in situ vs mining bitcoins

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The main takeaway from the article: Brady plans every detail of his life so he can play football as long as possible, and he'll do anything he can to get an edge. He diets all year round, takes scheduled naps in the offseason, never misses a workout, eats what his teammates call "birdseed," and does cognitive exercises to keep his brain sharp. Brady struggles to unwind after games and practices. He's still processing, thinking about what's next.

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In situ vs mining bitcoins

If mining asteroids sounds out-of-this-world to you, you may be surprised by the amount of effort companies are dedicating to space mining projects. To learn more, read these blog posts:. Your email address will not be published. Underground mines are more expensive and are often used to reach deeper deposits. Surface mines are typically used for more shallow and less valuable deposits. Placer mining is used to sift out valuable metals from sediments in river channels, beach sands, or other environments.

In-situ mining, which is primarily used in mining uranium, involves dissolving the mineral resource in place then processing it at the surface without moving rock from the ground. Space mining If mining asteroids sounds out-of-this-world to you, you may be surprised by the amount of effort companies are dedicating to space mining projects. Leave a Reply Cancel reply Your email address will not be published.

Smithsonian Acquires Valuable Geological Collection. Get news and research reviews on the topic of your choice, right in your inbox. Subscribe Now. A common feature of the previously mentioned studies is that the assessment of environmental impacts is built on ad-hoc methods. Despite the substantial uncertainties in the data and choices used in previous models, an explicit uncertainty assessment is lacking in previous studies.

There is thus the need to use a solid methodological basis to increase the transparency, validity, and replicability of the environmental assessment of Bitcoin. Summing up, previous studies assessing the impact of the Bitcoin mining network show contrasting and arguably overestimated results, and a key challenge in this assessment is the scarcity of accurate data on key factors determining the impact of the mining network.

This study wants to bring new insights in this area by providing a more detailed analysis of the hotspots of environmental impact in the Bitcoin mining network and by increasing the accuracy in the modeling of regional electricity mixes. Furthermore, this study wants to add a prospective approach by considering how electricity generation or the geography of the mining network might change in the future.

The added value of this analysis is adopting LCA as robust scientific methodology, the use of established databases for assessing environmental impact, including the impact of mining equipment in the analysis, and providing an outlook of future impacts. Methods and Materials. This study takes both a retrospective and a prospective approach, and two different system models were respectively used.

Figure 1 shows the structure of the product system that was analyzed in both cases. The ecoinvent v3. In the text, the IPCC method is reported for the carbon footprint. To understand the uncertainty associated with the background data, Monte Carlo simulations with iterations were carried out for the attributional baseline model and each consequential scenario. High Resolution Image. The functional unit of the attributional model was defined as computing 1 TH.

The information currently available on the location of Bitcoin miners is scarce and inaccurate. However, this information is crucial for estimating the environmental impact of the Bitcoin network, which is highly dependent on the electricity mix of the geographical locations where mining is performed. A geographical distribution of the Bitcoin mining network was developed in this study based on information available from two previous studies, Bendiksen et al. Table 1 shows the geographical distribution of the miners used in the attributional baseline model for Table 1.

Besides the energy mix, the electricity consumption of the Bitcoin network depends also on the equipment used for mining as it determines the efficiency of mining, namely the electricity consumption per TH computed. The types of equipment included in the model are taken from Bendiksen et al. Details on the methodology used to derive these values are provided in SI Section 4.

The use of mining equipment involves three main activities: electricity consumption, production, and end-of-life EoL of the equipment. The main contributor to electricity consumption is the use of electricity for mining, determined according to the product specifications of each machine.

Large facilities, especially in warmer climates, may require additional energy for cooling and other inefficiency. The amount of equipment that is produced and hence needs to be disposed of is approximated using machine lifetime. According to Digiconomist, 43 Bitcoin mining equipment has an average lifetime of 1.

For the production of mining equipment, the ecoinvent v3. Similarly, for the end-of-life of the machines, the ecoinvent v3. A sensitivity analysis was carried out to identify how key modeling parameters and modeling assumptions affect the results. First, the sensitivity to the electricity mix and geographical distribution of miners was investigated. Then, three divergent geographic distributions were modeled. Next, the sensitivity of the baseline model with respect to other key parameters was tested.

