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High energy use is increasingly a feature of many technology processing industries, including quantum computing, artificial intelligence, natural language processing, and cryptocurrency mining (cryptomining). Estimates suggest that technology processing passed the milestone of consuming 1% of world energy in 2010 and is on trajectory to increase to 6% by 2030 (Masanet et al. 2020, Andrae and Edler 2015). In recent years, data centres and Bitcoin mining alone consumed 0.9% and 0.5% of global electricity, respectively (Andrae 2017, Cambridge Center for Alternative Finance).

This intensive electricity consumption results in two negative externalities. The first is the carbon emissions resulting from electricity production of cryptomining, with the website Digiconomist estimating that the global CO2 emissions from Bitcoin mining alone are equivalent to those of Libya (see also De Vries 2018, Blandin et al. 2020).

Our work concerns a second, unstudied externality the real effects of technology processing on local economies (Benetton et al. 2023). What, for example, are the spillovers from cryptomining on households and small businesses? How does the interaction of supply and demand impact prices and delivery of electricity to homes and small businesses? As we shall see below, cryptomining also has positive externalities in that cryptomines produce local tax revenues, raising questions about net costs and benefits.

Before describing our methodology and findings, a brief note about cryptomining, which is the clearing of payment transactions for decentralised blockchain-based cryptocurrencies that rely on the proof-of-work protocol. Any person or firm can become a cryptominer, which involves solving increasingly complex computational puzzles to verify the validity of transactions. This has led to an arms race among firms who run large cryptomines, essentially warehouses full of specialized computers, crunching numbers across the world. And the key point here is that these cryptomines need lots of electricity, while employing very few people.

We analyse the negative externalities of the high electricity use of cryptomining through two channels: prices and quantity rationing.

In the first case, we study New York State, specifically Upstate NY, excluding New York City and Long Island, which was an early market for cryptomining in the US due to its cold climate, cheap electricity, and proximity to large hydropower sources (see Figure 1).

Figure 1 Bitcoin prices and electricity consumption

The regions grid operator employs a marginal supply pricing algorithm, whereby upward pressure on prices from demand gets passed to all users, including households and small businesses. We combine detailed data on these local electricity prices, electricity usage, and other economic outcomes with hand-collected data on the likely location of cryptominers, to analyse whether and how the use of electricity by cryptominers affects local communities.

Our findings include the following:

We now turn to China, a country that employed a quantity rationing system for electricity, and which hosted 6582% of the worlds cryptomining during the last decade before a ban in 2021. In a quantity rationing system, when total demand increases, prices do not adjust; rather, the electricity supply is rationed among locations to align with physical infrastructure. To explore possible externalities associated with the rationing of electricity in local economies, we exploit an annual panel of statistics at the city level for China (cities in the data include the surrounding areas). We focus on the 218 inland China city-areas, which have a mean population of 355,000, and do not include the large coastal metropolitan areas. There is evidence of cryptomining in 52 of these inland city-areas, of which we find the following:

We present novel empirical evidence of the real effects of cryptomining on local economies.

First, we focus on a setting in Upstate New York, where cryptomining led to an increase in electricity prices and a resultant consumer surplus loss. On the other hand, local governments saw an increase in tax revenues, although this only offsets a fraction of the consumer surplus loss through higher electricity prices. We then turn to China, where we reveal a negative impact on the labor market as well as fixed asset investments.

What does this mean for policymakers? Though likely tempting for some, the optimal response is likely not to ban cryptomining, which would only shift the problem to a more permissive jurisdiction and restrict any possible tax revenue gains. Rather, a better response would be to introduce electricity pricing schemes or dynamic quotas that minimise the adverse impact on the local community. In addition, the effects on local communities we document should be weighed against any other costs (notably, from pollution) and potential benefits arising from the growth of proof-of-work cryptocurrencies.

Andrae, A (2017), Total consumer power consumption forecast, Nordic Digital Business Summit 10.

Andrae, A S G and T Edler (2015), On Global Electricity Usage of Communication Technology: Trends to 2030, Challenges 6(1): 117-157.

Benetton, M, G Compiani and A Morse (2023), When Cryptomining Comes to Town: High Electricity-use Spillovers to the Local Economy, NBER Working Paper 31312.

Blandin, A, G Pieters, Y Wu, T Eisermann, A Dek, S Taylor and D Njoki (2020), Third global cryptoasset benchmarking study, Technical report, Cambridge Centre for Alternative Finance, University of Cambridge, Judge Business School.

De Vries, A (2018), Bitcoin's growing energy problem, Joule 2(5): 801-805.

Masanet, E, A Shehabi, N Lei, S Smith, and J Koomey (2020), Recalibrating global data center energy-use estimates, Science 367(6481): 984-986.

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