ETSI GS PDL 031 V1.1.1 (2025-11)

Permissioned Distributed Ledger (PDL); Energy Consumption Data Sharing based on PDL Service

DGS/PDL-0031_Energy_CDS

General Information

• Status: Not Published
• Technical Committee: PDL - Permissioned Distributed Ledger
• Current Stage: 12 - Citation in the OJ (auto-insert)
• Due Date: 03-Dec-2025
• Completion Date: 27-Nov-2025

Standards Content (Sample)

GROUP SPECIFICATION
Permissioned Distributed Ledger (PDL);
Energy Consumption Data Sharing based on PDL Service
Disclaimer
The present document has been produced and approved by the Permissioned Distributed Ledger (PDL) ETSI Industry
Specification Group (ISG) and represents the views of those members who participated in this ISG.
It does not necessarily represent the views of the entire ETSI membership.

2 ETSI GS PDL 031 V1.1.1 (2025-11)

Reference
DGS/PDL-0031_Energy_CDS
Keywords
data sharing, distributed ledger,
energy consumption
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3 ETSI GS PDL 031 V1.1.1 (2025-11)
Contents
Intellectual Property Rights . 5
Foreword . 5
Modal verbs terminology . 5
1 Scope . 6
2 References . 6
2.1 Normative references . 6
2.2 Informative references . 6
3 Definition of terms, symbols and abbreviations . 7
3.1 Terms . 7
3.2 Symbols . 7
3.3 Abbreviations . 7
4 Fine-grained EC metering in mixed deployment environments . 9
4.1 EC measurement in 6G networks . 9
4.1.1 Importance of fine-grained EC measurement . 9
4.1.2 Key features of fine-grained EC measurement . 9
4.1.3 Techniques and approaches enabling fine-grained EC metering . 10
4.1.4 Service types impacting EC . 12
4.2 Use cases and metrics . 13
4.2.1 Use cases of EC measurement . 13
4.2.1.1 Use case on media streaming carbon footprint transparency . 13
4.2.1.2 Use case on digital sobriety . 13
4.2.1.3 Use case on economic incentives for digital sobriety. 13
4.2.1.4 Use case on behavioural incentives for digital sobriety . 13
4.2.1.5 Use case on watch TV over 5G . 13
4.2.1.6 Use case on any Service provider . 13
4.2.1.7 Use case on carbon certificate as a service . 13
4.2.1.8 Use case on energy profiling on network nodes . 14
4.2.2 Key Metrics for EC measurement . 14
4.2.2.1 Metric of EC of a single user end-to-end session . 14
4.2.2.2 Metric of EC of a HW component/device . 14
4.2.2.3 Metric of EC measurement of a software component . 14
4.2.2.4 Metric of EC measurement of a software processes . 15
4.2.2.5 Metric of EC measurement of a complete service . 15
4.2.2.6 Metric of EC of virtualized environment . 15
4.2.2.7 Metric of residual EC of a service . 16
4.2.2.8 Metric of residual EC of a user session . 16
4.2.2.9 Metric of energy production of local green sources . 16
4.3 Deployment of EC measurement techniques . 17
4.3.1 Measurement points in the network . 17
4.3.2 Data reporting and feedback requirements . 18
5 Architecture enhancement with PDL service capability for supporting End-to-End (E2E) EC data
collection . 18
5.1 Background . 18
5.2 PDL Overview . 19
5.3 Architecture enhancement with PDL service capability . 19
5.3.1 Overview of existing telecom network architecture (5G) . 19
5.3.2 Architecture enhancement to telecom networks . 20
5.3.2.1 General architecture . 20
5.3.2.2 Reference point representation . 20
5.3.2.2.1 Option#1: EIF directly interfacing with cUPFs . 20
5.3.2.2.2 Option#2: EIF indirectly interfacing with cUPFs via network domain CP NFs . 21
5.3.2.3 Additional functionalities . 22
5.3.2.3.1 Access control . 22
5.3.2.3.2 Policy enforcement with smart contracts . 22
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5.3.2.3.3 Zero-Knowledge Proof (ZKP) . 22
5.3.2.3.4 On-Chain/Off-Chain Data Storage . 22
5.4 Summary . 23
6 Distributed consensus mechanisms for EC metering data post-verification and service
enforcement with smart contracts . 23
6.1 Post-verification of EC Metering Data . 23
6.1.1 General introduction . 23
6.1.2 EC data distributed verification . 24
