Architectural Considerations for Environmental Sustainability
draft-pignataro-enviro-sustainability-architecture-03

Network Working Group                                  C. Pignataro, Ed.
Internet-Draft                                      Blue Fern Consulting
Intended status: Informational                                 A. Rezaki
Expires: 14 May 2026                                               Nokia
                                                             S. Krishnan
                                                                   Cisco
                                                                J. Arkko
                                                                Ericsson
                                                                A. Clemm
                                                               Sympotech
                                                            H. ElBakoury
                                                  Independent Consultant
                                                               S. Prabhu
                                                                   Nokia
                                                        10 November 2025

     Architectural Considerations for Environmental Sustainability
         draft-pignataro-enviro-sustainability-architecture-03

Abstract

   This document describes several of the design tradeoffs involved in
   optimizing for sustainability along with the other common networking
   metrics such as performance and availability.

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   Copyright (c) 2025 IETF Trust and the persons identified as the
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Terminology . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Architectural Considerations of Environmental
           Sustainability  . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Design Tradeoffs  . . . . . . . . . . . . . . . . . . . .   3
     2.2.  Multi-Objective Optimization  . . . . . . . . . . . . . .   4
     2.3.  How Much Resiliency is Really Needed? . . . . . . . . . .   5
       2.3.1.  Redundancy and Sustainability . . . . . . . . . . . .   6
     2.4.  How Much are Performance and Quality of Experience
           Compromised?  . . . . . . . . . . . . . . . . . . . . . .   6
     2.5.  End-to-End Sustainability . . . . . . . . . . . . . . . .   7
     2.6.  Attributional and Consequential Models  . . . . . . . . .   7
     2.7.  The Role of Network Management and Orchestration  . . . .   8
   3.  Sustainability Requirements and Phases  . . . . . . . . . . .  11
     3.1.  Phase 1: Visibility . . . . . . . . . . . . . . . . . . .  11
     3.2.  Phase 2: Insights and Recommendations . . . . . . . . . .  12
     3.3.  Phase 3: Self-optimization and Automation . . . . . . . .  12
       3.3.1.  Cycle of Phases . . . . . . . . . . . . . . . . . . .  12
   4.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  13
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  13
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  13
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

1.  Introduction

   Over the past decade, there has been increased awareness of the
   environmental sustainability impact produced by the widespread
   adoption of the Internet and internetworking technologies.  The
   impact of Internet technologies has been overwhelmingly positive over
   the past years (e.g., providing alternatives to travel, enabling
   remote and hybrid work, enabling technology-based endangered species
   conservation, etc.), and there is still room for improvement.

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   At the same time, internetworking technologies themselves have a
   significant environmental footprint.  Reducing this footprint and
   making network deployments environmentally more sustainable becomes a
   matter of increasing importance to network providers for various
   reasons, including the desire to reduce operational expenses
   associated with energy usage, regulatory pressures related to Net
   Zero mandates, and corporate citizenship demands from customers to
   become "greener".

   Efforts to make internetworking more sustainable may in some cases
   conflict with other important goals, such as network availability,
   resilience against sudden spikes in traffic demand, and (at least in
   some cases) Quality of Experience.  For example, a network operator
   might want to include excess capacity in their network to be able to
   absorb sudden spikes in demand and provision additional paths to
   increase resilience against failures.  However, doing so may involve
   powering up additional ports and equipment, resulting in higher
   energy usage and increased greenhouse gas emissions, thus
   compromising sustainability goals.  This indicates that there are
   certain choices that a network operator has to make, some of which
   may involve tradeoffs between goals.

   This document describes some of the tradeoffs that could be involved
   while optimizing for sustainability in addition to or in lieu of
   traditional metrics such as performance or availability.  Further, it
   discusses how Internet technologies can be used to help other fields
   become more sustainable.

   Specifically, this document details environmental sustainability
   implications to Internet protocols, architectures, and technologies.

1.1.  Terminology

   This document leverages the terminology and concepts defined in
   [I-D.pignataro-green-enviro-sust-terminology], and readers are
   expected to be familiar with those.

2.  Architectural Considerations of Environmental Sustainability

2.1.  Design Tradeoffs

   Traditionally, digital communication networks are optimized for a
   specific set of criteria that serve as proxies for business metrics.
   A network operator providing services to their customers intends to
   maximize profits, by increasing top-line revenue and decreasing
   bottom-line associated costs.  This directly translates to goals of
   optimizing performance and availability, while reducing various
   costs.

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   Most recently, various forces elevate the need for sustainability in
   networking technologies and architectures, to quantify and minimize
   negative environmental impact.

   Optimizing only network availability (e.g., by having excess capacity
   and backup paths) or optimizing only performance (e.g., by increasing
   speeds or selecting paths based solely on delays) can seemingly be in
   opposition to optimizing sustainability objectives.  For a given
   application, use-case, or vertical realization of technology, a
   Pareto-efficient choice can potentially improve sustainability
   without sacrificing availability or performance beyond the
   application tolerance.  That is, a win-win.

   Consequently, network architects and designers are presented with a
   set of new design tradeoffs: a multi-objective optimization that
   satisfies broader requirements and global optima for availability,
   performance, and sustainability simultaneously.  This is not unlike
   the doughnut economics model concept described in [Doughnut].

2.2.  Multi-Objective Optimization

   To understand this new model, we can analyze a simplified example.
   Assume the following topology, passing traffic from A to B:

                           A
                           |
                      +----------+
                      | Router 1 |------------+
                      +----------+            |
                       | | | | |         +----------+
                       | | | | |         | Router 3 |
                       | | | | |         +----------+
                      +----------+            |
                      | Router 2 |------------+
                      +----------+
                           |
                           B

       Figure 1: Simplified Network for Multi-Objective Optimization

   Router 1 is directly connected to Router 2 through five parallel
   links, of 10 Gbps each.  Router 1 can also reach Router 2 through
   Router 3 with 40 Gbps links between Router 1 and Router 3, and
   between Router 3 and Router 2.  Let's assume that the capacity-
   planned traffic between A and B equals 15 Gbps.

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   In this scenario, a topology optimized for performance and
   availability/resiliency would have all links and routers on, and
   would likely forward traffic using two of the parallel links.
   Utilizing the path through Router 3 might lower performance, but it
   serves as a backup path.

   On the other hand, when we add sustainability as a consideration,
   different options present themselves, each of which involves a
   tradeoff.  One of the options is to remove from the topology Router 3
   and associated links, and shutdown links and optics in two or three
   of the parallel links.  This will allow the conservation of energy
   that will no longer be required to operate Router 3 and the affected
   links.  Another option is to completely shutdown all the parallel
   links and route traffic through Router 3 (i.e., not maximizing
   performance alone, but maximizing at the time performance,
   availability and resiliency, and sustainability.)  The choice between
   these two options, as well as the option to stick with the original
   topology, will depend on choices that the network operator will have
   to make, taking into account the aggregate sustainability metrics of
   network elements in each of the two topologies as well as the effect
   these choices will have on availability/resiliency which will be
   reduced as a result.

   Another option is to use flexible Ethernet, where the five links
   combined are aggregated into one active virtual link which has 15
   Gbps, and another inactive link of 35 Gbps of capacity -- although a
   physical link cannot be half-active and half-inactive as far as PHY
   and optics are concerned.

2.3.  How Much Resiliency is Really Needed?

   When we add sustainability considerations, resiliency is not the
   single objective to optimize.

   There are many methods to improve network resiliency, including a
   design eliminating single-points-of-failure, performing software
   safe-release selections and upgrades, deploying network real-time
   testing systems (including operations, administration, and
   maintenance (OAM) tools, monitoring systems (e.g., [RFC8403]), chaos-
   based testing, and site reliability engineering (SRE) principles),
   and utilizing redundancy across network elements as well as across a
   topology.  Each one of these methods incurs also a sustainability
   cost.  Yet, the functions for resiliency improvement and
   sustainability cost for each of these methods are not the same.
   Considering sustainability means quantifying its impact in the
   decision of how to improve resiliency, and how much is needed.

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2.3.1.  Redundancy and Sustainability

   Let's first explore redundancy.  For example, consider the ratio of
   overall network capacity (in bandwidth, compute power, etc.) over the
   used network capacity, and let's call it "Redundancy Index".  If this
   number is one, there's no redundancy; and as the ratio grows, so does
   the potentially unused capacity that could be utilized in a failure
   event.  Similarly, consider the values of sustainability metrics for
   when the Redundancy Index is one and for when it is two.  These
   border points might give an indication of the slope for each
   objective function.

