A novel IoT-based dynamic carbon footprint approach to reducing uncertainties in carbon footprint assessment of a solar PV supply chain

Solar Photovoltaic (PV) system is considered as one of the most important renewable energy sources as global community pledging to reduce greenhouse gas (GHG). However, there is environment cost for producing PV systems, including all carbon emissions generated from all phases of manufacturing supply chain. Thus, energy payback time (EPBT) has become important criteria to evaluate the performance PV systems. Estimating EPBT of PV systems relies on life cycle analysis (LCA) assessment. Current carbon footprint (CF) calculation uses long-run CF averaging method, which generates static CF information. However, solar PV Supply chain is quite dynamic because PV system components may be manufactured in different regions and use different power sources. Thus, the averaging method is not able to capture the dynamics of CF and that also implies that the uncertainties and data quality of CF auditing are negatively impacted. This study proposes an IoT-based dynamic carbon footprint approach to address the challenges above. The proposed method aims to capture LCA measuring data from IoT sensors and calculate dynamic CF of PV module across PV supply chain. The dynamic CF allows each PV module to have unique CF pedigree information based on its source of parts, the mix of energy types used during the manufacturing stage, and other CF related allocation data. The dynamic CF information can greatly reduce the information asymmetry between data providers and CF auditors, thus lowering the uncertainties and improving the data quality of CF auditing. A case study of a solar PV supply chain is presented in this paper, and the proposed approach is evaluated against the current practice.