Google has released new data showing the energy, carbon, and water use linked to its Gemini AI system. The report is one of the most detailed disclosures from a major tech company about the environmental footprint of artificial intelligence.
The numbers per query seem small, but the rise of AI worldwide makes these findings crucial. They help us grasp the larger sustainability challenge. Let’s examine Google’s report findings.
From Queries to Carbon: Measuring AI’s True Cost
Artificial intelligence systems require powerful data centers to process user prompts. These data centers run on large amounts of electricity and water for cooling. To provide more transparency, Google calculated the average environmental cost of a single Gemini AI text query.
The company reported that one prompt:
- Uses about 0.24 watt-hours of electricity (similar to watching TV for less than nine seconds)
- Produces about 0.03 grams of CO₂ equivalent (CO₂e)
- Consumes about 0.26 milliliters of water (roughly five drops)
Google looked at the energy used to run AI and also considered the electricity used when the system is idle. Additionally, it factored in the extra infrastructure that supports data centers.
The chart below shows how much energy different AI models use for each prompt. The results come from two types of data: estimates (gray) and direct measurements (black, red, and blue).

It also shows that results can vary a lot depending on how the energy use is measured. For example, the model Llama 3.1 (70B) was found to handle anywhere from about 580 prompts to 3,600 prompts per kilowatt-hour, depending on the method used.
By comparison, Google’s Gemini Apps prompts had a narrower and more consistent range of results. By releasing these figures, the company hopes to create a standard way of reporting AI’s environmental impact.
Smarter, Faster—But Still Energy Hungry
Google reported that Gemini has become much more efficient compared to earlier versions. On a per-query basis, the AI now uses about 33 times less energy than it did a year ago. This improvement comes from advances in hardware, optimized algorithms, and better data center operations.

However, efficiency gains do not necessarily mean lower overall emissions. The demand for AI services is growing rapidly, leading to more total queries. As a result, even though each prompt is cheaper in energy terms, the combined usage continues to increase.
This trend is an example of the “Jevons paradox,” where greater efficiency can lead to higher total consumption when demand rises quickly. Google’s own environmental report shows this effect. The company’s total greenhouse gas emissions have risen 51% since 2019, with AI being a key driver.

Data Centers and Their Rising Power Needs
Google’s data centers are at the heart of AI operations. In 2024, these facilities consumed 30.8 million megawatt-hours of electricity, more than double the amount in 2020. This sharp rise highlights the scale of resources required to support AI growth.
At the same time, Google has made efforts to reduce the climate impact of its facilities. Even as electricity demand increased by 27%, the company cut its direct data center emissions by 12%. This was achieved through clean energy contracts, efficiency upgrades, and improved cooling technologies.

