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India Forest, Wildlife & CRZ Clearances — PARIVESH

PARIVESH 2.0 · beyond Environmental Clearance · generated 2026-07-22

India's other green clearances — Forest, Wildlife & CRZ

The same open PARIVESH API that serves Environmental Clearances serves three more central clearances. Together they are the full green-clearance funnel a project passes through — and Forest Clearance carries something EC does not: the actual area of land, in hectares.

Forest Clearance
FC · majorClearanceType
28,999
6,134 granted 8,479 in process 2,663 dead 11,723 delisted
Grant rate of decided: 42.0% · 37 states/UTs
25,546 fresh (Form-A) · 2,097 re-diversion (Form-E) · 213 renewal · 261 transfer
Wildlife Clearance
WLC · majorClearanceType
4,919
517 granted 4,319 in process 83 dead 0 delisted
Grant rate of decided: 10.7% · 37 states/UTs
CRZ Clearance
CRZ · majorClearanceType
2,264
468 granted 1,025 in process 82 dead 689 delisted
Grant rate of decided: 31.3% · 17 states/UTs

Forest Clearance is a second land-creation channel

Every Environmental Clearance answers "may this project pollute?"; Forest Clearance answers "may this project take this forest land?" — so it is the one register that measures land quantum. Of the 1.05 million ha in the pipeline (inflated by delisted applications and one ~74,000-ha outlier), the trustworthy figure is the forest land with final diversion orders granted:

49,641 ha
forest land — final diversion granted
122,666
acres, granted
6,134
approved proposals of 28,999

For scale: the entire national industrial land bank shows ~253,720 developed-and-vacant acres. Forest diversion adds ~122,666 more acres of granted land-use change — a different channel (mining, linear infra, hydro), on a different clock.

Forest area by state

State / UTGranted haGranted acresPipeline ha
Tripura44108184,758
Chhattisgarh8,06119,920146,970
Madhya Pradesh21,14652,252146,905
Maharashtra1,0872,68688,062
Andhra Pradesh7381,82471,232
Assam7,74619,14149,780
Odisha1,6113,98044,518
Arunachal Pradesh1,0842,67942,080
Rajasthan1,3433,32035,289
Meghalaya92134,793
Jharkhand174226,582
Karnataka8120026,109

Top user agencies (FC)

AgencyProposals
MAHESH NAIR1,697
Jal Shakti Department1,223
HIMACHAL PRADESH PUBLIC WORK DEPARTMENT769
Public Works Department708
BHARAT SANCHAR NIGAM LIMITED409
Rural Development384
JIO DIGITAL FIBRE PRIVATE LTD321
Department of Irrigation279

What the three funnels say

Forest (42.0% of decided granted) — 88% are fresh Form-A diversions, but 40% of the register is auto-delisted, so the live decided pool is smaller than the headline count. Wildlife (10.7% granted) is the real bottleneck: 4,319 of 4,919 sit in process, most "Pending at Proponent" — projects near protected areas stall here for the National/State Board for Wildlife recommendation. CRZ (31.3%) is small and coastal (17 states/UTs) — the gate for ports, coastal industry and waterfront projects.

Source — PARIVESH 2.0 open API (advanceSearchData?majorClearanceType=2|3|4), full Forest / Wildlife / CRZ registers, fetched 2026-07-22. Grant rate = granted ÷ (granted + in-process); 'dead' (rejected/withdrawn/returned) and 'delisted' (PARIVESH auto-cleanup) are excluded from the denominator, as in the EC analysis. FC 'granted' = "Final Diversion Order Issued" or "Stage-II Accorded"; Stage-I in-principle is not counted. The forest-area pipeline total mixes all statuses and is skewed by a single ~74,000-ha outlier — the granted figure is the reliable one. Pipeline snapshot, not a time series. Data + refresh: github.com/herrrickshaw/india-ec-subsector-analysis. Corrections are displayed, never silent.
Disclaimer: This analysis is based on publicly available government and market data cited in the article above. It is provided for informational and research purposes only and does not constitute investment, legal, or policy advice. Figures may be revised as source data is updated — verify against the original source before relying on them for decisions.

AI Disclosure: This article was researched and written with AI assistance (Claude Sonnet), drawing on publicly available government, industry, and academic sources cited above. AI-generated text can occasionally misstate figures or "hallucinate" details even when working from real source material — readers should treat this piece as a synthesis aid, verify any figure that matters to a decision against the cited primary source, and focus on the underlying material rather than this summary alone.

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