The battle between OpenAI’s ChatGPT and Google’s Gemini is one of the most talked-about stories in technology today. These two artificial intelligence (AI) chatbots dominate the market for generative AI tools. They power smart responses, summaries, writing help, and more.
As users and businesses rely on AI more, questions about market competition and environmental impacts have grown. This article compares the two leaders in terms of market share, energy use, carbon footprint, and water consumption to give a clear picture of where the AI landscape stands in 2026.
Market Share: Where ChatGPT and Gemini Stand
As of early 2026, ChatGPT still leads the AI chatbot market. ChatGPT has around 68% of the market share based on visits and user interactions. This is less than its previous dominance.
In comparison, Google Gemini accounts for about 18.2% of the market share, showing rapid growth over the past year. This shift marks a major change in how users choose AI tools worldwide.
ChatGPT has maintained a large user base with around 800-900 million weekly active users and billions of monthly visits. But Gemini is also growing fast. Its user numbers have increased as Google adds it to more services.
Other AI platforms, such as DeepSeek, Grok, Perplexity, and Claude, hold smaller shares of the market but are growing in niche areas. ChatGPT and Gemini lead the global chatbot market. This shows a duopoly trend, with two main players in control.
The market positions of ChatGPT and Gemini reflect their different strategies. OpenAI built ChatGPT as a standalone AI platform with powerful language skills. It became popular early and gained millions of users quickly.
Google, meanwhile, embedded Gemini into search engines, Android devices, and other Google apps. This gives Gemini a wide reach, helping it grow faster in recent years as users encounter it automatically.
For users, this means choice. Some prefer ChatGPT’s deep text-generation and creative outputs. Others choose Gemini for quick answers tied to search and Android use.
As both platforms grow, competition will likely push innovation in AI quality, safety, and usefulness. And for climate-conscious and environmentalists, this means taking a closer look at the platforms’ growing energy use, carbon emissions, and water use.
AI’s Energy Footprint: Data Centers and Electricity
As AI use expands rapidly, the energy footprint of the technology has become an important topic. AI models like ChatGPT and Gemini run on large networks of servers housed in data centers. These facilities use electricity to power computing tasks and to keep equipment cool.
In 2024, data centers used around 415 terawatt-hours (TWh) of electricity. This is about 1.5% of the world’s total electricity consumption. AI workloads are a growing part of this total.
- The International Energy Agency predicts that data center electricity use may double to around 945 TWh by 2030.
This increase comes as AI and other digital services grow. Another research shows the same trend:
AI electricity use varies by task. Training large models—such as initial versions of GPT and other deep learning systems—can consume very large amounts of power. For example, training early large language models used tens of gigawatt-hours of electricity.
- Running the model for user queries (called inference) uses much less energy per request but occurs far more frequently.
In a direct comparison of per-prompt energy use, Google found that a typical Gemini text prompt consumes about 0.24 watt-hours (Wh) of electricity. This is roughly equivalent to the energy used by a small household device running for a few seconds.
ChatGPT queries, on the other hand, use about 0.34 Wh of electricity. That’s similar to running a lightbulb for a short time. This makes per-query energy costs relatively low but still significant when scaled to billions of daily uses. Over time, improvements in hardware and software have greatly reduced energy and carbon use per prompt.
Carbon in the Cloud: Emissions of AI Systems
Carbon emissions from AI are tied closely to electricity use. Where the electricity comes from—renewable sources versus fossil fuels—greatly affects emissions. Data centers powered by coal or gas produce more carbon than those using wind, solar or hydroelectric power.
Global AI and data centers are currently responsible for a small but growing share of carbon emissions. Combined data center emissions contribute to the broader trend of digital technologies impacting climate change.
Projections show that by 2035, AI’s carbon footprint may vary greatly. This depends on future energy mixes and how AI is deployed. Estimates suggest possible annual emissions ranging from 300 to 500 million tonnes of CO₂ by the mid-2030s. The exact share attributable to AI specifically will vary based on how much AI workloads grow within overall data center use.
ChatGPT and Google’s Gemini differ in their carbon footprints per query. A typical ChatGPT query generates about 0.15 grams of CO₂ per text prompt. In comparison, a typical Google Gemini query emits around 0.03 grams of CO₂ per prompt. This means Gemini’s per-query carbon footprint is about five times lower than ChatGPT’s based on current estimates.
Both companies promise to cut carbon intensity. They plan to do this by improving data center efficiency, buying renewable energy, and upgrading hardware.
