Assessing private R&D spending in Europe for climate change mitigation technologies via patent data

The concept of eco-innovation addresses a reduction in negative environmental impacts and the more efficient use of resources. As an integral part of eco-innovation, green technologies are receiving increasing attention due to growing environmental concerns. Patent data are one of the measures of the output of technological eco-innovation. However, understanding the patenting of eco-innovation comes with challenges. The aim of this study is to measure the output of eco-innovation and to analyse the trends in green technologies based on environment-related patents in the world’s leading countries from 2000 to 2017. For this research, a range of data collection techniques based on patent data from leading countries such as China, Korea, Japan, United States and Germany were employed. The study provides a comprehensive overview of changes and trends in the development of environmental technologies using different domains. In particular, significant progress has been made in the areas of environmental technologies and climate change mitigation technologies related to energy generation, transmission or distribution. These technologies are closely linked to international environmental policies such as climate change mitigation and green industry transformation. The study also contributes to the literature on measuring the output of eco-innovation.

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

10.31235/osf.io/zyb3j ◽

2021 ◽

Author(s):

Shruti Kulkarni

Keyword(s):

Climate Change ◽

Technology Transfer ◽

Climate Change Mitigation ◽

Latent Dirichlet Allocation ◽

Patent Data ◽

Current State ◽

Patent Landscape ◽

Competitor Analysis ◽

Research Questions ◽

Change Mitigation

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.

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