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Building a Global CO₂ Emissions Estimator Using Machine Learning

Key results
Carbon footprint prediction with a convenient heatmap visualisation
A 20-year visual retrospective of global CO₂ emissions
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Summary

In line with ELEKS’ commitment to Sustainable Development Goals, our Data Science team created a global CO₂ emission estimator that forecasts emissions for the coming year using machine learning techniques. To present the research results, we developed an interactive heat map using Streamlit’s framework. Users can select a year with a slider bar to view the emission distribution across countries. The map also allows users to examine CO₂ emission changes over the past 20 years.
Location
Europe
Services used
Data science
Industry
Environment
The situation

ELEKS wanted a visualisation and prediction tool that could allow it to track global CO₂ emissions

ELEKS has committed to two of the UN’s core Sustainable Development Goals; Goal 9: Industry, innovation and infrastructure and Goal 13: Climate action, to cement sustainability within its operations. The company dedicates a significant amount of its efforts to exploring climate-positive technology solutions.

ELEKS’ Data Science team resolved to build a model to predict the following year’s global CO₂ emissions using Machine Learning approaches. They aimed to explore the availability and global coverage of data, as well as modelling possibility. To visualise the obtained results, they created an interactive heat map on top of Streamlit, an open-source framework that allows Machine Learning and Data Science teams to develop and share data apps.

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The solution

A Machine Learning-powered precision model that predicts global emissions for the coming year by location and fuel type

ELEKS’ Data Science team built a global CO₂ emission estimator, which can forecast emissions for the following year using natural Machine Learning approaches. Predictions are modelled from data taken from the World DataBank, which allows users to access various global datasets.

The team opted to include estimates for all four CO₂-related targets; total CO₂ emissions, solid fuel CO₂ emissions, liquid fuel CO₂ emissions, and gaseous fuel CO₂ emissions, using a top-down approach and estimating the component parts based on total CO₂ emissions.

Once they had the data, the experts filtered it to give the model the most accurate outcomes. Then, the team applied several strategies to reduce the dimension of the matrix and refine obtained results by removing collinear features.

They evaluated multiple models and algorithms to estimate the CO₂ emissions and carried out model validation to show those that worked best. Our experts then applied different strategies and techniques to improve the precision of results. They evaluated the model with the multirun holdout validation, calculating the variance and concluding on the robustness of the model.

Tools and methodology:
01
Dimensionality reduction and features influence
Matrix spectral analysis decision tree regressor, random forest regressor, gradient boosting regressor, ridge regressor, SVM regressor, and a multilayer perceptron
02
Model validation
Cross-validation, depending on the MAPE score and standard deviation on the folds, multirun validation
The result

ELEKS has created a model that predicts future global emissions and gives a 20-year historical view for comparison

To visualise the research results, we have built an interactive heat map on top of Streamlit’s framework.

With the slider bar, a user can choose the year they’re interested in and see what the emission distribution is in countries across the globe. The map also serves as a simple tool for examining changes in CO₂ emissions throughout the past 20 years.

data science machine learning sustainability web
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