Ciencia de datos aplicada la obtención de neutralidad de carbono en Latinoamérica

Latin-American aims to reach carbon neutrality. The Nationally Determined Contributions (NDC) of every nation, commits the region to reach net-zero emissions of greenhouse gases by 2050 and sets targets for emissions to be reduced progressively over time. To comply with the goals of these NDCs, each country must consider a set of complex sectoral transformations, such as closing coal-fired power plants, promoting electric mobility, and increasing forest captures which, taken together, could bring emissions down to zero. However, these transformations are subject to a wide range of economic, environmental, and technological uncertainties. This talk will describe how data science methods have been used to identify the vulnerabilities of each nation NDC, that is, we combine simulation models, computational experimentation, and machine learning algorithms to identify under what conditions sectoral transformations are insufficient to achieve net-zero emissions. It then quantifies options for making sectoral plans to deliver the NDC more robust, that is to reduce the likelihood of not achieving carbon neutrality.