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doi:10.3808/jeil.201900011
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Review of Climate Research and Funding 1993 ~ 2017: A Multinomial Logistic Regression Approach

Y. Odeyemi1, M. Pollind1, R. Peeler2, K. Nozawa2, D. Vesely2, A. Page2, C. Rakovski3, and H. El-Askary3,4,5 *

  1. Computational and Data Sciences Graduate Program, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
  2. Research Group, VOXX Analytics, Orange, CA 92843, USA
  3. Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA
  4. Center of Excellence in Earth Observing, Chapman University, Orange, CA 92866, USA
  5. Department of Environmental Sciences, Alexandria University, Moharem Bek, Alexandria 21522, Egypt

*Corresponding author. Tel.: +1 (714) 289-2053; E-mail address: elaskary@chapman.edu (H. El-Askary).

Abstract


This research builds a multinomial regression framework to conduct a meta-analysis of trends in climate research and funding as related to the state of affairs in the last twenty-five years in this area of research. We used a climate research query-based strategy searching the Web of Science, National Science Foundation, Australia Department of Environment and Energy, African Development Bank’s African Climate Change Fund, the Asian Development Bank Climate Change Fund and Australia’s Department of Environment and Energy databases to perform quantitative and qualitative trend analysis. Data were harvested using a web scraper and filtered for the 1993 ~ 2017 window. Comparative analysis was carried out to evaluate the climate research output per continent. Also, we evaluated the role funding plays in the climate research outcomes. Different text processing and mining techniques were used to extract information and data needed for trend analysis and statistical modeling. The text processing revealed trends such as major key- words, key opinion leaders, and individual country’s contribution, monthly and yearly spread of published articles in the climate research domain. From these trends, we engineered some of the variables to build a multinomial regression model to further understand future trends in the climate research space. It is probabilistic in nature with the assumption of no inter correlation between variables, hence outputs are more significant. We found that funding for climate research has been on a steady increase in the last twenty-five years, with the US and European investing hundreds of millions of dollars in alternative and renewable energy. Lastly, the multinomial logistic regression assesses the impact of number of investigators, abstract word count and institution types on the class of grant awarded by NSF. This research builds a multinomial regression framework to conduct a meta-analysis of trends in climate research and funding as related to the state of affairs in the last twenty-five years in this area of research. We used a climate research query-based strategy searching the Web of Science, National Science Foundation, Australia Department of Environment and Energy, African Development Bank’s African Climate Change Fund, the Asian Development Bank Climate Change Fund and Australia’s Department of Environment and Energy databases to perform quantitative and qualitative trend analysis. Data were harvested using a web scraper and filtered for the 1993 ~ 2017 window. Comparative analysis was carried out to evaluate the climate research output per continent. Also, we evaluated the role funding plays in the climate research outcomes. Different text processing and mining techniques were used to extract information and data needed for trend analysis and statistical modeling. The text processing revealed trends such as major keywords, key opinion leaders, and individual country’s contribution, monthly and yearly spread of published articles in the climate research domain. From these trends, we engineered some of the variables to build a multinomial regression model to further understand future trends in the climate research space. It is probabilistic in nature with the assumption of no inter correlation between variables, hence outputs are more significant. We found that funding for climate research has been on a steady increase in the last twenty-five years, with the US and European investing hundreds of millions of dollars in alternative and renewable energy. Lastly, the multinomial logistic regression assesses the impact of number of investigators, abstract word count and institution types on the class of grant awarded by NSF.

Keywords: climate change fund, climate research, EU-LIFE program, multinomial logistic regression, Natural Science Foundation, natural language processing, Paris Climate Agreement


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