Abstract
The UNFCCC Paris Agreement, entered into force on 4 November 2016, represents a step forward in involving all countries in mitigation actions, even though it is based on a voluntary approach and lacks the active participation of some major polluting countries. The underinvestment in mitigation actions depends on market and policy failures and the absence of price signals internalizing the economic losses due to climatic damage. This contributes to underestimating potential benefits from global action. In this paper we discuss how crucial is the assessment of the vulnerability of a country to climate change in defining the threat and action strategies. A dynamic climate-economy CGE model is developed that includes a monetary evaluation of regional damages associated with climate change. By considering alternative damage profiles, results show that internalizing climatic costs might change the bargaining position of countries in climate negotiations. Consequently, damage costs should be given greater importance when defining the implementation of a global climate agreement.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The GCF, discussed and approved during the COP16 held in Cancun in 2010 and officially launched the following year at COP17, is explicitly individuated as a key mechanism for international support by several developing and emerging countries in their NDCs, such as China, Gabon, Morocco, Sudan, among the others.
The Climate Vulnerable Forum (CVF) is an international cooperation group founded in 2009 by the Maldives, that now includes 20 countries that face significant insecurity due to climate change.
DICE (RICE) is the (regional) Dynamic Integrated model of Climate and the Economy model.
These impacts are derived from specific applied models. In particular, the impact on coastal land loss due to the sea level rise is driven by results from the DIVA (Dynamic Integrated Vulnerability Assessment) model (Vafeidis et al. 2008). The ClimateCrop model (Iglesias et al. 2009, 2010) is used for changes in the average productivity of crops in agriculture sector, while data on for the energy sector, as the changes in residential energy demand due to increasing temperatures, are derived from the POLES (Prospective Outlook on Long-term Energy Systems) model (Criqui 2001; Criqui et al. 2009).
An example is the CIRCLE project “Costs of Inaction and Resource Scarcity: Consequences for Long-term Economic Growth Project”, where the dynamic general equilibrium ENV-linkages model is used to express climate impacts in monetary term and links them to GDP. In this case, the impacts covered are: loss of land and capital due to sea level rise, capital damages from hurricanes, changes in crop yields, fisheries catches, labour productivity, tourism flows, health care expenditures due to diseases and heat stress and energy demand for cooling and heating.
For an extensive review on sectoral impacts see Markandya et al. (2017).
The included electricity generating technologies are Coal, Gas, Oil, Hydro, Wind, Solar, Nuclear and Other Base Load Power sources, while Gas, Oil, Hydro and Solar generating technologies are further divided between Base and Peak Load. All details on the aggregation choice for this GDynEP model version are reported in “Appendix”. In order to merge GDynE and GTAP-Power, it is worth mentioning that in this model version we have adopted two simplifying assumptions. First, the transmission and distribution sector for electricity is included in the service sector and it is not taken as a distinguished one. This implies that there is no technical difference between renewables and the other energy sources in the transmission of electricity. This conservative assumption is adopted because we have no region-based data on distinguished institutional and technical features for the electricity transmission and distribution. Second, given that GDynEP is not a bottom up technical model, it deserves specific exogenous behavioral parameters for each stage of the production function. By introducing the renewable electricity sector, it is necessary to add a specific substitution elasticity parameter between fossil-based and renewable electricity. Given that there is not a specific value provided in the GTAP database, we have derived it from calibrating the BAU scenario in order to have a dynamic trend in renewable electricity production up to 2050 in line with BAU provided by IEA Outlook (IEA 2015). We acknowledge that this is an extremely conservative hypothesis, especially when carbon mitigation scenarios are considered. Nonetheless, in this paper we test only the emission trading policy option without exploring the role of public support to clean technologies, and this allows taking this substitution parameter as constant. Future research lines would require specific efforts in empirically estimating substitution elasticities at least at the country level as well shaping the evolution of such parameter over time.
NOAA estimates the concentration of CO2 in the atmosphere at 404.06 PPM in 2011.
The stock of GHG concentrated in the atmosphere used for the calculation of the average damage cost is taken from the PPM concentration measure available from IPCC (2014) and expressed in ton of CO2-eq by applying the conversion criteria used by IPCC: 1 PPM CO2 = 2.12 Gton Carbon; 1 ton Carbon = 3.66 ton CO2; 1 PPM of CO2 rise in the atmosphere is equal to 2.12 × 3.66 Gton CO2 emission.
For a comparison of alternative damage functions used in other IAM and CGE models, see Markandya et al. (2017).
