：The Qinghai−Tibet Plateau (QTP) is among the most sensitive regions to global environmental change worldwide. Although the climate change and engineering construction on the QTP have jointly modified the regional vegetation activity, little is known about how vegetation variation responds. Using Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data during 2000−2021, this study investigated the spatiotemporal variation of vegetation activity and the compound effects of climate change and construction along the G318 highway on the QTP (TG318) through the integration of trend, residual, and partial correlation analyses, as well as structural equation modeling. The results showed that the growing season EVI increased significantly at a rate of about 0.0020/year in the western QTP after reconstruction, but fluctuated in the east. Reconstruction generally had a significant effect on the growing season EVI, with contributions of 7.67%, 19.12%, 18.24%, and −4.15% in different sections of TG318, whereas climate change contributed −10.14% to 8.84% of the total variation. The growing season EVI negatively correlated with snow cover and minimum temperature in humid and sub-humid regions, whereas positively related to vapor pressure in semi-arid regions. Moreover, there existed an obvious lag effect of climate change on the growing season EVI, with lag time generally decreasing from west to east and apparent heterogeneity among different months and regions. These findings would help better understand the environmental impacts along the engineering corridors and provide a scientific basis for ecological conservation in the QTP region.

Exploring the driving factors of ecosystem services (ESs) trade-offs/synergies is crucial for ecosystem management, especially in ecological conservation red line (ECRL) areas that maintain regional and national ecological security. Soil conservation (SC), water yield (WY) and carbon sequestration (CS) were simulated in the Beijing ECRL areas. Geographical weighted regression was used to explore the trade-offs/synergies, and the geographical detector was applied to quantitatively identify their driving factors. Results show that (1) the SC and CS show marked synergy which characterized more than 80% of each ECRL area; the proportion of the space area of trade-off and synergy between SC and WY, and WY and CS was roughly 3 to 7 and 4 to 6 in each ECRL area, respectively. (2) The synergy of the three pairs of ESs was most sensitive to terrain factors. The precipitation erodibility of soil and its necessity for vegetation make it a determinant of the trade-off between SC and CS; temperature was the determinant in the trade-off between WY and CS, with an explanatory power of 32.8%; potential evapotranspiration was best able to explain the spatial distribution of the trade-off between SC and WY. (3) The interaction between precipitation and other factors had the greatest explanatory power on the spatial relationship between SC and WY. Precipitation and relief amplitude are the main interactive factors respectively affecting the spatial trade-off and synergy between SC and CS. The trade-off and synergy between WY and CS were most sensitive to the interaction between climate factors and terrain factors.