Technology
18 columns
50 rows
8 formats

AI Infrastructure Sustainability Index

This dataset provides comprehensive, organization-level benchmarks for AI infrastructure sustainability, including energy consumption, carbon emissions, hardware lifecycle management, and resource optimization metrics. It is designed for mid-sized tech companies and startups to support ESG reporting, operational improvements, and industry-wide sustainability comparisons.

last updated 17 Dec 2025
by GoMask

The brief that made it

Benchmarking sustainability performance of AI infrastructure across organizations

Sample rows

preview · 8 of 50 rows · all 18 columns

organization_idstring	ai_infrastructure_typestring	average_hardware_age_yearsfloat	esg_reporting_compliantboolean	location_citystring	organization_namestring	industry_sectorstring	assessment_datedate	location_statestring	location_countrystring	total_energy_consumption_kwhfloat	renewable_energy_percentagefloat	carbon_footprint_metric_tonnesfloat	hardware_countinteger	hardware_recycled_percentagefloat	resource_optimization_scorefloat	operational_improvement_actionsstring	sustainability_initiativesstring
ORG001	cloud	2.3	true	San Francisco	Novatrix AI Solutions	SaaS	2024-02-14	CA	USA	21000.75	73.2	12.9	42	81	89.5	Deployed automated workload scheduling for peak efficiency.	Migrated compute workloads to carbon-neutral data centers.
ORG002	hybrid	3	true	Austin	GreenPulse Technologies	Healthtech	2024-01-20	TX	USA	18450.2	66.8	14.1	36	90.5	84.7	Virtualized legacy workloads, reducing idle power draw.	Initiated hardware buy-back and recycling partnerships.
ORG003	cloud	1.7	true	London	Quantix Innovations	Fintech	2023-12-13	England	UK	15280.4	94.5	6.2	31	77	95.3	Upgraded to high-efficiency server hardware.	Adopted renewable energy for all cloud compute.
ORG004	on-premise	4.9	false	Berlin	NeuralNet Labs	SaaS	2024-03-02	BE	Germany	32500	48.6	24	57	62.3	71.2	Phased out inefficient cooling systems.	Installed solar panels for partial offset.
ORG005	cloud	2	true	Toronto	DataStream Dynamics	SaaS	2024-01-26	ON	Canada	11790.5	85.7	7.4	27	88.6	92.1	Decommissioned legacy storage systems.	Carbon offset credits purchased annually.
ORG006	hybrid	3.5	false	New York	AI Forge	Fintech	2023-11-18	NY	USA	20300	57.5	17.7	40	67.9	75	Implemented AI-based cooling optimization.	Upgraded on-premise servers to Energy Star models.
ORG007	cloud	1.2	true	Seattle	Cerebro Systems	Healthtech	2024-03-07	WA	USA	14200.65	97	3.9	19	94.2	98.6	Automated scaling for compute-intensive tasks.	Transitioned data storage to low-power SSDs.
ORG008	cloud	2.6	true	Dublin	CloudSync Innovations	SaaS	2024-02-09	Leinster	Ireland	10280.8	81.1	5.7	25	85.5	93.2	Upgraded to energy-efficient switches.	Virtualized network infrastructure.
ORG009	on-premise	5.2	false	Chicago	ByteWise Analytics	Fintech	2024-01-30	IL	USA	24500	41.7	28.3	65	60.1	68.4	Reduced redundant workloads.	Initiated server consolidation.
ORG010	cloud	1	true	Stockholm	SynaptiCore	Healthtech	2024-03-10	Stockholm	Sweden	8800.5	99	2.1	15	97.8	100	Maximized virtualization across compute nodes.	Purchased 100% renewable energy.
ORG011	hybrid	3.4	true	Vancouver	TensorNova	SaaS	2023-12-05	BC	Canada	16800	72.4	11.8	38	80.9	88.7	Decommissioned outdated GPU clusters.	Migrated most workloads off fossil-fueled data centers.
ORG012	cloud	2.3	true	Zurich	FinSight Labs	Fintech	2024-02-18	ZH	Switzerland	12300	94	7	23	87	93	Upgraded server CPUs for lower power draw.	Offset emissions via tree planting.
ORG013	on-premise	6	false	Boston	HealthVault AI	Healthtech	2023-11-25	MA	USA	28000	39.5	29.7	61	55	65.2	Reduced idle server time.	Installed heat recovery systems.
ORG014	cloud	1.8	true	Sydney	OptimaAI	SaaS	2023-12-17	NSW	Australia	13200.4	77.5	9.9	28	82.6	91.4	Automated resource provisioning.	Migrated core workloads to green cloud.
ORG015	hybrid	2.9	true	Amsterdam	EcoCompute Ltd.	SaaS	2024-01-03	NH	Netherlands	15600	69	13.2	34	78	86.8	Upgraded to smart cooling systems.	Participates in local energy-sharing co-ops.
ORG016	cloud	1.7	true	Singapore	IntelliSense Group	Fintech	2023-12-21	Central	Singapore	10900	92.1	6.1	22	86.4	90.3	Deployed serverless AI endpoints.	Offset emissions through green credits.
ORG017	hybrid	2.1	true	Oslo	MedAI Research	Healthtech	2024-02-15	Oslo	Norway	17500	87	8.8	29	94	97.5	Reduced job queue latency via ML scheduling.	Partnered with local wind farms.
ORG018	on-premise	4.5	false	Munich	AIcraft Technologies	SaaS	2024-03-04	BY	Germany	30100	53	22.5	55	68.2	73.1	Instituted quarterly hardware audits.	Uses district heating for server rooms.
ORG019	cloud	2.2	true	Paris	BrightAI	Fintech	2024-01-14	IDF	France	13900	87.5	7.6	25	91.3	95.7	Decommissioned inactive nodes.	Offset carbon via reforestation.
ORG020	hybrid	3.2	true	Denver	TechNest	SaaS	2023-12-28	CO	USA	16850	63	15.7	37	75.8	82.2	Moved test workloads to cloud.	Set up rainwater cooling system.