The Vulnerability Index measures a country’s exposure, sensitivity and adaptive capacity (components) to the negative effects of climate change. It considers six life-supporting sectors: food, water, health, ecosystem service, human habitat, and infrastructure. 36 indicators (two per component in each sector) contribute to the measure of vulnerability, obtained as a simple mean of the sector scores, which are the average scores of component indicators. Readiness measures the ability of a country’s private and public sectors to absorb investment resources and successfully apply them to reduce climate change vulnerability. Readiness includes indicators for three components (social, economic and governance indicators) not weighted equally (Economic Readiness is 50% of the readiness score while governance and social readiness are 25%).
The ratio between the vulnerability and readiness indices has been normalized (min = 0; max = 2) and then it is kept constant over time, as there is not information about future projections, especially because of uncertainties with regard to readiness issues. Thus, the variation in the regional distribution of damage cost is due to variations in population dynamics data. Population data do not take into account deaths caused by climate change since the vulnerability measure provided by ND-GAIN already includes number of deaths. In particular, the health component captures a country’s vulnerability of public health to climate change, including projected change of deaths from climate change induced diseases.
Although Farmer et al. (2015) emphasize the role of uncertainty in shaping the cost of climate change into IAMs, for the sake of simplicity in this work we ignore this factor that will be part of future work. By considering country vulnerability to climate change fixed over time in physical term, our modelling choice underestimates future damages that could be larger if vulnerability raises with increasing temperatures.
Equation (8) provides a stylized description of how damage cost is considered into the capital stock function of GDynEP. More precisely, GDynEP adopts the same capital accumulation function structure of GDyn (Ianchovichina and McDougall 2000) in which international capital mobility is allowed. Accordingly, the cost of climate change can be considered as a negative component of the available net investments at the regional level (that derive from national and foreign savings). To this purpose, the adoption of a weak sustainability approach allows including all forms of capital (economic, natural and ecologic) into a unique total capital stock measures assuming full substitutability of different forms of capital (Hamilton 1996; Neumayer 2003). This allows treating the damage cost as a negative component of capital accumulation whatever form of capital is considered. Given that in GDynEP, economic capital (\(K_{r,t}\)) is the only form of capital that is dynamically modelled and changes over time according to market mechanisms, we have modelled the cost of climatic damage within the economic capital accumulation function.
Asian developing and emerging countries have also been distinguished in Rest of South Asia and South East Asian representing, respectively, developing and emerging countries according to their level of development.
We acknowledge that the burden sharing adopted for the policy scenario is compatible with technological capabilities of regions but is not chosen on the basis of real policy feasibility (for instance strongly affected by the US defection). The exclusion of the US from mitigation actions would force to recalculate the burden sharing for all the other regions if the final goal is to reach anyway a 450 PPM concentration. This modelling choice will reduce comparability across scenarios selected for this specific paper. Further work could be done in the future to evaluate the effect of alternative burden sharing options.
In this paper we have not modeled the relation between the value adopted for parameter α and the level of risk-aversion of the social planner since we consider a common and univocal mitigation path that is driven by the IPCC and IEA bottom up climate-energy models. In the case of mitigation efforts endogenously decided according to the risk aversion of the policy maker, the value for parameter α would also influence timing profiles for mitigation actions. We will develop this relation in our future research agenda.
In order to obtain accurate results, the discount rate should be both differentiated by region and declining over time (Philibert 2003). However, in order to reduce uncertainty and facilitate the interpretability of results, we apply a single discount rate equal to 3%. This value is the most commonly used in SCC calculations and corresponds to the intermediate value applied by the US Government in its latest SCC computation (US Government 2015).
More specific targets are expressed in terms of use of renewable sources, afforestation and emission intensity.
Further research work on this specific issue will be part of the next agenda on modelling climatic damage in GDynEP for assessing climate policy optimality under different uncertainty conditions.
References
Anderson, E. (2006). Potential impacts of climate change on $2-a-day poverty and child mortality in Sub-Saharan Africa and South Asia. Overseas Development Institute (ODI) Working Paper No. 1759, London, UK.
Arndt, C., Tarp, F., & Thurlow, J. (2015). The economic costs of climate change: A multi-sector impact assessment for Vietnam. Sustainability, 7, 4131–4145.
Bosello, F., & De Cian, E. (2014). Documentation on the development of damage functions and adaptation in the WITCH model. Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) Research Paper, RP0228.
Bosello, F., De Cian, E., Eboli, F., & Parrado, R. (2009). Macroeconomic assessment of climate change impacts: a regional and sectoral perspective. Impacts of climate change and biodiversity effects. Final report of the CLIBIO project, European Investment Bank, University Research Sponsorship Programme.
