Five ways the ABACUS label advances nature-based carbon removal

From more-accurate measurement of carbon dioxide removal to greater diversity in restoration design, the ABACUS label’s requirements help advance the integrity of restoration projects in the voluntary carbon market.

Amazon cofounded the Climate Pledge in 2019 to commit to reaching net-zero carbon by 2040. The first priority of the pledge is to implement decarbonization strategies — in line with the Paris Agreement — through operational changes such as improving efficiency, driving forward scalable carbon-free energy sources, reducing waste, and innovating materials.

However, alongside real business change that directly reduces greenhouse gas emissions, there is also need for large-scale investments in climate change mitigation outside of our value chain (what we call carbon neutralization). At Amazon, we do this through both nature-based solutions and technological carbon dioxide removal.

Nature-based carbon removal harnesses the power of photosynthesis to sequester carbon in natural and managed ecosystems. This means altering land management in alignment with nature through native reforestation, agroforestry, and other forms of high-quality restoration. These activities alone have the potential to remove 2–4 billion tons of carbon per year; that’s almost half of the estimated 5–10 billion tons per year that experts estimate is likely needed through the end of the century in order to keep our global temperatures at safe levels.

While the voluntary carbon market has the potential to bring billions of dollars of finance to restoration projects, less than 3% of credits issued to date come from nature-based carbon removal. This is due to the voluntary carbon market’s prices’ falling below the costs of high-quality nature-based restoration.

That’s where ABACUS comes in. ABACUS is a set of principles and requirements, codified within Verra’s Verified Carbon Standard, that helps advance the integrity of restoration projects within the voluntary carbon market. ABACUS was developed by a working group of expert practitioners, conservation professionals, and scientists — including Amazon’s own carbon neutralization scientists — in an effort to raise the quality bar for agroforestry and native-restoration projects. The ABACUS label has already begun to raise the quality bar for leading buyers.

Below are five big ideas within ABACUS that help raise the bar on scientific rigor and transparency.

  1. Dynamic baseline to measure additionality

    Historically, restoration carbon projects assume that whatever land use was occurring before a project takes place — pasture or agriculture, for example — would have continued unaltered without the project intervention. This assumption ignores the myriad ecological, economic, and policy dynamics that could affect carbon removal without assistance from the voluntary carbon market.

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    In addition to demonstrating that a project would not be viable without carbon credit finance, ABACUS requires a treatment-control approach to measuring additionality, or the carbon removal resulting from the project above and beyond what would have occurred otherwise. This means matching the project “treatment” area — based on historical, satellite-based proxies for biomass — to a population of “control” plots that are followed through time. Each of these controls represents a potential alternate reality for the project in the absence of restoration.

    If the control plots regain forest carbon at pace with the project, this indicates that the project may have regained forest carbon on its own, without the intervention. If the control plots remain low-carbon, degraded land, we can be more confident that the project’s climate impacts are additional. By treating additionality as dynamic instead of static, we’re able to obtain a more data-driven estimation of the true impact of restoration.

  2. Carbon projects as engines for agricultural production

    Carbon removal cannot come at the expense of food production; in fact, these challenges are inextricably linked. Under some projections, agricultural production will need to double by 2050, even as the least productive pasture and croplands are restored to forest cover. Sustainably intensifying agriculture to increase food production, while sparing land for carbon removal — or, better, integrating carbon removal within productive agricultural systems — is critical to reconciling these needs.

    Drone footage of a mature cocoa, coconut, and mahogany agroforestry system, adjacent to a degraded pasture in southeast Pará, Brazil.
    ABACUS seeks to restore degraded pasturelands to diverse agroforestry systems like this one. (Drone footage courtesy of Eric Plançon)

    But the voluntary carbon market is not equipped to tackle this challenge. Carbon removal projects that displace agricultural production often result in indirect land use change and associated emissions, as agricultural markets replace lost production to serve growing demand (“leakage”).

    These crop- and region-specific leakage effects are difficult to quantify reliably. Conventional leakage methodologies impose standardized deductions based on default carbon leakage rates when agricultural production is displaced. This creates a persistent source of uncertainty and risk of over-crediting, and the approach misses an opportunity to build synergies between restoration and agricultural production.

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    ABACUS instead takes a “food-forward” approach to leakage accounting. Rather than using an imprecise default value to quantify leakage effects, ABACUS requires projects to eliminate leakage by maintaining or enhancing agricultural production in the project areas and surrounding landscapes. By recognizing the land-sparing effect of enhancing production of different types of commodities, ABACUS encourages projects to co-optimize for carbon and agricultural production and avoids locking regions into specific agricultural products. The working group is engaging partners to create commodity-specific leakage metrics based on land-carbon “opportunity costs” to estimate, and mitigate, the impacts of leakage.