For example, Google reported dramatic reductions in energy and carbon footprints for Gemini queries over a one-year period due to efficiency gains and cleaner energy sourcing.
Cooling Costs: Water Use in AI Data Centers
Water consumption is another environmental concern for AI because data centers use water for cooling. Keeping servers cool in large facilities often requires water-cooled systems, especially in warmer climates.
Global AI-related water withdrawal has been rising. Estimates suggest that AI data centers might use 4.2–6.6 billion cubic meters per year by 2027, which is equivalent to 4.2–6.6 billion tonnes of water. This amount is similar to the yearly water use of medium-sized countries.
At the individual query level, water use is very small. For example, OpenAI’s CEO has stated that a single ChatGPT query uses about 0.000085 gallons of water (or ~0.32 ml)—a tiny amount comparable to a few drops. But at scale, with billions of queries each day, total water demand becomes significant in the context of data center cooling systems.
Google’s data reveals that a typical Gemini text prompt uses about 0.26 milliliters of water. That’s about the same as a few drops, considering data center operations.
The Bigger Picture: AI’s Environmental Footprint
AI’s environmental footprint extends beyond individual models and queries. Data centers are expanding rapidly because of increased AI adoption and other online services. Data center electricity use might reach almost 3% of global demand by 2030. This growth highlights the importance of sustainable practices in the tech industry.
While per-query energy and carbon figures can seem small, the aggregate impact of billions of daily AI interactions adds up. Power use and cooling needs can stress local energy grids and water supplies. This happens if companies don’t use renewable sources and efficient technologies.
Major tech companies have made public commitments to use renewable energy and improve energy efficiency at data centers. Experts say that real transparency in environmental impacts needs better reporting. It also requires standardized metrics throughout the AI industry.
So, Who Wins the AI Race?
In the AI chatbot market, ChatGPT continues to lead with about 68% market share in 2026, while Google’s Gemini holds approximately 18.2% and is growing fast. Their competition reflects differences in strategy, reach, and integration into broader technology ecosystems.
On environmental performance, both AI systems contribute to energy use, carbon emissions, and water consumption through data centers. Per-query measurements such as 0.24–0.30 Wh of electricity and tiny amounts of water per request show that individual impacts are small.
However, the aggregate resource use of running AI at scale is significant and growing. Global demand for electricity in data centers is expected to rise sharply by 2030. Water use might also increase as AI adoption expands.
Understanding these footprints and market dynamics helps users, developers, and policymakers see the costs and benefits of AI. AI tools like ChatGPT and Gemini will keep changing tech markets. They will also influence talks about sustainability in our digital world.
- MUST READ: AI Drives a Transformative Wave in Global Data Centers – and Energy Is the Real Bottleneck
The post ChatGPT vs. Gemini: Who Leads the AI Race and at What Environmental Cost? appeared first on Carbon Credits.
Insetting program design starts with your supplier map. The carbon claim comes much later.
Consider a coffee procurement team that funds a farm intervention before checking its sourcing data. The supplier aggregates coffee lots from several regions. The buyer lacks farm-level traceability. Another purchaser expects to report the same outcome. A more attractive project brochure solves none of these problems.
If the distinction between a value-chain intervention and a purchased credit is still open, read CCC’s Insetting vs Offsetting guide before you set the brief.
Insetting program design begins with the purchasing relationship and works outward. You need to decide 1) where an intervention belongs, 2) what business problem it addresses, 3) who pays for it, 4) how results will be measured, and 5) what the evidence will let you say about them. This five-step playbook turns that sequence into a procurement plan.
Step 1: Insetting program design starts with supplier mapping
Start with the commodities that matter most to your Scope 3 inventory and sourcing continuity. Ask finance for spend by supplier, sustainability for the emissions baseline, and category managers for origin and volume records. Then identify which suppliers can trace material to a jurisdiction, sourcing region, producer group, or individual land-management unit.
The level of traceability affects what you can account for. Under the GHG Protocol Land Sector and Removals Standard, the spatial boundary for purchased land-based products depends on the traceability associated with each volume. Farm-specific factors or removals require physical traceability through an acceptable chain-of-custody model. A contract with a tier-one trader, by itself, does not establish a link to the farms behind its deliveries.[1]
Build a first-pass table with five columns: commodity and annual volume; supplier and origin; current traceability level; land-use and production risks; and the business owner who can change the purchasing terms. Mark missing data plainly. A blank cell is cheaper to fix before a program starts than after a claim is drafted.
