“Sensitivity of projected long term CO2 emissions across the Shared Socioeconomic Pathways” published by Nature Climate Change
Variation in projected long term CO2 emissions mostly driven by future income and energy efficiency. Economic growth and energy intensity are found to be the most important determinants of future CO2 emission across different socio-economic pathways.
CO2 emissions from energy combustion are the biggest determinant of men-made climate change, and have been growing over the past decades. In order to grasp where we are heading, it is therefore imperative to have a better understanding of future long term emissions, as a function of the evolution of different socio-economic and technical underlying forces.
A new study published in ‘Nature Climate Change’ has just done it. Bringing together 6 different energy-economy-climate models from 6 different European research institutes, the study has decomposed the sensitivity of future long term CO2 emissions to their major drivers: population, income, energy intensity, fossil resources availability, and low carbon technologies development.
The study has confronted 3 possible future worlds, as delineated by Shared Socio-Economic Pathways (SSPs, see here): a sustainable, middle of the road and challenging world. These scenarios represent baseline assumptions with increasing challenges to mitigating and adapting to climate change. Using advanced statistical approaches, the study has disentangled the impacts of each of the above-mentioned drivers in isolation as well as in interaction with the others.
‘The results’ –explains Giacomo Marangoni, researcher at FEEM and Politecnico di Milano and leader of the study ‘ clearly point to income and energy efficiency as key determinants of future emissions. Projected population seems to matter less in determining future emissions. Fossil fuel and low carbon resources rank in between. These results tend to hold across models, over different time horizons, and also in the presence of a climate policy. The different drivers interact: for example a richer world will lead to lower emission increase if it is a sustainable one, and vice versa. Neglecting these interactions would lead to inaccurate sensitivity rankings’.
Massimo Tavoni – a co-author of the study and program coordinator at FEEM and faculty member at Politecnico di Milano – says that the study also helps us thinking about how climate change can be solved. ‘Economic growth is a political priority and is needed, especially in emerging and developing economies. But there are policies which can make this objective compatible with lower emissions: energy efficiency and disincentives to fossil resources, especially coal, are high priorities. The alternative of high climate change would be far worse.’
The uncertainty ranges spanned by the scenario story-lines and the different modelling choices in implementing them have the potential to influence several years of climate policy research to come. The study helps prioritizing this research, suggesting that modelling and policy communities could benefit from shifting part of their attention from the traditional energy-supply domain also to elements like energy efficiency and economic wellbeing.
Researchers from the following institutions contributed to the research: Fondazione Eni Enrico Mattei (FEEM), Politecnico di Milano, Bocconi University, National Technical University of Athens, International Institute for Applied Systems Analysis (IIASA), PBL Netherlands Environmental Assessment Agency, Utrecht University, Centre International de Recherche sur l’Environnement et le Développement (CIRED), Ecole des Ponts and University College London.
G. Marangoni, M. Tavoni, V. Bosetti, E. Borgonovo, P. Capros, O. Fricko, D. E. H. J. Gernaat, C. Guivarch, P. Havlik, D. Huppmann, N. Johnson, P. Karkatsoulis, I. Keppo, V. Krey, E. Ó Broin, J. Price, D. P. van Vuuren, “Sensitivity of projected long term CO2 emissions across the Shared Socioeconomic Pathways”, Nature Climate Change, DOI: 10.1038/nclimate3199
** The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement n° 308329 (ADVANCE).