   Adequate Redundancy:
      In order to determine how much redundancy needs to be built into
      the overall network capacity, which can be referred to as
      "adequate redundancy to avoid network outings", it will be
      important to (1) measure the bandwidth of attacks against the
      overall network capacity; and (2) understand how quickly "high
      bandwidth" attacks can be detected and diverted.  Measuring these
      results over time may lead the "adequate redundancy" to become
      higher over time.

   Justified Redundancy:
      Traditionally, network operators would be much less worried about
      energy use than about the possibility that the network would have
      brownout or backout outages - thus the measuring will help better
      balance the "adequate redundancy" against the related energy use,
      resulting in turn in "justified redundancy": a balance between
      costs and benefits, with energy use as well as material use as a
      clear cost factor.

   Please note that "justified redundancy" may be higher than "adequate
   redundancy" when we manage to organize the redundancy in a multi-
   layer fashion: (1) capacity that is "always on" and always uses
   energy; (2) capacity that can turn on quickly when needed; (and
   possibly (3) capacity that is "on the shelf" (even in the box) but
   ready to be deployed quickly when needed.)

2.4.  How Much are Performance and Quality of Experience Compromised?

   Network performance and Quality of Experience have always played an
   important role in the development of networking technology.  Key
   parameters such as latency, jitter, and loss as well as their impact
   on the quality that the users of networked applications experience
   are well understood, the optimization of those parameters and the
   adherence to corresponding service level objectives being an
   important goal in most network deployments.  However, the desire to
   improve sustainability and energy efficiency can conflict with those

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   goals.  For example, in order to ensure minimal latency, a network
   operator may need to provision additional paths that require
   additional ports that need to be powered, instead of relying on a
   topology with fewer links and nodes.  Such a topology might result in
   greater power efficienc as a result more resources being shared, but
   it could also result in longer paths and an increased possibility for
   congestion, both of which would be detrimental to latency and
   associated Quality of Experience.

   This implies a tradeoff between different goals.  The challenge for
   operators lies in finding the sweet spot in which acceptable network
   performance is obtained and a point of diminishing returns is reached
   at which any incremental further performance improvement would come
   at the expense of significant deterioration in energy usage.

2.5.  End-to-End Sustainability

   The networking industry is in the starting phases of addressing this
   objective.  We are seeing a sprinkling of sustainability features
   across the networking stack and components of devices, whether it is
   on forwarding chips, power supplies, optics, and compute.  Many of
   those optimizations and features are typically local in nature, and
   widely scattered across different elements of a network architecture.
   An opportunity for maximizing the positive environmental impact of
   these technologies calls for a more cohesive and complementary view
   that spans the complete product lifecycle for hardware and software,
   as well as how some of these features work in unison.

   For example, features that provide energy saving modes for devices
   can be dynamically managed when the network utilization is such that
   performance would not significantly suffer.  A core router, instead
   of becoming obsolete due to the need for higher throughput in the
   core, could become a future edge/access router.  That is an example
   of reuse and repurpose, before recycling.  There are benefits of
   macro-optimizations by clustering in specific features, versus micro-
   optimizing locally without awareness of the network context.

2.6.  Attributional and Consequential Models

   Many of the subtleties and nuances of the measurement of GHG and
   environmental impacts stem from the very important distinction
   between attributional and consequential models.  Detailed definitions
   can be found at [UNEP-LCA].

   Attributional:

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      Also referred to as Allocational models, start by allocating or
      attributing quantities (e.g., GHG emissions) to entities (e.g., a
      router, a building, a town), and performing comparisons between
      the measurements (or estimates) of the quantity by the entities.

   Consequential:
      Perform the measurement of the quantity by establishing a baseline
      scenario (e.g., before feature introduction) and a modified
      scenario (e.g., after the feature introduction).

   While both models are quite different, they do use the same terms and
   frames of references, measures, and language.  Without explicit
   clarifications, they are prone to confusion.

   For example, measuring the carbon footprint attributed to a batch
   process or a workload based on its energy efficiency would not
   consider that the hardware is still there running.  When is it most
   effective to charge battery-powered devices, during the night when
   there's less load, or during the day when there's solar energy?  In
   other words, if a person who commutes by train to their office five
   days a week starts working from home two days a week, there could be
   an attributional reduction of GHG emissions, yet the train still
   continues running equally.  However, if that person commutes by
   combustion-engine car alone, the consequences are different.

