Computing has long treated efficiency as an engineering and cost concern. Climate policy is making it an emissions concern too. The code that chooses a model, allocates a server, retains data or schedules a batch job can change how much hardware and electricity a service needs. At the scale of cloud platforms and artificial intelligence, those decisions begin to affect grid planning.
The International Energy Agency estimated that data centers consumed about 415 terawatt-hours in 2024, roughly 1.5 percent of global electricity. Its 2025 base case projected about 945 TWh in 2030, just under 3 percent of world demand, with consumption growing around 15 percent annually from 2024 in its Energy and AI analysis. Those are scenarios, not fixed outcomes: the IEA modeled hardware, software and adoption uncertainty explicitly. That uncertainty is precisely why software efficiency matters.
A program has a physical supply chain
Green software begins by rejecting the idea that code is weightless. A service causes operational emissions through electricity and embodied emissions through the manufacture and use of servers, accelerators, storage and networking equipment. Its impact also depends on what the software accomplishes: emissions per request, training run, active user or another functional unit can be more informative than a facility-wide total.
The Software Carbon Intensity specification formalized that approach as a rate combining operational emissions and allocated embodied emissions, divided by a functional unit. It says "lower numbers are better than higher numbers" and excludes offsets from lowering the score. That matters because it directs teams toward physical changes: use less energy, use less hardware, or run on lower-carbon electricity.
In March 2024, that methodology became ISO/IEC 21031:2024. ISO says the standard is designed to make software sustainability more transparent and help teams compare design, development and deployment choices in the standard's abstract. A common metric does not guarantee accuracy, but it gives procurement officers, developers and auditors a language for asking reproducible questions.
Three levers sit inside the stack
The first lever is energy efficiency: reduce computation for the same useful result. Examples include smaller models, efficient queries, caching, compression, fewer network transfers and turning off idle resources. The right metric is workload-specific. A lower-energy response that is substantially less accurate, inaccessible or insecure is not necessarily an equivalent service.
The second is hardware efficiency. Higher utilization can spread embodied emissions across more work and reduce the number of machines provisioned for peaks. Yet utilization alone can mislead if it encourages unnecessary work. Teams need boundaries that include storage, networks and accelerators, not only CPU time visible to one application.
The third is carbon awareness: shifting flexible computation across time or location when electricity is less carbon-intensive. The Green Software Foundation's Carbon Aware SDK, for example, supplies emissions data so an application can schedule model building, deployment or updates in cleaner windows in its project documentation. This is best suited to deferrable work. Emergency services, user-facing latency and data-residency rules limit when and where a workload can move.
Reporting is moving from voluntary to regulated
Policy has historically focused on data-center buildings: power usage effectiveness, cooling and renewable procurement. The European Union is widening the measurement base. Commission Delegated Regulation 2024/1364 requires covered facilities to report indicators that support power usage effectiveness, water usage effectiveness, energy reuse and renewable-energy factors. It defines size categories beginning at 100 kilowatts of installed IT power and requires a public database at aggregated national and EU levels in the regulation.
Those facility indicators cannot identify which application caused demand. Software metrics can connect infrastructure totals to services, but only if organizations disclose system boundaries and functional units. Otherwise, a team can make its score look better by excluding a network stage, choosing a convenient denominator or moving emissions to a supplier.
The IEA's projection also shows why location claims need care. In 2024, renewables supplied about 27 percent of electricity used by data centers globally, while coal supplied about 30 percent and gas 26 percent. Renewables were projected to meet nearly half of demand growth through 2030, but fossil generation still expands in the base case in the agency's supply analysis. Annual renewable contracts are not the same as matching consumption with clean power hour by hour.
Policy should reward measured outcomes
Governments can make green software operational through procurement requirements, research funding, interoperable telemetry and reporting standards. Buyers can ask vendors for an SCI-style rate alongside absolute energy, water and lifecycle emissions. Regulators should allow different technical methods while requiring consistent boundaries, documentation and verification.
Efficiency also produces rebound risk: cheaper computation can increase total use. That is why per-unit improvements must be reported beside absolute consumption. A service that halves energy per task but runs ten times as many low-value tasks has not solved the climate problem.
Green software is not a substitute for cleaner grids or efficient hardware. It is the control layer that determines how intensively those physical systems are used. Treating it as climate infrastructure makes responsibility visible where many consequential choices are actually made: in product requirements, architecture diagrams and deployment pipelines.