Is there more to bitcoin mining than carbon emissions?

Abstract

Critics decry cryptocurrency mining as a huge waste of energy, while proponents insist on claiming that it is a green industry. Is Bitcoin mining really worth the energy it consumes? The high power consumption of cryptocurrency mining has become the latest global flashpoint. In this paper, we define the Mining Domestic Production (MDP) as a method to account for the final outcome of the Bitcoin mining industry’s production activities in a certain period time, calculate the carbon emission per unit output value of the Bitcoin mining industry in China, and compare it with three other traditional industries. The results show that Bitcoin mining does not always have the highest when compared with others. The contribution of this paper is that we give a new perspective on thinking whether Bitcoin mining is more efficient to make more profit, in terms of the same amount of carbon emissions per unit compared to other industries. Moreover, it could even be argued that Bitcoin may present an opportunity for some developing countries to build out their electrical capacity and generate revenue.

1. Introduction

The rise of Cryptocurrency has gained tremendous popularity since its inception of Bitcoin in 2009. Cryptocurrency is anticipated as an important means of payment and might replace traditional paper currency worldwide in the future. As the largest cryptocurrency, Bitcoin is a digital currency based on a cryptographically secured distributed ledger and represents the first and best-known blockchain application [1]. Bitcoin is widely followed by investors, regulatory authorities, policy makers, technicians, entrepreneurs, and academics [2]. The recent surge in the price of Bitcoin (BTC) has led to a significant increase in the amount of electricity consumed by Bitcoin miners, triggering a wave of debates on the ecology of cryptocurrencies in the crypto community. According to the latest data from Digiconomist's Bitcoin Energy Consumption Index, Bitcoin's energy consumption has been at the highest level in history since the end of 2020. It is estimated that the annual consumption of Bitcoin is about 77.8 TWh, which is equivalent to the consumption in Chile. Bitcoin has gained much attention over the last several years. Bitcoin mining is energy intensive and contributes to global emissions with associated environmental damages [3], such as air pollution [4] and e-waste [5]. Mora et al. [6] point out that the cumulative GHG emissions of Bitcoin could push global warming above 2 °C. Even though this statement is considered to be overestimated [7,8], what is undeniable is that the increasing energy consumption will bring environmental problems. On average, "each $1 in BTC market value created was responsible for $0.35 in global climate damages" [9]. There have been many researchers revealed the benefits of Bitcoin such as security [10], low transaction cost [11], high return [12], and as for alternative instruments for a country’s bailout mechanism [13] and use for employees’ wages [14]. It has also been noted that Bitcoin could be considered as a superior safe-haven asset option compared to gold during financial crisis or epidemics [15,16]. Despite that, there are also researchers pointing out the risk and drawbacks of using this digital coin, in terms of lack of regulation [[17], [18], [19]], high electricity bills due to energy consumption [20,21], lack of security [22,23] and other issues such as anonymity [24] and switching cost [25].

Even though the debate on Bitcoin mining has not been resolved, an indisputable fact is that the number of Bitcoin mining pools all over the world is still increasing year by year, from 850 in 2017–8630 in2021,1 especially in China. As of April 2020, China accounts for more than 75% of the global Bitcoin blockchain operation. Some rural areas in China are considered to be the ideal destinations for Bitcoin mining mainly due to the cheaper electricity price and largely undeveloped land for pool construction. Without any policy interventions, the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 TWh [26]. The high energy consumption of Bitcoin mining has brought many pressures on the governments and many of them are taking measures to limit the development of the Bitcoin mining industry. For example, China’s Inner Mongolia region has banned new cryptocurrency mining projects and shut down existing activity in a bit to reduce the energy-consuming operation. Meanwhile, and generating 130.50 million metric tons of carbon emission correspondingly, high energy consumption brings a lot of carbon dioxide emissions. The carbon emission of the Bitcoin blockchain in China will be 130.50 million metric tons per year in 2024, which would exceed the total annualized greenhouse gas emission output of the Czech Republic and Qatar. Domestically, it ranks in the top 10 among 182 cities and 42 industrial sectors in China [26]. The future of Bitcoin mining is a topic of considerable discussion.

When Satoshi Nakamoto first released the Bitcoin network in 2009, the security behind the Bitcoin network protocol was guarded by a small number of miners. After more than a decade of development, Bitcoin mining now has become a booming industry, forming a capital chain that continues to grow and develop along with Bitcoin. Based on this, this paper proposes the concept of the Bitcoin Mining Industry (BMI) and defines it as "the organizational structure system of business units or individuals engaged in Bitcoin production". We use the Gross Domestic Product (GDP) measure to account for the results of Bitcoin mining activities from the perspective of the income generated by the production process, and name it Mining Domestic Production (MDP). Specifically, we use the time-series data of the hash distribution in mining pools and the daily operating income of mining to estimate the electricity consumed by mining and its theoretical daily profit rate. The trend of cryptocurrency mining profitability is used to estimate whether the cryptocurrency mining industry is worth running. We also track data through the daily electricity consumption and daily carbon emission of the cryptocurrency mining in China from 2016 to 2020, as well as the gross value of production generated by cryptocurrency mining during the same period as the main measurement object, and compare it with the carbon emission and GDP ratio of similar industries during the same period, through the observation of time series data, we can estimate that Bitcoin mining is not a high energy consumption industry.

