OpenAI confirmed that ChatGPT now attracts 700 million weekly active users, up from around 500 million users in March. ChatGPT has grown four times compared to last year, showing a quick growth in both consumer and business areas.
The surge includes users from free, Plus, Pro, Enterprise, Team, and educational plans. This demonstrates broad AI adoption among individuals, businesses, and schools.
ChatGPT Soars Past 700 Million Weekly Active Users
ChatGPT is one of the fastest-growing online platforms ever. Its natural language skills, wide range of functions, and global workflow integration fuel this growth.
OpenAI’s official figures show ChatGPT’s user base quadrupled in less than a year, as the platform expanded voice, coding, and data tools. This huge growth matches the rising interest in AI tools.
There is a growing demand for virtual assistants. Also, machine learning is being used more in business, education, and media.
The rise of ChatGPT brings not just innovation but also environmental responsibility into focus. As artificial intelligence grows, so does the need for electricity, cooling, and computing power. This raises key questions about carbon emissions, energy use, and water consumption.
ChatGPT’s Environmental Footprint: Carbon, Energy, and Water Use
Let’s look closely at each of these footprints to grasp the chatbot’s environmental impact.
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Carbon Emissions from AI Queries: Emissions per Prompt
Each time a user enters a prompt into ChatGPT, servers housed in large data centers activate to generate a response. While a single query might seem harmless, the emissions can add up quickly when repeated millions—or billions—of times a week.
Recent research shows that each ChatGPT query consumes about 0.3 to 0.4 watt-hours of electricity. Depending on the energy source powering the data center, this results in around 0.15 grams of CO₂ per response.
That’s less than the footprint of a Google search but still meaningful when scaled up. Multiply it by millions of daily queries, and it equates to hundreds of thousands of kilograms of CO₂ emissions per month.
One estimate says ChatGPT might release over 260,000 kilograms of CO₂ each month. That’s like the emissions from 260 round-trip flights between New York and London. This amount would increase even more if users shift to longer or more complex prompts, which require more processing time and energy.
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The Energy Hunger of AI
Energy use is at the core of ChatGPT’s footprint. OpenAI uses powerful servers equipped with GPUs (graphics processing units) or AI accelerators like those from NVIDIA. These systems require large amounts of electricity for both computation and cooling.
To support ChatGPT’s scale—700 million weekly users—OpenAI may be operating thousands of servers running 24/7. Estimates show that daily inference needs more than 340 megawatt-hours (MWh) of electricity. That’s about the same as what 30,000 U.S. homes use in a day.
And that’s just for inference. The training phase of large language models (LLMs) like GPT-3 or GPT-4 uses even more energy.
- Training GPT-3 used 1,287 megawatt-hours of energy. This caused about 550 metric tons of CO₂ emissions. That’s like a car driving 1.2 million miles.
Training newer, larger models—like GPT-4 and beyond—will likely require even more energy. Emissions depend on the energy mix, like renewables versus fossil fuels. Even in the best cases, high-performance computing still uses a lot of energy.
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Water Usage for AI Cooling
One lesser-known but equally important resource consumed by ChatGPT is water. Data centers use water to cool hot-running servers, often in combination with air conditioning. Water either evaporates in cooling towers or comes from nearby freshwater sources. It is then released at higher temperatures.
A study estimates that every 20 to 50 queries to ChatGPT uses about half a liter of water. Most of this water is for cooling the hardware that processes those responses. That means even a casual user engaging with ChatGPT 10 times a day may indirectly use several liters of water per week.
The impact magnifies when considering model training. Training large AI models has used millions of liters of water. This is especially true in dry areas where cooling systems rely more on water than air.
Globally, the AI industry is expected to draw 4.2 to 6.6 billion cubic meters of water per year by 2027 if growth continues at the current pace. That’s equal to the annual water use of several million households.
Prompts, Processors & Power Grids: What Makes AI Greener?
Several factors influence how large or small ChatGPT’s environmental footprint becomes:
Prompt length and complexity:
A short sentence uses far less energy than a long essay or technical code. Complex prompts need more processing power, which raises energy use and emissions. A recent report shows they can use up to 50 times more energy per query.
Model size and efficiency:
GPT-4 and newer models are larger and more powerful than previous versions, but also more energy hungry. Smaller models like GPT-3.5 or distilled versions use less energy. They are great for simple tasks.
Data center location and power source:
Using renewable-powered data centers in cooler climates reduces both carbon and water footprints. Conversely, data centers relying on coal or natural gas contribute more to emissions.
Cooling methods:
Facilities that rely on advanced air-cooling or closed-loop water systems tend to have lower water footprints than traditional open cooling towers.
Here’s a glance at the chatbot’s environmental footprint:
ChatGPT Environmental Footprint
Industry Response: Moving Toward Sustainable AI
OpenAI and other AI leaders are increasingly aware of their environmental responsibilities. Many companies have committed to using renewable energy for data center operations.
Some companies are using carbon offset programs. They are also investing in energy-efficient chips from NVIDIA and AMD, which lower the power needed for each AI query.
Cloud service providers—such as Microsoft (a key OpenAI partner), Google, and Amazon—have all pledged to run their operations on 100% renewable energy by the end of the decade. Some already claim carbon neutrality for select cloud regions, although these claims often rely on offsets.
AI developers are also exploring ways to improve model efficiency, reducing the number of computations needed to produce high-quality responses. This helps not only lower costs but also shrink carbon and water footprints.
Users, too, have a role to play. The community can help lessen the environmental impact of tools like ChatGPT. They can do this by using better prompts, avoiding extra questions, and supporting companies that focus on green AI.
Navigating ChatGPT Use and Sustainability
Clearly, ChatGPT supports billions of interactions with minimal per-query footprint, yet scale causes cumulative environmental impact. Experts now call for more sustainable AI practices, such as:
- Choose concise prompts to reduce processing time and energy.
- Use smaller, more efficient models when possible.
- Developers should deploy energy-efficient hardware and renewable-powered data centers.
- Companies like OpenAI, Google, and Microsoft aim for carbon-neutral operations. However, changing supply chains and inference grid sources is also key.
Some studies point out that certain types of AI prompts—especially long or complex ones—can use up to 50 times more energy than simpler requests. That means user behavior significantly affects environmental costs, making user education part of the solution.
Reducing the carbon and water footprint of ChatGPT is not just an operational concern. It is important for public trust, business use, and following regulations. This is especially true in areas focused on ESG standards.
As ChatGPT’s weekly active users approach 700 million, the opportunity—and responsibility—for sustainable scaling grows. OpenAI should balance bigger server pools and improved models with efficiency.
The post ChatGPT Hits 700M Weekly Users, But at What Environmental Cost? appeared first on Carbon Credits.
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.
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.
More and more small and medium-sized businesses are hearing the same question from their larger customers: What is your carbon footprint? That question now travels down entire supply chains, and it arrives next to tender requirements, certification criteria, and rising customer expectations.
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