## Executive Summary

## Source details

**Canonical URL:** [Executive Summary](https://www.imf.org/-/media/files/publications/ftn063/2022/english/ftnea2022006.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/ftn063/2022/english/ftnea2022006.pdf.md)
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---

### Overview
- The term “digital currencies” in this paper refers to crypto assets and central bank digital currencies (CBDCs).
- The payment system is evolving rapidly, with new forms of digital currencies offering opportunities and raising debates about environmental costs.
- Payments—whether via cash (printing, distribution, disposal) or via card and bank payments (processing and maintenance)—entail nonnegligible energy and environmental costs.
- A key transformation in the payment system is the rise of crypto assets that rely on cryptography and distributed ledger technologies (DLTs).

### Energy drivers and design elements
- Two DLT design elements produce large variance in energy consumption across crypto assets:
  - Consensus mechanism: energy needs range from very intensive (proof-of-work (PoW), e.g., Bitcoin) to orders of magnitude lower when non-PoW mechanisms are used.
  - Level of control over the architecture: permissionless networks (anyone may join as a validator) versus permissioned networks (certified participants, control over number and role of nodes, node location, ease of updating code).
- Permissioned networks allow stronger controls on parameters that influence the energy consumption of the core processing infrastructure.

### Comparative energy implications
- Some crypto asset design options can yield higher energy efficiency than the current payment system.
- Academic and industry estimates indicate that non-PoW permissioned networks are significantly more energy efficient than current credit card processing centers, in part because credit card processing involves energy-inefficient legacy systems.
- Crypto assets that employ purely digital solutions (rather than physical payment means such as cash or cards and terminals) can further improve on traditional payment systems in terms of energy consumption.

### Implications for CBDC design
- CBDCs could be designed to use infrastructures that are less energy intensive than the current payment system.
- CBDCs that rely on non-PoW permissioned networks could harness efficiency gains from those networks and from relying on digital means of payment.
- Depending on the number and location of the nodes of a particular design, CBDCs could further optimize energy use.
- Non-DLT CBDCs could also be more efficient than the current payment system if central banks select platform, hardware, and other ecosystem elements with energy efficiency as a criterion.

### Additional considerations affecting environmental impact
- Potential positive environmental impacts depend on additional factors beyond core infrastructure choice:
  - Regulation and compliance costs can be an important source of energy spending.
  - Additional CBDC features not commonly part of crypto assets—such as increased resilience measures or offline capabilities—may affect energy consumption.
- Methodologies and data for a full assessment of the payment chain are currently a work in progress.
- Energy metrics provide only a partial assessment of environmental impact.
- Policymakers face multiple trade-offs, many outside the remit of this study, when evaluating broader adoption and regulation of digital currencies.

---

### BOX 2. Assessing the Environmental Impact of Payment Systems

### Scope and methodological considerations
- A complete environmental footprint requires a life cycle analysis considering products and services from conception to disposal; for digital infrastructure this includes production of servers, people involved in organizations, and waste. Methodologies and data to conduct such analyses are still incomplete.
- Energy necessary to maintain underlying networks should be considered: data transfers over the internet require infrastructures such as long-distance communication and routers that require energy to operate (Ahvar, Orgerie, and Lebre 2019).
- The Paris Agreement requires improvement in both energy efficiency and the amount of carbon emissions per unit of energy consumed; carbon intensity of energy and indirect emissions (e.g., worker commuting or travel) should be explored.
- Beyond greenhouse gas emissions, other environmental factors of importance include:
  - production of hardware, including the use of rare metals and freshwater;
  - e-waste and the environmental costs of recycling the hardware (Forti and others 2020);
  - several electronic components can have a damaging chemical impact on landfills.