Most of the existing literature is limited to the advantages and disadvantages regarding the functionality of Bitcoin, as well as its potential financial risks or regulatory issues it may pose. As for Bitcoin mining, most of the literature is conducted on the energy consumption generated by its mining process. In this paper, in contrast to the previous literature, we refer to the GDP calculation model and design the MDP as a method to illustrate the results of bitcoin mining activities in terms of the carbon emission per unit output value generated from the production process. By comparing the C/MDP of Bitcoin mining with the C/GDP of three other traditional industries in China, we give a new angle of thinking about whether Bitcoin mining is more effective or whether it is really less green. This research is thus a first attempt to argue that Bitcoin may provide an opportunity for some developing countries to use their extra power capacity and generate revenue.

2. Data and methods

2.1. Data

Gallersdörfer (2020) argues that the changes in the electricity consumption of cryptocurrencies are mainly from the renewal of mining hardware equipment and the changes in the hash rate of the network [1]. Therefore, in this paper, when calculating the energy consumption of cryptocurrencies, we put the hash rate of the network and the efficiency of mining hardware into consideration. By using suitable mining hardware in different periods and considering the depreciation cost of mining hardware and the fluctuation range of electricity costs, the range of electricity consumption and the corresponding profitability in the cryptocurrency mining process is estimated.

Given that the electricity cost fluctuates depending on weather changes, hydroelectricity is cheap during the rainy season, and it can go up during dry spells. This paper estimates electricity prices by collecting data on Chinese miners’ websites and assumes that the electricity prices are constant from 2016 to 2021. Meanwhile, electricity prices vary in different regions of China. Thus this paper estimates the cost range of cryptocurrency electricity consumption in China by delineating the upper and lower bounds and choosing the average value. The main mining sites in China are located in Xinjiang, Sichuan, and Yunnan, so the actual electricity prices are mainly considered in these areas.

The estimation model in this paper is based on the assumptions which listed to the supplementary materials. The specific formulas of the model are detailed in the appendix. The main data sources of the model are highlighted here. The data on hash rate, mining difficulty, and China’s share of global hash power from 2016 to 2020 are taken from the website of the blockchain (https://bch.btc.com/). The models of mining hardware used during the estimation period are all from the Avalon mining hardware of Jia Nan Technology (https://canaan-creative.com/), and the common statistics of 6 models are used for estimation. The daily closing data used in the calculation of daily income have been obtained from the website of Coinmarketcap (https://coinmarketcap.com/). The website does have complete statistics on the mining hash rate by region over 5 years, so the hash distribution of mining pools was used to track the global share of hash in that region to calculate the power consumed over the period. Since cryptocurrency mining is significantly influenced by technological advances, segmented statistics are taken in the selection of miners to fully consider the impact of technology. In addition, The unit price of electricity is influenced by significant regional resource and climate differences. And in this paper, the highest and lowest values of electricity prices in each region are used as the upper and lower limits of electricity prices, and the average price is used as the middle value for estimation. To overcome the influence of data metric differences among different industries, this paper takes carbon emissions per unit of GDP (C/GDP and C/MDP) of each industry for data processing.

2.2. Carbon emissions and MDP measurement

The carbon density provided by Digiconomist can be used by us to estimate the carbon emissions generated during cryptocurrency mining process. The carbon density data for each country are shown in Table 3, and the formula is as in equation (1)

$$C=CI\times E$$ (1)

Table 3.

The monthly upper/lower limits of profit margin and C/MDP(T) for the Sichuan province from 2016 to 2020. (PL: upper limits. LL: lower limits).