6.1.2.1 Single-domain EC data aggregation . 24
6.1.2.2 Cross-domain EC data verification . 25
6.1.3 Real-time auditability and monitoring . 25
6.1.4 Robustness through multi-tool attestation . 26
6.2 Smart contracts for service enforcement . 26
6.2.1 Smart Contract Design . 26
6.2.2 Usage Scenarios and Invocation Mechanisms . 27
6.2.3 Deployment and Governance . 27
7 Conclusion . 27
History . 28

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5 ETSI GS PDL 031 V1.1.1 (2025-11)
Intellectual Property Rights
Essential patents
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pertaining to these essential IPRs, if any, are publicly available for ETSI members and non-members, and can be
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Foreword
This Group Specification (GS) has been produced by ETSI Industry Specification Group (ISG) Permissioned
Distributed Ledger (PDL).
Modal verbs terminology
In the present document "shall", "shall not", "should", "should not", "may", "need not", "will", "will not", "can" and
"cannot" are to be interpreted as described in clause 3.2 of the ETSI Drafting Rules (Verbal forms for the expression of
provisions).
"must" and "must not" are NOT allowed in ETSI deliverables except when used in direct citation.

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6 ETSI GS PDL 031 V1.1.1 (2025-11)
1 Scope
Energy Consumption (EC) is a key metric to assess the energy efficiency of a service. When a service is operated over
multiple domains under different authorities, sharing the EC data is challenging without a centralized authority. Given
that, the present document aims to study how EC data can be trustworthily shared with PDL service. Specifically, the
present document targets to address the following problems:
• Study the existing methods for fine-grained EC metering in physical and virtualized environments; study PDL
service architecture enhancement for supporting E2E EC metering data collection.
• Study distributed consensus mechanisms for EC metering data post-verification and service enforcement with
smart contracts.
2 References
2.1 Normative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
Referenced documents which are not found to be publicly available in the expected location might be found in the
ETSI docbox.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents are necessary for the application of the present document.
[1] ETSI GS PDL 012: "Permissioned Distributed Ledger (PDL); Reference Architecture".
[2] ETSI GS PDL 011: "Permissioned Distributed Ledger (PDL); Specification of Requirements for
Smart Contracts' architecture and security".
[3] ETSI GS PDL 013: "Permissioned Distributed Ledger (PDL); Supporting Distributed Data
Management".
[4] ETSI GS PDL 024: "Permissioned Distributed Ledgers (PDL); Architecture enhancements for
PDL service provisioning in telecom networks".
2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or
non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the
referenced document (including any amendments) applies.
NOTE: While any hyperlinks included in this clause were valid at the time of publication, ETSI cannot guarantee
their long-term validity.
The following referenced documents may be useful in implementing an ETSI deliverable or add to the reader's
understanding, but are not required for conformance to the present document. ®
[i.1] Weaver, V.: "Reading RAPL energy measurements from Linux ", University of Maine. ®
NOTE: Linux is the registered trademark of Linus Torvalds in the U.S. and other countries.
[i.2] ARM Ltd.: "Energy Probe overview", Arm Developer Documentation.
[i.3] NETIO Products a.s.: "PowerBOX 4Kx", Smart PDU with Electrical Measurement, NETIO
Official Product Documentation.
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7 ETSI GS PDL 031 V1.1.1 (2025-11)
[i.4] NETIO Products a.s.: "PowerPDU 8KF", Professional Smart PDU with Metered & Switched
Outlets, NETIO Official Product Documentation.
