A modified eco-efficiency framework and methodology for advancing the state of practice of sustainability analysis as applied to green infrastructure
Ghimire, S. AND JohnM Johnston. A modified eco-efficiency framework and methodology for advancing the state of practice of sustainability analysis as applied to green infrastructure. Integrated Environmental Assessment and Management. Allen Press, Inc., Lawrence, KS, 13(5):821-831, (2017). https://doi.org/10.1002/ieam.1928
Our objective is to demonstrate the use of a modified EE framework within a novel sustainability analysis methodology for various RWH design configurations as Decision Management Objectives (DMOs), minimizing the limitations of CDEA and eliminating the EE nonuniqueness problem. We utilize LCA and LCCA to calculate sustainability indicators and DEA to calculate the modified EE measures as sustainability scores. The indicators consist of 1 economic indicator (life cycle cost), 4 environmental indicators (blue water use, ecotoxicity, cumulative energy demand, and global warming potential), and 1 social indicator (human health cancer impacts). We also address the performance of 10 weighting schemes, 5 existing weighting schemes (EI99, equal weights [EQWT], SSIS, NIST, and CDEA) and 5 derived schemes based on impact thresholds. Our methodology considered at least 1 indicator from each dimension of sustainability and weighting schemes with equal and unequal thresholds consistent with best practice. To our knowledge, no other study has combined these methods to analyze GI sustainability, and the methodology is applicable to other environmental, industrial, and engineered systems. In the following sections, we describe the modified EE framework and demonstrate the methodology.
We propose a modified eco-efficiency (EE) framework and novel sustainability analysis methodology for green infrastructure (GI) practices used in water resource management. Green infrastructure practices such as rainwater harvesting (RWH), rain gardens, porous pavements, and green roofs are emerging as viable strategies for climate change adaptation. The modified framework includes 4 economic, 11 environmental, and 3 social indicators. Using 6 indicators from the framework, at least 1 from each dimension of sustainability, we demonstrate the methodology to analyze RWH designs. We use life cycle assessment and life cycle cost assessment to calculate the sustainability indicators of 20 design configurations as Decision Management Objectives (DMOs). Five DMOs emerged as relatively more sustainable along the EE analysis Tradeoff Line, and we used Data Envelopment Analysis (DEA), a widely applied statistical approach, to quantify the modified EE measures as DMO sustainability scores. We also addressed the subjectivity and sensitivity analysis requirements of sustainability analysis, and we evaluated the performance of 10 weighting schemes that included classical DEA, equal weights, National Institute of Standards and Technology's stakeholder panel, Eco-Indicator 99, Sustainable Society Foundation's Sustainable Society Index, and 5 derived schemes. We improved upon classical DEA by applying the weighting schemes to identify sustainability scores that ranged from 0.18 to 1.0, avoiding the nonuniqueness problem and revealing the least to most sustainable DMOs. Our methodology provides a more comprehensive view of water resource management and is generally applicable to GI and industrial, environmental, and engineered systems to explore the sustainability space of alternative design configurations.
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