A spatial perspective on green technology adoption in China : insights from patent licensing data
(2023) In Innovation and Development- Abstract
In the transition to more sustainable regional economies, the widespread adoption of green technologies is crucial. However, little is known about the geography of green technology adoption and the relationship between regional demand and supply of green technologies. In this paper, we shed light on the (regional) factors explaining whether innovation adopters use green technologies that have been developed locally or green technologies that have been developed in other places. We analyze a unique data set of 8825 licensing agreements for Chinese patents in green technologies, which we use as an indicator to measure innovation diffusion. Our results suggest that the regional context plays a key role in predicting whether innovation... (More)
In the transition to more sustainable regional economies, the widespread adoption of green technologies is crucial. However, little is known about the geography of green technology adoption and the relationship between regional demand and supply of green technologies. In this paper, we shed light on the (regional) factors explaining whether innovation adopters use green technologies that have been developed locally or green technologies that have been developed in other places. We analyze a unique data set of 8825 licensing agreements for Chinese patents in green technologies, which we use as an indicator to measure innovation diffusion. Our results suggest that the regional context plays a key role in predicting whether innovation adopters use local or non-local green technologies. We show, among other things, that the use of locally developed green technologies is more likely in regions characterized by green technology specializations and high innovation capacity than in less innovative regions.
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- author
- Losacker, Sebastian LU ; Horbach, Jens and Liefner, Ingo
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- China, environmental innovation, green technology, Innovation diffusion, patent licensing
- in
- Innovation and Development
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:85164613604
- ISSN
- 2157-930X
- DOI
- 10.1080/2157930X.2023.2233199
- language
- English
- LU publication?
- yes
- id
- ccce7c3f-c2c6-441f-84ae-cef5d0d802e6
- date added to LUP
- 2023-09-27 11:20:00
- date last changed
- 2024-01-19 02:10:56
@article{ccce7c3f-c2c6-441f-84ae-cef5d0d802e6,
abstract = {{<p>In the transition to more sustainable regional economies, the widespread adoption of green technologies is crucial. However, little is known about the geography of green technology adoption and the relationship between regional demand and supply of green technologies. In this paper, we shed light on the (regional) factors explaining whether innovation adopters use green technologies that have been developed locally or green technologies that have been developed in other places. We analyze a unique data set of 8825 licensing agreements for Chinese patents in green technologies, which we use as an indicator to measure innovation diffusion. Our results suggest that the regional context plays a key role in predicting whether innovation adopters use local or non-local green technologies. We show, among other things, that the use of locally developed green technologies is more likely in regions characterized by green technology specializations and high innovation capacity than in less innovative regions.</p>}},
author = {{Losacker, Sebastian and Horbach, Jens and Liefner, Ingo}},
issn = {{2157-930X}},
keywords = {{China; environmental innovation; green technology; Innovation diffusion; patent licensing}},
language = {{eng}},
publisher = {{Taylor & Francis}},
series = {{Innovation and Development}},
title = {{A spatial perspective on green technology adoption in China : insights from patent licensing data}},
url = {{http://dx.doi.org/10.1080/2157930X.2023.2233199}},
doi = {{10.1080/2157930X.2023.2233199}},
year = {{2023}},
}