[i.5] NETIO Products a.s.: "PowerDIN 4PZ", DIN Rail Power Meter and Smart Switch, NETIO
Official Product Documentation.
[i.6] NETIO Products a.s.: "Official Website of NETIO Smart Power Monitoring Solutions".
[i.7] Amaral, Marcelo, et al.: "Kepler, A framework to calculate the energy consumption of
th
containerized applications", 2023 IEEE™ 16 international conference on cloud computing
(CLOUD).
[i.8] van Kemenade, Tim: "Real-time Scaphandre Energy Metrics Pipeline Integrated with Escheduler".
Diss. Vrije Universiteit Amsterdam, 2024.
[i.9] ETSI GR PDL 009: "Permissioned Distributed Ledger (PDL); Federated Data Management".
[i.10] ETSI GR PDL 004: "Permissioned Distributed Ledgers (PDL); Smart Contracts; System
Architecture and Functional Specification".
[i.11] ETSI ES 202 336-12: "Environmental Engineering (EE); Monitoring and control interface for
infrastructure equipment (power, cooling and building environment systems used in
telecommunication networks); Part 12: ICT equipment power, energy and environmental
parameters monitoring information model".
[i.12] ETSI TS 128 554: "5G; Management and orchestration; 5G end to end Key Performance
Indicators (KPI) (3GPP TS 28.554 Release 18)".
[i.13] 4E EDNA.(2019): "Total Energy Model for Connected Devices".
[i.14] JRC135926_01: "Energy Consumption in Data Centres and Broadband communication networks
in the EU", Kamiya & Bertoldi, 2024.
3 Definition of terms, symbols and abbreviations
3.1 Terms
Void.
3.2 Symbols
Void.
3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply:
rd
3GPP 3 Generation Partnership Project
5G Fifth Generation
6G Sixth Generation
ABAC Attribute Based Access Control
AI Artificial Intelligence
AMF Access and Mobility management Function
API Application Programming Interface
ARM Advanced RISC Machines
BMC Baseboard Management Controller
CO2 Carbon Dioxide
CP Control-Plane
CPU Central Process Unit
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CU Centralized Unit
cUPF computing User Plane Function
DLE Distributed Ledger Enabler
DLE-Peer Distributed Ledger Enabler -Peer
DU Distributed Unit
E2E End-to-End
EC Energy Consumption
EE Energy Efficiency
EIF Energy Information Function
GDPR General Data Protection Regulation
GHG Greenhouse Gas
GPU Graphic Process Unit
GR Group Report
HW Hardware
I/O Input/Output
ICT Information and Communication Technologies
IoT Internet of Things
IP Internet Protocol
IPMI Intelligent Platform Management Interface
NEF Network Exposure Function
NF Network Function
OEF On-site Energy Fraction
OTLP Open Telemetry Protocol
OTT Over-The-Top
PBFT Practical Byzantine Fault Tolerance
PCF Policy Control Function
PDL Permissioned Distributed Ledger
RAM Random-Access Memory
RAN Radio Access Network
RAPL Running Average Power Limit
RF Radio Frequency
SC-ITM Smart Contract of Incentive & Token Management
SC-MV Smart Contract of Measurement Validator
SC-PE Smart Contract of Policy Enforcement
SDIA Sustainable Digital Infrastructure Alliance
SLA Service Level Agreement
SMF Session Management Function
TS Technical Specification
TV Television
UDM Unified Data Management
UE User Equipment
UPF User Plane Function
VM Virtual Machine
ZKP Zero Knowledge Proof
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4 Fine-grained EC metering in mixed deployment
environments
4.1 EC measurement in 6G networks
4.1.1 Importance of fine-grained EC measurement
In the context of 6G networks, Energy Consumption (EC) measurement is critical to ensuring that the deployment and
operation of the network are sustainable, efficient, and cost-effective. In the transition to 6G, Energy Efficiency (EE)
will become even more important due to the expected exponential increase in the number of connected devices, higher
data rates, and more complex network services.
