The Energy Consumption of Digital Currencies
Depending on the two design features of the underlying DLT network, the energy consumption of cryptographic assets might vary significantly. The consensus mechanism, which is the initial component, is what allows for agreement on the network’s current state. When proof-of-work (PoW) algorithms, like the one employed in Bitcoin, are utilised, the resulting energy requirements range from extremely high to orders of magnitude lower than when non-PoW processes are used. The second feature is the amount of control that can be exerted on the underlying architecture (for instance, control over the number of nodes, capacity to assign participants roles, placement of the nodes, and simplicity of code update). Stronger controls on the variables that affect how much energy the central processing infrastructure uses are possible in permissioned networks as opposed to permissionless systems, which let anyone join as a validator.
Compared to the current payment system, some design decisions taken in crypto assets may allow for improved energy efficiency. Non-PoW permissioned networks are estimated to be much more energy efficient than present credit card processing centres, in part because the latter utilise energy-inefficient legacy technology. These estimates come from academic and industry sources. Furthermore, because they only use digital solutions rather than physical payment methods, these crypto assets might further reduce the energy consumption of the current payment system (that is, cash or cards and terminals).
Additionally, CBDCs might be developed to utilise infrastructures that consume less energy than the existing payment system. CBDCs that use non-PoW permissioned networks could benefit from the increased efficiency that comes with using those networks and electronic payment methods. CBDCs could further optimise energy utilisation depending on the quantity and position of the nodes of a specific design. If central banks choose the platform, hardware, and other components of the CBDC ecosystem with energy efficiency as a consideration, non-DLT CBDCs may also be more energy-efficient than the current payment system.
These possible environmental benefits will also be influenced by other variables. Costs associated with regulations and compliance, for instance, can be a significant source of energy expenditure. It will also depend on if and how extra characteristics that aren’t often included in crypto assets, such boosted resilience safeguards or offline capabilities, are judged vital for CBDCs. Techniques and data for a comprehensive analysis of the payment chain are still being developed.
It is crucial to understand how digital currencies might impact the environmental footprint of payments. Cash and credit cards are two common forms of payment that are known to use insignificant amounts of energy. Different technologies are linked to a significant variation in energy costs for digital currency.
Analysing Energy Utilisation for Different Crypto Assets
As mentioned at the outset, the components of their DLT systems’ design determine how much energy cryptographic assets use. The two most important dimensions are examined in this section. The consensus mechanism, which is the initial component, is what allows for agreement on the network’s current state. The degree of control that can be exercised on the underlying architecture is the second component.
Comparison of or Consensus Mechanisms: PoW and Non-PoW DLT Systems
The design of PoW systems, including the one employed by Bitcoin, the first and most well-known crypto asset, takes into account the requirement for high processing power. On January 3, 2009, the first Bitcoin was created with the intention of enabling digital payments independent of reliable middlemen. Due to the absence of a central authority, the network of collaborating users is in charge of deciding if a transaction is genuine. This is accomplished for Bitcoin via the PoW Nakamoto consensus process.
Anyone may make a computer a Bitcoin node that can validate transactions by downloading the free Bitcoin software. Based on the amount of computing power used to solve an algorithmic problem, a node’s likelihood of adding the following collection of transactions to the ledger (by forming a “block”) is determined. The newly created coins and transaction fees received as compensation serve as an incentive for validators to add transactions to the ledger. Because blocks containing invalid transactions can be rejected by other nodes, adding invalid transactions has a disincentive because rewards are only collected if legitimate transactions are contributed. Such rejection suggests that the validator experiences a net loss since it used computational resources without receiving any benefit. However, over the years, Bitcoin mining has become industrialised as the difficulty increased, thus deploying more computational and energy resources to validate transactions.
PoW techniques have two significant adverse effects on the environment: energy consumption and e-waste. The computations carried out by nodes competing for transaction validation in a DLT system based on PoW need a lot of electricity. For instance, the Cambridge Bitcoin Electricity Usage Index estimates that as of July 19, 2022, the yearly electricity consumption of the Bitcoin network is 143.08 terawatt hours (TWh). This is comparable to the combined yearly electricity usage of Finland and Austria.
