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.
The accounting differences that decide whether your nature investment shows up in inventory, in BVCM, or nowhere at all.
The question reaches a procurement team about three weeks before the next sustainability committee meeting. Someone has read about insetting. Someone else has just signed off on an offset purchase. The CSO wants to know if the two are interchangeable. The answer is no, and the GHG Protocol Land Sector and Removals Standard is the reason why.
This article walks through what each term means at audit-grade specificity, what the standards actually say about how each gets counted, and how to decide which tool fits which target. The insetting vs offsetting question is one of the most-searched in corporate climate strategy, and one of the most poorly answered. By the end of this piece, you should be able to brief a committee on the difference without notes.
The two definitions, in plain English
Offsetting means buying carbon credits generated outside your value chain and retiring them against your residual emissions. The reduction happens somewhere else, financed by you, and the credit is the receipt.
Insetting means investing in emission reductions or removals inside your own value chain, typically with suppliers, where the reduction is directly linked to the products and services you buy. The reduction happens inside the boundary of your Scope 3 inventory, and the accounting treatment is fundamentally different.
The shorthand from the University of Oxford’s Nature-based Insetting Initiative is useful: insetting is what you do with the supply chain you have; offsetting is what you do with the supply chain you do not have.
What the GHG Protocol Land Sector Standard actually says
The GHG Protocol Land Sector and Removals Standard, finalised in 2024 after a multi-year pilot, sets the rules for how land-based emission reductions and removals enter corporate inventories. The Standard distinguishes between inventory accounting (Scope 1, 2, and 3) and project or intervention accounting (a separate methodology for crediting).
For insetting, the practical implication is that supplier-level interventions, when properly measured and attributed, can reduce your Scope 3 category 1 (purchased goods and services) emissions in your inventory. The reduction is not a credit retired against the inventory; it is a lower inventory number, period.
For offsetting, the credit is retired separately. It can be reported as a contribution toward a net-zero claim under the SBTi Beyond Value Chain Mitigation framework or as part of a VCMI Carbon Integrity claim, but it does not lower the inventory number.
A practical consequence: if your Science Based Target requires a 50% absolute reduction in Scope 3 emissions by 2030, insetting moves you toward the target. Offsetting does not. This single point of difference reshapes the procurement decision.
When insetting counts toward Scope 3 (and when it does not)
Insetting counts toward Scope 3 only when several conditions are met:
- The intervention must occur with an entity in your value chain.
- The emissions reduction or removal must be measured against a defensible baseline.
- The reduction must be attributed to your share of that supplier’s output, not double-counted with other buyers.
- It must follow the inventory accounting rules in the GHG Protocol Land Sector Standard, not the project accounting rules used to generate credits.
The most common failure mode is double counting. If your supplier sells the same reduction as a credit on the voluntary market and also reports it to you as a Scope 3 reduction, the math breaks. The Standard requires you to address this risk, typically by purchasing and retiring the supplier-issued credit as part of your inventory or by contractual provisions that prevent the supplier from selling the reduction twice.
When insetting does not count toward Scope 3: when the intervention sits with a supplier you do not buy from, when the baseline is not defensible, when the attribution is unclear, or when the documentation does not survive audit. Those cases default to Beyond Value Chain Mitigation, which is still useful but operates on a different ledger.
The procurement and supplier engagement question
Insetting is harder than offsetting. That is the unfashionable truth most buyers eventually confront. Offsetting is a transaction; insetting is a relationship.
To run an insetting program, you need supplier mapping precise enough to know which farms or facilities sit at which Scope 3 boundary. You need an engagement model that gets suppliers to participate, which usually requires multi-year commitments and shared economics. You need an MRV architecture that measures the right things and produces audit-ready documentation. And you need a contractual structure that prevents double counting and protects both sides.
The trade-off you receive in return is significant. Reductions count against your inventory rather than your residual. Supplier relationships deepen, which protects sourcing continuity. Yield and quality improvements often follow regenerative interventions, which reduces your input cost over time. And the regulatory file, under CSRD, CSDDD, EUDR, and the SBTi FLAG Guidance, is materially stronger.
Choosing the right tool for the right target
A practical decision rule. If your target is a science-based Scope 3 reduction and you operate in a FLAG sector or source FLAG commodities, insetting is the structurally correct tool. If your target is a net-zero claim that includes neutralising hard-to-abate residual emissions outside your value chain, BVCM via high-integrity offsets is the structurally correct tool. Most companies with material Scope 3 exposure need both, in different proportions, sequenced over time.
The sequencing matters. Insetting takes longer to stand up but produces a permanent reduction in the inventory. Offsetting can be transacted faster but does not change the inventory and now sits under tighter claim restrictions. Treat them as complementary tools with different jobs, not as substitutes. The Accountability Framework Initiative and the IUCN Global Standard for Nature-based Solutions both provide useful guardrails for the insetting side, with biodiversity, human rights, and benefit-sharing requirements that go beyond carbon math.
If you are mapping a Scope 3 reduction roadmap and need to scope which interventions count toward your inventory versus which sit in Beyond Value Chain Mitigation, the carbon and sustainability experts at Carbon Credit Capital can help you structure a nature-based supply chain investment program that fits your FLAG exposure, your target architecture, and your audit horizon. Schedule a consultation.
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