We propose a novel approach to quantify the contribution of technological diffusion to climate change mitigation. First, we use a parametric model of epidemic diffusion to estimate from micro-level data the determinants and the structure of the networks of diffusion for three key mitigation technologies: electro-mobility, renewable energy and agriculture. We then simulate the propagation of new technological vintages on these networks and quantify the reduction of emissions induced by the diffusion process using a tailored feedback centrality measure labelled “emission centrality”. Finally, we investigate how new forms of international collaboration such as climate clubs can contribute to mitigation by catalysing the adoption of new technologies. Our approach can be used directly to measure the contribution of technological diffusion to mitigation or indirectly by providing estimates of global technological diffusion to integrated assessment models.
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Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47
Archibugi D, Coco A (2004) A new indicator of technological capabilities for developed and developing countries (Arco). World Dev 32(4):629–654
Balint T, Lamperti F, Mandel A, Napoletano M, Roventini A, Sapio A (2017) Complexity and the economics of climate change: a survey and a look forward. Ecol Econ
Battiston S, Puliga M, Kaushik R, Tasca P, Caldarelli G (2012) Debtrank: too central to fail? Financial networks, the fed and systemic risk. Sci Rep 2:541
Bloch F, Jackson MO, Tebaldi P (2017) Centrality measures in networks. Available at SSRN 2749124
De Cian E, Bosetti V, Tavoni M (2012) Technology innovation and diffusion in less than ideal climate policies: an assessment with the witch model. Clim Chang 114(1):121–143
de Coninck H, Sagar A (2015) Making sense of policy for climate technology development and transfer. Clim Pol 15:1–11
EVvolumes (2017) The electric vehicle world sales database. http://www.ev-volumes.com/
FAO (2018) Dataset of agricultural inputs. http://www.fao.org/faostat/en/#data. Accessed 01 Feb 2019
Glachant M, Dechezleprêtre A (2017) What role for climate negotiations on technology transfer? Clim Pol 17(8):962–981. 19
Gomez Rodriguez M, Leskovec J, Krause A (2010) Inferring networks of diffusion and influence. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 1019–1028
Grubb M, De Coninck H, Sagar AD (2015) From Lima to Paris, part 2: injecting ambition. Clim Pol 15(4):413–416
Halleck-Vega SH, Mandel A (2018) Technology diffusion and climate policy: a network approach and its application to wind energy. Ecol Econ 145:461–471
Halleck-Vega S, Mandel A, Millock K (2018) Accelerating diffusion of climate-friendly technologies: a network perspective. Ecol Econ 152:235–245
Hovi J, Sprinz DF, Sælen H, Underdal A (2016) Climate change mitigation: a role for climate clubs? Palgrave Communications 2:16020
Jackson MO (2010) Social and economic networks. Princeton university press, Princeton
Keohane RO, Victor DG (2016) Cooperation and discord in global climate policy. Nat Clim Chang 6(6):570–575
Lamperti F, Dosi G, Napoletano M, Roventini A, Sapio A (2018) Faraway, so close: coupled climate and economic dynamics in an agent-based integrated assessment model. Ecol Econ 150:315–339
Nordhaus W (2015) Climate clubs: overcoming free-riding in international climate policy. Am Econ Rev 105(4):1339–1370
NSF-Kellogg-Institute (2018) Economic Integration Agreements Database 4. https://kellogg.nd.edu/nsf-kellogg-institute-data-base-economic-integration-agreements. Accessed 01 Feb 2019
Paroussos L, Mandel A, Fragkiadakis K, Fragkos P, Hinkel J, Vrontisi Z (2019) Climate clubs and the macro-economic benefits of international cooperation on climate policy. Nature Climate Change 9, 542:546
Tavoni M, De Cian E, Luderer G, Steckel JC, Waisman H (2012) The value of technology and of its evolution towards a low carbon economy. Climatic Change, 114(1), 39–57
WindPower (2016). Wind Power database, retrieved https://www.thewindpower.net/. Accessed 01 Feb 2019
WRI (2018). CAIT Climate Data Explorer, retrieved from https://cait.wri.org/. Accessed 01 Feb 2019
Wu X, Kumar A, Sheldon D, Zilberstein S (2013) Parameter Learning for Latent Network Diffusion. Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI), 2923–2930
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This article is part of a Special Issue on Win-Win Solutions to Climatic Change edited by Diana Mangalagiu, Alexander Bisaro, Jochen Hinkel, and Joan David Tãbara
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Mandel, A., Halleck Vega, S. & Wang, DX. The contribution of technological diffusion to climate change mitigation: a network-based approach. Climatic Change 160, 609–620 (2020). https://doi.org/10.1007/s10584-019-02517-3