Optimising spatial planning for green infrastructure from an environmental efficiency perspective: A case study of the Taipei basin

Climate change has resulted in diverse risks to our environment. Green infrastructure is a strategically planned network of urban green spaces that conserve ecosystem functions and benefit people. Resource limitations mean there is a need to measure the environmental efficiency of green infrastructu...

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Veröffentlicht in:Journal of environmental management Jg. 393; S. 126849
Hauptverfasser: Lin, Zih-Hong, Laffan, Shawn W., Metternicht, Graciela
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Elsevier Ltd 01.10.2025
Schlagworte:
Climate Change
Conservation of Natural Resources - economics
Conservation of Natural Resources - methods
Cost-Benefit Analysis
Ecosystem
Ecosystem services
Environmental efficiency
Green infrastructure
Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA)
Taiwan
Taiwan
Ecosystem services
Green infrastructure
Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA)
Environmental efficiency
ISSN:0301-4797, 1095-8630, 1095-8630
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Abstract Climate change has resulted in diverse risks to our environment. Green infrastructure is a strategically planned network of urban green spaces that conserve ecosystem functions and benefit people. Resource limitations mean there is a need to measure the environmental efficiency of green infrastructure to support its planning and implementation. However, previous studies on the cost-benefit analysis of green infrastructure often focus on a single benefit, potentially neglecting other co-benefits, or convert multiple benefits into monetary values, which can overlook the non-monetary value of ecosystem services. To address these research gaps, a comprehensive study integrating co-benefits and costs is needed, with a focus on environmental efficiency to yield higher benefits with lower costs. This research uses Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA) for integrating multi-input and multi-output indices in evaluating the environmental efficiency of green infrastructure. The results indicate that among the 1415 decision-making units (DMUs), 398 are environmentally efficient. The mean efficiency score is 0.71, suggesting an overall moderate level of efficiency. The slack variable analysis identifies the required improvements for each indicator within each inefficient DMU. DMUs that are more environmentally efficient are priority areas for planning or investing in new green infrastructure, ensuring benefits are maximised while minimising costs. The model provides a comprehensive and replicable approach for prioritising future green infrastructure and developing practical strategies to enhance its efficiency. •Increasing the environmental efficiency of GI is crucial for its implementation.•Super-SBM-DEA is applied to measure the environmental efficiency of GI.•Out of the 1415 evaluated GI locations, 398 (28 %) are deemed efficient.•Slack variable analysis reveals the necessary improvements for inefficient GIs.
AbstractList Climate change has resulted in diverse risks to our environment. Green infrastructure is a strategically planned network of urban green spaces that conserve ecosystem functions and benefit people. Resource limitations mean there is a need to measure the environmental efficiency of green infrastructure to support its planning and implementation. However, previous studies on the cost-benefit analysis of green infrastructure often focus on a single benefit, potentially neglecting other co-benefits, or convert multiple benefits into monetary values, which can overlook the non-monetary value of ecosystem services. To address these research gaps, a comprehensive study integrating co-benefits and costs is needed, with a focus on environmental efficiency to yield higher benefits with lower costs. This research uses Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA) for integrating multi-input and multi-output indices in evaluating the environmental efficiency of green infrastructure. The results indicate that among the 1415 decision-making units (DMUs), 398 are environmentally efficient. The mean efficiency score is 0.71, suggesting an overall moderate level of efficiency. The slack variable analysis identifies the required improvements for each indicator within each inefficient DMU. DMUs that are more environmentally efficient are priority areas for planning or investing in new green infrastructure, ensuring benefits are maximised while minimising costs. The model provides a comprehensive and replicable approach for prioritising future green infrastructure and developing practical strategies to enhance its efficiency. •Increasing the environmental efficiency of GI is crucial for its implementation.•Super-SBM-DEA is applied to measure the environmental efficiency of GI.•Out of the 1415 evaluated GI locations, 398 (28 %) are deemed efficient.•Slack variable analysis reveals the necessary improvements for inefficient GIs.