Province	Year		2016		2017		2018		2019		2020
	Month	Metric	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)
Sichuan	Jan.	PL	78.06%	0.003805016	54.54%	0.004717061	87.03%	0.001203343	46.44%	0.019477914	40.78%	0.005319287
		LL	64.88%	0.003270973	36.43%	0.003924947	82.06%	0.001141478	10.12%	0.010648706	19.42%	0.004341442
	Feb.	PL	70.30%	0.005564916	52.63%	0.004930214	76.95%	0.002170763	40.18%	0.025363577	47.44%	0.004693292
		LL	52.45%	0.004500695	33.75%	0.004079703	68.12%	0.001986093	−0.38%	0.012223428	28.49%	0.003916378
	Mar.	PL	68.25%	0.00606701	50.62%	0.005171009	71.98%	0.002661579	44.12%	0.021316159	20.45%	0.007296333
		LL	49.18%	0.004829724	30.94%	0.004231838	61.24%	0.002385781	6.22%	0.011215277	−8.23%	0.005524626
	Apr.	PL	67.40%	0.006288333	49.48%	0.005292802	65.61%	0.00329672	54.19%	0.014172206	20.92%	0.007224992
		LL	47.81%	0.004969151	29.36%	0.004325237	52.43%	0.002891811	23.12%	0.008812237	−7.59%	0.005535419
	May	PL	66.47%	0.006544584	64.85%	0.003566166	62.94%	0.003570032	65.36%	0.008870222	−5.69%	0.009962042
		LL	46.32%	0.005122186	50.85%	0.00309523	48.73%	0.003096582	41.86%	0.006376901	−43.80%	0.006879116
	Jun.	PL	74.47%	0.004582025	69.23%	0.003092205	46.13%	0.005335857	68.41%	0.00762572	−25.21%	0.01192892
		LL	59.14%	0.003833632	56.96%	0.002734301	25.47%	0.00434196	47.00%	0.005732943	−70.35%	0.007919654
	Jul.	PL	55.29%	0.010505906	63.27%	0.003741215	43.53%	0.005612687	66.68%	0.008261681	−31.87%	0.012638015
		LL	28.43%	0.007052514	48.63%	0.003224177	21.88%	0.004530994	44.09%	0.006089254	−79.42%	0.008231237
	Aug.	PL	40.13%	0.016469391	72.08%	0.002792406	72.58%	0.00617747	60.43%	0.008246505	−9.50%	0.010277415
		LL	4.15%	0.009699176	60.95%	0.002492484	53.99%	0.004888242	36.72%	0.006101596	−48.98%	0.007166808
	Sep.	PL	38.66%	0.017257341	70.36%	0.002974543	70.95%	0.006683701	63.33%	0.007453284	−16.05%	0.008878793
		LL	1.80%	0.009970361	58.55%	0.002636722	51.25%	0.005209383	41.35%	0.00562559	−54.16%	0.006453557
	Oct.	PL	53.56%	0.004823329	73.57%	0.002632849	68.16%	0.007633455	52.04%	0.011039275	−9.80%	0.008408524
		LL	35.05%	0.004006562	63.04%	0.002367007	46.57%	0.005772604	23.30%	0.007527182	−45.86%	0.006199844
	Nov.	PL	56.33%	0.00450858	78.44%	0.00212677	62.11%	0.010346822	35.37%	0.005832792	17.51%	0.006342007
		LL	38.93%	0.003787417	69.86%	0.001948433	36.42%	0.007076841	12.07%	0.004674767	−9.58%	0.004998567
	Dec.	PL	56.80%	0.004456029	91.56%	0.000777268	57.08%	0.012639211	29.79%	0.006364419	31.81%	0.005252695
		LL	39.59%	0.003748686	88.32%	0.000751976	27.98%	0.008165736	4.48%	0.005016908	9.42%	0.004285362
Ave. Y		PL	60.48%	0.007572705	65.89%	0.003484542	65.42%	0.00561097	52.19%	0.01200198	6.73%	0.008185193
		LL	38.98%	0.005399256	52.30%	0.002984338	48.01%	0.004290625	24.16%	0.007503732	−25.89%	0.005954334

The income method is the total productive value generated from the production activities. Under this method of accounting, the sum of value added is obtained from four major factors: labor compensation, net taxes on production, depreciation of fixed assets, and operating surplus. GDP is based on the accounting reality that all expenditures in an economy should equal the total income. Considering the availability of the data, we propose to calculate the gross mining product under the GDP caliber based on the income method, referred to as MDP (Mining domestic product). Since we only consider a single cryptocurrency, Bitcoin, the specific relationship can be expressed in the following equation:

MDP = workers compensation + net taxes on production + depreciation of fixed assets + operating surplus (2)

In reality, the operating surplus can be approximated by adjusting the operating profit, that is, based on operating profit, plus the expenses such as administrative expenses in the bad and doubtful debts loss, afforestation fees, etc. during the production process while belonging to the operating surplus, and minus the labor compensation expended by the profit. The relationship between operating surplus and operating profit can be expressed in the following formula.

Operating surplus = Operation profit + Bad and doubtful debts loss, greening fee, etc.- labor compensation (3)

Taking industry characteristics into account, the MDP is calculated based on the following assumptions：

• 1)Labor compensation is all paid by profits.
• 2)Operating income is derived from BTC transactions, and operating costs are mainly composed of electricity costs.
• 3)Depreciation of mining hardware is assumed using the straight-line method, with a useful life of 1.5 years.
• 4)Bitcoin transactions are completed through the network, and there is no problem with bad and doubtful debts.
• 5)Based on anonymity and decentralization, it is difficult to charge green fees to Bitcoin-related counter parties.

