Amazon Invests $30 Million in Indian Rice Farming Carbon Removal Initiative

Amazon Inks $30 Million Carbon Removal Deal with the Good Rice Alliance in India

April 22, 2026

Amazon has entered a significant carbon removal agreement with the Good Rice Alliance, a move that underscores the growing corporate push to invest in nature-based climate solutions within key agricultural markets. The deal highlights the strategic importance of addressing methane emissions from rice cultivation, a major global source of the potent greenhouse gas.

According to reports, the e-commerce and cloud computing giant will invest $30 million into the initiative. In return, Amazon will secure over 685,000 carbon removal credits generated during the project's initial phase. The Good Rice Alliance is a subsidiary of the German conglomerate Bayer, formed in collaboration with investment firms GenZero and Temasek, along with Shell Nature-Based Solutions.

The Alliance operates across India, working directly with more than 13,000 smallholder farmers. Its program aims to reduce methane emissions by providing training, on-ground support, and financial incentives to transition from traditional, flood-based rice farming to sustainable practices. Conventional rice paddies, when flooded, create anaerobic conditions that produce methane, accounting for up to 10 percent of global methane emissions. The Alliance states that adopting improved water management techniques can significantly cut these emissions.

This investment further solidifies India's position as a burgeoning hub for carbon removal projects. The country has recently attracted similar commitments from other tech leaders. In March of last year, Microsoft signed a 30-year agreement to purchase 1.5 million tons of removal credits from a large-scale afforestation project. Google followed in January with a deal for 100,000 tons of credits from Indian biochar firm Varaha. Amazon's latest deal signals continued confidence in India's potential to deliver high-quality, nature-based carbon credits at scale.

Source: datacenterdynamics

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