Written by Adam Covati, VP of Product, Greenplaces
The rapid adoption of large language models (LLMs) like ChatGPT has spotlighted concerns around their significant energy consumption and environmental impacts. Thoughtful implementation, however, can help businesses leverage AI responsibly and sustainably.
This blog offers practical steps for developers, product teams, and operational leaders to minimize AI’s carbon footprint, especially during inference, rather than the initial training phase.
AI’s growing energy footprint: Understanding the scale
Mid-sized data centers consume roughly 300,000 gallons of water daily, equivalent to the usage of about 1,000 U.S. households.
The global energy demands of data centers are expected to more than double between 2022 and 2026, driven heavily by increased AI adoption. And by 2030, data centers may represent up to 21% of global electricity demand, dramatically escalating from today’s 1, 2%.
1. Track and measure AI usage with granular precision
Precise tracking is essential for reducing emissions. Measuring AI operations at a granular level reveals energy-intensive tasks, uncovering optimization opportunities and driving smarter sustainability decisions.
Key metrics to track:
- Token usage per API call/session
- Model type/version (e.g., GPT-4, Llama 3.1)
- Cloud region or data center location (due to varying emissions intensities)
- Frequency and volume of queries
Why this matters: Processing a million tokens (around $1 of compute) can emit as much carbon as driving a gasoline-powered car up to 20 miles. And generating a single image using AI equals the energy needed to fully charge a smartphone.
“As generative AI grows, transparent measurement of per-model emissions becomes critical. It’s now possible, and essential, to quantify your AI’s environmental impact down to each inference.”
, Alex Lassiter, CEO of Greenplaces
2. Design AI workflows for efficiency
Optimizing prompts and workflows reduces unnecessary computing demands. Efficient designs don’t just save money, they significantly reduce your emissions footprint.
Practical recommendations:
- Use smaller AI models for simpler tasks (GPT-3.5 vs. GPT-4)
- Run non-urgent tasks during low-emissions grid periods
- Avoid redundant queries and excessive verbosity
- Implement caching for repeated requests
Real-world impact: Optimizing AI workflows and carefully scheduling workloads can achieve emission reductions of 10, 20% for data centers.
3. Strategically test AI models and infrastructure
Regular testing of AI workflows reveals inefficiencies and sustainability opportunities. A structured testing approach enables efficient model selection and lowers carbon emissions.
Testing benefits:
- Confirms output accuracy for sustainability tradeoffs
- Identifies models providing optimal balance of performance and energy use
- Facilitates easier shifts to greener technology as it emerges
Pro tip: Use open-source carbon tracking tools like CodeCarbon or ML CO₂ Calculator. These integrate directly with your systems to provide real-time carbon insights per AI task, enabling granular tracking for reporting and optimization.
4. Prioritize sustainable infrastructure choices
Choosing eco-friendly infrastructure has the most immediate impact on your AI’s emissions profile. Data centers powered by renewable energy or optimized cooling systems drastically reduce emissions.
Infrastructure selection strategies:
- Favor providers with transparent emissions dashboards
- Opt for data centers powered predominantly by renewables (Oregon, Iceland)
- Employ carbon-aware scheduling for compute-intensive tasks
Case in point: Advancements like Microsoft’s cold-plate cooling technology could cut emissions and energy consumption by approximately 15% and reduce water usage by 30, 50% compared to traditional air cooling. Learn more.
Free guide: Navigating sustainability in software & tech
Explore practical insights and actionable strategies shaping sustainability across the tech industry.
5. Contextualize AI vs. traditional computing appropriately
Comparisons between LLMs and traditional web searches must be contextualized properly. Though LLMs use more energy per interaction, one comprehensive AI query can often replace multiple traditional searches, potentially offering net efficiency gains.
Factors to consider:
- Multiple traditional searches vs. one complex AI task
- Energy for indexing and browsing conventional searches
- User-side energy consumption in conventional research methods
6. Manage usage growth with governance
Efficiency alone isn’t enough. Due to “Jevons Paradox,” efficiency can inadvertently lead to increased total usage. Organizations must actively manage and govern AI adoption to avoid offsetting sustainability gains.
Actionable steps:
- Clearly communicate expectations for AI usage
- Employ governance frameworks to guide AI deployment
- Foster industry-wide collaborations to enhance sustainable practices
Spotlight: How Photoroom cut 1,000+ metric tons of CO₂
By strategically scheduling workloads and optimizing AI inference, Photoroom achieved impressive carbon savings while boosting performance and reducing costs.