Due to the complexity of the taxes included in the production tax, the production tax is assumed the sum of depreciation of fixed assets and operating profit, multiplying the taxer rate on production. As the taxer rate on production fluctuates within the range, formula 3 can be further expressed as:

MDP = net taxes on production *(depreciation of fixed assets + Operation profit) (4)

Bitcoin mining's carbon emissions mainly from the electricity consumed. According to the data disclosed by Digiconomist on Bitcoin energy consumption, Bitcoin mining has a carbon emission of 0.711 kg per kWh of electrical energy consumed based on the calculations detailed in the appendix. We assume that this rate is stable in recent years, and from the consumed electrical energy we can calculate the total carbon emissions of Bitcoin. Furthermore, we introduce carbon emission per unit MDP to measure the degree of environmental impact of the Bitcoin mining industry and compare it with Carbon emission per GDP of other industries with the following formula:

Carbon emission per MDP = 0.711* Electricity Resources/MDP (5)

Before presenting the results, there is a statistical validation to describe our data, as shown in Table 1. According to skewness, only carbon emission is not skewed.std of carbon emission is too high, which means that the distribution is not average. All Kurtosis are over 0 mean that all data distribution are non-iid.

Table 1.

Statistical validation to describe relate variables.

Variable	Obs	Mean	Std.dev.	Min	Max	Variance	Skewness	Kurtosis
dailyrevenue	619	0.5762785	0.5282931	0.0663874	3.081758	0.2790936	1.379811	4.832999
profitmargin	619	0.2550646	0.3422568	−0.7671466	0.9066066	0.1171397	−0.762784	3.101965
Carbon emission	619	4.80E+07	3.33E+07	4325418	1.12E+08	1.11E+15	0.0811294	1.529689
mdp	619	7860209	6431336	387432.2	3.18E+07	4.14E+13	0.899617	4.081305
c/mdp	619	8.03727	4.238022	0.8306439	22.58396	17.96083	1.263053	4.607581

3. Results

3.1. Cost and profit of bitcoin mining

Until 2020, cryptocurrency operating incomes are much greater than operating costs, and the profit margin of mining remains high and oscillates around 60% by 2019, which means that cryptocurrency mining is a highly profitable industry. While the situation changed in 2020, the profit of the cryptocurrency mining industry rapidly declined and even incurred negative profit margins. It was not until the fourth quarter that the profit margin picked up again (Fig. 1).

Fig. 1.

The hash rate of China, the Power of mining hardware, and Electricity price-avg. The horizontal x-axis represents time (from 2016 to 2020). “Hash rate of China” is shown by blue bars, using “TH/s” as a unit. “Power of mining hardware” is displayed by red bars, using “kw·h” as the unit. “Electricity price-avg” is shown in a purple line, using “￥” as a unit.

3.2. Carbon emissions per unit of bitcoin produced

Bitcoin mining's carbon emissions come mainly from the electricity consumed in the mining process, where coal makes up around 60% of the cryptocurrency mining.2 According to the data disclosed by Digiconomist on Bitcoin energy consumption, Bitcoin mining has a carbon emission of 0.711 kg per kWh of electrical energy consumed. In this paper, we assume that this rate is stable in recent years, and from the consumed electrical energy we can calculate the total carbon emissions of Bitcoin. In addition, we introduce carbon emissions per unit of MDP to measure its environmental impact on the Bitcoin mining industry.

In this paper, based on the availability of data, we selected Animal husbandry and fishery (primary industry), Construction (secondary industry), and Wholesale and retail (tertiary industry) as the comparison industries. These three major industries, make the comparative analysis of C/MDP* more comprehensive and integrated. This paper takes carbon emissions per unit of GDP (C/GDP and C/MDP) of each industry for data processing.

From the analysis, the value of C/MDP was lower than all the compared industries in 2017, but from 2018, C/MDP started to climb, gradually surpassing the Construction industry, and Wholesale and retail industry, and even approaching to C/GDP of Animal husbandry and fishery (Fig. 2). During 2016–2017, C/MDP decreased from 0.00681 to 0.00380, a decrease of about 40% (Table 2). This is because the price of Bitcoin increased substantially in 2017 compared to 2016, while the increase rate of hash value did not exceed the rate of Bitcoin price, making C/MDP more 'environmentally friendly' than other industries in 2017 (see Fig. 3).

Fig. 2.

C/MDP of Bitcoin mining from 2016 to 2021.The time series describing the daily C/MDP of Bitcoin mining (Red Line), annual C/MDP of Bitcoin mining (Purple Line), annual C/GDP of the Animal husbandry and fishery (Orange Line), Construction Industry (Green Line), Wholesale and Retail (Blue Line) in China synchronize after the connection time.

