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Study Shows How AI Can Cut Over 5 Billion Tons of Carbon Emissions in 3 Key Sectors

Artificial intelligence (AI) is rapidly changing how industries operate, and it could also help fight climate change. A major study published in npj Climate Action finds that AI could cut global carbon emissions by up to 5.4 billion tonnes per year by 2035. That’s more than the total annual emissions of the United States.

The study is led by researchers from the London School of Economics and Systemiq. The report entitled “Green and intelligent: the role of AI in the climate transition” shows that applying AI to three key sectors—food, electricity, and mobility—can unlock enormous environmental benefits.

AI’s strength lies in its ability to process large datasets, identify patterns, and optimize systems in real time. When used strategically, this can translate into greater efficiency, lower energy use, and less waste. These improvements are essential to reduce greenhouse gas emissions and slow climate change.

A Sector-by-Sector Breakdown: Where AI Delivers the Most Cuts

The study highlights three areas where AI can drive the biggest reductions in carbon dioxide equivalent (CO₂e) emissions:

  • Food: 0.9–1.6 billion tonnes CO₂e per year (up to 3.0 GtCO₂e in a highly ambitious scenario)
  • Energy (Electricity): Up to 1.8 billion tonnes CO₂e per year
  • Mobility (Transport): 0.5–0.6 billion tonnes CO₂e per year
Total emissions and emissions savings from AI
Source: Stern, N. et al. (2025) https://doi.org/10.1038/s44168-025-00252-3.

These figures are significant. Together, they represent 8% to 10% of total global greenhouse gas emissions. That’s a substantial contribution to international efforts like the Paris Agreement, which aims to limit global warming to well below 2°C.

In the food and agriculture sector, AI can improve productivity while reducing environmental harm. Smart sensors and machine learning tools help farmers use just the right amount of water, fertilizer, and pesticides.

AI also enables precision farming, reducing waste and cutting emissions from overuse of chemicals. It can predict crop yields and improve food distribution. This helps cut spoilage and lowers emissions from storage and transport.

AI helps the clean energy transition in electricity generation. It manages supply and demand more efficiently. Moreover, AI algorithms can predict electricity use. They also enhance energy storage and optimize the integration of solar and wind power.

Additionally, AI helps stabilize power grids and boosts low-carbon energy use. This cuts down the need for dirty backup systems that run on coal or gas.

For mobility and transport, AI improves logistics, reduces fuel use, and supports the development of cleaner vehicles. Fleet managers use AI to plan efficient routes, avoid traffic, and reduce idle times. AI is key to making self-driving cars. These vehicles could boost road safety and cut emissions even more.

The chart below shows the projected global emissions by 2035, with AI adoption differing from business-as-usual and ambitious reduction scenarios for the three sectors identified.

Projected annual global emissions in AI
Note: the ambitious emissions reduction scenario is calculated using the IEA’s net zero emissions scenario for Power and Light Road Vehicles and UNEP’s 2050 Paris-aligned target3 for Meat and Dairy. Source: Stern, N. et al. (2025) https://doi.org/10.1038/s44168-025-00252-3.

AI Carbon Reductions in Other Sectors

AI is also critical in industries like cement and steel, where emissions are hard to abate. Machine learning helps monitor production processes and reduce energy waste. AI also enables real-time emissions tracking and reporting, helping companies stay accountable to their climate goals.

A recent McKinsey report shows that AI technologies can help businesses lower CO₂ emissions by up to 10%. They can also reduce energy costs by 10–20%. Additionally, buildings could save 20% on energy, while transportation systems might save 15%.

Complementing this, the International Energy Agency (IEA) estimates that adopting existing AI applications across end-use sectors like energy, industry, transport, and buildings could reduce emissions by about 1.4 gigatons of CO₂ annually.

AI emission reductions IEA
Source: IEA

Together, these findings underscore AI’s significant role in accelerating decarbonization across multiple sectors. And the good news? These AI applications already exist and are being tested or deployed by companies around the world. What’s needed now is rapid scaling.

The Role of Policy and Industry Action

The study authors say AI’s benefits will only happen with strong guidance from policymakers and investors. Without supportive rules and incentives, AI might raise emissions. It could increase demand for power-hungry data centers. Also, it may automate processes that lead to more production and consumption.

To avoid these risks, the researchers call for:

  • Public and private investment in climate-focused AI tools
  • Open access to high-quality environmental datasets
  • Standards and guardrails to guide responsible use

They also warn against “AI rebound effects,” where efficiency gains are offset by increased consumption. For example, making vehicles more fuel-efficient might encourage people to drive more. That’s why careful planning and strong governance are essential.

Another key recommendation: include developing countries in the AI transition. These regions often face the greatest climate risks but have limited access to technology. Thus, international partnerships and funding will be needed to ensure AI’s climate benefits are shared globally.

AI as a Climate Enabler, Not Just a Tool

AI can also strengthen other climate solutions. For example:

  • Carbon removal. AI helps track carbon storage in forests and soils, improving the quality of carbon credits and offset programs.
  • Resilience planning. AI models assist cities in getting ready for floods, heat waves, and other climate effects. They do this by simulating different scenarios and testing response plans.
  • Energy optimization. AI manages heating, cooling, and lighting in buildings. It cuts energy waste while keeping comfort high.

These applications make climate solutions smarter, cheaper, and faster. AI doesn’t just reduce emissions—it helps manage the clean energy transition more effectively.

Governments are starting to notice. The European Union and Canada have launched initiatives to support green AI. Companies like Google, Microsoft, and Amazon are also building AI tools for climate forecasting, carbon tracking, and energy management.

Tech vs. Time: Can AI Help Us Beat the Climate Clock?

The new study offers compelling evidence that AI could play a leading role in slashing global carbon emissions. The estimated 3.2 to 5.4 billion tonnes of CO₂e reductions by 2035 are not just theoretical; they’re within reach if the right steps are taken.

These findings come at a time when many countries are off track in meeting their 2030 and 2050 climate goals. AI may help close that gap by offering fast, reliable, and affordable emissions cuts in important sectors.

Private companies, too, are under pressure to deliver on net-zero commitments. For them, AI can provide tools to track emissions, meet regulatory standards, and optimize energy use. Investors are also watching closely, with many ESG (environmental, social, governance) funds now looking for AI-powered climate solutions.

The bottom line? AI can become one of the world’s most powerful climate allies. But its impact depends on how it’s used, who controls it, and whether its benefits are shared widely. By focusing on climate-smart applications in food, electricity, and transport, AI can help build a cleaner, more resilient future.

The post Study Shows How AI Can Cut Over 5 Billion Tons of Carbon Emissions in 3 Key Sectors appeared first on Carbon Credits.

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Carbon Footprint

Insetting vs Offsetting: Which Actually Counts Toward Your Scope 3 Targets

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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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Carbon Footprint

Net zero needs nature: a carbon credit guide

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Net zero is often described as a balancing act: cut what you can, account for the rest, and reach zero on the ledger. That framing is useful, but it leaves something out. It treats every tonne of carbon as interchangeable and every route to zero as equally sound, while the science tells a more specific story.

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Carbon Footprint

Deforestation in Malawi: causes and solutions

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Malawi has lost a striking share of its forests over the past three decades. Woodlands that once covered well over a third of the country now cover less than a quarter, and the pressure on what remains is increasing. Behind those figures sit two practical questions: what is driving the loss, and what reverses it?

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