Introduction:
One amazing thing about Canada is we have the opportunity to experience the change of seasons in what most people consider their most prolific states. The flora blossoming in spring, the warm and sunny summers, the falling autumn leaves, and the snowfall of winter. To us, Climate Change could just be the loss of the dichotomy between seasons, to others it is floods, hurricanes, droughts, and matters of life. Climate Change is a global issue and while some would monetize it or weaponize it in politics, the scientific community once again proves to earn its reputation as the community beyond borders.
Analysis:
In a study led by the Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice in 2021, contributors were examining the opportunity of Machine Learning to help assess Climate Change. Specifically focusing on the ego-network centered on GIS (visualized below):
This network provided the baseline of what the study considered to be relevant scientific production to be analyzed. Amount of scientific production and relationships of collaboration between countries’ scientists are visualized below:
So, what do we see here? Unsurprisingly “wealthy” countries lead the way for scientific production. There is also a heavy relationship between the amount of scientific production and the country’s susceptibility to climate change, found by the study.
Now consider the collaborations between countries. In the early 2000s to middle 2010s we see what you might expect if someone simply said “draw relationships between countries” without a clear criterion. We see the “anglosphere” (English speaking predominant countries) collaborating, and then with their “allies” at least in a political sense. Recall that back then Climate Change was still considered “Global Warming” and while it was understood much earlier, it wasn’t at the forefront of people’s minds just yet. At the end of this period, we can see a coalition of strong ties between political allies, such as the United States and Western Europe, and then a coalition really of everyone else, who are not collaborating as much.
However, in the recent half-decade of 2015-2020, we see that barrier of political allies break down. In fact., it all just forms one large coalition. Following the property of Strong Triadic Closure as well as real-world analysis it is easy to see why. As one member of the collaborating coalition works with someone outside of the community, it creates a weak tie to the collaborating coalition. In the real world, as someone brings them into this collaborating scientific community, their work begins to get spread throughout the group and becomes a topic that others begin wanting to collaborate on.
Conclusion:
And that is a good thing, despite rising political tensions between China and the West, despite the current political climate of division and trying to weaponize Climate Change. It is important and critical that the scientific community continues as it always had, that it had built a reputation on. In a world that is easier than ever to talk to people around the globe, it would be a disservice not to have the bright minds of the world work together and collaborate, especially on an issue as global as Climate Change.
Resources:
Federica Zennaro, Elisa Furlan, Christian Simeoni, Silvia Torresan, Sinem Aslan, Andrea Critto, Antonio Marcomini, “Exploring machine learning potential for climate change risk assessment”, Earth-Science Reviews, Volume 220, 2021, 103752, ISSN 0012-8252,
https://doi.org/10.1016/j.earscirev.2021.103752.
(https://www.sciencedirect.com/science/article/pii/S0012825221002531)