Green AI: Sustainable, Efficient AI

• Green AI is a field dedicated to reducing AI’s energy, carbon, and resource footprints through efficient practices, ensuring high performance without excessive compute cost.
• Key methodologies include data-centric approaches, model compression, and hardware optimization to significantly reduce energy consumption and emissions.
• Empirical studies show that applying Green AI techniques can achieve up to a 90% reduction in carbon emissions and 25% energy savings in real-world applications.

Green AI refers to the field, methodologies, and practices devoted to minimizing the environmental impact of artificial intelligence systems across their entire lifecycle. This includes reducing energy consumption, carbon and resource footprints, and embodied emissions associated with model training, inference, deployment, hardware use, and supporting infrastructure. Green AI counters previous trends in "Red AI," where advances prioritized raw accuracy or scale at steep environmental and social costs. Research in Green AI robustly spans algorithmic, architectural, system, data-centric, and regulatory fronts, integrates rigorous quantitative metrics, and increasingly interfaces with both software engineering and policy regimes.

1. Foundations and Definitions

The foundational definition of Green AI is AI research and practice that yields novel results or maintains—or improves—performance "without increasing (and ideally reducing) computational cost," thus improving the efficiency of model development, training, deployment, and operation (Schwartz et al., 2019). The principal motivation arises from several factors:

• Exponential compute scaling: Compute resources required to achieve state-of-the-art in deep learning increased 300,000× over 2012–2018.
• Environmental footprint: These computations entail significant carbon emissions, with the ICT sector's software-related emissions accounting for 2.1–3.9% of global emissions and expected to rise with AI proliferation (Cruz et al., 26 Jun 2024).
• Inclusivity: Steep computational and financial costs limit access for researchers and institutions without abundant resources, reinforcing disparities.
• Diminishing returns: Linear improvements in model accuracy increasingly require exponential resources.

Green AI therefore frames efficiency—not only accuracy—as a core criterion alongside transparency in reporting financial and environmental cost, typically via hardware-agnostic metrics such as floating point operations (FPOs), total energy, and carbon equivalence (Schwartz et al., 2019). A synthesized field definition posits: Green AI encompasses practices aimed at mitigating the resource and environmental impacts of AI and/or harnessing AI itself as a tool for broader environmental sustainability (Verdecchia et al., 2023).

2. Key Principles and Methodologies

Green AI encompasses a diverse methodological spectrum. Practices are often subdivided along the following principal axes:

2.1 Data-Centric Approaches

Data-centric Green AI seeks efficiency by manipulating the volume, quality, and structure of training data:

• Data pruning/reduction: Reducing number of data points or irrelevant features yields energy savings up to 92%, with minimal performance loss in most algorithms (Verdecchia et al., 2022, Sabella et al., 23 Jul 2025).
• Feature selection: Employing techniques like Chi-Square, Information Gain, or Recursive Feature Elimination can substantially lower training and inference runtime and energy (Pereira et al., 11 Nov 2024).
• Data quality filtering: Selecting high-quality or well-labeled subsets boosts both accuracy and sustainability by reducing wasteful computation (Sabella et al., 23 Jul 2025).
• Dataset distillation: Compact synthetic datasets can maintain original accuracies, e.g., distilling MNIST to 10 images per class (Cruz et al., 26 Jun 2024).

2.2 Model-Centric Approaches

• Efficient architectures: Smaller or structurally optimized models can match or surpass the performance of larger counterparts at lower resource cost (e.g., Phi SLMs) (Cruz et al., 26 Jun 2024).
• Model compression: Pruning, quantization, binarization, and tensor network decompositions (e.g., TT, CP), often yield orders-of-magnitude resource reductions with negligible accuracy impact (Memmel et al., 2022).
• Transfer learning: Pretraining and reuse improve training efficiency by up to 15× over from-scratch training (Cruz et al., 26 Jun 2024).
• Training optimization: Bayesian hyperparameter tuning dominates grid or random search in energy and time, converging in ≤27 rounds for typical problems (Yarally et al., 2023).

2.3 System- and Lifecycle-Centric Approaches

• Hardware selection: Choosing efficient accelerators (e.g., Nvidia A100 vs T4) can reduce energy and emissions by 83% or more during large model training (Liu et al., 1 Apr 2024).
• Batched and edge inference: Batching queries and deploying models close to data can amortize overheads and decrease operational emissions (Cruz et al., 26 Jun 2024).
• Lifecycle thinking: Systematic assessment and minimization of embodied, operational, and end-of-life carbon through Life Cycle Assessment (LCA), covering models, data, hardware, and cloud (Clemm et al., 1 May 2024).
• Energy-aware scheduling: Compute alignment with green grid availability (temporal shifting), hardware utilization optimization, and environmental-aware logistics contribute to systemic sustainability (Zhao et al., 2023, Ranpara, 28 Mar 2025).

