Assessing the determinants of scale effects on carbon efficiency in China's wastewater treatment plants using causal machine learning

Abstract

The debate over the merits of centralized versus decentralized wastewater treatment plants (WWTPs) has gained prominence considering pressing sustainable development objectives and the need to reduce greenhouse gas (GHG) emissions. This highlights the importance of innovative analytical tools to shape forthcoming policies. Using causal machine learning, we evaluate the impact of WWTP scale on GHG emission intensities and investigate contributing factors. Results show GHG intensity typically decreases as WWTPs scale up. However, this trend varies based on regional environmental, economic, and infrastructure elements. Specifically, regions with fewer industrial wastewater contributions, increased rainwater composition, and elevated temperatures show smaller scale effects. This suggests limited GHG reductions from merely expanding WWTPs in such areas, as the benefits of handling fluctuating inflow volumes, tackling heavy pollution, and operating in cooler conditions offered by larger WWTPs are compromised. This research lays the foundation for comprehensive models promoting sustainable wastewater treatment strategies.