If your company sets an SBTi Forest, Land and Agriculture target (FLAG), keep its land-related target boundary separate from energy and industry emissions. The current FLAG guidance also requires relevant companies to assess primary deforestation-linked commodities in their value chains.[2]
Step 2: Choose a commodity and jurisdiction for a pilot
Select one supply corridor where the sourcing connection can be established and procurement has a reason to stay. A useful pilot has repeat purchases, suppliers willing to share records, an intervention matched to a diagnosed problem, and a defined plan to measure the result. Carbon potential alone is a poor selection rule if the supplier cannot deliver the crop your business needs.
Compare candidate regions on land-use risk, supplier concentration, local rights and tenure, the availability of baseline data, and exposure to drought, erosion, or forest conversion. Check the relevant legal requirements separately. A nature project does not satisfy a due-diligence obligation merely because it has a carbon method attached.
Consider a coffee buyer sourcing from two districts. In the first, producer-group records identify the farms, while recent dry seasons have increased rejected green coffee lots. In the second, deliveries pass through several intermediaries and farm origins are unknown. Pilot shade management in the first district, with local agronomic advice and quality checks built into the plan. The buyer can trace purchases there and test the intervention against a defined sourcing problem.
Use a short decision memo to record why the selected corridor beats the alternatives, what remains uncertain, and what would stop the pilot. That memo is the first control against a project drifting toward whatever happens to photograph well.
Step 3: Design the supplier engagement and commercial model
The intervention needs an owner on each side of the contract. Decide who supplies planting material or training, who maintains the practice, who bears the cost if a harvest disappoints, and how long the buyer expects to purchase from participating suppliers. Where smallholders participate, budget cash flow and technical support for practices that offer a later return.
Put these terms in a supplier agreement or pilot addendum. It should cover eligible farms and volumes, consent to data collection, access for monitoring, any land or carbon rights that matter, benefit sharing, and what happens if the purchasing relationship ends. Ask local counsel and producer representatives to review the arrangement. The Accountability Framework is a useful reference for supply-chain work involving forests, ecosystems, and human rights.[3]
Avoid assigning an automatic carbon benefit to the buyer. The contract should say which party has the right to report each outcome, how overlapping claims will be checked, and how any issued credits will be handled. A reduction reported in a buyer’s inventory cannot simply be sold for someone else’s offset claim without an accounting review. SBTi FLAG guidance excludes purchased credits used as offsets from near-term FLAG target achievement.[2]
Set commercial measures beside environmental ones: delivered volume, rejection rate, supplier retention, and procurement cost. They tell you whether the program works as a supply-chain investment, even if carbon accounting takes longer to resolve.
Step 4: Build monitoring, reporting, and verification before rollout
Agree on the baseline and data flow before paying for a full rollout. Identify the land units or sourcing regions in scope, the practices that change, the records suppliers already keep, and the additional measurements the program requires. Decide which data can come from field visits, transaction records, or remote sensing, and who checks each source.
For carbon, separate three questions. Did the practice happen? Did emissions or removals change under an applicable method? Can the buyer attribute a defensible share to its purchased goods without double counting? The GHG Protocol’s 2026 guidance includes dedicated chapters on spatial boundaries, data and methods, evaluating actions, credited outcomes, and assurance.[1] These are distinct jobs. A photograph of a planted tree answers only part of the first.
CCC’s MRV and Additionality article explains the separate evidence questions a project reviewer will ask.
Create an evidence file for every reporting period: dated supplier and farm records, purchase volumes, methods and emission factors, uncertainty, quality checks, and changes from the baseline. Specify who can challenge the data and how corrections are recorded. If a project also seeks credit certification, keep project accounting and corporate inventory accounting separate. The GHG Protocol says its land standard focuses on entity-level inventories and does not set the rules for credit certification.[4]
Start with the minimum viable measurement design that can survive review. Add complexity when the pilot tells you which data actually changes a decision.
Step 5: Decide what can be reported and claimed
Insetting program design needs reporting rules before the marketing copy. Procurement, sustainability, finance, and legal should agree on four distinct outputs: operational performance; observed land or nature outcomes; inventory effects, if the accounting criteria are met; and any separately issued credits. One dashboard can display them, but they should not share a single unqualified headline.
SBTi FLAG guidance allows certain land-based removals within a company’s operations or supply chain to contribute to FLAG pathways, subject to criteria including permanence, traceability, data quality, and allocation. It does not allow purchased offsets to meet near-term FLAG targets.[2] Eligibility therefore depends on evidence and the applicable target rules, not on calling an activity “insetting.”
