├── .github └── ISSUE_TEMPLATE │ ├── action_item.yaml │ ├── agenda.md │ ├── bug_report.yaml │ ├── documentation.yaml │ └── question.yaml ├── .gitignore ├── CODE-OF-CONDUCT.md ├── Cloud_Region_Metadata.csv ├── Cloud_Region_Metadata_estimate.csv ├── Cloud_Region_Metadata_specification.md ├── License.md ├── README.md ├── code ├── README.md ├── estimate_current_region_metadata.py ├── expected_output.csv ├── requirements.txt ├── test.sh ├── test_input.csv └── test_input_estimate.csv └── sup_file ├── AWS_renewable_energy_2023.csv ├── Amazon-Carbon-Free-Energy-Projects-2024.csv ├── Amazon-Carbon-Free-Energy-Projects-2025.csv ├── Miro RTC Screenshot 2023-08-28 at 10.28.38 AM.png ├── PRFAQ for RealTimeCarbonMetrics.md ├── place_holder.md ├── rtc-miro-2023-10-09.png ├── rtc-miro-2023-12-18.png └── rtc-miro-2024-07-01.png /.github/ISSUE_TEMPLATE/action_item.yaml: -------------------------------------------------------------------------------- 1 | name: Action Item 2 | description: Task to be completed 3 | title: "[AI]" 4 | labels: ["action item"] 5 | projects: ["Real-Time Cloud"] 6 | assignees: 7 | - adrianco 8 | - PindyBhullar 9 | body: 10 | - type: textarea 11 | id: what-happened 12 | attributes: 13 | label: Outline Action Item Details 14 | description: Outline Action Item Details 15 | placeholder: Tell us what you see 16 | value: "Add details" 17 | validations: 18 | required: true 19 | - type: dropdown 20 | id: TF-Groups 21 | attributes: 22 | label: Issue dependency with other WGs Groups 23 | multiple: true 24 | options: 25 | - No Dependency 26 | - Community WG 27 | - Opensource WG 28 | - Policy WG 29 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/agenda.md: -------------------------------------------------------------------------------- 1 | ---- 2 | ## 22023.mm.dd Agenda/Minutes 3 | --- 4 | Time HH:MM (GMT) - See the time in your [timezone](https://everytimezone.com/s/a8399b00) 5 | 6 | - Co-Chair – Adrian Cockcroft 7 | - Co-Chair - Add here 8 | - Convener – Sean Mcilroy (Linux Foundation) 9 | 10 | ## Antitrust Policy 11 | Joint Development Foundation meetings may involve participation by industry competitors, and the Joint Development Foundation intends to conduct all of its activities in accordance with applicable antitrust and competition laws. It is, therefore, extremely important that attendees adhere to meeting agendas and be aware of, and not participate in, any activities that are prohibited under applicable US state, federal or foreign antitrust and competition laws. 12 | 13 | If you have questions about these matters, please contact your company counsel or counsel to the Joint Development Foundation, DLA Piper. 14 | 15 | ## Recordings 16 | This meeting recording will be available until the next scheduled meeting. 17 | 18 | ## Roll Call 19 | Please *add 'Attended'* to this issue during the meeting to denote attendance. 20 | 21 | Any untracked attendees will be added by the GSF team below: 22 | - Full Name, Affiliation, (optional) GitHub username 23 | 24 | ## Agenda 25 | - [ ] Approve agenda 26 | - [ ] Approve [previous Meeting Minutes]() 27 | 28 | ## Add Topic 29 | - [ ] Add topic 30 | 31 | ## Add Topic 32 | - [ ] Add topic 33 | 34 | ## Add Topic 35 | - [ ] Add topic 36 | 37 | ## Weekly Reminders 38 | - [ ] - Newsletter submission - Topics that should be added to this week's GSF Digest 39 | - Articles have been submitted 40 | 41 | ## AOB 42 | - [ ] Any other topics to be added 43 | 44 | ## Next Meeting 45 | - [ ] DD MM 46 | 47 | ## Adjourn 48 | - [ ] Motion to adjourn 49 | 50 | -------- 51 | 52 | ## Meeting Action Items / Standing Agenda / Future Agenda submissions 53 | - [ ] Add details here 54 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/bug_report.yaml: -------------------------------------------------------------------------------- 1 | name: Bug report 2 | description: Create a report to help us improve 3 | title: "[Bug report]" 4 | labels: ["bug"] 5 | projects: ["Real-Time Cloud"] 6 | assignees: 7 | - adrianco 8 | - PindyBhullar 9 | body: 10 | - type: textarea 11 | id: what-happened 12 | attributes: 13 | label: Outline bug 14 | description: Outline Outline bug Details 15 | placeholder: Tell us what you see 16 | value: "Add details" 17 | validations: 18 | required: true 19 | - type: dropdown 20 | id: WG 21 | attributes: 22 | label: Issue dependency with other TF Groups 23 | multiple: true 24 | options: 25 | - No Dependency 26 | - Policy WG 27 | - Community WG 28 | - OS WG 29 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/documentation.yaml: -------------------------------------------------------------------------------- 1 | name: Documentation 2 | description: Specification observations 3 | title: "[Documentation]" 4 | labels: ["documentation"] 5 | projects: ["Real-Time Cloud"] 6 | assignees: 7 | - adrianco 8 | - PindyBhullar 9 | body: 10 | - type: textarea 11 | id: what-happened 12 | attributes: 13 | label: Outline Action Item Details 14 | description: Outline Action Item Details 15 | placeholder: Tell us what you see 16 | value: "Add details for proposed changes or additions." 17 | validations: 18 | required: true 19 | - type: dropdown 20 | id: TF-Groups 21 | attributes: 22 | label: Issue dependency with other WG Groups 23 | multiple: true 24 | options: 25 | - No Dependency 26 | - Community WG 27 | - Opensource WG 28 | - Policy WG 29 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/question.yaml: -------------------------------------------------------------------------------- 1 | name: Question 2 | description: Specification questions 3 | title: "[Question]" 4 | labels: ["question"] 5 | projects: ["Real-Time Cloud"] 6 | assignees: 7 | - adrianco 8 | - PindyBhullar 9 | body: 10 | - type: textarea 11 | id: Question 12 | attributes: 13 | label: Question 14 | description: Question submission for group members 15 | placeholder: Tell us what you see 16 | value: "Question submission details" 17 | validations: 18 | required: true 19 | - type: dropdown 20 | id: Groups 21 | attributes: 22 | label: Issue dependency with other WGs 23 | multiple: true 24 | options: 25 | - No Dependency 26 | - Community WG 27 | - Opensource WG 28 | - Policy WG 29 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | .DS_Store 162 | downloads/ 163 | -------------------------------------------------------------------------------- /CODE-OF-CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | We as members, contributors, and leaders pledge to make participation in our 6 | community a harassment-free experience for everyone, regardless of age, body 7 | size, visible or invisible disability, ethnicity, sex characteristics, gender 8 | identity and expression, level of experience, education, socio-economic status, 9 | nationality, personal appearance, race, caste, color, religion, or sexual identity 10 | and orientation. 11 | 12 | We pledge to act and interact in ways that contribute to an open, welcoming, 13 | diverse, inclusive, and healthy community. 14 | 15 | ## Our Standards 16 | 17 | Examples of behavior that contributes to a positive environment for our 18 | community include: 19 | 20 | - Demonstrating empathy and kindness toward other people 21 | - Being respectful of differing opinions, viewpoints, and experiences 22 | - Giving and gracefully accepting constructive feedback 23 | - Accepting responsibility and apologizing to those affected by our mistakes, 24 | and learning from the experience 25 | - Focusing on what is best not just for us as individuals, but for the 26 | overall community 27 | 28 | Examples of unacceptable behavior include: 29 | 30 | - The use of sexualized language or imagery, and sexual attention or 31 | advances of any kind 32 | - Trolling, insulting or derogatory comments, and personal or political attacks 33 | - Public or private harassment 34 | - Publishing others' private information, such as a physical or email 35 | address, without their explicit permission 36 | - Other conduct which could reasonably be considered inappropriate in a 37 | professional setting 38 | 39 | ## Enforcement Responsibilities 40 | 41 | Community leaders are responsible for clarifying and enforcing our standards of 42 | acceptable behavior and will take appropriate and fair corrective action in 43 | response to any behavior that they deem inappropriate, threatening, offensive, 44 | or harmful. 45 | 46 | Community leaders have the right and responsibility to remove, edit, or reject 47 | comments, commits, code, wiki edits, issues, and other contributions that are 48 | not aligned to this Code of Conduct, and will communicate reasons for moderation 49 | decisions when appropriate. 50 | 51 | ## Scope 52 | 53 | This Code of Conduct applies within all community spaces, and also applies when 54 | an individual is officially representing the community in public spaces. 55 | Examples of representing our community include using an official e-mail address, 56 | posting via an official social media account, or acting as an appointed 57 | representative at an online or offline event. 58 | 59 | ## Enforcement 60 | 61 | Instances of abusive, harassing, or otherwise unacceptable behavior may be directly 62 | reported to the Executive Director of the Green Software Foundation at exec@greensoftware.foundation or any community leaders responsible for enforcement. 63 | 64 | All complaints will be reviewed and investigated promptly and fairly. 65 | 66 | All community leaders are obligated to respect the privacy and security of the 67 | reporter of any incident. 68 | 69 | ## Enforcement Guidelines 70 | 71 | Community leaders will follow these Community Impact Guidelines in determining 72 | the consequences for any action they deem in violation of this Code of Conduct: 73 | 74 | ### 1. Correction 75 | 76 | **Community Impact**: Use of inappropriate language or other behavior deemed 77 | unprofessional or unwelcome in the community. 78 | 79 | **Consequence**: A private, written warning from community leaders, providing 80 | clarity around the nature of the violation and an explanation of why the 81 | behavior was inappropriate. A public apology may be requested. 82 | 83 | ### 2. Warning 84 | 85 | **Community Impact**: A violation through a single incident or series 86 | of actions. 87 | 88 | **Consequence**: A warning with consequences for continued behavior. No 89 | interaction with the people involved, including unsolicited interaction with 90 | those enforcing the Code of Conduct, for a specified period of time. This 91 | includes avoiding interactions in community spaces as well as external channels 92 | like social media. Violating these terms may lead to a temporary or 93 | permanent ban. 94 | 95 | ### 3. Temporary Ban 96 | 97 | **Community Impact**: A serious violation of community standards, including 98 | sustained inappropriate behavior. 99 | 100 | **Consequence**: A temporary ban from any sort of interaction or public 101 | communication with the community for a specified period of time. No public or 102 | private interaction with the people involved, including unsolicited interaction 103 | with those enforcing the Code of Conduct, is allowed during this period. 104 | Violating these terms may lead to a permanent ban. 105 | 106 | ### 4. Permanent Ban 107 | 108 | **Community Impact**: Demonstrating a pattern of violation of community 109 | standards, including sustained inappropriate behavior, harassment of an 110 | individual, or aggression toward or disparagement of classes of individuals. 111 | 112 | **Consequence**: A permanent ban from any sort of public interaction within 113 | the community. 114 | 115 | ## Attribution 116 | 117 | This Code of Conduct is adapted from the [Contributor Covenant][homepage], 118 | version 2.0, available at 119 | [https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0]. 120 | 121 | Community Impact Guidelines were inspired by 122 | [Mozilla's code of conduct enforcement ladder][mozilla coc]. 123 | 124 | For answers to common questions about this code of conduct, see the FAQ at 125 | [https://www.contributor-covenant.org/faq][faq]. Translations are available 126 | at [https://www.contributor-covenant.org/translations][translations]. 127 | 128 | [homepage]: https://www.contributor-covenant.org 129 | [v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html 130 | [mozilla coc]: https://github.com/mozilla/diversity 131 | [faq]: https://www.contributor-covenant.org/faq 132 | [translations]: https://www.contributor-covenant.org/translations 133 | 134 | --- 135 | -------------------------------------------------------------------------------- /Cloud_Region_Metadata.csv: -------------------------------------------------------------------------------- 1 | year,cloud-provider,cloud-region,cfe-region,em-zone-id,wt-region-id,location,geolocation,provider-cfe-hourly,provider-cfe-annual,power-usage-effectiveness,water-usage-effectiveness,provider-carbon-intensity-market-annual,provider-carbon-intensity-average-consumption-hourly,grid-carbon-intensity-average-consumption-annual,grid-carbon-intensity-marginal-consumption-annual,grid-carbon-intensity-average-production-annual,grid-carbon-intensity,total-ICT-energy-consumption-annual,total-water-input,renewable-energy-consumption,renewable-energy-consumption-goe,renewable-energy-consumption-ppa,renewable-energy-consumption-onsite 2 | 2023,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,0.16,,,,,646.0,702.03,,701.92,,,,,,, 3 | 2023,Google Cloud,asia-east1,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",0.18,,1.12,,0.0,451.0,537.64,,537.76,,,,,,, 4 | 2023,Google Cloud,asia-east2,Hong Kong,HK,HK,Hong Kong,"22.3,114.2",0.28,,,,0.0,360.0,435.36,,435.36,,,,,,, 5 | 2023,Google Cloud,asia-northeast1,Tokyo,JP-TK,JP_TK,Tokyo,"35.6897,139.692",0.16,,,,0.0,459.0,536.06,,545.67,,,,,,, 6 | 2023,Google Cloud,asia-northeast2,Kansai,JP-KN,JP_KN,Osaka,"34.6939,135.502",0.3,,,,0.0,385.0,370.3,,357.05,,,,,,, 7 | 2023,Google Cloud,asia-northeast3,South Korea,KR,KOR,Seoul,"37.56,126.99",0.35,,,,0.0,378.0,443.05,,443.01,,,,,,, 8 | 2023,Google Cloud,asia-south1,Maharashtra,IN-WE,IND,Mumbai,"19.0761,72.8775",0.14,,,,0.0,648.0,746.98,,762.24,,,,,,, 9 | 2023,Google Cloud,asia-south2,Uttar Pradesh,IN-NO,IND,Delhi,"28.6092,76.9798",0.29,,,,0.0,529.0,563.1,,562.0,,,,,,, 10 | 2023,Google Cloud,asia-southeast1,Singapore,SG,SGP,Singapore,"1.3,103.8",0.04,,1.16,,0.0,372.0,487.52,,487.06,,,,,,, 11 | 2023,Google Cloud,asia-southeast2,Indonesia,ID,ID,Jakarta,"-6.2507,106.869",0.13,,,,0.0,580.0,652.36,,652.36,,,,,,, 12 | 2023,Google Cloud,australia-southeast1,New South Wales,AU-NSW,NEM_NSW,Sydney,"-33.8678,151.21",0.33,,,,0.0,501.0,545.01,,546.1,,,,,,, 13 | 2023,Google Cloud,australia-southeast2,Victoria,AU-VIC,NEM_VIC,Melbourne,"-37.8142,144.963",0.4,,,,0.0,456.0,498.19,,514.1,,,,,,, 14 | 2023,Google Cloud,europe-central2,Poland,PL,PL,Warsaw,"52.23,21.0111",0.31,,,,0.0,723.0,749.28,,794.76,,,,,,, 15 | 2023,Google Cloud,europe-north1,Finland,FI,FI,Finland,"60.1708,24.9375",0.98,,1.09,,0.0,46.0,82.34,,89.24,,,,,,, 16 | 2023,Google Cloud,europe-southwest1,Spain,ES,ES,Madrid,"40.3333,-3.8667",0.76,,,,0.0,131.0,153.86,,154.56,,,,,,, 17 | 2023,Google Cloud,europe-west1,Belgium,BE,BE,Belgium,"51.1333,4.5667",0.82,,1.09,,0.0,122.0,177.58,,172.79,,,,,,, 18 | 2023,Google Cloud,europe-west2,Great Britain,GB,UK,London,"51.726,-0.3",0.92,,,,0.0,136.0,187.89,,200.18,,,,,,, 19 | 2023,Google Cloud,europe-west3,Germany,DE,DE,Frankfurt,"50.1106,8.6822",0.9,,,,0.0,345.0,370.93,,394.14,,,,,,, 20 | 2023,Google Cloud,europe-west4,Netherlands,NL,NL,Eemshaven,"51.9167,4.5",0.8,,1.07,,0.0,236.0,283.17,,292.61,,,,,,, 21 | 2023,Google Cloud,europe-west6,Switzerland,CH,CH,Zurich,"47.3744,8.5411",0.92,,,,0.0,59.0,83.8,,52.58,,,,,,, 22 | 2023,Google Cloud,europe-west8,North Italy,IT-NO,IT,Milan,"45.4669,9.19",0.52,,,,0.0,249.0,298.62,,379.97,,,,,,, 23 | 2023,Google Cloud,europe-west9,France,FR,FR,Paris,"48.8567,2.3522",0.94,,,,0.0,34.0,52.87,,45.21,,,,,,, 24 | 2023,Google Cloud,europe-west12,North Italy,IT-NO,IT,Turin,"45.0792,7.6761",0.52,,,,0.0,249.0,298.62,,379.97,,,,,,, 25 | 2023,Google Cloud,me-central1,United Arab Emirates,AE,,Doha,,0.0,,,,,575.0,381.54,,381.54,,,,,,, 26 | 2023,Google Cloud,me-central2,Saudi Arabia,SA,,Damman,,0.0,,,,,569.0,,,,,,,,,, 27 | 2023,Google Cloud,me-west1,Israel,IL,IL,Tel Aviv,"32.0167,34.7667",0.05,,,,0.0,463.0,533.04,,533.04,,,,,,, 28 | 2023,Google Cloud,northamerica-northeast1,Quebec,CA-QC,HQ,Montréal,"45.5089,-73.5617",1.0,,,,0.0,2.0,30.51,,28.68,,,,,,, 29 | 2023,Google Cloud,northamerica-northeast2,Ontario,CA-ON,IESO_NORTH,Toronto,"43.7417,-79.3733",0.87,,,,0.0,47.0,76.42,,75.79,,,,,,, 30 | 2023,Google Cloud,southamerica-east1,Central Brazil,BR-CS,BRA,São Paulo,"-3.45,-68.95",0.9,,,,0.0,56.0,89.55,,90.41,,,,,,, 31 | 2023,Google Cloud,southamerica-west1,Chile,CL-SEN,CHL,Santiago,"-33.4372,-70.6506 ",0.91,,1.12,,0.0,56.0,272.26,,272.2,,,,,,, 32 | 2023,Google Cloud,us-central1,MISO,US-MIDW-MISO,MISO_MASON_CITY,Iowa,"41.5725,-93.6105 ",0.95,,1.1,,0.0,430.0,501.42,,518.84,,,,,,, 33 | 2023,Google Cloud,us-east1,SC,US-CAR-SC,SC,South Carolina,"34.8354,-82.3646 ",0.29,,1.1,,0.0,560.0,649.07,,797.96,,,,,,, 34 | 2023,Google Cloud,us-east4,PJM,US-MIDA-PJM,PJM_DC,Northern Virginia,"39.1057,-77.5544 ",0.52,,1.09,,0.0,322.0,396.25,,396.77,,,,,,, 35 | 2023,Google Cloud,us-east5,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,Columbus,"41.4366,-97.3565 ",0.52,,1.09,,0.0,322.0,396.25,,396.77,,,,,,, 36 | 2023,Google Cloud,us-south1,ERCOT,US-TEX-ERCO,ERCOT_NORTHCENTRAL,Dallas,"44.9221,-123.313 ",0.79,,1.12,,0.0,321.0,388.76,,388.25,,,,,,, 37 | 2023,Google Cloud,us-west1,BPA,US-NW-BPAT,BPA,Oregon,"45.5372,-122.3955 ",0.84,,1.07,,0.0,94.0,119.76,,68.82,,,,,,, 38 | 2023,Google Cloud,us-west2,CAISO,US-CAL-CISO,LDWP,Los Angeles,"34.1141,-118.4068 ",0.55,,,,0.0,198.0,261.28,,239.67,,,,,,, 39 | 2023,Google Cloud,us-west3,PACE,US-NW-PACE,PACE,Salt Lake City,"40.7776,-111.9311 ",0.29,,,,0.0,588.0,647.78,,657.95,,,,,,, 40 | 2023,Google Cloud,us-west4,NVE,US-NW-NEVP,NEVP,Las Vegas,"35.6011,-105.2206 ",0.26,,1.08,,0.0,373.0,459.13,,499.52,,,,,,, 41 | 2023,Amazon Web Services,us-east-2,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,US East (Ohio),"39.8689,-84.3292 ",,,1.12,0.18,0.0,,396.25,,396.77,,,,,,, 42 | 2023,Amazon Web Services,us-east-1,PJM,US-MIDA-PJM,PJM_DC,US East (N. Virginia),"39.1057,-77.5544 ",,,1.15,0.18,0.0,,396.25,,396.77,,,,,,, 43 | 2023,Amazon Web Services,us-west-1,CAISO,US-CAL-CISO,CAISO_NORTH,US West (N. California),"36.7783,-119.417931",,,1.17,0.18,0.0,,261.28,,239.67,,,,,,, 44 | 2023,Amazon Web Services,us-west-2,BPA,US-NW-BPAT,BPA,US West (Oregon),"45.5371,-122.65 ",,,1.13,0.18,0.0,,119.76,,68.82,,,,,,, 45 | 2023,Amazon Web Services,af-south-1,South Africa,ZA,ZA,Africa (Cape Town),"-33.9253,18.4239 ",,,1.24,0.18,,,702.03,,701.92,,,,,,, 46 | 2023,Amazon Web Services,ap-east-1,Hong Kong,HK,HK,Asia Pacific (Hong Kong),"22.3,114.2",,,,0.18,,,435.36,,435.36,,,,,,, 47 | 2023,Amazon Web Services,ap-south-2,Hyderabad,IN-SO,IND,Asia Pacific (Hyderabad),"17.385,78.4867 ",,,1.5,0.18,0.0,,551.31,,549.72,,,,,,, 48 | 2023,Amazon Web Services,ap-southeast-3,Indonesia,ID,ID,Asia Pacific (Jakarta),"-6.175,106.8275 ",,,1.35,0.18,,,652.36,,652.36,,,,,,, 49 | 2023,Amazon Web Services,ap-southeast-4,Victoria,AU-VIC,NEM_VIC,Asia Pacific (Melbourne),"-37.8142,144.963",,,1.08,0.18,,,498.19,,514.1,,,,,,, 50 | 2023,Amazon Web Services,ap-south-1,Maharashtra,IN-WE,IND,Asia Pacific (Mumbai),"19.0761,72.8775",,,1.44,0.18,0.0,,746.98,,762.24,,,,,,, 51 | 2023,Amazon Web Services,ap-northeast-3,Kansai,JP-KN,JP_KN,Asia Pacific (Osaka),"34.6939,135.502",,,,0.18,0.0,,370.3,,357.05,,,,,,, 52 | 2023,Amazon Web Services,ap-northeast-2,South Korea,KR,KOR,Asia Pacific (Seoul),"37.56,126.99",,,,0.18,,,443.05,,443.01,,,,,,, 53 | 2023,Amazon Web Services,ap-southeast-1,Singapore,SG,SGP,Asia Pacific (Singapore),"1.3,103.8",,,1.3,0.18,,,487.52,,487.06,,,,,,, 54 | 2023,Amazon Web Services,ap-southeast-2,New South Wales,AU-NSW,NEM_NSW,Asia Pacific (Sydney),"-33.8678,151.21",,,1.15,0.18,,,545.01,,546.1,,,,,,, 55 | 2023,Amazon Web Services,ap-northeast-1,Tokyo,JP-TK,JP_TK,Asia Pacific (Tokyo),"35.6897,139.692",,,1.3,0.18,0.0,,536.06,,545.67,,,,,,, 56 | 2023,Amazon Web Services,ca-central-1,Quebec,CA-QC,HQ,Canada (Central),"45.5089,-73.5617",,,1.22,0.18,0.0,,30.51,,28.68,,,,,,, 57 | 2023,Amazon Web Services,eu-central-1,Germany,DE,DE,Europe (Frankfurt),"50.1106,8.6822",,,1.33,0.18,0.0,,370.93,,394.14,,,,,,, 58 | 2023,Amazon Web Services,eu-west-1,Ireland,IE,IE,Europe (Ireland),"53.35,-6.2603 ",,,1.1,0.18,0.0,,403.4,,416.03,,,,,,, 59 | 2023,Amazon Web Services,eu-west-2,Great Britain,GB,UK,Europe (London),"51.726,-0.3",,,,0.18,0.0,,187.89,,200.18,,,,,,, 60 | 2023,Amazon Web Services,eu-south-1,North Italy,IT-NO,IT,Europe (Milan),"45.4669,9.19",,,,0.18,0.0,,298.62,,379.97,,,,,,, 61 | 2023,Amazon Web Services,eu-west-3,France,FR,FR,Europe (Paris),"48.8567,2.3522",,,,0.18,0.0,,52.87,,45.21,,,,,,, 62 | 2023,Amazon Web Services,eu-south-2,Spain,ES,ES,Europe (Spain),"40.3333,-3.8667",,,1.11,0.18,0.0,,153.86,,154.56,,,,,,, 63 | 2023,Amazon Web Services,eu-north-1,Sweden,SE,SE,Europe (Stockholm),"59.3294,18.0686 ",,,1.12,0.18,0.0,,24.71,,21.28,,,,,,, 64 | 2023,Amazon Web Services,eu-central-2,Switzerland,CH,CH,Europe (Zurich),"47.3744,8.5411",,,,0.18,0.0,,83.8,,52.58,,,,,,, 65 | 2023,Amazon Web Services,il-central-1,Israel,IL,IL,Israel (Tel Aviv),"32.0167,34.7667",,,,0.18,,,533.04,,533.04,,,,,,, 66 | 2023,Amazon Web Services,me-south-1,Bahrain,BH,BH,Middle East (Bahrain),"26.219,50.538 ",,,1.32,0.18,,,700.0,,700.0,,,,,,, 67 | 2023,Amazon Web Services,me-central-1,United Arab Emirates,AE,AE,Middle East (UAE),"25.2631,55.2972 ",,,1.36,0.18,,,381.54,,381.54,,,,,,, 68 | 2023,Amazon Web Services,sa-east-1,Central Brazil,BR-CS,BRA,South America (São Paulo),"-3.45,-68.95",,,1.18,0.18,,,89.55,,90.41,,,,,,, 69 | 2023,Amazon Web Services,us-gov-east-1,PJM,US-MIDA-PJM,PJM_DC,GovCloud (US East),"39.1057,-77.5544 ",,,,0.18,0.0,,396.25,,396.77,,,,,,, 70 | 2023,Amazon Web Services,us-gov-west-1,BPA,US-NW-BPAT,BPA,GovCloud (US West),"45.5371,-122.65 ",,,,0.18,0.0,,119.76,,68.82,,,,,,, 71 | 2023,Amazon Web Services,cn-north-1,,,,China (Beijing),"39.904,116.4075 ",,,,0.18,0.0,,,,,,,,,,, 72 | 2023,Amazon Web Services,cn-northwest-1,,,,China (Ningxia),"38.4795,106.2254 ",,,1.26,0.18,0.0,,,,,,,,,,, 73 | 2023,Microsoft Azure,southeastasia,southeastasia,SG,SGP,Singapore,"1.352,103.835",,,1.34,,,,487.52,,487.06,,,,,,, 74 | 2023,Microsoft Azure,brazilsouth,Brazil,BR-CS,BRA,Brazil,"-22.939, -47.045",,,,,,,89.55,,90.41,,,,,,, 75 | 2023,Microsoft Azure,northeurope,Finland,FI,FI,Finland,"60.2065, 24.6753",,,,,,,82.34,,89.24,,,,,,, 76 | 2023,Microsoft Azure,asiapacific,Indonesia,ID,ID,Indonesia,"-6.219, 106.867",,,,,,,652.36,,652.36,,,,,,, 77 | 2023,Microsoft Azure,newzealandnorth,New Zealand,NZ,NZ,New Zealand,"-36.867, 174.751",,,,,,,96.81,,96.81,,,,,,, 78 | 2023,Microsoft Azure,swedencentral,Sweden,SE,SE,Sweden,"59.329,18.067",1.0,,1.16,,0.0,,24.71,,21.28,,,,,,, 79 | 2023,Microsoft Azure,australiaeast,New South Wales,AU-NSW,NEM_NSW,Australia East,"-33.889, 151.067",,,,,,,545.01,,546.1,,,,,,, 80 | 2023,Microsoft Azure,australiasoutheast,Victoria,AU-VIC,NEM_VIC,Australia Southeast,"-37.725, 145.067",,,,,,,498.19,,514.1,,,,,,, 81 | 2023,Microsoft Azure,denmarkeast,Denmark,DK-DK2,DK,Denmark,"55.685, 12.584",,,,,,,136.81,,184.21,,,,,,, 82 | 2023,Microsoft Azure,greececentral,Greece,GR,GR,Greece Central,"37.987, 23.745",,,,,,,368.75,,342.79,,,,,,, 83 | 2023,Microsoft Azure,italynorth,North Italy,IT-NO,IT,Italy North,"45.4669,9.19",,,,,,,298.62,,379.97,,,,,,, 84 | 2023,Microsoft Azure,polandcentral,Poland,PL,PL,Poland Central,"52.23,21.0111",,,,,,,749.28,,794.76,,,,,,, 85 | 2023,Microsoft Azure,taiwannorth,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",,,,,,,537.64,,537.76,,,,,,, 86 | 2023,Microsoft Azure,austriaeast,Austria,AT,AT,Austria East,"48.217, 16.493",,,,,,,160.36,,81.79,,,,,,, 87 | 2023,Microsoft Azure,northeurope,Ireland,IE,IE,Ireland,"53.35,-6.2603 ",,,1.19,,,,403.4,,416.03,,,,,,, 88 | 2023,Microsoft Azure,westeurope,Netherlands,NL,NL,Netherlands,"51.9167,4.5",,,1.14,,,,283.17,,292.61,,,,,,, 89 | 2023,Microsoft Azure,indiasouthcentral,India,IN-SO,IND,India South Central,"17.385,78.4867 ",,,,,,,551.31,,549.72,,,,,,, 90 | 2023,Microsoft Azure,mexicocentral,Mexico,MX,MX_SIN,Mexico Central,"20.631, -100.428",,,,,,,,,,,,,,,, 91 | 2023,Microsoft Azure,spaincentral,Spain,ES,ES,Spain Central,"40.44,-3.679",,,,,,,153.86,,154.56,,,,,,, 92 | 2023,Microsoft Azure,westus3,,,,Arizona: west US 3,"33.471,-112.056",,,1.18,,,,,,,,,,,,, 93 | 2023,Microsoft Azure,eastus3,SOCO,US-SE-SOCO,SOCO,Georgia: East US 3,"33.76,-84.401",,,,,,,435.76,,440.35,,,,,,, 94 | 2023,Microsoft Azure,northcentralus,MISO,US-MIDW-MISO,MISO,Illinois: North Central US,"41.832,-87.673",,,1.35,,,,501.42,,518.84,,,,,,, 95 | 2023,Microsoft Azure,centralus,MISO,US-MIDW-MISO,MISO,Iowa: Central US,"41.573,-93.608",,,1.16,,0.0,,501.42,,518.84,,,,,,, 96 | 2023,Microsoft Azure,southcentralus,ERCOT,US-TEX-ERCO,ERCOT,Texas: South Central US,"29.453,-98.508",,,1.28,,0.0,,388.76,,388.25,,,,,,, 97 | 2023,Microsoft Azure,eastus,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US,"36.68,-78.377",,,1.14,,,,396.25,,396.77,,,,,,, 98 | 2023,Microsoft Azure,eastus2,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US 2,"36.68,-78.377",,,1.14,,,,396.25,,396.77,,,,,,, 99 | 2023,Microsoft Azure,westus,BPA,US-NW-BPAT,BPA,Washington: West US,"47.247,-119.82",,,1.15,,,,119.76,,68.82,,,,,,, 100 | 2023,Microsoft Azure,westcentralus,,,,Wyoming: West Central US,"41.130,-104.83",,,1.11,,,,,,,,,,,,, 101 | 2022,Google Cloud,asia-east1,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",0.18,,1.12,,0.0,453.0,535.48,,535.49,,,,,,, 