EC measurement has long been a critical aspect of managing network resources and optimizing EE. Traditional
methods of EC measurement often provide a broad, system-level view of power usage, typically aggregated across
entire systems or network components. However, as modern networks, particularly 6G, evolve to support increasingly
complex and dynamic services, a higher level of precision is required to capture the nuances of EC.
Fine-grained EC metering, which provides detailed, process-level, or even thread-level measurement, represents a
paradigm shift towards more sophisticated energy management. This approach enables the identification of energy
usage patterns at a granular level, offering insights into the exact power consumption of specific processes, applications,
or components within the system.
4.1.2 Key features of fine-grained EC measurement
For fine-grained EC metering to be effective, several key features shall be present.
First, granularity of measurement is crucial. The EC meter shall be able to track EC down to the process, thread, or
program level. This includes per-process metering, where the device identifies and monitors EC of each individual
process on the system. This involves measuring CPU consumption per process, where the power used for CPU cycles is
tracked, as well as memory access power consumption, which involves monitoring energy used by memory operations
(RAM reads and writes) for each process.
Second, Input/Output (I/O) operations shall also be tracked, as they consume energy when interacting with disks,
networks, or peripherals. Beyond per-process metering, the device needs to offer thread-level precision to differentiate
between energy used by various threads within a process. Integration with hardware-level energy meters, such as Intel
RAPL [i.1] or ARM energy monitors [i.2], is essential for capturing EC of specific hardware components like CPUs,
GPUs, or network devices.
Third, the sampling rate of the EC metering shall be high to ensure the instrument can capture fine-grained data. A
high-frequency sampling rate, ideally in the range of microseconds (μs) or milliseconds (ms), ensures that even
short-lived processes are captured accurately. For instance, in high-performance computing environments, rapid
processes should not go unnoticed. The EC meter shall be capable of real-time monitoring, where short-term EC spikes
(such as during I/O bursts or GPU operations) are captured instantly. Additionally, adaptive sampling could be
employed, where the sampling interval is adjusted based on process duration, allowing for detailed monitoring of
shorter processes while minimizing data logging overhead for longer tasks.
Fourth, support for virtualized and distributed environments is critical for modern cloud and distributed systems. Many
applications now run across Virtual Machines (VMs) or containers. The metering shall therefore be able to monitor EC
in virtualized environments by interfacing with hypervisors such as VMware, KVM, or Hyper-V, and attribute power
usage to each individual VM instead of merely the physical host. For containerized applications like those running in
Docker or Kubernetes environments, the EC meter shall track energy used by individual containers, even though they
share underlying hardware resources. In distributed systems, the EC meter shall gather and consolidate energy data
from multiple physical or virtual nodes, including edge and cloud nodes, ensuring accurate attribution of power
consumption. When shared resources like CPU cores or network interfaces are used by multiple processes, the
instrument should allocate energy usage accordingly based on their utilization.
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4.1.3 Techniques and approaches enabling fine-grained EC metering
Fine-grained Energy Consumption (EC) metering methods refer to techniques used to measure EC with a high level of
granularity. These methods aim to provide detailed and accurate measurements of energy usage in various
environments, including both physical and virtualized settings.
Traditional approaches to EC metering in physical environments have been foundational in managing and recording
electricity usage. Direct metering techniques involve the use of physical meters to measure EC directly at the point of
use. These methods are fundamental to energy management in various settings and have evolved over time, starting
with analog meters and progressing to more sophisticated digital meters.
Analog meters have been the traditional tool for measuring electricity consumption. They operate using a mechanical
rotating disc that moves in proportion to the amount of electricity consumed. The simplicity and robustness of analog
meters make them reliable; they do not require an external power source and can operate for extended periods with
minimal maintenance. However, these meters come with significant drawbacks. They require manual reading and
recording, which is labour-intensive and prone to human error. The infrequency of readings, often limited to monthly
intervals, means that the data collected lacks granularity, making it difficult to analyse consumption patterns or identify
opportunities for efficiency improvements.