   Considering the attributional versus consequential distinction, there
   are some implications and a potential corollaries:

   *  For an environmental-impact analysis, it is critical to explicitly
      cite the model used, as well as clearly define the boundary.

   *  The activities that we embark upon as internetworking and protocol
      designers - including the ones targeting reduction of negative
      environmental impacts - have an energy footprint of themselves.

   *  "Do no harm" in the context of improving sustainability of
      networks is to look beyond bounded attributions and consider (both
      intended and unintended) consequences.

2.7.  The Role of Network Management and Orchestration

   Deployment and operational aspects play a critical role in making
   networks more sustainable.  A detailed explanation of that role, the
   associated challenges, as well as an outline of solution approaches
   is provided in [RFC9845].  Here are some areas in which network
   management can help make networks more sustainable; for a more
   extensive treatment, please refer to that document.

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   Dimensioning:
      Networks should be deployed and configured with sufficient
      capacity to serve their intended purpose.  At the same time,
      overprovisioning and providing too many resources should be
      avoided, as this results in waste and unnecessary environmental
      impact.  Network management can facilitate proper dimensioning of
      networks by providing the methods and tools that allow to assess
      network usage, determine required capacities, identify trends to
      allow to proactively accommodate traffic growth and new services.

   Network Optimization:
      Network management applications can help solve difficult network
      optimization problems involving multiple parameters, multiple and
      sometimes conflicting objectives, and mitigation of tradeoffs.
      Network optimization examples include maximization of utilization
      or of aggregate QoE scores, minimization of the possibility of SLA
      violations with a given amount of network resources, or
      optimization of the cost of configured paths.  Network metrics
      related to sustainability are another set of parameters that can
      be optimized.

   Rapid Discovery and Provisioning Schemes:
      One of the biggest potential opportunities in reducing
      environmental impact of networks concerns the ability to power
      resources such as equipment or line cards down when they are
      momentarily not needed due to swings in traffic demands.  In
      general, this involves fully automated management control loops
      with very short time scales.  Network management can enable such
      schemes, involving algorithms that determine and control the rapid
      de- and re-commissioning of networking resources, as well as the
      necessary control protocols that facilitate aspects such as rapid
      resource discovery, reprovisioning, or reconvergence of management
      state.

   Policy-Driven Sustainability Enforcement:
      Network management systems can play a pivotal role in enforcing
      explicit sustainability policies, much like how QoS, security, or
      routing policies are enforced today.  These policies can express
      environmental objectives such as limiting power consumption within
      specific network domains, prioritizing traffic through paths
      powered by renewable energy, or dynamically adjusting service
      parameters to meet carbon footprint targets.  Such policy-driven
      approaches allow sustainability intents to be specified at a high
      level and translated into actionable configurations using
      orchestration frameworks.  By embedding sustainability into policy
      and intent-based networking models, operators gain precise control
      over how environmental goals are operationalized and maintained
      across diverse services and tenants.

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   Inter-Domain Sustainability Coordination:
      Sustainability optimization must extend beyond individual
      administrative domains to realize its full potential at Internet
      scale.  Network management and orchestration systems can be
      enhanced to support inter-domain coordination mechanisms that
      allow operators to share sustainability-related metadata, such as
      real-time carbon intensity of regional infrastructure, green
      routing preferences, or energy availability status.  By enabling
      cooperative decision-making, networks can collectively route
      traffic in ways that reduce aggregate environmental impact.  This
      requires the definition of interoperable data models, trust
      frameworks, and privacy-preserving methods for sharing
      sustainability metrics across organizational boundaries.  As
      sustainability becomes a global imperative, inter-domain
      orchestration will be essential to align local optimizations with
      broader planetary goals.

      To enable end-to-end sustainability, environmental objectives must
      persist across the full service path, including ingress, transit,
      and egress domains.  This requires a mechanism for expressing
      sustainability intents (e.g., “carbon-sensitive” or “low-energy”)
      at the service origin and ensuring they are respected downstream.
      These intents may influence route selection, resource allocation,
      and power state decisions in intermediate networks.  Failure to
      propagate such goals may result in sustainability regressions that
      cancel out upstream efforts.

      Achieving this coordination demands standardized sustainability
      telemetry formats and semantic models.  Exchanged data may include
      per-domain carbon intensity, real-time energy sourcing, or
      equipment-level energy efficiency indicators.  Agreement on common
      ontologies and encoding formats will be essential to ensure
      compatibility across vendor and operator implementations.

