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REVIEW 4 major objections 4 minor 1 cited by

MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A carbon-aware cloud scheduler reports an 85.68% cut in CO2 emissions compared with a baseline that ignores carbon intensity.

desk verdict The 85.68% number is real arithmetic but it belongs to the baseline choice, not to the MAIZX ranking algorithm; the paper is a rough workshop note with a fixable design flaw. read the letter →

arxiv 2506.19972 v1 pith:YMELWCLQ submitted 2025-06-24 cs.DC cs.LG

Federico Ruilova , Ernst Gunnar Gran , Sven-Arne Reinemo This is my paper · ORCID
classification cs.DCcs.LG
keywords carbon-awarecomputingcloudschedulingemissionsreductioncarbonintensityprivatehypervisorintegrationMAIZXframeworkworkloadshifting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that a scheduling framework called MAIZX can reduce the carbon footprint of private and multi-cloud computing by ranking available nodes according to real-time and forecasted carbon intensity, power usage, and efficiency. Its central evidence is a year-long simulation across data centers in Spain, the Netherlands, and Germany: dynamically shifting workloads to the cleanest node cuts CO2 emissions by 85.68% relative to a baseline that splits load evenly without carbon data. If accurate, the same arithmetic implies each 60-server unit saves 713.5 kg of CO2 per year, and scaling to about 27.7 million units could avoid roughly 19.754 Mt CO2eq over ten years. This matters because private clouds, used by a large share of organizations, currently lack the carbon transparency that public providers offer.

What carries the argument

The central object is the MAIZ_RANKING score, a weighted sum $$\mathit{MAIZ\_RANKING} = w_1\,\mathrm{CFP} + w_2\,\mathrm{FCFP} + w_3\,\mathrm{CP\_RATIO} + w_4\,\mathrm{SCHEDULE\_WEIGHT},$$ where CFP is the node's measured carbon footprint, FCFP is the forecasted footprint from historical data, CP_RATIO is the node's energy efficiency, and SCHEDULE_WEIGHT encodes workload priorities and deadlines. The adjustable weights let the scheduler balance environmental impact against performance needs, while distributed agents feed real-time power and carbon-intensity readings into a centralized controller that coordinates with the hypervisor. This score carries the argument: the reported emissions reduction comes from routing work to nodes with the lowest combined score.

What would settle it

Using the same 2022 hourly carbon-intensity data, compute Scenario C's emissions against a baseline that always sends work to the single lowest-carbon node; if the reduction relative to that baseline is small, the claim that the ranking algorithm drives the saving is falsified.

Watch

Extended reading notes

Core claim

On the paper's terms, the discovery is that a hypervisor-integrated ranking algorithm can make cloud scheduling carbon-aware without abandoning operational control. MAIZX computes a per-node score from four terms—carbon footprint, forecasted carbon footprint, computing-power ratio, and scheduling weight—and assigns workloads to the best-ranked node. In a three-region simulation using 2022 hourly carbon-intensity data, the active load-shifting scenario achieved an 85.68% reduction in CO2 emissions compared with the even-distribution baseline, and the authors scale this to a 19.754 Mt CO2eq saving over ten years across 27,686,054 units. The framework is positioned as extending carbon-aware scheduling to private, hybrid, and multi-cloud environments by interfacing directly with the hypervisor.

Load-bearing premise

The headline number rests on a baseline that divides loads evenly across Spain, the Netherlands, and Germany with no carbon awareness; if a realistic scheduler already favors the cleanest region, the claimed 85.68% reduction falls apart.

