کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
994916 | 936151 | 2011 | 10 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Introducing technology learning for energy technologies in a national CGE model through soft links to global and national energy models Introducing technology learning for energy technologies in a national CGE model through soft links to global and national energy models](/preview/png/994916.png)
This paper describes a method to model the influence by global policy scenarios, particularly spillover of technology learning, on the energy service demand of the non-energy sectors of the national economy. It is exemplified by Norway. Spillover is obtained from the technology-rich global Energy Technology Perspective model operated by the International Energy Agency. It is provided to a national hybrid model where a national bottom-up Markal model carries forward spillover into a national top-down CGE1 model at a disaggregated demand category level. Spillover of technology learning from the global energy technology market will reduce national generation costs of energy carriers. This may in turn increase demand in the non-energy sectors of the economy because of the rebound effect. The influence of spillover on the Norwegian economy is most pronounced for the production level of industrial chemicals and for the demand for electricity for residential energy services. The influence is modest, however, because all existing electricity generating capacity is hydroelectric and thus compatible with the low emission policy scenario. In countries where most of the existing generating capacity must be replaced by nascent energy technologies or carbon captured and storage the influence on demand is expected to be more significant.
► Spillover of global technology learning may be forwarded into a macroeconomic model.
► The national electricity price differs significantly between the different global scenarios.
► Soft-linking global and national models facilitate transparency in the technology learning effect chain.
Journal: Energy Policy - Volume 39, Issue 6, June 2011, Pages 3327–3336