Date of Award
Master of Science (MS)
The focus of this research is to predict the greenhouse gas emissions and the funding to help combat this global problem. There must be consistent funding to support and sustain the planet ecosystems. This research is motivated by the global concern of climate change caused by greenhouse gas emissions and the need to consider a multinational strategy to provide funding to combat it. The goal of the funding is to provide adequate financial backing and support for innovations needed to combat this problem.
This research leverages the capabilities of machine learning found in Weka and forecasting and visualization in Tableau. The models are expected to predict a carbon tax rate that could be used multi-nationally. The results and performance measures will be scrutinized to identify the model that is the best fit for the proposed solution. The economic, population, land temperature, current multinational carbon tax rates and reverse carbon initiatives data will be interrogated by supervised machine learning models or classifiers (Frank et al., 2011). The CO2emissions for China, India and the United States will also be predicted to show expected increases in emission based on historical data through Tableau forecasting.
This study concluded that a carbon rate can adequately be created and predicted using machine learning models. And, CO2emissions can also be predicted using public open data sources that provide economic, population and surface temperature features.
Davis, Edward, "Climate Change Analytics: Predicting Carbon Price and CO2 Emissions" (2019). Analytics Capstones. 1.