Software may feel weightless, but it always runs on physical equipment. A search request, database query, video stream or machine-learning model uses processors, memory, storage and networks. Those systems consume electricity, and manufacturing them also creates emissions.
Green software is software designed, developed and operated to cause fewer carbon emissions. It does this by using less energy, making better use of hardware and, where workloads are flexible, running at times or in places where electricity is less carbon-intensive.
This is not simply about writing shorter code or moving an application to a cloud provider that buys renewable energy. Green software engineering considers the whole system: what useful work the software performs, how many resources it needs, when and where it runs, and how long its supporting hardware remains useful.
Green Software at a Glance
The term overlaps with several neighbouring ideas, but the boundaries are useful when deciding what to measure or change:
| Term | Main focus | Typical question |
|---|---|---|
| Green software | The emissions caused by developing and operating software | How can this system deliver the same value with less carbon? |
| Green coding | Implementation and development practices | Can this code perform the same task with less work? |
| Green IT | Technology estates, procurement and operations | How should devices and infrastructure be bought, used and retired? |
| Climate tech | Technology intended to address a climate problem | What climate outcome does this product enable? |
| Approach | What it changes | Simple example |
|---|---|---|
| Energy efficiency | Electricity used to complete the same task | Removing repeated database queries |
| Hardware efficiency | Physical resources needed to provide the service | Right-sizing servers instead of leaving excess capacity idle |
| Carbon awareness | When or where flexible computing happens | Running a non-urgent batch job when the local grid is cleaner |
| Measurement | Whether a change produces a real improvement | Comparing energy per completed transaction before and after an update |
The important phrase is the same task. An application has not become more efficient if it saves energy only by becoming unusable, inaccessible or unable to meet its purpose.
Why Green Software Matters
Digital services are expanding quickly, and their infrastructure is becoming a significant source of electricity demand. The International Energy Agency estimates that data centres consumed about 485 terawatt-hours of electricity worldwide in 2025 and projects consumption of roughly 950 terawatt-hours in 2030 in its central case. AI is not the only source of this growth, but it is an important one.
Those figures do not mean every developer should feel guilty about every function call. They show why efficiency matters at scale. A small improvement repeated across millions of requests can reduce the amount of computing capacity, electricity and equipment a service needs. Conversely, an inefficient feature used at enormous scale can create a large footprint even if one use appears trivial.
The relationship is not always simple. A faster program may finish sooner but draw more power while it runs. Compressing data may reduce network traffic while adding processing work. Distributing an application closer to users may reduce data travel but require more equipment in more locations. Green software engineering replaces assumptions with measurement and treats these effects as system-wide trade-offs.
For a closer look at one fast-growing workload, see Arianna Danielle’s evidence-based guide to how much energy AI uses.
Green Software, Green Coding and Green IT
These terms overlap, but they are not identical:
- Green software is the product or system designed to reduce emissions.
- Green software engineering is the discipline used to design, build, operate and improve that software.
- Green coding usually refers more narrowly to efficient programming choices, such as reducing unnecessary calculations, memory use or data transfer.
- Green IT is broader. It can include hardware procurement, device repair, data-centre cooling, renewable electricity, electronic waste and organisational technology policies.
- Climate tech uses technology to address climate problems. A grid-management platform may be climate tech because of what it does, but its own software is not automatically efficient.
A useful climate application can still run wastefully. Equally, highly efficient accounting software may qualify as green software even though its purpose is not environmental. One term describes the software’s operational qualities; the other may describe the problem it is trying to solve.
The Six Principles of Green Software Engineering
The Green Software Foundation’s learning material organises the discipline around six connected principles. The first four describe what to improve, measurement establishes whether it worked, and climate commitments help teams distinguish real emissions reductions from claims that do not change the physical system.
1. Carbon Efficiency
Carbon efficiency is the overall goal: create the greatest useful value for the least greenhouse-gas emissions. It keeps the focus on outcomes rather than treating any single technical metric as the answer.
For example, reducing the energy required for an API request is helpful only if the new version still returns the correct result reliably. Measuring emissions per successful request is often more meaningful than measuring the server’s total emissions without considering the work completed.
2. Energy Efficiency
Energy-efficient software uses less electricity to perform an equivalent function. Developers might eliminate redundant computation, choose a better algorithm, reduce excessive logging, cache appropriate results or stop idle resources.
Performance and energy efficiency frequently support one another, but they are not interchangeable. The only safe way to claim an energy improvement is to test comparable outputs under comparable conditions. Arianna’s ten green-coding experiments show how beginners can compare common programming choices without pretending that runtime alone is a perfect energy measurement.
For the programming decisions behind this principle, see the companion introduction to energy-efficient code for beginners.
3. Carbon Awareness
The carbon intensity of electricity changes by location and time. A grid may have abundant solar generation in the middle of one day and rely more heavily on fossil-fuel generation after sunset. Carbon-aware software responds to that variation.
