Romania vs Venezuela, Bolivarian Republic of: Crude steelmaking capacity
Crude steelmaking capacity over time
- Romania
- Venezuela, Bolivarian Republic of
How they compare
Venezuela, Bolivarian Republic of currently reports 5.64 Tonnes against 5.17 Tonnes in Romania, a difference of 0.47 Tonnes.
That makes Venezuela, Bolivarian Republic of's figure about 1.1 times Romania's.
The two have swapped places 4 times across 26 shared years of data; in 2000 it was Venezuela, Bolivarian Republic of ahead.
Romania ranks 34th and Venezuela, Bolivarian Republic of ranks 31st of 115 countries.
Across the 3 decades both report, Romania averaged higher in 1 and Venezuela, Bolivarian Republic of in 2.
Head to head by decade
| Decade | Romania | Venezuela, Bolivarian Republic of | Difference | Ahead |
|---|---|---|---|---|
| 2000s | 5.66 Tonnes | 5.5 Tonnes | 0.161 Tonnes | Romania |
| 2010s | 5.34 Tonnes | 5.64 Tonnes | 0.309 Tonnes | Venezuela, Bolivarian Republic of |
| 2020s | 5.17 Tonnes | 5.64 Tonnes | 0.475 Tonnes | Venezuela, Bolivarian Republic of |
Averages of every year both report within each decade.
Frequently asked questions
- Which has higher crude steelmaking capacity, Romania or Venezuela, Bolivarian Republic of?
- Venezuela, Bolivarian Republic of, at 5.64 Tonnes against 5.17 Tonnes in Romania as of 2025.
- What is the difference in crude steelmaking capacity between Romania and Venezuela, Bolivarian Republic of?
- 0.47 Tonnes, with Venezuela, Bolivarian Republic of ahead.
- How many years of comparable data are there for Romania and Venezuela, Bolivarian Republic of?
- 26 years are reported by both, from 2000 to 2025.
- How do Romania and Venezuela, Bolivarian Republic of rank globally for crude steelmaking capacity?
- Romania ranks 34th and Venezuela, Bolivarian Republic of ranks 31st of 115 countries.
- Where does this data come from?
- Organisation for Economic Co-operation and Development, published as Crude steelmaking capacity. Statizoid refreshes it automatically from the source and publishes the full history for both places.
Individual pages
About this data
This dataset provides information on crude steelmaking capacity for 132 OECD Member countries and non-OECD economies over the period 2000–2025. Definitions Steelmaking capacity Nominal crude steelmaking capacity is defined as the maximum theoretical output of installed steelmaking equipment. This definition does not account for yield losses, maintenance downtime, or other factors affecting actual production. As a result, the steelmaking capacity figures provided by the OECD should not be interpreted as effective capacity. Capacity is expressed in million metric tonnes (mmt). Annual capacity figures reflect all steelmaking capacity existing at the end of each calendar year. Steelmaking equipment Steelmaking equipment includes all equipment used to produce crude steel. The definition excludes ironmaking equipment, which is considered upstream, as well as casting, rolling, and finishing equipment, which are considered downstream. Specifically, crude steelmaking equipment includes electric arc furnaces (EAF), induction furnaces (IF) and LD basic oxygen furnaces (BOF). Methodology Steelmaking capacity developments are derived from data on existing capacity, investments, and closures. Capacity changes reflect new additions and permanent closures and therefore represent net changes. In some cases, capacity may be overestimated due to the assumption that projects classified as underway with expired start dates have become operational. Only permanent closures are included in capacity calculations, although some closures cannot be clearly classified due to limited available information. Data sources OECD calculations based on desk research, company websites, publicly available news sources and information exchanges within the OECD Steel Committee. Cite this dataset OECD (2025). Crude steelmaking capacity dataset. Accessed on [Day Month Year]. Further information OECD Steel Outlook 2025 OECD Steel Committee Global Forum on Steel Excess Capacity For questions or comments regarding this dataset, please contact Maika Sakamoto (Maika.SAKAMOTO@oecd.org) or Masanobu Nakamizu (Masanobu.NAKAMIZU@oecd.org).