This paper develops and operationalizes the Data-Constrained Greenwashing Asymmetry Coefficient (C-GAC), a novel metric designed to measure corporate greenwashing under the actual disclosure conditions of emerging markets. Applied to a purposive cross-industry panel of six NIFTY 50 constituents—Infosys (Information Technology), HDFC Bank (Financials), Hindustan Unilever (Consumer Staples), Tata Steel (Materials), Mahindra & Mahindra (Consumer Discretionary), and Reliance Industries (Energy)—supplemented by Sun Pharma (Healthcare) as a seventh case, over fiscal years 2022 to 2025, the C-GAC relies exclusively on verifiable emissions intensity and a transparent Disclosure Intensity rubric. We treat data scarcity not as a methodological inconvenience but as the central empirical fact of Indian ESG markets, shaping the reliability of corporate disclosures, limiting investor verification, and creating substantial challenges for accurately identifying and measuring greenwashing. Our results reveal a bifurcated landscape: Infosys and HDFC Bank exhibit authentic alignment (C-GAC < 1). Classifications at the extremes are sensitive to benchmark choice: against the full calculable-sample benchmark only Tata Steel breaches the threshold Γ, reflecting the structural emissions intensity of steelmaking, while excluding it flags Sun Pharma in both FY24 and FY25. Mahindra & Mahindra's coefficient rises but remains below Γ. At the sector level, Energy exceeds Γ in FY22-23 and FY23-24 and sits at the threshold in FY24-25 once non-reporting firms are imputed. Reliance Industries shows an information blackout, where missing emissions data makes the coefficient impossible to calculate, creating another potential form of greenwashing through opacity. The paper offers both a mathematical case study and policy recommendations, devoting equal analytical weight to the formal properties of the coefficient and to eight concrete regulatory recommendations for SEBI. We further present the full GAC—incorporating NLP-based linguistic scoring, green marketing expenditure, and sector-specific elasticity—as a suggestive aspirational framework that becomes implementable once the data infrastructure gaps identified herein are closed.