Abstract
In this paper we argue that when a subgroup of countries cooperate on emission reduction, the optimal response of non-signatory countries reflects the interaction between three potentially opposing factors, the incentive to free-ride on the environmental benefits of cooperation, the incentive to expand energy consumption, and the incentive to adopt the cleaner technologies introduced by the coalition. Using an Integrated Assessment Model with a game-theoretic structure we find that the equilibrium abatement of the coalition composed by OECD countries would be moderate, in line with the Pledges subscribed in Copenhagen, but increasing. The mitigation strategy would consist of investments in energy R&D and deployment of cleaner technologies with high learning potentials. International knowledge and technology externalities would facilitate the diffusion of cleaner technologies in non-signatory countries, offsetting the free-riding incentive and reducing their emissions. If the OECD group curbs emissions beyond the optimal equilibrium level, reaching reduction rates between 40 and 45 % below 2005 levels in 2050, the benefits of technology externalities would no longer compensate the effect of lower fossil fuel prices. Our results suggest that a moderate unilateral climate policy could induce a virtuous behaviour in non-signatory countries and that policies promoting the international transfer of technologies and knowledge could represent an effective complement to mitigation targets.
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Notes
For example, the EU decided to protect trade-exposed sectors by guaranteeing them a free allocation of allowances, see the recent Communication released by the European Union “Analysis of options to move beyond 20 % greenhouse gas emission reductions and assessing the risk of carbon leakage”, COM(2010) 265.
It must also be noted that the non-cooperative solution already assumes some degree of cooperation because each player represents a macro-region of the world, assuming that within each region countries cooperate and all externalities are internalised. Therefore in the non-cooperative solution each region internalises only the regional externalities associated with climate change damages, the accumulation of knowledge, and the use of fossil fuels, but not the international ones.
Electricity can be generated using fossil-fuel-based technologies and carbon-free options. Fossil-fuel-based technologies include natural gas combined cycle (NGCC), oil- and pulverised coal-based power plants. Integrated gasification combined cycle power plants equipped with carbon capture and storage (CCS) are also modelled. Zero carbon technologies include hydroelectric and nuclear power plants, wind turbines and photovoltaic panels (Wind & Solar). The end-use sector uses traditional biomass, biofuels, coal, gas, and oil. Oil and gas together account for more than 70 % of energy consumption in the non-electric sector. Instead, the use of coal and traditional biomass is limited to some developing regions and decreases over time. First generation biofuels consumption is currently low in all regions of the world and the overall penetration remains modest over time given the conservative assumptions on their large scale deployment.
The temperature increase above the pre-industrial levels is driven by the radiative forcing of different GHGs. Radiative forcing depends on CO\(_2\) and non-CO\(_2\) atmospheric concentrations. Stoichiometric coefficients are applied to the use of fossil fuels to derive related CO\(_2\) emissions. Although the module formulation is based on the three-box layer module from Nordhaus and Boyer (2000), CO\(_2\) concentrations in the atmosphere have been updated to 2005 at roughly 385 ppm and temperature increase above pre-industrial at \(0.76\,{^\circ }\text{ C }\), in accordance with IPCC 4th Assessment Report (2007). Climate sensitivity is set to 3.
The model simplifies the representation of the innovation process by assuming a deterministic specification.
A two-time period (corresponding to 10 years) lag is assumed for R&D, to capture the inertia of bringing research to the market.
ENERDATA (2008). Energy Statistics.
In more recent versions of the model we do actually assume Learning-By-Researching on the energy penalty of adding the CCS component to different plants. The data of the expert elicitation we used to model this were not yet available when we performed this analysis. However, what we found when eliciting the future cost of CCS technologies is that these technologies are, in a sense, known. Experts do not feel the need of large R&D investments. Rather they mostly believe that deployment will be necessary to lower the cost. In the model we use rather conservative numbers for investment costs and energy penalty.
In the OECD coalition we include the following model’s regions: USA, Europe (WEURO, EEURO), Canada-Japan-New Zealand (CAJANZ), and South Korea-South Africa-Australia (KOSAU).