Google has made deals with utilities in Indiana and Tennessee. These agreements enable Google to lower data center power use when grid demand is high. This strategy, known as demand response, helps prevent blackouts and lowers stress on local power systems.
Beyond Wind and Solar: Google’s Nuclear Bet
While renewable energy remains central to Google’s strategy, the company is also exploring new approaches to meet the constant power needs of AI. Key actions include:
- Advanced nuclear power: The company partners with Kairos Power and the Tennessee Valley Authority. This will support molten salt nuclear reactors. They can deliver reliable, low-carbon energy.
- Demand-response agreements: Reducing electricity use during peak times in states like Indiana and Tennessee to ease grid strain.
- Expanded clean energy contracts: Securing renewable sources to match rising data center demand.
These steps show that Google is pursuing a mix of solutions beyond traditional renewables. Nuclear power, in particular, is seen as a stable complement to solar and wind for 24/7 operations.
Transparency or Greenwashing? The Debate Over Metrics
Google’s decision to share detailed per-query metrics has been praised as a step toward industry-wide accountability. Few tech companies have provided such clear data. This transparency helps policymakers, researchers, and the public see the real costs of AI.
At the same time, experts have raised concerns about what the report leaves out. Some argue that Google’s calculations do not fully account for indirect emissions or the impact of where electricity is sourced. Others note that per-query figures, while helpful, may downplay the large-scale effects of billions of queries worldwide.
The contrast between small individual costs and large overall emissions illustrates the complexity of the issue. It shows why companies need efficient technology and broad strategies. They have to manage total demand and align with climate targets.
Can AI Innovation Outpace Emissions?
Google’s disclosure highlights the balancing act facing the entire AI industry. On one hand, new technology can drive efficiency, reduce per-query energy use, and open pathways to sustainable power. On the other hand, the sheer scale of AI adoption risks outpacing these improvements.
For AI to grow in a sustainable way, companies will need to combine efficiency gains with renewable energy, nuclear solutions, and smarter grid management. Transparency will also play a central role in building trust and creating common standards across the sector.
As more companies adopt AI and integrate it into daily life, the question of energy and carbon costs will become even more urgent. Google’s report is an early attempt to measure and address this challenge.
Whether the industry can keep total emissions in check while meeting growing demand will shape the future relationship between AI and the environment.
Google’s new data provides a clearer picture of what it takes to run AI systems like Gemini. One prompt uses just a few drops of water and a tiny bit of carbon. But when billions of people interact, the environmental impact grows massively.
By being transparent about these numbers, Google has set a benchmark for the industry. The next challenge will be to turn these insights into broader changes that ensure AI grows without driving emissions sharply higher. The path forward will require innovation, investment, and cooperation across the technology and energy sectors.
The post Google Reveals the Environmental Cost of Gemini AI Query appeared first on Carbon Credits.
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Most businesses that decide to act on their net-zero targets reach the same point of friction. Buying carbon credits has meant tracking down brokers, sitting through sales calls, and requesting a quote just to learn a price, sometimes with limited proof of what you are buying.
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Climate-Linked Supply Chain Risk Is Already in Your P&L
The earnings calls that quietly reframed climate from sustainability question to operating risk.
Three earnings calls in the last 18 months tell the story without any help from a press release.
Hershey, May 2024: cocoa price exposure compresses margin, and the company attributes part of the cost shock to West African weather. Olam, July 2024: coffee climate exposure quantified in the annual report. JBS, January 2025: supply chain climate disclosures expanded materially in response to investor pressure and regulatory expectation. None of these companies issued the announcement as climate news. They issued it as financial news. The climate-linked supply chain risk did not arrive with a sustainability framing; it arrived as a P&L line.
You are probably reading this article because you suspect the same thing is happening to your business. This piece walks through what is showing up on which earnings calls, how procurement and finance leaders are quantifying the exposure, and what serious corporates are doing about it before the regulator asks.
Where climate risk has already appeared in earnings
The pattern is consistent across resource-intensive sectors. A weather event compresses supply, the price spikes, the cost flows through the income statement, and the analyst on the call asks whether the event is anomalous or structural. Increasingly, the honest answer is the second one.
Cocoa is the cleanest example. The 2023 to 2024 West African harvest fell sharply on the back of erratic rainfall and disease. Cocoa futures more than tripled. Companies with concentrated West African sourcing absorbed the cost; companies with diversified sourcing absorbed less. The exposure was not climate as ESG topic. It was climate as cost of goods.
Coffee follows the same pattern. Brazilian and Vietnamese harvests have moved on weather more sharply across the last several seasons. Roasters with long-tenor supplier relationships and origin diversification have managed the volatility; roasters with spot-market exposure have not. Wheat, sugar, palm oil, beef: the same dynamic in different commodities, a pattern the IPCC AR6 Working Group II report projects will intensify across agricultural systems through mid-century.
What this means: climate risk is no longer a footnote in the 10-K. It is a line item the CFO has to explain on the call.
The three commodity exposures that hit margin first
For most companies with material Scope 3 exposure, three exposures dominate the near-term P&L risk.
- Concentrated single-origin sourcing in a climate-vulnerable region. If your tier-one supply for any material commodity sits in one geography, you have a concentration risk that climate amplifies. Diversification across origins is the obvious hedge, but it takes years to build and requires relationships you cannot acquire by tender.
- Supplier financial fragility under climate stress. Smallholder farmers, who supply a large share of the global cocoa, coffee, and palm oil market, do not carry the balance sheets to absorb yield shocks. When yields collapse, they exit. When they exit, your supply base shrinks, and the surviving suppliers raise prices. The risk is structural, not cyclical.
- Logistics and storage exposure to extreme weather. Hurricane disruptions to Gulf shipping, drought-driven Panama Canal restrictions, flooding in European inland waterways: each of these has moved input costs in the last three years, a pattern documented in Munich Re’s natural catastrophe data. The exposure shows up as a one-quarter event in the financial press but accumulates over time on the cost line.
TCFD and ISSB disclosure changes
The disclosure architecture has now caught up with the risk. The Task Force on Climate-related Financial Disclosures, whose recommendations are now embedded in the ISSB’s IFRS S2 climate standard, requires companies to disclose climate-related risks across physical and transition categories, with quantification where possible.
For physical risk specifically (the climate-linked supply chain risk you are reading about), the disclosure must address both acute exposures (extreme weather events) and chronic exposures (gradual changes in temperature, precipitation, and growing seasons). The disclosure must address the time horizon over which the risk is material, the parts of the value chain exposed, and the financial impact under different scenarios.
The CSRD imposes similar requirements under European law, with double materiality (both financial and impact materiality) embedded in the assessment. The practical effect: your auditors and your investor relations team now need a defensible answer to the climate-linked supply chain risk question, and the answer needs to be quantified.
What procurement and finance can do now
Three actions matter near-term.
Map your exposure. Most companies do not have a clear view of which tier-one and tier-two suppliers sit in which climate-vulnerable geographies. Without the map, you cannot quantify the risk, and without the quantification, you cannot disclose it credibly. The map is the foundation, and World Resources Institute climate risk research provides useful public tooling to start.
Diversify and deepen, in that order. Diversification across origins reduces concentration risk, but the deeper move is to invest in the resilience of the suppliers you already have. Regenerative practices, agroforestry, soil health interventions: these reduce yield volatility under climate stress and protect your input cost trajectory.
Embed the climate spend inside procurement, not outside it. Treating climate risk as a sustainability cost line subordinates it to the ESG budget. Treating it as a procurement and resilience investment puts it in the budget that matters, which is the cost-of-goods budget that the CFO defends quarterly.
Nature-based supply chain investments are the asset class designed for exactly this purpose. They sit inside the value chain, they reduce climate-linked supply risk, they generate verifiable Scope 3 reductions, and they produce the documentation an auditor and a regulator can both test.
If you are quantifying climate-linked supply chain risk in advance of the next earnings cycle or the next disclosure period, the carbon and sustainability experts at Carbon Credit Capital can help you map your exposure and structure a Dual-Value Model response that addresses reduction, resilience, and disclosure-readiness in a single program. Schedule a consultation.
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Where should an SME start with a carbon action plan?
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