Skip to main content

A Good Opening: The Key to Make the Most of Unilateral Climate Action

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Notes

  1. 1.

    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.

  2. 2.

    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.

  3. 3.

    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.

  4. 4.

    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.

  5. 5.

    The model simplifies the representation of the innovation process by assuming a deterministic specification.

  6. 6.

    A two-time period (corresponding to 10 years) lag is assumed for R&D, to capture the inertia of bringing research to the market.

  7. 7.

    ENERDATA (2008). Energy Statistics.

  8. 8.

    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.

  9. 9.

    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).

  10. 10.

    We thank an anonymous referee for pointing this out.

  11. 11.

    Similar results are shared by Manne and Richels (2004); Mendelsohn et al. (2000) and Pearce (2003).

  12. 12.

    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.

  13. 13.

    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.

  14. 14.

    The group of Annex I countries largely overlaps with our model definition of OECD region.

  15. 15.

    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.

  16. 16.

    The oil price reduction increases with the level of abatement and it reaches the highest reduction of 34 % in 2100.

  17. 17.

    Consider that the base year of the model is 2005.

  18. 18.

    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.

  19. 19.

    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.

  20. 20.

    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.

  21. 21.

    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.

  22. 22.

    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.

References

  1. Barker T, Bashmakov I, Alharthi A, Amann M, Cifuentes L, Drexhage J, Duan M, Edenhofer O, Flannery B, Grubb M, Hoogwijk M, Ibitoye FI, Jepma CJ, Pizer WA, Yamaji K (2007) Mitigation from a cross-sectoral perspective. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds) Climate change 2007: mitigation contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

    Google Scholar 

  2. Barreto L, Klaassen G (2004) Emissions trading and the role of learning-by-doing spillovers in the bottom-up energy-systems ERIS model. Int J Energy Tech Policy 2:70–95

    Article  Google Scholar 

  3. Barreto L, Kypreos S (2002) Multi-regional technological learning in the energy-systems MARKAL model. Int J Global Energy Issues 17:189–213

    Google Scholar 

  4. Bauman Y, Lee M, Seeley K (2008) Does technological innovation really reduce marginal abatement costs some theory, algebraic evidence, and policy implications. Environ Resourc Econ 40:507–527

    Article  Google Scholar 

  5. Bohm P (1993) Incomplete international cooperation to reduce CO\(_2\) emissions: alternative policies. J Environ Econ Manag 24:258–71

    Google Scholar 

  6. Böhringer C, Lange A, Rutherford T (2010) Optimal emission pricing in the presence of international spillovers: decomposing leakage and terms-of-trade motives. NBER working paper 15899.

  7. Bosetti V, Carraro C, Galeotti M, Massetti E, Tavoni T (2006) WITCH: a world induced technical change hybrid model. The Energy J, Special Issue. Hybrid modelling of energy-environment policies: reconciling bottom-up and top-down, pp 13–38.

  8. Bosetti V, Carraro C, Tavoni M (2008a) Delayed participation of developing countries to climate agreements: should action in the EU and US be postponed? FEEM WP FEEM working paper No. 70.2008.

  9. Bosetti V, De Cian E, Sgobbi A, Tavoni M (2009) The 2008 WITCH model: new model features and baseline, FEEM working paper 85.

  10. Bosetti V, Carraro C, Massetti E, Sgobbi A, Tavoni M (2008b) International energy R &D spillovers and the economics of greenhouse gas atmospheric stabilization. Energy Econ 30:2912–2929

    Article  Google Scholar 

  11. Bosetti V, Carraro C, De Cian E, Massetti E, Tavoni M (2013) Incentives and stability of international climate coalitions: an integrated assessment. Energy Policy 55:44–56

    Google Scholar 

  12. Burniaux J, Oliveira-Martins J (2000) Carbon emission leakages: a general equilibrium view. OECD Economics Department working paper 242, OECD Economics Department

  13. Carraro C, De Cian E, Nicita L, Massetti E, Verdolini E (2010) Environmental policy and technical change: a survey. Int Rev Environ Resourc Econ 4:1–57

    Article  Google Scholar 

  14. Carraro C, Massetti E (2012) Beyond Copenhagen: a realistic climate policy in a fragmented world. Clim Change 110:523–542

    Article  Google Scholar 

  15. Chander P, Tulkens H (1997) The core of an economy with multilateral environmental externalities. Int J Game Theory 26:379–401

    Article  Google Scholar 

  16. De Cian E, Tavoni M (2012) Do technology externalities justify restrictions on emission permit trading? Resourc Energy Econ 34:624–64618