We thank an anonymous referee for pointing this out.
We did not adjust the curvature of the utility function to reflect the lower pure rate of time preference and to keep the interest unchanged according to the Ramsey rule. As shown in Nordhaus (2007a), lowering the pure rate of time preference and adjusting accordingly the curvature of the utility function leads to a result that is basically unchanged from that based on the original parameter value. Instead, we base the simulation on an interest rate that is exceptionally low, following a normative approach, to observe the effects and compare them with simulations based on a higher pure rate of time preference. The next section will analyse how myopic behaviour, modelled with a higher discounting, affects the results.
The chosen damage and a pure rate of time preference are such that global cooperation results in the \(2.5\,{^\circ }\text{ C }\) degree target.
The group of Annex I countries largely overlaps with our model definition of OECD region.
See the UNEP Assessment “The Emission Gap Report” available on the website http://www.unep.org/publications/ebooks/emissionsgapreport/. Carraro and Massetti (2012) estimated that Annex I’s emissions in 2020 would be between 7 and 13 % lower than 2005 under low and high pledges, respectively.
The oil price reduction increases with the level of abatement and it reaches the highest reduction of 34 % in 2100.
Consider that the base year of the model is 2005.
In order to keep our focus on the reaction of non-signatories, we assume OECD countries commit to the same emissions target they had under the lower pure rate of time preference case.
As in Fig. 3 we use the reduction in aggregate cumulative emissions throughout the century, in percentage change with respect to the non-cooperative baseline, as indicator of the non-OECD countries’ best response.
We assume that countries that are not in the coalition can participate in the global carbon market and trade emission reductions with respect to their non-cooperative level of emissions. Non-OECD countries are allocated an amount of permits equal to their emissions in the non-cooperative solution. OECD countries continue to face the 45 % constraint compared to 2005. All countries can choose whether to reduce emissions below non-cooperative levels and sell the carbon credits on the international carbon market or not. The equilibrium in the global carbon market is reached when excess demand of permits is equal to zero at any given time period. This condition is met by iteratively adjusting the international price of oil as in a Walrasian tatônnement process.
In this scenario non-OECD countries can free ride until 2050 and they do not face any cap on their emissions until that date. Starting in 2050, non-OECD countries are forced to keep their emissions constant. This case does not assume global trade. Carbon trade is allowed only between OECD countries.
Figure 6 shows the percentage change of cumulative CO\(_2\) emissions with respect to the non-cooperative baseline (2010–2100) in non-OECD countries while leakage rate is defined as ratio between percentage emission change in the non-OECD and the percentage emission change in the OECD.
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This paper is part of the research being carried out by the Sustainable Development Programme of the Fondazione Eni Enrico Mattei. The research leading to these results has also received funding from the European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013) / ERC Grant Agreement no. 240895—project ICARUS “Innovation for Climate Change Mitigation: a Study of energy R&D, its Uncertain Effectiveness and Spillovers”. The authors wish to thank Carlo Carraro, Emanuele Massetti and Massimo Tavoni for their valuable help. The usual disclaimers apply.
Appendix
Appendix
Production nest and the elasticity of substitution. KL Capital-labour aggregate, K Capital invested in the production of final good, L Labour, ES Energy services, HE Energy R&D capital, ENEnergy, EL Electric energy, NEL Non-electric energy, OGB Oil, breakthrough (BACKnel), gas and biofuel nest, ELFF Fossil fuel electricity nest, W&S Wind and solar, ELj Electricity generated with technology j (IGCC plus CCS, Oil, Coal, Gas, Breakthrough (ELBACK), Nuclear, Wind plus Solar); TradBiom Traditional Biomass, TradBio Traditional Biofuels, AdvBio Advanced Biofuels
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Bosetti, V., De Cian, E. A Good Opening: The Key to Make the Most of Unilateral Climate Action. Environ Resource Econ 56, 255–276 (2013). https://doi.org/10.1007/s10640-013-9643-1
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DOI: https://doi.org/10.1007/s10640-013-9643-1