This allowed to understand the effect of improving mining efficiency or increasing electricity consumption. The consequential approach is fundamentally different from the attributional one as it focuses on quantifying the effect of an increase in the demand for mining. In the consequential LCA, three different scenarios were modeled. The first model describes a business-as-usual BAU scenario that differs from the attributional baseline model only in the background system: the consequential version of the ecoinvent v3.

The second model describes a technology-sensitive scenario where an increase in demand for mining will be met by installing new mining capacity and investing in the most efficient mining equipment. In other words, in this model only the marginal mining technologies are included. The third model describes a location-sensitive scenario where an increase in demand for mining is met not only by installing efficient mining capacity, but also by changing the geographical distribution of the miners toward locations that allow for more competitive conditions e.

The functional unit of the consequential model was defined as increase in demand for computing 1 additional TH. The consequential model thus investigates the effect associated with a marginal increase in mining rather than the total absolute impact of the whole mining. In the BAU and technology scenarios, the same geographical distribution of miners was maintained as in the attributional baseline model Table 1.

In the location scenario, the geographical distribution was adjusted to only include locations where miners are opening new facilities. With a changing political environment in China, 46,47 miners are looking for new locations with cheap electricity, fast Internet, and low temperatures. According to Bendiksen et al. Thus, in the location scenario the miners were assumed to be equally distributed among Norway, Sweden, Iceland, Russia, Canada, and the U.

In the BAU scenario, the same mining equipment as in the attributional model was used, which has an overall efficiency of 0. In the technology and location scenarios the model includes only the most efficient mining equipment currently on the market. With this distribution of mining equipment an overall efficiency of 0.

Regarding additional electricity for cooling and other inefficiency as well as the lifetime of mining equipment, all three consequential scenarios maintain the same assumptions as in the attributional baseline model. In contrast to the attributional model, all three consequential scenarios are linked to the ecoinvent v3. Results and Discussion. In the attributional baseline model, the energy consumption for every TH mined is That means that the Bitcoin network consumed Deviations from previous studies are due to the fact that, for example, de Vries, 17 Stoll et al.

The study by McCook 22 further uses different assumptions regarding the production of mining equipment and from the documentation available it is not entirely clear how his calculations were done. For , this makes a total of Additionally, the methods of calculating the carbon footprint deviate.

Thus, Digiconomist takes average emission factor of the Chinese grid and multiplies it by 0. Figure 2 displays the carbon footprint of the Bitcoin network in together with the hashrate and the Bitcoin price in USD. The curves for the hashrate and the carbon footprint are directly proportional as the same impact factor is applied for the entire year i.

The hashrate reflects the size of the Bitcoin network, of how many miners are trying to gain the right to add the next block. However, the hashrate does not reflect the market price or the amount of transaction throughput meaning it can—in the short term—increase or decrease independently of both the market price and the transaction throughput.

Table 2 displays the results for computing 1 TH for all the midpoint impact categories considered in this study. McCook 22 also calculates values for eutrophication, acidification, and ecotoxicity based on the global electricity mix. However, the limited documentation provided by McCook 22 on the methodology used does not allow making a comparison with the results of this study. Table 2. A contribution analysis showed that the use phase is the major contributor to carbon footprint with Equipment production and EoL only contribute 0.

The table also shows that the share of carbon footprint is larger than the share in mining for a number of locations including Inner Mongolia, Alberta, and Russia. Other locations such as Quebec, Iceland or Sichuan show only a minor individual contribution to the total carbon footprint. This is due to less carbon intensive electricity mixes in these regions.

Therefore, installing new mining facilities in those locations, would lead to a decrease in the carbon footprint per TH. Table 3. Contribution to the Carbon Footprint by Location. Looking at the contribution by equipment type, the equipment used in the attributional baseline model contributes to a similar share to mining and to the carbon footprint.

For example, the Bitmain Antminer S9 makes up The influence of changes in electricity mix on the environmental impact is substantial. Figure 3 shows the results of the sensitivity analysis considering the three different electricity mixes and three different geographical distributions. The main differences between the three different geographical distributions is largely explained by the different assumptions used in modeling the Chinese miners.