102 | 2022,Google Cloud,asia-east2,Hong Kong,HK,HK,Hong Kong,"22.3,114.2",0.28,,,,0.0,360.0,435.36,,435.36,,,,,,, 103 | 2022,Google Cloud,asia-northeast1,Tokyo,JP-TK,JP_TK,Tokyo,"35.6897,139.692",0.16,,,,0.0,463.0,546.82,535.9531592,554.76,536.0,,,,,, 104 | 2022,Google Cloud,asia-northeast2,Kansai,JP-KN,JP_KN,Osaka,"34.6939,135.502",0.32,,,,0.0,383.0,420.67,550.5322311,410.06,551.0,,,,,, 105 | 2022,Google Cloud,asia-northeast3,South Korea,KR,KOR,Seoul,"37.56,126.99",0.31,,,,0.0,425.0,455.53,686.0136038,455.52,686.0,,,,,, 106 | 2022,Google Cloud,asia-south1,Maharashtra,IN-WE,IND,Mumbai,"19.0761,72.8775",0.24,,,,0.0,555.0,747.67,679.4097431,760.68,679.0,,,,,, 107 | 2022,Google Cloud,asia-south2,Uttar Pradesh,IN-NO,IND,Delhi,"28.6092,76.9798",0.23,,,,0.0,632.0,552.52,679.4097431,552.19,679.0,,,,,, 108 | 2022,Google Cloud,asia-southeast1,Singapore,SG,SGP,Singapore,"1.3,103.8",0.04,,1.2,,0.0,372.0,491.09,409.1860208,490.42,409.0,,,,,, 109 | 2022,Google Cloud,asia-southeast2,Indonesia,ID,ID,Jakarta,"-6.2507,106.869",0.13,,,,0.0,580.0,652.36,,652.36,,,,,,, 110 | 2022,Google Cloud,australia-southeast1,New South Wales,AU-NSW,NEM_NSW,Sydney,"-33.8678,151.21",0.27,,,,0.0,538.0,586.3,770.3638355,585.48,770.0,,,,,, 111 | 2022,Google Cloud,australia-southeast2,Victoria,AU-VIC,NEM_VIC,Melbourne,"-37.8142,144.963",0.34,,,,0.0,490.0,534.85,664.5903366,534.85,665.0,,,,,, 112 | 2022,Google Cloud,europe-central2,Poland,PL,PL,Warsaw,"52.23,21.0111",0.24,,,,0.0,738.0,882.36,852.3941505,936.01,852.0,,,,,, 113 | 2022,Google Cloud,europe-north1,Finland,FI,FI,Finland,"60.1708,24.9375",0.97,,1.09,,0.0,112.0,132.69,784.9591618,147.0,785.0,,,,,, 114 | 2022,Google Cloud,europe-southwest1,Spain,ES,ES,Madrid,"40.3333,-3.8667",0.67,,,,0.0,160.0,220.63,370.288102,220.31,370.0,,,,,, 115 | 2022,Google Cloud,europe-west1,Belgium,BE,BE,Belgium,"51.1333,4.5667",0.8,,1.09,,0.0,123.0,196.35,411.1523178,174.17,411.0,,,,,, 116 | 2022,Google Cloud,europe-west2,Great Britain,GB,UK,London,"51.726,-0.3",0.85,,,,0.0,166.0,220.45,427.7283704,223.87,428.0,,,,,, 117 | 2022,Google Cloud,europe-west3,Germany,DE,DE,Frankfurt,"50.1106,8.6822",0.96,,,,0.0,413.0,474.46,741.7175367,488.94,742.0,,,,,, 118 | 2022,Google Cloud,europe-west4,Netherlands,NL,NL,Netherlands,"51.9167,4.5",0.57,,1.07,,0.0,317.0,337.25,487.2176839,348.62,487.0,,,,,, 119 | 2022,Google Cloud,europe-west6,Switzerland,CH,CH,Zurich,"47.3744,8.5411",0.85,,,,0.0,118.0,148.39,,49.5,,,,,,, 120 | 2022,Google Cloud,europe-west8,North Italy,IT-NO,IT,Milan,"45.4669,9.19",0.42,,,,0.0,323.0,369.48,385.8847276,445.96,386.0,,,,,, 121 | 2022,Google Cloud,europe-west9,France,FR,FR,Paris,"48.8567,2.3522",0.87,,,,0.0,71.0,91.23,383.7390836,72.0,384.0,,,,,, 122 | 2022,Google Cloud,europe-west12,North Italy,IT-NO,IT,Turin,"45.0792,7.6761",0.42,,,,0.0,323.0,369.48,385.8847276,445.96,386.0,,,,,, 123 | 2022,Google Cloud,me-west1,Israel,IL,IL,Tel Aviv,"32.0167,34.7667",0.02,,,,0.0,476.0,541.78,,541.78,,,,,,, 124 | 2022,Google Cloud,northamerica-northeast1,Quebec,CA-QC,HQ,Montréal,"45.5089,-73.5617",1.0,,,,0.0,0.0,28.22,462.2451779,27.92,462.0,,,,,, 125 | 2022,Google Cloud,northamerica-northeast2,Ontario,CA-ON,IESO_NORTH,Toronto,"43.7417,-79.3733",0.9,,,,0.0,36.0,63.01,356.8894651,63.46,357.0,,,,,, 126 | 2022,Google Cloud,southamerica-east1,Central Brazil,BR-CS,BRA,São Paulo,"-3.45,-68.95",0.89,,,,0.0,65.0,99.93,571.4267992,103.92,571.0,,,,,, 127 | 2022,Google Cloud,southamerica-west1,Chile,CL-SEN,CHL,Santiago,"-33.4372,-70.6506 ",0.9,,1.09,,0.0,165.0,321.66,615.8448463,321.66,616.0,,,,,, 128 | 2022,Google Cloud,us-central1,MISO,US-MIDW-MISO,MISO_MASON_CITY,Iowa,"41.5725,-93.6105 ",0.92,,1.11,,0.0,445.0,526.14,539.2510271,546.23,539.0,,,,,, 129 | 2022,Google Cloud,us-east1,SC,US-CAR-SC,SC,South Carolina,"34.8354,-82.3646 ",0.26,,1.1,,0.0,532.0,620.85,727.5301036,769.37,728.0,,,,,, 130 | 2022,Google Cloud,us-east4,PJM,US-MIDA-PJM,PJM_DC,Northern Virginia,"39.1057,-77.5544 ",0.6,,1.08,,0.0,354.0,429.84,565.3840343,430.11,565.0,,,,,, 131 | 2022,Google Cloud,us-east5,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,Columbus,"41.4366,-97.3565 ",0.6,,1.13,,0.0,354.0,429.84,565.1671385,430.11,565.0,,,,,, 132 | 2022,Google Cloud,us-south1,ERCOT,US-TEX-ERCO,ERCOT_NORTHCENTRAL,Dallas,"44.9221,-123.313 ",0.41,,1.14,,0.0,342.0,405.38,441.0116857,404.88,441.0,,,,,, 133 | 2022,Google Cloud,us-west1,BPA,US-NW-BPAT,BPA,Oregon,"45.5372,-122.3955 ",0.89,,1.1,,0.0,67.0,86.61,427.1886293,58.5,427.0,,,,,, 134 | 2022,Google Cloud,us-west2,CAISO,US-CAL-CISO,LDWP,Los Angeles,"34.1141,-118.4068 ",0.56,,,,0.0,202.0,264.4,501.0230748,247.55,501.0,,,,,, 135 | 2022,Google Cloud,us-west3,PACE,US-NW-PACE,PACE,Salt Lake City,"40.7776,-111.9311 ",0.31,,,,0.0,606.0,658.67,664.0421037,676.5,664.0,,,,,, 136 | 2022,Google Cloud,us-west4,NVE,US-NW-NEVP,NEVP,Las Vegas,"35.6011,-105.2206 ",0.27,,1.09,,0.0,396.0,465.92,456.7534925,508.35,457.0,,,,,, 137 | 2022,Amazon Web Services,us-east-2,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,US East (Ohio),"39.8689,-84.3292 ",,1.0,1.12,0.19,0.0,,429.84,565.1671385,430.11,565.0,,,,,, 138 | 2022,Amazon Web Services,us-east-1,PJM,US-MIDA-PJM,PJM_DC,US East (N. Virginia),"39.1057,-77.5544 ",,1.0,1.16,0.19,0.0,,429.84,565.3840343,430.11,565.0,,,,,, 139 | 2022,Amazon Web Services,us-west-1,CAISO,US-CAL-CISO,CAISO_NORTH,US West (N. California),"36.7783,-119.417931",,1.0,1.17,0.19,0.0,,264.4,392.7840553,247.55,393.0,,,,,, 140 | 2022,Amazon Web Services,us-west-2,BPA,US-NW-BPAT,BPA,US West (Oregon),"45.5371,-122.65 ",,1.0,1.13,0.19,0.0,,86.61,427.1886293,58.5,427.0,,,,,, 141 | 2022,Amazon Web Services,af-south-1,South Africa,ZA,ZA,Africa (Cape Town),"-33.9253,18.4239 ",,,1.36,0.19,,,705.0,,705.05,,,,,,, 142 | 2022,Amazon Web Services,ap-east-1,Hong Kong,HK,HK,Asia Pacific (Hong Kong),"22.3,114.2",,,,0.19,,,435.36,,435.36,,,,,,, 143 | 2022,Amazon Web Services,ap-south-2,Hyderabad,IN-SO,IND,Asia Pacific (Hyderabad),"17.385,78.4867 ",,1.0,,0.19,0.0,,504.69,679.4097431,501.99,679.0,,,,,, 144 | 2022,Amazon Web Services,ap-southeast-3,Indonesia,ID,ID,Asia Pacific (Jakarta),"-6.175,106.8275 ",,,1.39,0.19,,,652.36,,652.36,,,,,,, 145 | 2022,Amazon Web Services,ap-southeast-4,Victoria,AU-VIC,NEM_VIC,Asia Pacific (Melbourne),"-37.8142,144.963",,,,0.19,,,534.85,664.5903366,534.85,665.0,,,,,, 146 | 2022,Amazon Web Services,ap-south-1,Maharashtra,IN-WE,IND,Asia Pacific (Mumbai),"19.0761,72.8775",,1.0,1.43,0.19,0.0,,747.67,679.4097431,760.68,679.0,,,,,, 147 | 2022,Amazon Web Services,ap-northeast-3,Kansai,JP-KN,JP_KN,Asia Pacific (Osaka),"34.6939,135.502",,,,0.19,,,420.67,550.5322311,410.06,551.0,,,,,, 148 | 2022,Amazon Web Services,ap-northeast-2,South Korea,KR,KOR,Asia Pacific (Seoul),"37.56,126.99",,,,0.19,,,455.53,686.0136038,455.52,686.0,,,,,, 149 | 2022,Amazon Web Services,ap-southeast-1,Singapore,SG,SGP,Asia Pacific (Singapore),"1.3,103.8",,,1.33,0.19,,,491.09,409.1860208,490.42,409.0,,,,,, 150 | 2022,Amazon Web Services,ap-southeast-2,New South Wales,AU-NSW,NEM_NSW,Asia Pacific (Sydney),"-33.8678,151.21",,,1.15,0.19,,,586.3,770.3638355,585.48,770.0,,,,,, 151 | 2022,Amazon Web Services,ap-northeast-1,Tokyo,JP-TK,JP_TK,Asia Pacific (Tokyo),"35.6897,139.692",,,1.32,0.19,,,546.82,535.9531592,554.76,536.0,,,,,, 152 | 2022,Amazon Web Services,ca-central-1,Quebec,CA-QC,HQ,Canada (Central),"45.5089,-73.5617",,1.0,1.26,0.19,0.0,,28.22,462.2451779,27.92,462.0,,,,,, 153 | 2022,Amazon Web Services,eu-central-1,Germany,DE,DE,Europe (Frankfurt),"50.1106,8.6822",,1.0,1.32,0.19,0.0,,474.46,741.7175367,488.94,742.0,,,,,, 154 | 2022,Amazon Web Services,eu-west-1,Ireland,IE,IE,Europe (Ireland),"53.35,-6.2603 ",,1.0,1.1,0.19,0.0,,404.89,507.95177,404.88,508.0,,,,,, 155 | 2022,Amazon Web Services,eu-west-2,Great Britain,GB,UK,Europe (London),"51.726,-0.3",,1.0,,0.19,0.0,,220.45,427.7283704,223.87,428.0,,,,,, 156 | 2022,Amazon Web Services,eu-south-1,North Italy,IT-NO,IT,Europe (Milan),"45.4669,9.19",,1.0,,0.19,0.0,,369.48,385.8847276,445.96,386.0,,,,,, 157 | 2022,Amazon Web Services,eu-west-3,France,FR,FR,Europe (Paris),"48.8567,2.3522",,1.0,,0.19,0.0,,91.23,383.7390836,72.0,384.0,,,,,, 158 | 2022,Amazon Web Services,eu-south-2,Spain,ES,ES,Europe (Spain),"40.3333,-3.8667",,1.0,,0.19,0.0,,220.63,370.288102,220.31,370.0,,,,,, 159 | 2022,Amazon Web Services,eu-north-1,Sweden,SE,SE,Europe (Stockholm),"59.3294,18.0686 ",,1.0,1.12,0.19,0.0,,25.82,781.7946219,22.63,782.0,,,,,, 160 | 2022,Amazon Web Services,eu-central-2,Switzerland,CH,CH,Europe (Zurich),"47.3744,8.5411",,1.0,,0.19,0.0,,148.39,778.7844238,49.5,779.0,,,,,, 161 | 2022,Amazon Web Services,il-central-1,Israel,IL,IL,Israel (Tel Aviv),"32.0167,34.7667",,,,0.19,,,541.78,,541.78,,,,,,, 162 | 2022,Amazon Web Services,me-south-1,Bahrain,BH,BH,Middle East (Bahrain),"26.219,50.538 ",,,1.34,0.19,,,700.0,,700.0,,,,,,, 163 | 2022,Amazon Web Services,me-central-1,United Arab Emirates,AE,AE,Middle East (UAE),"25.2631,55.2972 ",,,,0.19,,,325.52,,325.52,,,,,,, 164 | 2022,Amazon Web Services,sa-east-1,Central Brazil,BR-CS,BRA,South America (São Paulo),"-3.45,-68.95",,,,0.19,,,99.93,571.4267992,103.92,571.0,,,,,, 165 | 2022,Amazon Web Services,us-gov-east-1,PJM,US-MIDA-PJM,PJM_DC,GovCloud (US East),"39.1057,-77.5544 ",,1.0,,0.19,0.0,,429.84,565.3840343,430.11,565.0,,,,,, 166 | 2022,Amazon Web Services,us-gov-west-1,BPA,US-NW-BPAT,BPA,GovCloud (US West),"45.5371,-122.65 ",,1.0,,0.19,0.0,,86.61,427.1886293,58.5,427.0,,,,,, 167 | 2022,Amazon Web Services,cn-north-1,,,,China (Beijing),"39.904,116.4075 ",,1.0,,0.19,0.0,,,,,,,,,,, 168 | 2022,Amazon Web Services,cn-northwest-1,,,,China (Ningxia),"38.4795,106.2254 ",,1.0,1.28,0.19,0.0,,,,,,,,,,, 169 | 2022,Microsoft Azure,southeastasia,southeastasia,SG,SGP,Singapore,"1.352,103.835",,0.13,1.358,2.06,,,491.09,409.1860208,490.42,409.0,,,,,, 170 | 2022,Microsoft Azure,brazilsouth,Brazil,BR-CS,BRA,Brazil,"-22.939, -47.045",,0.05,1.12,0.039,,,99.93,571.4267992,103.92,571.0,,,,,, 171 | 2022,Microsoft Azure,northeurope,Finland,FI,FI,Finland,"60.2065, 24.6753",,0.1,1.12,0.01,,,132.69,784.9591618,147.0,785.0,,,,,, 172 | 2022,Microsoft Azure,asiapacific,Indonesia,ID,ID,Indonesia,"-6.219, 106.867",,0.13,1.32,1.9,,,652.36,,652.36,,,,,,, 173 | 2022,Microsoft Azure,newzealandnorth,New Zealand,NZ,NZ,New Zealand,"-36.867, 174.751",,,1.12,0.0,,,96.35,,96.35,,,,,,, 174 | 2022,Microsoft Azure,swedencentral,Sweden,SE,SE,Sweden,"59.329,18.067",1.0,1.0,1.172,0.16,0.0,,25.82,781.7946219,22.63,782.0,,,,,, 175 | 2022,Microsoft Azure,australiaeast,New South Wales,AU-NSW,NEM_NSW,Australia East,"-33.889, 151.067",,0.28,1.12,0.012,,,586.3,,585.48,,,,,,, 176 | 2022,Microsoft Azure,australiasoutheast,Victoria,AU-VIC,NEM_VIC,Australia Southeast,"-37.725, 145.067",,0.28,1.12,0.012,,,534.85,,534.85,,,,,,, 177 | 2022,Microsoft Azure,denmarkeast,Denmark,DK-DK2,DK,Denmark,"55.685, 12.584",,0.1,1.16,0.01,,,165.42,659.8866462,211.49,660.0,,,,,, 178 | 2022,Microsoft Azure,greececentral,Greece,GR,GR,Greece Central,"37.987, 23.745",,,1.12,0.102,,,434.85,437.5870529,424.27,438.0,,,,,, 179 | 2022,Microsoft Azure,italynorth,North Italy,IT-NO,IT,Italy North,"45.4669,9.19",,0.15,1.12,0.023,,,369.48,385.8847276,445.96,386.0,,,,,, 180 | 2022,Microsoft Azure,polandcentral,Poland,PL,PL,Poland Central,"52.23,21.0111",,,1.12,0.023,,,882.36,852.3941505,936.01,852.0,,,,,, 181 | 2022,Microsoft Azure,taiwannorth,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",,,1.2,1.0,,,535.48,,535.49,,,,,,, 182 | 2022,Microsoft Azure,austriaeast,Austria,AT,AT,Austria East,"48.217, 16.493",,0.79,1.12,0.023,,,244.81,632.1527284,131.56,632.0,,,,,, 183 | 2022,Microsoft Azure,northeurope,Ireland,IE,IE,Ireland,"53.35,-6.2603 ",,0.49,1.197,0.03,,,404.89,507.95177,404.88,508.0,,,,,, 184 | 2022,Microsoft Azure,westeurope,Netherlands,NL,NL,Netherlands,"51.9167,4.5",,0.79,1.158,0.08,,,337.25,487.2176839,348.62,487.0,,,,,, 185 | 2022,Microsoft Azure,indiasouthcentral,India,IN-SO,IND,India South Central,"17.385,78.4867 ",,0.46,1.43,0.0,,,504.69,679.4097431,501.99,679.0,,,,,, 186 | 2022,Microsoft Azure,mexicocentral,Mexico,MX,MX_SIN,Mexico Central,"20.631, -100.428",,,1.12,0.056,,,,421.9293015,,422.0,,,,,, 187 | 2022,Microsoft Azure,spaincentral,Spain,ES,ES,Spain Central,"40.44,-3.679",,0.15,1.12,,,,220.63,370.288102,220.31,370.0,,,,,, 188 | 2022,Microsoft Azure,westus3,,,,Arizona: west US 3,"33.471,-112.056",,0.47,1.223,2.24,,,,,,,,,,,, 189 | 2022,Microsoft Azure,eastus3,SOCO,US-SE-SOCO,SOCO,Georgia: East US 3,"33.76,-84.401",,0.08,1.12,0.06,,,460.38,533.5899463,467.07,534.0,,,,,, 190 | 2022,Microsoft Azure,northcentralus,MISO,US-MIDW-MISO,MISO,Illinois: North Central US,"41.832,-87.673",,0.7,1.346,0.79,,,526.14,,546.23,,,,,,, 191 | 2022,Microsoft Azure,centralus,MISO,US-MIDW-MISO,MISO,Iowa: Central US,"41.573,-93.608",,1.0,1.16,0.19,0.0,,526.14,,546.23,,,,,,, 192 | 2022,Microsoft Azure,southcentralus,ERCOT,US-TEX-ERCO,ERCOT,Texas: South Central US,"29.453,-98.508",,1.0,1.307,1.82,0.0,,405.38,,404.88,,,,,,, 193 | 2022,Microsoft Azure,eastus,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US,"36.68,-78.377",,0.7,1.144,0.17,,,429.84,,430.11,,,,,,, 194 | 2022,Microsoft Azure,eastus2,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US 2,"36.68,-78.377",,0.7,1.144,0.17,,,429.84,,430.11,,,,,,, 195 | 2022,Microsoft Azure,westus,BPA,US-NW-BPAT,BPA,Washington: West US,"47.247,-119.82",,0.47,1.156,1.09,,,86.61,427.1886293,58.5,427.0,,,,,, 196 | 2022,Microsoft Azure,westcentralus,,,,Wyoming: West Central US,"41.130,-104.83",,0.47,1.125,0.23,,,,,,,,,,,, 197 | 2021,Google Cloud,asia-east1,Taiwan,TW,TW,Taiwan,,0.17,,,,0.0,456.0,542.08,,542.09,,,,,,, 198 | 2021,Google Cloud,asia-east2,Hong Kong,HK,HK,Hong Kong,,0.28,,,,0.0,360.0,435.36,,435.36,,,,,,, 199 | 2021,Google Cloud,asia-northeast1,Tokyo,JP-TK,JP_TK,Tokyo,,0.16,,,,0.0,464.0,549.03,,557.48,,,,,,, 200 | 2021,Google Cloud,asia-northeast2,Kansai,JP-KN,JP_KN,Osaka,,0.31,,,,0.0,384.0,402.94,,394.06,,,,,,, 201 | 2021,Google Cloud,asia-northeast3,South Korea,KR,KOR,Seoul,,0.31,,,,0.0,425.0,489.38,,489.38,,,,,,, 202 | 2021,Google Cloud,asia-south1,Maharashtra,IN-WE,IND,Mumbai,,0.1,,,,0.0,670.0,744.77,,761.97,,,,,,, 203 | 2021,Google Cloud,asia-south2,Uttar Pradesh,IN-NO,IND,Delhi,,0.08,,,,0.0,671.0,525.89,,525.92,,,,,,, 204 | 2021,Google Cloud,asia-southeast1,Singapore,SG,SGP,Singapore,,0.04,,,,0.0,372.0,491.36,,491.21,,,,,,, 205 | 2021,Google Cloud,asia-southeast2,Indonesia,ID,,Jakarta,,0.13,,,,0.0,580.0,652.36,,652.36,,,,,,, 206 | 2021,Google Cloud,australia-southeast1,New South Wales,AU-NSW,NEM_NSW,Sydney,,0.21,,,,0.0,598.0,654.6,,652.27,,,,,,, 207 | 2021,Google Cloud,australia-southeast2,Victoria,AU-VIC,NEM_VIC,Melbourne,,0.31,,,,0.0,521.0,587.39,,585.78,,,,,,, 208 | 2021,Google Cloud,europe-central2,Poland,PL,PL,Warsaw,,0.2,,,,0.0,576.0,856.54,,906.84,,,,,,, 209 | 2021,Google Cloud,europe-north1,Finland,FI,FI,Finland,,0.91,,,,0.0,127.0,169.48,,176.36,,,,,,, 210 | 2021,Google Cloud,europe-southwest1,Spain,ES,ES,Madrid,,0.0,,,,0.0,121.0,174.36,,175.13,,,,,,, 211 | 2021,Google Cloud,europe-west1,Belgium,BE,BE,Belgium,,0.82,,,,0.0,110.0,167.99,,151.67,,,,,,, 212 | 2021,Google Cloud,europe-west2,Great Britain,GB,UK,London,,0.57,,,,0.0,172.0,226.12,,235.87,,,,,,, 213 | 2021,Google Cloud,europe-west3,Germany,DE,DE,Frankfurt,,0.6,,,,0.0,269.0,437.1,,453.17,,,,,,, 214 | 2021,Google Cloud,europe-west4,Netherlands,NL,NL,Netherlands,,0.53,,,,0.0,283.0,368.15,,388.04,,,,,,, 215 | 2021,Google Cloud,europe-west6,Switzerland,CH,CH,Zurich,,0.85,,,,0.0,86.0,130.82,,59.49,,,,,,, 216 | 2021,Google Cloud,europe-west8,North Italy,IT-NO,IT,Milan,,0.0,,,,0.0,298.0,327.2,,392.53,,,,,,, 217 | 2021,Google Cloud,europe-west9,France,FR,FR,Paris,,0.0,,,,0.0,59.0,60.54,,51.84,,,,,,, 218 | 2021,Google Cloud,northamerica-northeast1,Quebec,CA-QC,HQ,Montréal,,1.0,,,,0.0,0.0,28.39,,28.42,,,,,,, 219 | 2021,Google Cloud,northamerica-northeast2,Ontario,CA-ON,IESO_NORTH,Toronto,,0.92,,,,0.0,29.0,53.61,,53.86,,,,,,, 220 | 2021,Google Cloud,southamerica-east1,Central Brazil,BR-CS,BRA,São Paulo,,0.78,,,,0.0,129.0,170.18,,169.95,,,,,,, 221 | 2021,Google Cloud,southamerica-west1,Chile,CL-SEN,CHL,Santiago,,0.69,,,,0.0,190.0,405.23,,405.64,,,,,,, 222 | 2021,Google Cloud,us-central1,MISO,US-MIDW-MISO,MISO_MASON_CITY,Iowa,,0.97,,,,0.0,394.0,561.94,,580.89,,,,,,, 223 | 2021,Google Cloud,us-east1,SC,US-CAR-SC,SC,South Carolina,,0.25,,,,0.0,434.0,652.2,,806.81,,,,,,, 224 | 2021,Google Cloud,us-east4,PJM,US-MIDA-PJM,PJM_DC,Northern Virginia,,0.64,,,,0.0,309.0,441.45,,442.26,,,,,,, 225 | 2021,Google Cloud,us-east5,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,Columbus,,0.64,,,,0.0,309.0,441.45,,442.26,,,,,,, 226 | 2021,Google Cloud,us-south1,ERCOT,US-TEX-ERCO,ERCOT_NORTHCENTRAL,Dallas,,0.4,,,,0.0,296.0,426.3,,425.98,,,,,,, 227 | 2021,Google Cloud,us-west1,BPA,US-NW-BPAT,BPA,Oregon,,0.88,,,,0.0,60.0,90.95,,67.69,,,,,,, 228 | 2021,Google Cloud,us-west2,CAISO,US-CAL-CISO,LDWP,Los Angeles,,0.53,,,,0.0,190.0,274.9,,254.26,,,,,,, 229 | 2021,Google Cloud,us-west3,PACE,US-NW-PACE,PACE,Salt Lake City,,0.31,,,,0.0,448.0,633.54,,669.78,,,,,,, 230 | 2021,Google Cloud,us-west4,NVE,US-NW-NEVP,NEVP,Las Vegas,,0.21,,,,0.0,365.0,498.72,,537.76,,,,,,, 231 | 2021,Amazon Web Services,us-east-2,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,US East (Ohio),,,0.95,,,,,441.45,,442.26,,,,,,, 232 | 2021,Amazon Web Services,us-east-1,PJM,US-MIDA-PJM,PJM_DC,US East (N. Virginia),,,0.95,,,,,441.45,,442.26,,,,,,, 233 | 2021,Amazon Web Services,us-west-1,CAISO,US-CAL-CISO,CAISO_NORTH,US West (N. California),,,0.95,,,,,274.9,,254.26,,,,,,, 234 | 2021,Amazon Web Services,us-west-2,BPA,US-NW-BPAT,BPA,US West (Oregon),,,0.95,,,,,90.95,,67.69,,,,,,, 235 | 2021,Amazon Web Services,af-south-1,South Africa,ZA,ZA,Africa (Cape Town),,,,,,,,710.75,,710.81,,,,,,, 236 | 2021,Amazon Web Services,ap-east-1,Hong Kong,HK,HK,Asia Pacific (Hong Kong),,,,,,,,435.36,,435.36,,,,,,, 237 | 2021,Amazon Web Services,ap-south-2,Hyderabad,IN-SO,IND,Asia Pacific (Hyderabad),,,,,,,,499.35,,498.15,,,,,,, 238 | 2021,Amazon Web Services,ap-southeast-3,Indonesia,ID,ID,Asia Pacific (Jakarta),,,,,,,,652.36,,652.36,,,,,,, 239 | 2021,Amazon Web Services,ap-southeast-4,Victoria,AU-VIC,NEM_VIC,Asia Pacific (Melbourne),,,,,,,,587.39,,585.78,,,,,,, 240 | 2021,Amazon Web Services,ap-south-1,Maharashtra,IN-WE,IND,Asia Pacific (Mumbai),,,,,,,,744.77,,761.97,,,,,,, 241 | 2021,Amazon Web Services,ap-northeast-3,Kansai,JP-KN,JP_KN,Asia Pacific (Osaka),,,,,,,,402.94,,394.06,,,,,,, 242 | 2021,Amazon Web Services,ap-northeast-2,South Korea,KR,KOR,Asia Pacific (Seoul),,,,,,,,489.38,,489.38,,,,,,, 243 | 2021,Amazon Web Services,ap-southeast-1,Singapore,SG,SGP,Asia Pacific (Singapore),,,,,,,,491.36,,491.21,,,,,,, 244 | 2021,Amazon Web Services,ap-southeast-2,New South Wales,AU-NSW,NEM_NSW,Asia Pacific (Sydney),,,,,,,,654.6,,652.27,,,,,,, 245 | 2021,Amazon Web Services,ap-northeast-1,Tokyo,JP-TK,JP_TK,Asia Pacific (Tokyo),,,,,,,,549.03,,557.48,,,,,,, 246 | 2021,Amazon Web Services,ca-central-1,Quebec,CA-QC,HQ,Canada (Central),,,0.95,,,,,28.39,,28.42,,,,,,, 247 | 2021,Amazon Web Services,eu-central-1,Germany,DE,DE,Europe (Frankfurt),,,0.95,,,,,437.1,,453.17,,,,,,, 248 | 2021,Amazon Web Services,eu-west-1,Ireland,IE,IE,Europe (Ireland),"53.35,-6.2603 ",,0.95,,,,,375.84,,378.18,,,,,,, 249 | 2021,Amazon Web Services,eu-west-2,Great Britain,GB,UK,Europe (London),,,0.95,,,,,226.12,,235.87,,,,,,, 250 | 2021,Amazon Web Services,eu-south-1,North Italy,IT-NO,IT,Europe (Milan),,,0.95,,,,,327.2,,392.53,,,,,,, 251 | 2021,Amazon Web Services,eu-west-3,France,FR,FR,Europe (Paris),,,0.95,,,,,60.54,,51.84,,,,,,, 252 | 2021,Amazon Web Services,eu-north-1,Spain,ES,SE,Europe (Stockholm),,,0.95,,,,,174.36,,175.13,,,,,,, 253 | 2021,Amazon Web Services,il-central-1,Israel,IL,IL,Israel (Tel Aviv),,,,,,,,550.86,,550.86,,,,,,, 254 | 2021,Amazon Web Services,me-south-1,Bahrain,BH,BH,Middle East (Bahrain),,,,,,,,,,,,,,,,, 255 | 2021,Amazon Web Services,me-central-1,United Arab Emirates,AE,AE,Middle East (UAE),,,,,,,,,,,,,,,,, 256 | 2021,Amazon Web Services,sa-east-1,Central Brazil,BR-CS,BRA,South America (São Paulo),,,,,,,,170.18,,169.95,,,,,,, 257 | 2021,Amazon Web Services,us-gov-east-1,PJM,US-MIDA-PJM,PJM_DC,GovCloud (US East),,,0.95,,,,,441.45,,442.26,,,,,,, 258 | 2021,Amazon Web Services,us-gov-west-1,BPA,US-NW-BPAT,BPA,GovCloud (US West),,,0.95,,,,,90.95,,67.69,,,,,,, 259 | 2021,Amazon Web Services,cn-north-1,,,,China (Beijing),,,,,,,,,,,,,,,,, 260 | 2021,Amazon Web Services,cn-northwest-1,,,,China (Ningxia),,,,,,,,,,,,,,,,, 261 | 2020,Google Cloud,asia-east1,Taiwan,TW,TW,Taiwan,,0.18,,,,0.0,540.0,540.14,,540.17,,,,,,, 262 | 2020,Google Cloud,asia-east2,Hong Kong,HK,HK,Hong Kong,,0.0,,,,0.0,453.0,435.36,,435.36,,,,,,, 263 | 2020,Google Cloud,asia-northeast1,Tokyo,JP-TK,JP_TK,Tokyo,,0.12,,,,0.0,554.0,558.18,,564.61,,,,,,, 264 | 2020,Google Cloud,asia-northeast2,Kansai,JP-KN,JP_KN,Osaka,,0.0,,,,0.0,442.0,472.07,,478.86,,,,,,, 265 | 2020,Google Cloud,asia-northeast3,South Korea,KR,KOR,Seoul,,0.31,,,,0.0,457.0,490.0,,490.0,,,,,,, 266 | 2020,Google Cloud,asia-south1,Maharashtra,IN-WE,IND,Mumbai,,0.12,,,,0.0,721.0,686.92,,698.28,,,,,,, 267 | 2020,Google Cloud,asia-south2,Uttar Pradesh,IN-NO,IND,Delhi,,0.0,,,,0.0,657.0,502.51,,507.31,,,,,,, 268 | 2020,Google Cloud,asia-southeast1,Singapore,SG,SGP,Singapore,,0.04,,,,0.0,493.0,492.5,,491.94,,,,,,, 269 | 2020,Google Cloud,asia-southeast2,Indonesia,ID,ID,Jakarta,,0.0,,,,0.0,647.0,652.36,,652.36,,,,,,, 270 | 2020,Google Cloud,australia-southeast1,New South Wales,AU-NSW,NEM_NSW,Sydney,,0.11,,,,0.0,727.0,692.71,,692.61,,,,,,, 271 | 2020,Google Cloud,australia-southeast2,Victoria,AU-VIC,NEM_VIC,Melbourne,,0.0,,,,0.0,691.0,633.0,,633.0,,,,,,, 272 | 2020,Google Cloud,europe-central2,Poland,PL,PL,Warsaw,,0.0,,,,0.0,622.0,828.05,,900.97,,,,,,, 273 | 2020,Google Cloud,europe-north1,Belgium,BE,BE,Finland,,0.94,,,,0.0,133.0,221.1,,214.14,,,,,,, 274 | 2020,Google Cloud,europe-west1,Great Britain,GB,UK,Belgium,,0.79,,,,0.0,212.0,206.99,,209.32,,,,,,, 275 | 2020,Google Cloud,europe-west2,Germany,DE,DE,London,,0.59,,,,0.0,231.0,385.61,,399.25,,,,,,, 276 | 2020,Google Cloud,europe-west3,Netherlands,NL,NL,Frankfurt,,0.63,,,,0.0,293.0,372.07,,403.44,,,,,,, 277 | 2020,Google Cloud,europe-west4,Switzerland,CH,CH,Netherlands,,0.6,,,,0.0,410.0,108.24,,68.73,,,,,,, 278 | 2020,Google Cloud,europe-west6,North Italy,IT-NO,IT,Zurich,,0.0,,,,0.0,87.0,305.91,,364.44,,,,,,, 279 | 2020,Google Cloud,northamerica-northeast1,Quebec,CA-QC,HQ,Montréal,,0.0,,,,0.0,27.0,28.48,,28.52,,,,,,, 280 | 2020,Google Cloud,southamerica-east1,Central Brazil,BR-CS,BRA,São Paulo,,0.88,,,,0.0,103.0,147.68,,98.67,,,,,,, 281 | 2020,Google Cloud,us-central1,MISO,US-MIDW-MISO,MISO_MASON_CITY,Iowa,,0.93,,,,0.0,454.0,525.69,,552.43,,,,,,, 282 | 2020,Google Cloud,us-east1,SC,US-CAR-SC,SC,South Carolina,,0.27,,,,0.0,480.0,597.85,,752.21,,,,,,, 283 | 2020,Google Cloud,us-east4,PJM,US-MIDA-PJM,PJM_DC,Northern Virginia,,0.58,,,,0.0,361.0,420.99,,421.62,,,,,,, 284 | 2020,Google Cloud,us-west1,BPA,US-NW-BPAT,BPA,Oregon,,0.9,,,,0.0,78.0,68.35,,56.64,,,,,,, 285 | 2020,Google Cloud,us-west2,CAISO,US-CAL-CISO,LDWP,Los Angeles,,0.54,,,,0.0,253.0,270.13,,253.12,,,,,,, 286 | 2020,Google Cloud,us-west3,PACE,US-NW-PACE,PACE,Salt Lake City,,0.28,,,,0.0,533.0,675.84,,732.5,,,,,,, 287 | 2020,Google Cloud,us-west4,NVE,US-NW-NEVP,NEVP,Las Vegas,,0.19,,,,0.0,455.0,501.39,,543.8,,,,,,, 288 | 2019,Google Cloud,asia-east1,Taiwan,TW,TW,Taiwan,,0.19,,,,0.0,541.0,539.3,,539.33,,,,,,, 289 | 2019,Google Cloud,asia-east2,Hong Kong,HK,HK,Hong Kong,,0.0,,,,0.0,506.0,435.36,,435.36,,,,,,, 290 | 2019,Google Cloud,asia-northeast1,Tokyo,JP-TK,JP_TK,Tokyo,,0.0,,,,0.0,569.0,561.77,,569.67,,,,,,, 291 | 2019,Google Cloud,asia-northeast2,Kansai,JP-KN,JP_KN,Osaka,,0.0,,,,0.0,414.0,407.42,,407.6,,,,,,, 292 | 2019,Google Cloud,asia-northeast3,South Korea,KR,KOR,Seoul,,0.0,,,,0.0,490.0,490.27,,490.27,,,,,,, 293 | 2019,Google Cloud,asia-south1,Maharashtra,IN-WE,IND,Mumbai,,0.0,,,,0.0,752.0,706.62,,707.47,,,,,,, 294 | 2019,Google Cloud,asia-southeast1,Singapore,SG,SGP,Singapore,,0.03,,,,0.0,493.0,493.32,,492.9,,,,,,, 295 | 2019,Google Cloud,asia-southeast2,Indonesia,ID,ID,Jakarta,,0.0,,,,0.0,647.0,652.36,,652.36,,,,,,, 296 | 2019,Google Cloud,australia-southeast1,New South Wales,AU-NSW,NEM_NSW,Sydney,,0.11,,,,0.0,725.0,711.83,,711.82,,,,,,, 297 | 2019,Google Cloud,europe-north1,Victoria,AU-VIC,NEM_VIC,Finland,,0.77,,,,0.0,181.0,648.97,,648.97,,,,,,, 298 | 2019,Google Cloud,europe-west1,Belgium,BE,BE,Belgium,,0.68,,,,0.0,196.0,209.8,,202.19,,,,,,, 299 | 2019,Google Cloud,europe-west2,Great Britain,GB,UK,London,,0.54,,,,0.0,257.0,230.3,,233.65,,,,,,, 300 | 2019,Google Cloud,europe-west3,Germany,DE,DE,Frankfurt,,0.61,,,,0.0,319.0,432.36,,445.06,,,,,,, 301 | 2019,Google Cloud,europe-west4,Netherlands,NL,NL,Netherlands,,0.61,,,,0.0,474.0,416.29,,448.38,,,,,,, 302 | 2019,Google Cloud,europe-west6,Switzerland,CH,CH,Zurich,,0.0,,,,0.0,87.0,130.78,,78.12,,,,,,, 303 | 2019,Google Cloud,northamerica-northeast1,Quebec,CA-QC,HQ,Montreal,,0.0,,,,0.0,27.0,28.46,,28.52,,,,,,, 304 | 2019,Google Cloud,southamerica-east1,Central Brazil,BR-CS,BRA,Sao Paulo,,0.87,,,,0.0,109.0,107.07,,104.01,,,,,,, 305 | 2019,Google Cloud,us-central1,MISO,US-MIDW-MISO,MISO_MASON_CITY,Iowa,,0.78,,,,0.0,479.0,581.2,,600.27,,,,,,, 306 | 2019,Google Cloud,us-east1,SC,US-CAR-SC,SC,South Carolina,,0.19,,,,0.0,500.0,627.68,,768.4,,,,,,, 307 | 2019,Google Cloud,us-east4,PJM,US-MIDA-PJM,PJM_DC,Northern Virginia,,0.41,,,,0.0,383.0,454.22,,449.69,,,,,,, 308 | 2019,Google Cloud,us-west1,BPA,US-NW-BPAT,BPA,Oregon,,0.89,,,,0.0,117.0,115.03,,74.15,,,,,,, 309 | 2019,Google Cloud,us-west2,CAISO,US-CAL-CISO,LDWP,Los Angeles,,0.55,,,,0.0,248.0,253.75,,223.36,,,,,,, 310 | 2019,Google Cloud,us-west3,PACE,US-NW-PACE,PACE,Salt Lake