A flexible system might delay a backup, build, data-processing job or model-training run until lower-carbon electricity is available. Some workloads can shift between regions; others cannot because of latency, privacy, regulatory or data-residency requirements. Carbon awareness should never override safety, security or the needs of users.
4. Hardware Efficiency
Servers, laptops, phones and networking equipment carry embodied emissions from extracting materials, manufacturing components, transport and disposal. Software influences how much hardware is required and how quickly devices become obsolete.
Applications that work on modest or older devices can help extend useful hardware life. In data centres, increasing productive utilisation may allow a service to meet demand with fewer machines. The goal is not to push every device permanently to maximum load; it is to avoid reserving, purchasing and replacing more equipment than the service genuinely needs.
This principle also connects software decisions with device longevity. Arianna’s guide to extending a laptop’s lifespan explores the user side of keeping existing hardware useful.
5. Measurement
Green claims need a baseline. Teams must decide what part of the system they are measuring, which data is observed and which is estimated, and what unit of useful work makes comparisons fair.
Depending on the service, a useful functional unit might be one API request, one active user, one completed transaction, one hour of video delivered or one model-training run. “Our application emitted 500 kilograms of carbon” says little without a time period, system boundary and measure of output.
6. Climate Commitments
Different environmental actions work through different mechanisms. Directly avoiding energy or hardware use is not the same as purchasing an offset after emissions occur. Buying renewable electricity or certificates is also different from shifting a workload in response to the physical electricity available in a particular grid.
Teams should explain what changed and avoid letting a broad “carbon-neutral” claim obscure an inefficient system. Credible green software work begins with reducing or avoiding emissions within the software system itself.
Where Software Emissions Come From
The environmental impact of software has two major components.
Operational Emissions
Operational emissions arise from the electricity used while software runs. They depend on both the energy consumed and the carbon intensity of that electricity.
A simplified relationship is:
Operational emissions = energy consumed × carbon intensity of electricity
This is why reducing computation and using cleaner electricity are related but distinct actions. A workload can consume less energy without changing where it runs, or it can use the same energy at a time when the grid produces fewer emissions.
Embodied Emissions
Embodied emissions are associated with producing and eventually disposing of the hardware. Software receives a share of those emissions because it occupies hardware capacity for a period of time.
Provisioning two servers where one would meet the requirement can increase the hardware allocated to the service. Requiring users to replace otherwise functional devices can move environmental costs outside the data centre. That is why a complete green-software assessment cannot look only at the electricity meter.
Practical Green Software Examples
| Area | Possible improvement | What to verify |
|---|---|---|
| Application code | Replace repeated linear searches with an appropriate indexed structure | Correctness, memory use and energy per completed task |
| Database | Return only required fields and remove duplicate queries | Query energy, latency and cache behaviour under realistic load |
| Web delivery | Resize images, remove unused JavaScript and avoid unwanted autoplay | Transferred bytes, processing on the user’s device and accessibility |
| Cloud infrastructure | Right-size instances and shut down unused development environments | Actual utilisation, capacity headroom and reliability requirements |
| Scheduled workloads | Run flexible jobs during cleaner grid periods | Location-based grid data, deadlines and regional constraints |
| Machine learning | Use a smaller suitable model or an existing trained model | Accuracy, inference demand and total lifecycle impact |
| Continuous integration | Avoid rebuilding unaffected components and cancel superseded jobs | Compute saved without weakening essential tests |
The Green Software Foundation maintains a catalogue of vendor-neutral green software patterns covering requirements, architecture, design, development and operations. A pattern is a candidate intervention, not a guarantee. Its effect still needs to be tested within the actual system.
A Sensible Order for Greening Software
Teams can waste time polishing tiny functions while much larger sources of resource use remain untouched. A practical order of work is:
- Define the useful outcome. Decide what unit of work must remain reliable and comparable.
- Measure a baseline. Record workload, energy or resource use under representative conditions.
- Remove work that delivers no value. Look for idle environments, abandoned data, duplicate requests, unnecessary builds and unused features.
- Right-size the system. Match provisioned compute, memory, storage and network capacity to realistic demand.
- Improve high-impact operations. Optimise the code paths, queries and transfers that account for meaningful consumption.
- Shift flexible demand. Where appropriate, move non-urgent work to lower-carbon times or locations.
- Protect hardware life. Avoid unnecessary minimum specifications and test important experiences on older or lower-powered devices.
- Measure again. Use the same boundary, functional unit and conditions wherever possible.
This order is deliberately unglamorous. Turning off an unused environment may achieve more than rewriting a small function. Measuring first helps direct effort toward changes that matter.
How Is Green Software Measured?
There is no universal “green score” that can describe every application without context. One important framework is the Software Carbon Intensity specification, known as SCI. It expresses emissions as a rate:
SCI = (operational emissions + allocated embodied emissions) per functional unit
Lower values are better. The functional unit makes the result meaningful: grams of carbon dioxide equivalent per transaction, per user or per another defined unit of work. The specification requires the system boundary and calculation method to be disclosed.