The CCAF distribution assumes that The average electricity mix in China has a different impact than the average mix in Sichuan province, China. On average, 1MJ in China produces 0. The amount of cooling required for Bitcoin mining varies depending on climate, scale of mining facility, and mining equipment used.

Improving the efficiency of mining equipment is likely to reduce the impact per TH. A decrease in lifetime of the mining equipment from 1. While the attributional model answered the question on what was the past impact of the Bitcoin mining network under specific assumptions, the consequential models answer the question of how the carbon footprint would change by increasing the computing demand.

Table 2 displays the impact of mining one additional TH for all the midpoint categories considered. The underlying model assumes that an increase in demand for electricity will be met by the marginal suppliers of electricity in each country. The carbon footprint of mining one additional TH in the technology scenario assuming more efficient mining equipment was 7. Mining one additional TH in the location scenario leads to a carbon footprint of 3.

Compared to the previous two scenarios the impact categories ozone depletion, marine ecotoxicity and freshwater ecotoxicity increase slightly see Table 2. This shows that while the carbon footprint in the new locations decreases, renewable energies have higher impacts in other categories. This study showed that the location of the miners has the highest impact on the environmental impact of the Bitcoin network. Miners will move to locations where electricity prices are very low.

Locations with very low electricity prices include those with unused electricity from hydropower e. The case of Plattsburg New York constitutes a recent example of how miners flocking to a city with cheap electricity can increase its energy consumption to the point where the city is no longer able to provide cheap electricity and has to import it from elsewhere. One way to make sure that Bitcoin mining is truly sustainable would be if the miners established new capacity of renewable energy production ensuring that the marginal electricity consumption is environmentally friendly.

One important challenge in the making of this study was the lack of reliable data sources. Many references listed in this study come from news outlets and grey literature. While Bitcoin has gained a lot of attention in popular media, the academic literature on Bitcoin mining is scarce. Furthermore, the data in peer-reviewed literature is outdated 56,57 considering that in the past couple of months the Bitcoin network has grown substantially see Figure 2 and any data before late analyzed a much smaller system than the present one.

Due to this scarce and diverging data basis it is important to highlight that this analysis and its results are characterized by an intrinsic uncertainty. Carrying out sensitivity analyses for all parameters was a way to make this uncertainty explicit and to provide an insight on the range of possible outcomes. Further research should focus on a more solid base of data regarding miner location, and mining equipment used.

This could be done using both expert interviews and a survey among the miners. Since these two parameters are major influencer of environmental impact, using even more accurate data would substantially decrease model uncertainties. Another possible way to increase the accuracy of the model is to consider the Bitcoin network as a whole and not focus on Bitcoin mining only. Such research should include impacts related to nonmining nodes and the growing number of off-chain transactions.

The inclusion of these factors was not coherent with the proposed model and therefore outside the scope of this study. A simple estimation of the lower bound of the energy consumption related to nonmining nodes carried out during this study showed that in nonmining nodes consumed 0. Details on the calculation used to derive this energy consumption is provided in SI Section 6.

Uncertainty of this calculation is high, though, as changing the assumptions regarding the computers used by the nodes could lead to a much higher impact, and this uncertainty should be addressed in future research. This analysis of the Bitcoin mining network contributes with a strictly technical perspective to the broader discussion on the sustainability of the international cryptocurrency.

The results should be considered in the larger context of a borderless currency that is difficult to regulate and where political and economic concerns play as important a role as technical and environmental ones. Bitcoin is not only difficult to regulate because it is a global currency, but also because of its governance structure.

Any changes of protocol would have to be proposed by developers and then be supported by a sufficient number of miners and users 11 involving a large number of people in the process. Therefore, it is important to remember socio-political aspects, but any discussion concerning regulation should be founded on a technical understanding. This analysis of the Bitcoin network is not transferable to all applications of blockchain but is limited to the Bitcoin PoW blockchain.

The environmental impact of different kinds of blockchains that use a consensus mechanism other than PoW, such as proof-of-stake PoS , can be expected to be much lower since no electricity-intensive mining is necessary. In order to add a new block in PoS, users who stake a certain amount of cryptocurrency are randomly selected. This study further adds a forward-looking perspective. The consequential model helps understanding the environmental impacts associated with future developments of the Bitcoin network.

