# Cause-and-effect relationships in a nonlinear model of Bitcoin’s energy use and price volatility effect

## TL;DR

This paper explores how Bitcoin's energy use and price volatility are linked through a nonlinear model, showing that energy trends can predict market behavior.

## Contribution

The study introduces a novel nonlinear Granger causality test using ANNs to analyze Bitcoin's energy and price volatility relationship.

## Key findings

- Bitcoin's price volatility significantly causes changes in energy consumption.
- Energy usage patterns serve as indicators of Bitcoin market behavior.
- Nonlinear models reveal causal relationships missed by linear approaches.

## Abstract

The environmental impact of Bitcoin (BTC) has been a source of concern due to its substantial energy consumption, which is a result of its proof-of-work mining algorithm and transaction processes. The global usage levels of Bitcoin are comparable to those of some affluent nations. This study examines the nonlinear causal relationship between the energy consumption of Bitcoin and its price volatility. In order to evaluate causality, we implement a nonlinear Granger causality test that is bolstered by artificial neural networks (ANNs). These networks are capable of recognizing intricate, nonlinear relationships that conventional linear models may be unable to identify. Our results indicate a substantial causal relationship between the price volatility of Bitcoin and fluctuations in its energy consumption, indicating that energy usage patterns can be used as indicators of market behavior. These findings have significant implications for regulators and investors, under-scoring the necessity of monitoring energy consumption trends to gain a more comprehensive understanding of the Bitcoin market dynamics and to inform policy decisions.