Bosello, F., Eboli, F., & Pierfederici, R. (2012a). Assessing the economic impacts of climate change. An updated CGE point of view. FEEM Nota di Lavoro 2.2012.
Bosello, F., Nicholls, R., Richards, J., Roson, R., & Tol, R. (2012b). Economic impacts of climate change in Europe: sea-level rise. Climatic Change, 112, 63–81.
Bosetti, V., Carraro, C., & Galeotti, M. (2006a). The dynamics of carbon and energy intensity in a model of endogenous technical change. Energy Journal, 27, 93–107.
Bosetti, V., Carraro, C., Galeotti, M., Massetti, E., & Tavoni, M. (2006b). WITCH: A world induced technical change hybrid model. Energy Journal, 27, 13–38.
Brunnée, J., & Streck, C. (2013). The UNFCCC as a negotiation forum: towards common but more differentiated responsibilities. Climate Policy, 13, 589–607.
Chen, C., Noble, I., Hellmann, J., Coffee, J., Murillo, M., & Chawla, N. (2015). Notre dame global adaptation index (ND-GAIN). USA: Detailed Methodology Report.
Ciscar, J.C., Feyen, L., Soria, A., Lavalle, C., Raes, F., & Perry, M., et al. (2014). Climate impacts in Europe. The JRC PESETA II Project. JRC Scientific and Policy Reports, EUR 26586EN. Publications Office of the European Union, Luxembourg.
Costantini, V., Sforna, G., & Zoli, M. (2016). Interpreting bargaining strategies of developing countries in climate negotiations. A quantitative approach. Ecological Economics, 121, 128–139.
Criqui, P. (2001). POLES: Prospective outlook on long-term energy systems. Grenoble, France: Institut d’Economie et de Politique de l’Energie.
Criqui, P., Menanteau, P., & Mima, S. (2009). The trajectories of new energy technologies in carbon constraint cases with the POLES world energy model. In IOP Conference series: Earth and environmental science 6, IOP Publishing.
Crost, B., & Traeger, C. P. (2014). Optimal CO2 mitigation under damage risk valuation. Nature Climate Change, 4, 631–636.
DARA. (2012a). Climate vulnerability monitor (2nd Edition). A guide to the cold calculus of a hot planet. Washington D.C., USA.
DARA. (2012b). Methodological documentation for the climate vulnerability monitor (2nd Edition).
Darwin, R. F., & Tol, R. S. (2001). Estimates of the economic effects of sea level rise. Environmental and Resource Economics, 19, 113–129.
De Cian, E., Bosetti, V., & Tavoni, M. (2012). Technology innovation and diffusion in “less than ideal” climate policies: An assessment with the WITCH model. Climatic Change, 114, 121–143.
Dellink, R., Lanzi E., Chateau J., Bosello F., Parrado R., & De Bruin K. (2014). Consequences of climate change damages for economic growth: A dynamic quantitative assessment. OECD Economics Department Working Papers, No. 1135, OECD Publishing.
Dellink, R., Dekker, T., & Ketterer, J. (2013). The fatter the tail, the fatter the Climate Agreement. Simulating the influence of Fat Tails in climate change damages on the success of international climate negotiations. Environmental Resource Economics, 56, 277–305.
Eboli, F., Parrado, R., & Roson, R. (2010). Climate change feedback on economic growth: explorations with a dynamic general equilibrium model. IEFE-Bocconi Working Paper 29.
European Union (EU). (2011). Climate cost: The full costs of climate change, summary of results from the climate cost project. Funded by the European Community’s Seventh Framework Programme.
Farmer, J. D., Hepburn, C., Mealy, P., & Teytelboym, A. (2015). A third wave in the economics of climate change. Environmental and Resource Economics, 62, 329–357.
Fussel, H. (2010). How inequitable is the global distribution of responsibility, capability, and vulnerability to climate change: A comprehensive indicator-based assessment. Global Environmental Change, 20, 597–611.
Fussel, H., & Klein, R. J. T. (2006). Climate change vulnerability assessments: an evolution of conceptual thinking. Climatic Change, 75, 301–329.
Golub, A. (2013). Analysis of climate policies with GDyn-E. Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University GTAP Technical Papers 32.
Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., et al. (2016). Shock waves: Managing the impacts of climate change on poverty. World Bank, Washington, DC: Climate Change and Development.
Hamilton, K. (1996). Pollution and pollution abatement in the national accounts. Review of Income and Wealth, 42, 13–33.