### Payment system components (core processing and user payment means)
- Payment systems have two main components:
  - Core processing: instructions and operations involved in processing and settlement of payments.
  - User payment means: technologies, devices, and actors that enable those payments.
- Component mappings summarized:
  - CASH
    - CORE PROCESSING: Creation, distribution, use, and disposal of banknotes and coins
    - USER PAYMENT MEANS: Websites, checks, bank branches
  - BANK TRANSFERS
    - CORE PROCESSING: Bank data centers, RTGS
    - USER PAYMENT MEANS: Websites, checks, bank branches
  - CREDIT CARDS
    - CORE PROCESSING: Card issuer and bank data centers, RTGS
    - USER PAYMENT MEANS: Physical cards and point-of-sale terminals
  - PERMISSIONED DLT-BASED DIGITAL CURRENCIES
    - CORE PROCESSING: Accredited nodes
    - USER PAYMENT MEANS: DLT digital wallets and third-party providers
  - PERMISSIONLESS DLT-BASED DIGITAL CURRENCIES
    - CORE PROCESSING: Any node that joins the network to validate transactions
    - USER PAYMENT MEANS: DLT digital wallets and third-party providers
- In traditional digital payment systems, trusted agencies perform core processing and provide user payment access points (banks, central banks, card issuers). In DLT systems, consensus mechanisms underlie trust in core processing and digital wallets are the main user payment means.

### Consensus mechanisms and environmental implications
- Design elements of DLT systems that most impact energy consumption:
  - Consensus mechanism used to achieve agreement about the network state.
  - Level of control over the underlying architecture (permissioned vs permissionless).
- Proof of Work (PoW) / Nakamoto consensus:
  - Nakamoto consensus comprises a computational competition, PoW, and a selection rule where the chain with the most computations is valid.
  - Anyone can download Bitcoin software to run a node; probability of adding the next block depends on computational power expended solving an algorithmic challenge; rewards are newly minted coins and transaction fees.
  - Environmental implications of PoW:
    - High energy consumption from computations performed by competing nodes.
    - E-waste from constant hardware upgrades to remain competitive.
  - Key statistics cited:
    - As of April 25, 2022, the annual electricity consumption of the Bitcoin network is estimated at 144 terawatt hours (TWh) per year according to the Cambridge Bitcoin Electricity Consumption Index.
    - This amounts to about 0.6 percent of total global electricity consumption.
    - Average life span of Bitcoin mining devices is 1.3 years.
    - Bitcoin network cycles through 30.7 metric kilotons of equipment per year (e-waste), roughly equivalent to the e-waste generated by a small advanced economy like the Netherlands.
    - On a per-transaction basis, e-waste: a single Bitcoin transaction generates 272 grams of e-waste (De Vries and Stoll 2021), comparable to throwing away two iPhone 13 Minis (141 grams each).
- Non-PoW mechanisms (example: Proof of Stake, PoS):
  - Probability of adding the next block does not depend on computational energy expended; in PoS it depends on the amount of crypto assets “staked.”
  - Validators are incentivized to add valid transactions; misbehavior can be punished by losing part or all of their stake.
  - Ethereum is transitioning from PoW toward PoS (acceptance, implementation, and deployment are ongoing).

### Comparing energy use across payment systems
- Figure 1 (described) presents energy consumption estimates for core processing of different payment systems, using estimates from private companies and academic studies; comparisons are made on a per-transaction basis.
- Classification of DLT-based payment systems in the comparison:
  - permissionless PoW;
  - permissionless non-PoW;
  - permissioned non-PoW.
- Caveats on per-transaction comparisons:
  - Number of payments can exceed number of transactions due to batching of multiple payments into a single transaction and use of layer 2 protocols to increase scalability and reduce costs.
  - Example (Bitcoin):
    - Bitcoin stands at around 100 million transactions per year, but considering batching and layer 2 transactions, it can be estimated that Bitcoin currently processes approximately 250 million payments per year.
    - As of March 2022: The Bitcoin network confirms approximately 250,000 transactions per day, which translates to 91.25 million transactions a year.
    - On average, a Bitcoin transaction represents 2.5 payments through batching, leading to approximately 230 million payments per year.
    - For Lightning (largest layer 2 on Bitcoin), there are 19,000 participating nodes with active channels (March 2022, Acinq). Assuming each participant makes 2 payments a day (average from the 2020 Survey of Consumer Choice), a rough estimate is an additional 14 million payments per year. Arcane Research provides an estimate of 8 million payments per year.
- Additional considerations:
  - Comparisons based on energy consumption can diverge from comparisons based on CO2 emissions because carbon intensity of the energy source matters and miners may locate near cheap energy sources with varying shares of renewables.
  - Use of renewables by mining may involve an opportunity cost by reducing availability of renewables for other uses.

