IoT-Based Carbon Footprint Monitoring and Reduction Platform For Urban Homes

Abstract

Household energy needs should be governed according to the global warming challenge. However, current IoT solutions for measuring carbon footprint are usually costly, inaccurate or have no user-friendly feedback. The key objective of this study was the development of a new low-cost IoT system that tracks real-time electricity consumption and indoor CO₂ concentration to promote sustainable behaviour. A key design feature of the system is its modularity, with an ESP32 microcontroller at the heart of the system allowing easy integration of calibrated SGP30 gas sensor, energy sensors (ZMPT101B, ZMCT103C), and multi-channel communication. The new software solution used for this study was the CallMeBot API that transmits the data directly to WhatsApp, making it very user-friendly by removing the need to use a mobile app only for this purpose. The tests carried out demonstrated that the average error of the voltage sensor was 1.8%, the Wi-Fi data transmission success was 99.2%, and the SGP30 sensor had a successful dynamic baseline calibration, ensuring accuracy to within ±50 ppm of reference values in steady-state conditions. The experiments revealed the existence of energy consumption differences by a substantial margin: the LED bulb (8.7 W) consumed 92% less energy than the incandescent bulb (107.46 W) and the DC fan (7.5 W) 91% less than the traditional AC fan (82.0 W). The indoor CO₂ levels correlated with the occupancy and ventilation, from a base of approximately 400 ppm to over 1250 ppm in areas with no ventilation. This study demonstrated that a powerful open-source IoT platform can enable households to monitor and reduce their ecological footprint to save energy and improve the indoor environment.