Hamilton, K., & Clemens, M. (1999). Genuine savings rates in developing countries. The World Bank Economic Review, 13, 333–356.
Hartwick, J. M. (1977). Intergenerational equity and the investing of rents from exhaustible resources. The American Economic Reviews, 67, 972–974.
Hartwick, J. M. (1978). Substitution among exhaustible resources and intergenerational equity. The Review of Economic Studies, 45, 347–354.
Hope, C. (2011). The social cost of CO2 from the PAGE09 model. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1973863.
Ianchovichina, E., & McDougall, R.A. (2000). Theoretical structure of dynamic GTAP. GTAP Technical Paper 17/2000.
Iglesias, A., Garrote, L., Quiroga, S., & Moneo, M. (2009). Impacts of climate change in agriculture in Europe. JRC Scientific and Technical Reports. EUR 24107 EN. Joint Research Centre, Institute for Prospective Technological Studies. Luxembourg: Office for Official Publications of the European Communities.
Iglesias, A., Quiroga, S., & Garrote, L. (2010). Report analysis for Europe. Deliverable D2B.2, Work package 2B Agriculture and water, ClimateCost project (FP7).
Intergovernmental Panel on Climate Change (IPCC) (2012). Summary for policymakers. In: C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, P.M. Midgley (Eds.), Managing the risks of extreme events and disasters to advance climate change adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (pp. 1–19). Cambridge University Press, Cambridge, UK, and New York, NY, USA.
Intergovernmental Panel on Climate Change (IPCC) (2014). Climate Change 2014: Mitigation of Climate Change. In Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, UK and New York, NY, USA: Cambridge University Press.
International Energy Agency (IEA). (2015). World energy outlook (WEO) 2015. Paris: International Energy Agency.
Kelly, P. M., & Adger, W. N. (2000). Theory and practice in assessing vulnerability to climate change and facilitating adaptation. Climatic Change, 47, 325–352.
Kotchen, M. J. (2018). Which social cost of carbon? A theoretical perspective. Journal of the Association of Environmental and Resource Economists, 5, 673–694.
Manne, A., Mendelsohn, R., & Richels, R. (1995). MERGE: A model for evaluating regional and global effects of GHG reduction policies. Energy Policy, 23, 17–34.
Manne, A.S., & Richels, R.G. (2005). MERGE: an integrated assessment model for global climate change. In Energy and environment (pp. 175–189). USA: Springer.
Markandya, A. (2014). Incorporating climate change into adaptation programmes and project appraisal: Strategies for uncertainties. In A. Markandya, I. Galarraga, & E. Sainz de Murieta (Eds.), Routledge handbook of the economics of climate change adaptation (pp. 97–119). London: Routledge.
Markandya, A., Antimiani, A., Costantini, V., Martini, C., Palma, A., & Tommasino, C. (2015). Analysing trade-offs in international climate policy options: The case of the green climate fund. World Development, 74, 93–107.
Markandya, A., Paglialunga, E., Costantini, V., & Sforna, G. (2017). Global and regional economic damages from climate change, Oxford research encyclopedia of environmental economics. Oxford: Oxford University Press.
Matthews, H. D. (2016). Quantifying historical carbon and climate debts among nations. Nature Climate Change, 6, 60–64.
Méjean, A., Lecocq, F., & Mulugetta, Y. (2015). Equity, burden sharing and development pathways: reframing international climate negotiations. International Environmental Agreements, 15, 387–402.
Mendelsohn, R., Dinar, A., & Williams, L. (2006). The distributional impact of climate change on rich and poor countries. Environment and Development Economics, 11, 159–178.
Mendelsohn, R. O., Morrison, W. N., Schlesinger, M. E., & Andronova, N. G. (1998). Country-specific market impacts of climate change. Climatic Change, 45, 553–569.
Moore, F., & Diaz, D. B. (2015). Temperature impacts on economic growth warrant stringent mitigation policy. Nature Climate Change, 5, 127–131.
Neumayer, E. (2003). Weak versus strong sustainability: exploring the limits of two opposing paradigms. Cheltenham, UK: Edward Elgar Publishing.
Neumayer, E., Plümper, T., & Barthel, F. (2014). The political economy of natural disaster damage. Global Environmental Change, 24, 8–19.
Nordhaus, W. D. (2008). A question of balance. Weighing the options on global warming policies. New Haven: Yale University Press.
Nordhaus, W.D. (2011). Integrated economic and climate modeling. Cowles Foundation, Discussion Paper No. 1839.
Nordhaus, W. D. (2013). The climate Casino. Risk, uncertainty and economics for a warming world. New Haven: Yale University Press.
