction by about 30% as compared to the baseline. The results show that the RE scenario is able to more-or-less half the gap between baseline CO2 emissions and the emission reductions required to restrict temperature increase - with a high degree of probability - to a maximum of 2°C.
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[[File:ResourceEfficiency.png|left|400px|alt=Resource efficiency case |Resource efficiency case]]

Latest revision as of 15:22, 22 May 2019

Link to framework components overview

Parts of Energy demand/Policy issues

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references
Component is implemented in:
Aggregated component:Components:
Related IMAGE components
Projects/Applications
Key publications
References
TIMER model, energy demand module
Some sectors are represented in a generic way as shown here, the sectors transport, residential and heavy industry are modelled in specific modules.

Baseline developments

The model shows that under a typical baseline scenario such as the one of the Rio+20 study, energy demand is projected to grow significantly during the 21st century. Most growth will be driven by an increase in energy use in low-income countries. Per capita use in high-income countries is projected to remain more or less constant, consistent with recent historical trends. The increase in energy demand in the first half of the century will be mostly met by fossil fuels and electricity. In this model simulation, hydrogen becomes competitive in the transport sector in the second half of the century, as a result of increasing oil prices and the assumed progress in hydrogen technologies. An alternative assumption could result in a similar role for electricity.


Global final energy demand under a baseline scenario
Between 2010 and 2050 energy demand for transport and industry, and for natural gas and electricity contribute most to the overall increase.

Policy interventions

Various policy interventions can be implemented in the energy demand submodules in different ways (see also the Policy interventions Table below):

  • Energy tax and carbon tax. This changes the prices for the energy carriers influences the choice of technology.
  • Discount rate/payback time. In the residential submodule , the perceived costs of capital (discount rate) influence the extent of energy efficiency improvement (PIEEI) and the choice of fuel and/or technology in the residential submodule.
  • Preferences. Fuel choice can be influenced by correction factors, representing aspects that influence fuel choice but are not incorporated in the price, such as fuel characteristics (e.g., cleanliness, availability), comfort and speed considerations, and infrastructure.
  • Efficiency standards. Such improvements can be introduced for the submodules that focus on specific technologies, for example, in transport, heavy industry and households.
  • Enforced market shares of fuel types. Such an analysis could, for instance, provide insight into the implications in the model of increasing the use of biofuels, electricity or hydrogen (Van Ruijven et al., 2007).

Effects of policy interventions on this component

Policy interventionDescriptionEffect
Carbon tax (*) A tax on carbon leads to higher prices for carbon intensive fuels (such as fossil fuels), making low-carbon alternatives more attractive. The higher fossil fuel prices result in a shift towards less carbon-intensive energy carriers and (assuming a higher overall energy price) more energy efficiency. There can also be changes in end-use technologies ( e.g. electric cars in the transport sector, blast furnaces with CCS to produce iron and steel).
Change market shares of fuel types (*) Exogenously set the market shares of certain fuel types. This can be done for specific analyses or scenarios to explore the broader implications of increasing the use of, for instance, biofuels, electricity or hydrogen and reflects the impact of fuel targets. (Reference:: Van Ruijven et al., 2007) The share of the fuel in final energy consumption will be at least equal to the target.
Change the use of electricity and hydrogen It is possible to promote the use of electricity and hydrogen at the end-use level. An increase in the use of electricity and hydrogen at the end use level. Given the high flexibility in the choice of feedstock in electricity and hydrogen production this can increase the ability of the total system to reduce greenhouse gas emissions in a mitigation scenario.
Improving energy efficiency (*) Exogenously set improvement in efficiency. Such improvements can be introduced for the submodels that focus on particular technologies, for example, in transport, heavy industry and households submodels. More efficient use of final energy, change in end use technologies, which leads to lower energy demand.
Provision on improved stoves for traditional bio-energy (*) Increases the efficiency of bio-energy use. Reduces demand for bio-energy.
Subsidies on modern energy (*) Reduces the costs of modern energy to reduce traditional energy use (can be targeted to low income groups). Reduces traditional energy use, while increasing modern energy use.
(*) Implemented in this component.
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