DPSR-based study and assessment of the influence pathways of Shanghai urban development level on river methane emission potential during 2011–2020

Abstract

Numerous studies have shown that urban rivers are becoming a source of methane emissions, which poses a challenge to developing a “net zero carbon city.” Most studies at this stage focus on the influence of single urban elements on river methane emissions, such as land use, water facilities, etc. It is necessary to consider the impact of urban development on river methane emissions in an integrated manner. This study aggregated relevant data sets for seven municipal districts in Shanghai during 2011–2020, collected through literature and statistical yearbooks. We constructed an “urban river methane emission” system based on the Driver-Pressure-State-Response model and used partial least squares-path modeling to verify the rationality of the system’s influence pathways. The results showed (goodness-of-fitness = 0.4446) that driver (population density, urbanization rate), pressure (annual water supply, total yearly household waste), and state (deteriorating water quality environment) all increased the methane emission potential of urban rivers (total effect = 0.1917; 0.3932; 0.1394). Response (sewage treatment rate, environmental investment) would mitigate river methane emissions (total effect = -0.2230). An “urban river methane emission potential” assessment model was then developed. Partial least squares-path modeling and generalized linear mixed model were used to calculate the “urban river methane emission potential” index in seven municipal districts of Shanghai over ten years, respectively. And the results of the two methods were similar. The results showed that (e.g., partial least squares-path modeling method) Pudong District and Putuo District maintained a high methane emission potential of urban rivers (the average index was 46.15%, and 25.94%, respectively) during this decade. The emission potential of Qingpu District and Jinshan District were significantly lower during this decade (p < 0.05, the average index was −1.28% and 0.77%, respectively). We believed that higher human activity intensities and economic levels before or in the lead-up to a transition to a “net zero carbon city” would mean a higher urban river methane emission risk. The development and quantification of the “urban river methane emission potential” assessment model would provide new assessment perspectives and methods for policymakers or urban planners to control urban greenhouse gas emissions and promote the “net zero carbon city” process. Meanwhile, the “urban river methane emission” system pointed out the carbon emission risk at the pollution end. It would provide a new idea to improve products’ full-lifecycle carbon footprint accounting.