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Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network

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  • Jana, Rabin K.
  • Ghosh, Indranil
  • Wallin, Martin W.

Abstract

The Bitcoin mining hosted in the blockchain network consumes enormous amounts of energy and generates electronic waste at an alarming rate. The paper aims to model and predict the future values of these two hazardous variables linked to conventional Bitcoin mining. We develop two predictive models using Facebook's Prophet algorithm and deep neural networks to identify and explain energy consumption and electronic waste generation patterns. The models rely on several explanatory features linked to the blockchain microstructure and the Bitcoin marketplace. We assess the predictive performance of the two models based on daily data of energy consumption and electronic waste generation and eleven key input features. We use local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) for explaining how these inputs can predict and control energy consumption and electronic waste generation. The findings assist in accurately estimating the future figures of energy discharge and electronic waste accumulation in the present Bitcoin mining setup. The study also reveals the block size to be the major driver.

Suggested Citation

  • Jana, Rabin K. & Ghosh, Indranil & Wallin, Martin W., 2022. "Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:tefoso:v:178:y:2022:i:c:s0040162522001160
    DOI: 10.1016/j.techfore.2022.121584
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    5. Kais Tissaoui & Taha Zaghdoudi & Sahbi Boubaker & Besma Hkiri & Mariem Talbi, 2024. "Testing the Nonlinear Long- and Short-Run Distributional Asymmetries Effects of Bitcoin Prices on Bitcoin Energy Consumption: New Insights through the QNARDL Model and XGBoost Machine-Learning Tool," Energies, MDPI, vol. 17(12), pages 1-19, June.
    6. Wang, Ning & Guo, Ziyu & Shang, Dawei & Li, Keyuyang, 2024. "Carbon trading price forecasting in digitalization social change era using an explainable machine learning approach: The case of China as emerging country evidence," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    7. Ghosh, Indranil & Jana, Rabin K., 2024. "Clean energy stock price forecasting and response to macroeconomic variables: A novel framework using Facebook's Prophet, NeuralProphet and explainable AI," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    8. Yerushalmi, Erez & Paladini, Stefania, 2023. "Blockchain in Financial Intermediation and Beyond: What are the Main Barriers for Widespread Adoption?," CAFE Working Papers 22, Centre for Accountancy, Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University.
    9. Armenia, Stefano & Franco, Eduardo & Iandolo, Francesca & Maielli, Giuliano & Vito, Pietro, 2024. "Zooming in and out the landscape: Artificial intelligence and system dynamics in business and management," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    10. Pramanik, Paritosh & Jana, Rabin K. & Ghosh, Indranil, 2024. "AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    11. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
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