Green and Low-Carbon Assessment of Wastewater Treatment Plants: A Critical Review of Methodologies, Indicators, and Decision Support Frameworks

Wastewater treatment plants (WWTPs) contribute approximately 5% of global non-CO2 greenhouse gas emissions, driving an urgent need for robust assessment methodologies to guide their transition from pollution control toward low-carbon resource recovery. This review provides a comprehensive examination of WWTP green and low-carbon assessment methodologies, indicator systems, and decision support frameworks, based on a systematic literature review of 150 peer-reviewed articles. Carbon accounting methods span from emission factor approaches—widely used but limited by parameter localization issues—to direct measurement, mass balance, modeling, and life cycle assessment. Indicator systems have evolved from single carbon footprint metrics to multi-dimensional frameworks encompassing environmental, economic, technical, and social dimensions, though social indicators remain underdeveloped. Data-driven methods—including machine learning, soft sensing, and digital twins—are enabling real-time emission prediction and process optimization, but they face persistent challenges in data quality, model interpretability, cross-plant generalization, and data privacy. Despite these advances, the field remains fragmented: carbon accounting, indicator development, and data-driven modeling communities largely operate in isolation, and policy frameworks often lack methodological grounding. To address this, the review proposes an integrated “assessment–diagnosis–optimization” framework that links evaluation results to actionable decisions. Key priorities include standardized frameworks, dynamic assessment, hybrid mechanistic data-driven models, privacy-preserving data sharing, and validation against full-scale operational data. A comparative analysis of MCDM applications is presented, and the suitability of each method for different decision-making contexts is discussed. Moving beyond fragmented approaches toward integrated frameworks is essential for transforming WWTPs into carbon-neutral, climate-smart infrastructures.

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Publication Details

Journal
Sustainability
Published
2026-11-05
DOI
https://doi.org/10.3390/su182111294
Primary Topic
Wastewater Treatment and Reuse
Type
article
Field-Weighted Citation Impact
0.00
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article

Green and Low-Carbon Assessment of Wastewater Treatment Plants: A Critical Review of Methodologies, Indicators, and Decision Support Frameworks

Qitao Pei, Xian Wu, Xuejun Xiong, Zheng Fang et al.
Sustainability
Wastewater Treatment and Reuse
article

Green and Low-Carbon Assessment of Wastewater Treatment Plants: A Critical Review of Methodologies, Indicators, and Decision Support Frameworks

Qitao Pei, Xian Wu, Xuejun Xiong, Zheng Fang, Yinhang Xu, Yajun Shi, Weihang Zhang, Zhenlin Wang, Wei Wu
article en

Abstract

Wastewater treatment plants (WWTPs) contribute approximately 5% of global non-CO2 greenhouse gas emissions, driving an urgent need for robust assessment methodologies to guide their transition from pollution control toward low-carbon resource recovery. This review provides a comprehensive examination of WWTP green and low-carbon assessment methodologies, indicator systems, and decision support frameworks, based on a systematic literature review of 150 peer-reviewed articles. Carbon accounting methods span from emission factor approaches—widely used but limited by parameter localization issues—to direct measurement, mass balance, modeling, and life cycle assessment. Indicator systems have evolved from single carbon footprint metrics to multi-dimensional frameworks encompassing environmental, economic, technical, and social dimensions, though social indicators remain underdeveloped. Data-driven methods—including machine learning, soft sensing, and digital twins—are enabling real-time emission prediction and process optimization, but they face persistent challenges in data quality, model interpretability, cross-plant generalization, and data privacy. Despite these advances, the field remains fragmented: carbon accounting, indicator development, and data-driven modeling communities largely operate in isolation, and policy frameworks often lack methodological grounding. To address this, the review proposes an integrated “assessment–diagnosis–optimization” framework that links evaluation results to actionable decisions. Key priorities include standardized frameworks, dynamic assessment, hybrid mechanistic data-driven models, privacy-preserving data sharing, and validation against full-scale operational data. A comparative analysis of MCDM applications is presented, and the suitability of each method for different decision-making contexts is discussed. Moving beyond fragmented approaches toward integrated frameworks is essential for transforming WWTPs into carbon-neutral, climate-smart infrastructures.

SustainabilityVol. 18(21)
Wuhan University (CN), Wuhan Municipal Engineering Design & Research Institute (CN)
Openalex Percentile: Top 14%
Wastewater Treatment and Reuse
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