The hashrate of the network is expected to continue growing. For example McCook 22 estimates this growth to be around 5. Growing mining efficiency is likely to increase the overall hashrate as a lower electricity consumption per TH means lower electricity costs for the miners. However, in the long term, the hashrate might stagnate as network security reaches a satisfactory level and rates of return for miners might decrease with the shift from Bitcoin rewards to transaction fees as the primary income.

Supporting Information. Author Information. Consensus in the Age of Blockchains.

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Surface mines are typically used for more shallow and less valuable deposits. Placer mining is used to sift out valuable metals from sediments in river channels, beach sands, or other environments. In-situ mining, which is primarily used in mining uranium, involves dissolving the mineral resource in place then processing it at the surface without moving rock from the ground. Space mining If mining asteroids sounds out-of-this-world to you, you may be surprised by the amount of effort companies are dedicating to space mining projects.

Leave a Reply Cancel reply Your email address will not be published. Smithsonian Acquires Valuable Geological Collection. Get news and research reviews on the topic of your choice, right in your inbox. Subscribe Now. I would like to receive information about content, events, products, services and promotions from Thermo Fisher Scientific and its affiliates. I agree for the Thermo Fisher Scientific group thermofisher. I can withdraw my consent and unsubscribe at any time by emailing reply thermofisher.

This field is for validation purposes and should be left unchanged. Such files may be downloaded by article for research use if there is a public use license linked to the relevant article, that license may permit other uses. More by Massimo Pizzol. Cite this: Environ. Article Views Altmetric -. Citations 2. Abstract High Resolution Image. Today, there are many expectations that blockchain technology will change the world for the better. A consensus mechanism is how the peers in the Bitcoin network continuously agree on the order of newly added blocks and thus secure the data in a decentralized fashion.

The miners compete in solving a puzzle, which requires substantial computational power. Every time the miners guess the nonce value an algorithm is applied that maps the data of their suggested block—including the guessed nonce value——to a value of a fixed length.

This output value is called a hash. A miner wins the right to add a new block when this hash is lower than a target value. The hashrate corresponds to the number of hashes guessed per second. In , the hashrate of the entire Bitcoin network ranged from around 15 to 60 million Tera hashes TH per second. With the increasing popularity of cryptocurrencies concerns were raised regarding the sustainability of Bitcoin, under the rationale that since the Bitcoin network uses a high amount of electricity for mining, its environmental impact might be substantial.

Stoll et al. These numbers are contested by Bendiksen et al. A common feature of the previously mentioned studies is that the assessment of environmental impacts is built on ad-hoc methods. Despite the substantial uncertainties in the data and choices used in previous models, an explicit uncertainty assessment is lacking in previous studies. There is thus the need to use a solid methodological basis to increase the transparency, validity, and replicability of the environmental assessment of Bitcoin.

Summing up, previous studies assessing the impact of the Bitcoin mining network show contrasting and arguably overestimated results, and a key challenge in this assessment is the scarcity of accurate data on key factors determining the impact of the mining network. This study wants to bring new insights in this area by providing a more detailed analysis of the hotspots of environmental impact in the Bitcoin mining network and by increasing the accuracy in the modeling of regional electricity mixes.

Furthermore, this study wants to add a prospective approach by considering how electricity generation or the geography of the mining network might change in the future. The added value of this analysis is adopting LCA as robust scientific methodology, the use of established databases for assessing environmental impact, including the impact of mining equipment in the analysis, and providing an outlook of future impacts. Methods and Materials. This study takes both a retrospective and a prospective approach, and two different system models were respectively used.

Figure 1 shows the structure of the product system that was analyzed in both cases. The ecoinvent v3. In the text, the IPCC method is reported for the carbon footprint. To understand the uncertainty associated with the background data, Monte Carlo simulations with iterations were carried out for the attributional baseline model and each consequential scenario.

High Resolution Image. The functional unit of the attributional model was defined as computing 1 TH. The information currently available on the location of Bitcoin miners is scarce and inaccurate. However, this information is crucial for estimating the environmental impact of the Bitcoin network, which is highly dependent on the electricity mix of the geographical locations where mining is performed.

