NITI Aayog unveils PACT and ZET marketplace to accelerate electric truck adoption

NITI Aayog has launched two new initiatives—the Platform for Aggregating Clean Transport (PACT) and the Zero Emission Truck (ZET) Marketplace—to accelerate the electrification of India's freight sector. The announcement was made by NITI Aayog Member Rajiv Gauba at the 5th E-Fast India Summit 2026.

Gauba emphasized that scaling up zero-emission trucking requires a market-led approach, combining demand aggregation, innovative financing, and coordinated charging corridors. He noted that heavy trucks, which form just 3-4% of India's vehicle fleet, contribute over a third of transport-sector carbon emissions.

With nearly 70% of India's freight moving by road, strategic electrification of key corridors is critical. However, Gauba pointed out that only around 800 heavy-duty electric trucks were sold in 2025, underscoring the scale of the challenge.

Key barriers include fragmentation among logistics operators, high financing costs, and a lack of coordinated corridor planning. PACT and the ZET Marketplace are designed to address these gaps through demand aggregation, financing innovation, and corridor-based projectisation.

Gauba called for blended finance mechanisms, leasing models, and stronger data systems to bring the cost of capital for electric trucks closer to diesel vehicles. He urged logistics providers, manufacturers, charge point operators, and financiers to collaborate on developing freight corridors and commercial models.

The launch builds on existing government support for electric mobility, including production-linked incentive schemes, charging infrastructure expansion, and demand-side programmes such as FAME, PM E-DRIVE, and PM e-Bus Sewa.

Gauba described the transition to electric mobility as an economic and strategic imperative, tied to India's energy security and its net-zero emissions target for 2070. He noted that PACT reflects a broader lesson: government is most effective when it creates systems that allow markets to discover outcomes, rather than directly allocating them.