Optimal Energy Investment and R&D Strategies to Stabilise Greenhouse Gas Atmospheric Concentrations

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. 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. Terms of use: Documents inJEL Code: H4, O3, Q4.

What should we expect from innovation? A model-based assessment of the environmental and mitigation cost implications of climate-related R&D

“…These findings are qualitatively robust to sensitivity analysis on key model parameters, notably returns to R&D, learning rates and international knowledge spillovers in the various technological areas (see Bosetti et al, 2009b). At the same time, some limitations to our analysis should be acknowledged, which call for caution in interpreting our quantitative results.…”

“…These scarcities limit the ability of R&D to keep increasing the efficiency of clean energy capital. Regarding parameter uncertainty, in previous analysis (Bosetti et al, 2009b), we have performed extensive sensitivity analysis on the key parameters, both on those regulating diffusion and innovation. The main finding of that assessment, which is still relevant for the analysis of this paper, is that essentially only the specification of backstop technologies has a significant bearing on projected carbon prices and mitigation policy costs.…”

Technology spillovers and stability of international climate coalitions

“…Golombek and Hoel (2005) assume that the technology level of the region depends on own investments in R&D and R&D investment in other countries (signatories) using a certain rate of technology diffusion, and R&D activities in cooperating countries will lower abatement costs in noncooperating countries due to technology diffusion. Bosetti et al (2007) have recently taken up this issue in an applied setting, using the multi-regional endogenous growth model WITCH. Carraro (2005a, b, 2006) explore the interactions between technology and coalition formation using the FEEM-RICE model.…”

Nuclear Versus Coal plus CCS: a Comparison of Two Competitive Base-Load Climate Control Options

“…Given that nuclear energy continues to remain unpopular in many countries, largely for reasons related to its inalienable risks, we also evaluate the improvements of its main 4 base-load electricity production competitor -coal-fired power plants complemented with CCS technology -needed to significantly scale down the prospects for nuclear power on purely (non-constrained) economic grounds. Bosetti et al (2009) evaluate the optimal portfolio of investments in energy technologies and energy R&D from an economic viewpoint, for a range of stabilization scenarios. This paper extends their work by explicitly focusing on the role of nuclear electricity vis-à-vis other non-carbon power generation technologies.…”