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This article preserves that 2025 forecast while separating enduring operating principles from provider-reported figures published for 2025 and considerations that still matter in 2026.
Why green IT was difficult to prioritise in 2025
Technology leaders faced competing demands: reduce budgets immediately, expand cloud capacity, deploy AI and maintain resilience. Sustainability projects that looked discretionary were vulnerable to delay, particularly when their payback depended on capital investment such as cooling upgrades, renewable-energy contracts or datacentre redesign.
That framing misses a large group of no-regret actions. Removing idle cloud resources, extending laptop and server life, reducing duplicated data and improving software efficiency can lower both environmental impact and operating expenditure. Other decisions involve genuine trade-offs: relocating a workload may reduce grid emissions but increase latency or violate data-residency rules; aggressive rightsizing may reduce energy use but harm service levels.
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- No-regret actions: delete unused resources, schedule non-production systems, improve utilisation, reduce unnecessary processing and redeploy equipment.
- Capital-intensive actions: cooling retrofits, facility redesign and long-term renewable-energy procurement.
- Trade-off actions: workload relocation, lower-performance hardware, reduced redundancy or slower batch processing.
The original forecast also highlighted stakeholder pressure, employee expectations and regulatory developments in the EU and California. Those pressures make weak data and unclear accountability a financial risk, not merely a reputational issue. Read the original Computer Weekly analysis.
What green IT actually includes
Green IT covers environmental impacts across the entire technology lifecycle:
- Raw-material extraction and manufacturing.
- Transport, deployment and installation.
- Electricity and water used during operation.
- Software, storage and data-processing demand.
- Maintenance, upgrades and replacement decisions.
- Reuse, resale, refurbishment and redeployment.
- Recycling and disposal at end of life.
Operational electricity is only one component. Manufacturing servers, networking equipment, phones and laptops creates embodied emissions before a device reaches a datacentre or office. Replacing functioning equipment early can therefore erase gains from a more efficient model. The Computer Weekly article makes the same lifecycle argument, extending responsibility beyond cloud-region selection to acquisition, use and disposal.
Give sustainability an owner and a budget
A workable operating model connects sustainability to the people who can change demand, architecture and purchasing:
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| Function | Accountability |
|---|---|
| CIO or CTO | Technology strategy, priorities and service-level trade-offs |
| CFO and FinOps | Cost-carbon business cases, chargeback and investment decisions |
| Infrastructure and platform teams | Utilisation, power, cooling, autoscaling and capacity |
| Software engineering | Efficient algorithms, data movement, retention and runtime demand |
| Procurement and IT asset management | Repairability, supplier evidence, redeployment and end-of-life controls |
| Facilities | Datacentre energy, cooling, water and backup power |
| Sustainability or ESG teams | Boundaries, emissions factors, assurance and reporting |
| Business owners | Application rationalisation and demand reduction |
Governance should answer four practical questions: who owns the emissions number, who pays for inefficient usage, can engineers see the effect of their choices, and are targets linked to budgets and performance reviews? A dashboard without an accountable cost owner rarely changes behaviour.
Use FinOps and GreenOps to remove cloud waste
Cloud cost controls are often the fastest route to measurable improvement, but cost and carbon are not interchangeable. Apply the following controls to each account, subscription or project:
- Delete unattached disks, snapshots, load balancers and other unused resources.
- Rightsize underutilised virtual machines, databases and containers.
- Schedule development and test environments to stop outside working hours.
- Use autoscaling instead of provisioning permanently for rare peaks.
- Select efficient CPU architectures where applications are compatible.
- Compress, deduplicate and tier storage; set retention rules for logs, backups and telemetry.
- Reduce unnecessary cross-region and cross-zone data movement.
- Use managed or serverless services when they genuinely remove idle capacity.
- Use spot or interruptible capacity for workloads that tolerate interruption.
- Shift flexible batch work by region or time only when reliability, compliance and service objectives permit.
The cheapest region may not have the lowest carbon intensity, and the lowest-carbon region may fail latency, sovereignty, availability, water-stress or resilience requirements. Evaluate those constraints before moving a workload.
Google’s sustainability architecture guidance treats workload design as affecting energy, water and carbon, including for small applications and machine-learning models: Google Cloud sustainability architecture guidance. Microsoft similarly recommends tracking emissions, energy and water and provides estimation guidance for comparing infrastructure choices: Microsoft sustainability guidance.
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Datacentres: electricity, water and resilience
Electricity and efficiency
Measure server utilisation, cooling overhead, power usage effectiveness (PUE), workload scheduling, grid intensity and stranded capacity. Renewable-energy purchases can reduce reported emissions, but annual matching is not the same as consuming carbon-free electricity every hour. Backup generation, transmission constraints and local grid reliability also matter.
Google reports a 2025 fleet-wide average PUE of 1.09 and says its datacentres used 83% less overhead energy than its stated industry average. These are Google-owned and operated, provider-reported fleet figures, not guarantees for every region or customer workload: Google datacentre operations.
Water is a separate constraint
Do not use water withdrawal and water consumption as synonyms. Record whether water is potable, reclaimed or freshwater, and distinguish direct cooling from water used to generate electricity. Evaporative cooling can reduce electricity while increasing water withdrawal; dry or air cooling can do the reverse. Local watershed stress determines which outcome is preferable.
AWS reports a 2025 global datacentre water usage effectiveness (WUE) of 0.12 litres withdrawn per kilowatt-hour of IT load, describing a 20% improvement from 2024. It is a global AWS figure, not a region-specific customer result: AWS sustainability. Google says its cooling decisions balance energy and water and that 88% of waste from Google-owned and operated datacentres was diverted from disposal in 2025: Google operations sustainability.
