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This discussion brief aims to provide insights into ways that individual researchers can aim to shift rather than reinforce unequal power relations in climate and environment research that disadvantage marginalized communities and the Global South. It seeks to launch wider discussions and actions on the subject to rectify colonial-era legacies that continue to affect power dynamics, detrimentally skewing research and its uptake.

In the 25 years from 1990 to 2015, annual global carbon emissions grew by 60%, approximately doubling total global cumulative emissions. This has brought the world perilously close to exceeding 2°C of warming, and it is now on the verge of exceeding 1.5°C. This paper examines the starkly different contributions of different income groups to carbon emissions in this period. It draws on new data that provides much improved insight into global and national income inequality, combined with national consumption emissions over this 25-year period, to provide an analysis relating emissions to income levels for the populations of 117 countries. Future scenarios of carbon inequality are also presented based on different possible trajectories of economic growth and carbon emissions, highlighting the challenge of ensuring a more equitable distribution of the remaining and rapidly diminishing global carbon budget.

Astronomical datasets are typically very large, and manually classifying the data in them is effectively impossible. We use machine learning algorithms to provide classifications (as stars, quasars and galaxies) for more than one billion objects given photometrically in the Third Data Release of the Sloan Digital Sky Survey (SDSS-III). We have used k NN, SVM and random forest algorithms in a distributed environment over the cloud to classify 1,183,850,913 unclassified photometric objects present in the SDSSIII catalog. This catalog contains photometric data for all objects viewed through a telescope and spectroscopic data for a small part of these. Although it is possible to classify all the objects using spectroscopic data, it is impractical to obtain such data for each one of them. To classify such a big dataset on a single machine would be impractically slow, so we have used the Spark cluster computing framework to implement a distributed computing environment over the cloud. We found that writing results (dozens of gigabytes) to the cloud storage is very slow while using k NN. Though writing the results with SVM is faster as it is done in parallel, its accuracy is only around 87%, due to lack of a kernel implementation of it in Spark. We then used the random forest algorithm to classify the entire set of 1,183,850,913 objects with an accuracy of 94% in about 17 hours of processing time. The result set is significant as even collecting spectroscopic data for these many objects would take decades, and our classifications can help astronomers and astrophysicists carry out further studies.