City,,0.25,,,,0.0,561.0,704.22,,772.14,,,,,,, 311 | 2019,Google Cloud,us-west4,NVE,US-NW-NEVP,NEVP,Las Vegas,,0.13,,,,0.0,491.0,540.58,,575.79,,,,,,, 312 | -------------------------------------------------------------------------------- /Cloud_Region_Metadata_estimate.csv: -------------------------------------------------------------------------------- 1 | year,cloud-provider,cloud-region,cfe-region,em-zone-id,wt-region-id,location,geolocation,provider-cfe-hourly,provider-cfe-annual,power-usage-effectiveness,water-usage-effectiveness,provider-carbon-intensity-market-annual,provider-carbon-intensity-average-consumption-hourly,grid-carbon-intensity-average-consumption-annual,grid-carbon-intensity-marginal-consumption-annual,grid-carbon-intensity-average-production-annual,grid-carbon-intensity,total-ICT-energy-consumption-annual,total-water-input,renewable-energy-consumption,renewable-energy-consumption-goe,renewable-energy-consumption-ppa,Unnamed: 23,renewable-energy-consumption-onsite 2 | 2025,Amazon Web Services,af-south-1,South Africa,ZA,ZA,Africa (Cape Town),"-33.9253,18.4239 ",,,1.12,0.17,,,,,,,,,,,,, 3 | 2025,Amazon Web Services,ap-east-1,Hong Kong,HK,HK,Asia Pacific (Hong Kong),"22.3,114.2",,,,0.17,,,,,,,,,,,,, 4 | 2025,Amazon Web Services,ap-northeast-1,Tokyo,JP-TK,JP_TK,Asia Pacific (Tokyo),"35.6897,139.692",,,1.28,0.17,0,,,535.9531592,,536.0,,,,,,, 5 | 2025,Amazon Web Services,ap-northeast-2,South Korea,KR,KOR,Asia Pacific (Seoul),"37.56,126.99",,,,0.17,,,,686.0136038,,686.0,,,,,,, 6 | 2025,Amazon Web Services,ap-northeast-3,Kansai,JP-KN,JP_KN,Asia Pacific (Osaka),"34.6939,135.502",,,,0.17,0,,,550.5322311,,551.0,,,,,,, 7 | 2025,Amazon Web Services,ap-south-1,Maharashtra,IN-WE,IND,Asia Pacific (Mumbai),"19.0761,72.8775",,1.0,1.45,0.17,0,,,679.4097431,,679.0,,,,,,, 8 | 2025,Amazon Web Services,ap-south-2,Hyderabad,IN-SO,IND,Asia Pacific (Hyderabad),"17.385,78.4867 ",,1.0,1.5,0.17,0,,,679.4097431,,679.0,,,,,,, 9 | 2025,Amazon Web Services,ap-southeast-1,Singapore,SG,SGP,Asia Pacific (Singapore),"1.3,103.8",,,1.27,0.17,,,,409.1860208,,409.0,,,,,,, 10 | 2025,Amazon Web Services,ap-southeast-2,New South Wales,AU-NSW,NEM_NSW,Asia Pacific (Sydney),"-33.8678,151.21",,,1.15,0.17,,,,770.3638355,,770.0,,,,,,, 11 | 2025,Amazon Web Services,ap-southeast-3,Indonesia,ID,ID,Asia Pacific (Jakarta),"-6.175,106.8275 ",,,1.31,0.17,,,,,,,,,,,,, 12 | 2025,Amazon Web Services,ap-southeast-4,Victoria,AU-VIC,NEM_VIC,Asia Pacific (Melbourne),"-37.8142,144.963",,,1.08,0.17,,,,664.5903366,,665.0,,,,,,, 13 | 2025,Amazon Web Services,ca-central-1,Quebec,CA-QC,HQ,Canada (Central),"45.5089,-73.5617",,1.0,1.18,0.17,0,,,462.2451779,,462.0,,,,,,, 14 | 2025,Amazon Web Services,cn-north-1,,CN,,China (Beijing),"39.904,116.4075 ",,1.0,,0.17,0,,,,,,,,,,,, 15 | 2025,Amazon Web Services,cn-northwest-1,,CN,,China (Ningxia),"38.4795,106.2254 ",,1.0,1.24,0.17,0,,,,,,,,,,,, 16 | 2025,Amazon Web Services,eu-central-1,Germany,DE,DE,Europe (Frankfurt),"50.1106,8.6822",,1.0,1.34,0.17,0,,,741.7175367,,742.0,,,,,,, 17 | 2025,Amazon Web Services,eu-central-2,Switzerland,CH,CH,Europe (Zurich),"47.3744,8.5411",,1.0,,0.17,0,,,778.7844238,,779.0,,,,,,, 18 | 2025,Amazon Web Services,eu-north-1,Sweden,SE,SE,Europe (Stockholm),"59.3294,18.0686 ",,1.0,1.12,0.17,0,,,781.7946219,,782.0,,,,,,, 19 | 2025,Amazon Web Services,eu-south-1,North Italy,IT-NO,IT,Europe (Milan),"45.4669,9.19",,1.0,,0.17,0,,,385.8847276,,386.0,,,,,,, 20 | 2025,Amazon Web Services,eu-south-2,Spain,ES,ES,Europe (Spain),"40.3333,-3.8667",,1.0,1.11,0.17,0,,,370.288102,,370.0,,,,,,, 21 | 2025,Amazon Web Services,eu-west-1,Ireland,IE,IE,Europe (Ireland),"53.35,-6.2603 ",,1.0,1.1,0.17,0,,,507.95177,,508.0,,,,,,, 22 | 2025,Amazon Web Services,eu-west-2,Great Britain,GB,UK,Europe (London),"51.726,-0.3",,1.0,,0.17,0,,,427.7283704,,428.0,,,,,,, 23 | 2025,Amazon Web Services,eu-west-3,France,FR,FR,Europe (Paris),"48.8567,2.3522",,1.0,,0.17,0,,,383.7390836,,384.0,,,,,,, 24 | 2025,Amazon Web Services,il-central-1,Israel,IL,IL,Israel (Tel Aviv),"32.0167,34.7667",,,,0.17,,,,,,,,,,,,, 25 | 2025,Amazon Web Services,me-central-1,United Arab Emirates,AE,AE,Middle East (UAE),"25.2631,55.2972 ",,,1.36,0.17,,,,,,,,,,,,, 26 | 2025,Amazon Web Services,me-south-1,Bahrain,BH,BH,Middle East (Bahrain),"26.219,50.538 ",,,1.3,0.17,,,,,,,,,,,,, 27 | 2025,Amazon Web Services,sa-east-1,Central Brazil,BR-CS,BRA,South America (São Paulo),"-3.45,-68.95",,,1.18,0.17,,,,571.4267992,,571.0,,,,,,, 28 | 2025,Amazon Web Services,us-east-1,PJM,US-MIDA-PJM,PJM_DC,US East (N. Virginia),"39.1057,-77.5544 ",,1.0,1.14,0.17,0,,,565.3840343,,565.0,,,,,,, 29 | 2025,Amazon Web Services,us-east-2,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,US East (Ohio),"39.8689,-84.3292 ",,1.0,1.12,0.17,0,,,565.1671385,,565.0,,,,,,, 30 | 2025,Amazon Web Services,us-gov-east-1,PJM,US-MIDA-PJM,PJM_DC,GovCloud (US East),"39.1057,-77.5544 ",,1.0,,0.17,0,,,565.3840343,,565.0,,,,,,, 31 | 2025,Amazon Web Services,us-gov-west-1,BPA,US-NW-BPAT,BPA,GovCloud (US West),"45.5371,-122.65 ",,1.0,,0.17,0,,,427.1886293,,427.0,,,,,,, 32 | 2025,Amazon Web Services,us-west-1,CAISO,US-CAL-CISO,CAISO_NORTH,US West (N. California),"36.7783,-119.417931",,1.0,1.17,0.17,0,,,392.7840553,,393.0,,,,,,, 33 | 2025,Amazon Web Services,us-west-2,BPA,US-NW-BPAT,BPA,US West (Oregon),"45.5371,-122.65 ",,1.0,1.13,0.17,0,,,427.1886293,,427.0,,,,,,, 34 | 2025,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,0.16,,,,,646.0,,,,,,,,,,, 35 | 2025,Google Cloud,asia-east1,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",0.178,,1.12,,0,428.5,,,,,,,,,,, 36 | 2025,Google Cloud,asia-east2,Hong Kong,HK,HK,Hong Kong,"22.3,114.2",0.35,,,,0,323.5,,,,,,,,,,, 37 | 2025,Google Cloud,asia-northeast1,Tokyo,JP-TK,JP_TK,Tokyo,"35.6897,139.692",0.2,,,,0,431.5,,535.9531592,,536.0,,,,,,, 38 | 2025,Google Cloud,asia-northeast2,Kansai,JP-KN,JP_KN,Osaka,"34.6939,135.502",0.375,,,,0,377.75,,550.5322311,,551.0,,,,,,, 39 | 2025,Google Cloud,asia-northeast3,South Korea,KR,KOR,Seoul,"37.56,126.99",0.438,,,,0,350.0,,686.0136038,,686.0,,,,,,, 40 | 2025,Google Cloud,asia-south1,Maharashtra,IN-WE,IND,Mumbai,"19.0761,72.8775",0.175,,,,0,622.0,,679.4097431,,679.0,,,,,,, 41 | 2025,Google Cloud,asia-south2,Uttar Pradesh,IN-NO,IND,Delhi,"28.6092,76.9798",0.387,,,,0,486.333,,679.4097431,,679.0,,,,,,, 42 | 2025,Google Cloud,asia-southeast1,Singapore,SG,SGP,Singapore,"1.3,103.8",0.042,,1.12,,0,341.75,,409.1860208,,409.0,,,,,,, 43 | 2025,Google Cloud,asia-southeast2,Indonesia,ID,ID,Jakarta,"-6.2507,106.869",0.162,,,,0,563.25,,,,,,,,,,, 44 | 2025,Google Cloud,australia-southeast1,New South Wales,AU-NSW,NEM_NSW,Sydney,"-33.8678,151.21",0.385,,,,0,445.0,,770.3638355,,770.0,,,,,,, 45 | 2025,Google Cloud,australia-southeast2,Victoria,AU-VIC,NEM_VIC,Melbourne,"-37.8142,144.963",0.533,,,,0,377.667,,664.5903366,,665.0,,,,,,, 46 | 2025,Google Cloud,europe-central2,Poland,PL,PL,Warsaw,"52.23,21.0111",0.413,,,,0,756.667,,852.3941505,,852.0,,,,,,, 47 | 2025,Google Cloud,europe-north1,Finland,FI,FI,Finland,"60.1708,24.9375",1.0,,1.09,,0,12.25,,784.9591618,,785.0,,,,,,, 48 | 2025,Google Cloud,europe-southwest1,Spain,ES,ES,Madrid,"40.3333,-3.8667",1.0,,,,0,136.0,,370.288102,,370.0,,,,,,, 49 | 2025,Google Cloud,europe-west1,Belgium,BE,BE,Belgium,"51.1333,4.5667",0.855,,1.09,,0,103.5,,411.1523178,,411.0,,,,,,, 50 | 2025,Google Cloud,europe-west12,North Italy,IT,IT,Turin,"45.0792,7.6761",0.62,,,,0,175.0,,385.8847276,,386.0,,,,,,, 51 | 2025,Google Cloud,europe-west2,Great Britain,GB,UK,London,"51.726,-0.3",1.0,,,,0,105.75,,427.7283704,,428.0,,,,,,, 52 | 2025,Google Cloud,europe-west3,Germany,DE,DE,Frankfurt,"50.1106,8.6822",0.972,,,,0,351.5,,741.7175367,,742.0,,,,,,, 53 | 2025,Google Cloud,europe-west4,Netherlands,NL,NL,Eemshaven,"51.9167,4.5",0.848,,1.07,,0,176.5,,487.2176839,,487.0,,,,,,, 54 | 2025,Google Cloud,europe-west6,Switzerland,CH,CH,Zurich,"47.3744,8.5411",1.0,,,,0,52.0,,,,,,,,,,, 55 | 2025,Google Cloud,europe-west8,North Italy,IT,IT,Milan,"45.4669,9.19",0.78,,,,0,224.5,,385.8847276,,386.0,,,,,,, 56 | 2025,Google Cloud,europe-west9,France,FR,FR,Paris,"48.8567,2.3522",1.0,,,,0,21.5,,383.7390836,,384.0,,,,,,, 57 | 2025,Google Cloud,me-central1,Qatar,QA,,Doha,,0,,,,,575.0,,,,,,,,,,, 58 | 2025,Google Cloud,me-central2,Saudi Arabia,SA,,Damman,,0,,,,,569.0,,,,,,,,,,, 59 | 2025,Google Cloud,me-west1,Israel,IL,IL,Tel Aviv,"32.0167,34.7667",0.08,,,,0,450.0,,,,,,,,,,, 60 | 2025,Google Cloud,northamerica-northeast1,Quebec,CA-QC,HQ,Montréal,"45.5089,-73.5617",1.0,,,,0,0,,462.2451779,,462.0,,,,,,, 61 | 2025,Google Cloud,northamerica-northeast2,Ontario,CA-ON,IESO_NORTH,Toronto,"43.7417,-79.3733",0.845,,,,0,56.0,,356.8894651,,357.0,,,,,,, 62 | 2025,Google Cloud,southamerica-east1,Central Brazil,BR-CS,BRA,São Paulo,"-3.45,-68.95",0.908,,,,0,42.75,,571.4267992,,571.0,,,,,,, 63 | 2025,Google Cloud,southamerica-west1,Chile,CL-SEN,CHL,Santiago,"-33.4372,-70.6506 ",1.0,,1.15,,0,0,,615.8448463,,616.0,,,,,,, 64 | 2025,Google Cloud,us-central1,MISO,US-MIDW-MISO,MISO_MASON_CITY,Iowa,"41.5725,-93.6105 ",0.992,,1.09,,0,417.75,,539.2510271,,539.0,,,,,,, 65 | 2025,Google Cloud,us-east1,SC,US-CAR-SC,SC,South Carolina,"34.8354,-82.3646 ",0.315,,1.1,,0,575.0,,727.5301036,,728.0,,,,,,, 66 | 2025,Google Cloud,us-east4,PJM,US-MIDA-PJM,PJM_DC,Northern Virginia,"39.1057,-77.5544 ",0.548,,1.1,,0,306.75,429.0,565.3840343,,565.0,,,,,,, 67 | 2025,Google Cloud,us-east5,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,Columbus,"41.4366,-97.3565 ",0.46,,1.05,,0,328.5,429.0,565.1671385,,565.0,,,,,,, 68 | 2025,Google Cloud,us-south1,ERCOT,US-TEX-ERCO,ERCOT_NORTHCENTRAL,Dallas,"44.9221,-123.313 ",0.985,,1.1,,0,333.5,,441.0116857,,441.0,,,,,,, 69 | 2025,Google Cloud,us-west1,BPA,US-NW-BPAT,BPA,Oregon,"45.5372,-122.3955 ",0.827,,1.04,,0,88.25,,427.1886293,,427.0,,,,,,, 70 | 2025,Google Cloud,us-west2,CAISO,US-CAL-CISO,LDWP,Los Angeles,"34.1141,-118.4068 ",0.55,,,,0,185.5,,501.0230748,,501.0,,,,,,, 71 | 2025,Google Cloud,us-west3,PACE,US-NW-PACE,PACE,Salt Lake City,"40.7776,-111.9311 ",0.3,,,,0,594.75,,664.0421037,,664.0,,,,,,, 72 | 2025,Google Cloud,us-west4,NVE,US-NW-NEVP,NEVP,Las Vegas,"35.6011,-105.2206 ",0.292,,1.07,,0,343.5,,456.7534925,,457.0,,,,,,, 73 | 2025,Microsoft Azure,asiapacific,Indonesia,ID,ID,Indonesia,"-6.219, 106.867",,0.13,1.32,1.9,,,,,,,,,,,,, 74 | 2025,Microsoft Azure,australiaeast,New South Wales,AU-NSW,NEM_NSW,Australia East,"-33.889, 151.067",,0.28,1.12,0.012,,,,,,,,,,,,, 75 | 2025,Microsoft Azure,australiasoutheast,Victoria,AU-VIC,NEM_VIC,Australia Southeast,"-37.725, 145.067",,0.28,1.12,0.012,,,,,,,,,,,,, 76 | 2025,Microsoft Azure,austriaeast,Austria,AT,AT,Austria East,"48.217, 16.493",,0.79,1.12,0.023,,,,632.1527284,,632.0,,,,,,, 77 | 2025,Microsoft Azure,brazilsouth,Brazil,BR-CS,BRA,Brazil,"-22.939, -47.045",,0.05,1.12,0.039,,,,571.4267992,,571.0,,,,,,, 78 | 2025,Microsoft Azure,centralus,MISO,US-MIDW-MISO,MISO,Iowa: Central US,"41.573,-93.608",,1.0,1.16,0.19,0,,,,,,,,,,,, 79 | 2025,Microsoft Azure,denmarkeast,Denmark,DK-DK2,DK,Denmark,"55.685, 12.584",,0.1,1.16,0.01,,,,659.8866462,,660.0,,,,,,, 80 | 2025,Microsoft Azure,eastus,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US,"36.68,-78.377",,0.7,1.136,0.17,,,,,,,,,,,,, 81 | 2025,Microsoft Azure,eastus2,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US 2,"36.68,-78.377",,0.7,1.136,0.17,,,,,,,,,,,,, 82 | 2025,Microsoft Azure,eastus3,SOCO,US-SE-SOCO,SOCO,Georgia: East US 3,"33.76,-84.401",,0.08,1.12,0.06,,,,533.5899463,,534.0,,,,,,, 83 | 2025,Microsoft Azure,greececentral,Greece,GR,GR,Greece Central,"37.987, 23.745",,,1.12,0.102,,,,437.5870529,,438.0,,,,,,, 84 | 2025,Microsoft Azure,indiasouthcentral,India,IN-SO,IND,India South Central,"17.385,78.4867 ",,0.46,1.43,0.0,,,,679.4097431,,679.0,,,,,,, 85 | 2025,Microsoft Azure,italynorth,North Italy,IT-NO,IT,Italy North,"45.4669,9.19",,0.15,1.12,0.023,,,,385.8847276,,386.0,,,,,,, 86 | 2025,Microsoft Azure,mexicocentral,Mexico,MX,MX_SIN,Mexico Central,"20.631, -100.428",,,1.12,0.056,,,,421.9293015,,422.0,,,,,,, 87 | 2025,Microsoft Azure,newzealandnorth,New Zealand,NZ,NZ,New Zealand,"-36.867, 174.751",,,1.12,0.0,,,,,,,,,,,,, 88 | 2025,Microsoft Azure,northcentralus,MISO,US-MIDW-MISO,MISO,Illinois: North Central US,"41.832,-87.673",,0.7,1.354,0.79,,,,,,,,,,,,, 89 | 2025,Microsoft Azure,northeurope,Finland,FI,FI,Finland,"60.2065, 24.6753",,0.1,1.225,0.01,,,,784.9591618,,785.0,,,,,,, 90 | 2025,Microsoft Azure,northeurope,Ireland,IE,IE,Ireland,"53.35,-6.2603 ",,0.1,1.225,0.01,,,,784.9591618,,785.0,,,,,,, 91 | 2025,Microsoft Azure,polandcentral,Poland,PL,PL,Poland Central,"52.23,21.0111",,,1.12,0.023,,,,852.3941505,,852.0,,,,,,, 92 | 2025,Microsoft Azure,southcentralus,ERCOT,US-TEX-ERCO,ERCOT,Texas: South Central US,"29.453,-98.508",,1.0,1.253,1.82,0,,,,,,,,,,,, 93 | 2025,Microsoft Azure,southeastasia,southeastasia,SG,SGP,Singapore,"1.352,103.835",,0.13,1.322,2.06,,,,409.1860208,,409.0,,,,,,, 94 | 2025,Microsoft Azure,spaincentral,Spain,ES,ES,Spain Central,"40.44,-3.679",,0.15,1.12,,,,,370.288102,,370.0,,,,,,, 95 | 2025,Microsoft Azure,swedencentral,Sweden,SE,SE,Sweden,"59.329,18.067",1.0,1.0,1.148,0.16,0,,,781.7946219,,782.0,,,,,,, 96 | 2025,Microsoft Azure,taiwannorth,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",,,1.2,1.0,,,,,,,,,,,,, 97 | 2025,Microsoft Azure,westcentralus,,US-NW-PACE,,Wyoming: West Central US,"41.130,-104.83",,0.47,1.095,0.23,,,,,,,,,,,,, 98 | 2025,Microsoft Azure,westeurope,Netherlands,NL,NL,Netherlands,"51.9167,4.5",,0.79,1.122,0.08,,,,487.2176839,,487.0,,,,,,, 99 | 2025,Microsoft Azure,westus,BPA,US-NW-BPAT,BPA,Washington: West US,"47.247,-119.82",,0.47,1.144,1.09,,,,427.1886293,,427.0,,,,,,, 100 | 2025,Microsoft Azure,westus3,,US-SW-AZPS,,Arizona: west US 3,"33.471,-112.056",,0.47,1.137,2.24,,,,,,,,,,,,, 101 | 2024,Amazon Web Services,af-south-1,South Africa,ZA,ZA,Africa (Cape Town),"-33.9253,18.4239 ",,,1.12,0.17,,,,,,,,,,,,, 102 | 2024,Amazon Web Services,ap-east-1,Hong Kong,HK,HK,Asia Pacific (Hong Kong),"22.3,114.2",,,,0.17,,,,,,,,,,,,, 103 | 2024,Amazon Web Services,ap-northeast-1,Tokyo,JP-TK,JP_TK,Asia Pacific (Tokyo),"35.6897,139.692",,,1.28,0.17,0,,,535.9531592,,536.0,,,,,,, 104 | 2024,Amazon Web Services,ap-northeast-2,South Korea,KR,KOR,Asia Pacific (Seoul),"37.56,126.99",,,,0.17,,,,686.0136038,,686.0,,,,,,, 105 | 2024,Amazon Web Services,ap-northeast-3,Kansai,JP-KN,JP_KN,Asia Pacific (Osaka),"34.6939,135.502",,,,0.17,0,,,550.5322311,,551.0,,,,,,, 106 | 2024,Amazon Web Services,ap-south-1,Maharashtra,IN-WE,IND,Asia Pacific (Mumbai),"19.0761,72.8775",,1.0,1.45,0.17,0,,,679.4097431,,679.0,,,,,,, 107 | 2024,Amazon Web Services,ap-south-2,Hyderabad,IN-SO,IND,Asia Pacific (Hyderabad),"17.385,78.4867 ",,1.0,1.5,0.17,0,,,679.4097431,,679.0,,,,,,, 108 | 2024,Amazon Web Services,ap-southeast-1,Singapore,SG,SGP,Asia Pacific (Singapore),"1.3,103.8",,,1.27,0.17,,,,409.1860208,,409.0,,,,,,, 109 | 2024,Amazon Web Services,ap-southeast-2,New South Wales,AU-NSW,NEM_NSW,Asia Pacific (Sydney),"-33.8678,151.21",,,1.15,0.17,,,,770.3638355,,770.0,,,,,,, 110 | 2024,Amazon Web Services,ap-southeast-3,Indonesia,ID,ID,Asia Pacific (Jakarta),"-6.175,106.8275 ",,,1.31,0.17,,,,,,,,,,,,, 111 | 2024,Amazon Web Services,ap-southeast-4,Victoria,AU-VIC,NEM_VIC,Asia Pacific (Melbourne),"-37.8142,144.963",,,1.08,0.17,,,,664.5903366,,665.0,,,,,,, 112 | 2024,Amazon Web Services,ca-central-1,Quebec,CA-QC,HQ,Canada (Central),"45.5089,-73.5617",,1.0,1.18,0.17,0,,,462.2451779,,462.0,,,,,,, 113 | 2024,Amazon Web Services,cn-north-1,,CN,,China (Beijing),"39.904,116.4075 ",,1.0,,0.17,0,,,,,,,,,,,, 114 | 2024,Amazon Web Services,cn-northwest-1,,CN,,China (Ningxia),"38.4795,106.2254 ",,1.0,1.24,0.17,0,,,,,,,,,,,, 115 | 2024,Amazon Web Services,eu-central-1,Germany,DE,DE,Europe (Frankfurt),"50.1106,8.6822",,1.0,1.34,0.17,0,,,741.7175367,,742.0,,,,,,, 116 | 2024,Amazon Web Services,eu-central-2,Switzerland,CH,CH,Europe (Zurich),"47.3744,8.5411",,1.0,,0.17,0,,,778.7844238,,779.0,,,,,,, 117 | 2024,Amazon Web Services,eu-north-1,Sweden,SE,SE,Europe (Stockholm),"59.3294,18.0686 ",,1.0,1.12,0.17,0,,,781.7946219,,782.0,,,,,,, 118 | 2024,Amazon Web Services,eu-south-1,North Italy,IT-NO,IT,Europe (Milan),"45.4669,9.19",,1.0,,0.17,0,,,385.8847276,,386.0,,,,,,, 119 | 2024,Amazon Web Services,eu-south-2,Spain,ES,ES,Europe (Spain),"40.3333,-3.8667",,1.0,1.11,0.17,0,,,370.288102,,370.0,,,,,,, 120 | 2024,Amazon Web Services,eu-west-1,Ireland,IE,IE,Europe (Ireland),"53.35,-6.2603 ",,1.0,1.1,0.17,0,,,507.95177,,508.0,,,,,,, 121 | 2024,Amazon Web Services,eu-west-2,Great Britain,GB,UK,Europe (London),"51.726,-0.3",,1.0,,0.17,0,,,427.7283704,,428.0,,,,,,, 122 | 2024,Amazon Web Services,eu-west-3,France,FR,FR,Europe (Paris),"48.8567,2.3522",,1.0,,0.17,0,,,383.7390836,,384.0,,,,,,, 123 | 2024,Amazon Web Services,il-central-1,Israel,IL,IL,Israel (Tel Aviv),"32.0167,34.7667",,,,0.17,,,,,,,,,,,,, 124 | 2024,Amazon Web Services,me-central-1,United Arab Emirates,AE,AE,Middle East (UAE),"25.2631,55.2972 ",,,1.36,0.17,,,,,,,,,,,,, 125 | 2024,Amazon Web Services,me-south-1,Bahrain,BH,BH,Middle East (Bahrain),"26.219,50.538 ",,,1.3,0.17,,,,,,,,,,,,, 126 | 2024,Amazon Web Services,sa-east-1,Central Brazil,BR-CS,BRA,South America (São Paulo),"-3.45,-68.95",,,1.18,0.17,,,,571.4267992,,571.0,,,,,,, 127 | 2024,Amazon Web Services,us-east-1,PJM,US-MIDA-PJM,PJM_DC,US East (N. Virginia),"39.1057,-77.5544 ",,1.0,1.14,0.17,0,,,565.3840343,,565.0,,,,,,, 128 | 2024,Amazon Web Services,us-east-2,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,US East (Ohio),"39.8689,-84.3292 ",,1.0,1.12,0.17,0,,,565.1671385,,565.0,,,,,,, 129 | 2024,Amazon Web Services,us-gov-east-1,PJM,US-MIDA-PJM,PJM_DC,GovCloud (US East),"39.1057,-77.5544 ",,1.0,,0.17,0,,,565.3840343,,565.0,,,,,,, 130 | 2024,Amazon Web Services,us-gov-west-1,BPA,US-NW-BPAT,BPA,GovCloud (US West),"45.5371,-122.65 ",,1.0,,0.17,0,,,427.1886293,,427.0,,,,,,, 131 | 2024,Amazon Web Services,us-west-1,CAISO,US-CAL-CISO,CAISO_NORTH,US West (N. California),"36.7783,-119.417931",,1.0,1.17,0.17,0,,,392.7840553,,393.0,,,,,,, 132 | 2024,Amazon Web Services,us-west-2,BPA,US-NW-BPAT,BPA,US West (Oregon),"45.5371,-122.65 ",,1.0,1.13,0.17,0,,,427.1886293,,427.0,,,,,,, 133 | 2024,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,0.16,,,,,646.0,,,,,,,,,,, 134 | 2024,Google Cloud,asia-east1,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",0.178,,1.12,,0,428.5,,,,,,,,,,, 135 | 2024,Google Cloud,asia-east2,Hong Kong,HK,HK,Hong Kong,"22.3,114.2",0.35,,,,0,323.5,,,,,,,,,,, 136 | 2024,Google Cloud,asia-northeast1,Tokyo,JP-TK,JP_TK,Tokyo,"35.6897,139.692",0.2,,,,0,431.5,,535.9531592,,536.0,,,,,,, 137 | 2024,Google Cloud,asia-northeast2,Kansai,JP-KN,JP_KN,Osaka,"34.6939,135.502",0.375,,,,0,377.75,,550.5322311,,551.0,,,,,,, 138 | 2024,Google Cloud,asia-northeast3,South Korea,KR,KOR,Seoul,"37.56,126.99",0.438,,,,0,350.0,,686.0136038,,686.0,,,,,,, 139 | 2024,Google Cloud,asia-south1,Maharashtra,IN-WE,IND,Mumbai,"19.0761,72.8775",0.175,,,,0,622.0,,679.4097431,,679.0,,,,,,, 140 | 2024,Google Cloud,asia-south2,Uttar Pradesh,IN-NO,IND,Delhi,"28.6092,76.9798",0.387,,,,0,486.333,,679.4097431,,679.0,,,,,,, 141 | 2024,Google Cloud,asia-southeast1,Singapore,SG,SGP,Singapore,"1.3,103.8",0.042,,1.12,,0,341.75,,409.1860208,,409.0,,,,,,, 142 | 2024,Google Cloud,asia-southeast2,Indonesia,ID,ID,Jakarta,"-6.2507,106.869",0.162,,,,0,563.25,,,,,,,,,,, 143 | 2024,Google Cloud,australia-southeast1,New South Wales,AU-NSW,NEM_NSW,Sydney,"-33.8678,151.21",0.385,,,,0,445.0,,770.3638355,,770.0,,,,,,, 144 | 2024,Google Cloud,australia-southeast2,Victoria,AU-VIC,NEM_VIC,Melbourne,"-37.8142,144.963",0.533,,,,0,377.667,,664.5903366,,665.0,,,,,,, 145 | 2024,Google Cloud,europe-central2,Poland,PL,PL,Warsaw,"52.23,21.0111",0.413,,,,0,756.667,,852.3941505,,852.0,,,,,,, 146 | 2024,Google Cloud,europe-north1,Finland,FI,FI,Finland,"60.1708,24.9375",1.0,,1.09,,0,12.25,,784.9591618,,785.0,,,,,,, 147 | 2024,Google Cloud,europe-southwest1,Spain,ES,ES,Madrid,"40.3333,-3.8667",1.0,,,,0,136.0,,370.288102,,370.0,,,,,,, 148 | 2024,Google Cloud,europe-west1,Belgium,BE,BE,Belgium,"51.1333,4.5667",0.855,,1.09,,0,103.5,,411.1523178,,411.0,,,,,,, 149 | 2024,Google Cloud,europe-west12,North Italy,IT-NO,IT,Turin,"45.0792,7.6761",0.62,,,,0,175.0,,385.8847276,,386.0,,,,,,, 150 | 2024,Google Cloud,europe-west2,Great Britain,GB,UK,London,"51.726,-0.3",1.0,,,,0,105.75,,427.7283704,,428.0,,,,,,, 151 | 2024,Google Cloud,europe-west3,Germany,DE,DE,Frankfurt,"50.1106,8.6822",0.972,,,,0,351.5,,741.7175367,,742.0,,,,,,, 152 | 2024,Google Cloud,europe-west4,Netherlands,NL,NL,Eemshaven,"51.9167,4.5",0.848,,1.07,,0,176.5,,487.2176839,,487.0,,,,,,, 153 | 2024,Google Cloud,europe-west6,Switzerland,CH,CH,Zurich,"47.3744,8.5411",1.0,,,,0,52.0,,,,,,,,,,, 154 | 2024,Google Cloud,europe-west8,North Italy,IT-NO,IT,Milan,"45.4669,9.19",0.78,,,,0,224.5,,385.8847276,,386.0,,,,,,, 155 | 2024,Google Cloud,europe-west9,France,FR,FR,Paris,"48.8567,2.3522",1.0,,,,0,21.5,,383.7390836,,384.0,,,,,,, 156 | 2024,Google Cloud,me-central1,Qatar,QA,,Doha,,0,,,,,575.0,,,,,,,,,,, 157 | 2024,Google Cloud,me-central2,Saudi Arabia,SA,,Damman,,0,,,,,569.0,,,,,,,,,,, 158 | 2024,Google Cloud,me-west1,Israel,IL,IL,Tel Aviv,"32.0167,34.7667",0.08,,,,0,450.0,,,,,,,,,,, 159 | 2024,Google Cloud,northamerica-northeast1,Quebec,CA-QC,HQ,Montréal,"45.5089,-73.5617",1.0,,,,0,0,,462.2451779,,462.0,,,,,,, 160 | 2024,Google Cloud,northamerica-northeast2,Ontario,CA-ON,IESO_NORTH,Toronto,"43.7417,-79.3733",0.845,,,,0,56.0,,356.8894651,,357.0,,,,,,, 161 | 2024,Google Cloud,southamerica-east1,Central Brazil,BR-CS,BRA,São Paulo,"-3.45,-68.95",0.908,,,,0,42.75,,571.4267992,,571.0,,,,,,, 162 | 2024,Google Cloud,southamerica-west1,Chile,CL-SEN,CHL,Santiago,"-33.4372,-70.6506 ",1.0,,1.15,,0,0,,615.8448463,,616.0,,,,,,, 163 | 2024,Google Cloud,us-central1,MISO,US-MIDW-MISO,MISO_MASON_CITY,Iowa,"41.5725,-93.6105 ",0.992,,1.09,,0,417.75,,539.2510271,,539.0,,,,,,, 164 | 2024,Google Cloud,us-east1,SC,US-CAR-SC,SC,South Carolina,"34.8354,-82.3646 ",0.315,,1.1,,0,575.0,,727.5301036,,728.0,,,,,,, 165 | 2024,Google Cloud,us-east4,PJM,US-MIDA-PJM,PJM_DC,Northern Virginia,"39.1057,-77.5544 ",0.548,,1.1,,0,306.75,429.0,565.3840343,,565.0,,,,,,, 166 | 2024,Google Cloud,us-east5,PJM,US-MIDA-PJM,PJM_SOUTHWEST_OH,Columbus,"41.4366,-97.3565 ",0.46,,1.05,,0,328.5,429.0,565.1671385,,565.0,,,,,,, 167 | 2024,Google Cloud,us-south1,ERCOT,US-TEX-ERCO,ERCOT_NORTHCENTRAL,Dallas,"44.9221,-123.313 ",0.985,,1.1,,0,333.5,,441.0116857,,441.0,,,,,,, 168 | 2024,Google Cloud,us-west1,BPA,US-NW-BPAT,BPA,Oregon,"45.5372,-122.3955 ",0.827,,1.04,,0,88.25,,427.1886293,,427.0,,,,,,, 169 | 2024,Google Cloud,us-west2,CAISO,US-CAL-CISO,LDWP,Los Angeles,"34.1141,-118.4068 ",0.55,,,,0,185.5,,501.0230748,,501.0,,,,,,, 170 | 2024,Google Cloud,us-west3,PACE,US-NW-PACE,PACE,Salt Lake City,"40.7776,-111.9311 ",0.3,,,,0,594.75,,664.0421037,,664.0,,,,,,, 171 | 2024,Google Cloud,us-west4,NVE,US-NW-NEVP,NEVP,Las Vegas,"35.6011,-105.2206 ",0.292,,1.07,,0,343.5,,456.7534925,,457.0,,,,,,, 172 | 2024,Microsoft Azure,asiapacific,Indonesia,ID,ID,Indonesia,"-6.219, 106.867",,0.13,1.32,1.9,,,,,,,,,,,,, 173 | 2024,Microsoft Azure,australiaeast,New South Wales,AU-NSW,NEM_NSW,Australia East,"-33.889, 151.067",,0.28,1.12,0.012,,,,,,,,,,,,, 174 | 2024,Microsoft Azure,australiasoutheast,Victoria,AU-VIC,NEM_VIC,Australia Southeast,"-37.725, 145.067",,0.28,1.12,0.012,,,,,,,,,,,,, 175 | 2024,Microsoft Azure,austriaeast,Austria,AT,AT,Austria East,"48.217, 16.493",,0.79,1.12,0.023,,,,632.1527284,,632.0,,,,,,, 176 | 2024,Microsoft Azure,brazilsouth,Brazil,BR-CS,BRA,Brazil,"-22.939, -47.045",,0.05,1.12,0.039,,,,571.4267992,,571.0,,,,,,, 177 | 2024,Microsoft Azure,centralus,MISO,US-MIDW-MISO,MISO,Iowa: Central US,"41.573,-93.608",,1.0,1.16,0.19,0,,,,,,,,,,,, 178 | 2024,Microsoft Azure,denmarkeast,Denmark,DK-DK2,DK,Denmark,"55.685, 12.584",,0.1,1.16,0.01,,,,659.8866462,,660.0,,,,,,, 179 | 2024,Microsoft Azure,eastus,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US,"36.68,-78.377",,0.7,1.136,0.17,,,,,,,,,,,,, 180 | 2024,Microsoft Azure,eastus2,PJM,US-MIDA-PJM,PJM_DC,Virginia: East US 2,"36.68,-78.377",,0.7,1.136,0.17,,,,,,,,,,,,, 181 | 2024,Microsoft Azure,eastus3,SOCO,US-SE-SOCO,SOCO,Georgia: East US 3,"33.76,-84.401",,0.08,1.12,0.06,,,,533.5899463,,534.0,,,,,,, 182 | 2024,Microsoft Azure,greececentral,Greece,GR,GR,Greece Central,"37.987, 23.745",,,1.12,0.102,,,,437.5870529,,438.0,,,,,,, 183 | 2024,Microsoft Azure,indiasouthcentral,India,IN-SO,IND,India South Central,"17.385,78.4867 ",,0.46,1.43,0.0,,,,679.4097431,,679.0,,,,,,, 184 | 2024,Microsoft Azure,italynorth,North Italy,IT-NO,IT,Italy North,"45.4669,9.19",,0.15,1.12,0.023,,,,385.8847276,,386.0,,,,,,, 185 | 2024,Microsoft Azure,mexicocentral,Mexico,MX,MX_SIN,Mexico Central,"20.631, -100.428",,,1.12,0.056,,,,421.9293015,,422.0,,,,,,, 186 | 2024,Microsoft Azure,newzealandnorth,New Zealand,NZ,NZ,New Zealand,"-36.867, 174.751",,,1.12,0.0,,,,,,,,,,,,, 187 | 2024,Microsoft Azure,northcentralus,MISO,US-MIDW-MISO,MISO,Illinois: North Central US,"41.832,-87.673",,0.7,1.354,0.79,,,,,,,,,,,,, 188 | 2024,Microsoft Azure,northeurope,Finland,FI,FI,Finland,"60.2065, 24.6753",,0.1,1.225,0.01,,,,784.9591618,,785.0,,,,,,, 189 | 2024,Microsoft Azure,northeurope,Ireland,IE,IE,Ireland,"53.35,-6.2603 ",,0.1,1.225,0.01,,,,784.9591618,,785.0,,,,,,, 190 | 2024,Microsoft Azure,polandcentral,Poland,PL,PL,Poland Central,"52.23,21.0111",,,1.12,0.023,,,,852.3941505,,852.0,,,,,,, 191 | 2024,Microsoft Azure,southcentralus,ERCOT,US-TEX-ERCO,ERCOT,Texas: South Central US,"29.453,-98.508",,1.0,1.253,1.82,0,,,,,,,,,,,, 192 | 2024,Microsoft Azure,southeastasia,southeastasia,SG,SGP,Singapore,"1.352,103.835",,0.13,1.322,2.06,,,,409.1860208,,409.0,,,,,,, 193 | 2024,Microsoft Azure,spaincentral,Spain,ES,ES,Spain Central,"40.44,-3.679",,0.15,1.12,,,,,370.288102,,370.0,,,,,,, 194 | 2024,Microsoft Azure,swedencentral,Sweden,SE,SE,Sweden,"59.329,18.067",1.0,1.0,1.148,0.16,0,,,781.7946219,,782.0,,,,,,, 195 | 2024,Microsoft Azure,taiwannorth,Taiwan,TW,TW,Taiwan,"25.0375,121.5625",,,1.2,1.0,,,,,,,,,,,,, 196 | 2024,Microsoft Azure,westcentralus,,US-NW-PACE,,Wyoming: West Central US,"41.130,-104.83",,0.47,1.095,0.23,,,,,,,,,,,,, 197 | 2024,Microsoft Azure,westeurope,Netherlands,NL,NL,Netherlands,"51.9167,4.5",,0.79,1.122,0.08,,,,487.2176839,,487.0,,,,,,, 198 | 2024,Microsoft Azure,westus,BPA,US-NW-BPAT,BPA,Washington: West US,"47.247,-119.82",,0.47,1.144,1.09,,,,427.1886293,,427.0,,,,,,, 199 | 2024,Microsoft Azure,westus3,,US-SW-AZPS,,Arizona: west US 3,"33.471,-112.056",,0.47,1.137,2.24,,,,,,,,,,,,, 200 | -------------------------------------------------------------------------------- /Cloud_Region_Metadata_specification.md: -------------------------------------------------------------------------------- 1 | --- 2 | version: 1.0.0 3 | --- 4 | 5 | ## Cloud Region Metadata Specification 6 | 7 | ### Introduction 8 | 9 | Cloud providers are recognized as significant global procurers of renewable energy. This specification addresses the need for accurate and timely carbon information provision to customers utilizing cloud services, aiming to align with regulatory requirements across various jurisdictions. 