Introduction

Mitigation and adaptation are key elements when it comes to responding to climate change. Mitigation measures are actions taken to reduce greenhouse gas emissions. This requires changes in electricity systems, transportation, buildings, industry, and land use. Examples of mitigation include increasing energy efficiency, switching to low-carbon energy sources, removing carbon dioxide and increasing the capacity of carbon sinks, e.g. through reforestation. 

The science and data shows that even the most effective climate change mitigation will not be enough to prevent further climate change impacts, thus making the need for adaptation unavoidable. Climate change adaptation aims to build resilience i.e. the capacity to prepare, plan for, recover and adapt to weather-related disasters, extreme events, and natural hazards. This necessitates a good understanding of weather predictions and the science of climate models and predictions.

While climate change is well-established there are unknown details about underlying mechanisms and impacts on society. Building a digital picture of our natural environment allows for better monitoring of the impacts of climate change in agriculture, biodiversity, oceans, land, water, and the cryosphere. The proactive stewardship and restoration of these ecosystems will help make the UK more resilient to climate change and can contribute one-third of the cost-effective climate mitigation needed between now and 2030. 

At the Turing, there are a number of research projects underway to explore how machine learning and data science can be part of the solution to climate change. 

In addition, the Institute’s Chair of the Board of Trustees Howard Covington joined a panel discussion (in June 2020) hosted by Mark Carney, the United Nations Special Envoy for Climate Action and Finance and former Governor of the Bank of England to explore the need for a whole economy transition to achieve net zero. 

 

Publications