· Public charity
Clean Air California
To promote the common good and social welfare of the people of California by educating the public and advocating on important issues of public policy, including clean energy and clean air, that will improve the quality of life and the environment in California.
Where the money goes
Grants by size, and the largest recipients.
How this is measured
By grant size · FY2022
- Under $10k3 grants · $24k
- $10k–50k2 grants · $23k
| Recipient | Amount |
|---|---|
| Democratic Party of Orange Co | $13,000 |
| SF Democratic Party | $10,000 |
| San Diego Co Democratic Par | $9,000 |
| Sacramento Co Democratic CC | $7,500 |
| Los Angeles Co Democratic Par | $7,000 |
Does the money reach high-need areas?
Each grant placed by the hardship in its grantee’s ZIP, against the area’s typical level.
Dollar for dollar, its grants (FY22–22) land where the poverty rate runs at 12%, against an area that typically sits at 11%. 70% of its dollars go to grantees based in above-average-need neighborhoods. Most of its grants land in higher-need ZIPs.
How this is measured
How this is measured
0 grantees tracked through their own filings, 2022–2022.
Each grant beside the grantee’s revenue from every source, year by year.
How this is measured
Counted here: distinct organizations funded across 2022 to 2022, not grant rows in a single year, so this will not match the grant count on the cover. 0 of the 5 grantees resolved in that span have returns of their own we could reconcile.
Each grantee is resolved to its own IRS returns. Association, dated; never a causal claim.
Where the money sits, by cause and then by grantee
Each grantee by its size and the funder’s share of it; top left is load-bearing
Grantee by grantee: a decade per organization, before and after funding
A decade per grantee: revenue shaded, grants as bars, all rows on one timeline.
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How do I get to Clean Air California?
Find your warmest path to Clean Air California through trustees and officers whose names appear on both boards. Search for your organization and Plinth traces the shortest route it can evidence.
Each link is a name appearing on two organizations’ public IRS 990 filings, matched on that name and, where the filings support it, on location. A same-state match has geographic support, which is a second matching feature rather than confirmation that the two are one person; a cross-state one is the likeliest to be two people who share a name. Every hop shows its tier, low-confidence and distant paths are held back rather than guessed, and it is worth confirming the person before you use the introduction.