A geographical distribution of the Bitcoin mining network was developed in this study based on information available from two previous studies, Bendiksen et al. Table 1 shows the geographical distribution of the miners used in the attributional baseline model for Table 1. Besides the energy mix, the electricity consumption of the Bitcoin network depends also on the equipment used for mining as it determines the efficiency of mining, namely the electricity consumption per TH computed.

The types of equipment included in the model are taken from Bendiksen et al. Details on the methodology used to derive these values are provided in SI Section 4. The use of mining equipment involves three main activities: electricity consumption, production, and end-of-life EoL of the equipment. The main contributor to electricity consumption is the use of electricity for mining, determined according to the product specifications of each machine.

Large facilities, especially in warmer climates, may require additional energy for cooling and other inefficiency. The amount of equipment that is produced and hence needs to be disposed of is approximated using machine lifetime. According to Digiconomist, 43 Bitcoin mining equipment has an average lifetime of 1.

For the production of mining equipment, the ecoinvent v3. Similarly, for the end-of-life of the machines, the ecoinvent v3. A sensitivity analysis was carried out to identify how key modeling parameters and modeling assumptions affect the results. First, the sensitivity to the electricity mix and geographical distribution of miners was investigated. Then, three divergent geographic distributions were modeled. Next, the sensitivity of the baseline model with respect to other key parameters was tested.

This allowed to understand the effect of improving mining efficiency or increasing electricity consumption. The consequential approach is fundamentally different from the attributional one as it focuses on quantifying the effect of an increase in the demand for mining.

In the consequential LCA, three different scenarios were modeled. The first model describes a business-as-usual BAU scenario that differs from the attributional baseline model only in the background system: the consequential version of the ecoinvent v3. The second model describes a technology-sensitive scenario where an increase in demand for mining will be met by installing new mining capacity and investing in the most efficient mining equipment. In other words, in this model only the marginal mining technologies are included.

The third model describes a location-sensitive scenario where an increase in demand for mining is met not only by installing efficient mining capacity, but also by changing the geographical distribution of the miners toward locations that allow for more competitive conditions e.

The functional unit of the consequential model was defined as increase in demand for computing 1 additional TH. The consequential model thus investigates the effect associated with a marginal increase in mining rather than the total absolute impact of the whole mining. In the BAU and technology scenarios, the same geographical distribution of miners was maintained as in the attributional baseline model Table 1. In the location scenario, the geographical distribution was adjusted to only include locations where miners are opening new facilities.

With a changing political environment in China, 46,47 miners are looking for new locations with cheap electricity, fast Internet, and low temperatures. According to Bendiksen et al. Thus, in the location scenario the miners were assumed to be equally distributed among Norway, Sweden, Iceland, Russia, Canada, and the U.

In the BAU scenario, the same mining equipment as in the attributional model was used, which has an overall efficiency of 0. In the technology and location scenarios the model includes only the most efficient mining equipment currently on the market.

With this distribution of mining equipment an overall efficiency of 0. Regarding additional electricity for cooling and other inefficiency as well as the lifetime of mining equipment, all three consequential scenarios maintain the same assumptions as in the attributional baseline model. In contrast to the attributional model, all three consequential scenarios are linked to the ecoinvent v3. Results and Discussion. In the attributional baseline model, the energy consumption for every TH mined is That means that the Bitcoin network consumed Deviations from previous studies are due to the fact that, for example, de Vries, 17 Stoll et al.

The study by McCook 22 further uses different assumptions regarding the production of mining equipment and from the documentation available it is not entirely clear how his calculations were done. For , this makes a total of Additionally, the methods of calculating the carbon footprint deviate. Thus, Digiconomist takes average emission factor of the Chinese grid and multiplies it by 0. Figure 2 displays the carbon footprint of the Bitcoin network in together with the hashrate and the Bitcoin price in USD.

The curves for the hashrate and the carbon footprint are directly proportional as the same impact factor is applied for the entire year i. The hashrate reflects the size of the Bitcoin network, of how many miners are trying to gain the right to add the next block.

However, the hashrate does not reflect the market price or the amount of transaction throughput meaning it can—in the short term—increase or decrease independently of both the market price and the transaction throughput. Table 2 displays the results for computing 1 TH for all the midpoint impact categories considered in this study. McCook 22 also calculates values for eutrophication, acidification, and ecotoxicity based on the global electricity mix.