10 | 11 | Historically, cloud providers have supplied carbon information to their customers every month, with a delay of several months, and provided public information on an annual basis with six to eighteen months lag. Consequently, customers have been compelled to estimate the real-time carbon footprint of their cloud workloads using incomplete public information. 12 | 13 | Cloud providers will be required to supply carbon metrics to meet regulatory standards in the UK, Europe, California, and emerging elsewhere. So far, cloud providers have developed custom silicon and system designs to optimize low power consumption, mitigated the carbon footprint within supply chains, and invested in renewable energy production. They have published generic estimates of efficiency gains achieved with renewable energy purchases compared to data centre alternatives. Still, the data needed for a customer to make the same comparison for a specific workload, and to make comparisons across cloud regions, is lacking. 14 | 15 | This specification outlines the necessity for real-time carbon reporting to address these concerns and proposes a standardized approach to achieve accurate and timely carbon footprint estimates for cloud workloads. Additionally, it highlights the significance of metadata disclosure by cloud providers for the regions they operate in and the ongoing efforts to consolidate and distribute this information as a singular data source. 16 | 17 | ### Objective 18 | This specification aims to standardize and clarify annual cloud region metadata for efficient and accurate usage by cloud service providers and users. It also seeks to address discrepancies and variations in data reporting methodologies and definitions among cloud providers and promote alignment toward standard definitions, such as the European Union Energy Efficiency Directive for data centres in future updates. 19 | 20 | ### Scope 21 | 22 | The project aims to enhance the accuracy of the carbon emissions model for cloud-based workloads. This will involve establishing a standard mechanism for cloud providers to share more detailed and useful information using the same data schema. The scope also includes enabling real-time updates to provide minute-level granularity for energy usage and hourly or daily granularity for carbon intensity. 23 | 24 | - **Cloud Region Metadata:** 25 | - Define standard parameters for cloud region metadata, including cloud provider and region specifications. 26 | - Establish guidelines for annual updates and data lag management (6-18 months), with emphasis on specifying the year or using the latest available data. 27 | - Clarify the annual average location-based marginal grid-carbon-intensity value for SCI-o and its availability and handling of not-available (NA) data. 28 | - **Standardizing Carbon Models and Data Reporting:** 29 | - Identify and clarify the multiple carbon models used by different cloud providers. 30 | - Address the variability of carbon data availability and handling of blank or not-available metrics. 31 | - **Real-time Data Lookup and Provider Keys:** 32 | - Define the process for real-time lookup of cloud region data via APIs provided by data providers such as Electricity Maps and WattTime. 33 | - Establish protocols for annual average carbon intensity reporting for each grid region under each cloud provider's model. 34 | - **Carbon-Free Energy and Renewable Energy Definitions:** 35 | - Define carbon-free energy and its inclusion of nuclear energy, which is distinct from the definition of renewable energy. 36 | - Address the absence of carbon-free energy data for regions that are not yet operational. 37 | - **Power and Water Usage Effectiveness (PUE and WUE):** 38 | - Standardize reporting of power usage effectiveness (PUE) and water usage effectiveness (WUE) for each cloud region. 39 | - Align WUE reporting among cloud providers and address the variation in PUE data publication schedules. 40 | - **Net Zero Reporting and Goals:** 41 | - Define the market method for calculating Net Zero goals, including energy-based offsets such as PPAs, RECs, and carbon offsets. 42 | - Report and align net carbon data on a region-by-region basis and identify regions that achieve zero net carbon emissions. 43 | - **Standard Definitions and Alignment:** 44 | - Establish guidelines for standard definitions and alignment of cloud region metadata, carbon models, and data reporting methodologies among cloud providers (e.g., AWS and Azure aligning with Google's location-based carbon data). 45 | 46 | ### Normative references 47 | There are no normative references in this document. 48 | 49 | ### Terms and definitions 50 | 51 | For this document, the following terms and definitions apply. 52 | 53 | ISO and IEC maintain terminological databases for use in standardization at the following addresses: 54 | - ISO Online browsing platform: available at https://www.iso.org/obp 55 | - IEC Electropedia: available at http://www.electropedia.org/ 56 | 57 | ### Description 58 | 59 | A user of the cloud region metadata can specify which cloud provider and region they use to run a workload and get all the relevant metadata about that region. Cloud region metadata is published annually and lags by 6-18 months, so the year must be specified, or the latest data should be used. The annual average location-based marginal grid-carbon-intensity value required for SCI-o is provided when available. Because of differences between cloud providers, data providers and reporting methodologies, there are several possible carbon models, and data may not be available (NA). Attempting to consume a not-available or blank metric should cause any calculations to fail. 60 | 61 | The data provider keys for Electricity Maps and WattTime are returned to allow real-time lookup via their APIs, and the annual average carbon intensity is reported for each grid region. 62 | 63 | Cloud providers have their own private carbon-free generation capacity, and they report a proportion of their energy consumption offset by carbon-free energy flowing within a “Carbon-Free Energy grid region”. This can reduce their effective grid carbon intensity, which is taken into account by the market method used for Net Zero reporting but not included in the location-based method that the SCI requires. The carbon-free energy calculation can be performed on a 24x7 hourly basis and accumulated over the year or on an annual total basis. Carbon free energy data is missing for regions that are not yet operational. 64 | 65 | Carbon-free energy includes nuclear and is distinct from the definition of renewable energy. 66 | 67 | Each cloud region has a power usage effectiveness (PUE) and a water usage effectiveness (WUE) that may be reported. Energy usage at the system level should be multiplied by the PUE ratio to account for losses due to cooling and energy distribution and storage within the cloud provider’s facilities. WUE is measured as litres per kilowatt-hour and was reported for each Azure region in 2022. AWS provides a global average WUE, and Google does not currently provide WUE data [Gap: we request AWS and Google match what Azure provides]. PUE data is published on different schedules; Google currently provides annual, quarterly and trailing 12-month data for data centre facilities that it owns, which is a subset of its cloud regions and we have matched the names of data centres to cloud region names. AWS also provides annual PUE data for a subset of regions. Azure provided 2022 PUE and WUE data that matched all its regions, but the 2023 data it published was less comprehensive. The 2022 data was removed from the Azure website but has been preserved by this project. 68 | 69 | Cloud providers have Net Zero goals, calculated using the market method. This method allows for energy-based offsets, including private Power Purchase Agreements (PPAs), tradable Renewable Energy Credits (RECs), and carbon offsets. Cloud providers report their net carbon on a region-by-region basis, using in-market energy based offsets, and a global figure that uses cross region RECs and offsets. For many regions, the market method carbon is already zero. 70 | 71 | Cloud providers have different definitions for the data they currently provide. Part of the goal of the GSF real-time cloud project is to clarify those differences and request that standard definitions and alignment occur in future updates. [Gap: Google provides location-based carbon data. Request AWS and Azure match what Google provides.] 72 | 73 | The European Union Energy Efficiency Directive (EED) for data centres (DCs) comes into force in 2024 for all DCs over 500 kW, which will include all cloud provider DCs sited in the EU. It mandates full disclosure to a confidential central EU registry of very detailed information on the specifications of DCs and how they are operated, and public disclosure of data subject to trade secrests and confidentiality. Since the data must be produced, key elements of the data have been added to the cloud region carbon metadata table to encourage standardized disclosure. 74 | 75 | ### Metric Naming Scheme 76 | 77 | - **Provider vs. Grid** - Some data is cloud **provider** specific, and some is generic data for the local **grid**. 78 | - **24x7 vs. Hourly vs. Annual** — Some provider metrics use a 24x7 hourly energy matching scheme and report data based on an **hourly** weighted average, labelled hourly (rather than 24x7). Other metrics are generated based on annual averages and labelled **annual**. 79 | **Location vs. Market** — The Greenhouse Gas Protocol specifies **location** and **market** methodologies for carbon reporting. Market methodology allows energy to be matched across grids, but AWS states that it matches energy exclusively within grids for 22 of its regions for 2023 and reports **market** data on a per-grid basis. 80 | **Consumption vs. Production** — Within a grid, the energy sources add up to a production-based metric; however, energy flows between grids across interconnects, and the actual energy mix **consumption** in a region takes this into account. 81 | - **Average vs. Marginal** - The **average** carbon intensity gives the total emissions mixture over a time period. The **marginal** emissions account for changes in demand and depend on what kind of energy source is used to supply variable demand, with other energy sources providing base load capacity. For example, many regions use gas-powered peaker plants or for overnight loads so that marginal carbon could be purely from gas. At other times, the same region may be curtailing solar power during the day, so marginal carbon would be purely from solar. The average carbon would report the proportional mix of these sources. 82 | - **Not Available** - Accessing blank or unavailable data should cause an exception and interrupt an Impact Framework calculation. 83 | 84 | ### Cloud Region Metadata Table 85 | 86 | | Name | Units | Example | Description | 87 | | ---------------------------------------------------- | -------------------------- | --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | 88 | | year | numeric | 2022 | Specify which calendar year the data is averaged over. The IF timestamp is used to select a year. | 89 | | cloud-provider | string | “Google Cloud” | Cloud Provider name. One of the required input keys for IF model. | 90 | | cloud-region | string | “asia-northeast-3” | Cloud provider region. One of the required input keys for IF model. | 91 | | cfe-region | string | “South Korea” | Carbon Free Energy grid region name as reported by the cloud provider. | 92 | | em-zone-id | string | “KR” | Electricity Maps zone identifier for this region. Can be used to get real-time data from their API. | 93 | | wt-region-id | string | “KOR” | WattTime region identifier. Can be used to get real-time data from their API. | 94 | | location | string | “Seoul” | Location of the region, as reported by the cloud provider. | 95 | | geolocation | numeric, numeric | 37.532600, 127.024612 | Latitude and longitude of the location, city level, not exact datacenter coordinates. | 96 | | provider-cfe-hourly | numeric proportion 0.0-1.0 | 0.31 | Carbon Free Energy proportion for this cloud provider and region, weighted by the hourly usage through the year. | 97 | | provider-cfe-annual | numeric proportion 0.0-1.0 | 0.28 | Carbon Free Energy proportion for this cloud provider and region, calculated on an annual totals basis. | 98 | | power-usage-effectiveness | numeric | 1.18 | Power Usage Effectiveness (PUE) ratio for the region, averaged across individual datacenters. | 99 | | water-usage-effectiveness | litres/kWh | 2.07 | Water Usage Effectiveness in litres per kilowatt hour for the region, averaged across individual datacenters. | 100 | | provider-carbon-intensity-market-annual | gCO2e/kWh | 0 | Scope 2 market-based carbon intensity, including any energy and carbon offsets obtained by the provider, that rolls up to their Net Zero reporting. | 101 | | provider-carbon-intensity-average-consumption-hourly | gCO2e/kWh | 354 | Electricity Maps consumption-based carbon intensity weighted by the provider’s hourly usage through the year as part of a 24x7 calculation. | 102 | | grid-carbon-intensity-average-consumption-annual | gCO2e/kWh | 429 | Electricity Maps consumption-based carbon intensity annual average for the em-zone-id | 103 | | grid-carbon-intensity-marginal-consumption-annual | gCO2e/kWh | 686.0136038 | WattTime marginal carbon intensity annual average for the wt-region-id | 104 | | grid-carbon-intensity | gCO2e/kWh | 686 | Specific named output for Impact Framework model consumed by SCI-o. SCI defines it as location-based and is currently set to the same value as grid-carbon-intensity-marginal-consumption-annual. | 105 | | total-ICT-energy-consumption-annual | kWh | 100000000 | EED total energy for all datacenters in cloud region | 106 | | total-water-input | litres | 100000000 | EED total water for all datacenters in cloud region | 107 | | renewable-energy-consumption | kWh | 90000000 | EED total renewable energy | 108 | | renewable-energy-consumption-goe | kWh | 10000000 | EED total renewable energy from Guarantees of Origin/Renewable Energy Certificates | 109 | | renewable-energy-consumption-ppa | kWh | 75000000 | EED total renewable energy from power purchase agreements | 110 | | renewable-energy-consumption-onsite | kWh | 5000000 | EED total renewable energy from on-site generation | 111 | 112 | 113 | 114 | ### References 115 | - [Amazon Renewable Energy Methodology](https://sustainability.aboutamazon.com/renewable-energy-methodology.pdf) 116 | - [Amazon Carbon Methodology](https://sustainability.aboutamazon.com/carbon-methodology.pdf) 117 | - [Azure Datacenter Fact Sheets for 2022](https://web.archive.org/web/20240308233631/https://datacenters.microsoft.com/globe/fact-sheets/) 118 | - [Google Carbon-Free Energy by Region](https://cloud.google.com/sustainability/region-carbon) 119 | - [Google Sustainability Report for 2023](https://www.gstatic.com/gumdrop/sustainability/google-2023-environmental-report.pdf#page=90) 120 | -------------------------------------------------------------------------------- /License.md: -------------------------------------------------------------------------------- 1 | Materials in this repository other than source code are provided as follows: 2 | 3 | Copyright (c) 2021 Joint Development Foundation Projects, LLC, GSF Series and 4 | its contributors. All rights reserved. THESE MATERIALS ARE PROVIDED "AS IS." The 5 | parties expressly disclaim any warranties (express, implied, or otherwise), 6 | including implied warranties of merchantability, non-infringement, fitness for a 7 | particular purpose, or title, related to the materials. The entire risk as to 8 | implementing or otherwise using the materials is assumed by the implementer and 9 | user. IN NO EVENT WILL THE PARTIES BE LIABLE TO ANY OTHER PARTY FOR LOST PROFITS 10 | OR ANY FORM OF INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY 11 | CHARACTER FROM ANY CAUSES OF ACTION OF ANY KIND WITH RESPECT TO THIS DELIVERABLE 12 | OR ITS GOVERNING AGREEMENT, WHETHER BASED ON BREACH OF CONTRACT, TORT (INCLUDING 13 | NEGLIGENCE), OR OTHERWISE, AND WHETHER OR NOT THE OTHER MEMBER HAS BEEN ADVISED 14 | OF THE POSSIBILITY OF SUCH DAMAGE. 15 | 16 | The patent mode selected for materials developed by this Working Group is W3C Mode. 17 | For specific details, see this Working Group's Charter at: 18 | 19 | 20 | Source code in this repository is provided under the MIT License, as follows: 21 | 22 | Copyright (c) 2021 Joint Development Foundation Projects, LLC, GSF Series and 23 | its contributors. 24 | 25 | Permission is hereby granted, free of charge, to any person obtaining a copy of 26 | this software and associated documentation files (the "Software"), to deal in 27 | the Software without restriction, including without limitation the rights to 28 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of 29 | the Software, and to permit persons to whom the Software is furnished to do so, 30 | subject to the following conditions: 31 | 32 | The above copyright notice and this permission notice shall be included in all 33 | copies or substantial portions of the Software. 34 | 35 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 36 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 37 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 38 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 39 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 40 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 41 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Real Time Energy and Carbon Standards for Cloud Providers 2 | Cloud providers are the largest purchasers of renewable energy in the world, but so far they have provided their customers with carbon information on a monthly basis, a few months in arrears, using a variety of metrics and models. Customers have had to produce their own real-time estimates for cloud workloads, using public information that doesn't include renewable energy purchases and overestimates carbon footprints. As part of the information technology supply chain, cloud providers need to supply real-time carbon metrics that can be aggregated by workload, allocated and apportioned through the supply chain to satisfy regulations that are in place in the UK and Europe, on the way in California, and emerging elsewhere. Cloud providers build their own custom silicon and systems designs, and optimize them for low power consumption and to reduce the carbon footprint of their supply chain. With standardized metrics the efficiency benefits combined with the renewable energy purchases of cloud providers can be compared with each other and to datacenter alternatives for specific workloads. 3 | 4 | ## Motivation 5 | "All models are wrong, some models are useful". The goal of this project is to make the carbon emissions model for cloud based workloads less wrong, by defining a standard mechanism for cloud providers to share more information, and more useful, by having the same metadata schema for all cloud providers. We need "real time" data that can be used to estimate the carbon footprint of a workload that is running today, or is planned for future deployment. 6 | 7 | ## Target Users of this Standard 8 | Platform teams and software as a service (SaaS) providers run multi-tenant workloads on cloud providers. To supply their own customers with carbon footprint estimates, the instance level energy and carbon data needs to be allocated and attributed across workloads. The [Kepler project](https://github.com/sustainable-computing-io/kepler) hosted by the Cloud Native Computing Foundation allocates the energy usage of a host node to the active pods and containers running in that node, so that energy and carbon data can be reported for workloads running on Kubernetes. In datacenter deployments Kepler can directly measure energy usage and obtain carbon intensity data from the datacenter operator. Cloud providers block direct access to energy usage metrics as part of their multi-tenant hypervisor security model, and in that case Kepler uses estimates for CPU energy usage. However, detailed GPU energy usage *is* available in real time for most GPUs. Given the energy usage, the next step is to lookup the cloud provider information for the region so that it can be combined with Power Usage Effectiveness, the Carbon Free Energy ratio, and the local Carbon Intensity to produce a carbon emissions estimate. We have gathered all the cloud region data we can find into a common standard for this project. 9 | 10 | ## Cloud Providers 11 | The cloud providers disclose metadata about regions annually, around six months after the year ends. This data may include Power and Water Usage Effectiveness, carbon-free energy percentage, and the location and grid region for each cloud region. This project is gathering, normalising and releasing this metadata as a single data source, and lobbying the cloud providers to release annual metadata that is more closely aligned across providers. 12 | 13 | In this V1.0 release, annual metadata from Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) has been normalised into a single data source. That data, which is only currently provided for 2023 and earlier, has been projected into a 2024 and 2025 estimate. 2025 data on AWS power generation project locations has also been archived here. 14 | 15 | Please create issues and pull requests to provide corrections and updates. We encourage cloud providers to nominate a representative that the project can contact. GSF members (including Azure and GCP) participated in defining the standard, but we also welcome discussions with non-members who can provide data. 16 | 17 | ## Updates and Version Stability 18 | Work has started to include Oracle Cloud, and we encourage other cloud providers to release annual data aligned with the annual Cloud Region Metadata standard we have established. 19 | 20 | In past years, Microsoft has published updates in May, Google in June and Amazon in July. When all three providers have published new data, we plan to update the metadata tables here for a new minor version release (V1.1). If the metadata standard is changed to add new columns of data we will increment the major version (V2.0). To maintain backward compatibility we will not remove, rename or re-order existing columns of data. 21 | 22 | ## Collected Context on Data Sources and Tools 23 | The initial work focused on collecting and discussing existing information, and a context miro board was created that is being used to crowdsource relevant information about power and carbon data sources and how they are created and used from end to end. The miro is [publicly readable here](https://miro.com/app/board/uXjVM1o59N4=/?share_link_id=388311040102) and screenshots are stored in this repo. It is proposed that slowly changing reference data will be shared via the [GSF Impact Framework](https://github.com/Green-Software-Foundation/if), and so far this includes Power Usage Efficiency (PUE), Water Usage Efficiency (WUE), Carbon Free Energy, a placeholder for EU Datacenter disclosure data, and Power Purchase Agreement location information. There are issues tracking the development of each of these. 24 | 25 | ![Miro Summary](./sup_file/rtc-miro-2024-07-01.png) 26 | 27 | ## Estimation Code 28 | A Python script with test input and output has been produced to generate trended estimates for the current year based on previous years. 29 | 30 | - [Cloud_Region_Metadata_estimate.csv](https://github.com/Green-Software-Foundation/real-time-cloud/blob/main/Cloud_Region_Metadata_estimate.csv) 31 | - [estimate_current_region_metadata code](https://github.com/Green-Software-Foundation/real-time-cloud/blob/main/code/estimate_current_region_metadata.py) 32 | 33 | # History 34 | This standard was initially proposed as part of a talk by Adrian Cockcroft at QCon London in March 2023, that was updated and presented again at the CNCF Sustainability Week in October 2023. [March slides,](https://github.com/adrianco/slides/blob/master/Cloud%20DevSusOps%20London.pdf) [October slides.](https://github.com/adrianco/slides/blob/master/Cloud%20DevSusOps%20Oct23.pdf) that summarized the currently available carbon footprint information from the three largest cloud providers, AWS, Azure and GCP. These monthly resolution summaries are aimed at audit reporting, and the proposal was that real time data would enable new kinds of reporting, optimization and tools, and that all the cloud providers should provide the same data. 35 | 36 | In June 2023 this proposal was [written up as a PRFAQ](https://github.com/Green-Software-Foundation/real-time-cloud/blob/main/PRFAQ%20for%20RealTimeCarbonMetrics.md) and discussed with the GSF Standards Working Group, who decided to recommend that it become a project, which was created by the GSF in July 2023. 