Abstract
In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.
Similar content being viewed by others
Notes
Directive 2009/28/EC of the European parliament and of the council of 23 April 2009
See the model website, http://www.witchmodel.org/
It should be remarked that WITCH is a macroeconomic model, and thus cannot capture many barriers that prevent the full internalisation of innovation market failures at a finer scale, as it is often the case for small consumers and producers.
A logarithmic utility function implies that the relative risk aversion coefficient is equal to one. The pure rate of time preference is 3%, yielding interest rates consistent with historical saving rates [30].
Whereas zero or very low carbon taxes are as probable as very high taxes, the degree of uncertainty surrounding technology costs is more restricted, at least for mature technologies such as nuclear and renewable. This motivates the assumption of a 30% spread in the case of technology uncertainty as opposed to the larger variation considered in the case of uncertain carbon tax.
Assuming 100% capture rate would avoid the decrease of CCS in the second half of the century, though CCS would nonetheless stabilise because of the increasing cost of injecting CO2 underground, which are endogenous in the model. Introducing biomass plus CCS (BECS) would further foster the role of CCS since it would allow to absorb CO2 from atmosphere, but we do not include it here because of concerns over the scalability of this technology due to competition with land use.
See Renewables Global Status Report, 2009 Update available at http://www.ren21.net/pdf/RE_GSR_2009_Update.pdf
We focus on this technology because in the case of nuclear and renewables there are no major changes and the year of maximum hedging remains always right before the date of learning.
In this case we show hedging capacity in 2030 rather than in 2035 because in 2035 investment costs are already different across scenarios.
As discussed in the previous section, the effect of technology risk on renewables and nuclear is monotonic for spreads above 50%. The effect of policy riskiness is always monotonic.
The target 20% has been chosen for illustrative purposes. In most countries RPS for 2020 are around 20% (China 21% from wind by 2020, India 15% from renewables by 2015, U.S. 20% from wind by 2030, EU 20% by 2020). Some countries in Middle East and North Africa have also similar targets. Regional policies are available at www.ren21.org. Regional and global renewable targets considered here are in line with those proposed by other studies such as the ETP Blue scenario by the International Energy Agency www.iea.org
A 20% RPS, either in 2020 or 2030, has a much larger impact than the EPS considered above and therefore the two instruments cannot be compared.
References
Bahn, O., Haurie, A., & Malham, R. (2008). A stochastic control model for optimal timing of climate policies. Automatica, 44, 1545–1558.
Baker, E., & Shittu, E. (2006). Profit maximizing R&D investment in response to a random carbon tax. Resource and Energy Economics, 28, 105–192.
Blanford, G. J. (2009). R&D investment strategy for climate change. Energy Economics, 31(S1), S27–S36.
Borer, M. J., & Wustenhagen, M. (2009). Which renewable energy policy is a venture capitalist' s best friend? Empirical evidence from a survey of international cleantech investors. Energy Policy, 37(12), 4997–5006.
Bosetti, V., Carraro, C., Massetti, E., Tavoni, M. (2006). WITCH: A world induced technical change hybrid model. The Energy Journal, Special Issue. Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-up and Top-down, 13–38 (2006).
Bosetti, V., Carraro, C., Sgobbi, A., & Tavoni, M. (2009). Delayed action and uncertain targets. How much will climate policy cost? Climatic Change, 96(3), 299–312.
Bosetti, V., & Tavoni, M. (2009). Uncertain R&D, backstop technology and GHGs stabilization. Energy Economics, 31, S18–S26.
Bosetti, V., Carraro, C., Duval, R., Tavoni, M. (2010). What should we expect from innovation? A model-based assessment of the environmental and mitigation cost implications of climate-related R&D. FEEM Working Paper No. 42, Milan.
De Cian, E., Tavoni, M. (2010). The role of international carbon offsets in a second-best climate policy: A numerical evaluation. FEEM Working Paper, No. 33, Milan.
Dixit, A., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton: Princeton Univ Press.
Ecofys (2009). Scenarios on the introduction of CO2 emission performance standards for the EU power sector. http://www.ecofys.com/com/publications/documents/FinalReportEcofys_EPS_Scenarios_13Jan2009.pdf
Fisher, A. (2003). Irreversibility and catastrophic risk in climate change. In E. van Ierland, H. Weikard, & J. Wesseler (Eds.), Risk and uncertainty in environmental and resource economics. Wagenigen: Wagenigen University.