However, the limited documentation provided by McCook 22 on the methodology used does not allow making a comparison with the results of this study. Table 2. A contribution analysis showed that the use phase is the major contributor to carbon footprint with Equipment production and EoL only contribute 0. The table also shows that the share of carbon footprint is larger than the share in mining for a number of locations including Inner Mongolia, Alberta, and Russia.

Other locations such as Quebec, Iceland or Sichuan show only a minor individual contribution to the total carbon footprint. This is due to less carbon intensive electricity mixes in these regions. Therefore, installing new mining facilities in those locations, would lead to a decrease in the carbon footprint per TH. Table 3. Contribution to the Carbon Footprint by Location. Looking at the contribution by equipment type, the equipment used in the attributional baseline model contributes to a similar share to mining and to the carbon footprint.

For example, the Bitmain Antminer S9 makes up The influence of changes in electricity mix on the environmental impact is substantial. Figure 3 shows the results of the sensitivity analysis considering the three different electricity mixes and three different geographical distributions.

The main differences between the three different geographical distributions is largely explained by the different assumptions used in modeling the Chinese miners. The CCAF distribution assumes that The average electricity mix in China has a different impact than the average mix in Sichuan province, China.

On average, 1MJ in China produces 0. The amount of cooling required for Bitcoin mining varies depending on climate, scale of mining facility, and mining equipment used. Improving the efficiency of mining equipment is likely to reduce the impact per TH.

A decrease in lifetime of the mining equipment from 1. While the attributional model answered the question on what was the past impact of the Bitcoin mining network under specific assumptions, the consequential models answer the question of how the carbon footprint would change by increasing the computing demand. Table 2 displays the impact of mining one additional TH for all the midpoint categories considered. The underlying model assumes that an increase in demand for electricity will be met by the marginal suppliers of electricity in each country.

The carbon footprint of mining one additional TH in the technology scenario assuming more efficient mining equipment was 7. Mining one additional TH in the location scenario leads to a carbon footprint of 3. Compared to the previous two scenarios the impact categories ozone depletion, marine ecotoxicity and freshwater ecotoxicity increase slightly see Table 2.

This shows that while the carbon footprint in the new locations decreases, renewable energies have higher impacts in other categories. This study showed that the location of the miners has the highest impact on the environmental impact of the Bitcoin network. Miners will move to locations where electricity prices are very low.

Locations with very low electricity prices include those with unused electricity from hydropower e. The case of Plattsburg New York constitutes a recent example of how miners flocking to a city with cheap electricity can increase its energy consumption to the point where the city is no longer able to provide cheap electricity and has to import it from elsewhere.

One way to make sure that Bitcoin mining is truly sustainable would be if the miners established new capacity of renewable energy production ensuring that the marginal electricity consumption is environmentally friendly. One important challenge in the making of this study was the lack of reliable data sources.

Many references listed in this study come from news outlets and grey literature. While Bitcoin has gained a lot of attention in popular media, the academic literature on Bitcoin mining is scarce. Furthermore, the data in peer-reviewed literature is outdated 56,57 considering that in the past couple of months the Bitcoin network has grown substantially see Figure 2 and any data before late analyzed a much smaller system than the present one.

Due to this scarce and diverging data basis it is important to highlight that this analysis and its results are characterized by an intrinsic uncertainty. Carrying out sensitivity analyses for all parameters was a way to make this uncertainty explicit and to provide an insight on the range of possible outcomes. Further research should focus on a more solid base of data regarding miner location, and mining equipment used.

This could be done using both expert interviews and a survey among the miners. Since these two parameters are major influencer of environmental impact, using even more accurate data would substantially decrease model uncertainties.

Another possible way to increase the accuracy of the model is to consider the Bitcoin network as a whole and not focus on Bitcoin mining only. Such research should include impacts related to nonmining nodes and the growing number of off-chain transactions. The inclusion of these factors was not coherent with the proposed model and therefore outside the scope of this study.

A simple estimation of the lower bound of the energy consumption related to nonmining nodes carried out during this study showed that in nonmining nodes consumed 0. Details on the calculation used to derive this energy consumption is provided in SI Section 6.

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