37 | 38 | A summary of the state of AWS sustainability at the end of 2023 was [written up here](https://adrianco.medium.com/sustainability-talks-and-updates-from-aws-re-invent-2023-969100c46a6a). There were no substantive announcements but renewable energy purchases are continuing to grow. A comparison of the three main cloud providers disclosures for calendar year 2023 was published as a story in [The New Stack](https://thenewstack.io/sustainability-how-did-amazon-azure-google-perform-in-2023/). 39 | 40 | In December 2024, AWS provided PUE data for 2022 and 2023 for the first time. The PUE disclosures of AWS, Azure and GCP were analyzed and published as a story in [The New Stack](https://thenewstack.io/cloud-pue-comparing-aws-azure-and-gcp-global-regions/) in January 2025. 41 | -------------------------------------------------------------------------------- /code/README.md: -------------------------------------------------------------------------------- 1 | # Generating Estimates for Current Year Metadata 2 | 3 | Python code to automatically generate trended data for current years (for two years since 2023 data is all we have until mid-2025) 4 | 5 | ``` 6 | python3 -m venv venv 7 | ``` 8 | Windows: 9 | ``` 10 | venv\Scripts\activate 11 | ``` 12 | macOS/Linux: 13 | ``` 14 | source venv/bin/activate 15 | ``` 16 | ``` 17 | pip install -r requirements.txt 18 | ``` 19 | 20 | Run the script, by default it estimates one year, with an optional parameter it will estimate N years. 21 | ``` 22 | % python code/estimate_current_region_metadata.py Cloud_Region_Metadata.csv 2 23 | Most recent year in dataset: 2023 24 | Estimating data for years: [np.int64(2024), np.int64(2025)] 25 | Generating estimate for year: 2024 26 | Generating estimate for year: 2025 27 | Estimates saved to Cloud_Region_Metadata_estimate.csv 28 | ``` 29 | 30 | # Test Script and Simplified Input Data 31 | ``` 32 | % cd code 33 | % sh test.sh 34 | Running estimation on test_input.csv... 35 | Most recent year in dataset: 2023 36 | Estimating data for years: [np.int64(2024), np.int64(2025)] 37 | Generating estimate for year: 2024 38 | Generating estimate for year: 2025 39 | Estimates saved to test_input_estimate.csv 40 | Comparing test_input_estimate.csv with expected_output.csv... 41 | TEST PASSED! Output matches expected file. 42 | ``` -------------------------------------------------------------------------------- /code/estimate_current_region_metadata.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | import sys 4 | 5 | def estimate_next_years(input_file, num_years=1): 6 | # Load the dataset 7 | df = pd.read_csv(input_file) 8 | 9 | # Ensure the 'year' column exists and is treated as an integer 10 | if 'year' not in df.columns: 11 | raise KeyError("The dataset must contain a 'year' column.") 12 | df['year'] = df['year'].astype(int) 13 | 14 | # Get the most recent year 15 | max_year = df['year'].max() 16 | print(f"Most recent year in dataset: {max_year}") 17 | 18 | # Identify numeric and textual columns, excluding 'year' from numeric columns 19 | numeric_columns = [col for col in df.select_dtypes(include=[np.number]).columns if col != 'year'] 20 | textual_columns = [col for col in df.columns if col not in numeric_columns] 21 | 22 | # Keep track of decimal precision for each numeric column 23 | decimal_places = { 24 | col: df[col].apply(lambda x: len(str(x).split('.')[-1]) if isinstance(x, float) else 0).max() 25 | for col in numeric_columns 26 | } 27 | 28 | # Extract the most recent data 29 | most_recent_data = df[df['year'] == max_year] 30 | 31 | # Prepare estimates for the specified number of years 32 | estimated_years = [max_year + i for i in range(1, num_years + 1)] 33 | print(f"Estimating data for years: {estimated_years}") 34 | 35 | estimated_data = [] 36 | 37 | # Define constraints 38 | carbon_intensity_columns = [ 39 | 'provider-carbon-intensity-market-annual', 40 | 'provider-carbon-intensity-average-consumption-hourly', 41 | 'grid-carbon-intensity-average-consumption-annual', 42 | 'grid-carbon-intensity-marginal-consumption-annual', 43 | 'grid-carbon-intensity-average-production-annual', 44 | 'grid-carbon-intensity' 45 | ] 46 | 47 | for year in estimated_years: 48 | print(f"Generating estimate for year: {year}") 49 | next_year_df = most_recent_data.copy() 50 | next_year_df['year'] = year 51 | 52 | for col in numeric_columns: 53 | if col in df.columns: 54 | historical_data = df[['year', 'cloud-provider', 'cloud-region', col]].dropna() 55 | latest_values = most_recent_data.set_index(['cloud-provider', 'cloud-region'])[col].to_dict() 56 | 57 | # Get all historical values for each provider-region pair 58 | all_values = {} 59 | for _, row in historical_data.iterrows(): 60 | key = (row['cloud-provider'], row['cloud-region']) 61 | if key not in all_values: 62 | all_values[key] = [] 63 | all_values[key].append((row['year'], row[col])) 64 | 65 | trends = {} 66 | for (provider, region), group in historical_data.groupby(['cloud-provider', 'cloud-region']): 67 | if len(group) > 1: 68 | group = group.sort_values(by='year') 69 | trend = group[col].diff().mean() 70 | trends[(provider, region)] = trend 71 | else: 72 | trends[(provider, region)] = 0 73 | 74 | new_values = [] 75 | for idx, row in next_year_df.iterrows(): 76 | key = (row['cloud-provider'], row['cloud-region']) 77 | trend = trends.get(key, 0) 78 | latest_value = latest_values.get(key, np.nan) 79 | 80 | if not np.isnan(latest_value): 81 | # If we have a value for the most recent year, use it with the trend 82 | new_value = latest_value + trend 83 | if col in carbon_intensity_columns: 84 | new_value = max(0, new_value) # Ensure carbon intensity does not go negative 85 | elif col in ['provider-cfe-hourly', 'provider-cfe-annual']: 86 | new_value = min(1.0, max(0, new_value)) # Clamp values between 0 and 1 87 | elif col == 'power-usage-effectiveness': 88 | new_value = max(1.04, new_value) # Ensure PUE is >= 1.04 89 | new_values.append(round(new_value, decimal_places[col])) 90 | else: 91 | # If no value for most recent year, check if we have any historical value 92 | previous_values = all_values.get(key, []) 93 | if previous_values: 94 | # Use the most recent historical value available 95 | previous_values.sort(key=lambda x: x[0], reverse=True) 96 | previous_value = previous_values[0][1] 97 | new_values.append(previous_value) 98 | else: 99 | # No historical values at all 100 | new_values.append("") 101 | 102 | next_year_df[col] = new_values 103 | 104 | estimated_data.append(next_year_df) 105 | 106 | # Concatenate estimated years and sort in descending order 107 | final_df = pd.concat(estimated_data) 108 | final_df = final_df.sort_values(by=['year', 'cloud-provider', 'cloud-region'], ascending=[False, True, True]) 109 | 110 | # Specific handling for water-usage-effectiveness for us-east1 111 | final_df.loc[(final_df['cloud-region'] == 'us-east1') & 112 | (final_df['water-usage-effectiveness'].isna()), 'water-usage-effectiveness'] = 0.1 113 | 114 | # Ensure empty columns remain blank 115 | for col in df.columns: 116 | if df[col].isna().all(): 117 | final_df[col] = "" 118 | 119 | # Save output 120 | output_file = input_file.replace('.csv', f'_estimate.csv') 121 | final_df.to_csv(output_file, index=False) 122 | print(f"Estimates saved to {output_file}") 123 | 124 | if __name__ == "__main__": 125 | if len(sys.argv) < 2: 126 | print("Usage: python estimate_current_region_metadata.py [num_years]") 127 | else: 128 | input_file = sys.argv[1] 129 | num_years = int(sys.argv[2]) if len(sys.argv) > 2 else 1 130 | estimate_next_years(input_file, num_years) 131 | -------------------------------------------------------------------------------- /code/expected_output.csv: -------------------------------------------------------------------------------- 1 | year,cloud-provider,cloud-region,cfe-region,em-zone-id,wt-region-id,location,geolocation,provider-cfe-hourly,provider-cfe-annual,power-usage-effectiveness,water-usage-effectiveness,provider-carbon-intensity-market-annual,provider-carbon-intensity-average-consumption-hourly,grid-carbon-intensity-average-consumption-annual,grid-carbon-intensity-marginal-consumption-annual,grid-carbon-intensity-average-production-annual,grid-carbon-intensity,total-ICT-energy-consumption-annual,total-water-input,renewable-energy-consumption,renewable-energy-consumption-goe,renewable-energy-consumption-ppa,renewable-energy-consumption-onsite 2 | 2025,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,,,1.4,,600.0,620.0,650.0,660.0,670.0,680.0,80000.0,4500.0,,,, 3 | 2025,Google Cloud,asia-south1,India,IN-MH,IND,Mumbai,,0.4,,1.2,0.2,480.0,500.0,530.0,540.0,550.0,560.0,95000.0,5000.0,,,, 4 | 2025,Google Cloud,australia-southeast1,Australia,AU,AU,Sydney,,0.3,0.35,1.04,0.2,80.0,90.0,110.0,120.0,130.0,140.0,55000.0,3200.0,40000.0,22000.0,18000.0, 5 | 2025,Google Cloud,europe-west1,Belgium,BE,BE,St. Ghislain,,0.85,0.9,1.05,,190.0,200.0,220.0,230.0,240.0,250.0,75000.0,4100.0,47000.0,27000.0,20000.0, 6 | 2025,Google Cloud,us-east1,Virginia,US-VA,US,Richmond,,0.6,0.725,1.07,0.5,380.0,408.0,438.0,448.0,458.0,468.0,110000.0,5400.0,64000.0,32000.0,32000.0, 7 | 2024,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,,,1.4,,600.0,620.0,650.0,660.0,670.0,680.0,80000.0,4500.0,,,, 8 | 2024,Google Cloud,asia-south1,India,IN-MH,IND,Mumbai,,0.4,,1.2,0.2,480.0,500.0,530.0,540.0,550.0,560.0,95000.0,5000.0,,,, 9 | 2024,Google Cloud,australia-southeast1,Australia,AU,AU,Sydney,,0.3,0.35,1.04,0.2,80.0,90.0,110.0,120.0,130.0,140.0,55000.0,3200.0,40000.0,22000.0,18000.0, 10 | 2024,Google Cloud,europe-west1,Belgium,BE,BE,St. Ghislain,,0.85,0.9,1.05,,190.0,200.0,220.0,230.0,240.0,250.0,75000.0,4100.0,47000.0,27000.0,20000.0, 11 | 2024,Google Cloud,us-east1,Virginia,US-VA,US,Richmond,,0.6,0.725,1.07,0.5,380.0,408.0,438.0,448.0,458.0,468.0,110000.0,5400.0,64000.0,32000.0,32000.0, 12 | -------------------------------------------------------------------------------- /code/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==2.2.2 2 | pandas==2.2.3 3 | python-dateutil==2.9.0.post0 4 | pytz==2025.1 5 | six==1.17.0 6 | tzdata==2025.1 7 | -------------------------------------------------------------------------------- /code/test.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Check if Python virtual environment is activated 4 | if [ -z "$VIRTUAL_ENV" ]; then 5 | echo "Python virtual environment not detected." 6 | echo "Please set up and activate the virtual environment:" 7 | echo "python3 -m venv venv" 8 | echo "source venv/bin/activate (macOS/Linux) or venv\\Scripts\\activate (Windows)" 9 | echo "pip install -r requirements.txt" 10 | exit 1 11 | fi 12 | 13 | # Clean up any existing output file 14 | rm -f test_input_estimate.csv 15 | 16 | # Run the estimation algorithm 17 | echo "Running estimation on test_input.csv..." 18 | python estimate_current_region_metadata.py test_input.csv 2 19 | 20 | # Check if the output file was created 21 | if [ ! -f test_input_estimate.csv ]; then 22 | echo "ERROR: Output file test_input_estimate.csv was not created." 23 | exit 1 24 | fi 25 | 26 | # Compare the output with the expected output using diff 27 | echo "Comparing test_input_estimate.csv with expected_output.csv..." 28 | if diff -q test_input_estimate.csv expected_output.csv > /dev/null; then 29 | echo "TEST PASSED! Output matches expected file." 30 | exit 0 31 | else 32 | echo "TEST FAILED! Output does not match expected file." 33 | echo "Differences:" 34 | diff test_input_estimate.csv expected_output.csv 35 | exit 1 36 | fi -------------------------------------------------------------------------------- /code/test_input.csv: -------------------------------------------------------------------------------- 1 | year,cloud-provider,cloud-region,cfe-region,em-zone-id,wt-region-id,location,geolocation,provider-cfe-hourly,provider-cfe-annual,power-usage-effectiveness,water-usage-effectiveness,provider-carbon-intensity-market-annual,provider-carbon-intensity-average-consumption-hourly,grid-carbon-intensity-average-consumption-annual,grid-carbon-intensity-marginal-consumption-annual,grid-carbon-intensity-average-production-annual,grid-carbon-intensity,total-ICT-energy-consumption-annual,total-water-input,renewable-energy-consumption,renewable-energy-consumption-goe,renewable-energy-consumption-ppa,renewable-energy-consumption-onsite 2 | 2021,Google Cloud,us-east1,Virginia,US-VA,US,Richmond,,0.45,0.5,1.3,,410,430,460,470,480,490,95000,4800,58000,29000,29000, 3 | 2022,Google Cloud,us-east1,Virginia,US-VA,US,Richmond,,0.5,0.6,1.2,0.5,400,420,450,460,470,480,100000,5000,60000,30000,30000, 4 | 2023,Google Cloud,us-east1,Virginia,US-VA,US,Richmond,,0.55,0.65,1.15,,390,415,445,455,465,475,105000,5200,62000,31000,31000, 5 | 2021,Google Cloud,europe-west1,Belgium,BE,BE,St. Ghislain,,0.7,0.75,1.2,,220,230,250,260,270,280,60000,3800,41000,21000,20000, 6 | 2022,Google Cloud,europe-west1,Belgium,BE,BE,St. Ghislain,,0.75,0.8,1.15,,210,220,240,250,260,270,65000,3900,43000,23000,20000, 7 | 2023,Google Cloud,europe-west1,Belgium,BE,BE,St. Ghislain,,0.8,0.85,1.1,,200,210,230,240,250,260,70000,4000,45000,25000,20000, 8 | 2022,Google Cloud,asia-south1,India,IN-MH,IND,Mumbai,,0.4,,1.4,0.4,520,540,570,580,590,600,85000,4600,,,, 9 | 2023,Google Cloud,asia-south1,India,IN-MH,IND,Mumbai,,0.4,,1.3,0.3,500,520,550,560,570,580,90000,4800,,,, 10 | 2022,Google Cloud,australia-southeast1,Australia,AU,AU,Sydney,,0.1,0.15,1.15,,120,130,150,160,170,180,45000,2800,30000,18000,12000, 11 | 2023,Google Cloud,australia-southeast1,Australia,AU,AU,Sydney,,0.2,0.25,1.05,0.2,100,110,130,140,150,160,50000,3000,35000,20000,15000, 12 | 2023,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,,,1.4,,600,620,650,660,670,680,80000,4500,,,, 13 | -------------------------------------------------------------------------------- /code/test_input_estimate.csv: -------------------------------------------------------------------------------- 1 | year,cloud-provider,cloud-region,cfe-region,em-zone-id,wt-region-id,location,geolocation,provider-cfe-hourly,provider-cfe-annual,power-usage-effectiveness,water-usage-effectiveness,provider-carbon-intensity-market-annual,provider-carbon-intensity-average-consumption-hourly,grid-carbon-intensity-average-consumption-annual,grid-carbon-intensity-marginal-consumption-annual,grid-carbon-intensity-average-production-annual,grid-carbon-intensity,total-ICT-energy-consumption-annual,total-water-input,renewable-energy-consumption,renewable-energy-consumption-goe,renewable-energy-consumption-ppa,renewable-energy-consumption-onsite 2 | 2025,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,,,1.4,,600.0,620.0,650.0,660.0,670.0,680.0,80000.0,4500.0,,,, 3 | 2025,Google Cloud,asia-south1,India,IN-MH,IND,Mumbai,,0.4,,1.2,0.2,480.0,500.0,530.0,540.0,550.0,560.0,95000.0,5000.0,,,, 4 | 2025,Google Cloud,australia-southeast1,Australia,AU,AU,Sydney,,0.3,0.35,1.04,0.2,80.0,90.0,110.0,120.0,130.0,140.0,55000.0,3200.0,40000.0,22000.0,18000.0, 5 | 2025,Google Cloud,europe-west1,Belgium,BE,BE,St. Ghislain,,0.85,0.9,1.05,,190.0,200.0,220.0,230.0,240.0,250.0,75000.0,4100.0,47000.0,27000.0,20000.0, 6 | 2025,Google Cloud,us-east1,Virginia,US-VA,US,Richmond,,0.6,0.725,1.07,0.5,380.0,408.0,438.0,448.0,458.0,468.0,110000.0,5400.0,64000.0,32000.0,32000.0, 7 | 2024,Google Cloud,africa-south1,South Africa,ZA,ZA,Johannesburg,,,,1.4,,600.0,620.0,650.0,660.0,670.0,680.0,80000.0,4500.0,,,, 8 | 2024,Google Cloud,asia-south1,India,IN-MH,IND,Mumbai,,0.4,,1.2,0.2,480.0,500.0,530.0,540.0,550.0,560.0,95000.0,5000.0,,,, 9 | 2024,Google Cloud,australia-southeast1,Australia,AU,AU,Sydney,,0.3,0.35,1.04,0.2,80.0,90.0,110.0,120.0,130.0,140.0,55000.0,3200.0,40000.0,22000.0,18000.0, 10 | 2024,Google Cloud,europe-west1,Belgium,BE,BE,St. Ghislain,,0.85,0.9,1.05,,190.0,200.0,220.0,230.0,240.0,250.0,75000.0,4100.0,47000.0,27000.0,20000.0, 11 | 2024,Google Cloud,us-east1,Virginia,US-VA,US,Richmond,,0.6,0.725,1.07,0.5,380.0,408.0,438.0,448.0,458.0,468.0,110000.0,5400.0,64000.0,32000.0,32000.0, 12 | -------------------------------------------------------------------------------- /sup_file/AWS_renewable_energy_2023.csv: -------------------------------------------------------------------------------- 1 | business,name,city,state,country,energyType,date,size,longitude,latitude,aws_percent,aws_size_MW 2 | AWS,Amazon Wind Farm Indiana - Fowler Ridge,Benton County,Indiana,US,Wind farm,2016,150,-87.269,40.641,100%,150 3 | AWS,Amazon Wind Farm Ohio - Timber Road,Paulding County,Ohio,US,Wind farm,2016,100,-84.546,41.132,100%,100 4 | AWS,Amazon Solar Farm Virginia - Eastern Shore,Accomack County,Virginia,US,Solar farm,2016,80,-75.598,37.905,100%,80 5 | AWS,Amazon Solar Farm Virginia - Buckingham,Buckingham County,Virginia,US,Solar farm,2017,20,-78.536,37.59,100%,20 6 | AWS,Amazon Solar Farm Virginia - Scott,Powhatan County,Virginia,US,Solar farm,2017,20,-77.902,37.554,100%,20 7 | AWS,Amazon Solar Farm Virginia - Sappony,Sussex County,Virginia,US,Solar farm,2017,20,-77.248,36.938,100%,20 8 | AWS,Amazon Solar Farm Virginia - Southampton,Southampton County,Virginia,US,Solar farm,2017,100,-77.185,36.674,100%,100 9 | AWS,Amazon Solar Farm Virginia - New Kent,New Kent County,Virginia,US,Solar farm,2017,20,-76.993,37.469,100%,20 10 | AWS,Amazon Wind Farm North Carolina - Desert Wind,Perquimans and Pasquotank counties,North Carolina,US,Wind farm,2017,208,-76.456,36.229,100%,208 11 | AWS,Amazon Wind Farm California - Tehachapi,Kern County,California,US,Wind farm,2020,46.8,-118.45,35.123,100%,46.8 12 | AWS,Amazon Solar Farm Virginia - Gretna,Pittsylvania County,Virginia,US,Solar farm,2020,45,-79.425,36.781,100%,45 13 | AWS,Amazon Wind Farm Ireland - Cork,Cork,Ireland,Ireland,Wind farm,2020,23.2,-8.565,51.921,100%,23.2 14 | "65% AWS, 35% Amazon.com",Amazon Solar Farm Spain - Cabrera,Seville,Spain,Spain,Solar farm,2020,149,-5.985,37.389,35%,52.15 15 | AWS,Amazon Wind Farm Sweden - Bäckhammar,Bäckhammar,Sweden,Sweden,Wind farm,2020,91,14.283,59.283,100%,91 16 | AWS,Amazon Wind Farm Kansas - Iron Star,Ford County,Kansas,US,Wind farm,2021,239,-99.89,37.65,100%,239 17 | AWS,Amazon Wind Farm Nebraska - Little Blue,Hastings City,Nebraska,US,Wind farm,2021,250,-98.69,40.255,100%,250 18 | AWS,Amazon Wind Farm Kansas - Irish Creek,Frankfort City,Kansas,US,Wind farm,2021,301.7,-96.244,39.415,100%,301.7 19 | AWS,Amazon Solar Farm Ohio - Hilllcrest,Brown County,Ohio,US,Solar farm,2021,200,-83.902,39.09,100%,200 20 | AWS,Amazon Solar Farm North Carolina - Hawtree Creek,Warren County,North Carolina,US,Solar farm,2021,65,-78.104,36.53,100%,65 21 | AWS,Amazon Solar Farm Virginia - Fort Powhatan,Prince George County,Virginia,US,Solar farm,2021,150,-77.08,37.26,100%,150 22 | AWS,Amazon Wind Farm Scotland - Beinn an Tuirc 3,Argyll and Bute,Scotland,United Kingdom,Wind farm,2021,50,-5.591,55.534,100%,50 23 | AWS,Amazon Solar Farm Spain - Hijar I,Zaragoza,Spain,Spain,Solar farm,2021,49,-0.889,41.649,100%,49 24 | "90% AWS, 10% Amazon.com",Amazon Solar Farm China - Shandong,Shandong,China,China,Solar farm,2021,100,118.88,37.67,90%,90 25 | AWS,Amazon Solar Farm Australia - Suntop,New South Wales,Australia,Australia,Solar farm,2021,105,148.829,-32.583,100%,105 26 | AWS,Amazon Solar Farm Australia - Gunnedah,New South Wales,Australia,Australia,Solar farm,2021,60,150.349,-30.941,100%,60 27 | "15% AWS, 55% Amazon.com, 30% Devices",Amazon Solar Farm Canada - Travers,Alberta,Alberta,Canada,Solar farm,2022,375,-112.73,50.25,55%,206.25 28 | AWS,Amazon Texas Great Prairie Wind Farm,Hansford County,Texas,US,Wind farm,2022,868.56,-101.392,36.497,100%,868.56 29 | AWS,Amazon Wind Farm Illinois - Midland,Henry County,Illinois,US,Wind farm,2022,100,-90.014,41.263,100%,100 30 | AWS,Amazon Wind Farm Illinois - Crescent Ridge,Bureau County,Illinois,US,Wind farm,2022,54,-89.853,41.238,100%,54 31 | AWS,Amazon Solar Farm Illinois - Junction,Lee County,Illinois,US,Solar farm,2022,100,-89.37,41.49,100%,100 32 | AWS,Amazon Solar Farm Kentucky - Garrard,Garrard County,Kentucky,US,Solar farm,2022,50,-84.574,37.595,100%,50 33 | AWS,Amazon Solar Farm Virginia - Powells Creek,Halifax County,Virginia,US,Solar farm,2022,70,-79.028,36.55,100%,70 34 | AWS,Amazon Solar Farm Virginia - Crystal Hill,Halifax County,Virginia,US,Solar farm,2022,65,-78.929,36.695,100%,65 35 | AWS,Amazon Solar Farm Virginia - Sunnybrook,Halifax,Virginia,US,Solar farm,2022,51,-78.83,36.8,100%,51 36 | AWS,Amazon Solar Farm Virginia - Carvers Creek,Gloucester County,Virginia,US,Solar farm,2022,130,-76.608,37.548,100%,130 37 | AWS,Amazon Solar Farm Delaware - Solidago,New Castle County,Delaware,US,Solar farm,2022,50,-75.754,39.395,100%,50 38 | AWS,Amazon Wind Farm Ireland - Donegal,Donegal,Ireland,Ireland,Wind farm,2022,91.2,-8.058,54.648,100%,91.2 39 | "55% AWS, 45% Amazon.com",Amazon Wind Farm UK - Kennoxhead (Phase 1),"Kennoxhead, South Lanarkshire",Scotland,United Kingdom,Wind farm,2022,60,-3.88,55.505,45%,27 40 | "50% AWS, 50% Amazon.com",Amazon Solar Farm Italy - Mazara,Italy,Italy,Italy,Solar farm,2022,40,12.644,37.798,50%,20 41 | "50% AWS, 50% Amazon.com",Amazon Solar Farm Italy - Paterno,Italy,Italy,Italy,Solar farm,2022,26.4,14.84,37.515,50%,13.2 42 | "90% AWS, 10% Amazon.com",Amazon Wind Farm Sweden - Hastkullen,Sweden,Sweden,Sweden,Wind farm,2022,275,17.729,63.428,10%,27.5 43 | AWS,Amazon Wind Farm Sweden – Nysäter,"Sundvall, Västernorrland",Sweden,Sweden,Wind farm,2022,122,17.729,63.428,100%,122 44 | AWS,Amazon Solar Farm South Africa - Adams,Northern Cape Province,South Africa,South Africa,Solar farm,2022,10,23.004,-27.388,100%,10 45 | AWS,Amazon Solar Farm Singapore II,Singapore,Singapore,Singapore,Solar farm,2022,14,103.634,1.298,100%,14 46 | AWS,Amazon Solar Farm Singapore I,Singapore,Singapore,Singapore,Solar farm,2022,50,103.82,1.352,100%,50 47 | "80% AWS, 20% Amazon.com",Amazon Wind Farm Australia - Hawkesdale,Hawkesdale,Victoria,Australia,Wind farm,2022,96.6,142.346,-38.133,80%,77.28 48 | AWS,Amazon Solar Farm Canada - Lathom,Newell,Alberta,Canada,Solar farm,2023,80,-113.23,50.72,100%,80 49 | AWS,Amazon Wind Farm Oklahoma – Ponderosa II,Beaver,Oklahoma,US,Wind farm,2023,100,-100.66,36.74,100%,100 50 | AWS,Amazon Solar Farm Missouri - Altona,Audrain County,Missouri,US,Solar farm,2023,30,-92.141,39.284,100%,30 51 | AWS,Amazon Solar Farm Missouri - Envoy,Audrain County,Missouri,US,Solar farm,2023,50,-91.835,39.154,100%,50 52 | AWS,Amazon Solar Farm Louisiana - Oak Ridge,Morehouse,Louisiana,US,Solar farm,2023,200,-91.788,32.575,100%,200 53 | AWS,Amazon Solar Farm Illinois - Warren,Warren County,Illinois,US,Solar farm,2023,35,-90.669,40.896,100%,35 54 | AWS,Amazon Solar Farm Arkansas - Big Cypress,Crittenden,Arkansas,US,Solar farm,2023,120,-90.194,35.185,100%,120 55 | AWS,Amazon Solar Farm Mississippi – Potlatch,Scott County,Mississippi,US,Solar farm,2023,175,-89.731,32.517,100%,175 56 | "0.4% AWS, 99.6% Amazon.com",Amazon Solar Farm Mississippi - Cane Creek,Clarke,Mississippi,US,Solar farm,2023,78.5,-88.69,32.04,4%,3.14 57 | AWS,Amazon Solar Farm Kentucky - Caldwell,Caldwell County,Kentucky,US,Solar farm,2023,100,-88,37.16,100%,100 58 | AWS,Amazon Solar Farm Indiana - Randolph,Randolph County,Indiana,US,Solar farm,2023,100,-85.135,40.035,100%,100 59 | AWS,Amazon Solar Farm Ohio - Timber Road,Paulding County,Ohio,US,Solar farm,2023,50,-84.726,41.092,100%,50 60 | AWS,Amazon Solar Farm Ohio - Mark Center,Defiance,Ohio,US,Solar farm,2023,90,-84.611,41.282,100%,90 61 | AWS,Amazon Solar Farm Ohio - Great Bear,Van Wert County,Ohio,US,Solar farm,2023,46,-84.58,40.94,100%,46 62 | AWS,Amazon Solar Farm Ohio - Blue Harvest,Putnam County,Ohio,US,Solar farm,2023,50,-84.274,41.092,100%,50 63 | AWS,Amazon Solar Farm Ohio - Nestlewood,Brown and Clermont Counties,Ohio,US,Solar farm,2023,80,-84.017,38.926,100%,80 64 | AWS,Amazon Solar Farm Ohio - Dodson Creek,Highland County,Ohio,US,Solar farm,2023,100,-83.5,39.22,100%,100 65 | AWS,Amazon Solar Farm Ohio - Ross County,Ross County,Ohio,US,Solar farm,2023,100,-83.35,39.35,100%,100 66 | AWS,Amazon Solar Farm Ohio - Yellowbud,Ross County,Ohio,US,Solar farm,2023,274,-83.15,39.55,100%,274 67 | AWS,Amazon Solar Farm Ohio - Union,Union County,Ohio,US,Solar farm,2023,293,-83,40,100%,293 68 | AWS,Amazon Solar Farm Ohio - Union Ridge,Licking County,Ohio,US,Solar farm,2023,108,-82.647,39.991,100%,108 69 | AWS,Amazon Solar Farm Virginia - Axton,Pittsylvania County,Virginia,US,Solar farm,2023,200,-79.696,36.691,100%,200 70 | AWS,Amazon Solar Farm Virginia - Alton Post,Halifax,Virginia,US,Solar farm,2023,75,-79.033,36.569,100%,75 71 | AWS,Amazon Solar Farm Pennsylvania - Ridgeway,McKean County,Pennsylvania,US,Solar farm,2023,90,-78.515,41.696,100%,90 72 | AWS,Amazon Solar Farm Virginia - Foxglove,Frederick County,Virginia,US,Solar farm,2023,55,-78.271,39.065,100%,55 73 | AWS,Amazon Solar Farm Pennsylvania - Potter,Potter County,Pennsylvania,US,Solar farm,2023,30,-78.044,41.835,100%,30 74 | AWS,Amazon Wind Farm Ireland - Ardderroo (Phase 1),Galway,Ireland,Ireland,Wind farm,2023,91,-8.83,53.35,100%,91 75 | AWS,Amazon Wind Farm Northern Ireland - Ballykeel,United Kingdom,United Kingdom,United Kingdom,Wind farm,2023,16.1,-5.914,54.856,100%,16.1 76 | "50% AWS, 50% Amazon.com",Amazon Solar Farm Spain - IV,Spain,Spain,Spain,Solar farm,2023,92,-5.767,39.303,50%,46 77 | "50% AWS, 50% Amazon.com",Amazon Solar Farm Spain lll,Spain,Spain,Spain,Solar farm,2023,364,-5.767,39.303,50%,182 78 | AWS,Amazon Solar Farm Spain – I,Spain,Spain,Spain,Solar farm,2023,28.6,-4.644,42.135,100%,28.6 79 | AWS,Amazon Solar Farm Spain – Alamo,Spain,Spain,Spain,Solar Farm,2023,100,-4.059,40.039,100%,100 80 | AWS,Amazon Solar Farm Spain III,Spain,Spain,Spain,Solar farm,2023,152,-2.65,40,100%,152 81 | AWS,Amazon Wind Farm Spain – I,Spain,Spain,Spain,Wind farm,2023,60,-1.089,40.667,100%,60 82 | AWS,Amazon Wind Farm - San Bartolome,"Municipality of Aguilon, in the province of Zaragoza (Aragon), Spain",Spain,Spain,Wind farm,2023,38.6,-0.955,41.305,100%,38.6 83 | "50% AWS, 50% Amazon.com",Amazon Solar Farm France - Prechac,France,France,France,Solar farm,2023,15,-0.365,44.428,50%,7.5 84 | AWS,Amazon Solar Farm Italy - Castelvetrano,Italy,Italy,Italy,Solar Farm,2023,39.6,12.771,37.737,100%,39.6 85 | AWS,Amazon Wind Farm Sweden - Kallamossen,Sweden,Sweden,Sweden,Wind farm,2023,258,20.666,65.327,100%,258 86 | AWS,Amazon Wind Farm Finland - Kalajoki,Finland,Finland,Finland,Wind farm,2023,63.84,23.927,64.196,100%,63.84 87 | AWS,Amazon Wind Farm Finland - Puskakorpi,Finland,Finland,Finland,Wind farm,2023,53,24.184,64.404,100%,53 88 | AWS,Amazon Wind Farm Finland - Kyyjärvi,Finland,Finland,Finland,Wind farm,2023,36,24.847,63.097,100%,36 89 | AWS,Amazon Solar Farm India - Rajasthan Jaisalmer,Rajasthan,India,India,Solar farm,2023,210,71.043,26.315,100%,210 90 | "25% AWS, 75% Amazon.com",Amazon Solar Farm India - Rajasthan Bhadla,Rajasthan,India,India,Solar farm,2023,100,72.477,27.508,75%,75 91 | AWS,Amazon Solar Farm India - Rajasthan Bikaner,Rajasthan,India,India,Solar farm,2023,110,73.023,28.854,100%,110 92 | AWS,Amazon Solar Farm Missouri - Blue Bird,Warren County,Missouri,US,Solar farm,2023,139,91.138,38.894,100%,139 93 | AWS,Amazon Wind Farm China – Qian'an,China,China,China,Wind farm,2023,100,123.65,44.95,100%,100 94 | AWS,Amazon Wind Farm China – Daqing,China,China,China,Wind farm,2023,100,124.867,46.533,100%,100 95 | AWS,Amazon Solar Farm Japan I,Japan,Japan,Japan,Solar farm,2023,22,139.421,36.311,100%,22 96 | AWS,Amazon Solar Farm Honshu,Japan,Japan,Japan,Solar farm,2023,38,140.376,35.705,100%,38 97 | AWS,Amazon Solar + Storage Farm California - Silver Peak,San Bernardino County,California,US,Solar farm,2024,50,-117.454,34.609,100%,50 98 | AWS,Amazon Solar + Storage California - Baldy Mesa,San Bernardino County,California,US,Solar farm,2024,150,-117.454,34.61,100%,150 99 | AWS,Amazon Solar + Storage Arizona - McFarland,Yuma County,Arizona,US,Solar farm,2024,300,-113.481,32.951,100%,300 100 | AWS,Amazon Solar Farm Texas - Desert Willow,Swisher County,Texas,US,Solar farm,2024,175,-102.967,34.484,100%,175 101 | AWS,Amazon Wind Farm Texas - Montgomery Ranch,Foard and Knox County,Texas,US,Wind farm,2024,200,-99.63,33.84,100%,200 102 | AWS,Amazon Solar Farm Texas – Lake Whitney,Bosque County,Texas,US,Solar farm,2024,200,-97.393,31.844,100%,200 103 | AWS,Amazon Solar Farm Louisiana – St Landry,St Landry Parish,Louisiana,US,Solar farm,2024,100,-91.967,30.511,100%,100 104 | AWS,Amazon Wind Farm Mississippi - Delta Wind,Tunica County,Mississippi,US,Wind farm,2024,184,-90.398,34.501,100%,184 105 | AWS,Amazon Solar Farm Mississippi - Ragsdale,Madison County,Mississippi,US,Solar farm,2024,100,-90,32.549999,100%,100 106 | AWS,Amazon Solar Farm Arkansas - Crooked Lake,Mississippi,Arkansas,US,Solar farm,2024,175,-89.79,35.92,100%,175 107 | AWS,Amazon Solar Farm Indiana - Maple,Huntington County,Indiana,US,Solar farm,2024,35,-85.38,40.694,100%,35 108 | AWS,Amazon Solar Farm Indiana - Randolph II,Randolph County,Indiana,US,Solar farm,2024,150,-85.17,40.145,100%,150 109 | AWS,Amazon Solar Farm Ohio - Highland,Highland County,Ohio,US,Solar farm,2024,300,-83.795,39.071,100%,300 110 | AWS,Amazon Solar Farm Ohio - Madison Fields,Madison County,Ohio,US,Solar farm,2024,180,-83.486,40.082,100%,180 111 | AWS,Amazon Solar Farm Ohio - Fayette,Fayette,Ohio,US,Solar farm,2024,47.5,-83.401,39.383,100%,47.5 112 | AWS,Ohio Solar Project I,Marion County,Ohio,US,Solar farm,2024,100,-83.15,40.62,100%,100 113 | AWS,Amazon Solar Farm Foxhound - Virginia,Halifax,Virginia,US,Solar farm,2024,72.9,-78.748,36.866,100%,72.9 114 | AWS,Amazon Solar Farm Virginia - Bartonsville 2,Frederick County,Virginia,US,Solar farm,2024,50,-78.232,39.086,100%,50 115 | AWS,Amazon Solar Farm Virginia - Bartonsville,Frederick County,Virginia,US,Solar farm,2024,80,-78.15,39.15,100%,80 116 | AWS,Amazon Solar Farm Delaware - Cedar Creek,Kent County,Delaware,US,Solar farm,2024,74.7,-75.57,39.37,100%,74.7 117 | "72% AWS, 28% Amazon.com",Amazon Solar Farm Brazil - Novo Oriente,Sao Paulo,Brazil,Brazil,Solar farm,2024,122,-51.341,-20.652,72%,87.84 118 | AWS & AMZL,Amazon Wind Farm Brazil - Serido,Rio Grande do Norte,Brazil,Brazil,Wind farm,2024,49.5,-6.766,-36.658,100%,49.5 119 | AWS,Amazon Solar Farm Spain - Zaratan,Spain,Spain,Spain,Solar farm,2024,20.09,-4.84,41.64,100%,20.09 120 | AWS,Amazon Solar Farm Spain - Arroyadas,Spain,Spain,Spain,Solar farm,2024,20.09,-4.78,41.52,100%,20.09 121 | AWS,Amazon Solar Farm Spain - Brazatortas,Spain,Spain,Spain,Solar farm,2024,37.75,-4.223,38.712,100%,37.75 122 | "55% AWS, 45% Amazon.com",Amazon Wind Farm UK - Kennoxhead,"Kennoxhead, South Lanarkshire",Scotland,United Kingdom,Wind farm,2024,69,-3.88,55.505,55%,37.95 123 | AWS,Amazon Solar Farm Spain – Armada,Spain,Spain,Spain,Solar farm,2024,74,-3.2,40.39,100%,74 124 | "20% AWS, 50% Amazon.com, 30% Devices",Amazon Wind Farm UK - Moray West,United Kingdom,United Kingdom,United Kingdom,Wind farm,2024,473,-2.758,58.126,50%,236.5 125 | AWS,Amazon Solar Farm UK - Warley,United Kingdom,United Kingdom,United Kingdom,Solar farm,2024,47,0.34,51.535,100%,47 126 | "90% AWS, 10% v",Amazon Shell HKN Offshore Wind,Netherlands,Netherlands,Netherlands,Wind farm,2024,380,4.573,52.461,90%,342 127 | "50% AWS, 50% Amazon.com",Amazon Solar Farm Spain - Carmonita South,Spain,Spain,Spain,Solar farm,2024,80,6.45,38.97,50%,40 128 | AWS,Amazon Solar Farm Greece - Macrichoria,Greece,Greece,Greece,Solar farm,2024,24,21.21,38.87,100%,24 129 | AWS,Amazon Wind Farm Finland - Mustalamminmaki,Finland,Finland,Finland,Wind farm,2024,23.6,24.904,62.856,100%,23.6 130 | AWS,Amazon Wind Farm Finland - Koiramaki,Finland,Finland,Finland,Wind farm,2024,23.6,24.985,62.903,100%,23.6 131 | AWS,Amazon Wind Farm Finland - Illevaara,Finland,Finland,Finland,Wind farm,2024,30,28.554,64.587,100%,30 132 | AWS,Amazon Solar+Wind Farm India - Rajgarh,India,India,India,Solar farm,2024,150,75.13,22.7,100%,150 133 | "50% AWS, 50% Amazon.com",Amazon Solar+Wind Farm India - Gadag,India,India,India,Solar farm,2024,200,75.897,15.743,50%,100 134 | AWS,Amazon Solar+Wind Farm India - Gadag Koppal,India,India,India,Solar farm,2024,150,75.936,15.251,100%,150 135 | AWS,Indonesia Green Tariff,Indonesia,Indonesia,Indonesia,Solar farm,2024,210,111.258,-7.502,100%,210 136 | AWS,Amazon Solar Farm II,Japan,Japan,Japan,Solar farm,2024,15,139.071,36.13,100%,15 137 | AWS & Devices,Amazon Solar Farm Australia - Wandoan,Australia,Australia,Australia,Solar farm,2024,125,149.77,-26.27,100%,125 138 | AWS,Amazon Wind Farm Canada - Buffalo Plains,Canada,Canada,Canada,Wind farm,2025,415,-112.727,50.361,100%,415 139 | AWS,Amazon Solar Farm Texas - Nazareth,Swisher and Castro,Texas,US,Solar farm,2025,170,-101.98,34.49,100%,170 140 | AWS,Amazon Solar Farm Texas - Rio Lago,Bandera County,Texas,US,Solar farm,2025,120,-99.112,29.757,100%,120 141 | AWS,Amazon Solar Farm Oklahoma - Kiowa,Kiowa,Oklahoma,US,Solar farm,2025,100,-98.962,34.67,100%,100 142 | AWS,Amazon Solar Farm Texas - Texas One,Milam County,Texas,US,Solar farm,2025,50,-97.155,30.599,100%,50 143 | AWS,Amazon Solar Farm Texas - Stoneridge,Milam County,Texas,US,Solar farm,2025,200,-97.11,30.55,100%,200 144 | AWS,Amazon Solar Farm Texas - Jungmann,Milan County,Texas,US,Solar farm,2025,40,-97.058,30.904,100%,40 145 | AWS,Amazon Solar Farm Arkansas - Prairie Mist,Ashley County,Arkansas,US,Solar farm,2025,99.9,-91.667,33.238,100%,99.9 146 | AWS,Amazon Solar Farm Arkansas – Horseshoe Lake,Crittenden County,Arkansas,US,Solar farm,2025,200,-90.35,35.009,100%,200 147 | AWS,Amazon Solar Farm Indiana - Crossroads,Fountain,Indiana,US,Solar farm,2025,200,-87.298,40.128,100%,200 148 | AWS,Amazon Solar Farm Ohio - Sycamore,Crawford,Ohio,US,Solar farm,2025,117,-82.862,40.936,100%,117 149 | AWS,Amazon Solar Farm Maryland - Morgnec,Kent,Maryland,US,Solar farm,2025,45,-76.051,39.232,100%,45 150 | AWS,Amazon Wind Farm Spain - Salguero,Spain,Spain,Spain,Wind farm,2025,24.8,-4.644,42.136,100%,24.8 151 | AWS,Amazon Wind Farm Spain - Monte Becerril,Spain,Spain,Spain,Wind farm,2025,17.28,-4.591,42.149,100%,17.28 152 | AWS,Amazon Solar Farm Spain - FV Baluarte Solar,Spain,Spain,Spain,Solar farm,2025,24.717,-4.4,42.56,100%,24.717 153 | AWS,Amazon Solar Farm Spain - FV Retama,Spain,Spain,Spain,Solar farm,2025,31.96,-4.39,42.07,100%,31.96 154 | AWS,Amazon Solar Farm Spain - FV Bombarda Solar,Spain,Spain,Spain,Solar farm,2025,24.717,-4.37,42.55,100%,24.717 155 | AWS,Amazon Solar Farm Spain - FV Almendro,Spain,Spain,Spain,Solar farm,2025,31.96,-4.35,42.06,100%,31.96 156 | AWS,Amazon Solar Farm Spain - FV Sauce Solar,Spain,Spain,Spain,Solar farm,2025,27.286,-2.98,40.71,100%,27.286 157 | AWS,Amazon Solar Farm Spain - Canes 1,Spain,Spain,Spain,Solar farm,2025,31.68,-2.962,40.303,100%,31.68 158 | AWS,Amazon Solar Farm Spain - La Pared,Spain,Spain,Spain,Solar farm,2025,174,-2.29,37.12,100%,174 159 | AWS,Amazon Solar Farm Spain - Mambar,Spain,Spain,Spain,Solar farm,2025,26.88,-2.29,37.12,100%,26.88 160 | AWS,Amazon Solar Farm Spain - Energias Barranquilla,Spain,Spain,Spain,Solar farm,2025,32,-2.069999,39.63,100%,32 161 | AWS,Amazon Solar Farm Spain - Valle Del Sol Energias Renovables,Spain,Spain,Spain,Solar farm,2025,32,-2.069,39.62,100%,32 162 | AWS,Amazon Solar Farm Spain - Valdezorita,Spain,Spain,Spain,Solar farm,2025,38.24,-1.74,40.325,100%,38.24 163 | AWS,Amazon Solar Farm Spain - Eiden,Spain,Spain,Spain,Solar farm,2025,26.88,-1.19,39.05,100%,26.88 164 | AWS,Amazon Solar Farm Spain - El Aguila,Spain,Spain,Spain,Solar farm,2025,26.88,-1.18,39.06,100%,26.88 165 | AWS,Amazon Solar Farm Spain - Chambo,Spain,Spain,Spain,Solar farm,2025,26.88,-1.17,39.06,100%,26.88 166 | AWS,Amazon Solar Farm Spain - Escatron,Spain,Spain,Spain,Solar farm,2025,69.69,-0.428,41.254,100%,69.69 167 | AWS,Amazon Solar Farm France - Saint Frichoux,Saint Frichoux,France,France,Solar farm,2025,23.4,2.551,43.256,100%,23.4 168 | AWS,Amazon Wind Farm - Baltic Eagle,Germany,Germany,Germany,Wind farm,2025,189.64,13.899,54.82,100%,189.64 169 | AWS,Amazon Wind Farm Sweden - Lyckas,Sweden,Sweden,Sweden,Wind farm,2025,40.5,14.319,57.843,100%,40.5 170 | AWS,Amazon Wind Farm India - Osmanabad,India,India,India,Wind farm,2025,198,76.247,18.384,100%,198 171 | AWS,Amazon Solar Farm Ohio - Clearview Solar,Champain County,Ohio,US,Solar farm,2025,99,83.995,40.261,100%,99 172 | AWS,Amazon Solar Farm Texas - Outpost,Webb,Texas,US,Solar farm,2025,500,99.177,27.851,100%,500 173 | AWS & Devices,Amazon Wind Farm China - Bobai,China,China,China,Wind farm,2025,150,110.108,22.084,100%,150 174 | AWS,Taean Nammyeon I,South Korea,South Korea,South Korea,Solar farm,2025,60,126.311,36.694,100%,60 175 | AWS,Amazon Solar Farm Maryland - CPV Backbone,Garrett County,Maryland,US,Solar farm,2026,130.5,-79.202,39.443,100%,130.5 176 | AWS,Amazon Wind Farm Finland - Pahkakoski,Finland,Finland,Finland,Wind farm,2026,150.48,26.026,65.322998,100%,150.48 177 | AWS,Amazon Wind Farm Europe,Europe,Europe,Europe,Wind farm,2027,129.9,4.556,55.687,100%,129.9 178 | AWS,Amazon Wind Farm Germany,Germany,Germany,Germany,Wind farm,2027,130,14.029,54.89,100%,130 179 | -------------------------------------------------------------------------------- /sup_file/Amazon-Carbon-Free-Energy-Projects-2024.csv: -------------------------------------------------------------------------------- 1 | Site Name,Project Type,City/County,State,Country,Operational Date,System Size (MW) 2 | Amazon Onsite Solar Ohio - Akron 1,On-site Solar,Akron,Ohio,United States,2023,4.00 3 | Amazon Onsite Solar California - Bakersfield 1,Solar + Battery,Bakersfield,California,United States,2023,5.43 4 | Amazon Onsite Solar Ohio - West Jefferson 1,On-site Solar,Columbus,Ohio,United States,2023,5.40 5 | Amazon Onsite Solar New York - East Syracuse 1,On-site Solar,East Syracuse,New York,United States,2021,0.72 6 | Amazon Onsite Solar California - Milpitas 1,On-site Solar,Milpitas,California,United States,2022,0.43 7 | Amazon Onsite Solar California - Hawthorne 1,On-site Solar,Hawthorne,California,United States,2022,0.54 8 | Amazon Onsite Solar California - Poway 1,On-site Solar,Poway,California,United States,2023,0.90 9 | Amazon Onsite Solar Canada - Nisku 1,On-site Solar,Nisku,Outside US,Canada,2021,0.21 10 | Amazon Onsite Solar Arizona - Goodyear,On-site Solar,Goodyear,Arizona,United States,2023,2.84 11 | Amazon Onsite Solar New York - Staten Island 1,On-site Solar,Staten Island,New York,United States,2021,7.09 12 | Amazon Onsite Solar Kentucky - Hebron 1,On-site Solar,Hebron,Kentucky,United States,2023,2.41 13 | Amazon Onsite Solar California - San Bernardino 3,Solar + Battery,San Bernardino,California,United States,2022,5.83 14 | Amazon Onsite Solar Nevada - Henderson,On-site Solar,Henderson,Nevada,United States,2023,1.25 15 | Amazon Onsite Solar California - Perris 2,On-site Solar,Perris,California,United States,2022,1.08 16 | Amazon Onsite Solar Illinois - Monee 1,On-site Solar,Monee,Illinois,United States,2022,3.00 17 | Amazon Onsite Solar Ohio - Rossford 1,On-site Solar,Rossford,Ohio,United States,2023,4.00 18 | Amazon Onsite Solar California - Beaumont 1,On-site Solar,Beaumont,California,United States,2021,4.74 19 | Amazon Onsite Solar California - San Diego,Solar + Battery,San Diego,California,United States,2024,5.56 20 | Amazon Onsite Solar California - Rialto 3,On-site Solar,Bloomington,California,United States,2023,3.37 21 | Amazon Onsite Solar California - San Bernardino 4,On-site Solar,San Bernardino,California,United States,2023,3.12 22 | Amazon Onsite Solar California - Stockton 2,On-site Solar,Stockton,California,United States,2023,3.35 23 | Amazon Onsite Solar California - Manteca 1,On-site Solar,Manteca,California,United States,2022,1.09 24 | Amazon Onsite Solar California - Stockton 3,On-site Solar,Stockton,California,United States,2022,1.10 25 | Amazon Onsite Solar California - Vacaville 2,On-site Solar,Vacaville,California,United States,2023,0.80 26 | Amazon Onsite Solar Arizona - Tucson 1,On-site Solar,Tucson,Arizona,United States,2022,3.33 27 | Amazon Onsite Solar Italy - Padova 1,On-site Solar,Padova,Italy,Italy,2020,0.02 28 | Amazon Onsite Solar Spain - Rubi 1,On-site Solar,Rubi,Spain,Spain,2020,0.05 29 | Amazon Onsite Solar Spain - Sevilla 1,On-site Solar,Sevilla,Spain,Spain,2020,0.03 30 | Amazon Onsite Solar United Kingdom - Belvedere 1,On-site Solar,Belvedere,United Kingdom,United Kingdom,2020,0.13 31 | Amazon Onsite Solar United Kingdom - Ipswich 1,On-site Solar,Ipswich,United Kingdom,United Kingdom,2020,0.04 32 | Amazon Onsite Solar United Kingdom - Deeside 1,On-site Solar,Deeside,United Kingdom,United Kingdom,2020,0.13 33 | Amazon Onsite Solar Spain - Murcia 1,On-site Solar,Murcia,Spain,Spain,2020,0.02 34 | Amazon Onsite Solar Italy - Burago di Molgora 1,On-site Solar,Burago di Molgora,Italy,Italy,2019,0.02 35 | Amazon Onsite Solar Italy - Brandizzo 1,On-site Solar,Brandizzo,Italy,Italy,2020,0.01 36 | Amazon Onsite Solar Italy - Genova 1,On-site Solar,Genova,Italy,Italy,2021,0.11 37 | Amazon Onsite Solar Italy - Castelguglielmo 1,On-site Solar,Castelguglielmo,Italy,Italy,2021,1.04 38 | Amazon Onsite Solar Italy - Castegnato 1,On-site Solar,Castegnato,Italy,Italy,2021,0.15 39 | Amazon Onsite Solar United Kingdom - Newport 1,On-site Solar,Newport,United Kingdom,United Kingdom,2020,0.00 40 | Amazon Onsite Solar Italy - Rome 1,On-site Solar,Rome,Italy,Italy,2021,2.00 41 | Amazon Onsite Solar Spain - Alicante 1,On-site Solar,Alicante,Spain,Spain,2021,0.10 42 | Amazon Onsite Solar Japan - Amagasaki 1,On-site Solar,Amagasaki,Japan,Japan,2021,2.52 43 | Amazon Onsite Solar United Kingdom - Tilbury 1,On-site Solar,Tilbury,United Kingdom,United Kingdom,2020,3.47 44 | Amazon Onsite Solar United Kingdom - Dunstable 1,On-site Solar,Dunstable,United Kingdom,United Kingdom,2021,1.51 45 | Amazon Onsite Solar United Kingdom - Coventry 1,On-site Solar,Coventry,United Kingdom,United Kingdom,2020,1.70 46 | Amazon Onsite Solar United Kingdom - Coaville 1,On-site Solar,Coaville,United Kingdom,United Kingdom,2021,2.19 47 | Amazon Onsite Solar United Kingdom - Dartford 1,On-site Solar,Dartford,United Kingdom,United Kingdom,2021,3.50 48 | Amazon Onsite Solar Spain - Alcalá de Henares 1,On-site Solar,Alcalá de Henares,Spain,Spain,2021,0.10 49 | Amazon Onsite Solar Poland - Chociule 1,On-site Solar,Chociule,Poland,Poland,2021,1.75 50 | Amazon Onsite Solar India - Karnataka 2,On-site Solar,Karnataka,India,India,2021,0.60 51 | Amazon Onsite Solar India - Hyderabad 2,On-site Solar,Hyderabad,India,India,2021,0.50 52 | Amazon Onsite Solar India - Jaipur 1,On-site Solar,Jaipur,India,India,2021,0.70 53 | Amazon Onsite Solar India - Lucknow 1,On-site Solar,Lucknow,India,India,2021,1.50 54 | Amazon Onsite Solar India - Chennai 2,On-site Solar,Chennai,India,India,2022,0.50 55 | Amazon Onsite Solar Japan - Oume 1,On-site Solar,Tokyo Oume,Japan,Japan,2021,2.07 56 | Amazon Onsite Solar Japan - Sagamihara 1,On-site Solar,Sagamihara,Japan,Japan,2022,2.79 57 | Amazon Onsite Solar Japan - Ageo 1,On-site Solar,Ageo,Japan,Japan,2022,2.16 58 | Amazon Onsite Solar Japan - Sayama 1,On-site Solar,Sayama,Japan,Japan,2022,2.64 59 | Amazon Onsite Solar Australia - Melbourne 1,On-site Solar,Melbourne,Australia,Australia,2021,0.48 60 | Amazon Onsite Solar UAE - Dubai 1,On-site Solar,Dubai,United Arab Emirates,United Arab Emirates,2021,2.73 61 | Amazon Onsite Solar Saudi Arabia - Jeddah 1,On-site Solar,Jeddah,Saudi Arabia,Saudi Arabia,2023,1.75 62 | Amazon Onsite Solar Australia - Sydney 1,On-site Solar,Sydney,Australia,Australia,2018,0.10 63 | Amazon Onsite Solar India - Chennai 1,On-site Solar,Chennai,India,India,2021,0.70 64 | Amazon Onsite Solar India - Dehli 1,On-site Solar,Delhi,India,India,2017,1.35 65 | Amazon Onsite Solar India - Dehli 2,On-site Solar,Delhi,India,India,2018,1.75 66 | Amazon Onsite Solar Japan - Chiba Minato 1,On-site Solar,Chiba Minato,Japan,Japan,2022,3.60 67 | Amazon Onsite Solar Japan - Sagamihara 2,On-site Solar,Sagamihara,Japan,Japan,2023,3.00 68 | Amazon Onsite Solar Japan - Sayama Saitama 1,On-site Solar,Sayama Saitama,Japan,Japan,2023,1.90 69 | Amazon Onsite Solar India - Delhi 5,On-site Solar,Delhi,India,India,2021,0.02 70 | Amazon Onsite Solar India - Gurgram 1,On-site Solar,Gurgram,India,India,2022,0.03 71 | Amazon Onsite Solar India - Bengaluru 1,On-site Solar,Bengaluru,India,India,2022,0.09 72 | Amazon Onsite Solar India - Bengaluru 2,On-site Solar,Bengaluru,India,India,2022,0.14 73 | Amazon Onsite Solar India - Hyderabad 3,On-site Solar,Hyderabad,India,India,2022,0.03 74 | Amazon Onsite Solar India - Hyderabad 4,On-site Solar,Hyderabad,India,India,2022,0.02 75 | Amazon Onsite Solar Australia - Melbourne 2,On-site Solar,Melbourne,Australia,Australia,2022,1.40 76 | Amazon Onsite Solar India - Bangalore 3,On-site Solar,Bangalore,India,India,2022,0.04 77 | Amazon Onsite Solar India - Jaipur 2,On-site Solar,Jaipur,India,India,2022,0.03 78 | Amazon Onsite Solar India - Gandhinagar 1,On-site Solar,Gandhinagar,India,India,2022,0.04 79 | Amazon Onsite Solar India - Hyderabad 5,On-site Solar,Hyderabad,India,India,2022,0.02 80 | Amazon Onsite Solar India - Chennai 3,On-site Solar,Chennai,India,India,2022,0.02 81 | Amazon Onsite Solar India - Mumbai 1,On-site Solar,Mumbai,India,India,2022,0.03 82 | Amazon Onsite Solar India - Mumbai 2,On-site Solar,Mumbai,India,India,2022,0.06 83 | Amazon Onsite Solar India - Chennai 4,On-site Solar,Chennai,India,India,2022,0.02 84 | Amazon Onsite Solar India - Pune 1,On-site Solar,Pune,India,India,2022,0.04 85 | Amazon Onsite Solar India - Pune 2,On-site Solar,Pune,India,India,2022,0.04 86 | Amazon Onsite Solar India - Nashik 1,On-site Solar,Nashik,India,India,2022,0.03 87 | Amazon Onsite Solar India - Vadodara 1,On-site Solar,Vadodara,India,India,2022,0.04 88 | Amazon Onsite Solar United Kingdom - Redditch 1,On-site Solar,Redditch,United Kingdom,United Kingdom,2021,0.30 89 | Amazon Onsite Solar United Kingdom - Swindon 1,On-site Solar,Swindon,United Kingdom,United Kingdom,2021,0.46 90 | Amazon Onsite Solar France - Morlaas 1,On-site Solar,Morlaas,France,France,2021,0.22 91 | Amazon Onsite Solar France - Fontaine 1,On-site Solar,Fontaine,France,France,2021,0.49 92 | Amazon Onsite Solar United Kingdom - Birminghman 1,On-site Solar,Birminghman,United Kingdom,United Kingdom,2021,0.10 93 | Amazon Onsite Solar Spain - Tarragona 1,On-site Solar,Tarragona,Spain,Spain,2021,0.10 94 | Amazon Onsite Solar Spain - Mollet del Vallès 1,On-site Solar,Mollet del Vallès,Spain,Spain,2021,0.10 95 | Amazon Onsite Solar Italy - Parma 1,On-site Solar,Parma,Italy,Italy,2021,0.14 96 | Amazon Onsite Solar United Kingdom - Ossett 1,On-site Solar,Ossett,United Kingdom,United Kingdom,2021,0.14 97 | Amazon Onsite Solar Spain - Coslada 1,On-site Solar,Coslada,Spain,Spain,2021,0.10 98 | Amazon Onsite Solar France - Gauchy 1,On-site Solar,Gauchy,France,France,2021,0.31 99 | Amazon Onsite Solar Spain - El Puerto de Santamaría 1,On-site Solar,El Puerto de Santamaría,Spain,Spain,2021,0.10 100 | Amazon Onsite Solar Spain - Valladolid 1,On-site Solar,Valladolid,Spain,Spain,2019,0.08 101 | Amazon Onsite Solar Italy - Cagliari 1,On-site Solar,Cagliari,Italy,Italy,2021,0.11 102 | Amazon Onsite Solar Italy - Catania 1,On-site Solar,Catania,Italy,Italy,2021,0.10 103 | Amazon Onsite Solar Italy - Pisa 1,On-site Solar,Pisa,Italy,Italy,2021,0.18 104 | Amazon Onsite Solar Italy - Perugia 1,On-site Solar,Perugia,Italy,Italy,2022,0.13 105 | Amazon Onsite Solar Austria - Wien 1,On-site Solar,Wien,Austria,Austria,2021,0.03 106 | Amazon Onsite Solar United Kingdom - Liverpool 1,On-site Solar,Liverpool,United Kingdom,United Kingdom,2021,0.10 107 | Amazon Onsite Solar United Kingdom - Warrington 2,On-site Solar,Warrington,United Kingdom,United Kingdom,2021,0.10 108 | Amazon Onsite Solar Spain - Zaragoza 1,On-site Solar,Zaragoza,Spain,Spain,2021,0.09 109 | Amazon Onsite Solar Germany - Neu-Ulm 1,On-site Solar,Neu-Ulm,Germany,Germany,2021,0.08 110 | Amazon Onsite Solar Spain - Illescas 2,On-site Solar,Illescas,Spain,Spain,2022,5.03 111 | Amazon Onsite Solar Spain - El Far d'Empordà 1,On-site Solar,El Far d'Empordà,Spain,Spain,2022,3.50 112 | Amazon Onsite Solar Spain - Picassent 1,On-site Solar,Picassent,Spain,Spain,2022,0.10 113 | Amazon Onsite Solar United Kingdom - Manchester 1,On-site Solar,Manchester,United Kingdom,United Kingdom,2022,1.51 114 | Amazon Onsite Solar Spain - Dos Hermanas 1,On-site Solar,Dos Hermanas,Spain,Spain,2022,5.26 115 | Amazon Onsite Solar Spain - Madrid 2,On-site Solar,Madrid,Spain,Spain,2022,0.65 116 | Amazon Onsite Solar Germany - Nuremberg 1,On-site Solar,Nuremberg,Germany,Germany,2022,0.24 117 | Amazon Onsite Solar Germany - Hof 1,On-site Solar,Hof,Germany,Germany,2022,4.00 118 | Amazon Onsite Solar Italy - Rome 2,On-site Solar,Rome,Italy,Italy,2022,0.02 119 | Amazon Onsite Solar Germany - Kaiserslautern 1,On-site Solar,Kaiserslautern,Germany,Germany,2022,3.69 120 | Whole Foods - 3rd & Fairfax,On-site Solar,Los Angeles,California,United States,2018,0.15 121 | Whole Foods - Arbor Trails,On-site Solar,Austin,Texas,United States,2012,0.02 122 | Whole Foods - Arlington Center,On-site Solar,Arlington,Massachusetts,United States,2017,0.06 123 | Whole Foods - Bellingham,On-site Solar,Bellingham,Massachusetts,United States,2009,0.08 124 | Whole Foods - Bend,On-site Solar,Bend,Oregon,United States,2018,0.08 125 | Whole Foods - Berkeley,On-site Solar,Berkeley,California,United States,2017,0.05 126 | Whole Foods - Blossom Hill,On-site Solar,San Jose,California,United States,2018,0.36 127 | Whole Foods - Brea,On-site Solar,Brea,California,United States,2018,0.11 128 | Whole Foods - Brentwood,On-site Solar,Los Angeles,California,United States,2018,0.15 129 | Whole Foods - Bridgeport,On-site Solar,Tigard,Oregon,United States,2018,0.20 130 | Whole Foods - Brooklyn,On-site Solar,Brooklyn,New York,United States,2013,0.31 131 | Whole Foods - Cambridge,On-site Solar,Cambridge,Massachusetts,United States,2009,0.08 132 | Whole Foods - Cherry Hill,On-site Solar,Cherry Hill,New Jersey,United States,2018,0.15 133 | Whole Foods - Coddingtown,On-site Solar,Santa Rosa,California,United States,2018,0.31 134 | Whole Foods - Colleyville,On-site Solar,Colleyville,Texas,United States,2018,0.06 135 | Whole Foods - Danbury,On-site Solar,Danbury,Connecticut,United States,2018,0.08 136 | Whole Foods - Darien,On-site Solar,Darien,Connecticut,United States,2018,0.15 137 | Whole Foods - Dedham,On-site Solar,Dedham,Massachusetts,United States,2009,0.08 138 | Whole Foods - Domain,On-site Solar,Austin,Texas,United States,2014,0.19 139 | Whole Foods - Dublin,On-site Solar,Dublin,California,United States,2018,0.12 140 | Whole Foods - Fairview,On-site Solar,Fairview,Texas,United States,2018,0.03 141 | Whole Foods - Folsom,On-site Solar,Folsom,California,United States,2018,0.11 142 | Whole Foods - Forest,On-site Solar,Dallas,Texas,United States,2015,0.20 143 | Whole Foods - Framingham,On-site Solar,Framingham,Massachusetts,United States,2010,0.09 144 | Whole Foods - Fremont,On-site Solar,Fremont,California,United States,2018,0.22 145 | Whole Foods - Hingham,On-site Solar,Hingham,Massachusetts,United States,2017,0.12 146 | Whole Foods - Lakewood,On-site Solar,Dallas,Texas,United States,2018,0.08 147 | Whole Foods - Los Altos,On-site Solar,Los Altos,California,United States,2018,0.28 148 | Whole Foods - Milburn Union,On-site Solar,Vauxhall,New Jersey,United States,2019,0.17 149 | Whole Foods - Milford,On-site Solar,Milford,Connecticut,United States,2018,0.09 150 | Whole Foods - North Atlantic Distribution Center,On-site Solar,Cheshire,Connecticut,United States,2012,0.15 151 | Whole Foods - Pacific Coast Highway,On-site Solar,El Segundo,California,United States,2012,0.29 152 | Whole Foods - Palm Desert,On-site Solar,Palm Desert,California,United States,2018,0.07 153 | Whole Foods - Park Lane,On-site Solar,Dallas,Texas,United States,2015,0.26 154 | Whole Foods - Princeton,On-site Solar,Princeton,New Jersey,United States,2018,0.15 155 | Whole Foods - Reno,On-site Solar,Reno,Nevada,United States,2018,0.56 156 | Whole Foods - Richmond Distribution Center,On-site Solar,Richmond,California,United States,2018,0.88 157 | Whole Foods - Ridgewood,On-site Solar,Ridgewood,New Jersey,United States,2006,0.08 158 | Whole Foods - Roseville,On-site Solar,Roseville,California,United States,2018,0.46 159 | Whole Foods - San Luis Obispo,On-site Solar,San Luis Obispo,California,United States,2018,0.15 160 | Whole Foods - Santa Clara,On-site Solar,Santa Clara,California,United States,2018,0.14 161 | Whole Foods - Somerville,On-site Solar,Somerville,Massachusetts,United States,2018,0.04 162 | Whole Foods - South Beach,On-site Solar,Miami Beach,Florida,United States,2018,0.03 163 | Whole Foods - Stevens Creek,On-site Solar,Cupertino,California,United States,2018,0.50 164 | Whole Foods - Sudbury,On-site Solar,Sudbury,Massachusetts,United States,2018,0.26 165 | Whole Foods - Swampscott,On-site Solar,Swampscott,Massachusetts,United States,2017,0.05 166 | Whole Foods - University,On-site Solar,Providence,Rhode Island,United States,2004,0.03 167 | Whole Foods - Valencia,On-site Solar,Santa Clarita,California,United States,2018,0.11 168 | Whole Foods - Vernon Distribution Center,On-site Solar,Vernon,California,United States,2018,0.62 169 | Whole Foods - Wellesley,On-site Solar,Wellesley,Massachusetts,United States,2017,0.06 170 | Whole Foods - West Hartford,On-site Solar,West Hartford,Connecticut,United States,2009,0.08 171 | Whole Foods - Woodland Hills,On-site Solar,Woodland Hills,California,United States,2018,0.15 172 | Whole Foods - Ygnacio Valley Road,On-site Solar,Walnut Creek,California,United States,2018,0.13 173 | Amazon Onsite Solar Nevada - Las Vegas 1,On-site Solar,Las Vegas,Nevada,United States,2018,0.56 174 | Amazon Onsite Solar Colorado - Thorton 1,On-site Solar,Aurora,Colorado,United States,2019,6.05 175 | Amazon Onsite Solar Maryland - Sparrows Point 1,On-site Solar,Sparrows Point,Maryland,United States,2019,1.98 176 | Amazon Onsite Solar California - Fontana 1,On-site Solar,Fontana,California,United States,2020,4.98 177 | Amazon Onsite Solar Italy - Milano 1,On-site Solar,Milano,Italy,Italy,2020,0.04 178 | Amazon Onsite Solar United Kingdom - Leeds 1,On-site Solar,Leeds,United Kingdom,United Kingdom,2021,0.22 179 | Amazon Onsite Solar India - Coimbatore 1,On-site Solar,Coimbatore,India,India,2021,1.20 180 | Amazon Onsite Solar India - Bangalore 2,On-site Solar,Bangalore,India,India,2022,1.00 181 | Amazon Onsite Solar India - Chennai 5,On-site Solar,Chennai,India,India,2022,1.30 182 | Amazon Onsite Solar California - Rialto 1,On-site Solar,Rialto,California,United States,2019,5.75 183 | Amazon Onsite Solar California - Eastvale 1,On-site Solar,Eastvale,California,United States,2018,2.20 184 | Amazon Onsite Solar Colorado - Aurora 2,On-site Solar,Aurora,Colorado,United States,2019,1.50 185 | Amazon Onsite Solar Italy - Vercelli 1,On-site Solar,Vercelli,Italy,Italy,2017,1.66 186 | Amazon Onsite Solar United Kingdom - Rugby 1,On-site Solar,Rugby,United Kingdom,United Kingdom,2016,0.03 187 | Amazon Onsite Solar California - Vacaville 1,On-site Solar,Vacaville,California,United States,2018,0.55 188 | Amazon Onsite Solar California - San Bernardino 1,On-site Solar,San Bernardino,California,United States,2018,2.86 189 | Amazon Onsite Solar California - Sacramento 1,On-site Solar,Sacramento,California,United States,2018,3.46 190 | Amazon Onsite Solar California - San Bernardino 2,On-site Solar,San Bernardino,California,United States,2016,1.12 191 | Amazon Onsite Solar California - Patterson 1,On-site Solar,Patterson,California,United States,2017,3.36 192 | Amazon Onsite Solar Maryland - North East 1,On-site Solar,North East,Maryland,United States,2018,4.10 193 | Amazon Onsite Solar California - Rialto 2,On-site Solar,Rialto,California,United States,2018,3.23 194 | Amazon Onsite Solar California - Perris 1,On-site Solar,Riverside,California,United States,2018,3.13 195 | Amazon Onsite Solar California - Redlands 1,On-site Solar,Redlands,California,United States,2019,2.79 196 | Amazon Onsite Solar California - Eastvale 2,On-site Solar,Eastvale,California,United States,2018,3.53 197 | Amazon Onsite Solar New Jersey - Edison 1,On-site Solar,Edison,New Jersey,United States,2018,4.05 198 | Amazon Onsite Solar Nevada - Las Vegas 2,On-site Solar,North Las Vegas,Nevada,United States,2019,1.20 199 | Amazon Onsite Solar Nevada - Las Vegas 3,On-site Solar,North Las Vegas,Nevada,United States,2018,1.13 200 | Amazon Onsite Solar California - Fresno 1,On-site Solar,Fresno,California,United States,2019,5.84 201 | Amazon Onsite Solar New Jersey - Carteret 1,On-site Solar,Carteret,New Jersey,United States,2017,7.51 202 | Amazon Onsite Solar New Jersey - Avenel 1,On-site Solar,Avenel,New Jersey,United States,2016,2.59 203 | Amazon Onsite Solar New Jersey - Avenel 2,On-site Solar,Avenel,New Jersey,United States,2017,2.94 204 | Amazon Onsite Solar Maryland - Baltimore 1,On-site Solar,Baltimore,Maryland,United States,2016,1.61 205 | Amazon Onsite Solar Maryland - Baltimore 2,On-site Solar,Baltimore,Maryland,United States,2017,2.07 206 | Amazon Onsite Solar Massachusetts - Fall River 1,On-site Solar,Fall River,Massachusetts,United States,2018,3.04 207 | Amazon Onsite Solar Connecticut - Windsor 1,On-site Solar,Windsor,Connecticut,United States,2018,2.02 208 | Amazon Onsite Solar New Jersey - Logan Township 1,On-site Solar,Logan Township,New Jersey,United States,2016,0.86 209 | Amazon Onsite Solar New Jersey - West Depford 1,On-site Solar,Clarksboro,New Jersey,United States,2019,5.14 210 | Amazon Onsite Solar New Jersey - Florence 1,On-site Solar,Florence,New Jersey,United States,2017,3.33 211 | Amazon Onsite Solar New Jersey - Carteret 2,On-site Solar,Carteret,New Jersey,United States,2016,4.84 212 | Amazon Onsite Solar Nevada - Reno 1,On-site Solar,Reno,Nevada,United States,2017,1.36 213 | Amazon Onsite Solar Delaware - Middletown 1,On-site Solar,Middletown,Delaware,United States,2017,2.72 214 | Amazon Onsite Solar California - Redlands 2,On-site Solar,Redlands,California,United States,2017,0.99 215 | Amazon Onsite Solar California - Newark 1,On-site Solar,Newark,California,United States,2017,1.56 216 | Amazon Onsite Solar Connecticut - North Haven 1,On-site Solar,North Haven,Connecticut,United States,2019,1.51 217 | Amazon Onsite Solar California - Tracy 2,On-site Solar,Tracy,California,United States,2018,3.80 218 | Amazon Onsite Solar Spain - Castellbisbal 1,On-site Solar,Castellbisbal,Spain,Spain,2020,0.06 219 | Amazon Onsite Solar Spain - San Fernando de Henares 1,On-site Solar,San Fernando de Henares,Spain,Spain,2018,0.10 220 | Amazon Onsite Solar Spain - Illescas 1,On-site Solar,Illescas,Spain,Spain,2019,0.10 221 | Amazon Onsite Solar Italy - Fara in Sabina 1,On-site Solar,Fara in Sabina,Italy,Italy,2018,0.96 222 | Amazon Onsite Solar Italy - Casirate d'Adda 1,On-site Solar,Casirate d'Adda,Italy,Italy,2018,0.79 223 | Amazon Onsite Solar Italy - Turin 1,On-site Solar,Turin,Italy,Italy,2019,1.20 224 | Amazon Onsite Solar United Kingdom - Dunfermline 1,On-site Solar,Dunfermline,United Kingdom,United Kingdom,2020,1.60 225 | Amazon Onsite Solar United Kingdom - Rochester 1,On-site Solar,Rochester,United Kingdom,United Kingdom,2018,0.25 226 | Amazon Onsite Solar United Kingdom - Warrington 1,On-site Solar,Warrington,United Kingdom,United Kingdom,2020,1.48 227 | Amazon Onsite Solar United Kingdom - Park Royal 1,On-site Solar,Park royal,United Kingdom,United Kingdom,2017,0.02 228 | Amazon Onsite Solar India - Thane 1,On-site Solar,Thane,India,India,2019,1.25 229 | Amazon Onsite Solar India - Hyderabad 1,On-site Solar,Hyderabad,India,India,2018,1.80 230 | Amazon Onsite Solar India - Kolkata 1,On-site Solar,Kolkata,India,India,2020,1.20 231 | Amazon Onsite Solar India - Bangalore 1,On-site Solar,Bangalore,India,India,2018,1.20 232 | Amazon Onsite Solar India - Delhi 4,On-site Solar,Delhi,India,India,2018,0.50 233 | Amazon Onsite Solar India - Ahmedabad 1,On-site Solar,Ahmedabad,India,India,2020,1.00 234 | Amazon Onsite Solar India - Karnataka 1,On-site Solar,Karnataka,India,India,2019,0.25 235 | Amazon Onsite Solar India - Delhi 3,On-site Solar,Delhi,India,India,2018,0.90 236 | Amazon Onsite Solar California - Stockton 1,On-site Solar,Stockton,California,United States,2019,3.53 237 | Amazon Onsite Solar New Jersey - Sweedsboro 1,On-site Solar,Sweedsboro,New Jersey,United States,2018,2.16 238 | Amazon Onsite Solar New York - Towanda 1,On-site Solar,Towanda,New York,United States,2021,0.72 239 | Amazon Onsite Solar New Jersey - Somerset 1,On-site Solar,Somerset,New Jersey,United States,2021,2.88 240 | Amazon Onsite Solar New Jersey - Burlington 1,On-site Solar,Burlington,New Jersey,United States,2020,5.49 241 | Amazon Onsite Solar Pennsylvania - Easton 1,On-site Solar,Easton,Pennsylvania,United States,2020,3.81 242 | Amazon Onsite Solar Illinois - Romeoville 1,On-site Solar,Romeoville,Illinois,United States,2020,3.00 243 | Amazon Onsite Solar India - Thane 2,On-site Solar,Thane,India,India,2020,0.75 244 | Amazon Onsite Solar Italy - Bitonto 1,On-site Solar,Bitonto,Italy,Italy,2022,0.05 245 | Amazon Onsite Solar Belgium - Antwerp 1,On-site Solar,Antwerp,Belgium,Belgium,2022,0.79 246 | Amazon Onsite Solar United Kingdom - Wakefield 1,On-site Solar,Wakefield,United Kingdom,United Kingdom,2022,4.00 247 | Amazon Onsite Solar United Kingdom - Haydock 1,On-site Solar,Haydock,United Kingdom,United Kingdom,2022,2.01 248 | Amazon Onsite Solar Italy - Novara 1,On-site Solar,Novara,Italy,Italy,2022,1.04 249 | Amazon Onsite Solar France - Briec 1,On-site Solar,Briec,France,France,2022,0.68 250 | Amazon Onsite Solar Spain - Picassent 1,On-site Solar,Siero,Spain,Spain,2022,4.00 251 | Amazon Onsite Solar Spain - Zaragoza 2,On-site Solar,Zaragoza,Spain,Spain,2023,1.54 252 | Amazon Onsite Solar Italy - Spilamberto 1,On-site Solar,Spilamberto,Italy,Italy,2023,0.70 253 | Amazon Onsite Solar France - Gauchy 2,On-site Solar,Gauchy,France,France,2022,0.30 254 | Amazon Onsite Solar United Kingdom - Milton Keynes 1,On-site Solar,Milton Keynes,United Kingdom,United Kingdom,2022,1.67 255 | Amazon Onsite Solar United Kingdom - Havant 1,On-site Solar,Havant,United Kingdom,United Kingdom,2023,0.10 256 | Amazon Onsite Solar United Kingdom - Baillieston 1,On-site Solar,Baillieston,United Kingdom,United Kingdom,2023,0.56 257 | Amazon Onsite Solar Spain - Badajoz 1,On-site Solar,Badajoz,Spain,Spain,2022,2.00 258 | Amazon Onsite Solar Spain - Onda 1,On-site Solar,Onda,Spain,Spain,2023,2.01 259 | Amazon Onsite Solar Italy - Trento 1,On-site Solar,Trento,Italy,Italy,2023,0.07 260 | Amazon Onsite Solar India - Pune 3,On-site Solar,Pune,India,India,2023,0.50 261 | Amazon Onsite Solar India - Ahmedabad 2,On-site Solar,Ahmedabad,India,India,2023,0.50 262 | Amazon Onsite Solar India - Pune 4,On-site Solar,Pune,India,India,2023,1.00 263 | Amazon Onsite Solar India - Gurugram 1,On-site Solar,Gurugram,India,India,2023,0.30 264 | Amazon Onsite Solar Spain - Murcia 2,On-site Solar,Murcia,Spain,Spain,2023,4.21 265 | Amazon Onsite Solar Italy - Pioltello 1,On-site Solar,Pioltello,Italy,Italy,2023,0.48 266 | Amazon Onsite Solar United Kingdom - Sutton-in-Ashfield 1,On-site Solar,Sutton-in-Ashfield,United Kingdom,United Kingdom,2023,3.13 267 | Amazon Onsite Solar Japan - Nagareyama 1,On-site Solar,Nagareyama,Japan,Japan,2023,2.78 268 | Amazon Onsite Solar India - Surat,On-site Solar,Surat,India,India,2024,0.94 269 | Amazon Onsite Solar Japan - Misato 1,On-site Solar,Misato,Japan,Japan,2023,0.40 270 | Amazon Onsite Solar Japan - Maebashi 1,On-site Solar,Maebashi,Japan,Japan,2023,0.40 271 | Amazon Onsite Solar Japan - Yokohama 1,On-site Solar,Yokohama,Japan,Japan,2023,0.44 272 | Amazon Onsite Solar United Kingdom - Doncaster 1,On-site Solar,Doncaster,United Kingdom,United Kingdom,2023,2.18 273 | Amazon Onsite Solar Japan - Nagareyama 1,On-site Solar,Nagareyama,Japan,Japan,2024,0.20 274 | Amazon Onsite Solar Spain - Barcelona 1,On-site Solar,Barcelona,Spain,Spain,2023,0.75 275 | Amazon Onsite Solar United Kingdom - Daventry 1,On-site Solar,Daventry,United Kingdom,United Kingdom,2019,0.60 276 | Amazon Onsite Solar Spain - Barberà del Vallès 1,On-site Solar,Barberà del Vallès,Spain,Spain,2023,0.89 277 | Amazon Onsite Solar Japan - Sakado 1,On-site Solar,Sakado,Japan,Japan,2024,2.20 278 | Amazon Onsite Solar California - Tracy 1,On-site Solar,Tracy,California,United States,2022,5.94 279 | Amazon Onsite Solar Spain - Mostoles 1,On-site Solar,Mostoles,Spain,Spain,2023,0.65 280 | Whole Foods - Jamboree (Tustin),On-site Solar,Tustin,California,United States,2020,0.41 281 | Amazon Onsite Solar Spain - Montcada i Reixac 1,On-site Solar,Montcada i Reixac,Spain,Spain,2023,0.75 282 | Amazon Onsite Solar Spain - Madrid 1,On-site Solar,Madrid,Spain,Spain,2023,1.18 283 | Amazon Onsite Solar - Dubai 2,On-site Solar,Dubai,United Arab Emirates,United Arab Emirates,2024,0.57 284 | Whole Foods - Concord,On-site Solar,Concord,California,United States,2020,0.21 285 | Whole Foods - West Hollywood,On-site Solar,West Hollywood,California,United States,2020,0.17 286 | Whole Foods - Huntington Beach,On-site Solar,Huntington Beach,California,United States,2020,0.14 287 | Whole Foods - Irvine,On-site Solar,Irvine,California,United States,2020,0.25 288 | Whole Foods - Long Beach,On-site Solar,Long Beach,California,United States,2020,0.14 289 | Whole Foods - Laguna Niguel,On-site Solar,Laguna Niguel,California,United States,2020,0.25 290 | Whole Foods - Melrose,On-site Solar,Melrose,Massachusetts,United States,2018,0.15 291 | Whole Foods - Playa Vista,On-site Solar,Playa Vista,California,United States,2020,0.16 292 | Whole Foods - Porter Ranch,On-site Solar,Porter Ranch,California,United States,2020,0.18 293 | Whole Foods - Upland,On-site Solar,Upland,California,United States,2019,0.17 294 | Amazon Solar Farm Australia - Wandoan,Solar,Australia,Queensland (QLD),Australia,2021,125 295 | Amazon Solar Farm Australia - Suntop,Solar,Australia,New South Wales (NSW),Australia,2020,105 296 | Amazon Wind Farm Australia - Hawkesdale,Wind,Australia,Victoria (VIC),Australia,2020,96.6 297 | Amazon Solar Farm Australia – Gunnedah,Solar,NSW,New South Wales (NSW),Australia,2019,60 298 | Amazon Solar Farm Brazil - Novo Oriente,Solar,Sao Paulo,Brazil,Brazil,2022,40.6 299 | Amazon Wind Farm Brazil - Serido,Wind,Rio Grande do Norte,Brazil,Brazil,2022,18 300 | Amazon Wind Farm Canada - Buffalo Plains,Wind,Canada,Canada,Canada,2027,415 301 | Amazon Solar Farm Canada - Travers,Solar,Vulcan,Canada,Canada,2021,400 302 | Amazon Solar Farm Canada - Lathom,Solar,County of Newell,Canada,Canada,2021,60 303 | Amazon Wind Farm China – Bobai,Wind,China,China,China,2023,150 304 | Amazon Wind Farm China – Qian'an,Wind,Jilin Province,China,China,2021,100 305 | Amazon Solar Farm China – Shandong,Solar,Shandong Province,China,China,2020,100 306 | Amazon Wind Farm China – Daqing,Wind,China,China,China,2022,100 307 | Amazon Wind Farm Finland - Pahkakoski,Wind,Finland,Finland,Finland,2023,150.48 308 | Amazon Wind Farm Finland - Kalajoki,Wind,Finland,Finland,Finland,2021,63.84 309 | Amazon Wind Farm Finland - Puskakorpi,Wind,Finland,Finland,Finland,2021,52.8 310 | Amazon Wind Farm Finland - Kinnula,Wind,Finland,Finland,Finland,2021,36 311 | Amazon Wind Farm Finland - Kyyjärvi,Wind,Finland,Finland,Finland,2021,36 312 | Amazon Wind Farm Finland - Illevaara,Wind,Finland,Finland,Finland,2023,30 313 | Amazon Wind Farm Finland - Koiramäki,Wind,Finland,Finland,Finland,2022,23.6 314 | Amazon Wind Farm Finland - Mustalamminmäki,Wind,Finland,Finland,Finland,2022,23.6 315 | Amazon Wind Farm Finland - Saarijärvi,Wind,Finland,Finland,Finland,2021,22.8 316 | Amazon Solar Farm France - Saint Frichoux,Solar,France,France,France,2021,23.4 317 | Amazon Solar Farm France – Prechac,Solar,France,France,France,2020,15 318 | Amazon Wind Farm Germany - BR3,Offshore Wind,Germany,Germany,Germany,2020,350 319 | Amazon Wind Farm Germany - Baltic Eagle,Offshore Wind,Germany,Germany,Germany,2022,189.64 320 | Amazon Wind Farm Europe,Offshore Wind,Germany,Germany,Germany,2022,129.9 321 | Amazon Solar Farm Greece - Macrichoria,Solar,Greece,Greece,Greece,2023,24 322 | Amazon Solar Farm India - Rajasthan Jaisalmer,Solar,India,India,India,2022,210 323 | Amazon Solar+Wind Farm India - Gadag,Solar + Wind,India,India,India,2022,202.6 324 | Amazon Solar+Wind Farm India - Gadag Koppal,Solar + Wind,India,India,India,2022,150 325 | Amazon Solar+Wind Farm India - Rajgarh,Solar + Wind,India,India,India,2022,150 326 | Amazon Solar Farm India - Rajasthan Bikaner,Solar,India,India,India,2022,110 327 | Amazon Solar Farm India - Rajasthan Bhadla,Solar,India,India,India,2022,100 328 | Amazon Solar Farm Indonesia ,Solar,Indonesia,Indonesia,Indonesia,2022,210 329 | Amazon Wind Farm Ireland - Donegal,Wind,Donegal,Ireland,Ireland,2019,91.2 330 | Amazon Wind Farm Ireland - Arderro Phase 1,Wind,Ireland,Ireland,Ireland,2020,91 331 | Amazon Wind Farm Ireland - Cork,Wind,Cork,Ireland,Ireland,2019,23.2 332 | Amazon Solar Farm Italy – Mazara,Solar,Italy,Italy,Italy,2020,40 333 | Amazon Solar Farm Italy - Castelvetrano,Solar,Italy,Italy,Italy,2021,39.6 334 | Amazon Solar Farm Italy – Paterno,Solar,Italy,Italy,Italy,2020,26.4 335 | Amazon Solar Farm Honshu I,Solar,Japan,Japan,Japan,2022,16 336 | Project Mitsubishi,Solar,Japan,Japan,Japan,2021,22 337 | Amazon Solar Farm Japan II,Solar,Japan,Japan,Japan,2023,15 338 | Amazon Wind Farm Netherlands - HKN,Offshore Wind,Netherlands,Netherlands,Netherlands,2020,130 339 | Amazon WInd Farm New Zealand – Turitea South,Wind,New Zealand,New Zealand,New Zealand,2023,51 340 | Amazon Solar Farm Poland – Milkowice,Solar,Wroclaw - Legnica,Poland,Poland,2022,87 341 | Amazon Solar Farm Singapore - JTC,Solar,Singapore,Singapore,Singapore,2021,62 342 | Amazon Solar Farm Singapore II,Solar,Singapore,Singapore,Singapore,2022,14 343 | Amazon Solar Farm South Africa - Adams,Solar,South Africa,South Africa,South Africa,2020,10 344 | Amazon Solar Farm Spain - Carmonita - Aurea,Solar,Extremadura,Spain,Spain,2021,136.8 345 | Amazon Solar Farm- La Pared 3,Solar,Andalusia,Spain,Spain,2023,34.72125 346 | Amazon Solar Farm Spain - Escuderos 2,Solar,Castilla–La Mancha,Spain,Spain,2021,40 347 | Amazon Solar Farm Spain - Cabrera,Solar,Andalusia,Spain,Spain,2026,148.73 348 | Amazon Solar Farm Spain - Arco I,Solar,Andalusia,Spain,Spain,2021,32 349 | Amazon Solar Farm Spain I,Solar,Extremadura,Spain,Spain,2021,37.1 350 | Amazon Solar Farm Spain - Alamo I,Solar,Castilla–La Mancha,Spain,Spain,2021,33.45 351 | Amazon Solar Farm Spain - Carmonita North,Solar,Extremadura,Spain,Spain,2021,40 352 | Amazon Solar Farm Spain - Carmonita South - Aquila,Solar,Extremadura,Spain,Spain,2022,40 353 | Amazon Solar Farm Spain - Armada,Solar,Castilla–La Mancha,Spain,Spain,2021,74 354 | Amazon Solar Farm Spain - Escatron I,Solar,Aragon,Spain,Spain,2023,23.23 355 | Amazon Wind Farm Spain – I,Wind,Aragon,Spain,Spain,2021,33.75 356 | Amazon Solar Farm Spain – Alcores I,Solar,Andalusia,Spain,Spain,2021,34.6 357 | Amazon Solar Farm Spain - Hijar I,Solar,Aragon,Spain,Spain,2019,49.49 358 | Amazon Solar Farm Spain - Antilia,Solar,Castilla La Mancha,Spain,Spain,2022,40 359 | Amazon Solar Farm Spain - Villanueva,Solar,Castilla La Mancha,Spain,Spain,2021,40 360 | Amazon Wind Farm - San Bartolome,Wind,Aragon,Spain,Spain,2021,38.6 361 | Amazon Solar Farm Virginia - Sappony,Solar,Castilla–La Mancha,Spain,Spain,2023,38.24 362 | Amazon Solar Farm Spain - Brazatortas,Solar,Castilla–La Mancha,Spain,Spain,2023,37.75 363 | Amazon Wind Farm Spain - San Isidro I,Wind,Aragon,Spain,Spain,2022,36.96 364 | Amazon Solar Farm Spain - Energias Barranquilla,Solar,Castilla–La Mancha,Spain,Spain,2023,32 365 | Amazon Solar Farm Virginia - Scott,Solar,Castilla–La Mancha,Spain,Spain,2023,32 366 | Amazon Solar Farm Spain - FV Almendro,Solar,Castile and León,Spain,Spain,2022,31.96 367 | Amazon Solar Farm Spain - FV Retama,Solar,Castile and León,Spain,Spain,2022,31.96 368 | Amazon Solar Farm Spain - Canes 1,Solar,Castilla-La Mancha,Spain,Spain,2023,31.68 369 | Amazon Solar Farm Spain – I,Solar,Castilla–La Mancha,Spain,Spain,2022,28.6 370 | Amazon Solar Farm Spain - Chambo,Solar,Valencia,Spain,Spain,2023,26.88 371 | Amazon Solar Farm Spain - Eiden,Solar,Valencia,Spain,Spain,2023,26.88 372 | Amazon Solar Farm Spain - El Aguila,Solar,Valencia,Spain,Spain,2023,26.88 373 | Amazon Solar Farm Spain - Mambar,Solar,Valencia,Spain,Spain,2023,26.88 374 | Amazon Wind Farm Spain - Salguero,Wind,Castile and León,Spain,Spain,2023,24.8 375 | Amazon Solar Farm Spain - FV Baluarte Solar,Solar,Castile and León,Spain,Spain,2023,24.717 376 | Amazon Solar Farm Spain - FV Bombarda Solar,Solar,Castile and León,Spain,Spain,2023,24.717 377 | Amazon Solar Farm Spain - Arroyadas,Solar,Castile and León,Spain,Spain,2022,20.09 378 | Amazon Solar Farm Spain - Zaratan,Solar,"Valladolid, Castile and León",Spain,Spain,2022,20.09 379 | Amazon Wind Farm Spain - Monte Becerril,Wind,Castile and León,Spain,Spain,2023,17.3 380 | Amazon Wind Farm Sweden - Hastkullen,Wind,Sweden,Sweden,Sweden,2020,275 381 | Amazon Wind Farm Sweden - Kallamossen,Wind,Sweden,Sweden,Sweden,2020,258 382 | Amazon Wind Farm Sweden - Nysater,Wind,Sweden,Sweden,Sweden,2019,122 383 | Amazon Wind Farm Sweden - Bäckhammar,Wind,Sweden,Sweden,Sweden,2019,91 384 | Amazon Wind Farm Sweden - Lyckas,Wind,Sweden,Sweden,Sweden,2023,40.5 385 | Amazon Wind Farm UK - Moray West,Offshore Wind,United Kingdom,United Kingdom,United Kingdom,2021,350 386 | Amazon Wind Farm UK - Kennoxhead II,Wind,United Kingdom,United Kingdom,United Kingdom,2020,68.7 387 | Amazon Wind Farm UK - Kennoxhead I,Wind,United Kingdom,United Kingdom,United Kingdom,2020,60 388 | Amazon Wind Farm Scotland - Bein an Tuirc 3,Wind,United Kingdom,United Kingdom,United Kingdom,2019,50 389 | Amazon Solar Farm UK - Warley,Solar,United Kingdom,United Kingdom,United Kingdom,2023,47 390 | Amazon Wind Farm Northern Ireland - Ballykeel,Wind,United Kingdom,United Kingdom,United Kingdom,2021,16.1 391 | Amazon Texas Great Prairie Wind Farm,Wind,Hansford County,Texas,United States,2021,355.32 392 | Amazon Solar Farm Virginia - Foxglove,Solar,Madison County,Ohio,United States,2021,150 393 | Amazon Solar Farm Texas - Frye,Solar,Swisher,Texas,United States,2021,500 394 | Amazon Solar Farm Texas - Outpost,Solar,Webb,Texas,United States,2023,500 395 | Amazon Wind Farm Kansas - Irish Creek,Wind,Marshall,Kansas,United States,2020,300.6 396 | Amazon Solar Farm Arizona - Atlas,Solar + Storage,La Paz,Arizona,United States,2021,300 397 | Amazon Solar Farm Ohio - Birch,Solar,Allen and Auglaize Counties,Ohio,United States,2020,300 398 | Amazon Solar Farms Texas - Danish Fields,Solar,Wharton and Matagorda Counties,Texas,United States,2021,300 399 | Amazon Solar Farm Illinois - High Point,Solar,Highland County,Ohio,United States,2020,300 400 | Amazon Solar + Storage Farm Arizona - McFarland,Solar + Storage,Yuma,Arizona,United States,2021,300 401 | Amazon Solar Farm Virginia - Gretna,Wind,Caddo County,Oklahoma,United States,2021,201.45 402 | Amazon Solar Farm Ohio - Union,Solar,Union County,Ohio,United States,2020,292.5 403 | Amazon Solar Farm Ohio - Yellowbud,Solar,Ross County,Ohio,United States,2020,274 404 | Amazon Wind Farm Nebraska - Little Blue,Wind,Webster and Franklin,Nebraska,United States,2020,250 405 | Amazon Solar Farm Texas - Porter,Solar,"Denton and Wise, TX",Texas,United States,2022,245 406 | Amazon Wind Farm Kansas - Iron Star,Wind,Ford County,Kansas,United States,2020,239.14 407 | Amazon Wind Farm Texas,Wind,Scurry County,Texas,United States,2016,228 408 | Amazon Wind Farm North Carolina - Desert Wind,Wind,Perquimans and Pasquotank Counties,North Carolina,United States,2015,208 409 | Amazon Solar Farm Indiana - Crossroads,Solar,Fountain,Indiana,United States,2022,200 410 | Amazon Wind Farm Texas - Montgomery Ranch,Wind,Foard and Knox County,Texas,United States,2022,200 411 | Amazon Solar Farm Texas - Stoneridge,Solar,Milam,Texas,United States,2022,200 412 | Amazon Solar Farm Virginia - Axton,Solar,Pittsylvania County,Virginia,United States,2021,200 413 | Amazon Solar Farm Texas - Garcitas Creek,Solar,Jackson County,Texas,United States,2022,200 414 | Amazon Solar Farm Ohio - Hillcrest,Solar,Brown County,Ohio,United States,2019,200 415 | Amazon Solar Farm Louisiana - Oak Ridge,Solar,Morehouse,Louisiana,United States,2022,200 416 | Amazon Solar Farm Texas - Fort Bend,Solar,Fort Bend County,Texas,United States,2022,192 417 | Amazon Wind Farm Mississippi - Delta Wind,Wind,Tunica County,Mississippi,United States,2022,184 418 | Amazon Solar Farm Ohio - Madison Fields,Solar,Madison,Ohio,United States,2021,180 419 | Amazon Solar Farm Texas - Desert Willow,Solar,Swisher,Texas,United States,2023,175 420 | Amazon Solar Farm Arkansas - Crooked Lake,Solar,Mississippi,Arkansas,United States,2022,175 