DP6549 Optimal Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations
|Author(s):||Valentina Bosetti, Carlo Carraro, Emanuele Massetti, Massimo Tavoni|
|Publication Date:||November 2007|
|Keyword(s):||climate policy, energy R&D, investments, stabilisation costs|
|JEL(s):||H4, O3, Q4|
|Programme Areas:||International Trade and Regional Economics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=6549|
The stabilisation of GHG atmospheric concentrations at levels expected to prevent dangerous climate change has become an important, global, long-term objective. It is therefore crucial to identify a cost-effective way to achieve this objective. In this paper we use WITCH, a hybrid climate-energy-economy model, to obtain a quantitative assessment of some cost-effective strategies that stabilise CO2 concentrations at 550 or 450 ppm. In particular, this paper analyses the energy investment and R&D policies that optimally achieve these two GHG stabilisation targets (i.e. the future optimal energy mix consistent with the stabilisation of GHG atmospheric concentrations at 550 and 450 ppm). Given that the model accounts for interdependencies and spillovers across 12 regions of the world, optimal strategies are the outcome of a dynamic game through which inefficiency costs induced by global strategic interactions can be assessed. Therefore, our results are somehow different from previous analyses of GHG stabilisation policies, where a central planner or a single global economy are usually assumed. In particular, the effects of free-riding incentives in reducing emissions and in investing in R&D are taken into account. Technical change being endogenous in WITCH, this paper also provides an assessment of the implications of technological evolution in the energy sector of different stabilisation scenarios. Finally, this paper quantifies the net costs of stabilising GHG concentrations at different levels, for different allocations of permits and for different technological scenarios. In each case, the optimal long-term investment strategies for all available energy technologies are determined. The case of an unknown backstop energy technology is also analysed.