A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in China
Water resource utilization is crucial for sustainable development, and enhancing water resource green efficiency (WRGE) is essential for addressing water scarcity. This study presents three key innovations: (1) It applies the super-efficiency epsilon-based measurement and global Malmquist–Luenberger...
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Elsevier
2025-06-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25004704 |
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| author | Naiming He Rijia Ding |
| author_facet | Naiming He Rijia Ding |
| author_sort | Naiming He |
| collection | DOAJ |
| description | Water resource utilization is crucial for sustainable development, and enhancing water resource green efficiency (WRGE) is essential for addressing water scarcity. This study presents three key innovations: (1) It applies the super-efficiency epsilon-based measurement and global Malmquist–Luenberger index (Super-EBM-GML) model from the perspective of high-quality economic development to analyze the spatiotemporal characteristics of WRGE across 30 Chinese provinces from 2014 to 2022. (2) It uses the Technology-Organization-Environment (TOE) framework and dynamic qualitative comparative analysis (QCA) model to identify the key drivers of WRGE and regional variations. (3) It integrates machine learning (light gradient-boosting machine with Shapley additive explanations; LightGBM-SHAP) with QCA to quantify the impact of variables, combining qualitative and quantitative analysis. Key findings include the following: (1) WRGE showed an upward trend, with higher efficiency in the eastern and economically developed regions. Growth in the GML index was mainly driven by green technological progress (GTC). (2) Although no single necessary condition was found to drive WRGE, three model types and four configuration paths were identified: technology–environment-driven, environment-driven, and organization–environment-driven. (3) The most influential factors were digital economy development, followed by industrial structure rationalization and environmental regulation. This study provides key policy recommendations, including the promotion of green technology, the strengthening of regulations, the enhancement of policy resilience, the implementation of region-specific strategies, and the integration of the digital economy with water resource management, thus offering valuable insights for regions facing water scarcity. |
| format | Article |
| id | doaj-art-fb504f68e5cf48569a9df7a9dbf0fe51 |
| institution | OA Journals |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | Ecological Indicators |
| spelling | doaj-art-fb504f68e5cf48569a9df7a9dbf0fe512025-08-20T01:57:12ZengElsevierEcological Indicators1470-160X2025-06-0117511354010.1016/j.ecolind.2025.113540A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in ChinaNaiming He0Rijia Ding1School of Management, China University of Mining and Technology (Beijing), No.11, Ding, College Road, Beijing 100083, ChinaCorresponding author.; School of Management, China University of Mining and Technology (Beijing), No.11, Ding, College Road, Beijing 100083, ChinaWater resource utilization is crucial for sustainable development, and enhancing water resource green efficiency (WRGE) is essential for addressing water scarcity. This study presents three key innovations: (1) It applies the super-efficiency epsilon-based measurement and global Malmquist–Luenberger index (Super-EBM-GML) model from the perspective of high-quality economic development to analyze the spatiotemporal characteristics of WRGE across 30 Chinese provinces from 2014 to 2022. (2) It uses the Technology-Organization-Environment (TOE) framework and dynamic qualitative comparative analysis (QCA) model to identify the key drivers of WRGE and regional variations. (3) It integrates machine learning (light gradient-boosting machine with Shapley additive explanations; LightGBM-SHAP) with QCA to quantify the impact of variables, combining qualitative and quantitative analysis. Key findings include the following: (1) WRGE showed an upward trend, with higher efficiency in the eastern and economically developed regions. Growth in the GML index was mainly driven by green technological progress (GTC). (2) Although no single necessary condition was found to drive WRGE, three model types and four configuration paths were identified: technology–environment-driven, environment-driven, and organization–environment-driven. (3) The most influential factors were digital economy development, followed by industrial structure rationalization and environmental regulation. This study provides key policy recommendations, including the promotion of green technology, the strengthening of regulations, the enhancement of policy resilience, the implementation of region-specific strategies, and the integration of the digital economy with water resource management, thus offering valuable insights for regions facing water scarcity.http://www.sciencedirect.com/science/article/pii/S1470160X25004704Water resource green efficiencyConfiguration pathsSuper-EBM modelTechnology-Organization-Environment (TOE) frameworkDynamic qualitative comparative analysis (QCA)LightGBM |
| spellingShingle | Naiming He Rijia Ding A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in China Ecological Indicators Water resource green efficiency Configuration paths Super-EBM model Technology-Organization-Environment (TOE) framework Dynamic qualitative comparative analysis (QCA) LightGBM |
| title | A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in China |
| title_full | A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in China |
| title_fullStr | A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in China |
| title_full_unstemmed | A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in China |
| title_short | A dual-method approach integrating dynamic QCA and LightGBM-SHAP algorithms to uncover the configuration paths and key drivers of water resource green efficiency in China |
| title_sort | dual method approach integrating dynamic qca and lightgbm shap algorithms to uncover the configuration paths and key drivers of water resource green efficiency in china |
| topic | Water resource green efficiency Configuration paths Super-EBM model Technology-Organization-Environment (TOE) framework Dynamic qualitative comparative analysis (QCA) LightGBM |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25004704 |
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