---

### BOX 3. Batching and Layer 2

### Mechanics of batching and Layer 2
- Blockchain transactions move funds from address to address; in permissionless systems, senders pay fees to validators as transactions are validated.
- Batching:
  - Panel (i): one payment per blockchain transaction.
  - Panel (ii): multiple payments (example: two payments) contained in a single blockchain transaction, lowering cost per payment despite the batched transaction being slightly larger.
  - Note: "The fee is proportional to the digital size or the computational intensity of the transaction. Although batched transactions are generally slightly larger than individual transactions and thus more expensive, their cost per payment is lower than individual transactions."
- Layer 2 (off-chain processing):
  - A user initiates a channel by submitting transactions on the main chain and locking funds in a smart contract.
  - The channel can be used to make many off-chain payments processed much faster and at a fraction of the fees versus on-chain transactions.
  - When the channel concludes, a final on-chain transaction settles the net outcome, so a single on-chain transaction may represent a large set of off-chain payments.
  - Examples of off-chain technologies: "the Lightning network, plasma chains, and ZK- and optimistic rollups."

### Energy implications highlighted in the surrounding analysis
- PoW-based DLT systems use many orders of magnitude more energy per transaction than non-PoW DLT systems.
- Estimates for energy use of smartphone apps (user-facing confirmations and UI) are on the order of 10^-7 kWh for a few minutes of app use and are considered negligible relative to core processing.
- A specific estimate cited for a novel non-DLT payment system (TIPS) is 4 × 10^-5 kWh per transaction (Tiberi (2021)), which is close to the upper bound of permissioned DLT estimates.
- Bitcoin is estimated to consume about 144 TWh per year; scalability solutions and increased usage might change energy costs per transaction but do not eliminate overall energy spending.

### How batching and Layer 2 affect energy and fee dynamics (scenarios and effects)
- Substitution effect (Layer 2 / batching): for a given level of asset transaction demand, Layer 2 reduces demand for on-chain transactions by allowing more off-chain transactions, which reduces transaction fees and miners’ incentives, thereby reducing energy consumption.
- Demand effect (Layer 2 / batching): by making crypto assets more attractive as means of payment and strengthening network effects, successful Layer 2 and batching could increase demand for the asset, raise its price, increase mining rewards (rewards paid in the asset), and thereby increase energy consumption.
- Net impact on PoW systems depends on the balance between the substitution effect and the demand effect.
- Operational notes:
  - Batching is commonly used by crypto asset exchanges to pay multiple users withdrawing funds concurrently.
  - Layer 2 channels require initial and final on-chain transactions; energy and fee savings accrue from performing many payments off-chain between those on-chain events.

---

### References — Key estimates, conclusion, and annex highlights

### Key estimates — Energy consumption of the current payment system
- Credit and debit card transactions constituted 74 percent of all cashless transactions in 2019 (CPMI Redbook Statistics).
- A specific card issuer estimated total 2019 energy usage at 0.377 TWh (or about 0.04 kWh per transaction).
- Scaling factors applied to estimate global annual energy consumption of the digital payment system:
  - Issuer market share: 2 percent → multiplication factor of 50.
  - Card payments to all digital payments: multiplication factor of 1.3 (based on CPMI Redbook Statistics).
  - CPMI coverage of world population: 62 percent → population scaling factor of 1 / 0.62 = 1.6.
  - Combined scaling calculation reported: 0.377 x 50.2 x 1.3 x 1.6 = 39 TWh (digital payments estimate).
- Cash energy estimate:
  - Hanegraaf and others (2020) estimate total annual environmental impact of cash in the Netherlands at 19 million kg CO2 equivalents.
  - Converted to energy metric: 0.0815 TWh.
  - Netherlands population share: 0.225 percent of world population → extrapolation gives 36 TWh.
  - Netherlands cash-in-circulation to GDP: 8.9 percent; global ratio: 9.6 percent.
  - Netherlands GDP per capita: $52,304; global GDP per capita: $10,926.
  - Correction factor constructed for higher Dutch CIC per capita: [(0.096)(10,926)] ⁄ ([(0.089)(52,304)] = 0.23.
  - Corrected global cash energy estimate: 0.23 x 36 TWh = 8.3 TWh.