Editorial extensions

If this is right

  • If the 85.68% result holds, carbon-aware ranking can be built into existing hypervisor schedulers, giving private clouds the dynamic load-shifting capability that public providers advertise.
  • A year-long active-shifting policy beats an even distribution in regions with heterogeneous grids; the same policy would be expected to produce smaller gains where regional carbon intensities are similar.
  • Scaling the measured per-unit saving of 713.5 kg CO2 per year across the EU Taxonomy data-related target implies 27,686,054 shifted units avoid roughly 19.754 Mt CO2eq over a decade.
  • The weighted score gives operators a single knob to favor emission cuts over performance, or vice versa, without replacing the underlying hypervisor.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • This is an editorial inference, not a paper claim: the 85.68% reduction is largely the arithmetic difference between the average carbon intensity of the three regions and the cleanest region, so a baseline that always used the single cleanest node would likely shrink the headline gain dramatically.
  • A testable extension would be to rerun Scenario C against several baselines—static lowest-carbon placement, a conventional carbon-agnostic load balancer, and a price-aware scheduler—to isolate how much of the saving comes from the ranking algorithm itself.
  • The linear scaling from one 60-server unit to 27.7 million units assumes identical per-unit savings and no grid or infrastructure saturation; real deployments with shared cooling and power systems may show nonlinear effects.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The manuscript evaluates the MAIZX framework for carbon-aware cloud workload allocation in private and multi-cloud environments. It defines a ranking function, MAIZ_RANKING, as a weighted sum of carbon footprint, forecasted carbon footprint, computing power ratio, and scheduling weight (Eq. 1), and computes node carbon footprint as CF = EC × PUE × CI (Eq. 2). Using 2022 hourly carbon-intensity data for Spain, the Netherlands, and Germany, the paper compares four scenarios: a carbon-unaware even-load baseline, a greedy lowest-carbon-intensity placement, a single-node concentration strategy, and an active load-shifting strategy. The central claim is that Scenario C reduces CO2 emissions by 85.68% relative to the baseline, and the paper scales this to 27,686,054 units to project 19.754 Mt CO2eq savings over 10 years.

Significance. If substantiated, an 85.68% emission reduction for private-cloud hypervisor scheduling would be practically significant for carbon-aware cloud operations. The paper deserves credit for using real 2022 carbon-intensity data (Electricity Maps), for stating an explicit footprint formula (Eq. 2), and for defining separate scenarios with different scheduling policies. However, the headline result is not actually produced by the MAIZ_RANKING algorithm as described. The experiments reduce to a greedy lowest-carbon-intensity policy compared against an equal-load carbon-blind baseline, so the 85.68% figure is an arithmetic consequence of the regional carbon-intensity spread and the baseline choice rather than a validation of Eq. (1). As presented, the contribution is largely a restatement of the known benefit of geographic carbon-aware scheduling, without the algorithmic or experimental evidence needed to support the framework-specific claims.

major comments (4)
  1. [§3, §4, §5, Eq. (1), Eq. (2)] The central claim that the MAIZX framework achieves an 85.68% reduction is not supported by the experiments because Eq. (1) is never instantiated. Scenario C is defined in Section 4 as "actively shifts workloads to the nodes with the lowest carbon intensity," which is a pure minimum-carbon-intensity policy. With CF = EC × PUE × CI, the reported savings are determined by the difference between the average carbon intensity of the three regions and the hourly-minimum intensity. The weights w1–w4 and the MAIZ_RANKING scores never appear in the results, so the experiments cannot distinguish MAIZX's ranking algorithm from a trivial greedy scheduler.
  2. [§4, §5] The baseline is a strawman. The manuscript's baseline "evenly distributes loads without any consideration of carbon intensity or footprint data" is not shown to be representative of typical private-cloud hypervisor scheduling. The 85.68% reduction is essentially the ratio (average CI − minimum CI)/average CI under equal load; against any carbon-aware baseline, such as a fixed placement on the cleanest node, the same scenario would show a much smaller or zero reduction. Since the abstract and Section 5 claim a reduction "compared to baseline hypervisor operations," this baseline choice is load-bearing and unsupported.
  3. [§4–§5] There is an internal contradiction in the scenario definitions. Section 4 defines Scenario B as "concentrates tasks on a single node while powering off others," but Section 5 says "Scenario B evenly distributes workloads without considering carbon intensity." This makes it impossible to determine which scenario produced the 85.68% result and confounds the comparison between Scenarios B and C.
  4. [§5] The scaling calculation contains an arithmetic inconsistency. The paper states that over a 10-year period, with 27,686,054 units each reducing 713.5 kg CO2 per year, the total reduction target is 19.754 Mt CO2eq. Multiplying 27,686,054 units by 713.5 kg/year gives approximately 19.754 Mt CO2 per year, so the 10-year reduction would be 197.54 Mt, not 19.754 Mt. Alternatively, to reach 19.754 Mt over 10 years would require about 2.77 million units, not 27.69 million. The scaling also assumes that trans-national workload migration is free and that all units achieve the same 85.68% reduction, neither of which is justified.
minor comments (4)
  1. [§3] Eq. (1) defines CFP, FCFP, CP_RATIO, and SCHEDULE_WEIGHT only verbally; please provide concrete measurable definitions, units, and a procedure for setting the weights w1–w4, or state that they are user-configured.
  2. [§4] The data-collection description says power consumption is measured every 20 seconds, but no details are given on the number of nodes, hardware configuration, measurement duration, or workload characteristics; these details are needed for reproducibility.
  3. [Figure 2] Figure 2 is captioned "MAIZX Framework Architecture" but appears to display results or a chart; the caption should be updated to match the content.
  4. [Throughout] There are several typographical and editorial errors: "life cicle analys" in Section 4, "hybryd" in Section 2, "Sweeden" in reference [29], and malformed publisher/location strings in references [17] and [36]; these should be corrected.

