How Have Maruti Suzuki’s CO₂ Emissions Per Vehicle Evolved Over the Last Decade, And What Simple Process Improvements Could Further Reduce Them?

This research provides a longitudinal analysis of Maruti Suzuki’s CO₂ emissions and energy intensity per vehicle over seven fiscal years (FY 2016–17 to FY 2022–23), examining how process-level interventions, product mix changes, and external grid factors influenced overall emission performance. Using company-published Integrated Reports and verified secondary data, the study traces the firm’s operational evolution—highlighting milestones such as the installation of variable-frequency drives (VFDs) in paint and pumping systems, the introduction of rooftop solar plants, and subsequent scaling to over 26 MWp of captive solar power. Quantitative time trend analysis is paired with qualitative timeline coding to isolate the effects of internal efficiency measures from external shocks, including the COVID-19 pandemic and national grid carbon intensity changes.

The findings reveal a clear pattern of plant-level efficiency gains, punctuated by temporary setbacks linked to production disruptions and heavier vehicle models. While measures such as low-temperature primer coats, steam-to-hot-water transitions, and increased solar generation significantly reduced energy consumption and emissions intensity, these improvements were partly offset by systemic challenges—particularly India’s coal-dependent electricity grid. The research underscores the paint shop as the single most energy-intensive process, consuming between 30% and 75% of total plant energy, making it the focal point for efficiency upgrades and waste-heat recovery interventions.

Ultimately, the study concludes that Maruti Suzuki’s emission performance reflects both technological progress and structural limitations inherent to industrial decarbonization in emerging economies. The company’s experience demonstrates how targeted process optimization and renewable integration can drive meaningful intensity reductions even in complex, high-throughput manufacturing environments. By correlating data-driven efficiency trends with specific operational changes, the paper not only documents progress but also identifies actionable pathways for future emission reductions through renewable adoption, logistics optimization, and energy recovery systems.