Digital meters represent a more advanced approach to direct metering. Unlike their analog counterparts, digital meters
provide electronic readings of EC. These meters can store data at much higher frequencies, allowing for more detailed
and accurate records. Digital meters can transmit data automatically to central systems via communication networks,
reducing the need for manual readings and minimizing errors. This capability enhances the granularity of the data,
enabling more precise monitoring and analysis of energy usage. However, digital meters can be more expensive to
install and maintain than analog meters and require a reliable power source and communication infrastructure.
Sensor-based monitoring systems offer a more granular and dynamic approach to EC metering. These systems use
various sensors to continuously measure different parameters of energy usage, providing real-time data that can be
analysed for immediate insights and long-term trends.
Smart sensors are at the core of these systems, capable of measuring voltage, current, power, and other relevant metrics
with high precision. These sensors are often installed at various points within an electrical network, including at
individual appliances, circuits, or distribution panels. The real-time data collected by these sensors is transmitted to a
central monitoring system, where it is aggregated and analysed. This continuous stream of data enables the detection of
anomalies, peak demand periods, and inefficient energy use, allowing for proactive management and optimization of
EC.
Figure 1: A metering infrastructure for campus distribution system
The integration of Internet of Things (IoT) technology enhances the capabilities of sensor-based monitoring systems.
IoT-enabled sensors can communicate wirelessly, facilitating easy installation and scalability. They can also be
integrated with advanced analytics platforms and machine learning algorithms to provide predictive insights and
automated responses to identified issues. For instance, a sensor detecting an unusual spike in energy usage can trigger
an alert or automatically shut down a malfunctioning device to prevent energy waste or damage.
In virtualized environments, where multiple Virtual Machines (VMs) or containers share underlying physical resources,
accurate and fine-grained metering of EC is crucial for optimizing resource allocation, improving energy efficiency, and
enabling fair billing practices. Two common approaches for EC metering in virtualized environments are hypervisor-
based monitoring and application-level instrumentation.
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Hypervisor-based monitoring leverages the virtualization layer, known as the hypervisor, to collect EC metrics from the
underlying physical hardware and the virtual entities (VMs or containers) running on top of it. The hypervisor has a
privileged view of the entire system and can gather detailed information about resource utilization, which can be
correlated with EC.
The hypervisor-based approach typically involves the following techniques:
• Hardware performance counters: Modern processors and other hardware components provide performance
counters that can be accessed by the hypervisor. These counters capture low-level metrics such as CPU cycles,
cache misses, and memory accesses, which can be used to estimate EC based on predetermined models or
calibration data.
• Power modelling: The hypervisor can employ power models that map resource utilization metrics (CPU,
memory, disk, network) to EC values. These models can be derived from empirical measurements or
vendor-provided data for specific hardware configurations.
• Direct energy measurement: In some cases, the hypervisor may have access to hardware sensors or
instrumentation that directly measures the EC of physical components, such as CPU packages, memory
modules, or entire server nodes.
Advantages of hypervisor-based monitoring include a comprehensive view of resource usage across all virtual entities,
minimal overhead as monitoring is performed at the hypervisor level, and the ability to monitor EC even for opaque or
closed-source applications.
Disadvantages include limited visibility into application-level metrics and behaviour, potential security concerns due to
the hypervisor's privileged access, and the need for accurate power models or calibration data for reliable energy
estimation.
Application-level instrumentation involves embedding monitoring code or agents within the applications running inside
the virtual machines or containers. These agents collect resource usage metrics and application-specific performance
indicators, which can be correlated with EC patterns.
The implementation of application-level instrumentation can take various forms, such as:
• Code instrumentation: Modifying the application's source code to include energy monitoring hooks or probes
that capture resource usage metrics and report them to a centralized monitoring system.