SCI is designed to respond to genuine changes in energy efficiency, hardware efficiency and carbon awareness. Offsets do not reduce the score because they do not remove emissions from the measured software system. The methodology has also been published as ISO/IEC 21031:2024.
Measurement may combine direct telemetry with models. Public-cloud customers, for example, may not have access to granular physical energy data. Estimates are still useful when their assumptions are disclosed and comparisons use a consistent method. False precision is not.
Tools for Green Software Development
Different tools answer different questions. Some measure the energy used by local code. Others estimate cloud emissions, inspect Kubernetes workloads, estimate emissions associated with data transfer or provide grid-carbon data for scheduling.
Before choosing a tool, ask:
- Does it measure physical energy, estimate energy from utilisation, or estimate emissions from another proxy?
- Does it include only operational energy, or also attempt to allocate embodied emissions?
- How current and geographically specific is its electricity data?
- Can it measure the same functional unit repeatedly?
- Does it support the operating system, language and deployment environment being tested?
Arianna’s guide to green coding tools compares several options and explains which kinds of projects they suit. Beginners can also start with ordinary profilers and operating-system resource monitors. CPU time, memory, storage and network traffic are not carbon measurements, but they can reveal waste worth investigating.
Common Green Software Mistakes
Assuming Faster Always Means Greener
Runtime is a useful clue, not proof. A faster method may use more parallel hardware or create a different memory cost. Compare energy or credible resource measurements for the same result.
Optimising What Is Easy Instead of What Matters
Reducing a tiny loop that runs once a week may have less value than fixing an oversized service that idles continuously. Profile the system and prioritise high-volume or resource-intensive work.
Treating the Cloud as Automatically Green
Shared infrastructure can improve utilisation, but cloud resources can also be over-provisioned and left running. Provider commitments do not replace good architecture, workload measurement or regional analysis.
Ignoring Rebound Effects
An efficiency improvement can lower the cost of a service and encourage much more use. Track total demand as well as emissions per functional unit so that a better rate does not conceal rapidly increasing overall consumption.
Sacrificing People for a Sustainability Metric
Disabling accessibility features, weakening security or making a service unreliable is not responsible engineering. Sustainability belongs alongside performance, safety, privacy, maintainability and user needs.
Making Claims the Data Cannot Support
Carbon estimates depend on boundaries, electricity data and modelling assumptions. Report those limitations. “This change reduced measured energy per transaction in our test” is more credible than calling an entire application carbon-free.
How Beginners Can Start
You do not need a specialist platform or corporate sustainability team to begin. Choose one program or feature and create a controlled comparison:
- Define one repeatable task and confirm both versions produce the same output.
- Run each version several times under similar conditions.
- Record runtime and relevant CPU, memory, disk or network activity.
- Change one important variable, such as an algorithm, query or file size.
- Repeat the test and explain what the measurements can and cannot prove.
Once those habits feel natural, explore dedicated measurement and carbon-aware tools. Microsoft also offers a free beginner module covering the principles of sustainable software engineering.
If this field appeals to you professionally, Arianna’s guide to starting a green-tech career maps software skills to wider sustainability roles.
Frequently Asked Questions
What Is Green Software?
Green software is software designed, built and operated to cause fewer carbon emissions. It aims to use less energy, require fewer physical resources and use electricity when or where its carbon intensity is lower, while still providing its intended function.
What Is Green Software Engineering?
Green software engineering is the discipline of applying climate-aware principles to software requirements, design, development, deployment and operation. It combines software engineering with knowledge of energy, hardware, electricity grids and carbon measurement.
Is Green Software the Same as Energy-Efficient Software?
Energy efficiency is one part of green software. A fuller assessment also considers the carbon intensity of electricity and the embodied emissions associated with hardware. Software can use the same amount of energy but cause fewer emissions when flexible work is shifted to a cleaner time or location.
Can Any Software Become Greener?
Most software systems contain opportunities to reduce waste, improve utilisation or extend hardware compatibility. The scale of improvement depends on the application, its architecture, its workload and the quality of available measurements.
Does Efficient Code Always Use Less Energy?
No. Faster or simpler code often uses less energy, but the relationship is not guaranteed. Parallelism, memory, storage, networking and hardware power behaviour can change the result. Test equivalent outputs rather than assuming performance is a perfect proxy.
Can Green Software Be Carbon-Neutral?
A company may make a carbon-neutral claim using renewable-energy purchasing or offsets, but that is different from eliminating the emissions caused by a software system. Green software engineering prioritises direct reductions through energy efficiency, hardware efficiency and carbon awareness.
Green Software Is Better Engineering With a Wider Boundary
Green software engineering asks developers to look beyond whether an application works. It asks how much physical capacity and electricity the application requires, what kind of electricity supplies it, how its demand changes at scale and whether its hardware can remain useful for longer.
The best starting point is not a sweeping sustainability claim. It is a clearly defined task, a credible baseline and one measurable improvement. Remove unnecessary work, right-size resources, optimise what matters, shift flexible demand and report the result honestly. That is how green software moves from a good intention to an engineering practice.