Table 2.

The time series describing the monthly C/MDP of Bitcoin mining from 2016 to 2020.

Metric	Year Month	2016	2017	2018	2019	2020	Ave. M
C/MDP	Jan.	0.00327	0.00392	0.00114	0.00114	0.00434	0.00276
	Feb.	0.0045	0.00408	0.00199	0.00199	0.00392	0.00329
	Mar.	0.00483	0.00423	0.00239	0.00239	0.00552	0.00387
	Apr.	0.00497	0.00433	0.00289	0.00289	0.00554	0.00412
	May	0.00512	0.0031	0.0031	0.0031	0.00688	0.00426
	Jun.	0.00383	0.00273	0.00434	0.00434	0.00792	0.00463
	Jul.	0.00705	0.00322	0.00453	0.00453	0.00823	0.00551
	Aug.	0.0097	0.00249	0.00489	0.00489	0.00717	0.00583
	Sep.	0.00997	0.00264	0.00521	0.00521	0.00645	0.0059
	Oct.	0.00401	0.00237	0.00577	0.00577	0.0062	0.00482
	Nov.	0.00379	0.00195	0.00708	0.00708	0.005	0.00498
	Dec.	0.00375	0.00075	0.00817	0.00817	0.00429	0.00502
Ave. Y		0.0054	0.00298	0.00429	0.00429	0.00595	0.00458

Fig. 3.

Bitcoin data in major global regions.(A) describes the global hash share in 2021, the darker the color, the greater the share. Where gray is the missing value. One can notice that China is significantly red. (B) focuses on the hash distribution in China, using different colors to highlight three important provinces: Xinjiang (C), Sichuan (D), and Yunnan (E). (C1), (D1), and (E1) depict the upper/lower margin limits for the three provinces from 2016/1/2 to 2021/1/29 respectively.Blue bars are the upper margin limits and orange bars are the lower margin limits. Similarly, (C2), (D2), and (E2) depict the upper/lower bounds of indicator C/MDP(T) for each of the three provinces at the same time. Where the color block indicates the upper limit of C/MDP(T) and the red line indicates the lower limit of C/MDP(T).

While from 2018 to 2020, C/MDP showed a downward trend. The price of Bitcoin had been breaking into all-time highs and being sought after by more and more investors, and the growth of hash value shows a blowout in these three years. The rapid growth of hash value has led to an increase in carbon emissions while also making the cost of electricity rise, resulting in the operating profit of the whole industry does not show a significant upward trend from 2018 to 2020, thus leading to a continuous rise in C/MDP.

In particular, based on Cambridge Bitcoin Electricity Consumption Index from https://ccaf.io/cbeci/mining_map, Sichuan (Table 3), Xinjiang (Table 4) and Yunnan (Table 5), three important provinces in China, contribute most of the hashrate of Bitcoin. However, there are differences among the three places due to their preference for hydropower generation in Sichuan and Yunnan and thermal power generation in Xinjiang. Although in 2020, all three will suffer from the fluctuation of global Bitcoin market price, resulting in a sharp drop in profit margins, Xinjiang will be more affected in terms of profit and loss due to the relatively low price of hydropower compared with thermal power.

Table 4.

The monthly upper/lower limits of profit margin and C/MDP(T) for the Xinjiang province from 2016 to 2020. (PL: upper limits. LL: lower limits).