Draft a claims register. For each proposed sentence, record the period, boundary, data owner, calculation method, reviewer, and whether the result is measured, estimated, or still a goal. If the evidence supports only “we funded a pilot with 200 farms,” say that. Do not turn a funding fact into a verified emissions reduction.
What good looks like by year three
Treat year three as a review point, not a promised payback date. A mature pilot should have mapped participating supply volumes, a stable supplier agreement, a repeatable data collection process, and a comparison of commercial and environmental results against the baseline. It should also have a documented answer for outcomes that cannot be included in the buyer’s inventory.
Scale only where the pilot improves a sourcing decision and the evidence can follow the product. If neither condition holds, redesign the intervention or change the corridor. A disciplined program can conclude that a site is unsuitable. That is useful procurement intelligence.
Discuss your insetting program with CCC
Carbon Credit Capital designs nature-based supply chain investments around sourcing needs and measurable sustainability outcomes. Schedule a consultation with our carbon and sustainability experts to map your tier-one supplier network, FLAG exposure, and first pilot decision.
Sources
[1] GHG Protocol, Land Sector and Removals Standard, version 1.1, and companion Guidance, June 2026: https://ghgprotocol.org/land-sector-and-removals-standard ; https://ghgprotocol.org/land-sector-and-removals-guidance
[2] Science Based Targets initiative, Forest, Land and Agriculture Science-Based Target-Setting Guidance, version 1.2, March 2026, especially pp. 16–17, 37, and 41: https://sciencebasedtargets.org/resources/files/SBTiFLAGGuidance.pdf
[3] Accountability Framework initiative, Accountability Framework and guidance for companies: https://accountability-framework.org/
[4] GHG Protocol, Frequently Asked Questions: Land Sector and Removals Standard, questions 10 and 18: https://ghgprotocol.org/blog/frequently-asked-questions-land-sector-and-removals-lsr-standard
Nature-based carbon investments deserve consideration when they address a specific business dependency and produce outcomes you can measure. Imagine Moonbucks, a global coffee roaster planning next season’s espresso blend. It is considering natural Grade 4 coffee from Jimma, in southwestern Oromia, Ethiopia. Its sourcing decisions affect the roasting schedule and the cash tied up in coffee purchases. Moonbucks needs to understand which measures could protect that supply, what they would cost and how it would judge the results.
1. Define the sourcing problem before choosing a project
Moonbucks needs a dependable component for its espresso blend. Its procurement team wants continuity of volume and an agreed cup profile. Replacing a shipment at short notice could require new sample approval and changes to the blend, and given the schedules for cultivation, processing, and freight delivery, is practically impossible. Moonbucks needs to get this right, and its initial concern is inconsistent deliveries. It commissions an assessment of the supplying farms and post-harvest chain. The questions cover land condition, harvest availability and whether drying or storage problems are affecting saleable lots.
This distinction changes the investment brief. A tree-planting proposal cannot be assumed to fix drying problems. Better storage cannot restore eroded land. The team must establish which problem exists before selecting the response.
CCC’s guide to finding nature-based solutions in your supply chain explains where to look. Here, the next task is to turn a broad concern into a testable proposal.
2. Develop nature-based carbon investments around the actual supply chain
The assessment would trace Moonbucks’ purchases back through the exporter and supplying producer groups. Which farms supply the contracted lots? Who controls land-management decisions? How long does the buyer expect to maintain the purchasing relationship?
Furthermore, Jimma natural Grade 4 cannot be treated as interchangeable with every Ethiopian coffee. Moonbucks would need to test any substitute against its own blend and quality requirements.
Suppose Moonbucks’ assessment identifies an opportunity to maintain shade trees and restore vegetation along exposed streambanks. The IPCC describes agroforestry as a way to store carbon in vegetation and soil, with potential benefits for erosion control and water quality.1 Moonbucks would need local advice on species, growing conditions and maintenance before deciding how these measures fit its sourcing plan.
The proposal now has a defined sourcing connection and a candidate intervention. The next steps are to test its effect on production, assess carbon eligibility and build the financial case.
3. Compare the proposal with other ways to protect supply
WBCSD’s Nature-based Solutions Blueprint recommends comparing nature-based options with other responses across their benefits and costs.2 For Moonbucks, that means evaluating the landscape proposal alongside drying improvements, storage controls and alternative sourcing arrangements.