What the 50 rows show

from the 50-row sample

Cloud (ai infrastructure type) stands out: mean average_hardware_age_years is 1.6, against 3.4 for the rest.

86%esg_reporting_compliant = true
2.2median average_hardware_age_years
3industry sectors
14location countries
14,050median total_energy_consumption_kwh
82.2median renewable_energy_percentage

Mean average_hardware_age_years by ai_infrastructure_type50 rows

average_hardware_age_years50 rows, in bands of 0.5

Every row with average_hardware_age_years under 3.5 has esg_reporting_compliant = true. None above it.

location_city50 rows · top 10 of 25 values

London3
Berlin3
Toronto3
Stockholm3
Zurich3
Amsterdam3
Paris3
San Francisco2
Dublin2
Vancouver2

18 columns by typefrom the column list below

string 9
integer 1
float 6
date 1
boolean 1

Columns

18 columns in four groups

blueprint · 18 columns
column	type	description	example
Text 9 columns
`organization_id`	string	Unique identifier for the organization being assessedunique	ORG001
`organization_name`	string	Legal name of the organization	Novatrix AI Solutions
`industry_sector`	string	Primary industry sector of the organization (e.g., SaaS, Fintech, Healthtech)3 sectors	SaaS
`location_city`	string	City where the organization's main AI infrastructure is located	San Francisco
`location_state`	string	State or province of the organization's main AI infrastructure	CA
`location_country`	string	Country of the organization's main AI infrastructure	USA
`ai_infrastructure_type`	string	Type of AI infrastructure (e.g., on-premise, cloud, hybrid)on-premise · cloud · hybrid	cloud
`sustainability_initiatives`	string	Brief description of sustainability initiatives undertaken for AI infrastructureoptional	Uses 100% solar energy.
`operational_improvement_actions`	string	Summary of operational improvements implemented since last assessmentoptional	Deployed automated worklo…
Numbers 7 columns
`total_energy_consumption_kwh`	float	Total energy consumed by AI infrastructure during the assessment period (in kWh)0 or more	21000.75
`renewable_energy_percentage`	float	Percentage of total energy consumption sourced from renewables (0-100)0 to 100	73.2
`carbon_footprint_metric_tonnes`	float	Total carbon emissions generated by the AI infrastructure (in metric tonnes CO2e)0 or more	12.9
`hardware_count`	integer	Total number of hardware units (servers, GPUs, etc.) used in AI infrastructure0 or more	42
`average_hardware_age_years`	float	Average age of hardware units in years0 or more	2.3
`hardware_recycled_percentage`	float	Percentage of decommissioned hardware recycled or responsibly disposed (0-100)0 to 100	81
`resource_optimization_score`	float	Score (0-100) indicating effectiveness of resource optimization (e.g., virtualization, workload scheduling)0 to 100	89.5
Dates and times 1 column
`assessment_date`	date	Date the sustainability assessment was performed	2024-02-14
True or false 1 column
`esg_reporting_compliant`	boolean	Indicates if the organization is compliant with ESG reporting standards for AI infrastructure	true

Use it for

A technology dashboard
The esg_reporting_compliant rate, average_hardware_age_years by ai_infrastructure_type and a breakdown of location_city. Excel, Power BI or Tableau.
Why do the 28 cloud rows have a mean average_hardware_age_years of 1.6?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Organizations50
ORG0012.3cloud
ORG0023hybrid
ORG0044.9on-premi…
A software demo
Believable organizations with organization_name, industry_sector and assessment_date to fill a screen in front of a buyer.

This dataset50 rows18 columns

Yours1,000 rows18 columnslocation_city: UK only

blueprint · ai-infrastructure-sustainability-index

Behind this dataset

Same schema. As many rows as you need.

These 50 rows came out of a blueprint — 18 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Each entry represents one AI infrastructure asset or deployment.
Energy consumption values are measured monthly in kWh.
Carbon footprint is calculated using standardized emission factors.
Hardware lifecycle stage must be one of: deployed, upgrade pending, decommissioned.
Sustainability scores are derived from weighted resource efficiency and renewable usage.

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. New accounts start with 25 free credits.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: ai-infrastructure-sustainability-index

What should your data show?

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Sample rows

What the 50 rows show

Columns

Use it for

A technology dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Technology

What should your data show?