Province	Year		2016		2017		2018		2019		2020
	Month	Metric	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)
Xinjiang	Jan.	PL	70.53%	0.00360831	44.20%	0.004419295	84.19%	0.001181945	25.68%	0.015248486	28.57%	0.004947636
		LL	69.27%	0.003555851	42.47%	0.004341087	83.72%	0.001175978	22.22%	0.014360296	26.54%	0.004850837
	Feb.	PL	60.10%	0.005157999	41.84%	0.004609785	71.90%	0.002105447	17.00%	0.01866099	36.61%	0.004402135
		LL	58.40%	0.00505251	40.04%	0.004525761	71.06%	0.002087513	13.14%	0.017353557	34.81%	0.004325478
	Mar.	PL	57.36%	0.005589634	39.37%	0.004814053	65.85%	0.002562641	22.47%	0.016392033	4.06%	0.006586787
		LL	55.54%	0.005466746	37.50%	0.004721094	64.82%	0.002535745	18.86%	0.015377641	1.33%	0.006409716
	Apr.	PL	56.20%	0.005777066	37.98%	0.00492544	58.08%	0.00314963	36.44%	0.011768789	4.63%	0.006557644
		LL	54.34%	0.005645922	36.07%	0.004829679	56.82%	0.00311	33.48%	0.011228152	1.91%	0.006389062
	May	PL	54.96%	0.005989635	56.85%	0.003393854	54.82%	0.003396677	51.93%	0.007838208	−27.47%	0.008653973
		LL	53.04%	0.00584805	55.52%	0.003347673	53.47%	0.003350242	49.69%	0.007587943	−31.10%	0.008343682
	Jun.	PL	65.71%	0.004301786	62.22%	0.002962896	34.32%	0.004956854	56.18%	0.006866415	−51.00%	0.010204201
		LL	64.25%	0.004227963	61.05%	0.00292792	32.36%	0.004858402	54.14%	0.006677135	−55.30%	0.009799891
	Jul.	PL	39.94%	0.009013536	54.91%	0.003551037	31.16%	0.005198525	53.77%	0.007380034	−59.05%	0.01072231
		LL	37.38%	0.008665455	53.51%	0.003500269	29.09%	0.005091275	51.62%	0.007162397	−63.57%	0.010277607
	Aug.	PL	19.57%	0.013356579	65.72%	0.002684545	61.96%	0.005677392	46.88%	0.007379908	−32.06%	0.008978191
		LL	16.15%	0.012673144	64.66%	0.002655275	60.19%	0.005549224	44.62%	0.007165121	−35.82%	0.008665267
	Sep.	PL	17.60%	0.013872645	63.61%	0.002852533	59.69%	0.006107248	50.77%	0.006720157	−37.83%	0.0078898
		LL	14.09%	0.013137392	62.49%	0.002819525	57.82%	0.005960428	48.68%	0.006537461	−41.46%	0.007646572
	Oct.	PL	42.98%	0.004516367	67.55%	0.002537768	55.82%	0.006892689	35.62%	0.009552848	−30.41%	0.007515331
		LL	41.22%	0.004435724	66.55%	0.002511864	53.76%	0.006706759	32.88%	0.009199076	−33.84%	0.007294097
	Nov.	PL	46.38%	0.004239471	73.54%	0.00206374	47.43%	0.008947273	22.06%	0.005387476	2.03%	0.0058202
		LL	44.73%	0.00416839	72.72%	0.002046424	44.98%	0.008618276	19.84%	0.00527255	−0.55%	0.005686592
	Dec.	PL	46.97%	0.004192241	89.71%	0.000768646	40.45%	0.010674998	15.33%	0.005841413	19.02%	0.00488433
		LL	45.33%	0.004122534	89.40%	0.000766219	37.68%	0.010223777	12.92%	0.005707412	16.89%	0.004788541
Ave. Y		PL	48.19%	0.006634606	58.13%	0.003298633	55.47%	0.005070943	36.18%	0.00991973	−11.91%	0.007263545
		LL	46.14%	0.00641664	56.83%	0.003249399	53.81%	0.004938968	33.51%	0.009469062	−15.01%	0.007039779

Table 5.

The monthly upper/lower limits of profit margin and C/MDP(T) for the Yunnan province from 2016 to 2020. (PL: upper limits. LL: lower limits).

