How Amazon achieved its 100% renewable energy goal

Investing in 500+ solar and wind projects, bringing carbon-free energy to dirty grids, and buying Renewable Energy Certificates all played a role.

Amazon recently announced that we met our 100% renewable energy goal, seven years early. Making progress like that—especially with operations as complex as ours—isn’t easy, so we wanted to share more about how we were able to get it done. We used a variety of mechanisms, which included investing in new solar and wind projects, supporting projects in countries that still rely heavily on fossil fuels to power their grids (where renewable energy projects are needed the most), and purchasing Renewable Energy Certificates (RECs).

Here's a bit more about seven key steps we took to achieve our goal:

  1. Investing in 240+ utility-scale projects, which deliver new sources of energy to the grid

    We signed agreements with developers and energy companies where we agreed to buy the renewable energy produced by hundreds of utility-scale solar and wind projects, which help make those projects possible and brings large amounts of new renewable energy online. The agreements – known as Power Purchase Agreements (PPAs) – give the developer confidence to buy materials, purchase land, and move forward with the construction. Once built, these projects provide new sources of renewable energy to the grid, helping decarbonize the grid mix – benefitting everyone who uses those grids (including us). In many cases, these projects would not have moved forward if it weren’t for our investment.

  2. Enabling 270 onsite solar projects on Amazon buildings, which bring renewable energy directly to our buildings or the local grid

    We invested in 270 solar arrays on the rooftops and grounds of Amazon fulfillment centers, Whole Foods Market stores, and other facilities. Many of these projects power the Amazon facilities directly, helping defray the amount of energy the buildings need from the grid. Other projects, like the rooftop solar array at the Amazon Air Hub at the Cincinnati/Northern Kentucky International Airport (CVG), sends power to the local grid, helping power nearby homes and businesses.

  3. Bringing new renewable energy to regions where our operations are located

    The majority of the utility-scale projects we invest in connect to grids where we have operations, including our data center regions. For example, in the IAD Region in Northern Virginia, Amazon has invested in 19 solar projects capable of producing a total of 1.3 gigawatts (GW) of renewable energy for the local grid. In our PDX Region in Oregon, Amazon recently invested in Leaning Juniper, a wind farm that is being repowered with enhanced turbine technology to help it produce more energy. In our DUB Region in Ireland, Amazon is collaborating with Bord na Mona Energy park in County Offaly to co-locate data centers with wind projects, and to invest in up to 800 megawatts (MW) of renewable projects across the country.

  4. Bringing new solar and wind projects to dirty grids

    While a lot of our projects bring carbon-free energy to the grids where we have operations, we’re also focused on making the grid cleaner and more reliable for everyone. An emissions-first approach – which prioritizes supporting renewable energy projects in locations and countries that still rely heavily on fossil fuels – is the fastest way to decarbonize global grids. We’ve done this by investing in projects in countries including Poland, Greece, South Africa, and India, where we enabled 50 solar and wind projects – enough to power 1.1 million homes in New Delhi each year. To demonstrate why location matters, by investing in seven utility-scale renewable energy projects in India, instead of a country like Sweden where the grid is already renewable-heavy, we’ve reduced carbon emissions by 18 times. And to encourage other corporate renewable energy buyers to do the same, we created the Emissions First Partnership alongside many other large buyers of renewable energy, including General Motors, Intel, Meta, and Rivian.

  5. Using grids that already have a large amount of renewable energy

    We’re often able to locate projects on grids that already have a large amount of renewable energy, and as part of our renewable energy methodology, we count the renewable energy mix in the grid toward our goal. This means in a country like the U.S., where the grid already runs on 31% renewable energy, we’re able to reach 100% by investing in additional solar and wind projects to cover the remaining 69% of our energy consumption.

  6. Purchasing Renewable Energy Certificates (RECs)

    No company with complex and growing operations is able today to only consume renewable energy – there simply aren’t enough sources in enough locations, and it takes a while for new projects to come online. Like most companies with ambitious climate goals like ours, we also purchase RECs. RECs are used to legally substantiate claims of renewable energy use, and are used by many companies and government agencies, and other entities, like local utilities. They also help make renewable energy investment more inclusive by allowing medium and small businesses to invest in renewable energy projects without having to purchase an entire wind or solar farm, allowing them to also play a role in addressing climate change.

    When Amazon or any other buyer purchases renewable energy, there’s no way to guarantee that the same electron generated by an Amazon-backed project will be the same electron consumed by our data centers, fulfillment centers, or other operations. RECs allow us to account for these electrons, which are still flowing through the grid, and helping power other needs, like energy to run homes and businesses.

  7. Using different kinds of RECs

    Amazon purchases “bundled” and “unbundled” RECs, which we use as a temporary measure. Bundled RECs are purchased alongside our investment in a wind or solar project, and act as a bridge until the project comes online. This is important, because due to grid constraints, many projects are delayed around the world. Across the United States, there are currently more than 260,000 megawatts (MW) of renewable energy and storage projects waiting to connect to the grid—enough carbon-free energy to power 63.3 million US homes.

    Unbundled RECs are purchased independent of a project. The revenue typically supports an existing project, helping with repairs, upgrades and other needs. Both forms of RECs are a fundamental component of the global renewable energy market – and are key to our society’s transition to a carbon-free energy future. Many current renewable energy technologies would not have been financed nor have become operable without the support of REC purchases, according to the American Council on Renewable Energy.

  8. Amazon’s commitment to sustainability

    We are focused on reaching our Climate Pledge to be net-zero carbon by 2040. We know our path won’t be linear, and we’re continuing to innovate in order to add new sources of carbon-free energy to grids around the world.

    We believe in transparency for our customers, employees and partners, and release a voluntary annual Sustainability Report that highlights the work and progress we’re making to become a more sustainable company, across every business in which we operate. This report, including our achievement of our 100% renewable energy goal, is verified by independent external third-party assurors.

    From investing in new carbon-free energy projects to advocating for grid modernization and collaborating with key stakeholders around the world, Amazon is working toward a cleaner energy future.

Research areas

Related content

IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
US, WA, Seattle
We’re working on the future. If you are seeking an iterative fast-paced environment where you can drive innovation, apply state-of-the-art technologies to solve large-scale real world delivery challenges, and provide visible benefit to end-users, this is your opportunity. Come work on the Amazon Prime Air Team! We are seeking a highly skilled weather scientist to help invent and develop new models and strategies to support Prime Air’s drone delivery program. In this role, you will develop, build, and implement novel weather solutions using your expertise in atmospheric science, data science, and software development. You will be supported by a team of world class software engineers, systems engineers, and other scientists. Your work will drive cross-functional decision-making through your excellent oral and written communication skills, define system architecture and requirements, enable the scaling of Prime Air’s operation, and produce innovative technological breakthroughs that unlock opportunities to meet our customers' evolving demands. About the team Prime air has ambitious goals to offer its service to an increasing number of customers. Enabling a lot of concurrent flights over many different locations is central to reaching more customers. To this end, the weather team is building algorithms, tools and services for the safe and efficient operation of prime air's autonomous drone fleet.