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Technology Diffusion and Climate Policy: A Network Approach and its Application to Wind Energy

Author

Listed:
  • Solmaria Halleck Vega

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UP1 UFR02 - Université Paris 1 Panthéon-Sorbonne - École d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Antoine Mandel

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

The role of technology transfer in the mitigation of climate change has been strongly emphasized in the recent policy debate. This paper offers a network-based perspective on the issue. First, we propose a methodology to infer from technology adoption data the network of diffusion and apply it to a detailed dataset on wind energy technologies installed globally since the 1980s. We then perform a statistical analysis of the network. It highlights a relatively inefficient organization, characterized in particular by the weakness of South-South links, which leads to relatively long lags in the diffusion process. Against this background, we characterize optimal transfer/seeding strategies for an agent that aims to introduce a new technology in a developing country in view of further diffusion. Our results suggest in particular that CDM projects have been too concentrated in large emerging economies and that developed countries should put a stronger weight on the positive externalities in terms of technology transfer of cooperating with less prominent developing countries.

Suggested Citation

  • Solmaria Halleck Vega & Antoine Mandel, 2018. "Technology Diffusion and Climate Policy: A Network Approach and its Application to Wind Energy," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01905996, HAL.
    • Handle: RePEc:hal:cesptp:halshs-01905996
    DOI: 10.1016/j.ecolecon.2017.11.023
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    Cited by:

    1. Brancaccio, Emiliano & Giammetti, Raffaele & Lopreite, Milena & Puliga, Michelangelo, 2019. "Monetary policy, crisis and capital centralization in corporate ownership and control networks: A B-Var analysis," Structural Change and Economic Dynamics, Elsevier, vol. 51(C), pages 55-66.
    2. Li, Jingjing & Jiao, Jianling & Tang, Yunshu, 2019. "An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network," Energy Policy, Elsevier, vol. 129(C), pages 1-12.
    3. Li, Fangyi & Cao, Xin & Ou, Rui, 2021. "A network-based evolutionary analysis of the diffusion of cleaner energy substitution in enterprises: The roles of PEST factors," Energy Policy, Elsevier, vol. 156(C).
    4. Zhenxu Guo & Jiarui Shen & Lihong Li, 2024. "Identifying the implementation effect of technology transfer policy using system dynamics: a case study in Liaoning, China," The Journal of Technology Transfer, Springer, vol. 49(2), pages 660-688, April.
    5. Carattini, Stefano & Fankhauser, Sam & Gao, Jianjian & Gennaioli, Caterina & Panzarasa, Pietro, 2023. "What does network analysis teach us about international environmental cooperation?," Ecological Economics, Elsevier, vol. 205(C).
    6. Halleck-Vega, Solmaria & Mandel, Antoine & Millock, Katrin, 2018. "Accelerating diffusion of climate-friendly technologies: A network perspective," Ecological Economics, Elsevier, vol. 152(C), pages 235-245.
    7. Shi, Y.Y. & Wei, Z.X. & Shahbaz, M., 2023. "Analyzing the co-evolutionary dynamics of consumers’ attitudes and green energy technologies based on a triple-helix model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    8. Antoine Mandel & Solmaria Halleck Vega & Dan-Xia Wang, 2020. "The contribution of technological diffusion to climate change mitigation: a network-based approach," Climatic Change, Springer, vol. 160(4), pages 609-620, June.
    9. Côme Billard & Anna Creti & Antoine Mandel, 2020. "How Environmental Policies Spread? A Network Approach to Diffusion in the U.S," Working Papers 2020.12, FAERE - French Association of Environmental and Resource Economists.
    10. Côme Billard, 2020. "Technology Contagion in Networks," Working Papers 2020.01, FAERE - French Association of Environmental and Resource Economists.
    11. Fadly, Dalia & Fontes, Francisco, 2019. "Geographical proximity and renewable energy diffusion: An empirical approach," Energy Policy, Elsevier, vol. 129(C), pages 422-435.
    12. Brandt, Urs Steiner & Svendsen, Gert Tinggaard, 2022. "Is the annual UNFCCC COP the only game in town?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Miremadi, I. & Saboohi, Y. & Arasti, M., 2019. "The influence of public R&D and knowledge spillovers on the development of renewable energy sources: The case of the Nordic countries," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 450-463.
    14. Dolphin, Geoffroy & Pollitt, Michael G., 2021. "The International Diffusion of Climate Policy: Theory and Evidence," RFF Working Paper Series 21-23, Resources for the Future.
    15. Pan, Xiuzhen & Wei, Zixiang & Han, Botang & Shahbaz, Muhammad, 2021. "The heterogeneous impacts of interregional green technology spillover on energy intensity in China," Energy Economics, Elsevier, vol. 96(C).
    16. Zhang, Jianhua & Ballas, Dimitris & Liu, Xiaolong, 2024. "Global climate change mitigation technology diffusion: A network perspective," Energy Economics, Elsevier, vol. 133(C).
    17. Zhou, Yong & Yang, Qijin & Lu, Shuo, 2023. "Research on the identification and formation mechanism of the main path of digital technology diffusion: Empirical evidence from China," Technology in Society, Elsevier, vol. 75(C).
    18. Ying Luo & Xiaowen Jie & Xiaoping Li & Liming Yao, 2018. "Ranking Chinese SMEs Green Manufacturing Drivers Using a Novel Hybrid Multi-Criterion Decision-Making Model," Sustainability, MDPI, vol. 10(8), pages 1-23, July.

    More about this item

    Keywords

    Technology transfer; Climate policy; Diffusion networks; Wind energy;
    All these keywords.

    JEL classification:

    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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