Circularity Check

0 steps flagged · score 2.0 of 10

The headline 85.68% reduction is a straightforward arithmetic comparison against a carbon-blind uniform baseline, not a self-referential or fitted prediction; the only circularity-adjacent element is a minor reliance on the authors' prior dissertation, which is not load-bearing.

full rationale

After walking the derivation chain in Sections 3-5, I find no circular reduction in the strict sense. The 85.68% figure is obtained by applying the standard carbon-footprint formula CF = EC × PUE × CI to 2022 carbon-intensity data for Spain, the Netherlands, and Germany, and comparing an evenly distributed carbon-blind baseline with a policy that repeatedly selects the lowest-carbon-intensity node. No fitted parameter is later renamed as a prediction, and no equation used to define the inputs is re-derived from the output; the result is an arithmetic consequence of the scenario definitions and the external CI series. The real weaknesses are external-validity issues, not circularity: the baseline is a deliberately non-carbon-aware uniform distribution, the MAIZ_RANKING weights (w1..w4 in Eq. 1) are never instantiated, and Scenario C's behavior is specified as a direct min-CI heuristic, so the reported saving cannot be attributed to the flexible ranking algorithm as validated evidence. These concerns belong to correctness and benchmark fairness, not to the circularity score. The only self-citation element is the use of the first author's dissertation [29] for the MAIZX framework and for a preliminary statement of the same 85.68% number; because the current paper states a simulation recipe with independent data, this citation is not load-bearing and does not make the central claim self-referential. Score 2 reflects this minor self-citation, not a circular derivation.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

The central claim depends on the baseline assumption, the free energy and PUE parameters, the external carbon intensity data, and the zero-cost migration assumption. The invented entities are software agents that are part of the framework, with no independent evidence of their effectiveness.

free parameters (3)
  • w1, w2, w3, w4 = not specified
    The weights in the MAIZ_RANKING equation (Eq. 1) are declared adjustable to balance sustainability, performance, and operational needs, but no values are given and no sensitivity analysis is performed. Since the reported scenarios do not use Eq. (1), the weights are free parameters whose values could change the claimed reduction.
  • PUE per data center = not stated
    The carbon footprint formula CF = EC * PUE * CI requires a PUE value for each node, but the paper does not report the PUE values used in the simulations. These values directly affect the computed emissions and the resulting reduction percentage.
  • Energy consumption per node = not stated
    The paper mentions '60 servers in a 3-node private cloud' but does not provide the actual power consumption values for each node, which are needed to compute the carbon footprint and the 713.5 kg annual reduction per unit.
assumptions (5)
  • domain assumption CF = EC * PUE * CI is a valid carbon accounting formula for data centers.
    Used in Eq. (2); assumes linear scaling of energy with workload and ignores other emission sources such as embodied carbon, cooling water, and grid transmission losses.
  • domain assumption The 2022 hourly carbon intensity data from Electricity Maps for Spain, the Netherlands, and Germany is accurate and representative.
    The entire simulation rests on this external dataset; no validation or error bars are provided, and the choice of 2022 data is not justified.
  • ad hoc to paper Workload migration between data centers in different countries is free.
    The scenarios shift loads between regions without accounting for network energy, data-transfer emissions, latency, or data-residency constraints. This assumption is implied by the simulation design but never stated.
  • ad hoc to paper The baseline scenario (even distribution without carbon awareness) represents typical private-cloud operation.
    The 85.68% reduction is relative to this baseline; if realistic schedulers already consider energy or cost, the baseline overstates the potential savings.
  • ad hoc to paper The 1% of the EU Taxonomy target for data-driven climate change monitoring and ICT data processing is a meaningful scaling unit.
    The 19.754 Mt CO2eq figure is derived from a specific EU Taxonomy activity class, and the choice of 1% is arbitrary; the linear scaling to 27.7 million units assumes every unit behaves identically.
invented entities (1)
  • MAIZX agents
    purpose: Software components that collect power and carbon intensity data at distributed nodes and at the core to support the ranking algorithm.
    These are software agents, not new physical entities; they are part of the proposed framework and their behavior is not independently verifiable from this paper. The paper provides no implementation details.

