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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsC performs very well in several programming-language energy benchmarks, but the evidence does not establish it as universally “the greenest.” Rankings apply to particular programs, implementations and test conditions. To know whether C would use less energy for your software, compare equivalent versions of the actual workload on the hardware where it will run.
What do the studies actually show?
Published comparisons have found C among the most energy-efficient languages tested, and some have placed it first. Their results are useful evidence about the benchmark programs they ran—not a universal ranking of every application written in C, Python or another language.
| Evidence | What was compared or reported | What it supports | What it does not establish |
|---|---|---|---|
| Pereira et al. (2021) | Implementations of 10 problems in up to 27 languages; the study measured energy, execution time and memory. | A broad benchmark comparison in which C was used as the normalized baseline in later accounts of the results. | A universal winner. The authors also report that rankings changed for one language when they checked a second set of implementations. |
| Preliminary TU Delft study (2017) | Small, independent tasks selected from Rosetta Code; the number of languages and tasks is not stated in the repository record. | C, C++, Java and Go were among the most energy-efficient compiled languages in that task set. | That C outperforms all other languages or implementations on other workloads. |
| Oxford Open Energy review (2023) | Summarized earlier benchmark results; the review reports that Python used 7,588% more energy than C on the compared task. | In that specific comparison, the measured Python implementation used substantially more energy. | That Python applications generally use 7,588% more energy than equivalent C applications. |
| “It’s Not Easy Being Green” (2024 arXiv preprint) | Examined how implementation differences and measurement factors can affect language-energy comparisons. | The authors report that, after controlling key factors, language implementation choice had no significant energy impact beyond execution time. | A settled consensus: this is a preprint’s reported finding, not proof that language and runtime choices never matter. |
| Publisher summary of a version-comparison study (2025) | Compared compiler or interpreter versions for C, Java and Python; exact measurements are not stated in the summary. | Version changes can affect measured energy, with no clear overall trend; C showed the largest energy difference and Python worsened in its latest tested version. | A general rule that newer versions use more or less energy. |
The 7,588% figure is especially easy to misread. It describes a benchmark result summarized by the 2023 review, not a forecast for rewriting an arbitrary Python program in C. The programs, algorithms, libraries, runtimes and test setup all shape the comparison.
Why “the greenest language” is hard to prove
A language is not the whole implementation
A source-language label can hide very different execution paths. A C program depends on its compiler and settings; a Python program depends on its interpreter and any libraries or compiled extensions it uses. Even programs written in the same language can differ substantially: the 2024 preprint discusses variation between interpreter and just-in-time (JIT) configurations.
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That distinction matters because a benchmark compares concrete implementations, not abstract languages. If one version uses a better algorithm, a more efficient library or different parallelism, its result cannot automatically be credited to the language itself.
Runtime and energy are related, but not identical
Energy use depends on how much power the system draws and for how long. A program that finishes sooner may use less energy, but elapsed time alone does not describe power draw, active CPU cores or memory activity. The 2024 preprint identifies these and other measurement factors as possible confounds, and reports that its controlled comparisons found no significant language effect beyond runtime.
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A benchmark is not a whole carbon footprint
Energy measurements for running code are not the same as a calculation of total carbon emissions. The comparisons described here do not establish lifecycle impact across electricity sources, hardware manufacture, deployment scale or other parts of a system’s life. A lower measured energy result for one program therefore cannot, by itself, prove a lower overall carbon footprint.
How to compare energy use for your own software
For a useful comparison, test representative implementations under controlled conditions. Keep the task and output equivalent, and record enough detail for someone else to understand what was measured.
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- Define the workload. Use the same inputs and require equivalent outputs. Include typical and demanding cases if both matter in production.
- Control the implementation. Record the algorithm, data structures, libraries, compiler or interpreter and exact version. Document optimization settings and, for runtimes that warm up, the warm-up policy.
- Keep the machine conditions consistent. Use the intended hardware where possible, and note the CPU, frequency conditions and number of active cores. Control parallelism rather than allowing one implementation to use more cores without recording it.
- Measure more than one number. Record elapsed time, energy and memory behavior. State the measurement boundary and tool—for example, whether the reading covers the CPU or the whole machine—and repeat runs enough to identify unstable results.
- State what the result means. Separate operational energy for the tested workload from claims about carbon emissions or lifecycle impact. Report the specific implementations and conditions rather than declaring a language universally greener.
This approach helps distinguish a language or runtime effect from differences in the program, hardware use or measurement method. It also gives teams a concrete basis for deciding whether an optimization is worthwhile on their own system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When does choosing C make sense?
The studies support taking C seriously when energy efficiency is important, especially where the team can implement and maintain the workload effectively. They do not show that moving existing software to C will automatically lower energy use. Such a change is only justified by a measured improvement on representative workloads, weighed against the engineering effort and the system’s real constraints.
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The broader lesson is to treat “greenest language” as a question to test, not a permanent property of a language. Benchmark rankings can identify promising candidates; production measurements determine whether a particular implementation is more efficient for a particular job.
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