Causal direct drivers of greenhouse gas emissions in 40,722 wastewater treatment plants in China

Wastewater treatment plants (WWTPs) are essential for public health but are highly energy-intensive, accounting for approximately 1.6% of global greenhouse gas (GHG) emissions. Developing cost-effective carbon mitigation strategies requires identifying the dominant drivers of these emissions. However, existing analyses often treat operational variables as independent predictors or lack the causal validity required to disentangle complex, interacting emission sources across diverse regions. Consequently, the fundamental mechanisms causing distinct GHG emissions in the resource-constrained wastewater sector remain poorly understood. Here we use an integrated machine learning and causal inference framework to quantify the drivers of GHG emissions across 40,722 Chinese WWTPs over a decade. We reveal that aggregate emission intensities have rebounded recently, driven primarily by increasingly stringent water discharge standards that necessitate chemical- and energy-intensive nitrogen removal. Although ammonia nitrogen removal exerts the strongest individual causal effect on emissions—where a 10% increase drives a 5.65% surge in total intensity—decarbonizing the regional power grid offers a highly potent and practical mitigation lever. Specifically, a 10% reduction in the regional grid electricity emission factor accounts for a 4.45% equivalent reduction in total emissions, demonstrating a profound cross-sectoral coupling. Our findings highlight a critical tension between local water quality improvements and global climate goals, suggesting that future environmental regulations must harmonize ecological benefits with their intrinsic carbon costs.