• Agent-based monitoring: Integrating a separate monitoring agent or library with the application, either through
dynamic linking or by running alongside the application process. The agent collects resource usage metrics
and sends them to an energy monitoring system.
• Energy-aware profiling tools: Utilizing profiling tools specifically designed for EC analysis, which can
instrument the application code or integrate with the runtime environment to capture energy-related metrics as
in Figure 2.
Application-level instrumentation can collect various metrics relevant to EC, such as:
• CPU usage: Tracking CPU cycles, instruction counts, and CPU-bound workloads.
• Memory usage: Monitoring memory footprint, memory access patterns, and memory-bound workloads.
• Disk I/O: Capturing disk read/write operations, disk throughput, and disk-bound workloads.
• Network I/O: Tracking network traffic, bandwidth utilization, and network-bound workloads.
• Application-specific metrics: Collecting application-level performance indicators, such as transaction rates,
response times, or custom metrics relevant to EC patterns.
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Figure 2: Runtime monitoring
Advantages of application-level instrumentation include granular insights into application-level EC patterns, the ability
to correlate energy usage with specific application behaviours or workloads, and fine-grained optimization
opportunities.
Disadvantages may include the need to modify or instrument the application code, potential overhead introduced by the
monitoring agents, and the challenge of accurately mapping application-level metrics to EC without hardware-level
calibration or modelling.
In practice, a combination of hypervisor-based monitoring and application-level instrumentation may be employed to
achieve comprehensive and accurate EC metering in virtualized environments. The hypervisor-based approach provides
a system-wide view, while application-level instrumentation offers granular insights into application behaviour and EC
patterns, enabling targeted optimization and energy-efficient resource allocation strategies.
4.1.4 Service types impacting EC
In 6G networks, services can be categorized into distinct types, each with different Energy Consumption (EC)
characteristics and computational requirements. These service types include transmission services, computation
services, and dynamic services:
• Transmission Services: These services primarily focus on data transfer between network nodes and end
devices. EC for transmission services is closely related to the volume of data being transmitted, the distance
between devices, and the network infrastructure (e.g. base stations or edge nodes). Power usage in these
services tends to scale with bandwidth demands and network traffic, requiring efficient protocols to optimize
energy use during data transfer.
• Computing Services: In contrast to transmission services, computation services are more resource-intensive,
involving data processing, analytics, and decision-making tasks at both the edge and cloud layers. These
services require significant computational power and are heavily dependent on the performance and efficiency
of the underlying hardware (e.g. GPUs or specialized processors). EC in computation services is influenced by
factors such as processing complexity, resource allocation, and task scheduling.
• Dynamic Services: Dynamic services in 6G networks are characterized by their ability to adapt to real-time
changes in network conditions and user requirements. These include services like network slicing, on-demand
resource provisioning, and real-time edge computing. The EC of dynamic services is variable, as it is affected
by factors like the level of service scaling, load balancing, and the agility of the network in adjusting to
varying demand. Efficiently managing the energy use of dynamic services requires advanced algorithms and
predictive models that can anticipate demand spikes and optimize resource allocation.
Each service type has distinct EC patterns, requiring different methods for accurate energy metering and management.
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4.2 Use cases and metrics
4.2.1 Use cases of EC measurement
4.2.1.1 Use case on media streaming carbon footprint transparency
Energy Consumption (EC) measurement plays a critical role in a variety of use cases across different sectors,
particularly in environments where sustainability and operational efficiency are key priorities. These use cases highlight
the need for precise and granular tracking of energy usage to optimize performance, minimize environmental impact,
and ensure compliance with regulatory standards. By integrating EC measurement into these scenarios, organizations
can make data-driven decisions that not only reduce EC but also contribute to broader efforts to mitigate climate
change. This clause explores the specific use cases that require accurate EC measurement to achieve these objectives.
