Carbon Footprint Prediction using Machine Learning Model

An accurate prediction of the carbon footprint will play a vital role in supporting a sustainable future and addressing climate change. Traditional emissions estimation techniques use static assumptions and require manual calculations, which reduces the way in which they can be adapted to be used across multiple regions or businesses. The research outlined here presents a carbon footprint prediction system based on machine learning techniques, along with what the authors refer to as Carbon Emission Analytics, which provides insight into the emission patterns on an ongoing basis for all vehicle types and energy sources used (including residential). Supervised learning techniques are applied to develop pattern recognition and to forecast carbon emissions. Results obtained confirm the validity of the predictive capabilities of the proposed system and the data-driven nature of the predictive emissions analytic capabilities of the authors’ research. In the past fifty years, rapid growth in industry, creating urban areas, and a higher dependence on fossil fuels have led to an increase in greenhouse gases being released into our atmosphere. All of these elements have contributed to a very large portion of the total greenhouse gases that have been produced on the earth and are currently present in the atmosphere. Transportation systems, residential energy consumption, and a change in lifestyle habits are the three main sources of carbon emissions on the planet. Monitoring and tracking carbon footprints to develop a trend over time will be critical to meeting global goals for sustainable development and reducing long-term environmental impact.