Nordhaus, W. D. (2015). Climate clubs: Overcoming free-riding in international climate policy. American Economic Review, 105, 1339–1370.
Nordhaus, W. D., & Boyer, J. G. (2000). Warming the world: The economics of the greenhouse effect. Cambridge, MA: MIT Press.
Nordhaus, W. D., & Sztorc, P. (2013). DICE 2013R: Introduction and user’s manual (2nd Edition). Yale University. http://www.econ.yale.edu/~nordhaus/homepage/homepage/documents/DICE_Manual_100413r1.pdf. Accessed 23 July 2018.
Organisation for Economic Cooperation and Development (OECD) (2015). The economic consequences of climate change. Paris: OECD Publishing. https://doi.org/10.1787/9789264235410-en.
Paul, B. K. (2011). Environmental hazards and disasters: Contexts, perspectives and management. Chichester: Wiley.
Peters, J. C. (2016). The GTAP-power data base: Disaggregating the electricity sector in the GTAP data base. Journal of Global Economic Analysis, 1, 209–250.
Philibert, C. (2003). Discounting the future. Internet Encyclopaedia of Ecological Economics: International Society for Ecological Economics.
Roson, R., & Sartori, M. (2016). Estimation of climate change damage functions for 140 regions in the GTAP9 database. In IEFE—Center for Research on Energy and Environmental. Working Paper no. 86.
Roson, R., & van der Mensbrugghe, D. (2012). Climate change and economic growth: Impacts and interactions. International Journal of Sustainable Economy, 4, 270–285.
Solow, R. M. (1986). On the intergenerational allocation of natural resources. Scandinavian Journal of Economics, 88, 141–149.
Stern, N. H. (2007). The economics of climate change: The Stern review. Cambridge, UK: Cambridge University Press.
Tol, R.S.J. (2015). Economic impacts of climate change. Working Paper Series No. 75-2015, University of Sussex.
US Government (2015). Interagency working group on social cost of carbon. Technical Support Document: Technical Update of the Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866, United States Government.
Vafeidis, A. T., Nicholls, R. J., McFadden, L., Tol, R. S., Hinkel, J., Spencer, T., et al. (2008). A new global coastal database for impact and vulnerability analysis to sea-level rise. Journal of Coastal Research, 24, 917–924.
Valentini, E., & Vitale, P. (2018). Optimal climate policy for a pessimistic social planner. Resource and Energy Economics (forthcoming).
van den Bergh, J. C. J. M., & Botzen, W. J. W. (2015). Monetary valuation of the social cost of CO2 emissions: A critical survey. Ecological Economics, 114, 33–46.
Verendel, V., Johansson, D. J. A., & Lindgren, K. (2016). Strategic reasoning and bargaining in catastrophic climate change games. Nature Climate Change, 6, 265–268.
Waldhoff, S. T., Anthoff, D., Rose, S., & Tol, R. S. (2014). The marginal damage costs of different greenhouse gases: An application of FUND. economics: The open-access. Open-Assessment E-Journal, 8, 1–33.
Weitzman, M. L. (1976). On the welfare significance of national product in a dynamic economy. Quarterly Journal of Economics, 90, 156–162.
Acknowledgements
We acknowledge financial support received by the EU D.G. Research (research project “CECILIA2050—Choosing efficient combinations of policy instruments for low-carbon development and innovation to achieve Europe’s 2050 climate targets”, Grant agreement no. 308680), the Italian Ministry of Education, University and Research (Scientific Research Program of National Relevance 2010 on “Climate change in the Mediterranean area: scenarios, economic impacts, mitigation policies and technological innovation”), the Regione Lazio (research project SMART ENVIRONMENTS) and the Department of Economics and the Centro Rossi-Doria of Roma Tre University. We are also indebted to the research group of the National Consortium CREA-ENEA-ROMATRE for the continuous scientific support in CGE modelling. We thank Dr. Mariangela Zoli for her review and highly appreciate the comments and suggestions that contributed to improving the quality of the publication. We also thank the journal Reviewers for several suggestions that have improved the paper. Errors and omissions are of course ours alone.
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
See Tables 3, 4, 5, 6, 7, 8 and 9.
Rights and permissions
About this article
Cite this article
Costantini, V., Markandya, A., Paglialunga, E. et al. Impact and distribution of climatic damages: a methodological proposal with a dynamic CGE model applied to global climate negotiations. Econ Polit 35, 809–843 (2018). https://doi.org/10.1007/s40888-018-0129-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40888-018-0129-z