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Watershed Customers Drive More Than $500 Million in New Clean Energy Investment

SAN FRANCISCO, CA, UNITED STATES, July 13, 2026 /EINPresswire.com/ -- Watershed, the sustainability AI platform, today announced that its customers have collectively driven more than $500 million in new clean energy investment, funding over 270 solar and wind projects across six countries, adding almost 400 MW of capacity to the grid, and avoiding more than 375,000 tonnes of CO2e annually—equivalent to taking more than 80,000 gas-powered cars off the road.

The projects are built or under construction today, generating clean electricity on grids that need it, funded by companies taking concrete action to reduce their emissions.

The announcement comes as corporate clean power purchasing agreements declined globally in 2025 for the first time in ten years, with just four companies accounting for nearly half of all global activity. Rising costs and the complexity of large-scale energy transactions are pushing smaller buyers out of the market. Watershed is closing that gap by aggregating demand from companies ready to act on their emissions, then matching them with new clean energy projects that need committed buyers to move forward.

Albany International, Alterra Mountain Company, Dayforce, and ServiceNow are the latest companies to invest in clean power through a Watershed VPPA (virtual power purchase agreement). Their cohort is funding new solar projects in Texas, Florida, and Illinois, adding over 130 MW of clean electricity capacity to the grid and avoiding over 145,000 tonnes of CO2e annually, equivalent to 30,000 homes' annual electricity use. The first project is coming online this summer.

They join a growing roster of customers building new clean power. In 2024, Canva and its North American print suppliers co-invested in a first-of-its-kind supply chain VPPA, funding four new solar farms in Illinois that reduced Canva's scope 3 emissions while expanding renewable energy access to local households. Block, Braze, DoorDash, Match Group, and Unity funded five solar projects in Michigan, adding 100 MW of capacity per year across five communities and avoiding more than 52,000 tonnes of CO2e annually.

Most companies that want to fund new clean energy can't access the deals on their own. Traditional renewable energy contracts require long commitments, complex negotiations, and minimum sizes that put them out of reach. Watershed's cohort-based buying model, designed with the help of Morgan Stanley, pools demand from multiple companies and matches them to new projects on the grids where clean power will displace the most fossil generation. Buyers get a fixed, predictable cost over a manageable time period.

Watershed has also built clean power management directly into its platform, enabling customers to manage their energy attribute certificates in-product, see exactly where their electricity is covered and where gaps remain, and allocate certificates directly to their footprint.

"We've proven that when you aggregate demand and direct it to the right grids, mid-sized companies can collectively finance projects that used to be reserved for the largest buyers," said Matt Konieczny, Head of Decarbonization and Energy at Watershed. "We're proud of the $500 million our customers have invested in clean energy, and we're excited to continue accelerating the renewable energy transition."

About Watershed
Watershed is the sustainability AI platform. Companies like Airbnb, Carlyle Group, FedEx, Visa, and Dr. Martens use Watershed to manage climate and ESG data, produce audit-ready metrics for voluntary and regulatory reporting including CSRD, and drive real decarbonization. Watershed is the platform of choice for companies seeking to reduce emissions; meet customer, investor and regulatory requirements; and modernize their sustainability programs. Watershed customers also have exclusive access to a marketplace of pre-vetted, high-quality carbon projects and groundbreaking virtual power purchase agreements.

Amelia Penniman
Watershed
amelia@watershed.com

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