### Conclusion (aggregate)
- Combined estimate for annual energy consumption by the global payment system: 47.3 TWh.
- This amounts to about 0.2 percent of total global electricity consumption.
- Comparable in annual electricity consumption to a small advanced economy like Portugal or a sizable developing economy like Bangladesh.

### Methodology and caveats highlighted in the Annex
- Estimates for the digital payment system focus on credit and debit cards because they constituted 74 percent of cashless transactions in 2019 and because data on their energy intensity are available.
- Assumption: card payments are comparable in energy use to other digital payments; authors note empirical evidence against which to judge this assumption is lacking but that cards’ 74 percent share limits potential error.
- CPMI country coverage assumption: CPMI covers countries making up 62 percent of world population; scaling by 1.6 used to approximate global totals.
- For cash, the Netherlands study (Hanegraaf and others, 2020) is used as the sole comprehensive study of cash lifecycle impacts; population and CIC/GDP corrections applied to extrapolate to global level.
- The file with the underlying calculations is available on request.

### Annex II — CBDC initiatives at developed stages (selected entries)
- Bahamas — 2019 — Sand Dollar — NZIA (including IBM and Zynesis) — DLT — N/A — Retail.
- Nigeria — 2021 — e-Naira — Bitt Inc. — DLT — N/A — Launched.
- Singapore — 2016 — Ubin — R3 Corda; Hyperledger Fabric; Quorum — DLT — Centralized; zero-knowledge proof — Pilot / Wholesale.
- Canada — 2016 — Jasper — Phase 1: Ethereum (private network); Phase 2: R3 Corda — DLT — Phase 2: Centralized notary node — Wholesale.
- South Africa — 2017 — Khokha — ConsenSys Quorum (Ethereum blockchain) — DLT — Istanbul Byzantine Fault Tolerance — Wholesale.
- China — 2020 — e-CNY — Feitian Technologies — Hybrid - DLT - — Retail.
- ECCU — 2021 — DCash — Bitt Inc.; Hyperledger Fabric — DLT — N/A — Retail.
- Uruguay — 2017 — e-Peso — Roberto Giori Company; IBM — Non-DLT — Retail.
- UAE + Saudi Arabia — 2019 — Aber — Hyperledger Fabric; IBM — DLT — N/A.
- Canada + Singapore — 2019 — Jasper-Ubin — R3 Corda; Quorum; JPMorgan; Accenture — DLT — Corda: Notary node; Quorum: Raft or Istanbul BFT — Wholesale.
- Euro Area — 2020 — Digital euro experiment: WS1 — TARGET Instant Payment Settlement (TIPS) — Non - DLT — N/A — Retail.
- BIS + Australia + Malaysia + Singapore + South Africa — 2022 — Project Dunbar — Corda; Partior; Quorum — DLT — Notary node; Istanbul BFT; Raft — Proof of concept.
- Additional projects and stages listed across multiple jurisdictions and technology types; table denotes technology types as DLT, Non-DLT, or Hybrid and lists consensus/validation mechanisms where available.

*International Monetary Fund — Fintech Notes (Executive Summary, ftnea2022006).*

### Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

### Executive Summary

### Overview
- The term “digital currencies” in this paper refers to crypto assets and central bank digital currencies (CBDCs).
- The payment system is evolving rapidly, with new forms of digital currencies offering opportunities and raising debates about environmental costs.
- Payments—whether via cash (printing, distribution, disposal) or via card and bank payments (processing and maintenance)—entail nonnegligible energy and environmental costs.
- A key transformation in the payment system is the rise of crypto assets that rely on cryptography and distributed ledger technologies (DLTs).