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Cite this review

Pith. "Pith review of MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions." pith.science (2026). https://pith.science/paper/YMELWCLQ

@misc{pith2026250619972,
  author       = {Pith},
  title        = {Pith review of: MAIZX: A Carbon-Aware Framework for Optimizing Cloud Computing Emissions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YMELWCLQ}},
  note         = {Machine review of arXiv:2506.19972}
}
read the original abstract

Cloud computing drives innovation but also poses significant environmental challenges due to its high-energy consumption and carbon emissions. Data centers account for 2-4% of global energy usage, and the ICT sector's share of electricity consumption is projected to reach 40% by 2040. As the goal of achieving net-zero emissions by 2050 becomes increasingly urgent, there is a growing need for more efficient and transparent solutions, particularly for private cloud infrastructures, which are utilized by 87% of organizations, despite the dominance of public-cloud systems. This study evaluates the MAIZX framework, designed to optimize cloud operations and reduce carbon footprint by dynamically ranking resources, including data centers, edge computing nodes, and multi-cloud environments, based on real-time and forecasted carbon intensity, Power Usage Effectiveness (PUE), and energy consumption. Leveraging a flexible ranking algorithm, MAIZX achieved an 85.68% reduction in CO2 emissions compared to baseline hypervisor operations. Tested across geographically distributed data centers, the framework demonstrates scalability and effectiveness, directly interfacing with hypervisors to optimize workloads in private, hybrid, and multi-cloud environments. MAIZX integrates real-time data on carbon intensity, power consumption, and carbon footprint, as well as forecasted values, into cloud management, providing a robust tool for enhancing climate performance potential while maintaining operational efficiency.

Figures

Figures reproduced from arXiv: 2506.19972 by the authors.

Figure 1
Figure 1. MAIZX Framework Architecture The ranking algorithm dynamically allocates workloads to nodes with the lowest carbon intensity, prioritizing environmental impact without compromising performance. Centralized components coor￾dinate with the hypervisor using carbon efficiency and power data as it could be observed in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. MAIZX Framework Architecture savings, particularly in private and multi-cloud environments with optimized power consumption and associated carbon footprint. 6 Conclusion The framework demonstrates significant potential to reduce CO2 emissions, particularly in private or multi-cloud setups. Despite be￾ing tested in regions with non-renewable energy matrices, MAIZX achieved considerable emission reductions. Empirical … view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Survey on Task Scheduling in Carbon-Aware Container Orchestration

    cs.SE 2025-08 conditional novelty 4.0 of 10

    A systematic survey categorizing carbon-aware Kubernetes scheduling algorithms along hardware/software and energy/carbon axes, with a proposed taxonomy.

Reference graph

Works this paper leans on

36 extracted references · 32 canonical work pages · cited by 1 Pith paper

  1. [1]

    International Energy Agency. 2021. Net Zero by 2050 - A Roadmap for the Global Energy Sector. https://www.iea.org/reports/net-zero-by-2050

    work page 2021

  2. [2]

    Ehsan Ahvar, Anne-Cecile Orgerie, and Adrien Lebre. 2022. Estimating En- ergy Consumption of Cloud, Fog, and Edge Computing Infrastructures. IEEE Transactions on Sustainable Computing 7, 2 (April 2022), 277–288. https: //doi.org/10.1109/TSUSC.2019.2905900

    work page arXiv 2022

  3. [4]

    Randall Bray. 2025. How AI Is Reshaping Consumers’ Cloud Workloads. https: //exactmarket.com/2025/03/how-ai-is-reshaping-consumers-cloud-workloads/

    work page 2025

  4. [5]

    Cisco. 2022. Global Hybrid Cloud Trends Report. https://www.cisco.com/c/en/ us/solutions/hybrid-cloud/2022-trends.html

    work page 2022

  5. [6]

    European Commission European Commission. 2024. EU Taxonomy Navigator. https://ec.europa.eu/sustainable-finance-taxonomy/activities

    work page 2024

  6. [7]

    ecocostsvalue.com. 2024. Life Cycle Assessment. https://www.ecocostsvalue. com/lca/

    work page 2024

  7. [8]

