A Blockchain AI Solution to Climate Change
SSRN Electronic Journal
2024
- 1Citations
- 668Usage
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
BACKGROUND: Climate change represents a critical global challenge, hindered by skepticismjavascript:void(0); towards data manipulation and politicization. Trust in climate data and the policies derived from it is essential for effective climate action. OBJECTIVE: This perspective paper explores the synergistic potential of blockchain technology and Large Language Models (LLMs) in addressing climate change. It aims to demonstrate how their integration can enhance the transparency, reliability, and accessibility of climate science, thus rebuilding public trust and fostering more effective climate action. METHODS: The paper analyzes the roles of blockchain technology in enhancing transparency, traceability, and efficiency in carbon credit trading, renewable energy certificates, and sustainable supply chain management. It also examines the capabilities of LLMs in processing complex datasets to distill actionable intelligence. The synergistic effects of integrating both technologies for improved climate action are discussed, alongside the challenges faced, such as scalability, energy consumption, and the necessity for high-quality data. RESULTS: Blockchain technology contributes to climate change mitigation by ensuring transparent and immutable recording of transactions and environmental impacts, thereby fostering trust among stakeholders and democratizing participation in climate initiatives. LLMs complement blockchain by providing deep insights and actionable intelligence from large datasets, facilitating evidence-based policymaking. The integration of both technologies promises enhanced data management, improved climate models, and more effective climate action initiatives. CHALLENGES: The paper identifies scalability and energy consumption of blockchain technologies, and the need for high-quality data for LLMs as significant challenges. It suggests advancements towards more energy-efficient consensus mechanisms and the development of sophisticated data preprocessing tools as potential solutions. CONCLUSION: The integration of blockchain technology and LLMs offers a transformative approach to climate change mitigation, enhancing the accuracy, transparency, and security of climate data and governance. This synergy not only addresses current limitations but also futureproofs climate strategies, marking a cornerstone for the next generation of environmental stewardship.
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