Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Lebanon
Lebanon: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 5.51 million million SLC in 2023. ◆ Volatile
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Lebanon, 1995–2023
Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.
Analysis
The most recent figure for consumption of fixed capital (agriculture, forestry and fishing) in Lebanon is 5.51 million million SLC, measured in 2023. That is the highest value across all 29 years on record.
The figure is up 66.6% on the previous year and up 3,168.9% over ten years.
Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in Lebanon peaked at 5.51 million million SLC in 2023 and was at its lowest, 70,402 million SLC, in 1995.
That places Lebanon 6th out of 181 countries with data for 2023, putting it in the top 10%.
The series is highly variable year to year, so single readings are best treated with caution.
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Lebanon, year by year
| Year | million SLC | Change |
|---|---|---|
| 1995 | 70,402 million SLC | — |
| 1996 | 70,893 million SLC | +0.7% |
| 1997 | 85,378 million SLC | +20.4% |
| 1998 | 88,240 million SLC | +3.4% |
| 1999 | 86,016 million SLC | -2.5% |
| 2000 | 82,486 million SLC | -4.1% |
| 2001 | 80,112 million SLC | -2.9% |
| 2002 | 82,421 million SLC | +2.9% |
| 2003 | 85,495 million SLC | +3.7% |
| 2004 | 88,563 million SLC | +3.6% |
| 2005 | 87,092 million SLC | -1.7% |
| 2006 | 110,371 million SLC | +26.7% |
| 2007 | 107,617 million SLC | -2.5% |
| 2008 | 117,318 million SLC | +9.0% |
| 2009 | 130,031 million SLC | +10.8% |
| 2010 | 126,822 million SLC | -2.5% |
| 2011 | 137,439 million SLC | +8.4% |
| 2012 | 156,749 million SLC | +14.0% |
| 2013 | 168,600 million SLC | +7.6% |
| 2014 | 173,125 million SLC | +2.7% |
| 2015 | 172,875 million SLC | -0.1% |
| 2016 | 167,794 million SLC | -2.9% |
| 2017 | 170,452 million SLC | +1.6% |
| 2018 | 174,449 million SLC | +2.3% |
| 2019 | 179,409 million SLC | +2.8% |
| 2020 | 544,256 million SLC | +203.4% |
| 2021 | 1.25 million million SLC | +129.3% |
| 2022 | 3.31 million million SLC | +165.0% |
| 2023 | 5.51 million million SLC | +66.6% |
Averages by decade
| Decade | Average | Lowest | Highest | Years |
|---|---|---|---|---|
| 1990s | 80,186 million SLC | 70,402 million SLC | 88,240 million SLC | 5 |
| 2000s | 97,151 million SLC | 80,112 million SLC | 130,031 million SLC | 10 |
| 2010s | 162,771 million SLC | 126,822 million SLC | 179,409 million SLC | 10 |
| 2020s | 2.65 million million SLC | 544,256 million SLC | 5.51 million million SLC | 4 |
Countries ranked near Lebanon
- 3 Cabo Verde 1,375 million SLC compare
- 3 Lao People's Democratic Republic 2.30 million million SLC compare
- 3 Uzbekistan 11.17 million million SLC compare
- 4 Republic of Korea 8.80 million million SLC compare
- 4 United Republic of Tanzania 1.97 million million SLC compare
- 5 Somalia 5.64 million million SLC compare
- 5 Syrian Arab Republic 1.04 million million SLC compare
- 6 Türkiye 191,619 million SLC compare
- 7 Colombia 5.48 million million SLC compare
- 7 Democratic Republic of the Congo 154,505 million SLC compare
- 8 Bolivia (Plurinational State of) 3,107 million SLC compare
- 8 Guinea 3.24 million million SLC compare
- 9 India 2.29 million million SLC compare
- 9 Timor-Leste 28.96 million SLC compare
More environment data for Lebanon
- Historical exposure to drought — Land soil moisture anomaly -12.11 Percentage change (2025)
- Historical exposure to drought — Cropland soil moisture anomaly -12.31 Percentage change (2025)
- Standard Deviation, annual growth rate 0 % change on previous year (2025)
- Standard Deviation 0.5 °C (2025)
- Temperature change 1.89 °C (2025)
- Roundwood, non-coniferous — Production, annual growth rate 0 % change on previous year (2024)
- Wood fuel — Production, annual growth rate -1.97 % change on previous year (2024)
- Wood fuel, non-coniferous — Production, annual growth rate 0 % change on previous year (2024)
- Industrial roundwood — Production, annual growth rate 0 % change on previous year (2024)
- Industrial roundwood — Import quantity, per unit of GDP 0 m3 per US$ of GDP (2024)
Frequently asked questions
- What is consumption of fixed capital (agriculture, forestry and fishing) in Lebanon?
