Abstract
The IPCC recommends the use of carbon capture and sequestration (CCS) technologies in order to achieve the Kyoto environmental goals. This paper sheds light on this issue by assessing the optimal strategy regarding the long-term use of CCS technologies. The aim is to analyze the optimal CCS policy when the sequestration rate is endogenous, being therefore one specific tool of the environmental policy. We develop a simple growth model to identify the main driving forces that should determine the optimal CCS policy. We show that, under some conditions on the cost of extractions, CCS may be a long-term solution to curb carbon emissions. We also show that over time the social planner will choose to decrease the rate of capture and sequestration. We then derive the decentralized equilibrium outcome by considering the programs of the fossil resource-holder and of the representative consumer. Finally, we determine the optimal environmental policy, i.e. the carbon tax scheme, as well as the dynamics of the fossil fuel price needed to implement it.
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Notes
It includes gaseous storage in various deep geological formations (including saline formations and exhausted gas fields), liquid storage in the ocean, and solid storage by reaction of CO2 with metal oxides to produce stable carbonates.
One direct extension, among others, is to take into account the uncertainty linked to CSS efficiency. The CSS projects in action are still recent and we do not know exactly the full consequences of such abatement technologies, in terms of environmental consequences (on oceans for instance), or in terms of efficiency once we consider the leakage problems.
See for example Heal [17] for a survey on these topics.
For the sake of computational convenience, we do not assume here that the sequestration cost depends on the accumulated past storage \(S_{t}\). If such an assumption was made, it would give rise to two possible effects acting in two opposite ways. First, the scarcity effect, capturing the idea that it becomes more and more costly to store carbon emissions as the stock already sequestered increases, would be taken into account by assuming that \( \partial D( .) /\partial S>0.\) Secondly, the learning effect, implying that the deployment of the CCS technology improves as the installed capacity increases, would require us to assume that \(\partial D\left ( .\right ) /\partial S<0.\) A discussion about these effects can be found in Amigues et al. [1].
This first term (\(\rho +\alpha \)) is often qualified as a modified discount rate, or environmental discount rate, in order to take into account that emitting an additional unit of carbon today yields a marginal return \(\rho \) tomorrow, but it also increases the future marginal regeneration of the atmosphere by \(\alpha \).
Some agreements also refer to carbon budgets so that the constraint concerns the accumulated emissions instead of the atmospheric carbon concentration.
As suggested by a referee, one would expect a sequentiality of decisions between resource holder and resource users. Doing so, we would assume that firstly, the resource holder could choose the level of extraction; secondly, the resource user could decide the consumption and the sequestration rates. This is an interesting extension that would require to change the framework and would complicate significantly the calculus.
Note that we conduct here a partial decentralization exercise since the behavior of agents on the financial market, which would allow us to determine the equilibrium level of the interest rate, is not examined. It is possible to introduce the budget constraint of the consumer who can save money and hold a stock of bonds \(B_{t}\). In its simplest form, this constraint writes \(\dot {B}_{t}=rB_{t}-p_{t}x_{t}-\beta x_{t}d(\gamma _{t})-\tau _{t}(1-\gamma _{t})\beta x_{t}\). However, it can be easily proved that this additional feature changes neither the properties of the equilibrium trajectories nor the optimum implementation rules.
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The authors are indebted to two anonymous referees for their helpful suggestions and comments on an earlier version of this article.
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Ayong Le Kama, A., Fodha, M. & Lafforgue, G. Optimal Carbon Capture and Storage Policies. Environ Model Assess 18, 417–426 (2013). https://doi.org/10.1007/s10666-012-9354-y
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DOI: https://doi.org/10.1007/s10666-012-9354-y