Fisher, A., & Narain, U. (2003). Global warming, endogenous risk, and irreversibility. Environmental and Resource Economics, 25, 395–416.
Goulder, L. H., & Schneider, S. H. (1999). Induced technological change and the attractiveness of CO2 abatement policies. Resource and Energy Economics, 21(3–4), 211–253.
Ha-Duong, M., Grubb, M. J., & Hourcade, J. C. (1997). Influence of socioeconomic inertia and uncertainty on optimal CO2 emission abatement. Nature, 30, 270–273.
Hendricks, C., Graus, W., Bergen, F. (2004). Global carbon dioxide storage potential and costs, Rijksinstituut voor Volksgezondheit en Milieu, TNO/ECOFYS.
Johansson, D. J. A., Persson, U. M., & Azar, C. (2008). Uncertainty and learning: Implications for the trade-off between short-lived and long-lived greenhouse gases. Climatic Change, 88(3–4).
Johnstone, N., Haščič, I., & Popp, D. (2010). Renewable energy policies and technological innovation: Evidence based on patent counts. Environmental and Resource Economics, 45, 133–155.
Karp, L., & Zhang, J. (2006). Regulation with anticipated learning about environmental damages. Journal of Environmental Economics and Management, 51, 259–280.
Keller, K., Bolker, B. M., & Bradford, D. F. (2004). Uncertain climate thresholds and optimal economic growth. Journal of Environmental Economics and Management, 48, 723–741.
Kolstad, C. (1996). Fundamental irreversibilities in stock externalities. Journal of Public Economics, 60, 221–233.
Kolstad, C. (1996). Learning and stock effects in environmental regulations: The case of greenhouse gas emissions. Journal of Environmental Economics and Management, 31, 1–18.
\Kriegler, E., Lorenz, A., Schmidt, M. (2010). The effect of uncertainty about catastrophic climate damages on optimal abatement levels revisited, presented at the International Energy Workshop, http://www.kth.se/polopoly_fs/1.61926!D3_Kriegler.pdf
Loulou, R., Labriet, M., & Kanudia, A. (2009). Deterministic and stochastic analysis of alternative climate targets under differentiated cooperation regimes. Energy Economics, 31(S2), S131–S143.
Manne, A., & Richels, R. (1995). The greenhouse debate. Economic efficiency, burden sharing and hedging strategies. The Energy Journal, 16(4), 1–37.
Massetti, E., Nicita, L. (2010). Optimal R&D investments and the cost of GHG stabilization when knowledge spills across sectors. CESifo Working Paper No 2988.
Nemet, G. F. (2010). Robust incentives and the design of a climate change governance regime. Energy Policy, 38(11), 7216–7225.
Nordhaus, W. D., & Popp, D. (1997). What is the value of scientific knowledge? An application to global warming using the price model. Energy Journal, 18(1), 1–45.
Nordhaus, W. D. (2007). A question of balance. Cambridge: MIT Press.
Nordhaus, W. D. (2007). A review of the stern review on the economics of climate change. Journal of Economic Literature, 45, 686–702.
Otto, V. M., Löschel, A., & Reilly, J. (2008). Directed technical change and differentiation of climate policy. Energy Economics, 30(6), 2855–2878.
Pindyck, R. (1992). Investments of uncertain costs. NBER Working Paper, No.4175.
Pindyck, R. (2000). Irreversibilites and the timing of environmental policy. Resource and Energy Economics, 22, 233–259.
Popp, D. (2006). R&D subsidies and climate policy: Is there a free lunch? Climatic Change, 77(3–4), 311–341.
Rothschild, M., & Stiglitz, J. (1970). Increasing risk I: A definition. Journal of Economic Theory, 2, 225–243.
Roughgarden, T., & Schneider, S. H. (1999). Climate change policy: Quantifying uncertainties for damages and optimal carbon taxes. Energy Policy, 27, 415–429.
Ulph, A., & Ulph, D. (1997). Global warming, irreversibility and learning. The Economic Journal, 107, 636–650.
Wigley, T. M. L., Richels, R., & Edmonds, J. (1996). Economic and environmental choices in the stabilization of atmospheric CO2 concentrations. Nature, 379, 240–243.
Yohe, G., Andronova, N., & Schlesinger, M. (2004). To hedge or not against an uncertain climate future. Science, 306, 416–417.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
De Cian, E., Massimo, T. Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty. Environ Model Assess 17, 123–136 (2012). https://doi.org/10.1007/s10666-011-9279-x
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10666-011-9279-x