Carbon capture and storage (CCS) has substantial potential for deep decarbonization of the steel sector. However, long-term transformations within this sector lead to significant changes in steel units, posing challenges for CCS deployment. Here, we integrate sector-level transformation pathways by 2060 to simulate the distribution of China’s steel units and generate optimal CCS deployment schemes using a source-sink matching model. Results indicate that CCS accounts for 31.4-40.7% of carbon mitigation effects in China’s steel sector by 2060. Following the sector-level pathways, over 650 steel units will either be eliminated or retrofitted. The optimal CCS deployment schemes can achieve carbon mitigation effects of 472.4-609.6 Mt at levelized costs of 187.4-193.5 Chinese Yuan t⁻¹ CO₂, demonstrating cost-effectiveness under future carbon price levels. Nevertheless, the proposed schemes will lead to energy and water consumption of 951.0-1427.3 PJ and 1.60-1.69 million m³, respectively, posing a risk of resource scarcity. These insights inform the development of CCS implementation strategies in China’s steel sector and beyond, promoting deep decarbonization throughout society.

Hydrogen-based direct reduced iron (H₂-DRI) is crucial for decarbonizing the steel sector but is limited by the availability of renewable energy. Here, we propose H₂-DRI deployment schemes in China’s steel sector at moderate and aggressive scales, incorporating three renewable energy sources with a resolution of 1 km × 1 km across 570 steel units. Results indicate that 52.6–55.8% of China’s current steel units lack sufficient renewable energy supply for H₂-DRI deployment due to uneven distribution of these energy sources. Renewable energy can fulfill 97-100% of hydrogen demand at the moderate scale, whereas the aggressive scale requires supplemented fossil fuels accounting for one-third to one-half. H₂-DRI can decarbonize steel production to 0.15–0.91 t CO₂ t^-1 steel at the moderate scale, but the emissions would raise by up to over sixfold at the aggressive scale. Furthermore, H₂-DRI fueled by solar and wind energy exhibits poorer economic and water usage performance at the aggressive scale. We highlight the necessity of avoiding excessive H₂-DRI deployment and recommend prioritizing its implementation in steel units located in regions with abundant solar and wind sources nearby.

Matching dependencies (MDs) have recently been proposed to make data dependencies tolerant to various information representations, and found useful in data quality applications such as record matching. Instead of the strict equality function used in traditional dependency syntax (e.g., functional dependencies), MDs specify constraints based on similarity and identification. However, in practice, MDs may still be too strict and applicable only in a subset of tuples in a relation. Thereby, we study the conditional matching dependencies (CMDs), which bind matching dependencies only in a certain part of a table, i.e., MDs conditionally applicable in a subset of tuples. Compared to MDs, CMDs have more expressive power that enables them to satisfy wider application needs. In this article, we study several important theoretical and practical issues of CMDs, including irreducible CMDs with respect to the implication, discovery of CMDs from data, reliable CMDs agreed most by a relation, approximate CMDs almost satisfied in a relation, and finally applications of CMDs in record matching and missing value repairing. Through an extensive experimental evaluation in real data sets, we demonstrate the efficiency of proposed CMDs discovery algorithms and effectiveness of CMDs in real applications.

This study corroborates the asymmetric and heterogeneous associations between clean energy intensity (CEI) and carbon dioxide (CO₂) emissions among the world's top ten renewable energy consumer countries using quarterly data from 1970Q1 to 2018Q4. We quantify the complete dependence structure between CEI and CO₂ at quantile distributions using a novel quantile-on-quantile (QQ) method proposed by Sim and Zhou (2015). Compared to classic approaches such as quantile regression and ordinary least squares, the QQ technique can provide more information on the overall relationship between CEI and CO₂. Furthermore, we also seek to determine causal relationships between CEI and CO₂ using a quantile Granger causality approach suggested by Troster. According to our empirical evidence, the link between the two variables is predominantly negative. Moreover, there are significant disparities across countries in the quantile ranges of CEI and CO₂. In particular, there is a weak positive link between CEI and CO₂ in the case of Sweden, Italy, Japan, and Australia, which may be because CEI has a minimal direct influence on CO₂ in these countries. The empirical findings clarify that policymakers should fund renewable energy industries to minimize carbon dioxide (CO₂) emissions and achieve Sustainable Development Goals (SDGs).