[Future / Now] — Man-made Environmental Disasters Become More Likely
AI+News: stories written by journalists empowered by AI
Nuclear power reactors provide 11% of the world electricity, and this number is increasing. As of October 2018, there are currently about 50 power reactors being constructed in different parts of the planet, mainly Asia and Russia, as reported by the World Nuclear Association. At the same time, there is a competition among oil-producing nations to become the biggest producer in the world, with the United States projected to reach the first position by year 2019, displacing Russia and Saudi Arabia.
Constantly increasing, fast production of nuclear power and oil only means increased risks of a man-made environmental disaster. Factoring in climate change, natural disasters and man-made disasters will eventually overlap. Oil rigs and refineries around the world are amongst the most sensitive types of infrastructure to be damaged by extra-destructive natural events; so are nuclear plants.
Another effect of climate change in relation to oil spills can be found in the Arctic, where a recent study published by AMBIO found that “future oil spills in a warming climate will in some cases result in greater areal coverage and increased shoreline exposure, due to reduced ice coverage’”.
This story was written by a journalist empowered by AI.
The journalist is Giomar Silva (@G_SV), founder of Migrante21 (@Migrante21). Giomar has an extensive background as a reporter and editor in Peru and Washington, D.C. After covering stories about human rights, culture, technology and politics in Peru, he focused on immigrant and minorities issues as a web editor at Washington Hispanic, the largest Spanish-language newspaper in the D.C. area. His interest in these topics led him to found Migrante21, a bilingual website that aims to document the immigrant experience in America.
Minerva leverages news data collections available in the Web and uses Artificial Intelligence based on Machine Learning (AI/ML) to discover the multiple relations among global risks, a data-driven approach that is more appealing in terms of timeliness and efficient discovery of such relations than current methodologies based on opinion surveys.