Energy supply/Data uncertainties limitations: Difference between revisions
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{{ComponentDataUncertaintyAndLimitationsTemplate | {{ComponentDataUncertaintyAndLimitationsTemplate | ||
|Status=On hold | |Status=On hold | ||
|Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; | |Reference=Mulders et al., 2006; Van Vuuren et al., 2009; Van Vuuren et al., 2010; Hoogwijk, 2004; Hendriks et al., 2004b; IPCC, 2005; van Vuuren et al., 2008; | ||
|Description=<h2>Data</h2> | |Description=<h2>Data</h2> | ||
Below some of the most important data sources used on the supply side of the TIMER model are indicated. The most important data used in the energy supply modules concern information on the size of the different resources at different production costs. | Below some of the most important data sources used on the supply side of the TIMER model are indicated. The most important data used in the energy supply modules concern information on the size of the different resources at different production costs. | ||
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Obviously, one of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly; especially non-conventional resource estimates for oil and natural gas. Equally important are the nature and rate of technological advances and the design and implementation of energy policies in different regions. | Obviously, one of the main uncertainties with respect to long-term supply is the size of the resource estimates at various production costs. Estimates of energy resources vary significantly; especially non-conventional resource estimates for oil and natural gas. Equally important are the nature and rate of technological advances and the design and implementation of energy policies in different regions. | ||
Various PBL publications have analysed the sensitivity of the model to various supply uncertainties. In particular, a Monte Carlo uncertainty analysis was performed as a function of different storylines (Van Vuuren et al., 2008). From this analysis, some model parameters were identified as important determinants of the future supply of each fuel (e.g. oil and natural gas resources and renewable energy learning rates). Interestingly, some of these factors were only important for a subset of scenario output. For instance, it was found that the size of oil resources directly influenced future oil production, but had very limited impact on future CO2 emissions. The main reason being that oil production in the medium term was constrained anyway given the competition from other fossil fuels and bio-energy. The results were also shown to be scenario dependent: fossil-fuel-related uncertainties were much more important for model behaviour in a scenario that resulted in a high fossil-fuel demand than in one with low demand. | Various PBL publications have analysed the sensitivity of the model to various supply uncertainties. In particular, a Monte Carlo uncertainty analysis was performed as a function of different storylines ([[Van Vuuren et al., 2008]]). From this analysis, some model parameters were identified as important determinants of the future supply of each fuel (e.g. oil and natural gas resources and renewable energy learning rates). Interestingly, some of these factors were only important for a subset of scenario output. For instance, it was found that the size of oil resources directly influenced future oil production, but had very limited impact on future CO2 emissions. The main reason being that oil production in the medium term was constrained anyway given the competition from other fossil fuels and bio-energy. The results were also shown to be scenario dependent: fossil-fuel-related uncertainties were much more important for model behaviour in a scenario that resulted in a high fossil-fuel demand than in one with low demand. | ||
==Limitations== | ==Limitations== | ||
The general limitations of TIMER also apply to the energy supply models, with a few more specific limitations. As a global model, TIMER specifies resource availability in 26 global regions. However, this to some degree ignores the underlying geographical dimensions of individual countries and specific areas. For fossil fuel, this issue leads to heterogeneity within a region (e.g. due to different tax systems), but it is even more important for renewable energy. Here, transport from one area to another can be really important and calculations require the use of other models. Another main limitation concerns the focus on production costs, when describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs, over the last few decades, we have seen that issues related to capacity constraints and market formation over longer periods of time also lead to fossil fuels prices that are very different from production costs. | The general limitations of TIMER also apply to the energy supply models, with a few more specific limitations. As a global model, TIMER specifies resource availability in 26 global regions. However, this to some degree ignores the underlying geographical dimensions of individual countries and specific areas. For fossil fuel, this issue leads to heterogeneity within a region (e.g. due to different tax systems), but it is even more important for renewable energy. Here, transport from one area to another can be really important and calculations require the use of other models. Another main limitation concerns the focus on production costs, when describing energy markets. Although long-term developments may be expected to be driven by long-term supply costs, over the last few decades, we have seen that issues related to capacity constraints and market formation over longer periods of time also lead to fossil fuels prices that are very different from production costs. | ||
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Revision as of 17:18, 9 December 2013
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