Climate change is one of the most pressing global challenges that could potentially threaten ecosystems, human populations, and weather patterns over time. Impacts including rising sea levels and soil salinization are caused by climate change, primarily driven by human activities such as fossil fuel combustion for energy production. The resulting greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂) emissions, amplify the greenhouse effect and accelerate global warming, underscoring the urgent need for effective mitigation strategies. This study investigates the performance and outcomes of various machine learning regression models for predicting CO₂ emissions. A comprehensive overview of performance metrics, including R², mean absolute error, mean squared error, and root-mean-squared error, and cross-validation scores for decision tree, random forest, multiple linear regression, k-nearest neighbors, gradient boosting, and support vector regression models was conducted. The biggest source of CO₂ emissions was coal (46.11%), followed by natural gas (25.49%) and electricity (26.70%). Random forest and gradient boosting both performed well, but multiple linear regression had the highest prediction accuracy among machine learning models (R² = 0.98 training, 0.99 testing). Support vector regression (SVR) and k-nearest neighbors (KNN) demonstrated lower accuracies, whereas decision tree displayed overfitting. The decision tree, random forest, multiple linear regression, and gradient boosting models were found to be extremely sensitive to coal, natural gas, and petroleum (transportation sector) based on sensitivity analysis. Random forest and gradient boosting demonstrated the most sensitivity to coal usage, whereas KNN and SVR maintained excellent R² scores (0.94–0.98) but were less susceptible to changes in the variables. This analysis provides insights into the agreement and discrepancies between predicted and actual CO₂ emissions, highlighting the models’ effectiveness and potential limitations.

Agricultural nonpoint source pollution is emerging as one of the increasingly serious environmental concerns all over the world. This study conducted field experiments in Zengcheng District, Guangzhou City, from 2019 to 2023 to explore the mechanisms by which different crop types, fertilization modes, and meteorological conditions affect the loss of nitrogen and phosphorus in agricultural nonpoint source pollution. In rice and corn, the CK and PK treatment groups showed significant fitting advantages, such as the R² of rice-CK reaching 0.309. MAE was 0.395, and the R² of corn-PK was as high as 0.415. For compound fertilization groups such as NPK and OF, the model fitting ability decreased, such as the R² of rice-NPK dropping to 0.193 and the R² of corn-OF being only 0.168. In addition, the overall performance of the model was limited in the modeling of total phosphorus. A relatively good fit was achieved in corn (such as NPK group R² = 0.272) and in vegetables and citrus. R² was mostly below 0.25. The results indicated that fertilization management, crop types, and meteorological conditions affected nitrogen and phosphorus losses in agricultural runoff. Cornfields under conventional nitrogen, phosphorus, and potassium fertilizer (NPK) and conventional nitrogen and potassium fertilizer treatment without phosphorus fertilizer (NK) treatments exhibited the highest nitrogen losses, while citrus fields showed elevated phosphorus concentrations under NPK and PK treatments. Organic fertilizer treatments led to moderate nutrient losses but greater variability. Organic fertilizer treatments resulted in moderate nutrient losses but showed greater interannual variability. Meteorological drivers differed among crop types. Nitrogen enrichment was mainly associated with high temperature and precipitation, whereas phosphorus loss was primarily triggered by short-term extreme weather events. Linear regression models performed well under simple fertilization scenarios but struggled with complex nutrient dynamics. Crop-specific traits such as flooding in rice fields, irrigation in corn, and canopy coverage in citrus significantly influenced nutrient migration. The findings of this study highlight that nutrient losses are jointly regulated by crop systems, fertilization practices, and meteorological variability, particularly under extreme weather conditions. These findings underscore the necessity of crop-specific and climate-adaptive nutrient management strategies to reduce agricultural nonpoint source pollution. By integrating long-term field observations with machine learning–based analysis, this study provides scientific evidence to support sustainable fertilizer management, protection of water resources, and environmentally responsible agricultural development in subtropical regions. The proposed approaches contribute to sustainable land and water resource utilization and climate-resilient agricultural systems, aligning with the goals of sustainable development in rapidly urbanizing river basins.