Energy Recovery (ERII) Operating Expenses (2010 - 2026)
Energy Recovery (ERII) reported Operating Expenses of $14.84 million for Q2 2026, down 9.9% from $16.48 million a year earlier and down 15.5% from the prior quarter.
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Analysis
Energy Recovery (ERII) Operating Expenses (2010 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Energy Recovery's Operating Expenses came in at $62.94 million, down 13.8% year-over-year; for FY2025, it was $64.04 million, down 17.1% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 1.5% (FY2020 to FY2025).
- By year, Operating Expenses came in at $77.21 million in FY2024 (+13.5%), $68.03 million in FY2023 (+8.8%), $62.53 million in FY2022 (+8.9%) and $57.4 million in FY2021 (-3.4%).
- Five-year quarterly Operating Expenses spans a low of $13.27 million in Q3 2021 and a high of $21.51 million in Q4 2024.
- Year over year, Operating Expenses gained in three of the last eight quarters, with an average decline of 6.2%.
- The high point for year-over-year Operating Expenses in five years was Q2 2022 (growth of 23.9%); the low point was Q4 2025 (a decline of 36.5%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $17.56 million (Q1 2026), $13.65 million (Q4 2025) and $16.9 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Ecolab | 80.39 Bn | 72.13 Bn | 1.95 Bn | 1.14 Bn |
| 2 | Veralto | 24.89 Bn | 17.53 Bn | 902.00 Mn | 587.00 Mn |
| 3 | Xylem | 24.60 Bn | 19.85 Bn | 963.00 Mn | 573.00 Mn |
| 4 | Badger Meter | 3.70 Bn | 2.97 Bn | 90.77 Mn | 51.40 Mn |
| 5 | Ceco Environmental | 2.47 Bn | 2.29 Bn | 86.47 Mn | 109.35 Mn |
| 6 | NWPX Infrastructure | 933.25 Mn | 894.72 Mn | 34.36 Mn | 13.21 Mn |
| 7 | Cadiz | 342.06 Mn | 310.50 Mn | -24,000.00 | 9.81 Mn |
| 8 | Energy Recovery | 341.46 Mn | 16.62 Mn | 8.96 Mn | 14.84 Mn |
| 9 | AirJoule Technologies | 214.78 Mn | 94.41 Mn | - | 5.04 Mn |
| 10 | LanzaTech Global | 58.30 Mn | -56.83 Mn | 1.91 Mn | 18.89 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 14.84 Mn |
| Mar 31, 2026 | 17.56 Mn |
| Dec 31, 2025 | 13.65 Mn |
| Sep 30, 2025 | 16.90 Mn |
| Jun 30, 2025 | 16.48 Mn |
| Mar 31, 2025 | 17.02 Mn |
| Dec 31, 2024 | 21.51 Mn |
| Sep 30, 2024 | 18.06 Mn |
| Jun 30, 2024 | 19.58 Mn |
| Mar 31, 2024 | 18.07 Mn |
| Dec 31, 2023 | 18.89 Mn |
| Sep 30, 2023 | 16.75 Mn |
| Jun 30, 2023 | 16.13 Mn |
| Mar 31, 2023 | 16.27 Mn |
| Dec 31, 2022 | 15.29 Mn |
| Sep 30, 2022 | 16.14 Mn |
| Jun 30, 2022 | 16.28 Mn |
| Mar 31, 2022 | 14.83 Mn |
| Dec 31, 2021 | 17.18 Mn |
| Sep 30, 2021 | 13.27 Mn |
API Access
Energy Recovery Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=ERII&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ERII", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=ERII&period=max&api_key=YOUR_API_KEY");
const data = await res.json();