Province	Year		2016		2017		2018		2019		2020
	Month	Metric	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)	Profit Margin	C/MDP (T)
Yunnan	Jan.	PL	78.06%	0.003635135	54.54%	0.004459479	87.03%	0.001184952	46.44%	0.015735517	40.78%	0.004997504
		LL	68.64%	0.003270973	41.61%	0.003924947	83.48%	0.001141478	20.49%	0.010648706	25.52%	0.004341442
	Feb.	PL	70.30%	0.005212424	52.63%	0.004652981	76.95%	0.002114533	40.18%	0.019391891	47.44%	0.004441493
		LL	57.55%	0.004500695	39.14%	0.004079703	70.64%	0.001986093	11.21%	0.012223428	33.90%	0.003916378
	Mar.	PL	68.25%	0.005653176	50.62%	0.004861944	71.98%	0.00257631	44.12%	0.016951211	20.45%	0.006679207
		LL	54.63%	0.004829724	36.56%	0.004231838	64.31%	0.002385781	17.05%	0.011215277	−0.04%	0.005524626
	Apr.	PL	67.40%	0.005844952	49.48%	0.004974761	65.61%	0.003169828	54.19%	0.012059618	20.92%	0.00664532
		LL	53.41%	0.004969151	35.11%	0.004325237	56.19%	0.002891811	32.00%	0.008812237	0.56%	0.005535419
	May	PL	66.47%	0.006063045	64.85%	0.003417431	62.94%	0.003420388	65.36%	0.007969949	−5.69%	0.008818386
		LL	52.08%	0.005122186	54.85%	0.00309523	52.79%	0.003096582	48.57%	0.006376901	−32.92%	0.006879116
	Jun.	PL	74.47%	0.004339682	69.23%	0.002980701	46.13%	0.005007615	68.41%	0.006965246	−25.21%	0.010419212
		LL	63.52%	0.003833632	60.47%	0.002734301	31.37%	0.00434196	53.12%	0.005732943	−57.45%	0.007919654
	Jul.	PL	55.29%	0.009198887	63.27%	0.003576989	43.53%	0.005253877	66.68%	0.007494009	−31.87%	0.010959478
		LL	36.10%	0.007052514	52.82%	0.003224177	28.06%	0.004530994	50.54%	0.006089254	−65.84%	0.008231237
	Aug.	PL	40.13%	0.01372685	72.08%	0.002699428	72.58%	0.005743752	60.43%	0.007492258	−9.50%	0.009143293
		LL	14.43%	0.009699176	64.13%	0.002492484	59.30%	0.004888242	43.49%	0.006101596	−37.70%	0.007166808
	Sep.	PL	38.66%	0.014272165	70.36%	0.002869335	70.95%	0.006183414	63.33%	0.006815552	−16.05%	0.008017331
		LL	12.33%	0.009970361	61.93%	0.002636722	56.88%	0.005209383	47.63%	0.00562559	−43.27%	0.006453557
	Oct.	PL	53.56%	0.004557801	73.57%	0.002550923	68.16%	0.006989578	52.04%	0.009740158	−9.80%	0.007631081
		LL	40.34%	0.004006562	66.05%	0.002367007	52.74%	0.005772604	31.51%	0.007527182	−35.56%	0.006199844
	Nov.	PL	56.33%	0.004275929	78.44%	0.002072511	62.11%	0.009122012	35.37%	0.005446853	17.51%	0.005889398
		LL	43.90%	0.003787417	72.31%	0.001948433	43.76%	0.007076841	18.73%	0.004674767	−1.84%	0.004998567
	Dec.	PL	56.80%	0.00422799	91.56%	0.000769866	57.08%	0.010916315	29.79%	0.005910802	31.81%	0.004933702
		LL	44.51%	0.003748686	89.25%	0.000751976	36.29%	0.008165736	11.71%	0.005016908	15.82%	0.004285362
Ave. Y		PL	60.48%	0.00675067	65.89%	0.003323862	65.42%	0.005140214	52.19%	0.010164422	6.73%	0.007381284
		LL	45.12%	0.005399256	56.18%	0.002984338	52.99%	0.004290625	32.17%	0.007503732	−16.57%	0.005954334

4. Discussion

4.1. Bitcoin mining industry is growing up

The profitability of cryptocurrency mining has remained consistently high since 2016 due to technological advances in hardware. As operating income during cryptocurrency mining is affected by the fluctuation of hash rate, the cryptocurrency mining margins subsequently undergo cyclical oscillations. The price of electricity in the early stage was mainly influenced by the efficiency of the mining hardware. As the technology of the mining hardware advanced, the impact brought by the efficiency gradually shrank, and the hash rate became the main reason affecting the fluctuation of the price of electricity. But cryptocurrency mining margins saw a significant drop after 2020. Operating incomes from mining show a lower level due to the fall of Bitcoin prices and the rapid rise in hardware costs as the miners advance, which leads to a rise in depreciation costs. Although the profitability of mining saw a significant drop in 2020, the overall profit of the mining industry has remained at a high level.

Consider that bitcoin volume and market price affect MDP due to its own cyclicality. MDP dropped sharply in July 2016 because when Bitcoin first launched, the reward was 50 Bitcoins. This reward was cut in half for the first time at 25 as of November 2012, and then the second halving occurred in July 2016. As the price of Bitcoin was relatively low at this time, MDP overall remained at a lower level. MDP reached a peak level first in late 2017 and then dropped sharply because the trading price of Bitcoin reached a new high at that time. And then the price of Bitcoin started to drop in 2018. The total hash value in the network first increased to a new high, a large number of miners chose to shut down and stop mining due to profit reasons, and the total hash value started to decline. Bitcoin price later saw a rebound in 2019, which again attracted a large number of miners who started mining and the total hash value oscillated up and continued to break new highs. This brought about an increase in total power consumption and the third halving of Bitcoin took place in May 2020, leaving the value of MDP no longer exceeding its peak in late 2017.

4.2. Is Bitcoin mining really not green?

With the price of Bitcoin continuing to rise and the hash rate expanding at a modest rate, our study find that the Bitcoin mining industry is greener compared to the Construction, Wholesale and Retail industries in terms of generating the same output. As more miners flock into this industry and the rewards of puzzle-solving is periodically cut in half due to Bitcoin’s design principle, a large number of miners are now at risk of consuming electricity but earning nothing. In this case, C/MDP is starting to exceed the C/GDP of the Wholesale and Retail as well as the Construction industry, becoming less green. If the number of mines is allowed to increase without any restrictions, the Bitcoin mining industry will end up harming the environment more than any other industry. With this in mind, it's not surprising that the Chinese government has outlawed illegal mining pools and gradually unplugging hardware.

