4.7 Article

Innovation and CO2 emissions: the complimentary role of eco-patent and trademark in the OECD economies

期刊

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
卷 26, 期 22, 页码 22878-22891

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-019-05558-4

关键词

CO2 emissions; Patent; Trademark; Economic growth; Urbanization

资金

  1. National Natural Science Foundation of China [71603105, 71673117]
  2. Ministry of Education of the Republic of Korea
  3. National Research Foundation of Korea [NRF-2018S1A5A2A03036952]
  4. Natural Science Foundation of Jiangsu, China [SBK2016042936]
  5. Science Foundation of Ministry of Education of China [16YJC790067]
  6. China Postdoctoral Science Foundation [2017M610051, 2018T110054]
  7. National Research Foundation of Korea [2018S1A5A2A03036952] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

Increasing global emissions has led to research on the role of innovations play combating emissions. Mitigations from innovation perspective have mainly been focused on the role of patent, ignoring the role of trademarks. We therefore investigate the mitigating power of patent and trademarks in the OECD economies, benchmarking patent as the traditional mitigation strategy. Examining the complimentary role, we created an interaction term between patent and trademark. Our study divided the OECD economies into four subpanels which are OECD America, OCED Asia, OECD Europe, and OECD Oceania. We employed the Im, Pesaran and Shin W-stat, Augmented Dickey-Fuller, and Phillips Perron unit root tests, as well as cross-sectional dependence and Westerlund cointegration tests for the preliminary test on the variables. We also adopted ARDL approach to cointegration, Granger causality test, and OLS in examining the relationship between CO2 and patent, trademark, urbanization, and economic growth. Findings show that jointly, eco-patents and trademarks mitigate CO2 emissions. Also, bidirectional or unidirectional causal relationship was established between our variables of study, an indication that most of our variables can be used in forecasting one other.

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