  3. Abbreviated crediting periods for durability assurance

    Carbon stored in ecosystems can be highly durable, but it faces persistent, long-term climate risks such as fire, drought, and land use change, which must be responsibly managed. Nature-based carbon removal should seek “effective permanence” — an actual net greenhouse gas benefit to the atmosphere that is equal to, or greater than, the net benefit represented by the credits. In addition, the removal should ensure that this balance can be maintained indefinitely.

    On the other hand, agroforestry and restoration projects can catalyze shifts to land use systems that durably enhance carbon storage even beyond what is credited. This can happen through spillover effects, continued carbon removal after the crediting period, and biophysical cooling feedbacks, among other factors. ABACUS includes several methods that improve the likelihood that nature-based carbon remains durably stored — for example, requiring projects to plant ecologically appropriate restoration systems and to create public plans for the longevity of project activities even after the support of carbon revenues.

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    One of ABACUS’s key innovations is to limit the crediting period in an effort to maximize uncredited removals. The ABACUS working group found that revenues from credits generated beyond year 30 are mostly immaterial to investment decisions today, due to their heavy discounts. By shortening the crediting period to 40 years maximum — as opposed to as much as 100 years under some voluntary carbon market standards — ABACUS will create a source of uncredited carbon removal that can serve as an additional buffer against future reversals.

    Additionally, ABACUS proposes that projects will be required to allocate a portion of carbon credits issued late in the crediting period (i.e., years 31–40) to a “long-term permanence mechanism” such as an enhanced buffer pool or insurance product. Achieving increased confidence in the effective permanence of nature-based carbon credits may require stringing together removals or replacing a moderate-durability credit with a high-durability credit, if and when previously credited removals are reversed. Economically, such a construct is currently likely to be cost effective compared to today’s high-durability carbon dioxide removal.

  4. Going beyond commercial monoculture plantations

    Forest plantations already cover nearly 300 million hectares globally — roughly equivalent to the entire area of India. That figure has more than doubled in the last 30 years, without a robust voluntary carbon market, and it is projected to continue growing to provide timber, pulpwood, firewood, and charcoal to increasing populations and a growing economy.

    Brazil_Drone.png
    Orthorectified mosaic capturing a range of land management types on a typical farm in the Amazon basin, Brazil. We can see the contrast between low-carbon-density pasture (left) and diverse agroforestry (center), which combines shade-tolerant commodity production with native, carbon-rich hardwood trees. ABACUS is designed to support native restoration and agroforestry interventions on formerly forested, degraded land.
    Photos captured and combined by ICRAF-Brazil on behalf of the Agroforestry Accelerator.

    As a first step, ABACUS prohibits most monocultures and requires project developers to use observed or modeled data to demonstrate that planted systems are ecologically appropriate for the landscape. This approach avoids projects seeking to reforest with systems that aren’t suitable for the location’s native biomass potential — a function of climate, soil type, water availability, and elevation, among other things. Credit buyers are encouraged to send demand signals that further encourage biodiverse, ecologically sound, and socially beneficial restoration.

  5. Transparency to foster competition on quality

    For some aspects of restoration, it’s challenging to prescribe universally applicable requirements without stifling innovation and local knowledge: every restored ecosystem is unique in its own way. ABACUS introduces multiple requirements for added transparency that will allow buyers, investors, and the public to better assess for themselves the effectiveness of project designs and measurement.

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    For example, ABACUS projects will need to publish their in-situ inventory measurements, systematically justify their use of allometric or other scaling models, and report on design approaches to avoid measurement or sampling bias. Instead of once every five years or so, ABACUS requires projects to annually map disturbances, to ensure that carbon credited and subsequently reversed is immediately identified. With enhanced transparency, the ABACUS working group hopes to incentivize project developers to compete on quality.

  6. ABACUS doesn’t solve all of the challenges of quantifying the complete climate impact of nature-based carbon removal, and it is no replacement for the stakeholder engagement necessary to ensure genuine socio-economic benefits on the ground. Many important improvements remain for future versions of the label’s principles and requirements. As we learn, the ABACUS working group will continue to enhance the scientific rigor of and public confidence in ecosystem restoration, catalyzing rural restoration economies and livelihoods and — if we succeed — helping to enable billions of tons of ecosystem carbon removal across the world.

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We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon. This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems. Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes. The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale. Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization. Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems. Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements. Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives. We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience. The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.
IN, KA, Bangalore
Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? If so, the WW Amazon Logistics, Business Analytics team is for you. We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed, Sr. Applied Scientist with good analytical skills to help manage projects and operations, implement scheduling solutions, improve metrics, and develop scalable processes and tools. The primary role of an Operations Research Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how the final phase of delivery is done at Amazon. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, and the ability to use data and research to make changes. This role requires robust program management skills and research science skills in order to act on research outcomes. This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences