Remote sensing is widely used for lake-water-quality monitoring, but the inversion of the total nitrogen (TN) and total phosphorus (TP) of rivers and non-optical parameters is still a difficult problem. The use of high spatial and temporal resolution multispectral imagery combined with machine learning techniques is an effective solution for this difficulty. Three machine learning methods based on support vector regression (SVR), neural network (NN) and random forest (RF) were used to invert TN and TP using actual water-quality measurement data and Sentine-2 remote-sensing images, and analyzed the factors influencing water quality in terms of pollutant emissions and land use. The results show that RF performs the best in both TN (R² = 0.800, RMSE = 0.640, MSE = 0.400, MAE = 0.480) and TP (R² = 0.830, RMSE = 0.033, MSE = 0.001, MAE = 0.022) inversion models, and that the optimal selection of feature variables improves model performance. The TN and TP concentrations in the Minjiang River Meishan Water Function Development Zone were the highest in the downstream section and in 2018. Analysis of the factors influencing water quality shows that pollution sources and amounts were closely related to land-use types, and land use in riparian zones at different spatial scales had different degrees of impact on water quality.

The new energy demonstration city policy is a significant pilot measure to promote the transition of China’s energy system, aiming at developing new, green, and low-carbon sources of energy. In this paper, the Non-radial Directional Distance Function (NDDF) was adopted to calculate the Energy-Carbon Performance Index (ECPI) of Chinese 182 cities, for measuring the Energy-Carbon Performance (ECP) level of each city. On this basis, it is possible to empirically analyse the impact that the policy orientation of constructing new energy demonstration cities has had on urban energy carbon performance by using a combination of Propensity Score Matching and Difference-in-Difference. Moreover, a mediating effect model is utilised to test the mediating effect of technological innovation. The results show that the new energy demonstration city policy can significantly improve the ECP. Technological innovation has a partial mediating effect between the policy orientation of new energy demonstration city construction and ECP, which accounts for 12.92% of the total effect. Optimising the industrial structure, improving the level of economic development, increasing carbon sink resources, and attracting foreign direct investment all have significant impacts on the improvement of China’s ECP, while the urbanisation process has an inhibitory effect on the improvement of ECP. Heterogeneity analysis shows that policy orientation has a better driving effect on eastern cities and western cities in promoting the improvement of ECP. The policy implications of this paper are that 1) The government should expand the scope of new energy city pilots in an orderly manner; 2) The lasting and long-term influence of policy orientation on ECP should make use of technological innovation intermediary channels; 3) Support policies are supposed to formulate according to local conditions.

This paper utilizes a localized Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to calculate the spatio-temporal distribution of water conservation in the river source area of northwestern Sichuan over the past 15 years. It explores the influence of climatic and topographic factors on water conservation under different vegetation cover systems (forest, alpine grassland, and plateau marsh) through trend analysis and correlation analysis. The study reveals an upward trend in total water conservation over the 15-year period, following a low–middle–high–low spatial pattern from east to west. Analyzing the correlation between precipitation, evapotranspiration (ET), elevation, and slope with water conservation under three vegetation cover systems, the study found ET negatively correlated with water conservation depth, with correlation coefficients (Rs) of −0.69, −0.71, and −0.70, respectively. Precipitation and elevation are positively correlated with water conservation depth, with R values of 0.21, 0.24, and 0.14 and 0.23, 0.05, and 0.21, respectively. Slope is negatively correlated in forests (R = −0.19), but positively correlated in alpine grassland and swamp systems (R = 0.02 and 0.29, respectively). These findings highlight the significant influence of climate, topography, and subsurface factors on water conservation, offering valuable insights for precise water resource management and ecological protection in the study area.