      Beyond metrics, cooperative orchestration protocols will be needed
      to act on this shared information.  For example, inter-domain
      green routing agreements may optimize for end-to-end energy
      profiles in addition to latency or cost.  Sustainability-aware
      SLAs could encode carbon or energy constraints alongside
      traditional service guarantees.  Trust boundaries, policy
      asymmetries, and privacy concerns may necessitate abstraction
      layers, optional disclosure levels, or brokered negotiation
      intermediaries.

   In addition to those aspects, perhaps the most important role of
   network management is to provide network operators with the necessary
   visibility into how and where power is used in their network.  This
   is required in order to assess where the network stands in terms of

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   sustainability.  It also allows to track progress over time, compare
   different alternatives for their effectiveness, and generally to
   facilitate network sustainability optimization.  Providing this
   visibility requires the definition of metrics and corresponding
   instrumentation of the network so that those metrics can be
   monitored, assessed, compared, and improved.

3.  Sustainability Requirements and Phases

   The architectural considerations for environmental sustainability
   cannot always be achieved at the same time and we expect the
   following high level phases:

   1.  Visibility: In this phase we focus on the measurement and
       collection of metrics.

   2.  Insights and Recommendations: In this phase we focus on deriving
       insights and providing recommendations that can be acted upon
       manually over large time scales.

   3.  Self-Optimization via Automation: In this phase we build
       awareness into the systems to automatically recognize
       opportunities for improvement and implement them.

3.1.  Phase 1: Visibility

   Visibility represents collecting and organizing data in a standard
   vendor agnostic manner.  The first step in improving our
   environmental impact is to actually measure it in a clear and
   consistent manner.  The IETF, IRTF and the IAB have a long history of
   work in this field, and this has greatly helped with the
   instrumentation of network equipment in collecting metrics for
   network management, performance, and troubleshooting.  On the
   environmental-impact side though, there has been a proliferation of a
   wide variety of vendor extensions based on these standards.  Without
   a common definition of metrics across the industry and widespread
   adoption we will be left with ill-defined, potentially redundant,
   proprietary, or even contradicting metrics.  Similarly, we also need
   to work on standard telemetry for collecting these metrics so that
   interoperability can be achieved in multi-vendor networks.

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3.2.  Phase 2: Insights and Recommendations

   Once the metrics have been collected, categorized, and aggregated in
   a common format, it would be straightforward to visualize these
   metrics and allow consumers to draw insights into their GHG and
   energy impact.  The visualizations could take the form of high-level
   dashboards that provide aggregate metrics and potentially some form
   of maturity continuum.  We think this can be accomplished using
   reference implementations of the standards developed in "Phase 1:
   Visibility".  We do expect vendors and other open projects to
   customize this and incorporate specific features.  This will allow
   identifying sources of environmental impact and address any potential
   issues through operational changes, creation of best-practices, and
   changes towards a greener, more environmentally friendly equipment,
   software, platforms, applications, and protocols.

3.3.  Phase 3: Self-optimization and Automation

   Manually making changes as mentioned in "Phase 2: Insights and
   Recommendations" works for changes needed on large timescales but
   does not scale to improvements on smaller scales (i.e., it is
   impractical in many levels for an operator to be looking at a
   dashboard monitoring usage and making changes in real-time 24x7).
   There is a need to provision some amount of self-awareness into the
   network itself, at various layers, so that it can identify
   opportunities for improvement, implement the necessary changes, and
   measure the effects to complete the feedback loop.  The goals of the
   consumers can be stated declaratively, and the networks can
   continually use mechanisms such as machine learning (ML), deep
   learning (DL), and artificial intelligence (AI) with an additional
   goal to optimize for improvements in the environmental impact.  These
   include, for example:

   *  Discovery and advertisement of networking characteristics that
      have either direct or indirect environmental impact,

   *  greener networking protocols that can move traffic onto more
      energy efficient paths, directing topological graphs to optimize
      environmental impacts, and

   *  protocols that can instruct equipment to move under-utilized links
      and devices into low-energy modes.

3.3.1.  Cycle of Phases

   The three phases run in an iterative fashion, such that after phases
   1, 2, and 3 are completed for an interation, there will be an added
   awareness of what (else) to collect back to phase 1.

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   Further, sustainability-aware self-optimization is something to
   explore in Autonomic Networking.

