Water footprint of China’s coal mining sector substantially underestimated

• Develops China’s coal mining water footprint framework based on 12,378 coal mines database. • Identifies 10.58-fold disparities in water footprint intensity across 208 technological configurations. • Demonstrates spatial mismatch: concentrated production vs. nationwide consumption. • Highlights prioritized COD, SS, BOD 5 , and heavy metal control throughout whole life cycle. • Quantifies technological structure, supply structure, and consumption demand as key transition drivers. China faces severe water scarcity intensified by the spatial mismatch between coal reserves and water resources. Accurately assessing the water footprint of coal mining is essential for quantifying water-related risks and guiding sustainable practices. However, existing studies often lack systematic frameworks that integrate grey water footprint accounting for heavy metals and other pollutants, leading to impact underestimation. To address this gap, we develop a comprehensive water footprint assessment framework based on an improved method incorporating 16 pollutants. It employs spatial analyses, long-term trends, and both production- and consumption-side perspectives, using data from 12,378 coal mines operated during 2013–2022. Key findings indicate that water footprint intensities vary from 3.16 to 33.44 m 3 /t across 208 technological configurations, with grey water contributing over 85 %. Large-surface mines show the lowest intensity, highlighting their water-saving advantage. Over the decade, China’s coal production water footprint intensity declined by 17.76 %, while the consumption-side fell by 16.75 %. Total water footprint for coal production and consumption decreased from 43.5 Gm 3 to 38 Gm 3 . Virtual water flows primarily follow a north–to–south and west–to–east pattern, peaking between Shanxi and Hebei (>1 Gm 3 ). Technological advancements reduced the national consumption-side water footprint by 7.99 Gm 3 . Supply chain optimization drove greater reductions in Fujian, Hunan, Jiangxi, and Chongqing. By rectifying grey water footprint underestimation and emphasizing heavy metal control, this study provides a scientific basis for spatially targeted water governance and cross-regional ecological compensation. The proposed framework has broad international applicability, especially for regions facing similar coal-water conflicts.

Authors

Institutions

Publication Details

Journal
Ecological Indicators
Published
2025-10-22
DOI
https://doi.org/10.1016/j.ecolind.2025.114310
Citations
3
Primary Topic
Water-Energy-Food Nexus Studies
Type
article
Field-Weighted Citation Impact
2.45
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Water footprint of China’s coal mining sector substantially underestimated

Yulong Yan, Jie Zhang, Junjie Li, Sheng Zhang et al.
3 citations
Ecological Indicators
Water-Energy-Food Nexus Studies
2.45
article

Water footprint of China’s coal mining sector substantially underestimated

Yulong Yan, Jie Zhang, Junjie Li, Sheng Zhang, Lin Peng, Yirong Wang, Zhen Li, Kechang Xie, Menggang Li
article en
3 citations

Abstract

• Develops China’s coal mining water footprint framework based on 12,378 coal mines database. • Identifies 10.58-fold disparities in water footprint intensity across 208 technological configurations. • Demonstrates spatial mismatch: concentrated production vs. nationwide consumption. • Highlights prioritized COD, SS, BOD 5 , and heavy metal control throughout whole life cycle. • Quantifies technological structure, supply structure, and consumption demand as key transition drivers. China faces severe water scarcity intensified by the spatial mismatch between coal reserves and water resources. Accurately assessing the water footprint of coal mining is essential for quantifying water-related risks and guiding sustainable practices. However, existing studies often lack systematic frameworks that integrate grey water footprint accounting for heavy metals and other pollutants, leading to impact underestimation. To address this gap, we develop a comprehensive water footprint assessment framework based on an improved method incorporating 16 pollutants. It employs spatial analyses, long-term trends, and both production- and consumption-side perspectives, using data from 12,378 coal mines operated during 2013–2022. Key findings indicate that water footprint intensities vary from 3.16 to 33.44 m 3 /t across 208 technological configurations, with grey water contributing over 85 %. Large-surface mines show the lowest intensity, highlighting their water-saving advantage. Over the decade, China’s coal production water footprint intensity declined by 17.76 %, while the consumption-side fell by 16.75 %. Total water footprint for coal production and consumption decreased from 43.5 Gm 3 to 38 Gm 3 . Virtual water flows primarily follow a north–to–south and west–to–east pattern, peaking between Shanxi and Hebei (>1 Gm 3 ). Technological advancements reduced the national consumption-side water footprint by 7.99 Gm 3 . Supply chain optimization drove greater reductions in Fujian, Hunan, Jiangxi, and Chongqing. By rectifying grey water footprint underestimation and emphasizing heavy metal control, this study provides a scientific basis for spatially targeted water governance and cross-regional ecological compensation. The proposed framework has broad international applicability, especially for regions facing similar coal-water conflicts.

Ecological IndicatorsVol. 180
Beijing Jiaotong University (CN), Taiyuan University of Technology (CN)
Openalex Percentile: Top 10%
Water-Energy-Food Nexus Studies
2.45
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.