Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Syrian Arab Republic

Syrian Arab Republic: Consumption of Fixed Capital (Agriculture, Forestry and Fishing) was 1.04 million million SLC in 2023. ◆ Volatile

Latest (2023)
1.04 million million SLC
Change on year
up 52.7%
World rank
5th
of 10 countries
All-time high
1.04 million million SLC
in 2023
All-time low
13,192 million SLC
in 1995
Years of data
29
1995–2023

Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Syrian Arab Republic, 1995–2023

0250.0k500.0k750.0k1.0M1995200920231995: 13.2k million SLC1996: 14.5k million SLC1997: 15.2k million SLC1998: 15.9k million SLC1999: 15.9k million SLC2000: 16.9k million SLC2001: 19.5k million SLC2002: 18.8k million SLC2003: 20.2k million SLC2004: 21.1k million SLC2005: 24.9k million SLC2006: 26.1k million SLC2007: 32.3k million SLC2008: 34.1k million SLC2009: 33.9k million SLC2010: 39.1k million SLC2011: 43.4k million SLC2012: 51.4k million SLC2013: 56.9k million SLC2014: 66.0k million SLC2015: 72.3k million SLC2016: 76.0k million SLC2017: 88.5k million SLC2018: 108.1k million SLC2019: 151.4k million SLC2020: 233.6k million SLC2021: 421.2k million SLC2022: 682.0k million SLC2023: 1.0M million SLC

Source: Food and Agriculture Organization of the United Nations. Measured in million SLC.

Analysis

Syrian Arab Republic recorded 1.04 million million SLC for consumption of fixed capital (agriculture, forestry and fishing) in 2023. That is the highest value across all 29 years on record.

That represents a change of up 52.7% on the previous year and up 1,730.1% over ten years.

Over the whole period, consumption of fixed capital (agriculture, forestry and fishing) in Syrian Arab Republic peaked at 1.04 million million SLC in 2023 and was at its lowest, 13,192 million SLC, in 1995.

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 Syrian Arab Republic, year by year

Annual values for Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value Standard Local Currency in Syrian Arab Republic, 1995 to 2023.
Year million SLC Change
1995 13,192 million SLC
1996 14,501 million SLC +9.9%
1997 15,155 million SLC +4.5%
1998 15,902 million SLC +4.9%
1999 15,883 million SLC -0.1%
2000 16,934 million SLC +6.6%
2001 19,454 million SLC +14.9%
2002 18,757 million SLC -3.6%
2003 20,171 million SLC +7.5%
2004 21,066 million SLC +4.4%
2005 24,896 million SLC +18.2%
2006 26,064 million SLC +4.7%
2007 32,297 million SLC +23.9%
2008 34,136 million SLC +5.7%
2009 33,934 million SLC -0.6%
2010 39,104 million SLC +15.2%
2011 43,393 million SLC +11.0%
2012 51,424 million SLC +18.5%
2013 56,911 million SLC +10.7%
2014 66,007 million SLC +16.0%
2015 72,280 million SLC +9.5%
2016 76,026 million SLC +5.2%
2017 88,548 million SLC +16.5%
2018 108,110 million SLC +22.1%
2019 151,422 million SLC +40.1%
2020 233,554 million SLC +54.2%
2021 421,228 million SLC +80.4%
2022 681,967 million SLC +61.9%
2023 1.04 million million SLC +52.7%

Averages by decade

DecadeAverage LowestHighest Years
1990s 14,927 million SLC 13,192 million SLC 15,902 million SLC 5
2000s 24,771 million SLC 16,934 million SLC 34,136 million SLC 10
2010s 75,323 million SLC 39,104 million SLC 151,422 million SLC 10
2020s 594,570 million SLC 233,554 million SLC 1.04 million million SLC 4

Countries ranked near Syrian Arab Republic

  1. 2 Nigeria 12.65 million million SLC compare
  2. 2 Viet Nam 100.34 million million SLC compare
  3. 3 Cabo Verde 1,375 million SLC compare
  4. 3 Lao People's Democratic Republic 2.30 million million SLC compare
  5. 3 Uzbekistan 11.17 million million SLC compare
  6. 4 Republic of Korea 8.80 million million SLC compare
  7. 4 United Republic of Tanzania 1.97 million million SLC compare
  8. 5 Somalia 5.64 million million SLC compare
  9. 6 Lebanon 5.51 million million SLC compare
  10. 6 Türkiye 191,619 million SLC compare
  11. 7 Colombia 5.48 million million SLC compare
  12. 7 Democratic Republic of the Congo 154,505 million SLC compare
  13. 8 Bolivia (Plurinational State of) 3,107 million SLC compare
  14. 8 Guinea 3.24 million million SLC compare

See the full ranking of 194 places →

More environment data for Syrian Arab Republic

All data for Syrian Arab Republic →

Frequently asked questions

What is consumption of fixed capital (agriculture, forestry and fishing) in Syrian Arab Republic?
Consumption of fixed capital (agriculture, forestry and fishing) in Syrian Arab Republic was 1.04 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 Syrian Arab Republic?
The highest recorded value was 1.04 million million SLC in 2023.
What is the lowest consumption of fixed capital (agriculture, forestry and fishing) recorded in Syrian Arab Republic?
The lowest recorded value was 13,192 million SLC in 1995.
How does Syrian Arab Republic rank for consumption of fixed capital (agriculture, forestry and fishing)?
Syrian Arab Republic ranks 5th out of 10 countries with data for 2023.
Is consumption of fixed capital (agriculture, forestry and fishing) rising or falling in Syrian Arab Republic?
Over the last ten years it is up 1,730.1%. The long-run trend across the full record is volatile.
Where does this Syrian Arab Republic 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.

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Consumption of Fixed Capital (Agriculture, Forestry and Fishing) in Syrian Arab Republic. Statizoid, drawing on Food and Agriculture Organization of the United Nations. Retrieved 11 September 2026, from https://environment.statizoid.com/stat/consumption-of-fixed-capital-agriculture-forestry-and-fishing-value-standard-local/syrian-arab-republic-2/

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About this data

Indicator
Consumption of Fixed Capital (Agriculture, Forestry and Fishing) — Value Standard Local Currency
Unit
million SLC
Source
Food and Agriculture Organization of the United Nations
Licence
CC BY-NC-SA 3.0 IGO (FAO)
Coverage
194 places, 5,516 data points, 1995–2023
Last refreshed

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.