4.  IANA Considerations

   This document has no IANA actions.

5.  Security Considerations

   Sustainable practices offer many environmental, economic, and social
   benefits, and security is a route to sustainability rather than a
   hurdle to clear.

      The creation of sustainability features for an element or a system
      should not weaken or compromise their security posture, nor should
      it increase the surface of attack or create attack vectors.

      -  Sustainability metrics and data models ought to describe how to
         secure the sustainability data exposed and surfaced through
         telemetry.

      -  Sustainability control capabilities, as for example for power
         management, should consider potential attacks leveraging those
         controls.  Setting a device on low-power or power-save modes
         during peak traffic can be a denial-of-service attack vector,
         negatively impacting end-to-end services.

      The development of security features should, in turn, balance the
      environmental impact and sustainability considerations detailed in
      this document.

      -  Computational increase on cryptographic operations can result
         in higher power use.  Since generally the increase of energy
         required is not linear with the increase of computational
         complexity, there's a desire to satisfy security requirements
         while minimizing environmental impact.

      -  Proof-of-Work schemes' and AI models' energy consumption also
         grows non-linearly as a function of the precision achieved.  In
         these, perfect is the enemy of good, and bounding precision
         through specifications supports sustainable compute
         considerations.

6.  Acknowledgements

   This document is created greatly leveraging ideas and text from
   [I-D.cparsk-eimpact-sustainability-considerations], and consequently
   acknowledges all the many contributions that improved it.

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7.  Informative References

   [Doughnut] Wikipedia, "Doughnut (economic model)", 13 October 2023,
              <https://en.wikipedia.org/wiki/Doughnut_(economic_model)>.

   [I-D.cparsk-eimpact-sustainability-considerations]
              Pignataro, C., Rezaki, A., Krishnan, S., ElBakoury, H.,
              and A. Clemm, "Sustainability Considerations for
              Internetworking", Work in Progress, Internet-Draft, draft-
              cparsk-eimpact-sustainability-considerations-07, 24
              January 2024, <https://datatracker.ietf.org/doc/html/
              draft-cparsk-eimpact-sustainability-considerations-07>.

   [I-D.pignataro-green-enviro-sust-terminology]
              Pignataro, C., Rezaki, A., ElBakoury, H., and S. Prabhu,
              "Environmental Sustainability Terminology and Concepts",
              Work in Progress, Internet-Draft, draft-pignataro-green-
              enviro-sust-terminology-02, 12 May 2025,
              <https://datatracker.ietf.org/doc/html/draft-pignataro-
              green-enviro-sust-terminology-02>.

   [RFC8403]  Geib, R., Ed., Filsfils, C., Pignataro, C., Ed., and N.
              Kumar, "A Scalable and Topology-Aware MPLS Data-Plane
              Monitoring System", RFC 8403, DOI 10.17487/RFC8403, July
              2018, <https://www.rfc-editor.org/info/rfc8403>.

   [RFC9845]  Clemm, A., Ed., Pignataro, C., Ed., Westphal, C.,
              Ciavaglia, L., Tantsura, J., and M. Odini, "Challenges and
              Opportunities in Management for Green Networking",
              RFC 9845, DOI 10.17487/RFC9845, October 2025,
              <https://www.rfc-editor.org/info/rfc9845>.

   [UNEP-LCA] "Global guidance principles for life cycle assessment
              databases : a basis for greener processes and products",
              2011, <https://www.lifecycleinitiative.org/library/global-
              guidance-principles-for-lca-databases-a-basis-for-greener-
              processes-and-products/>.

Authors' Addresses

   Carlos Pignataro (editor)
   Blue Fern Consulting
   United States of America
   Email: cpignata@gmail.com, carlos@bluefern.consulting

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Internet-Draft     Sustainability Arch Considerations      November 2025

   Ali Rezaki
   Nokia
   Germany
   Email: ali.rezaki@nokia.com

   Suresh Krishnan
   Cisco Systems, Inc.
   United States of America
   Email: sureshk@cisco.com

   Jari Arkko
   Ericsson
   Email: jari.arkko@ericsson.com

   Alexander Clemm
   Sympotech
   United States of America
   Email: ludwig@clemm.org

   Hesham ElBakoury
   Independent Consultant
   United States of America
   Email: helbakoury@gmail.com

   Shailesh Prabhu
   Nokia
   India
   Email: shailesh.prabhu@nokia.com

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