E-waste, or gadgets that are dumped after their useful life, is a second environmental concern. PoW consensus algorithms generate a lot of electronic waste because validators must update to the newest, fastest technology all the time to stay competitive. According to De Vries and Stoll (2021), the average lifespan of a Bitcoin mining device is 1.3 years, which means that the Bitcoin network goes through 30.7 metric kilotons of equipment every year, which is roughly equivalent to the amount of electronic waste produced by a small advanced country like the Netherlands.
Proof of stake (PoS), a non-PoW process, bases this probability on the number of cryptographic assets “staked.” Validators are rewarded for promptly adding legitimate transactions. If not, they run the danger of being penalised by having their investment reduced or eliminated. Systems using PoW DLTs need many orders of magnitude more energy for each transaction. On the orders of magnitude of the energy involved in transactions based on PoW DLT, estimates from the private sector and academia are rather closely aligned.
Despite the fact that estimates of the energy costs of various non-PoW DLT applications vary greatly, all of these estimates are still far lower than those for PoW DLT. Comparisons are likely to hold true even though scalability solutions and greater usage could alter the expenses per transaction.
For crypto assets, the energy costs associated with starting a transaction and showing confirmations on user interfaces are probably minimal. Estimates focus on the energy consumption of the fundamental processing processes that underlie cryptographic assets.
However, these energy expenditures are probably minimal given that the majority of crypto asset payments are made online utilising digital wallets. According to estimates for the energy consumption of smartphone applications, such as those in Wilke et al. (2013) work, just a few minutes of app use—the typical length of time required to perform a transaction on a digital wallet—can use somewhere between 10^7 kWh. Energy use for a digital wallet would be regarded as negligible to the extent that it is similar to that of other apps.
Effect of Control Level Enabled by a Permitted Network Compared to Permissionless Network
A safe, scalable, and trusted system requires a certain amount of processing capacity, in addition to the consensus mechanism. The number of nodes that make up the network, their relative roles (Gola and Sedlmeir 2022; Platt et al. 2021; Sedlmeir et al. 2020a), the distance between nodes (Ahvar et al. 2019), the versatility to update the code and nodes to optimise power consumption, and the capacity to impose energy criteria for eligibility to be a participant are all parameters that play a major role in energy use.
In terms of having control over these characteristics, permissioned DLT networks are inherently superior to permissionless networks. Every payment mechanism revolves around trust. Trust in a permissionless network arises from the fact that a ledger’s history cannot be altered unless a sufficient number of validators conspire to rewrite it. As a result, on such networks, confidence depends on the presence of several trustworthy nodes, such as validators, making it too expensive for a sufficient number of them to conspire and falsify the ledger. Since the protocol for Bitcoin is set up so that “honest nodes possess a majority of CPU power” (Nakamoto 2008), rewriting Bitcoin’s history would involve appropriating more than half of the validators’ computing power.
Because each validator in a permissioned network has been approved and identified by a central authority, trust in the infrastructure is inherent. Compared to their permissionless equivalents, these networks offer resilience while requiring fewer nodes to maintain a huge ledger. Less redundancy, which results in less computational capacity and, all other things being equal, less energy consumption, is implied by the presence of fewer nodes. A safe and functional network can be provided by central authorities, and they also have the authority to push protocols that are optimal for energy usage.
Existing permissioned DLT crypto assets are estimated to use less energy than permissionless DLT crypto assets. Although estimates for energy usage vary widely, especially for permissionless systems, the estimates for permissioned DLT systems are typically lower than those for the latter. Permissioned DLT systems may be especially well-positioned to gain from energy economies of scale if their usage increases. The growth of validators will determine the economies of scale of a DLT system, according to Platt et al. (2021): if the number of validating nodes increases quickly as the system processes more transactions, then energy economies of scale will deteriorate.
On the other hand, energy economies of scale are high if the number of validating nodes remains constant regardless of the size of a DLT system. Permissioned crypto assets may be better able to benefit from energy economies of scale than permissionless crypto assets. Although anyone can choose to become a validator node in permissionless systems, in permissioned systems the validators are typically a list of organisations allocated at the beginning of the project. As the system matures, this list may remain stable or hardly alter. Examples of permissioned crypto assets with a set number of validating nodes are available.
Potential Energy Gains Compared to Traditional Systems and Their Implications
The estimations for non-PoW permissioned DLT transaction energy consumption are less than those for core credit card processing. Estimates of energy consumption for DLT systems with non-PoW permissions are the lowest.