### Energy drivers and design elements
- Two DLT design elements produce large variance in energy consumption across crypto assets:
  - Consensus mechanism: energy needs range from very intensive (proof-of-work (PoW), e.g., Bitcoin) to orders of magnitude lower when non-PoW mechanisms are used.
  - Level of control over the architecture: permissionless networks (anyone may join as a validator) versus permissioned networks (certified participants, control over number and role of nodes, node location, ease of updating code).
- Permissioned networks allow stronger controls on parameters that influence the energy consumption of the core processing infrastructure.

### Comparative energy implications
- Some crypto asset design options can yield higher energy efficiency than the current payment system.
- Academic and industry estimates indicate that non-PoW permissioned networks are significantly more energy efficient than current credit card processing centers, in part because credit card processing involves energy-inefficient legacy systems.
- Crypto assets that employ purely digital solutions (rather than physical payment means such as cash or cards and terminals) can further improve on traditional payment systems in terms of energy consumption.

### Implications for CBDC design
- CBDCs could be designed to use infrastructures that are less energy intensive than the current payment system.
- CBDCs that rely on non-PoW permissioned networks could harness efficiency gains from those networks and from relying on digital means of payment.
- Depending on the number and location of the nodes of a particular design, CBDCs could further optimize energy use.
- Non-DLT CBDCs could also be more efficient than the current payment system if central banks select platform, hardware, and other ecosystem elements with energy efficiency as a criterion.

### Additional considerations affecting environmental impact
- Potential positive environmental impacts depend on additional factors beyond core infrastructure choice:
  - Regulation and compliance costs can be an important source of energy spending.
  - Additional CBDC features not commonly part of crypto assets—such as increased resilience measures or offline capabilities—may affect energy consumption.
- Methodologies and data for a full assessment of the payment chain are currently a work in progress.
- Energy metrics provide only a partial assessment of environmental impact.
- Policymakers face multiple trade-offs, many outside the remit of this study, when evaluating broader adoption and regulation of digital currencies.

*International Monetary Fund — Fintech Notes (Executive Summary, ftnea2022006).*

### BOX 2. Assessing the Environmental Impact of Payment Systems

### BOX 2. Assessing the Environmental Impact of Payment Systems

### Scope and methodological considerations
- A complete environmental footprint requires a life cycle analysis considering products and services from conception to disposal; for digital infrastructure this includes production of servers, people involved in organizations, and waste. Methodologies and data to conduct such analyses are still incomplete.
- Energy necessary to maintain underlying networks should be considered: data transfers over the internet require infrastructures such as long-distance communication and routers that require energy to operate (Ahvar, Orgerie, and Lebre 2019). This is particularly relevant in DLT as peer-to-peer activity can have different data traffic profiles depending on the geography of the network’s nodes, settings, and algorithms.
- The Paris Agreement requires improvement in both energy efficiency and the amount of carbon emissions per unit of energy consumed. Therefore, the carbon intensity of the energy used should be explored, as well as indirect emissions such as worker commuting or travel, which have been proven to represent a significant part of corporations’ emissions (Klaassen and Stoll 2021).
- Beyond greenhouse gas emissions, other environmental factors of importance include:
  - production of hardware, including the use of rare metals and freshwater;
  - e-waste and the environmental costs of recycling the hardware (Forti and others 2020);
  - several electronic components can have a damaging chemical impact on landfills.

### Payment system components (core processing and user payment means)
- Payment systems have two main components:
  - Core processing: instructions and operations involved in processing and settlement of payments.
  - User payment means: technologies, devices, and actors that enable those payments.
- Table 1 overview (components summarized from the source):
  - CASH
    - CORE PROCESSING: Creation, distribution, use, and disposal of banknotes and coins
    - USER PAYMENT MEANS: Websites, checks, bank branches
  - BANK TRANSFERS
    - CORE PROCESSING: Bank data centers, RTGS
    - USER PAYMENT MEANS: Websites, checks, bank branches
  - CREDIT CARDS
    - CORE PROCESSING: Card issuer and bank data centers, RTGS
    - USER PAYMENT MEANS: Physical cards and point-of-sale terminals
  - PERMISSIONED DLT-BASED DIGITAL CURRENCIES
    - CORE PROCESSING: Accredited nodes
    - USER PAYMENT MEANS: DLT digital wallets and third-party providers
  - PERMISSIONLESS DLT-BASED DIGITAL CURRENCIES
    - CORE PROCESSING: Any node that joins the network to validate transactions
    - USER PAYMENT MEANS: DLT digital wallets and third-party providers
- In traditional digital payment systems, trusted agencies perform core processing and provide user payment access points (banks, central banks, card issuers). In DLT systems, consensus mechanisms underlie trust in core processing and digital wallets are the main user payment means.

