A new report from venture firm a16z highlights a shifting race in generative artificial intelligence (AI). Google’s Gemini, China’s DeepSeek, and even Grok, backed by Elon Musk, are gaining ground on OpenAI’s ChatGPT.
But as these AI rivals advance, there’s an urgent question: how green are their growing footprints? Let’s take a closer look at each of the top three AI’s environmental footprints below.
Competitors Rise: How Google and Grok Are Gaining Ground on ChatGPT
The a16z report maps the top 100 generative AI apps, showing that ChatGPT has strong competition emerging. Google’s Gemini is expanding quickly, and Grok—new but promising—is stepping onto the field, too.

Gemini’s strength comes from Google’s massive infrastructure. Its backing allows faster improvements and better integration across services like search, Gmail, and cloud tools. Gemini’s smooth response and deep context give it a competitive edge.
Meanwhile, DeepSeek earns the third spot because it strikes a middle ground between efficiency and emissions. Much of its footprint comes from running on China’s coal-heavy power grid, which raises its carbon intensity compared to peers with greater access to renewable energy.
Meanwhile, ChatGPT stays strong thanks to its large user base and bold partnerships. OpenAI’s alignment with Microsoft means tight integration in Office, Azure, and more. ChatGPT also supports fine-tuning and plugins, making it more flexible for businesses and developers.

Despite their differences, the report shows all three top models are advancing quickly in user experience, expanding features, and market presence. It marks a growing field, not one dominated by ChatGPT alone anymore.
Watt for Watt: Who’s the Greenest Chatbot? Comparing AI Footprints
As AI usage grows, its environmental impact becomes critical. Let’s compare how these three models fare in energy use and emissions.
OpenAI ChatGPT
ChatGPT sits in the middle of the spectrum. Its exact footprint varies depending on which study you use, but most analyses suggest it consumes more energy and emits more carbon per query than Gemini.
Part of this comes from heavier model sizes and widespread usage. Improvements in hardware efficiency and energy sourcing are bringing numbers down, but its typical footprint is still higher than Google’s.
OpenAI’s Sam Altman claims a ChatGPT query uses as much power as running an oven for about one second. Independent estimates align with this level.
Although a single query uses moderate energy, the rapid growth in usage means overall consumption is significant. U.S. data centers—many of which power AI—could account for up to 8% of U.S. electricity use by 2030.
Greenly, a carbon accounting firm, estimates that using ChatGPT-4 to respond to one million emails monthly could generate 7,138 tonnes of CO₂, equating to about 4,300 round-trip flights Paris–New York per year.

- Energy use per prompt: ~3 Wh (can be lower in some estimates, ~0.3 Wh)
- CO₂ emissions per prompt: ~2–3 g (includes amortized training emissions)
SEE MORE: ChatGPT Hits 700M Weekly Users, But at What Environmental Cost?
Google Gemini
Google has been working to make its AI models more efficient, and Gemini reflects this push. According to Google’s own reporting, text-based queries in Gemini consume very little energy compared to earlier AI systems.
The company highlights dramatic efficiency gains in both energy use and carbon intensity, making Gemini one of the leaner large models when handling short, text-only prompts.
- According to Google, a median Gemini AI text prompt uses just 0.24 watt-hours, emits 0.03 grams of CO₂, and consumes 0.26 milliliters of water—about five drops.
Over the past year, Google claims a 33× reduction in energy use per prompt and a 44× reduction in carbon footprint while improving quality.

Experts warn Google’s method may understate environmental cost by excluding indirect water usage (e.g., power plant cooling) and relying on market-based carbon accounting.
- Energy use per prompt: ~0.24 Wh
- CO₂ emissions per prompt: ~0.03 g
- Water use per prompt: ~0.26 mL
READ MORE: Google Reveals the Environmental Cost of Gemini AI Query
DeepSeek R1
DeepSeek’s reasoning models work well with long, complex prompts. This makes them more energy-intensive than regular chat models.
DeepSeek hasn’t shared its exact CO₂ figures. However, benchmarking shows that its energy use per query is much higher than competitors. This is especially true for tasks that require multi-step reasoning or coding. This places DeepSeek at the high end of per-query emissions.
A recent academic study found that models like DeepSeek-R1 use more than 33 Wh per long prompt—over 70× the energy of smaller models like GPT-4.1 Nano. Large-scale inference, with 700 million queries daily, could use as much electricity as 35,000 U.S. homes. It would also need a forest the size of Chicago to offset its carbon emissions.
- Energy use per long reasoning prompt: >33 Wh
- CO₂ emissions per prompt: Likely an order of magnitude higher than ChatGPT (depends on grid mix): ~2–4 g
At first glance, Gemini seems the greenest per query (with footprints barely visible in the chart below), while ChatGPT has a moderate impact, and DeepSeek is the least efficient. But real-world AI use involves billions of queries daily. So, even small differences matter.

As AI scales, overall energy and CO₂ use skyrocket unless systems are optimized for efficiency.
Data Centers or Carbon Centers? The Stakes for Climate
The environmental stakes are real. Experts estimate global data center use could hit 945 terawatt-hours (TWh) by 2030, with AI responsible for 652 TWh—an 80× jump from today.
Generative AI alone may cause 18–246 million tons of CO₂ emissions per year by 2035, similar to entire industries like aviation or shipping.
Without green design, AI growth could claw back efforts to reduce climate impact. Companies need to think beyond speed and accuracy—AI must grow sustainably, too.
AI Growth Meets Climate Responsibility: What Comes Next
The AI competition is intensifying—with ChatGPT, Gemini, and Grok pushing each other forward. Users benefit from better tools, but rising usage means rising environmental costs. To move forward responsibly, analysts suggest these actions:
- Developers should optimize AI models for energy efficiency, just like Gemini’s leap.
- Companies should track and reveal full lifecycle impacts—not just inference costs.
- Cloud providers and AI firms need policies favoring renewable energy and efficient data center cooling.
- Public policy could reward low-carbon AI, possibly with incentives or carbon pricing.
The a16z report shows that generative AI has entered a new phase—competition among equals, not a single leader. ChatGPT, Gemini, and Grok are all driving innovation in AI. But with growing usage comes growing environmental responsibility.
As the field speeds up, AI’s impact on climate can’t be ignored. Models that combine high performance with low energy use will define the future. If innovators balance progress with sustainability, AI’s value could be even greater—and greener.
The post ChatGPT, Gemini, and DeepSeek Are on an AI Race – But at What Climate Cost? A Comparison appeared first on Carbon Credits.
Carbon Footprint
Designing an Insetting Program: A Step-by-Step Playbook for Procurement
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
Carbon Footprint
The Strategic Case for Nature-Based Carbon Investments
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.
Carbon Footprint
How to buy plastic credits, and what to check before you do
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.
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