There are three fundamental problems with comparing the energy used for each transaction in a credit card company’s data centre to the energy used for each transaction in a permissioned DLT crypto asset. The first restriction is that a credit card payment transaction involves parties besides the card issuer, like the bank of the merchant who is receiving the payment. The estimates of energy use for credit cards would increase if this element were taken into account.
The second restriction is that numerous payments may be batch processed during transactions on DLT systems. The third restriction is that at the moment, credit cards complete significantly more transactions than digital currency. The estimates for the permissioned DLT systems would probably be lower if an attempt were made to compare these systems on an equivalent scale (i.e., greatly increasing the number of transactions processed using a non-PoW permissioned crypto currency). Permissioned DLT systems are especially well positioned to gain from energy economies of scale, as was noted previously. It’s possible that outdated technologies are a factor in the increased energy use of credit card transactions. The cause is the dependence on old legacy systems by many traditional digital payment arrangements.
Digital currencies and other new payment methods hold the promise of more environmentally friendly consumer payment options. Some conventional digital payment methods include user payment access mechanisms that use a sizable amount of energy. Most crypto assets provide user access to payments through digital wallets, which probably use very little energy.
Reducing the energy used by user payment methods could potentially be accomplished by modifications to conventional payment systems. For instance, some significant e-commerce companies provide credit cards that can only be used to make transactions online. Cash might be viewed as a legacy payment method from an environmental standpoint. Although cash may have various benefits that digital forms of money cannot easily imitate, innovations like these may contribute to the development of digital payment methods that consume less energy for both core processing and user payment methods.
Implications for CBDC’s Environmental Aspects
The implementation of CBDCs is being considered by policymakers worldwide. While many central banks are still researching CBDCs, others have already conducted proofs-of-concept, pilots, or launched a CBDC. There are many reasons why central banks might consider CBDCs, as well as numerous concerns about the potential effects of doing so. The energy cost of CBDCs is the only topic of debate here, similar to the discussion of crypto assets, even though it is acknowledged that there are many other pertinent factors that should be taken into account when determining whether or not to issue a CBDC.
Modernised iterations of conventional payment systems or non-PoW permissioned DLT are the foundations for current CBDC developments. In comparison to currently used traditional payment methods, both DLT and non-DLT CBDCs have the potential to include lower energy use for key processing activities. On the one hand, compared to permissioned DLT crypto assets, CBDCs might need some more sophisticated features, which could result in higher energy costs. It is anticipated that additional physical security measures will be judged necessary at the central bank and participating institutions, along with server backups and hardware redundancy for improved resilience and availability.
Other energy needs might result from setting up new departments or research facilities focused on CBDCs, as well as developing custom teams and technologies to oversee regulatory compliance. Moreover, central banks’ ability to regulate node density and location might help with environmental optimisation. The quantity, function, and perhaps even location of each node in the network might be managed by the central bank in a CBDC based on permissioned DLT. The nodes can be situated in areas with access to renewable energy or where energy is produced in excess but is not utilised.
Identical factors may be taken into account for a non-DLT CBDC when deciding where the primary servers, including cloud systems, will be situated. The CBDC ecosystem’s other components also hold true in this regard. Private permissioned crypto assets may be able to influence where energy-consuming systems are located, but there is no assurance that this will be one of their objectives.
Additionally, the advantages of any new technology introduced by a CBDC project could be evaluated, including how much energy it consumed. The ideas of Green Software Engineering, which are used at numerous major technology businesses, could be utilised by central banks that incorporate an environmental goal in the creation of their CBDC.
This may entail optimising not only the programming used to create the CBDC ledger but also any other layer of the underlying technology stack, including auditing software and application programming interfaces. In order to choose a platform, hardware, and other components of the CBDC ecosystem, central banks might formally accept energy consumption and carbon footprint as criteria. For instance, in order to lower their carbon footprint while generating electricity, large cloud service providers are moving toward cooler climes and renewable energy sources like geothermal and hydropower.
The CBDC project and any subsequent central bank digitalisation projects can both benefit from adding the environmental footprint as a selection factor of a cloud partner. One subtlety is that some energy usage characteristics may not be under the control of central banks when they purchase CBDC technology as a platform from commercial vendors.
But since these technologies are still in their infancy, central banks may have a window of opportunity to shape their advancement by encouraging competition among suppliers on the energy impact of CBDC systems.