Negative elements of cryptocurrencies: Exploring the drivers of Bitcoin carbon footprints

https://doi.org/10.1016/j.frl.2023.104031Get rights and content

Highlights

  • This paper investigates factors driving the carbon footprint of Bitcoin.

  • Bitcoin prices, energy prices, carbon prices and financial indicators are considered.

  • Price dynamics are found to have significant impacts on emissions from Bitcoin mining.

  • Spillovers in the system are found to be stronger during the COVID-19 pandemic.

Abstract

This paper adopts an interactive network approach to investigate the factors driving the carbon footprint of Bitcoin, a negative aspect of cryptocurrencies. Our findings demonstrate that the dynamics of Bitcoin prices, including both returns and volatility, have a significant impact on the system comprising carbon emissions, energy prices, carbon prices, and financial indicators. During the first two years of the COVID-19 pandemic period, the spillover effects are observed to be particularly strong. Furthermore, we find that the dynamics of Bitcoin prices play a crucial role in driving its associated carbon emissions.

Introduction

Anthropogenic global warming is a multi-causal event. One element, by no means trivial, is the growth of energy intensive cryptocurrencies. These have significant global warming potential (Mora et al., 2018). While their effect is now known (Zhang et al., 2023), to date there has been little research on linking their prices with the associated carbon emissions. The cryptocurrency markets are also characterized by significant volatility and inter-connected with other financial assets (Ji et al., 2022), which leads to extensive research from a financial perspective (for example Corbet et al., 2018, Ji et al., 2020, Urquhart and Lucey, 2022).

Bitcoin mining is associated with significant carbon emissions (Mora et al., 2018, Stoll et al., 2019). Thus, Bitcoin prices should, in a well functioning market, reflect the cost of carbon (Di Febo et al., 2021), cost of energy use (Zhang et al., 2023), and also the level of carbon emissions (De Vries et al., 2022). To date research on the linkage of Bitcoin (and other cryptocurrencies) and energy markets has been focused on the nature of cleaner cryptocurrrency mining methods (Ren and Lucey, 2022), or on crypto-energy market linkages treating each as financial assets (Corbet et al., 2021). There has also been some limited research on the inter-relatedness of cryptocurrencies and environmental awareness (Wang et al., 2022).

Policymakers and market participants have started to pay attention to the environmental impacts of Bitcoin mining activities, especially following the global consensus to control carbon emissions and cope with climate change. However, the issue of emissions, and the cost of carbon, energy, and their connectedness with cryptocurrency prices, has remained under-researched. Simply plotting the historical trend of Bitcoin prices and emissions associated with mining would easily reveal two long-term upward sloping trends and a strong positive correlation. However, the short-term dynamic relationships and the underlying mechanisms are not straightforward. The situation could be more complicated from a financial perspective (Baur and Oll, 2022), where both returns and risks are important aspects to be considered.

In this letter, we endeavour to more directly look at the inter-relatedness of the prices of Bitcoin, a main representative of these new financial assets, and its carbon footprint with the cost of carbon, cost of energy and more traditional financial assets. As the process here takes into account the interrelatedness of all variables, it may allow us to surface linkages not apparent yet.

Section snippets

Data and methods

The method used in our study builds on Diebold and Yılmaz (2014), a popular model extensively used to uncover systemic linkages in financial markets, among other fields. In this model, all variables are assumed to interact with each other, forming a system. We utilize a vector-autoregressive (VAR) model to estimate the dynamic relationships between variables. After estimating the VAR model, we use the generalized forecast error variance decomposition (GFEVD) to calculate the relative

Static networks

Fig. 1 shows the net directional flow of information (over the whole sample period); its direction is given by the arrow, and its strength by the thickness. Turning to returns network, the largest net influence on the system is from Bitcoin prices, offering 8.493% net information spillover to the system. It is also the largest information contributor to carbon emissions (footprint). Overall, Bitcoin carbon footprint is the net information receiver from all factors in the system except Brent oil

Conclusions

The development of cryptocurrency, such as Bitcoin, has garnered significant attention in recent years. However, due to its intensive energy use and consequential environmental impacts, modelling Bitcoin prices and its associated carbon footprint becomes an important consideration for both financial markets and regulators. In this paper, we utilize a dynamic network approach to investigate the driving factors of Bitcoin’s carbon footprint, with a specific focus on changes and volatilities in

CRediT authorship contribution statement

Suwan(Cheng) Long: Investigation, Validation, Writing – original draft. Brian Lucey: Formal analysis, Validation, Investigation, Writing – original draft, Writing – review & editing. Dayong Zhang: Conceptualization, Validation, Supervision, Writing – original draft, Writing – review & editing, Project administration, Funding acquisition. Zhiwei Zhang: Formal analysis, Investigation, Validation, Data curation, Visualization.

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Cited by (4)

This research is supported by the National Natural Science Foundation of China (NSFC) Grant No. 71974159 and the 111 Project Grant No. B16040.

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