- Consumption of fixed capital (agriculture, forestry and fishing) in Lebanon was 5.51 million million SLC in 2023, according to Food and Agriculture Organization of the United Nations.
- What is the highest consumption of fixed capital (agriculture, forestry and fishing) recorded in Lebanon?
- The highest recorded value was 5.51 million million SLC in 2023.
- What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in Lebanon?
- The lowest recorded value was 70,402 million SLC in 1995.
- How does Lebanon rank for consumption of fixed capital (agriculture, forestry and fishing)?
- Lebanon ranks 6th out of 181 countries with data for 2023.
- Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in Lebanon?
- Over the last ten years it is up 3,168.9%. The long-run trend across the full record is volatile.
- Where does this Lebanon data come from?
- The figures come from Food and Agriculture Organization of the United Nations, published as part of Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value Standard Local Currency. Statizoid updates them automatically from the source API.
Download this data
CSV · JSON — 29 observations, free to reuse under CC BY-NC-SA 3.0 IGO (FAO).
About this data
As part of the FAO Agriculture Capital Stock (ACS) database, the Statistics Division of FAO publishes country-by-country data on physical investment in agriculture, forestry and fishing as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF). Additional variables included in the ACS are Net and Gross Capital Stock, Consumption of Fixed Capital, the Agriculture Investment ratio, and the Gross Fixed Capital Formation Agriculture Orientation Index. The FAO Agriculture Capital Stock Database is an analytical database: whenever available, the database integrates official National Accounts data harvested from the UNSD National Accounts Main Aggregates Database (UNSD AMA) and the OECD Annual National Accounts Database (OECD ANA). The database is further supplemented by OECD Structural Analysis database (OECD STAN) and, in a few cases, data from country’s statistics websites. If the full set of official data is not available for any specific country, imputation methods are applied to obtain estimates over the complete time series. Many data points in ACS are estimated and are flagged as such; they do not represent official submissions by Member Countries. With a view of producing internationally comparable net capital stock estimates, the Perpetual Inventory Method (PIM) with a constant geometric depreciation rate is employed to impute missing data. The Perpetual Inventory Method is a well-established economic model to calculate Net Capital Stocks (NCS) and Consumption of Fixed Capital (CFC) from time series of Gross Fixed Capital Formation (GFCF). Specifically, annual measures of the NCS are obtained from cumulating historical series on physical investment flows and deducting the part of assets that are depreciated (the Consumption of Fixed Capital that occurs in every period). In order to implement the PIM, long time series on aggregate GFCF in agriculture, forestry and fishing is required.An attempt is made to rely as much as possible on National Accounts data published by the OECD and UNSD. When country data are partially or fully missing, econometric techniques to impute missing observations are employed. Depending on the pattern of data missingness for the countries, different imputation methods are applied (from among the ARIMAX, PANEL regression, and OLS approaches) for the data series from 1995 to 2022. The values of Agriculture Capital Stock related indicators for 2023, including Agriculture Investment Ratio, Agriculture Orientation Index, Net Capital Stock, Gross Fixed Capital Formation and Consumption of Fixed Capital, are estimated using the Holt-Winters (HW) method (Cipra et al., 1995). The HW method is an exponential smoothing method for forecasting the annual values of economic variables. In this context, the HW method relies on existing (historical) values of the Agriculture Capital Stock. The predicted value is an extrapolation of the historical values to the specified target date, which extends the timeline without considering seasonality in the annual series.All data series in the database are provided both in national currencies and in US dollars as well as in current prices and constant prices with base year 2015.