Data-driven approach for selection of on-chain vs off-chain carbon credits data storage methods

As blockchain technology continues to revolutionize various industries, the efficient and secure storage of data has become a critical challenge. Inspired by blockchain’s distributed peer-to-peer networks, security and information storage solutions have found widespread application in high-information environments due to their efficiency, security, traceability, and decentralized nature. However, as blockchain applications expand and the volume of data grows exponentially, the interplay between on-chain and off-chain storage is rapidly becoming a crucial factor in blockchain data management. The motivation for this research stems from the pressing need to address the scalability and performance issues inherent in blockchain systems as they handle increasingly large datasets. Carbon credits data, specifically from a provenance perspective, is multimodal in nature. It can be captured using many different modes and devices and could include images, text, and unstructured data. Current literature lacks a comprehensive analysis of the trade-offs between on-chain and off-chain storage methods, particularly for multimedia data, in terms of their impact on system scalability, operational efficiency, and economic viability. This gap in knowledge hinders stakeholders from making informed decisions about optimal data disposition strategies for their blockchain implementations. This paper aims to bridge this gap by delivering a comprehensive explanation of the data storage methods adopted by blockchain systems, focusing on scalability, efficiency, and economic viability as key evaluation criteria. We analyze the unique connotations and implications of both on-chain and off-chain storage, considering their effects on data retrieval times and overall system performance. Through our analysis, we derive valuable insights to guide the decision-making process in designing and implementing blockchain solutions tailored to specific needs and application scenarios. By providing a thorough examination of these storage methods, this research enables stakeholders to make informed choices about blockchain data disposition, ensuring the long-term sustainability and efficiency of their blockchain implementations. Our findings will contribute to the optimization of blockchain systems, balancing performance, security, and cost-effectiveness in an era of rapidly expanding blockchain applications and data volumes. • Rigorous Research Methodology: A structured methodology for evaluating the trade-offs between Carbon Data storage methods, covering objective setting, prototype development, parameter selection, performance testing, security analysis, and cost evaluation. • Comprehensive Empirical Evaluation: The first comprehensive assessment of on-chain versus off-chain storage approaches for carbon credits, examining security, scalability, latency, and cost. • Addressing a Critical Research Gap: The paper fills a notable void in the literature by providing a holistic comparison of on-chain and off-chain approaches in the carbon credit domain. • Innovative Data Management Framework: Based on the empirical findings, the study proposes an optimized framework to guide stakeholders in adopting blockchain solutions that are efficient, secure, and cost-effective for carbon credit management. • Clarity on Blockchain Application: The research delineates the use of on-chain and off-chain methods in carbon credit systems, offering insights into their respective advantages and limitations.