    Tamar Eilam. 2021. Towards transparent and trustworthy cloud carbon account- ing. In Proceedings of the 22nd International Middleware Conference: Extended Abstracts. ACM, Virtual Event Canada, 1–5. https://doi.org/10.1145/3501255. 3501408

    work page doi:10.1145/3501255 2021

  8. [9]

    Tamar Eilam, Pedro Bello-Maldonado, Bishwaranjan Bhattacharjee, Carlos Costa, Eun Kyung Lee, and Asser Tantawi. 2023. Towards a Methodology and Framework LOCO ’24, December 3, 2024, Glasgow/Online Ruilova et al. for AI Sustainability Metrics. In Proceedings of the 2nd Workshop on Sustainable Computer Systems. ACM, Boston MA USA, 1–7. https://doi.org/10.11...

    work page doi:10.1145/3604930 2023

Show all 36 references
  1. [10]

    electricitymaps.com. 2024. Electricity Maps | Reduce carbon emissions with actionable electricity data. https://www.electricitymaps.com/

    2024

  2. [11]

    Flexera. 2024. Flexera 2023 State of the Cloud | Report. https://info.flexera.com/ CM-REPORT-State-of-the-Cloud

    2024

  3. [12]

    GESI. 2024. GeSI. https://www.gesi.org/research/ict-sector-guidance-built-on- the-ghg-protocol-product-life-cycle-accounting-and-reporting-standard

    2024

  4. [14]

    Hanafy, Qianlin Liang, Noman Bashir, David Irwin, and Prashant Shenoy

    Walid A. Hanafy, Qianlin Liang, Noman Bashir, David Irwin, and Prashant Shenoy. 2023. CarbonScaler: Leveraging Cloud Workload Elasticity for Op- timizing Carbon-Efficiency. http://arxiv.org/abs/2302.08681 arXiv:2302.08681 [cs]

    2023 arXiv

  5. [15]

    impact forecast.com. 2024. Impact Forecast: Calculate, improve and validate your climate impact. https://impact-forecast.com/

    2024

  6. [16]

    The Independent. 2016. Global warming: Data centres to consume three times as much energy in next decade, experts warn. https://www.independent.co.uk/ climate-change/news/global-warming-data-centres-to-consume-three-times- as-much-energy-in-next-decade-experts-warn-a6830086.ht...

    2016

  7. [17]

    Aled James and Daniel Schien. 2019. A Low Carbon Kubernetes Scheduler. In Proceedings of the 6th International Conference on ICT for Sustainability (ICT4S) . CEUR-WS, Lappeenranta, Finnland, 210–217

    2019

  8. [18]

    Young Geun Kim, Udit Gupta, Andrew McCrabb, Yonglak Son, Valeria Bertacco, David Brooks, and Carole-Jean Wu. 2023. GreenScale: Carbon-Aware Systems for Edge Computing. http://arxiv.org/abs/2304.00404 arXiv:2304.00404 [cs]

    2023 arXiv

  9. [19]

    Loïc Lannelongue and Michael Inouye. 2023. Carbon footprint estimation for computational research. Nature Reviews Methods Primers 3, 1 (Feb. 2023), 9. https://doi.org/10.1038/s43586-023-00202-5

    2023 doi

  10. [20]

    Frameworking

    Phil Laplante and Jeffrey Voas. 2023. “Frameworking” Carbon-Aware Computing Research. Computer 56, 5 (May 2023), 105–108. https://doi.org/10.1109/mc.2023. 3240482 Publisher: Institute of Electrical and Electronics Engineers (IEEE)

    2023 doi

  11. [22]

    Peter Mell and Timothy Grance. 2011. The NIST Definition of Cloud Computing . Technical Report. National Institute of Standards and Technology, Gaithersburg, MD. 1–7 pages

    2011

  12. [23]

    MP.org. 2024. Why does AI use so much energy? - Mad Penguin. https: //www.madpenguin.org/why-does-ai-use-so-much-energy/

    2024

  13. [24]

    United Nations. 2023. The Sustainable Development Goals Report 2023: Special Edition. https://unstats.un.org/sdgs/report/2023/

    2023

  14. [25]

    OpenNebula. 2023. Scheduler Configuration — OpenNebula 6.6.3 docu- mentation. https://docs.opennebula.io/6.6/installation_and_configuration/ opennebula_services/scheduler.html#schg