### Consensus mechanisms and environmental implications
- Design elements of DLT systems that most impact energy consumption:
  - Consensus mechanism used to achieve agreement about the network state.
  - Level of control over the underlying architecture (permissioned vs permissionless).
- Proof of Work (PoW) / Nakamoto consensus:
  - Nakamoto consensus comprises a computational competition, PoW, and a selection rule where the chain with the most computations is valid.
  - Anyone can download Bitcoin software to run a node; probability of adding the next block depends on computational power expended solving an algorithmic challenge; rewards are newly minted coins and transaction fees.
  - Environmental implications of PoW:
    - High energy consumption from computations performed by competing nodes.
    - E-waste from constant hardware upgrades to remain competitive.
  - Key statistics cited:
    - As of April 25, 2022, the annual electricity consumption of the Bitcoin network is estimated at 144 terawatt hours (TWh) per year according to the Cambridge Bitcoin Electricity Consumption Index.
    - This amounts to about 0.6 percent of total global electricity consumption.
    - Average life span of Bitcoin mining devices is 1.3 years.
    - Bitcoin network cycles through 30.7 metric kilotons of equipment per year (e-waste), roughly equivalent to the e-waste generated by a small advanced economy like the Netherlands.
    - On a per-transaction basis, e-waste: a single Bitcoin transaction generates 272 grams of e-waste (De Vries and Stoll 2021), comparable to throwing away two iPhone 13 Minis (141 grams each).
- Non-PoW mechanisms (example: Proof of Stake, PoS):
  - Probability of adding the next block does not depend on computational energy expended; in PoS it depends on the amount of crypto assets “staked.”
  - Validators are incentivized to add valid transactions; misbehavior can be punished by losing part or all of their stake.
  - Ethereum is transitioning from PoW toward PoS (acceptance, implementation, and deployment are ongoing).

### Comparing energy use across payment systems
- Figure 1 (described) presents energy consumption estimates for core processing of different payment systems, using estimates from private companies and academic studies; comparisons are made on a per-transaction basis.
- Classification of DLT-based payment systems in the comparison:
  - permissionless PoW;
  - permissionless non-PoW;
  - permissioned non-PoW.
- Caveats on per-transaction comparisons:
  - Number of payments can exceed number of transactions due to batching of multiple payments into a single transaction and use of layer 2 protocols to increase scalability and reduce costs.
  - Example (Bitcoin):
    - Bitcoin stands at around 100 million transactions per year, but considering batching and layer 2 transactions, it can be estimated that Bitcoin currently processes approximately 250 million payments per year.
    - As of March 2022: The Bitcoin network confirms approximately 250,000 transactions per day, which translates to 91.25 million transactions a year.
    - On average, a Bitcoin transaction represents 2.5 payments through batching, leading to approximately 230 million payments per year.
    - For Lightning (largest layer 2 on Bitcoin), there are 19,000 participating nodes with active channels (March 2022, Acinq). Assuming each participant makes 2 payments a day (average from the 2020 Survey of Consumer Choice), a rough estimate is an additional 14 million payments per year. Arcane Research provides an estimate of 8 million payments per year.
- Additional considerations:
  - Comparisons based on energy consumption can diverge from comparisons based on CO2 emissions because carbon intensity of the energy source matters and miners may locate near cheap energy sources with varying shares of renewables.
  - Use of renewables by mining may involve an opportunity cost by reducing availability of renewables for other uses.

