The EC register is the earliest public, machine-readable signal of where physical investment is headed — before IEM implementation, before commissioning. Flagship-project absences (e.g. POSCO-JSW JV, RRPCL) are as informative as the grants.
Headline numbers
What the dashboard shows
- State × sub-sector matrix — which states clear what: mining-heavy registers (MP, Chhattisgarh, Rajasthan minor minerals) vs industrial ECs (Gujarat, Maharashtra) vs infrastructure corridors.
- The approval funnel — the real attrition is auto-delisting (22%) and pipeline backlog, not rejection; see the companion post EC Approval Funnel.
- Fresh vs reuse — only 49% are new projects; 22% are mostly re-appraisals of expiring minor-mineral leases on the same plots, plus transfers, expansions and amendments.
- Beyond EC — the Forest / Wildlife / CRZ registers are covered in the companion post Forest, Wildlife & CRZ Clearances: Wildlife is the true bottleneck (only 10.7% of decided wildlife proposals are granted).
Why it matters
The headline 88.7% grant rate makes India's EC process look more permissive than the underlying dynamics suggest. Real attrition here isn't rejection — it's auto-delisting and pipeline backlog, and only about half of all proposals are genuinely new projects rather than re-appraisals, transfers, expansions and amendments of the same underlying assets. For anyone reading state-level clearance counts as a proxy for new industrial capacity, that fresh-vs-reuse split is the number that actually matters, and the state × sub-sector breakdown shows where the concentration sits — not evenly across India's economy, but clustered in a handful of mining- and industrial-heavy states.
Source: PARIVESH 2.0 open API, full EC register, fetched 22 Jul 2026. Data, methodology and refresh scripts: github.com/herrrickshaw/india-ec-subsector-analysis. The dashboard is served from GitHub Pages, so it always reflects the repo's latest data.
Why the state pattern isn't random
The state × sub-sector split matters because it separates two very different kinds of clearance activity that a national grant-rate number blends together. States like Madhya Pradesh, Chhattisgarh and Rajasthan carry disproportionately large EC registers because of minor-mineral mining — and mining clearances have a structural reason to recur on the same plots: a mining lease has a fixed term, and its renewal or re-appraisal shows up in the register as a new proposal even though no new land is being disturbed. That's a large share of what sits behind this piece's 49% fresh-vs-reuse split. Gujarat and Maharashtra's registers, by contrast, skew toward industrial and infrastructure ECs — plants, corridors, ports — which are more likely to represent genuinely new capacity landing rather than an existing lease renewing itself on paper.
That distinction is the reason this piece treats the 88.7% grant rate as a starting point rather than a conclusion. A state with a high grant rate built mostly from mining re-appraisals is not attracting more new investment than a state with a lower rate built mostly from first-time industrial ECs — the two numbers describe different things wearing the same label. Anyone using EC counts as a leading indicator of where capital is actually going (the way this piece's own opening line frames it, ahead of IEM implementation data and commissioning) needs the fresh-vs-reuse split and the sub-sector mix, not just the state total, or the read is backwards.
Caveats. The state and sub-sector breakdowns described above are drawn from the same PARIVESH 2.0 register this piece's headline figures use, via the linked GitHub-hosted dashboard; the exact per-state grant-rate figures are not reproduced in this article's own text because they update continuously on the dashboard itself (it re-pulls from the repo's latest data on every load) — treat this piece's qualitative pattern (mining-heavy states skew toward reuse, industrial states skew toward fresh capacity) as the takeaway, and the dashboard as the place to pull today's exact number for any one state.
About this article: Researched, written and edited by Umashankar Triplicane Dwarakanathan, with AI research assistance; every figure is meant to trace to the primary source cited. See the Editorial Policy for how sourcing, AI use and corrections work.