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AI changes the green-IT equation
AI makes power density, cooling, hardware refresh cycles and data movement board-level issues. Assess training, fine-tuning, batch inference and interactive inference separately. Before approving an AI feature, ask:
- Can a smaller model, retrieval, caching or distillation meet the requirement?
- Is hardware utilisation high enough, and can jobs be batched or delayed?
- Are precision, data pipelines and repeated transfers larger than necessary?
- Does the feature deliver enough business value to justify energy, water and embodied hardware impacts?
- Can inference scale down when demand falls?
Efficiency per task can improve while total demand rises through the rebound effect. Google says its Ironwood TPU is nearly 30 times more energy-efficient than its first Cloud TPU, but that is a vendor, generational comparison rather than evidence that total AI impact is falling: Google environmental report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Sustainable software engineering
Engineering teams influence demand long after infrastructure is purchased. Practical measures include:
- Reduce polling, retries and repeated recomputation.
- Cache results and batch background work where service levels allow.
- Optimise queries, indexes, payload sizes and data formats.
- Compress data and remove duplicate pipelines.
- Set retention and deletion policies for logs, backups and datasets.
- Scale services to demand and shut down development resources.
- Measure performance and energy proxies in CI/CD.
- Remove unused features and unnecessary high-frequency processing.
“Less CPU” is not a sufficient objective. A change that saves compute but increases network traffic, storage, latency, failures or retries may increase total impact. Optimise for the required service level with the least overall resource use.
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Extend hardware life and build circular IT
Reuse and life extension generally preserve more manufacturing value than recycling. Start with an accurate asset register and require redeployment before purchasing new equipment. Procurement and IT asset management should specify:
- Expected service life, warranty and support duration.
- Repairability, modular upgrades and spare-parts availability.
- Refurbished-equipment options and secure data erasure.
- Supplier take-back, chain-of-custody and certified recycling.
- Embodied-carbon, material and responsible-minerals disclosure.
- Asset-level reporting on repair, reuse, resale and disposal.
AWS describes its circular approach as “design better, operate longer and recover more”; customers should test whether their contracts enable equivalent practices: AWS sustainability. Recycling is the final step, not a substitute for repair, refurbishment or redeployment.
Measure what decisions can change
Build a scorecard that combines absolute and normalised measures. Useful indicators include:
| Area | Example metric |
|---|---|
| Cloud and infrastructure | Idle-resource percentage, utilisation, spend and kWh per workload |
| Datacentres | PUE, WUE, grid intensity and renewable-energy share |
| Emissions | Operational and embodied carbon, with boundaries and allocation rules |
| Software and data | Energy or cost per transaction, data-retention volume and transfer volume |
| AI | Energy or cost per inference, model utilisation and batch efficiency |
| Assets | Average device age, redeployment, repair, refurbishment and recovery rates |
| Data quality | Percentage of supplier and provider data that is assured |
Cloud dashboards are estimates or allocations, not automatically comparable measurements. AWS says its customer-emissions methodology and allocation approach are updated over time: AWS methodology. Google says its Cloud Carbon Footprint methodology was reviewed by third-party sustainability consultants as reasonable and appropriate under the GHG Protocol, but its boundaries still differ from AWS and Azure: Google Cloud Carbon Footprint. Document scope, emissions factors, accounting period and double-counting treatment before using figures externally.
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Cloud or on-premises? Compare the workload
| Cloud may fit better when | On-premises may fit better when |
|---|---|
| Demand is variable and existing utilisation is poor | Workloads are stable and hardware is highly utilised |
| Managed services remove idle capacity | Existing equipment has substantial remaining life |
| The provider offers credible, usable emissions data | Facility cooling and power are demonstrably efficient |
| Workloads can scale down or move flexibly | Residency, control or migration impacts dominate |
There is no universal rule that cloud is greener. Compare utilisation, hardware replacement timing, redundancy, location, energy source, water profile, data movement and the emissions created by migration itself.
A practical 30/90/365-day programme
First 30 days
- Name an executive sponsor and cross-functional working group.
- Inventory cloud accounts, datacentre equipment, applications and end-user devices.
- Find idle resources, duplicate datasets and imminent hardware replacements.
- Document provider boundaries for carbon, energy and water data.
- Freeze avoidable replacement purchases while reuse options are checked.
First 90 days
- Implement rightsizing, shutdown and retention policies.
- Add repairability, service life, take-back and disclosure to procurement scoring.
- Choose high-impact software, data and AI workloads for optimisation.
- Create secure redeployment, refurbishment and erasure workflows.
- Pilot workload-level carbon and water reporting with service owners.
Within 12 months
- Put sustainability checks into architecture and AI review boards.
- Set normalised targets tied to useful output and service levels.
- Include embodied emissions and supplier data in investment cases.
- Contract for auditable emissions information and lifecycle services.
- Publish an internally assured scorecard and revise targets as demand changes.
Common mistakes to avoid
- Claiming cloud is always greener than on-premises.
- Treating renewable-energy matching as zero-carbon, 24/7 operation.
- Comparing provider PUE, WUE or carbon figures without common boundaries.
- Counting recycling as circularity while replacing usable devices early.
- Optimising carbon alone while ignoring water, resilience, latency or sovereignty.
- Using a carbon dashboard that engineers cannot act on.
- Assuming lower unit energy for AI means total AI demand is falling.
- Replacing measured reductions with offsets or removals.
The Bottom Line
Green IT works best as disciplined technology management: remove waste, use efficient software and AI, extend hardware life, choose datacentre locations against carbon, water and resilience constraints, and publish metrics that decision-makers can verify. The 2025 opportunity was to make those controls part of FinOps, architecture, procurement and asset management—not to leave sustainability in a separate reporting function.
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