Prescriptive AI for sustainable policy making

The Carbon Emission and Reduction Analytics Initiative

Our Global Mission

Utilising data and AI to generate predictive analytics and data driven insights to analyse, understand and predict carbon emission and thereby help governments and industries make informed decisions to mitigate their environmental impact.

With data analytics and AI driving the fourth revolution, we believe that the answers to the climate change problem lie within data and AI too. It is time to put them at the centre and build on what we already know about causes and effects of carbon emissions.

The CERA initiative is set up to do just that. Our mission makes up our four founding principles, the CURB framework.

The CURB framework.

Computing

Using the power of computers and artificial intelligence to collect and analyse data in a meaningful way.

Computing

Understanding

Understanding and interpreting data analysed to generate predictive analytics and data insights.

Understanding

Reducing

Reducing carbon emission by creating awareness of impact of actions of communities locally and countries internationally

Reducing

Building

Building a sustainable future by recommending and encouraging governments and industries to make positive shifts in policy making and global cooperation to reduce the environmental impacts.

Building

Our Implementation Framework

Data collection

Gather necessary data on carbon emissions, inflation rates, changes in industrial output, and other relevant metrics for chosen industries and economies

Data processing

Clean, preprocess and standardize data to ensure accuracy, consistency and comparability.

Feature engineering

Create meaningful features and data sets from the collected data that can enhance the predictive power of the model.

Model selection

Select a suitable predictive analytics model, such as regression-based models or time-series analysis methods, based on the project requirements.

Training & Validating data

Split data into training and validation sets to train the selected model and validation set to check the model’s performance against expectations.

Comparative Analysis

Obtain similar data for selected economies of similar size and macroeconomic policies and normalize it to account for specific factors and ensure a fair comparison.

Analysis and interpretation

Apply the trained model to the normalized data to predict carbon emissions. Analyze the results and interpret the findings in terms of the impact on carbon emissions.

Visualisation and reporting

Create visual reports and summaries showing the analysis, findings and recommendations.

Prescriptive AI

Using data analytics and AI evaluate the effect of current policies of governments and industries on carbon emissions

Iterative Improvement

Evaluate the model’s performance and assess any limitations or areas for improvement.

Our Global Impact Areas

Ansh Soni

Ansh Soni, a diligent high school student from England, possesses a strong work ethic and an unwavering passion for computer programming. With a blend of analytical prowess and programming expertise, Ansh is not only committed to honing his skills to excel in his chosen field but also make an impact in the world.

Ansh thrived on collaborating with like-minded individuals on projects during his work experience at Rutherford Appleton Laboratory and competitions like the FIRST Robotics Competition. Recently he earned the Advanced Certificate on Global Citizenship for Social Impact from AFS and the Center for Social Impact Strategy at the University of Pennsylvania for participating in the AFS Global STEM Academies programme which covered a detailed study of UN’s SDGs. Ansh secured a scholarship for an AI course at Oxford University run by Immerse Education.

Ansh’s ambition is to make a significant impact in reducing carbon emissions using AI and contribute to the advancement of the 4th industrial revolution. He founded Carbon Emission and Reduction Analytics Initiative (CERA Initiative) out of his aspiration to help understand the cause and impact of carbon emissions using data driven analysis and influence policy making using predictive data analytics.