Uncovering the Drivers of Greenhouse Gas Emissions from Hydropower Reservoirs in China Based on Machine Learning

China is expanding hydropower capacity as a key climate change mitigation strategy, yet greenhouse gas (GHG) emissions from reservoirs can substantially offset this benefit. The influence of specific environmental drivers on these emissions remains poorly understood, and previous studies have rarely quantified their relative importance under multifactorial conditions. To fill this gap, this study quantifies CO2, CH4, and N2O emissions from 79 major hydroelectric reservoirs across China—representing over 60% of national hydropower generation—by integrating the G-res model and the IMAGE-DGNM model. We then employ a random forest (RF) model to evaluate the significance and marginal effects of 15 environmental drivers. Results show that reservoir-specific properties collectively explain 40.37% of the variance in total GHG emissions, and reservoir area emerges as the overwhelmingly dominant driver (MDI importance score = 1.41), far exceeding other key variables such as NH4+ concentration, dissolved oxygen, altitude, water temperature, catchment area, total phosphorus, and air temperature (all with MDI importance > 0.5). Partial dependence analysis further reveals that emissions rise sharply with expanding reservoir area, NH4+ concentrations above 0.15–0.2 mg/L, and catchment areas in the 360,000–680,000 km2 range, while elevated dissolved oxygen (6–9 mg/L) and higher altitude suppress emissions. This study moves beyond simple emission inventories by providing a national-scale, data-driven attribution of reservoir GHG emissions to interacting environmental factors, thereby offering actionable insights for sustainable hydropower planning.