Assessing the Carbon Footprint of Cryptoassets: Evidence from a Bivariate VAR Model

Abstract

AbstractDue to its massive energy consumption and large carbon footprint, Bitcoin's energy hunger has triggered a heated debate in academic literature. Unfortunately, it is difficult to measure Bitcoin's actual electricity consumption, and literature on the topic produces inconsistent estimates that lead to different assessments of the network's carbon footprint. The objective of this chapter is to provide a reasonable economic approximation and a meaningful forecast of the carbon footprint of cryptoassets. This chapter examines the relationship between the trading volumes of cryptocurrencies and Bitcoin's energy consumption using a vector autoregression (VAR) framework. Using causality tests, we find evidence of one-way directional causality from Bitcoin's trading volume to the network's electricity consumption. By making reasonable assumptions about network energy sources, we find that bitcoin mining was responsible for approximately 43 million metric tons of carbon in 2020 (the equivalent to 0.14% of the global total yearly carbon emissions in that year). Using an impulse-response analysis, we also find that the impact of one standard deviation shock in Bitcoin's trading volume on the network's energy consumption is persistent and amounts to 8.8% on average per month (or 63% annually) over a period of twelve months. This growth rate implies that the total electricity consumption of Bitcoin is expected to generate 492 million metric tons of carbon emissions by the end of 2026, translating to approximately 1.6% of current total carbon emissions worldwide. These alarming statistics demand attention and highlight the negative environmental impact of digital currencies.KeywordsCryptocurrencyEnergy consumptionCryptocurrency miningCarbon emissionVector autoregression