电力碳足迹因子对钢铁行业产品碳足迹的影响

Influence of electricity carbon footprint factors on the carbon footprint of iron and steel products

  • 摘要: 钢铁工业作为国民经济的基础产业,既是能源消耗密集型行业,也是全球碳排放的重要来源. 在应对气候变化和实现“双碳”目标的背景下,产品碳足迹逐渐成为国际贸易与供应链竞争力的核心指标. 由于单位产品碳足迹较高,我国钢铁行业在可持续发展方面面临严峻挑战. 本文基于生命周期评价(Life cycle assessment, LCA)方法,核算了钢铁产业典型的三种粗钢生产工艺的产品碳足迹,包括长流程(高炉–转炉)、45%废钢比电炉短流程以及全废钢电炉短流程. 同时,采用电网排放因子计算方法,对我国区域及省级2018—2022年的年度电力碳足迹因子进行了测算与分析,形成本地化的电力碳足迹因子. 研究结果表明,电力消耗是钢铁生产碳排放的重要影响因素,不同工艺路线的电力碳足迹贡献差异显著:长流程粗钢的电力碳排放仅占总碳足迹的约7%,45%废钢比电炉短流程占比约20%,而全废钢电炉短流程则高达约58%. 本地化的电力碳足迹因子与商业数据库因子核算碳足迹结果差异较大,全废钢电炉短流程差异可达35%. 此外,我国区域及省级电力结构差异明显,西南地区以水电为主的电力结构使其碳足迹因子低于0.40 kg·kW–1·h–1,而以火电为主的华北和华东地区因子超过1.00 kg·kW–1·h–1,呈现“北高南低、西低东高”的空间格局. 2018—2022年我国区域及省级电力碳足迹因子总体呈下降趋势,体现清洁能源发电比例提升带来的减排效果. 敏感性分析结果显示,电力因子每降低0.1 kg·kW–1·h–1,可使全废钢短流程粗钢产品碳足迹减少约50 ~ 70 kg·t–1,进一步验证了电力结构优化对行业减排潜力的显著影响. 本研究通过对电力碳足迹因子与钢铁产品碳足迹的系统性量化分析,揭示了中国电力碳足迹因子的空间分布规律及其随时间变化趋势,量化了电力碳足迹因子在不同钢铁生产工艺中的影响权重,验证了本地化因子在提高钢铁产品碳足迹核算准确性与区域可比性方面的显著作用. 研究结果可为钢铁行业的低碳化转型、产能空间布局优化以及区域能源结构优化提供数据支撑与方法参考.

     

    Abstract: As a pillar of the national economy, the iron and steel industry is both energy-intensive and a primary source of global carbon emissions. In the context of global climate mitigation and China’s “Dual Carbon” targets, the product carbon footprint (PCF) has emerged as a critical metric determining competitiveness in international trade and supply chains. Given the high carbon intensity per unit of product, the Chinese steel industry faces significant challenges in achieving sustainable development. This study employs life cycle assessment (LCA) to quantify the PCFs of three typical crude steel production routes: the blast furnace–basic oxygen furnace (BF–BOF) long process, an electric arc furnace (EAF) short process with 45% scrap input, and a full-scrap EAF short process. Furthermore, using the grid emission factor method, this study calculated and analyzed annual electricity carbon footprint factors at regional and provincial levels in China from 2018 to 2022, thereby establishing a localized dataset for electricity carbon intensity. Results indicate that electricity consumption is a critical determinant of carbon emissions in steel manufacturing, with the contribution of electricity-related emissions varying significantly across routes: approximately 7% for BF–BOF, 20% for the 45% scrap EAF, and rising to 58% for the full-scrap EAF. Moreover, significant discrepancies were observed between PCFs calculated using localized electricity factors and those derived from commercial databases such as Ecoinvent. For the full-scrap EAF route, this deviation reached 35%, underscoring the need to use region-specific emission factors for accurate PCF accounting. Additionally, significant spatial heterogeneity exists in regional and provincial power structures. In hydropower-dominant southwestern China, the electricity carbon footprint factor remained below 0.40 kg·kW–1·h–1, whereas in coal-reliant northern and eastern regions, it exceeded 1.00 kg·kW–1·h–1, exhibiting a spatial pattern of “high in the north and east, low in the south and west.” From 2018 to 2022, these factors showed a general downward trend, reflecting the emission reduction benefits of increased clean energy generation. Sensitivity analysis indicates that a 0.1 kg·kW–1·h–1 decrease in the electricity factor reduces the PCF of the full-scrap EAF route by approximately 50–70 kg·t–1, confirming the substantial impact of optimizing the power structure on the industry’s decarbonization potential. By systematically quantifying the relationship between electricity factors and steel PCFs, this study elucidates the spatial and temporal trends of China’s electricity carbon footprint factors. It further quantifies the influence of these factors across different steelmaking processes, confirming that localized factors are essential for improving the accuracy and regional comparability of PCF accounting. These findings provide empirical data and methodological references to support the low-carbon transformation of the steel industry, optimization of production capacity layout, and enhancement of regional energy planning.

     

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