Compliance with EU Commission environmental carbon emission reporting mandates can be a laborious process, prone to human error, and costly in terms of time and resources.
Introducing the WhatsApp-based chatbot designed to streamline and simplify carbon emission reporting, ensuring compliance with EU Commission standards.
- Dynamic Reporting: Utilise Natural Language Processing (NLP) and AI to send and receive text messages via WhatsApp, which auto-populate the carbon emission report with real-time data.
- Efficiency: Eliminate manual data entry and reduce reporting time significantly.
- User-Friendly: No complex installations or training required; WhatsApp familiarity ensures ease of use.
- Accuracy & Compliance: Ensure precise reporting to meet EU Commission requirements, minimising the risk of penalties.
- Real-Time Updates: Receive notifications and alerts regarding reporting deadlines and regulatory changes.
- Cost-Effective: A cost-efficient alternative to hiring additional personnel or outsourcing reporting.
- Environmental Impact: Simplifying reporting contributes to a company's broader sustainability efforts.
- Organizations and Businesses: Companies subject to EU carbon emission reporting mandates benefit from efficient and accurate reporting, reducing compliance risks.
- Regulatory Authorities: EU Commission and national environmental agencies benefit from improved data collection and compliance verification.
- Environmental Advocacy Groups: Organizations focused on sustainability appreciate efforts to reduce carbon emissions and improve reporting accuracy.
- Investors and Shareholders: Increased reporting accuracy enhances investor confidence and supports sustainability goals.
- Environmental Consultants and Auditors: These experts see value in a tool that streamlines reporting processes and enhances accuracy.
- Programming Languages: Python
- Frameworks: Flask
- Natural Language Processing Libraries: NLTK, Huggingface, Langchain
- Database: PostgreSQL
- Messaging Platform: WhatsApp API
- Machine Learning:
- Data Visualization: Matplotlib
Our approach involves:
- Data collection from various sources, including public agricultural ministry datasets and internal databases.
- Training and fine-tuning NLP models for text analysis.
- Developing a chatbot using Python and Flask, including defining user flow and minimizing questions asked until report is filled.
- Integration with WhatsApp API for user interaction.
We collect data from:
- Public agricultural ministry datasets
- EU Commission guidelines and datasets.
- Internal databases from user intraction
The chatbot is implemented using Flask and integrated with the WhatsApp API to enable real-time reporting. NLP models are used to analyze text messages and auto-populate reports.
The chatbot was rigorously tested against various reporting scenarios, and validation was performed to ensure it meets EU Commission standards for accuracy and compliance.
The EcoReportBot has reduced reporting time and improved accuracy to ensure compliance with EU Commission mandates. Key metrics include response time and user satisfaction.
In the future, we plan to:
- Expand language support.
- Incorporate advanced AI features for predictive analysis.
- Develop a mobile app for accessibility.
- Enhance reporting visualization.
User guides and technical documentation can be found here.
- Adnan Bhanji: Project Manager
- Beatrice Mossberg: Data Engineer
- Riyad Mazari: Data Scientist
- Sofia Morena Lasa: Data Scientist
- Khaled Akel: Machine Learning Engineer
- Hussein Soliman: MLOps Engineer
We would like to express our gratitude to our mentors, collaborators, and data providers who contributed to the success of this project.
For additional code snippets, data samples, and graphs, please refer to the appendix.