*Source: BOX 2. Assessing the Environmental Impact of Payment Systems (ftnea2022006).*

### BOX 3. Batching and Layer 2

### BOX 3. Batching and Layer 2

### Mechanics of batching and Layer 2
- Blockchain transactions move funds from address to address; in permissionless systems, senders pay fees to validators as transactions are validated.
- Batching:
  - Panel (i): one payment per blockchain transaction.
  - Panel (ii): multiple payments (example: two payments) contained in a single blockchain transaction, lowering cost per payment despite the batched transaction being slightly larger.
  - Note: "The fee is proportional to the digital size or the computational intensity of the transaction. Although batched transactions are generally slightly larger than individual transactions and thus more expensive, their cost per payment is lower than individual transactions."
- Layer 2 (off-chain processing):
  - A user initiates a channel by submitting transactions on the main chain and locking funds in a smart contract.
  - The channel can be used to make many off-chain payments processed much faster and at a fraction of the fees versus on-chain transactions.
  - When the channel concludes, a final on-chain transaction settles the net outcome, so a single on-chain transaction may represent a large set of off-chain payments.
  - Examples of off-chain technologies: "the Lightning network, plasma chains, and ZK- and optimistic rollups."

### Energy implications highlighted in the surrounding analysis
- PoW-based DLT systems use many orders of magnitude more energy per transaction than non-PoW DLT systems.
- Estimates for energy use of smartphone apps (user-facing confirmations and UI) are on the order of 10^-7 kWh for a few minutes of app use and are considered negligible relative to core processing.
- A specific estimate cited for a novel non-DLT payment system (TIPS) is 4 × 10^-5 kWh per transaction (Tiberi (2021)), which is close to the upper bound of permissioned DLT estimates.
- Bitcoin is estimated to consume about 144 TWh per year; scalability solutions and increased usage might change energy costs per transaction but do not eliminate overall energy spending.

### How batching and Layer 2 affect energy and fee dynamics (scenarios and effects)
- Substitution effect (Layer 2 / batching): for a given level of asset transaction demand, Layer 2 reduces demand for on-chain transactions by allowing more off-chain transactions, which reduces transaction fees and miners’ incentives, thereby reducing energy consumption.
- Demand effect (Layer 2 / batching): by making crypto assets more attractive as means of payment and strengthening network effects, successful Layer 2 and batching could increase demand for the asset, raise its price, increase mining rewards (rewards paid in the asset), and thereby increase energy consumption.
- Net impact on PoW systems depends on the balance between the substitution effect and the demand effect.

### Operational and policy-relevant notes on batching and Layer 2 usage
- Batching is commonly used by crypto asset exchanges to pay multiple users withdrawing funds concurrently.
- Layer 2 channels require initial and final on-chain transactions; energy and fee savings accrue from performing many payments off-chain between those on-chain events.
- While Layer 2 and batching reduce fees per payment and can reduce on-chain transaction load, their broader systemic energy impact depends on user adoption and price/reward dynamics in PoW systems.

*Source: Authors.*

### References

### ftnea2022006 - References

### Key estimates — Energy consumption of the current payment system
- Credit and debit card transactions constituted 74 percent of all cashless transactions in 2019 (CPMI Redbook Statistics).
- A specific card issuer estimated total 2019 energy usage at 0.377 TWh (or about 0.04 kWh per transaction).
- Scaling factors applied to estimate global annual energy consumption of the digital payment system:
  - Issuer market share: 2 percent → multiplication factor of 50.
  - Card payments to all digital payments: multiplication factor of 1.3 (based on CPMI Redbook Statistics).
  - CPMI coverage of world population: 62 percent → population scaling factor of 1 / 0.62 = 1.6.
  - Combined scaling calculation reported: 0.377 x 50.2 x 1.3 x 1.6 = 39 TWh (digital payments estimate).
- Cash energy estimate:
  - Hanegraaf and others (2020) estimate total annual environmental impact of cash in the Netherlands at 19 million kg CO2 equivalents.
  - Converted to energy metric: 0.0815 TWh.
  - Netherlands population share: 0.225 percent of world population → extrapolation gives 36 TWh.
  - Netherlands cash-in-circulation to GDP: 8.9 percent; global ratio: 9.6 percent.
  - Netherlands GDP per capita: $52,304; global GDP per capita: $10,926.
  - Correction factor constructed for higher Dutch CIC per capita: [(0.096)(10,926)] ⁄ ([(0.089)(52,304)] = 0.23.
  - Corrected global cash energy estimate: 0.23 x 36 TWh = 8.3 TWh.

