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Tracking artificial intelligence in climate inventions with patent data

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  • Vilhelm Verendel

    (Chalmers University of Technology)

Abstract

Artificial intelligence (AI) is spreading rapidly in many technology areas, and AI inventions may help climate change mitigation and adaptation. Previous studies of climate-related AI mainly rely on expert studies of literature, not large-scale data. Here I present an approach to track the relation between AI and climate inventions on an economy-wide scale. Analysis of over 6 million US patents, 1976 to 2019, shows that within climate patents, AI is referred to most often in transportation, energy and industrial production technologies. In highly cited patents, AI occurs more frequently in adaptation and transport than in other climate mitigation areas. AI in climate patents was associated with around 30–100% more subsequent inventions when counting all technologies. Yet AI-climate patents led to a greater share of citations from outside the climate field than non-AI-climate patents. This suggests the importance of tracking both increased invention activity and the areas where subsequent inventions emerge.

Suggested Citation

  • Vilhelm Verendel, 2023. "Tracking artificial intelligence in climate inventions with patent data," Nature Climate Change, Nature, vol. 13(1), pages 40-47, January.
    • Handle: RePEc:nat:natcli:v:13:y:2023:i:1:d:10.1038_s41558-022-01536-w
    DOI: 10.1038/s41558-022-01536-w
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    References listed on IDEAS

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    1. Rigby, R.A. & Stasinopoulos, D.M. & Akantziliotou, C., 2008. "A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 381-393, December.
    2. Matt Marx & Aaron Fuegi, 2020. "Reliance on science: Worldwide front‐page patent citations to scientific articles," Strategic Management Journal, Wiley Blackwell, vol. 41(9), pages 1572-1594, September.
    3. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
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    Cited by:

    1. Zhang, Jianhua & Ballas, Dimitris & Liu, Xiaolong, 2024. "Global climate change mitigation technology diffusion: A network perspective," Energy Economics, Elsevier, vol. 133(C).

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