In addition, it is worth noting that Bitcoin mining energy efficiency is improving. According to a recent report from Bitcoin Mining Council, bitcoin mining is 5,814% more efficient over the last 8 years, while the global J/TH efficiency is 48.9 [27]. The improvement of energy efficiency provides more possibilities for the green transformation of the industry.

4.3. An industry waiting to be recognized

At the same time, we can assume that cryptocurrency mining needs to be recognized as an industry by governments and related agencies. On the one hand, it not only brings income to cryptocurrency miners and hardware companies. For undeveloped areas, the support of cryptocurrency mining could stimulate the economy and provide job opportunities to local people. While cryptocurrency mining may not create as many jobs as other industries, access to the latest financing tools and technologies can enhance infrastructure construction and technological innovation. On the other hand, bitcoin mining tends to gather around areas where with relatively low renewable energy utilization rates. As time goes by, it will help loss-making renewable energy projects to achieve profits and drive economic growth [28]. However, the development of cryptocurrency mining industry is full of uncertainty because of its unstable value and government regulation and at least three more things need to be settled.

First, we need to exclude or replace fossil energy sources as much as possible in terms of the energy options needed for mining, and then move to cleaner clean energy sources such as hydropower, photovoltaic, and wind power. Meanwhile, the process of energy transformation should be without occupying the local people's renewable energy for domestic usage.

Second, we should be looking to provide the cryptocurrency community with more environmental protection technology improvement proposals. And to promote the environmental protection concept of all the cryptocurrency community, whether it is Bitcoin, Ether, or other cryptocurrency foundations and community members.

In addition, we should also push the consensus mechanism of cryptocurrency to switch from the PoW consensus mechanism that consumes high energy to the low energy consumption consensus mechanism such as PoS, DPoS or other consensus mechanisms, to promote the fundamental reduction of carbon emission from the technology and algorithm.

Last but not the least, it is also possible to make full use of the unevenness of clean energy in time and space, such as hydropower, which accounts for a relatively large share, such in the Yunnan and Sichuan regions in China and can make full use of the abandoned hydropower generation in the summer that cannot be connected to the grid in the surplus land, while part of the miners can be relocated to areas with more sufficient water resources in other countries at the same time for cryptocurrency mining in winter.

5. Conclusion

This paper provides a method to calculate the GDP of the Bitcoin mining industry in China. The accounting results show that the MDP of the Bitcoin mining industry started to rise dramatically in 2017 and began to decline gradually after reaching a peak at the end of 2017 and falling to the lowest point at the beginning of 2019. The results show that the mining industry consumes less energy per unit of production, a perspective that could be considered more “environment friendly” than other industries when the increase in Bitcoin value is higher than the magnitude of mining hash value, and vice versa. Also, for many developing countries with large energy resources, including hydropower capacity, it is often too expensive to build the transmission and distribution infrastructure to move electricity from where it is produced to where it is consumed. They do not have the money or the production capacity to readily use electricity. These resources are at this stage available for profitable bitcoin mining, which would be a potential source of income for these countries.

And by comparing the C/MDP with three other traditional industries in China, we detected that the carbon footprint of Bitcoin mining is not always higher and it can be greener with clean energy transition. The method can be widely used while China's Bitcoin energy structure is hydropower primarily, which limits the representatives of China's C/MDP to the global market. Future studies can expand the research to other parts of the world such as the US, Europe, the Middle and Southeast Asia, to compare the difference between MDP and C/MDP in different areas.

Meanwhile, this study lacks an in-depth analysis of economic and social impacts due to the constraints of data. These impacts vary with place and time. For example, in the early stage of Bitcoin mining in Erdos, Inner Mongolia, the economic development generated by the Bitcoin mining industry revitalize the ghost town. But with the increase in energy consumption and carbon emissions, the negative impact of Bitcoin mining on the local environment highlighted gradually. Therefore, future studies can take these factors into consideration and do some deeper analysis.

Author contribution statement

Feng Liu: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Linlin Wang: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Deli Kong: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Chen Shi: Performed the experiments; Analyzed and interpreted the data.

Zhefu Feng: Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Jiashen Zhou: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Jiaqi Liu: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Zhibin Li: Conceived and designed the experiments; Wrote the paper.

Funding statement

This work was supported by the China-Central and Eastern European Universities Joint Education Project 2020 on "Research on Techniques and Methods for Governance of Cross-border Data Flows" (Project No. 202033).

Data availability statement

Data included in article/supplementary material/referenced in article.

Declaration of interest’s statement

The authors declare no competing interests.

Appendix A. Supplementary data

The following is the Supplementary data to this article: