Clean Technology Meets Data Science: News and Articles

Last time, we looked at how models and data science are used in measuring, monitoring, predicting and responding to a changing climate in the latest IPCC reports. Today, let’s look at the results from the reports. First, we’ve now reached an average warming level of 1.1 0 C [0.95 0 C - 1.20 0 C] compared to pre-industrial levels (1850-1900) . This result is based on satellite and sensor observations taken from the land and the oceans. Further, when compared to paleoclimate data (for e.g. data from ice core samples existing millions of years ago), the key indicators of the climate system are increasingly at levels that have not been seen for centuries and are changing at rates that are unprecedented for the last 2000 years. Also, several studies have shown that the ocean absorbed a significant amount of heat between 1998-2012, a process that resulted in a smaller rate of increase in land surface temperatures. However, this effect appears to be temporary, with strong warming seen since 2...

I t’s Earth Month and the Inter-governmental Panel on Climate Change (IPCC) just released the third installment in the series of reports on the state of the planet. The first report dealt with the science of climate change and the second one looked at its impacts on society and the planet. The third report was released a few days ago and looked at our response to climate change as societies and what we can expect as a result. IPCC reports have been released approximately every decade since 1990 - the current one is the 6th installment. Several hundred scientists collaborate on the technical and scientific assessments - reviewing the latest research published globally, combining multiple scientific areas and disciplines - in order to develop as comprehensive a picture as possible of the state of the planet. Just as an example, the second report on impacts to the planet draws from 34,000 studies and involved 270 authors from 67 countries.   The first report , released in December 202...

In the first of our two-part conversation on machine learning in climate science, we talked about the main challenges in using machine learning in earth and environmental science. Today, let’s talk about why we go through the effort of using these tools in clean technology when they require a significant investment in understanding and modifying them. Why use machine learning? Three main reasons - 1) Do it better 2) Do it faster 3) Find unexplained trends or patterns. 1) Do it better : Last time we talked about the challenges of using machine learning in solving problems in clean technology. And that’s still true for unmodified, off-the shelf models. However, there’s a huge opportunity for scientists and engineers who are interested in understanding and adapting these models to make them work effectively with all the other tools in the tool box!   Let’s look at one such adaptation where machine learning algorithms can be used in concert with physics based models to generate more a...

  As the impacts of climate change on the planet become clearer, scientists and professionals in climate science are looking at the latest tools and technologies in AI and machine learning to help understand and mitigate the effects. At the same time, career opportunities in the field are growing and we’re seeing increasing numbers of students and early career professionals interested in developing and using their skills in ways that can help the planet.   So, when and where can machine learning and AI be used in climate science? And what are the pitfalls? If you’re working in environmental and earth sciences, you probably already have a pretty big toolbox that has been developed over several decades! It consists of standard statistical techniques including spatial and temporal statistics, a range of physics-based or process based models, and several data collection and data integration technologies at different scales.   What can machine learning add to this? Does it rep...

  What is climate tech? How much is the market worth - hint it's in trillions of dollars. How has it grown over the last decade? What are the trends driving the adoption of data science, AI and machine learning in the climate science space? What are the jobs like and what skills are required to work at the intersection of data science and climate? Join us for a lively discussion on this topic on March 25th !

  Perhaps one of the most interesting parts of trying to clean up the environment is the need to balance individual action with global behavior. Actions that are optimal at the individual level are often what lead to depletion of resources and environmental damage at the global or regional scale. Take climate change for example – even though the effects of a changing climate promise to be devastating to many countries and places around the world, it has often worked better for countries to focus on their short-term economic goals rather than look at what would work best for the economy and environment in the long term. A  recent approach  from scientists at Georgia Tech looks at how game theory can be used to help solve this problem. An assumption inherent in how many environmental policies and markets are designed is that actors will act rationally in their own interest and that the system doesn’t change drastically. Now, this is an assumption that doesn’t necessarily ho...

  Data science has been used extensively in building climate models, downscaling climate models to regions, monitoring and evaluating the accuracy of climate models through  paleoclimate data as well as developing methods to mitigate the effects of climate change and develop alternative markets. Today’s post will look at some of the more straightforward uses of data, machine learning and spatial statistics in monitoring carbon emissions as well as building alternative market systems. The first of course is   monitoring and measuring carbon emissions   and emissions of other gases that contribute to the changing climate. Our first example comes from Europe. Researchers in Europe    created a tool to map the 177 regions in 27 countries of the EU and the carbon footprint associated with them. They used a database (EXIOBASE 2.3 multiregional input-output database) with detailed information about the world economy in 2007 and built a model that looked at the dif...