### Conclusion (aggregate)
- Combined estimate for annual energy consumption by the global payment system: 47.3 TWh.
- This amounts to about 0.2 percent of total global electricity consumption.
- Comparable in annual electricity consumption to a small advanced economy like Portugal or a sizable developing economy like Bangladesh.

### Methodology and caveats highlighted in the Annex
- Estimates for the digital payment system focus on credit and debit cards because they constituted 74 percent of cashless transactions in 2019 and because data on their energy intensity are available.
- Assumption: card payments are comparable in energy use to other digital payments; authors note empirical evidence against which to judge this assumption is lacking but that cards’ 74 percent share limits potential error.
- CPMI country coverage assumption: CPMI covers countries making up 62 percent of world population; scaling by 1.6 used to approximate global totals.
- For cash, the Netherlands study (Hanegraaf and others, 2020) is used as the sole comprehensive study of cash lifecycle impacts; population and CIC/GDP corrections applied to extrapolate to global level.
- The file with the underlying calculations is available on request.

### Annex II — CBDC initiatives at developed stages (selected entries)
- Bahamas — 2019 — Sand Dollar — NZIA (including IBM and Zynesis) — DLT — N/A — Retail.
- Nigeria — 2021 — e-Naira — Bitt Inc. — DLT — N/A — Launched.
- Singapore — 2016 — Ubin — R3 Corda; Hyperledger Fabric; Quorum — DLT — Centralized; zero-knowledge proof — Pilot / Wholesale.
- Canada — 2016 — Jasper — Phase 1: Ethereum (private network); Phase 2: R3 Corda — DLT — Phase 2: Centralized notary node — Wholesale.
- South Africa — 2017 — Khokha — ConsenSys Quorum (Ethereum blockchain) — DLT — Istanbul Byzantine Fault Tolerance — Wholesale.
- China — 2020 — e-CNY — Feitian Technologies — Hybrid - DLT - — Retail.
- ECCU — 2021 — DCash — Bitt Inc.; Hyperledger Fabric — DLT — N/A — Retail.
- Uruguay — 2017 — e-Peso — Roberto Giori Company; IBM — Non-DLT — Retail.
- UAE + Saudi Arabia — 2019 — Aber — Hyperledger Fabric; IBM — DLT — N/A.
- Canada + Singapore — 2019 — Jasper-Ubin — R3 Corda; Quorum; JPMorgan; Accenture — DLT — Corda: Notary node; Quorum: Raft or Istanbul BFT — Wholesale.
- Euro Area — 2020 — Digital euro experiment: WS1 — TARGET Instant Payment Settlement (TIPS) — Non - DLT — N/A — Retail.
- BIS + Australia + Malaysia + Singapore + South Africa — 2022 — Project Dunbar — Corda; Partior; Quorum — DLT — Notary node; Istanbul BFT; Raft — Proof of concept.
- Additional projects and stages listed include Inthanon-LionRock (Hong Kong + Thailand), Stella phases (Japan + Euro Area), Helvetia (Switzerland + BIS), Project Jura (France + Switzerland + BIS), e-Krona (Sweden), Digital Yen (Japan), Digital Lira (Turkey), Digital Real (Brazil), and others across initiation, pilot, experiment, proof of concept, and retail/wholesale categorizations.
- Note: Table entries denote technology types as DLT, Non-DLT, or Hybrid, and list consensus/validation mechanisms where available (examples include Notary node; Proof of Authority; Proof of Authority; Istanbul Byzantine Fault Tolerance; Practical Byzantine Fault Tolerance (PBFT); Proof of Authority; Unique Node List (UNL)). The table source construction used cbdctracker.org, kiffmeister.blogspot.com, and internal IMF sources, with additional country and technology provider sources consulted.

*Italic: References and annex material as provided in ftnea2022006 - References.*

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_Source: https://www.imf.org/-/media/files/publications/ftn063/2022/english/ftnea2022006.pdf_