I led the development of ManoCO₂, a mobile app that helps Lithuanian citizens track and reduce their carbon footprint. Working alongside the Lithuanian Energy Agency, we identified a gap between traditional energy efficiency measures and real behavior change. Our market research revealed significant limitations in existing carbon tracking apps. None offered comprehensive lifestyle tracking or day-to-day monitoring capabilities. Most importantly, none were adapted to Lithuania’s specific context or integrated with local energy providers. Building on these insights, we developed ManoCO₂ to encompass multiple aspects of daily life, from energy services and mobility to food choices and waste management.
What sets ManoCO₂ apart is its deep integration with Lithuania’s energy infrastructure. We built technical capabilities for heat supplier data integration, with electricity consumption integration planned for the next development phase. The app provides both daily and monthly indicators, respecting user privacy while delivering actionable insights.
The development follows a clear vision that unfolds in three stages. We have completed the initial measurement and recommendations phase, and are now moving into creating a testing environment for more effective approaches through 2025. Our long-term vision includes developing savings transfer capabilities.
ManoCO₂ directly supports Lithuania’s Energy Saving Plan by connecting individual actions to national energy efficiency goals. The app analyzes user consumption patterns and matches them with relevant energy-saving guidelines from the Lithuanian Energy Agency, creating a bridge between policy and personal action.
The technical implementation leverages React Native for cross-platform compatibility, with a WordPress backend handling complex data processing and integration with energy providers. We implemented a robust data architecture that maintains user privacy while enabling detailed analytics and personalized recommendations. The app utilizes real-time data processing to convert various inputs into standardized carbon metrics, presenting them through an intuitive interface built with custom visualization components.
Future development phases will introduce machine learning capabilities for more sophisticated pattern recognition and recommendation systems, along with expanded API integrations for real-time energy consumption data from smart meters and other IoT devices.
