Causal Effects and Heterogeneity of Carbon Emissions in Urban Wastewater Treatment Plant Clusters: A Quasi-Experimental Panel Study of Twelve Facilities

Volume 10, Issue 1, February 2025     |     PP. 55-81      |     PDF (323 K)    |     Pub. Date: December 9, 2025
DOI: 10.54647/energy480260    12 Downloads     284 Views  

Author(s)

Naifei Wang, Three Gorges Ecological Environment Investment Co.,Ltd.
Lianjun Luo, Three Gorges Ecological Environment Investment Co.,Ltd.
Jiali Wei, Three Gorges Ecological Environment Investment Co.,Ltd.
Yi Guo, Three Gorges Ecological Environment Investment Co.,Ltd.
Hu Wang, Three Gorges Ecological Environment Investment Co.,Ltd.
Xiaoming Yang, Three Gorges Ecological Environment Investment Co.,Ltd.

Abstract
Urban wastewater treatment plants (WWTPs) constitute a material share of municipal greenhouse-gas footprints, yet management-level mitigation is obscured by static emission-factor inventories and scarce plant-cluster data. We assemble a two-year panel for twelve WWTPs in a middle Chinese city and integrate three components into a single workflow: (i) quasi-experimental difference-in-differences (DID) to identify net treatment effects of routine interventions (precise aeration, anaerobic digestion start-up, intelligent external carbon dosing); (ii) Bayesian hierarchical updating to construct a localized dynamic emission-factor library, with emphasis on N₂O; and (iii) machine-learning diagnostics to rank drivers of heterogeneity and distill interpretable control rules. Specific emissions per unit TN removed exhibit pronounced within-city dispersion (coefficient of variation = 0.35). Random-forest diagnostics attribute ≈42% of modeled between-plant variance to dissolved oxygen (DO) control, with influent C/N and sludge age as secondary drivers. DID estimates indicate that precise aeration reduced specific emissions by 0.15 kg CO₂e kg⁻¹ TN (≈18.3% relative to the pre-intervention mean), with aligned pre-trends and placebo timing supporting identification; the N₂O component declined by 0.023 (p < 0.01), while short-run electricity savings were statistically indistinct from zero. Bayesian updating yields a localized posterior for the N₂O emission factor of 0.016 kg N₂O-N kg⁻¹ TN, 42% below the IPCC default. A shallow decision tree centered on C/N ≈ 5.0 and DO ≈ 2.5 mg L⁻¹ generates transferable rules; citywide portfolios guided by these rules deliver an estimated 2,100 t CO₂e y⁻¹ abatement with 3.8-year payback. The framework provides decision-grade evidence for medium-sized cities operating under data constraints and supports annual Bayesian refresh of localized factors.

Keywords
Wastewater treatment plant · Carbon emission heterogeneity · Panel data · Difference-in-differences · Localized emission factor · Bayesian updating · Interpretable machine learning

Cite this paper
Naifei Wang, Lianjun Luo, Jiali Wei, Yi Guo, Hu Wang, Xiaoming Yang, Causal Effects and Heterogeneity of Carbon Emissions in Urban Wastewater Treatment Plant Clusters: A Quasi-Experimental Panel Study of Twelve Facilities , SCIREA Journal of Energy. Volume 10, Issue 1, February 2025 | PP. 55-81. 10.54647/energy480260

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