Self-Optimizing the Environmental Sustainability of Blockchain-Based Systems

Abstract

Blockchain technology has been widely adopted in many areas to provide more dependable and trustworthy systems, including digital infrastructure. Nevertheless, its widespread implementation is accompanied by significant environmental concerns, as it is considered a substantial contributor to greenhouse gas emissions. This environmental impact is mainly attributed to the inherent inefficiencies of its consensus algorithms, notably Proof of Work, which demands substantial computational power for trust establishment. This paper proposes a novel self-adaptive model to optimize the environmental sustainability of blockchain-based systems, addressing energy consumption and carbon emission without compromising the fundamental properties of blockchain technology. The model continuously monitors a blockchain-based system and adaptively selects miners, considering context changes and user needs. It dynamically selects a subset of miners to perform sustainable mining processes while ensuring the decentralization and trustworthiness of the system. The aim is to minimize blockchain-based systems' energy consumption and carbon emissions while maximizing their decentralization and trustworthiness. We conduct experiments to evaluate the efficiency and effectiveness of the model. The results show that our self-optimizing model can reduce energy consumption by 55.49% and carbon emissions by 71.25% on average while maintaining desirable levels of decentralization and trustworthiness by more than 96.08% and 75.12%, respectively. Furthermore, these enhancements can be achieved under different operating conditions compared to similar models, including the straightforward use of Proof of Work. Also, we have investigated and discussed the correlation between these objectives and how they are related to the number of miners within the blockchain-based systems.