Can Cryptocurrencies Be Sustainable? AI-Based CO₂ Emission Forecasting

Abstract

Purpose: This study evaluates the energy consumption and environmental impact of blockchain supported cryptocurrency systems, focusing on CO₂ emissions generated by Bitcoin mining. It investigates the relationship between cryptocurrency economics, energy usage, and ecological sustainability, and assesses the future viability of cryptocurrencies as a global financial system in alignment with the Sustainable Development Goals (SDGs).

Methods: A quantitative time series forecasting approach was adopted using 5030 days of historical CO₂ emission data obtained from the Cambridge Blockchain Network Sustainability Index (CBECI). Three energy scenarios, hydroelectric power, coal based power, and mixed energy sources, were analyzed. A Long Short-Term Memory (LSTM) neural network model was implemented to predict future emission trends and evaluate sustainability outcomes under each scenario.

Findings: The results indicate that hydroelectric energy produces the lowest CO₂ emissions as the most sustainable option, while coal-based mining results in the highest emissions, representing the worst-case scenario. The mixed energy model provides a feasible compromise, significantly reducing emissions compared to coal while maintaining mining efficiency. These findings identify certain environmental risks associated with energy intensive mining practices and emphasize the urgent need for sustainable operational strategies.

Implications: This research contributes to blockchain sustainability literature by providing a predictive framework for environmental assessment. Policymakers can leverage these insights to design regulations promoting renewable energy integration, while industry practitioners can adopt energy efficient mining models to reduce carbon footprints and achieve long term sustainability.

Originality: The study uniquely integrates deep learning-based forecasting with large scale longitudinal emission data to conduct scenario-based sustainability analysis, offering dynamic insights beyond conventional static evaluations.

Page Number : 103-116
Published Date : 2026-04-20
Keywords
CO2 emissions, Cryptocurrency, Sustainability, Energy consumption, Blockchain, Neural network analysis
DOI Number
10.15415/jtmge/2025.162008
Authors
Sanjeewani Sehgal and Divya Mehta

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