Using Machine Learning to Analyze Climate Change Technology Transfer (CCTT)

Shruti Kulkarni

No zyb3j, SocArXiv from Center for Open Science

Abstract: The objective of the present paper is to review the current state of climate change technology transfer. This research proposes a method for analyzing climate change technology transfer using patent analysis and topic modeling. A collection of climate change patent data from patent databases would be used as input to group patents in several relevant topics for climate change mitigation using the topic exploration model in this research. The research questions we want to address are: how have patenting activities changed over time in climate change mitigation related technology (CCMT) patents? And who are the technological leaders? The investigation of these questions can offer the technological landscape in climate change-related technologies at the international level. We propose a hybrid Latent Dirichlet Allocation (LDA) approach for topic modelling and identification of relationships between terms and topics related to CCMT, enabling better visualizations of underlying intellectual property dynamics. Further, a predictive model for CCTT is proposed using techniques such as social network analysis (SNA) and, regression analysis. The competitor analysis is also proposed to identify countries with a similar patent landscape. The projected results are expected to facilitate the transfer process associated with existing and emerging climate change technologies and improve technology cooperation between governments.

Date: 2020-04-25
New Economics Papers: this item is included in nep-big, nep-ene and nep-env
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:zyb3j

DOI: 10.31219/osf.io/zyb3j

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