This use case focuses on providing users with visibility into the carbon footprint of their video streaming activities,
including OTT platforms and video conferencing. Metrics such as "Instant Carbon Footprint" or "Total Daily CO " are
displayed to the user during streaming. This transparency allows users to make informed choices about the trade-off
between service quality and environmental impact, offering them the option to adjust settings that minimize their carbon
footprint.
4.2.1.2 Use case on digital sobriety
Building on UC introduced in clause 4.2.1.1, this use case empowers users to take active steps to reduce their carbon
footprint by providing alternative delivery modes such as lower video resolution or data rate options. These alternatives
are accompanied by their respective carbon footprint impacts, enabling users to select a service option that aligns with
their environmental goals.
4.2.1.3 Use case on economic incentives for digital sobriety
This use case adds an economic layer to UC introduced in clause 4.2.1.2 by offering users incentives for reducing their
carbon footprint. Users are rewarded with "environmental points" for selecting carbon-reducing alternatives, and at the
end of each month, those points contribute to a lottery, with the user's potential reward being proportional to their
accumulated points.
4.2.1.4 Use case on behavioural incentives for digital sobriety
Similar to UC introduced in clause 4.2.1.3, this use case seeks to motivate users to reduce their carbon footprint, but
through behavioural incentives. Peer pressure is used as a motivational tool, where users are privately informed of their
relative performance in terms of carbon footprint reduction, with comparisons to others in the same timeframe
(e.g. weekly performance).
4.2.1.5 Use case on watch TV over 5G
This use case introduces carbon footprint tracking in the context of watching TV over 5G networks. Similar to UC1,
metrics are provided to the user, but this use case highlights differences in connectivity types (specifically 5G) that may
affect the carbon footprint of the service session.
4.2.1.6 Use case on any Service provider
While UC introduced in clause 4.2.1.1 visualizes energy and carbon footprint data to the user, this use case extends this
functionality to all service providers involved in the service delivery chain. This ensures that carbon footprint
transparency is shared across the entire ecosystem, allowing each provider to assess and adjust their environmental
impact.
4.2.1.7 Use case on carbon certificate as a service
This use case envisions a carbon trading market where users receive carbon usage reports at the end of their billing
period. Based on these reports, users can trade their carbon credits, receiving compensation for unused carbon or
purchasing additional credits if their emissions exceed their allowance.
ETSI
14 ETSI GS PDL 031 V1.1.1 (2025-11)
4.2.1.8 Use case on energy profiling on network nodes
This use case involves the monitoring and aggregation of energy-related data at each node in the network infrastructure.
The collected data is analysed to create an energy profile for each node, with an efficiency index assigned to represent
the node's energy performance. This dynamic energy profiling sets the stage for optimizing load distribution across the
network, helping reduce overall EC.
4.2.2 Key Metrics for EC measurement
4.2.2.1 Metric of EC of a single user end-to-end session
Description: evaluation of EC generated by a single user session. EC contributions of every component included into
the end-to-end (multidomain) service path should be considered. The metric includes EC contributions of service
components required for the service to run (see EC#3, EC#5) and energy consumed at the client side (see EC#3). The
mathematical definition is:
∑
� = � � �� = � ∆� (1)
��#� ����������� � �����������
�
Formula 1: EC of a single user session based on integration or discrete summation of power usage over time:
• Unit: Joule (can be transformed to kWh).
• Accuracy: would depend on input metrics accuracy.
• Time resolution: would depend on input metrics time resolution.
4.2.2.2 Metric of EC of a HW component/device
Description: measuring EC of a HW device, i.e. the measurement would normally take place at the power outlet,
resulting in measurement of total EC. In case the device has multiple power inputs, EC of all of them shall be measured,
while total EC is sum of all partial measurement results. The mathematical definition is:

� = � �� = ∑ � ∆� (2)
�
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�
Formula 2: EC of a hardware device as the integral or discrete summation of power consumption over t
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