2.4 Adaptive and Hybrid Selection

• Dynamic model selection: Instance-adaptive strategies (e.g., cascading/routing) select the minimal sufficient model at inference, reducing energy by up to 25% while retaining 95% accuracy (Cruciani et al., 24 Sep 2025).
• Energy-aware ensemble selection: Optimizing for the GreenQuotientIndex (GQI) enables substantial energy reductions in production ensembles with minor accuracy trade-offs (Nijkamp et al., 21 May 2024).

3. Metrics and Quantification

Comprehensive evaluation of Green AI interventions requires rigorous, standardized metrics (Schwartz et al., 2019, Verdecchia et al., 2023, Liu et al., 1 Apr 2024, Liu et al., 7 Mar 2024, Clemm et al., 1 May 2024):

• Energy consumption: Measured in kWh or Joules, direct but hardware/context-dependent.
• Carbon emissions: $$\text{CO}_2\text{e} = \text{Energy (kWh)} \times \text{Carbon Intensity (kg CO}_2\text{e/kWh)}$$.
• Floating point operations (FLOPs/FPOs): Hardware-agnostic, analytically tractable, correlates with resource use.
• Tunables: Reporting energy/accuracy tradeoffs, accuracy per gram CO₂ (e.g., ApC metric: $$\displaystyle ApC = \frac{AUC-50}{CO_2E} \times 100$$) (Liu et al., 7 Mar 2024).
• Lifecycle metrics: Embodied emissions (from hardware manufacturing), use-phase operational emissions, water use, and e-waste are increasingly important, especially for large-scale deployments (Clemm et al., 1 May 2024).

Standardization is urged, including explicit energy reporting, data/model/circuit metadata, and harmonized units (e.g., via ISO/IEC 20226, Green Software Measurement Model) (Cruz et al., 26 Jun 2024).

4. Empirical Achievements and Technology Impact

Empirical studies consistently report substantial energy and emissions savings via Green AI techniques:

• Model/data reduction: Up to 90% reduction in carbon emissions in federated learning with maintained or improved accuracy (Sabella et al., 23 Jul 2025).
• OLEO paradigm: Caching content representations in recommender systems delivers up to 2992% improvement in ApC over standard end-to-end pipelines, preserving accuracy (Liu et al., 7 Mar 2024).
• Systemic frameworks: Multi-layered resource optimization can reduce energy by 25%, material recovery by 18–20%, and transportation emissions by 30% in circular economy scenarios (Ranpara, 28 Mar 2025).
• Programming language/implementation selection: Language and algorithmic implementation factor differences can yield up to 54× divergence in energy needs, emphasizing the necessity for context-aware stack choices (Marini et al., 31 Dec 2024, Pereira et al., 11 Nov 2024).

Patent landscape analysis reveals the scale, diffusion, and concentration of Green AI innovation, with data processing, distributed energy, and agriculture now dominating over legacy combustion or emissions domains. Stabilized market value and high scientific impact domains, such as clinical bioinformatics or agri-environmental management, coexist with others requiring policy incentive for further advancement (Emer et al., 12 Sep 2025).

5. Organizational and Societal Context

Despite academic maturity, industrial adoption of Green AI is limited. Empirical interview studies indicate sustainability is seldom prioritized in AI system adoption; most firms neither monitor nor systematically mitigate their AI's environmental impact, citing lack of regulatory driver, tool accessibility, or internal incentives (Sampatsing et al., 12 May 2025). Regulatory frameworks such as the EU AI Act or CSRD have modest influence, rarely prompting proactive efforts beyond minimal compliance.

Key challenges and recommendations include:

• Making environmental impacts visible, measurable, and mandatory via standard tools and disclosure.
• Bridging the gap between technical and sustainability teams in organizations.
• Incentivizing sustainable architecture and reporting at conferences and peer review venues (Zhao et al., 2023).
• Educating practitioners and including environmental cost in training pipelines and model selection decisions.

6. Future Directions and Challenges

Current research highlights several open issues and ongoing development areas:

• Lifecycle integration: Moving from training-phase-only focus to full-lifecycle and system-level LCA and ecodesign (Clemm et al., 1 May 2024).
• AI4greenAI: Leveraging AI (LLMs, RL, code synthesis) to optimize its own sustainability, automating ecodesign and resource optimization (Clemm et al., 1 May 2024).
• Standardization of metrics and reporting: Adoption of universal frameworks, reporting templates (ID-cards, GAISSALabel) for cross-comparable carbon/energy data.
• Tooling and industry translation: Most existing tools are academic; there is a need for robust, scalable, and user-friendly solutions fit for industrial and regulatory use.
• Policy and market design: Where private incentives lag social value (e.g., meteorological forecasting, emissions monitoring), targeted public intervention is advocated (Emer et al., 12 Sep 2025).

Adoption of proactive, interdisciplinary, and transparent Green AI practices—incorporating standardization, regulatory guidance, and automation not only in model training but throughout the system lifecycle—is critical for aligning the field's progress with global environmental and social imperatives.