Climate change has resulted in diverse risks to our environment. Green infrastructure is a strategically planned network of urban green spaces that conserve ecosystem functions and benefit people. Resource limitations mean there is a need to measure the environmental efficiency of green infrastructure to support its planning and implementation. However, previous studies on the cost-benefit analysis of green infrastructure often focus on a single benefit, potentially neglecting other co-benefits, or convert multiple benefits into monetary values, which can overlook the non-monetary value of ecosystem services. To address these research gaps, a comprehensive study integrating co-benefits and costs is needed, with a focus on environmental efficiency to yield higher benefits with lower costs. This research uses Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA) for integrating multi-input and multi-output indices in evaluating the environmental efficiency of green infrastructure. The results indicate that among the 1415 decision-making units (DMUs), 398 are environmentally efficient. The mean efficiency score is 0.71, suggesting an overall moderate level of efficiency. The slack variable analysis identifies the required improvements for each indicator within each inefficient DMU. DMUs that are more environmentally efficient are priority areas for planning or investing in new green infrastructure, ensuring benefits are maximised while minimising costs. The model provides a comprehensive and replicable approach for prioritising future green infrastructure and developing practical strategies to enhance its efficiency.
Climate change has resulted in diverse risks to our environment. Green infrastructure is a strategically planned network of urban green spaces that conserve ecosystem functions and benefit people. Resource limitations mean there is a need to measure the environmental efficiency of green infrastructure to support its planning and implementation. However, previous studies on the cost-benefit analysis of green infrastructure often focus on a single benefit, potentially neglecting other co-benefits, or convert multiple benefits into monetary values, which can overlook the non-monetary value of ecosystem services. To address these research gaps, a comprehensive study integrating co-benefits and costs is needed, with a focus on environmental efficiency to yield higher benefits with lower costs. This research uses Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA) for integrating multi-input and multi-output indices in evaluating the environmental efficiency of green infrastructure. The results indicate that among the 1415 decision-making units (DMUs), 398 are environmentally efficient. The mean efficiency score is 0.71, suggesting an overall moderate level of efficiency. The slack variable analysis identifies the required improvements for each indicator within each inefficient DMU. DMUs that are more environmentally efficient are priority areas for planning or investing in new green infrastructure, ensuring benefits are maximised while minimising costs. The model provides a comprehensive and replicable approach for prioritising future green infrastructure and developing practical strategies to enhance its efficiency.Climate change has resulted in diverse risks to our environment. Green infrastructure is a strategically planned network of urban green spaces that conserve ecosystem functions and benefit people. Resource limitations mean there is a need to measure the environmental efficiency of green infrastructure to support its planning and implementation. However, previous studies on the cost-benefit analysis of green infrastructure often focus on a single benefit, potentially neglecting other co-benefits, or convert multiple benefits into monetary values, which can overlook the non-monetary value of ecosystem services. To address these research gaps, a comprehensive study integrating co-benefits and costs is needed, with a focus on environmental efficiency to yield higher benefits with lower costs. This research uses Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA) for integrating multi-input and multi-output indices in evaluating the environmental efficiency of green infrastructure. The results indicate that among the 1415 decision-making units (DMUs), 398 are environmentally efficient. The mean efficiency score is 0.71, suggesting an overall moderate level of efficiency. The slack variable analysis identifies the required improvements for each indicator within each inefficient DMU. DMUs that are more environmentally efficient are priority areas for planning or investing in new green infrastructure, ensuring benefits are maximised while minimising costs. The model provides a comprehensive and replicable approach for prioritising future green infrastructure and developing practical strategies to enhance its efficiency.
ArticleNumber 126849
Author Lin, Zih-Hong
Metternicht, Graciela
Laffan, Shawn W.
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  surname: Metternicht
  fullname: Metternicht, Graciela
  organization: Earth and Sustainability Science Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia
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Keywords Ecosystem services
Green infrastructure
Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA)
Environmental efficiency
Language English
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Snippet Climate change has resulted in diverse risks to our environment. Green infrastructure is a strategically planned network of urban green spaces that conserve...
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SubjectTerms Climate Change
Conservation of Natural Resources - economics
Conservation of Natural Resources - methods
Cost-Benefit Analysis
Ecosystem
Ecosystem services
Environmental efficiency
Green infrastructure
Super-efficiency slack-based measure data envelopment analysis (Super-SBM-DEA)
Taiwan
Title Optimising spatial planning for green infrastructure from an environmental efficiency perspective: A case study of the Taipei basin
URI https://dx.doi.org/10.1016/j.jenvman.2025.126849
https://www.ncbi.nlm.nih.gov/pubmed/40834577
https://www.proquest.com/docview/3246402771
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