A sustainable path forward
The environmental impact of AI is considerable, but it doesn’t have to be prohibitive.
Businesses that proactively measure their emissions, implement efficient AI workflows, and strategically select sustainable infrastructure will significantly mitigate AI’s environmental footprint.
Sustainability in AI isn’t just attainable, it’s imperative and beneficial.
More resources from Greenplaces
[Merged from #59] AI & sustainability: how tech companies can accelerate climate impact
On August 20, Greenplaces hosted an engaging webinar, AI & sustainability: how tech companies can accelerate climate impact. The session brought together sustainability leaders from Sightly and TripleLift, alongside AI for Good researcher Nathaniel Burola, to explore how artificial intelligence can both accelerate progress and challenge climate goals.
The discussion highlighted the environmental footprint of large language models (LLMs), practical strategies for responsible AI use, and the regulatory shifts requiring organizations to measure and disclose AI-related emissions.
The environmental cost of AI
Panelists underscored that AI is not impact-free. Training a single large model can consume enough electricity to power 5,000 U.S. homes for a year, while everyday use continues to drive demand for electricity, cooling water, and network bandwidth.
For example, ChatGPT alone consumes an estimated 2.5 million liters of water daily to serve global prompts. As usage scales, inference can quickly outpace training in terms of cumulative environmental cost.
In the Q&A, panelists noted that many companies underestimate the everyday cost of inference. Nathaniel Burola explained that most client questions he hears are less about training emissions and more about the water and energy required for a single ChatGPT query. He emphasized that understanding these micro-costs is critical for setting realistic sustainability goals.
Practical steps to reduce AI’s footprint
The panel offered several actionable takeaways for organizations:
-
Model selection: Smaller, faster models often meet business needs with lower energy intensity.
-
Prompt optimization: Techniques like caching repeated queries, concise chain-of-thought prompting, and batch processing reduce unnecessary compute cycles.
-
Workload scheduling: Running non-urgent AI tasks during low-carbon grid hours can significantly cut emissions.
-
Measurement and transparency: Tracking token usage, data center region, and total query volume allows businesses to make informed decisions, and communicate impact credibly.
-
Governance: Establishing policies that weigh both business value and environmental impact prevents AI overuse and aligns technology choices with brand values.
During the Q&A, panelists expanded with concrete examples. Maeve Gordon shared that her team runs regular “data hygiene” cadences, deleting inactive accounts and redundant data to reduce unnecessary storage and compute waste. Kate Fiala highlighted that some companies are even “training models not to answer trivial, easily searchable questions” as a way to conserve compute resources. These small practices, panelists stressed, can add up to meaningful impact.
Regulatory readiness
California’s SB 253 and SB 261 will soon require companies above certain revenue thresholds to disclose AI-related emissions and climate risks. Panelists emphasized that organizations should start tagging AI-related usage data now, integrating it into Scope 2 and Scope 3 disclosures.
Compliance isn’t just about avoiding penalties; it can also build trust with investors and customers, improve operational efficiency, and position companies as leaders in sustainable innovation.
In response to an audience question, panelists advised that companies operating in California should not wait until 2026. Starting now with AI-specific tagging of emissions and building governance structures around AI use were recommended as proactive steps. This approach, they argued, not only ensures compliance but also helps avoid reputational risk when customers begin asking about AI’s climate impact.
Balancing innovation and responsibility
Throughout the session, panelists stressed that sustainable AI is not a limitation but an opportunity. Businesses that integrate AI with intention, using it where it adds the most value while minimizing unnecessary impact, can differentiate themselves in a crowded market.
As Nathaniel Burola noted, “It’s about evaluating not just the immediate benefits of AI, but also the downstream environmental and social impacts before scaling.”
The Q&A reinforced this theme. When asked how to balance governance with innovation, panelists emphasized multi-stakeholder engagement, bringing sustainability, product, and engineering leaders into the decision-making process early. This cross-functional governance helps ensure AI delivers business value without creating hidden environmental costs.
Key takeaway
AI is transforming industries, but it comes with real environmental costs. Companies that measure, optimize, and disclose their AI footprint will not only stay compliant but also strengthen credibility and unlock competitive advantage.
For organizations just beginning this journey, the panelists agreed: ask first whether AI is truly necessary for a given task, start small, measure everything, and make sustainability a core part of your AI strategy from day one.