421 | Amazon Solar Farm Mississippi – Potlatch,Solar,Scott,Mississippi,United States,2020,175 422 | Amazon Solar Farm Texas - Nazareth,Solar,Swisher and Castro,Texas,United States,2022,170 423 | Amazon Solar Farm Kentucky - Fleming,Solar,Fleming,Kentucky,United States,2020,169 424 | Amazon Solar Farm Illinois - Chariot,Solar,Saline,Illinois,United States,2021,160 425 | Amazon Solar Farm Virginia - Fort Powhatan,Solar,Prince George,Virginia,United States,2019,150 426 | Amazon Wind Farm Indiana - Fowler Ridge,Wind,Benton County,Indiana,United States,2014,150 427 | Amazon Solar + Storage Farm California - Baldy Mesa,Solar + Storage,San Bernardino,California,United States,2021,150 428 | Amazon Solar Farm Indiana - Randolph II,Solar,Randolph County,Indiana,United States,2022,150 429 | Amazon Solar Farm Ohio - Willowbrook,Solar,Highland and Brown,Ohio,United States,2021,150 430 | Amazon Solar Fam Missouri - Blue Bird,Solar,Warren County,Missouri,United States,2022,139 431 | Amazon Solar Farm Arkansas - Quartz,Solar,Cross County,Arkansas,United States,2020,135 432 | Amazon Solar Farm Maryland - CPV Backbone,Solar,Garrett County,Maryland,United States,2023,130.5 433 | Amazon Solar Virginia - Carvers Creek,Solar,Gloucster,Virginia,United States,2020,130 434 | Amazon Solar Farm Texas - Rio Lago,Solar,"Bandera County, TX",Texas,United States,2023,123 435 | Amazon Solar Farm Arkansas - Big Cypress,Solar,Crittenden,Arkansas,United States,2021,120 436 | Amazon Wind Farm Oklahoma - Glass Sands,Wind,Murray,Oklahoma,United States,2020,118.3 437 | Amazon Solar Farm Ohio - Sycamore,Solar,Crawford,Ohio,United States,2022,117 438 | Amazon Solar Farm Ohio - Union Ridge,Solar,Licking County,Ohio,United States,2021,107.7 439 | Amazon Solar Farm Indiana – Randolph,Solar,Randolph,Indiana,United States,2020,100.39 440 | Amazon Solar Farm Oklahoma - Kiowa,Solar,Kiowa,Oklahoma,United States,2022,100 441 | Amazon Solar Farm Ohio - Dodson Creek,Solar,Highland County,Ohio,United States,2020,117 442 | Amazon Solar Farm Arkansas - Huttig,Solar,Union,Arkansas,United States,2021,100 443 | Amazon Wind Farm Illinois - Midland,Wind,Henry County,Illinois,United States,2020,100 444 | Amazon Wind Farm Oklahoma – Ponderosa II,Wind,Beaver,Oklahoma,United States,2022,100 445 | Amazon Solar Farm Mississippi - Ragsdale,Solar,Madison County,Mississippi,United States,2022,100 446 | Amazon Solar Farm Ohio - Ross County,Solar,Ross County,Ohio,United States,2020,120 447 | Amazon Solar Farm Virginia - Southampton,Solar,Southampton,Virginia,United States,2016,100 448 | Amazon Wind Farm Ohio - Timber Road,Wind,Paulding County,Ohio,United States,2015,100 449 | Amazon Solar Farm California - Vega,Solar + Storage,Imperial Valley,California,United States,2020,100 450 | Amazon Solar Farm Arkansas - Prairie Mist,Solar,Ashley County,Arkansas,United States,2023,99.9 451 | Amazon Solar Farm Ohio - Clearview Solar,Solar,Champain County,Ohio,United States,2023,99 452 | Amazon Solar Farm Mississippi - Covington,Solar,Covington,Mississippi,United States,2021,96 453 | Amazon Solar Farm Ohio - Highland,Solar,Stephenson,Illinois,United States,2020,90 454 | Amazon Solar Farm Kentucky - Madison,Solar,Madison,Kentucky,United States,2021,90 455 | Amazon Solar Farm Ohio - Mark Center,Solar,Defiance,Ohio,United States,2021,90 456 | Amazon Solar Farm Pennsylvania - Ridgeway,Solar,McKean,Pennsylvania,United States,2020,90 457 | Amazon Solar Farm Michigan - Murch,Solar,Van Buren,Michigan,United States,2022,85 458 | Amazon Solar Farm Virginia - Maplewood,Solar,Pittsylvania County,Virginia,United States,2020,82 459 | Amazon Solar Farm Georgia - Alligator Creek,Solar,Wheeler,Georgia,United States,2022,80 460 | Amazon Solar Farm Virginia - Bartonsville,Solar,Frederick County,Virginia,United States,2022,80 461 | Amazon Solar Farm Georgia - Blackwater,Solar,Ware County,Georgia,United States,2021,80 462 | Amazon Solar Farm Georgia - Bulldog,Solar,Warren County,Georgia,United States,2026,80 463 | Amazon Solar Farm Georgia 2,Solar,Macon County,Georgia,United States,2022,80 464 | Amazon Solar Farm Virginia - Eastern Shore,Solar,Accomack County,Virginia,United States,2015,80 465 | Amazon Solar Farm Ohio - Nestlewood,Solar,Brown and Clermont County,Ohio,United States,2020,80 466 | Amazon Solar Farm Mississippi - Cane Creek,Solar,Clarke,Mississippi,United States,2025,78.5 467 | Amazon Solar Farm Mississippi - Moonshot,Solar,Hancock,Mississippi,United States,2021,78.5 468 | Amazon Solar Farm Virginia - Alton Post,Solar,Halifax,Virginia,United States,2021,75 469 | Amazon Solar Farm Delaware - Cedar Creek,Solar,Kent,Delaware,United States,2021,74.3 470 | Amazon Solar Farm Ohio - Fox Squirrel Solar 3,Solar,Halifax,Virginia,United States,2023,72.9 471 | Amazon Solar Farm Georgia 1,Solar,Burke County,Georgia,United States,2021,70 472 | Amazon Solar Farm Virginia - Powells Creek,Solar,"Halifax County, VA",Virginia,United States,2020,70 473 | Amazon Solar Farm Illinois - Staley,Solar,Kankakee County,Illinois,United States,2020,70 474 | Amazon Solar Farm Virginia - Crystal Hill,Solar,Halifax,Virginia,United States,2021,64.7 475 | Amazon Solar Farm North Carolina - Hawtree Creek,Solar,Warren County,North Carolina,United States,2019,65 476 | Amazon Solar Farm Ohio - Fox Squirrel Solar 2,Solar,Frederick County,Virginia,United States,2021,72 477 | Amazon Wind Farm Illinois - Crescent Ridge,Wind,Bureau,Illinois,United States,2021,54.5 478 | Amazon Solar Farm Virginia - Sunnybrook,Solar,Halifax County,Virginia,United States,2020,51 479 | Amazon Solar Farm Texas - Texas One,Solar,"Milam County, TX",Texas,United States,2023,50 480 | Amazon Solar Farm Virginia - Bartonsville 2,Solar,Frederick County,Virginia,United States,2022,50 481 | Amazon Solar Farm Kentucky - Garrard,Solar,Garrard,Kentucky,United States,2020,50 482 | Amazon Solar Farm Delaware - Solidago,Solar,Newcastle County,Delaware,United States,2020,50 483 | Amazon Solar + Storage Farm California - Silver Peak,Solar + Storage,San Bernadino,California,United States,2022,50 484 | Amazon Solar Farm Ohio - Blue Harvest,Solar,Putnam,Ohio,United States,2020,49.9 485 | Amazon Solar Farm Ohio - Timber Road,Solar,Paulding,Ohio,United States,2020,49.9 486 | Amazon Solar Farm Ohio - Fayette,Solar,Fayette,Ohio,United States,2022,47.5 487 | Amazon Wind Farm Germany- Windanker,Wind,Kern,California,United States,2018,46.5 488 | Amazon Solar Farm Ohio - Great Bear,Solar,Clermont County,Ohio,United States,2020,46 489 | Amazon Solar Farm Maryland - Morgnec,Solar,Kent,Maryland,United States,2023,45 490 | Amazon Wind Farm Oklahoma - White Rock,Solar,Pittsylvania,Virginia,United States,2019,45 491 | Amazon Solar Farm Georgia - Bird Dog,Solar,Burke County,Georgia,United States,2022,40 492 | Amazon Solar Farm Texas - Jungmann,Solar,"Milam County, TX",Texas,United States,2023,40 493 | Amazon Solar Farm Georgia - Sonny,Solar,Elbert,Georgia,United States,2021,40 494 | Amazon Solar Farm Georgia - Wolfskin,Solar,Oglethorpe County,Georgia,United States,2022,38 495 | Amazon Solar Farm Indiana - Maple,Solar,Huntington,Indiana,United States,2021,35 496 | Amazon Solar Farm Pennsylvania - Potter,Solar,Potter,Pennsylvania,United States,2020,30 497 | Amazon Solar Farm Spain - Valdezorita,Solar,"New Kent, Buckingham, Sussex, Powhatan",Virginia,United States,2016,20 498 | Amazon Solar Farm Virginia - New Kent,Solar,"New Kent, Buckingham, Sussex, Powhatan",Virginia,United States,2016,20 499 | Amazon Solar Farm Virginia - Buckingham,Solar,"New Kent, Buckingham, Sussex, Powhatan",Virginia,United States,2016,20 500 | Amazon Solar Farm Spain - Valle Del Sol Energias Renovables,Solar,"New Kent, Buckingham, Sussex, Powhatan",Virginia,United States,2016,19.8 501 | Amazon Solar Farm Texas - BT Signal Ranch,Solar,Hunt County,Texas,United States,2026,51.84 502 | Amazon Solar Farm South Africa - Springbok,Solar,Westmoreland,Pennsylvania,United States,2023,13.8 503 | Amazon Wind Farm - Leaning Juniper 2A,Wind,Gilliam,Oregon,United States,2024,90.3 504 | Amazon Wind Farm - Osagrove Flats,Wind,LaSalle,Illinois,United States,2024,150 505 | Amazon Solar + Storage- Bellefield,Solar + Storage,Kern County (Please do no put on interactive map),California,United States,2021,500 506 | Amazon Solar + Storage - Bellefield 2,Solar + Storage,Kern,California,United States,2024,500 507 | Amazon Solar Farm Japan III,Solar,"Greater Tokyo, Kansai, Chubu, Chugoku, and Tohoku",Japan,Japan,2023,16.335 508 | Amazon Solar Farm Spain - Tordesillas,Solar,Castile and León,Spain,Spain,2024,201.6 509 | Amazon Solar Farm Spain - Guadajoz,Solar,Andalucia,Spain,Spain,2023,32 510 | Amazon Solar Farm Pennsylvania – Spring Lane,Solar,South Africa,South Africa,South Africa,2023,18 511 | Amazon Solar Plant Spain - Don Rodrigo,Solar,Andalucia,Spain,Spain,2023,34.11 512 | Amazon Solar Farm Spain – Navarredonda,Solar,Madrid,Spain,Spain,2024,38.27 513 | Amazon Solar Farm Italy - Terzo D'Aquileia,Solar,Italy,Italy,Italy,2023,24.8 514 | Amazon Solar Farm Italy - La Manganizza,Solar,Italy,Italy,Italy,2023,6.4 515 | Amazon Solar Farm Spain – Cierzo IV,Solar,Navarre,Spain,Spain,2024,37.1 516 | Amazon Solar Farm Spain – Cierzo II,Solar,Navarre,Spain,Spain,2024,36.9 517 | Amazon Solar Farm Spain – Morata de Tajuna,Solar,Madrid,Spain,Spain,2023,36.4 518 | Amazon Solar Farm Spain - Las Coronadas,Solar,Andalucia,Spain,Spain,2023,32 519 | Amazon Wind Farm UK- East Anglia 3,Offshore Wind,United Kingdom,United Kingdom,United Kingdom,2024,159 520 | Amazon Wind Farm Spain - Rueda Sur Wind 1,Wind,Aragon,Spain,Spain,2023,36 521 | Amazon Wind Farm Poland – Jastrowie,Wind,Poland,Poland,Poland,2023,30.3 522 | Amazon Wind Farm Poland – Okonek,Wind,Poland,Poland,Poland,2023,22.8 523 | Amazon Wind Farm Spain - Rueda Sur Wind 2,Wind,Aragon,Spain,Spain,2023,36 524 | Amazon Wind Farm Spain - Rueda Sur Wind 3,Wind,Aragon,Spain,Spain,2023,36 525 | Amazon Solar Farm Spain – Ubierna,Wind,Castile and León,Spain,Spain,2024,36.45 526 | Amazon Wind Farm Ireland - Derrinlough,Wind,Ireland,Ireland,Ireland,2024,105 527 | Amazon Solar Farm Japan - Kadamatsu,Solar,Japan,Japan,Japan,2024,9.5 528 | -------------------------------------------------------------------------------- /sup_file/Miro RTC Screenshot 2023-08-28 at 10.28.38 AM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Green-Software-Foundation/real-time-cloud/890b94583e8d67649f4c20ec5b7d8a09c49a4cfb/sup_file/Miro RTC Screenshot 2023-08-28 at 10.28.38 AM.png -------------------------------------------------------------------------------- /sup_file/PRFAQ for RealTimeCarbonMetrics.md: -------------------------------------------------------------------------------- 1 | [THIS IS A DRAFT PR-FAQ EVERYTHING HERE IS SPECULATIVE, NO CLOUD PROVIDERS HAVE AGREED TO DO ANYTHING YET] 2 | 3 | **AMAZON, MICROSOFT AND GOOGLE JOINTLY ANNOUNCE SUPPORT FOR GREEN SOFTWARE FOUNDATION STANDARDIZED REAL-TIME ENERGY AND CARBON METRICS** 4 | 5 | Carbon measurement reports move from monthly totals to minute by minute metrics, allowing real time feedback and optimization of cloud workload carbon footprints by providing information that is otherwise only available to datacenter workloads. 6 | 7 | **Seattle, Washington–October 17th, 2023** – The three leading cloud providers have agreed to provide real-time energy and carbon metrics according to the Real Time Cloud standard defined by the Green Software Foundation (GSF-RTC). 8 | 9 | Cloud providers are the largest purchasers of renewable energy in the world, but so far they have provided their customers with carbon information on a monthly basis, a few months in arrears, so customers have had to produce their own real-time estimates for cloud workloads, using public information that doesn't include those purchases and overestimates carbon footprints. As part of the information technology supply chain, cloud providers need to supply real-time carbon metrics that can be aggregated by workload, allocated and apportioned through the supply chain to satisfy regulations that are in place in Europe and California, and emerging elsewhere. Cloud providers build their own custom silicon and systems designs, and optimize them for low power consumption and to reduce the carbon footprint of their supply chain. Using GSF-RTC the efficiency benefits combined with the renewable energy purchases of cloud providers can be compared directly to datacenter alternatives for specific workloads. 10 | 11 | Many software as a service (SaaS) providers run multi-tenant workloads on cloud providers. To supply their own customers with carbon footprint estimates, the instance level data from GSF-RTC needs to be allocated and attributed across workloads. The Kepler project hosted by the Cloud Native Computing Foundation allocates the energy usage of a host node to the active pods and containers running in that node, so that energy and carbon data can be reported for workloads running on Kubernetes. In datacenter deployments Kepler can directly measure energy usage and obtain carbon intensity data from the datacenter operator. Cloud providers block direct access to energy usage metrics as part of their multi-tenant security model, but can safely provide energy data to Kepler via GSF-RTC at one minute intervals. 12 | 13 | The carbon intensity of electricity obtained from the grid depends on location and varies continuously, but estimates are available on an hourly basis. These have been used for so-called "24x7 Location Model" monthly carbon reports by GCP in particular. However these estimates don't take into account private power purchase agreements (PPAs) where cloud providers have their own supply of renewable energy. The alternative is to report data based on the energy that has been purchased according to the so-called "Market model" which includes PPAs, and is the basis of the AWS and Azure monthly reports. GSF-RTC includes both of these standard reporting models, and AWS, Azure and GCP all plan to report data using both models. 14 | 15 | Energy usage is defined as Scope 2 by the greenhouse gas consortium standard. There is also a small amount of Scope 1 fuel burned in backup generators, in heating buildings, and by staff commuting to work. Scope 3 reports on the supply chain including silicon and computer hardware manufacturing, transport, datacenter construction, and recycling. The proportion of renewable energy is increasing over time, and as a result Scope 3 is tending to dominate carbon footprints. All three scopes are reported by GSF-RTC. 16 | 17 | The current monthly reports are delayed by several months so that there is time to gather accurate data in all regions around the world for a definitive report. In order to provide data in real time, preliminary estimates of the carbon intensity and supply chain data need to be supported. GSF-RTC reports energy as a single value, but uses a confidence interval and a most likely value for the carbon footprint of each scope. As better carbon intensity data becomes available over time, the energy data can be re-processed to produce new carbon data, and the confidence interval narrows. The same metric schema can be used to produce MEC-Monthly roll-up data that isn't useful for optimization, but is well suited for carbon audit reports. 18 | 19 | Customers and partners will access the GSF-RTC metrics as time-series data via the cloud provider's default metric interface: AWS CloudWatch, Azure Monitor, and Google Cloud Monitoring. For Kubernetes Kepler will export to Prometheus on all cloud platforms. 20 | 21 | Amazon VP Sustainability Kara Hurst said [MADE UP QUOTE]_"Our customers and partners asked us for detailed information on the carbon footprint of their workloads in a standard format, as they optimize for upcoming regulations and deliver on The Climate Pledge, and we're happy to be working with the GSF and cooperating with our colleagues at other cloud providers to meet this need."_ 22 | 23 | Google VP Sustainability Kate Brandt said [MADE UP QUOTE]_"Google pioneered the hourly 24x7 carbon measurement capability, to support optimizations in time and space, we're very happy to extend this into a standardized minute by minute data feed that is optimized to support Kubernetes based workloads"._ 24 | 25 | Microsoft VP Sustainability Melanie Nakagawa said [MADE UP QUOTE]_"When we helped launch the Green Software Foundation our intent was to collaborate across the industry to come up with standards that our partners and customers can use to reduce their carbon footprint. We're very happy to support this real-time data feed, and to provide the first reference_ _implementation as a proof of concept"._ 26 | 27 | Harness CEO Jyoti Bansai said [APPROVED REAL QUOTE] _"We always wanted to provide our customers the ability to view their carbon footprint in the context of their cloud cost spend and idle/unused resources across all cloud providers. By ingesting the GSF-RTC data, we may finally be able to get the information we need in a standard form"__._ 28 | 29 | Salesforce VP Sustainability Patrick Flynn said [MADE UP QUOTE]_"Salesforce is dedicated to using its full power to save the planet, and that means we need to be able to measure and optimize our own workloads, and to be able to tell our customers what the carbon footprint of their use of Salesforce amounts to. In the past we've used crude carbon footprint estimation methods, and we're excited to be able to give much more precise and actionable data to our engineers and customers"._ 30 | 31 | CloudZero CEO Erik Petersen said [APPROVED REAL QUOTE]_“Sustainability has long been a concern for cloud engineering teams. But for as long as it’s been on engineers’ minds, the missing link in making sustainability a non-functional requirement has been the data. Every engineering decision is a buying decision — and consequently, an emissions decision — but without real-time data on cloud infrastructure’s cost and carbon consequences, engineers haven’t been able to prioritize efficiency as they build. GSF-RTC is a crucial step in establishing a universal definition of cloud sustainability; now it’s up to organizations to quantify and optimize their cloud efficiency in the name of sustainability — an existentially urgent concern for all of us.” — Erik Peterson CTO and Founder, CloudZero_. 32 | 33 | To learn more, go to [https://greensoftware.foundation/projects](https://greensoftware.foundation/projects) and to see the GSF-RTC specification see [https://github.com/Green-Software-Foundation/real-time-cloud/](https://github.com/Green-Software-Foundation/real-time-cloud). 34 | 35 | **FREQUENTLY ASKED QUESTIONS** 36 | 37 | **Question:** Why are the quotes made up? 38 | 39 | **Answer:** The quotes are initially intended to indicate how we think key supporters will react to this announcement. The people are real, but the words are suggested. As this document is shared and refined, they will be replaced by real quotes. Jyoti Bansai of Harness and Erik Petersen of CloudZero approved their quotes. Other people mentioned have not been contacted directly, although versions of this document were supplied to AWS, Azure and GCP before it was published. 40 | 41 | **Question:** Why do cloud providers need to support GSF-RTC? Should other cloud providers implement it as well? 42 | 43 | **Answer:** The underlying information is only available internally at cloud providers, and there needs to be a common mechanism to share it, so that customers can measure the carbon footprint of their workloads, and so that cloud workloads aren't at a disadvantage compared to datacenter workloads. We encourage all cloud providers to adopt GSF-RTC. 44 | 45 | **Question:** How does GSF-RTC relate to other Green Software Foundation standards like Software Carbon Intensity (SCI)? 46 | 47 | **Answer:** GSF-RTC is needed to obtain underlying carbon measurements that are then apportioned to transactions and other business metrics so that SCI can be calculated for a cloud based workload. 48 | 49 | **Question:** What are the security issues around energy measurement? 50 | 51 | **Answer:** There is a class of attacks that use very accurate measurements of CPU energy use to detect the different code paths that decryption algorithms take when they check whether keys are valid, and these can be used to break the algorithm. In addition, in a multi-tenant platform there may be more than one customer workload sharing a physical host, and the energy usage of that host is measured as a whole, not on a per-virtual-machine basis, which breaks the strong isolation guarantees made by cloud providers. By providing energy data summaries at one minute intervals the energy data is good enough for carbon estimation, and if necessary can be dithered to mask any signal that could possibly cause security issues. There is an [Intel CVE](https://www.intel.com/content/www/us/en/developer/articles/technical/software-security-guidance/advisory-guidance/running-average-power-limit-energy-reporting.html) on this subject. 52 | 53 | **Question:** What metric format does GSF-RTC use? 54 | 55 | **Answer:** The initial proposal is that GSF-RTC uses the same OpenMetrics standard for metrics as Prometheus and other recent tools. Each data point consists of a timestamp, a metric, and name/value pairs that describe it. Metrics consist of metadata such as name, type, units, and a stream of data points. [https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md](https://github.com/OpenObservability/OpenMetrics/blob/main/specification/OpenMetrics.md) 56 | 57 | **Question:** What carbon footprint information is currently available from cloud providers? 58 | 59 | **Answer:** Monthly totals are provided by AWS, Azure and GCP, with varying levels of detail. Currently, AWS and Azure only provide data using the Market Model, and GCP only provides data using the Location Model. This is suitable for audit reports, but not useful for optimization tools and projects, and doesn't give enough detail to allow allocation and attribution for SaaS providers to pass on carbon footprint data to their customers. 60 | 61 | **Question:** Why are GSF-RTC carbon metrics reported as a confidence interval, how does that work, and how should they be produced and consumed? 62 | 63 | **Answer:** The input data comes from many sources of varying quality, in particular as cloud regions are scattered around the world, there are different standards and interfaces for obtaining carbon intensity from the grid, as well as high variability over time in some areas. Where estimates are being produced, the most likely value is reported, but in addition a confidence interval provides a separate upper value and lower value with 95% confidence that the actual value is in that range. When computing with imprecise data, a common technique is to use Monte-Carlo methods, which work with distributions as inputs or outputs that can be specified using these three values. In regions that have very low carbon grids like France (Nuclear) or Sweden (Hydro), the variation is low so the confidence interval will be narrow. In regions that rely on solar and wind backed up by carbon based generation, there will be a much wider confidence interval. 64 | 65 | **Question:** Why are confidence intervals also used for Scope 3 supply chain carbon metrics? 66 | 67 | **Answer:** For scope 3 supply chain data, there are a lot of unknowns and estimated values, as well as batch to batch variation in builds of otherwise identical hardware. As the data sources improve, confidence intervals will narrow over time. 68 | 69 | **Question:** How can optimization algorithms use confidence intervals? 70 | 71 | **Answer:** For statistically valid comparisons between two values, the _values are only significantly different if they have non-overlapping confidence intervals_. So an optimization algorithm should treat input metric confidence intervals that overlap as _not significantly different_, and try to generate results that don't overlap before claiming success. The energy metrics provide a more precise value to optimize for. 72 | 73 | **Question:** What is the California supply chain rule? 74 | 75 | **Answer: The rule is in progress as of June, but should be settled one way or the other by October, which is the suggested date of the PRFAQ. https://www.motherjones.com/environment/2023/06/california-bill-climate-corporate-data-accountability-supply-chain-carbon-emissions/** 76 | 77 | **Question:** 78 | 79 | **Answer:** 80 | 81 | **Question:** 82 | 83 | **Answer:** 84 | 85 | **Question:** 86 | 87 | **Answer:** 88 | 89 | **Question:** 90 | 91 | **Answer:** 92 | 93 | **Question:** 94 | 95 | **Answer:** 96 | -------------------------------------------------------------------------------- /sup_file/place_holder.md: -------------------------------------------------------------------------------- 1 | placeholder 2 | -------------------------------------------------------------------------------- /sup_file/rtc-miro-2023-10-09.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Green-Software-Foundation/real-time-cloud/890b94583e8d67649f4c20ec5b7d8a09c49a4cfb/sup_file/rtc-miro-2023-10-09.png -------------------------------------------------------------------------------- /sup_file/rtc-miro-2023-12-18.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Green-Software-Foundation/real-time-cloud/890b94583e8d67649f4c20ec5b7d8a09c49a4cfb/sup_file/rtc-miro-2023-12-18.png -------------------------------------------------------------------------------- /sup_file/rtc-miro-2024-07-01.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Green-Software-Foundation/real-time-cloud/890b94583e8d67649f4c20ec5b7d8a09c49a4cfb/sup_file/rtc-miro-2024-07-01.png --------------------------------------------------------------------------------