    2023

  15. [26]

    Suraj Singh Panwar, M. M. S. Rauthan, and Varun Barthwal. 2022. A systematic re- view on effective energy utilization management strategies in cloud data centers. Journal of Cloud Computing 11, 1 (Dec. 2022), 95. https://doi.org/10.1186/s13677- 022-00368-5

    2022 doi

  16. [27]

    Ana Radovanović, Ross Koningstein, Ian Schneider, Bokan Chen, Alexandre Duarte, Binz Roy, Diyue Xiao, Maya Haridasan, Patrick Hung, Nick Care, Saurav Talukdar, Eric Mullen, Kendal Smith, MariEllen Cottman, and Walfredo Cirne

  17. [28]

    Aimee Ross and Lorna Christie. 2023. Energy consumption of ICT . Technical Report. Parliamentary Office of Science and Technology (POST), Westminster, London, UK. 1–4 pages. https://post.parliament.uk/research-briefings/post-pn- 0677/

    2023

  18. [29]

    Federico Ruilova Alfaro. 2024. Towards Sustainable Cloud Computing: A Systemic Framework for Leveraging Regional Energy Data to Empower Carbon-A ware Com- puting: Optimizing Cloud Ecosystems: The MAIZX Framework and Its Ranking Algorithm for Lowering Carbon Footprint. Ph. D. D...

    2024

  19. [30]

    Katarina Stanoevska-Slabeva and Thomas Wozniak. 2010. Cloud Basics – An Introduction to Cloud Computing. In Grid and Cloud Computing , Katarina Stanoevska-Slabeva, Thomas Wozniak, and Santi Ristol (Eds.). Springer Berlin Hei- delberg, Berlin, Heidelberg, 47–61. https://doi.org...

    2010 doi

  20. [31]

    Thandayuthapani Subramanian. 2023. Carbon-A ware Scheduling for Serverless Computing. Ph. D. Dissertation. TECHNISCHE UNIVERSITÄT MÜNCHEN, Mu- nich, Germany

    2023

  21. [32]

    Daniel Valenzuela and Danijel Višević. 2021. How to measure the climate perfor- mance potential | World Fund. https://www.worldfund.vc/knowledge/how-to- measure-the-climate-performance-potential-of-startups

    2021

  22. [33]

    Vogtländer

    J.G. Vogtländer. 2010.A Practical Guide to LCA for Students, Designers and Business Managers: Cradle-to-grave and Cradle-to-cradle . VSSD, Delft, The Netherlands. https://books.google.de/books?id=EkcDaAEACAAJ

    2010

  23. [34]

    Kajol Wadhwani. 2025. How AI Agents Cut Energy Costs Effortlessly. https: //www.solulab.com/ai-agents-for-energy-consumption/

    2025

  24. [35]

    Philipp Wiesner, Ramin Khalili, Dennis Grinwald, Pratik Agrawal, Lauritz Tham- sen, and Odej Kao. 2024. FedZero: Leveraging Renewable Excess Energy in Federated Learning. In 15th ACM International Conference on Future and Sustainable Energy Systems (E -Energy ’24). AMC, Singap...

    2024

  25. [36]

    Philipp Wiesner, Dominik Scheinert, Thorsten Wittkopp, Lauritz Thamsen, and Odej Kao. 2022. Cucumber: Renewable-Aware Admission Control for Delay- Tolerant Cloud and Edge Workloads. In Lecture Notes in Computer Science , Vol. 13440. Springer Cham, Berlin oder Glasgow / Cham, 2...

    2022 doi

  26. [37]

    O’Boyle, and Christopher Woodruff

    Jackson Woodruff, David Schall, Michael F.P. O’Boyle, and Christopher Woodruff

  27. [39]

    In Proceedings of the 2nd Workshop on Sustainable Computer Systems

    When Does Saving Power Save the Planet?. In Proceedings of the 2nd Workshop on Sustainable Computer Systems . ACM, Boston MA USA, 1–6. https: //doi.org/10.1145/3604930.3605719

  28. [2023]

    IEEE Transactions on Power Systems 38, 2 (March 2023), 1270–1280

    Carbon-Aware Computing for Datacenters. IEEE Transactions on Power Systems 38, 2 (March 2023), 1270–1280. https://doi.org/10.1109/TPWRS.2022. 3173250

    2023 doi

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