Asymmetries in global scientific knowledge production: regional representations in climate change research
Lagerroos, Sofie (2023)
Lagerroos, Sofie
2023
Politiikan tutkimuksen maisteriohjelma - Master's Programme in Politics
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2023-03-07
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202302192506
https://urn.fi/URN:NBN:fi:tuni-202302192506
Tiivistelmä
This study examines regional representations in scientific climate change research. More specifically, the aim of this thesis is to map the geographical distribution of case studies in adaptation and mitigation related research, as well as to determine whether certain economy, education, research, and development related factors correlate with said distribution. As a starting point for this endeavour, three central factors can be highlighted: the social organisation of knowledge, spatial or geographical contexts, and resources. These factors emerge from the theoretical discussion about structural power and inequalities in the global knowledge economy, and underlying them is the idea that knowledge production has social, spatial, and economic importance, which is all tied to structural power.
The data of this study comprises 10 000 scientific articles about climate adaptation and mitigation, published between the years 2018 and 2022 and collected from Scopus -database. In this sample, there are 6 844 case studies that form the final dataset. The first part of the analysis examines the geographical distribution of these case studies, whereas the second part looks into the existence and strength of possible correlations between the recurrence of case study locations and the following indicators: Research and Development expenditure (as a percentage of GDP), GDP per capita (PPP), government expenditure on tertiary education (as a percentage of GDP), the number of researchers per million people, Human Development Index, and Global Innovation Index.
One of the main conclusions of the analysis is that there is clear variation in the spatial distribution of case study locations: certain countries and regions are much more studied than others, and there are some regional “clusters” that stand out due to a very large or a very small number of conducted case studies. In the original dataset, population sizes clearly influence the observed regional representations, considering that countries with particularly large populations stand out: for example USA, China, Brazil, India, Ethiopia, South-Africa, and Australia. When the number of case studies per country has been population-adjusted, the highest proportions of case studies can be found from Oceania, Northern Europe (especially from the Nordic countries), Northern America, and Southern Africa. It is clear that in the dataset of this thesis, case study locations are not evenly distributed across the globe.
Another important conclusion is that, as the correlation analysis shows, there is a positive, albeit only weak to moderate, association between the recurrence of case studies and all of the chosen indicators. The strongest correlation can be found between the number of case studies and the number of researchers, but R&D expenditures and the Global Innovation Index demonstrate moderate correlations to the recurrence of case study locations as well. These variables are, therefore, likely related to the levels of regional representation observed in the dataset. GDP per capita (PPP), tertiary education expenditures, and the Human Development Index, on the other hand, only show weak correlations, which would indicate a rather unsubstantial relationship between said variables and the number of case studies.
The data of this study comprises 10 000 scientific articles about climate adaptation and mitigation, published between the years 2018 and 2022 and collected from Scopus -database. In this sample, there are 6 844 case studies that form the final dataset. The first part of the analysis examines the geographical distribution of these case studies, whereas the second part looks into the existence and strength of possible correlations between the recurrence of case study locations and the following indicators: Research and Development expenditure (as a percentage of GDP), GDP per capita (PPP), government expenditure on tertiary education (as a percentage of GDP), the number of researchers per million people, Human Development Index, and Global Innovation Index.
One of the main conclusions of the analysis is that there is clear variation in the spatial distribution of case study locations: certain countries and regions are much more studied than others, and there are some regional “clusters” that stand out due to a very large or a very small number of conducted case studies. In the original dataset, population sizes clearly influence the observed regional representations, considering that countries with particularly large populations stand out: for example USA, China, Brazil, India, Ethiopia, South-Africa, and Australia. When the number of case studies per country has been population-adjusted, the highest proportions of case studies can be found from Oceania, Northern Europe (especially from the Nordic countries), Northern America, and Southern Africa. It is clear that in the dataset of this thesis, case study locations are not evenly distributed across the globe.
Another important conclusion is that, as the correlation analysis shows, there is a positive, albeit only weak to moderate, association between the recurrence of case studies and all of the chosen indicators. The strongest correlation can be found between the number of case studies and the number of researchers, but R&D expenditures and the Global Innovation Index demonstrate moderate correlations to the recurrence of case study locations as well. These variables are, therefore, likely related to the levels of regional representation observed in the dataset. GDP per capita (PPP), tertiary education expenditures, and the Human Development Index, on the other hand, only show weak correlations, which would indicate a rather unsubstantial relationship between said variables and the number of case studies.