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Tutorials - Jessica Greene, Chioma Onyekpere: Monitoring carbon emissions: making the impact...
Monitoring carbon emissions involves data collection, preprocessing, visualization, and applying machine learning algorithms to predict and reduce emissions.
- Collect data from various sources: Monitoring carbon emissions requires collecting data from various sources, including government agencies, companies, and organizations.
- Use worldbank and NASA datasets: Utilize datasets from the World Bank and NASA to gather information on greenhouse gas emissions.
- Data preprocessing is crucial: Proper preprocessing of data is necessary to ensure accuracy and quality of the data.
- Data visualization is important: Visualizing the data helps to identify trends and patterns, making it easier to understand and make decisions.
- Machine learning algorithms can be applied: Machine learning algorithms can be applied to predict future emissions and identify areas where reductions can be made.
- Combining data sources and machine learning: Combining data sources and using machine learning algorithms can help to improve the accuracy of emissions estimates.
- Addressing data gaps: Identifying and addressing data gaps are crucial to improving the accuracy of emissions estimates.
- ssolvents and carbon sinks: Understanding the role of solvents and carbon sinks is essential to accurately estimating carbon emissions.
- Next steps: Further research and collaboration are needed to improve the accuracy of carbon emissions estimates and to develop effective strategies for reducing emissions.