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AI Energy

AI is a powerful force driving innovation across industries in today’s rapidly evolving technological landscape. However, as AI capabilities expand, so does its appetite for energy. This phenomenon has brought the intersection of AI and energy into sharp focus, particularly in the context of global decarbonization efforts.

The Interplay of AI and Renewable Energy

The rise of AI has spurred an unprecedented demand for computing power, much of which is supplied by data centers. 

These data giants consume vast amounts of electricity, prompting concerns about their environmental impact and contribution to carbon emissions. Some argue that these companies have the resources and the motivation to invest in cleaner energy technologies. They can also advocate for policy changes to support decarbonization efforts. However, others raise concerns about the environmental impact and the need for greater transparency and accountability in their sustainability initiatives.

Amidst the urgency to transition to renewable energy sources, the energy consumed by AI presents a significant challenge to decarbonization efforts.

On one side, the influx of demand from tech giants could provide a financial boost to investments in renewables, potentially accelerating the transition to cleaner energy sources. However, there remains a tangible risk that the energy demands of AI will be met using conventional, fossil fuel-based methods, such as natural gas or coal. This scenario would undermine progress toward decarbonization goals and perpetuate reliance on non-renewable resources.

Thus, navigating this decarbonization dilemma requires balancing the transformative potential of AI and mitigating its environmental impact. 

  • It calls for strategic investments in renewable energy infrastructure with AI technology innovation to optimize energy efficiency. 
  • Collaborative efforts between tech companies, energy providers, policymakers, and environmental advocates are essential to charting a sustainable path forward.

A Bloomberg analysis reported, that traditional energy corporations like PPL Corp., Alliant Energy Corp., WEC Energy Group Inc., Entergy Corp., Duke Energy Corp., NextEra Energy Inc., DTE Energy Co., CenterPoint Energy Inc., and Vistra Corp., are also deeply involved in navigating the challenges and opportunities presented by AI and data centers.

These companies face pressure to optimize their operations for efficiency, reliability, and sustainability. AI technologies offer opportunities to enhance grid management, predict demand more accurately, optimize energy distribution, and improve maintenance scheduling. Moreover, these corporations will likely explore AI-driven solutions to meet regulatory requirements and customer demands for cleaner energy sources.

As AI becomes increasingly integral to various industries, including energy, investors will evaluate companies based on their AI capabilities and ability to adapt to technological advancements.

The automation era in the energy sector

This futuristic vision is swiftly materializing – the AI in energy and power market is forecasted to surge at a CAGR of 24.68%, from a value of US$3.103 billion in 2021 to US$14.527 billion by 2028.

AI energy

AI-Growth Drivers Transforming the Energy Companies

From predictive maintenance to demand forecast, AI-powered solutions are revolutionizing traditional practices and reshaping the industry.

1. Predictive Maintenance: Preventing Downtime, Maximizing Efficiency

By analyzing vast amounts of data from sensors and equipment, AI algorithms can detect anomalies and predict potential failures before they occur. This approach not only minimizes downtime but also maximizes the lifespan of critical assets. It further leads to substantial cost savings for energy companies.

2. Optimized Asset Management: Maximizing Returns on Investments

AI-driven asset management solutions enable energy companies to optimize the performance of their infrastructure. Through real-time monitoring and analysis, AI algorithms can identify opportunities for efficiency improvements and asset optimization. AI empowers companies to make data-driven decisions that enhance profitability and sustainability.

3. Dynamic Demand Forecasting: Balancing Supply and Demand

Accurate demand forecasting is essential for energy companies to manage supply and avoid costly overproduction or shortages. AI-powered demand forecasting models leverage historical data, weather patterns, market trends, and other variables to predict future demand with precision. By optimizing resource allocation and scheduling, energy companies can minimize waste and maximize revenue, ultimately improving cost efficiency.

4. Enhanced Customer Engagement: Personalized Services and Solutions

AI technologies also enable energy companies to enhance customer engagement by offering personalized services and solutions. Data analytics and machine learning empower companies to customize offerings based on individual customer preferences and behavior.

AI energy

source: Data Dynamics

Moving on, we can see the top energy giants using AI in their operations  

Top Energy Giants using AI in their Operations  

These energy companies exemplify the strategic adoption of AI to enhance their operational capabilities, driving efficiency gains and ultimately contributing to their bottom line.

Exxon Mobil

Exxon Mobil integrates AI to enhance operational efficiency and reliability across its operations. It collaborates with IBM to use quantum computing in advancing AI-driven simulations. Additionally, they use AI for critical calculations to optimize CCS methods. 

  • Enhances its operational efficiency, minimizes downtime, and reduces maintenance costs with AI-driven predictive maintenance and process optimization. 
  • The AI-powered analytics enable the company to optimize supply chain management.
  • Subsequently, it ensures timely delivery of products to customers while minimizing transportation costs and environmental impact.

ABB

The Swiss technology leader in electrification and automation is a pioneer in AI usage. The company

  • Utilizes AI to identify faults like pipeline and machinery cracks through image analysis. 
  • Manages distributed energy resources for reliable green power.
  • Employs AI to analyze seismic data for optimizing oil extraction.

Schneider Electric

It uses Microsoft’s machine learning to remotely monitor and configure pumps in oil and gas fields. AI can detect pump failures, prevent weeks of downtime, and repair costs of up to $1 million.

BP

The London-based gas and oil giant leverages AI to enhance decision-making processes, optimize resource allocation, and improve safety standards. AI boosts the oil extraction and recovery process with high-end sensors. It further lowers the cost/ barrel, reduces risk, and ensures compliance. 

Notably, BP is one of Amazon’s most trusted cloud computing clients.  It has used its technology to enhance the performance of its lubricants ERP system with 40% faster response times.

Royal Dutch Shell

Shell implements AI technologies to streamline operations, drive innovation, and enhance overall performance. It utilizes Microsoft’s cloud-centric platform, Azure. By leveraging AI technologies, Shell aims to boost revenue, cut costs, and enhance operational safety, such as monitoring data from drill sensors deep underground.

Gretchen Watkins, President of Shell Oil Company, revealed at the CERAWeek energy conference that,

Shell employs AI algorithms for drilling in wells in the Permian Basin. These algorithms, driven by machine learning, facilitate safe, reliable, and cost-effective operations.

The Top 10 AI-powered solutions in the Energy and Power Sector and their Stocks to Watch Out

AI

The U.S. Department of Energy established the Artificial Intelligence and Technology Office (AITO) to elevate it into a global leader in AI

AITO is responsible for reliable AI governance and capabilities in energy infrastructure, advising on trustworthy AI/ML strategies. It fosters partnerships, policies, and innovations in AI and energy across public, private, and international sectors. AITO further supports Department of Energy program offices to implement AI/ML strategies. 

Overall, the relationship between AI, energy, and decarbonization efforts is complex and multifaceted, and addressing the challenges it presents will require collaboration across industries and disciplines.

The post Wired for Change: AI, Energy, and the Decarbonization Dilemma appeared first on Carbon Credits.

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Designing an Insetting Program: A Step-by-Step Playbook for Procurement

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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

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The Strategic Case for Nature-Based Carbon Investments

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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

  1. 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).
  2. World Business Council for Sustainable Development (WBCSD), Nature-based Solutions Blueprint, 5 June 2024.
  3. 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.
  4. United Nations Environment Programme (UNEP), State of Finance for Nature 2023, 2023.
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How to buy plastic credits, and what to check before you do

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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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