    Article  Google Scholar 

  17. De Cian E, Carrara S, Tavoni M (2012) Innovation benefits from nuclear phase-out: can they compensate the costs? FEEM working paper n. 096, Milan

  18. Dechezleprêtre A, Glachant M, Ménière Y (2008) The clean development mechanism and the international diffusion of technologies: an empirical study. Energy Policy 36:1273–1283

    Article  Google Scholar 

  19. Eaton J, Kortum S (2001) Trade in capital goods. Eur Econ Rev 45(7):1195–1235

    Google Scholar 

  20. EIA (2008) Annual energy outlook. Energy Information Administration, Washington, DC

    Google Scholar 

  21. Gerlagh R, Kuik O (2007) Carbon leakage with international technology spillovers, FEEM working paper No. 33

  22. Golombek R, Hoel M (2004) Unilateral emission eeductions and cross-country technology spillovers. Adv Econ Anal Policy 3(2). doi:10.2202/1538-0637.1318

  23. Keller W (2010) International trade, foreign direct investment, and technology spillovers. In: Hall B, Rosenberg N (eds) Handbook of the economics of innovation, vol 2. Elsevier, Amsterdam

    Google Scholar 

  24. Hoel M (1992) International environment conventions: the case of uniform reductions of emissions. Environ Resour Econ 2(2):141–159

    Google Scholar 

  25. Hoel M (1994) Efficient climate policy in the presence of free riders. J Environ Econ Manage 27:259–274

    Article  Google Scholar 

  26. Hoel M (2009) Bush meets hotelling: effects of improved renewable energy technology on greenhouse gas emissions. FEEM working paper no. 01

  27. IEA (2007) World energy outlook 2007. OECD/IEA, Paris

    Book  Google Scholar 

  28. Jamasab T (2007) Technical change theory and learning curves: patterns of progress in electric generation technologies. Energy J 28:3

    Google Scholar 

  29. Jamet S, Corfee-Morlot J (2009) Assessing the impacts of climate change: a literature review of the impacts of climate change. OECD Economics Department working papers, p 691

  30. Manne AS, Richels RG (2004) MERGE: an integrated assessment model for global climate change. Stanford University, USA

    Google Scholar 

  31. Mendelsohn R, Morrison W, Schlesinger ME, Andronova NG (2000) Country specific market impacts of climate change. Clim Change 45:553–569

    Article  Google Scholar 

  32. Nordhaus WD (2007a) A review of the Stern review on the economics of climate change. J Econ Lit 45:686–702

    Article  Google Scholar 

  33. Nordhaus WD, Boyer JG (2000) Warming the world: the economics of the greenhouse effect. MIT Press, Cambridge, MA

    Google Scholar 

  34. Pearce D (2003) The social cost of carbon and its policy implications. Oxford Rev Econ Policy 19:362–384

    Article  Google Scholar 

  35. Roemer JE (2011) The ethics of intertemporal distribution in a warming planet. Environ Resour Econ 48: 363–390

    Article  Google Scholar 

  36. Seres S, Haites E, Murphy K (2009) Analysis of technology transfer in CDM projects: an update. Energy Policy 37:4919–4926

    Article  Google Scholar 

  37. Stern N (2006) The economics of climate change, the Stern Review. Cambridge University Press, Cambridge, UK

    Google Scholar 

  38. Tol RSJ (2002) Welfare specifications and optimal control of climate change: an application of FUND. Energy Econ 24:367–376

    Article  Google Scholar 

  39. UNFCCC (2007) Investments and financial flows to address climate change. Background paper on analysis of existing and planned investments and financial flows relevant to the development of effective and appropriate international response to climate change, p 273

  40. Van der Werf E, Di Maria C (2008) Carbon leakage revisited: unilateral climate policy with directed technical change. Environ Resour Econ 39:55–74

    Article  Google Scholar 

  41. Warren R et al. Centre for Climate Change Research working paper 91

  42. Weitzman ML (2009) On modelling and interpreting the economics of catastrophic climate. Rev Econ Stat 91:1–91

    Article  Google Scholar 

  43. Weiss M, Junginger M, Patel MK, Blok K (2010) A review of experience curve analyses for energy demand technologies. Technol Forecast Social Change 77:411–428

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Enrica De Cian.

Additional information

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

(See Table 2; Fig. 7)

Table 2 Distinguishing feature of the WITCH model
Fig. 7
figure7

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

Rights and permissions

Reprints and Permissions

About this article

Cite this article

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

Download citation

Keywords

  • Technology spillovers
  • Climate change
  • Partial cooperation

JEL Classification

  • Q54
  • Q55
  • C72