Key Takeaways
- Energy is the largest controllable operating cost in steelmaking; energy costs can represent roughly 20%–40% of steel production costs depending on region and process (cost share range)
- Steelmaking can generate around 0.8–1.2 tonnes of waste per tonne of crude steel in certain plant operations (waste generation range)
- Waste heat recovery projects in steel can improve overall energy efficiency by 5%–15% (efficiency from recovered heat)
- Steel production contributed about 7% of global industrial energy-related greenhouse gas emissions in 2020
- Nearly all (about 98%) blast furnaces and integrated steelworks use coal-based processes rather than direct reduction routes (global structure figure)
- In 2022, 9.7% of global crude steel capacity was based on electric arc furnaces (trend level)
- The share of scrap in steelmaking is projected to increase to about 30% by 2050 in many scenarios (scrap availability trend)
- EU steel producers must meet quarterly monitoring and reporting obligations under EU ETS rules for installations (compliance practice measure)
- Electric arc furnaces can be up to 60%–70% more energy efficient than basic oxygen furnace primary routes when run with modern scrap-based practice (energy efficiency comparison)
- In the European cementitious sector, clinker substitution rates of 15%–30% are shown to reduce CO2; by analogy, similar material-efficiency improvements are a key abatement lever in steel value chains (industrial decarbonization lever quantified)
- SCR (selective catalytic reduction) can reduce nitrogen oxides (NOx) emissions by about 70%–90% in combustion sources used in steel plants
- The global steel market size was about $1.8 trillion in 2023 (industry estimate)
- The EU CBAM requires reporting of embedded emissions for covered goods starting in a transition period in 2023
Energy dominates steel costs and emissions, making efficiency and cleaner routes like scrap EAFs key to decarbonization.
Cost Analysis
Cost Analysis Interpretation
Emissions & Intensity
Emissions & Intensity Interpretation
Industry Trends
Industry Trends Interpretation
Technology & Abatement
Technology & Abatement Interpretation
Market Size
Market Size Interpretation
How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Stefan Wendt. (2026, February 13). Sustainability In The Steel Industry Statistics. Gitnux. https://gitnux.org/sustainability-in-the-steel-industry-statistics
Stefan Wendt. "Sustainability In The Steel Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/sustainability-in-the-steel-industry-statistics.
Stefan Wendt. 2026. "Sustainability In The Steel Industry Statistics." Gitnux. https://gitnux.org/sustainability-in-the-steel-industry-statistics.
References
- 1iea.org/reports/iron-and-steel-sector-emissions
- 4iea.org/reports/carbon-capture-utilisation-and-storage
- 6iea.org/reports/global-hydrogen-review-2023
- 11iea.org/reports/the-future-of-hydrogen
- 13iea.org/reports/iron-and-steel-technology-pathways
- 18iea.org/reports/cement
- 2oecd-ilibrary.org/environment/the-iron-and-steel-sector-in-figures_9789264100956-en
- 3irena.org/publications/2017/Jan/Energy-Waste-Heat
- 10irena.org/publications/2021/Apr/World-iron-and-steel-in-figures-2021
- 5reuters.com/business/sustainable-business/low-carbon-steel-investment-passes-10-billion-in-2023-report-2024-01-
- 7worldbank.org/en/research/commodity-markets
- 8ember-climate.org/data/data-tools/european-energy-data/
- 9iiasa.ac.at/publication/energy-efficiency-in-industry-digitalization
- 12worldsteel.org/steel-issues/steel-statistics/
- 17worldsteel.org/steel-issues/steel-technology/
- 14eur-lex.europa.eu/eli/reg/2018/2066/oj
- 15eur-lex.europa.eu/eli/reg/2023/956/oj
- 16eur-lex.europa.eu/eli/reg_impl/2019/159/oj
- 19epa.gov/sites/default/files/2016-03/documents/cap-and-trade-no-control-tech.pdf
- 20epa.gov/sites/default/files/2017-09/documents/so2-control-techniques.pdf
- 21statista.com/statistics/270573/global-steel-production-value/
- 22taxation-customs.ec.europa.eu/carbon-border-adjustment-mechanism_en