The product’s natural-process designation matters. Moonbucks should examine the drying and dry-milling chain alongside land conditions. Its team would check storage practices, contamination risks and incoming-lot quality to see whether problems arise before or after the coffee leaves the farms.
The revised proposal might combine land stewardship with separate post-harvest controls. Each component needs its own purpose and budget. A sustainability label should not obscure which activity is expected to solve which problem.
The project develops as the evidence improves. Its scope should follow the diagnosis rather than preserve the first idea unchanged.
4. Establish what the carbon outcome could count toward
Moonbucks’ sustainability team now tests the proposed carbon case separately from the sourcing case. Is the objective a measurable change in Moonbucks’ value-chain emissions, verified credits for separate voluntary use, or distinct outcomes under carefully defined arrangements?
Scope 3 covers value-chain emissions. A purchasing relationship alone does not prove that a farm intervention can be included in Moonbucks’ inventory. The team needs an applicable accounting method, defensible boundaries and evidence supporting attribution.
SBTi’s FLAG Guidance (forest, land and agriculture), version 1.2, says purchased credits used as offsets cannot meet near-term FLAG or energy and industry targets. Eligible land-based removals are subject to accounting requirements.3 Moonbucks needs to test its proposal against those rules before including an outcome in its targets.
For Moonbucks’ Jimma proposal, maintaining existing trees and establishing new vegetation could involve different carbon questions. Specialists must examine the baseline and additionality, meaning what changes because the project proceeds. They must also assess measurement and the risk that stored carbon is lost. CCC’s MRV and additionality article introduces those tests.
Until that work is complete, Moonbucks should not budget an assumed Scope 3 reduction or credit-sale receipt. Direct emissions reductions remain the priority.
5. Build a financial case that survives weaker assumptions
Moonbucks’ finance team would now build a multi-year budget. UNEP’s State of Finance for Nature 2023 compares current finance with the investment needed to meet climate, biodiversity and land-restoration goals.4 The company would cost its proposed activities, farmer participation and ongoing monitoring. Verification, where required, is a separate expense. Maintenance and responsibility for replacing failed plantings must remain visible after the initial establishment budget ends.
Operational benefits need equally careful treatment. If fewer rejected lots reduce emergency purchasing, count that saving once. Do not add “supply resilience” as another financial benefit when it represents the same avoided cost.
Moonbucks should compare a base case with slower benefits, higher implementation costs and no carbon revenue. It should also distinguish production outcomes from quality and shipment outcomes. A single good harvest would not prove that the landscape intervention caused better delivery performance.
A project can remain worth funding even when its carbon case needs more work. That decision must rest on the operational evidence and the company’s objectives, without unsupported environmental claims.
6. Fund a pilot with a clear expansion decision
Moonbucks’ first approval would fund a bounded pilot, rather than commit the entire sourcing program. Before spending, Moonbucks would agree on participating farms, responsibilities and the evidence needed at review.
Its scorecard could track farmer participation, implementation costs and plant survival, alongside accepted coffee volumes and lot-quality results. Carbon measurements would follow the selected method. Farm-level traceability must connect the intervention to the actual sourcing relationship.
Suppose Moonbucks’ review later finds that storage changes explain most quality improvements, while the land measures need longer observation. Those findings would shape the next decision: expand the proven component, adjust the landscape work or pause further spending. They would not justify crediting all improvements to tree planting.
This gives nature-based carbon investments a practical place in a multi-year strategy. Funding increases as the evidence supports it, and the project can change direction before a larger commitment.
Discuss a sourcing opportunity with CCC
Carbon Credit Capital’s nature-based supply-chain investment service uses its Dual-Value Model to examine supply-chain economics alongside potential sustainability outcomes.
Schedule a consultation to discuss your sourcing exposure and the questions to resolve before allocating capital.
Sources
- Intergovernmental Panel on Climate Change (IPCC), Climate Change 2022: Mitigation of Climate Change, Chapter 7, Agriculture, Forestry and Other Land Uses, section 7.4.3.3 (Agroforestry).
- World Business Council for Sustainable Development (WBCSD), Nature-based Solutions Blueprint, 5 June 2024.
- Science Based Targets initiative (SBTi), Forest, Land and Agriculture Science-Based Target-Setting Guidance, version 1.2, March 2026, section 3.1.4, pp. 37–38.
- United Nations Environment Programme (UNEP), State of Finance for Nature 2023, 2023.
A plastic credit funds the collection of plastic waste in places where no formal system exists to collect it. Each credit represents a measured, independently audited quantity of plastic taken out of the environment or returned to use, tracked on a public registry.
