# Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
## 3 Results  
### Changes in NDVI and Its Factors  
At the city level, the NDVI values in 2018 were higher than in 2002 across all counties (Figure 2a). The linear trends of the annual NDVI in each district during 2018 and 2002 are plotted in Figure 2b. Across the study region, the average linear slope of the annual NDVI was 0.0037/yr. More than 85% of counties experienced a significant upward trend, and the trend was over 0.004 per year among 45% of the counties. The land cover map of the study area in 2002 based on MCD12Q1 (IGBP) is shown in Figure 3a. The study area was reclassified as MG, MC, MF, along with other types (urban/barren/water bodies). We calculated the difference between the NDVI in 2002 and 2018 for different land-use types(Figure 3b-d). In general, there was an increase in MG, MC, MF. Between 2002 and 2018, 44.01% of grassland area, 61.07% of cropland area, and 32.33% of forestland area had an increment of over 0.06.  
From 2002 to 2018, climate change exhibited a warming-wetting trend in the study area. The upward slopes of Pre and Tem are 2.16 mm/yr (\(p>0.05\)) and 0.03 \({}^{\circ}\)C/yr (\(p>0.05\)), respectively (Figure 4a-b). Spatially, Pre increased in 70.06% of the area, but only 5.35% of the area increased significantly (\(p<0.05\)). Pre showed significantly ascending trends in Beijing and Shanxi province, and the largest trend occurred in Wuttai county, reaching 12 mm/yr. Areas with increasing Tem accounted for 80.24% of the total. The trend of significantly increasing Tem mainly appeared in the south, in 28.44% of the total area, whereas the significantly decreasing trend occurred in only 0.9% of the area. Shanhaiguan district portrayed the largest significantly increasing Tem trend (0.13 \({}^{\circ}\)C/yr). Areas with a declining Tem mainly existed in the northern and central regions of the study area. For Rad, an average downward trend (\(-0.12\) W/m\({}^{2}\)) was noticed (\(p>0.05\)). Spatially, Rad showed a positive trend in the north and a negative trend in the central and southern regions (Figure 4c). Rad significantly increased in 8.28% of the total area, whereas it significantly declined in 16.47% of the total area. Generally, the study area experienced apparent climate change across the entire region, with a warming-wetting-dimming trend.  
Figure 2: (a) Increased normalized difference vegetation index (NDVI) in each county and (b) annual NDVI trends in each county, from 2002 to 2018. The black dots in (b) imply that the trends are statistically significant (\(p\)-value \(<0.05\)).  
Furthermore, a downward trend was found in AFF (\(-\)32.77 \(\mathrm{hm^{2}/yr}\), \(p<0.05\)) and an upward trend was detected in the NTL (0.31 per year, \(p<0.05\)) from 2002 to 2018 (Figure 4d-e). The majority of artificial afforestation was concentrated in the mountainous areas. In 20.69% of the area, there was a significant negative trend in afforestation areas. Despite the fact that the afforestation area is reducing, the accumulative growth of perennial trees cannot be overlooked. Therefore, we used AAF in the attribution analysis. Overall, significantly upward trends in NTL occupied over 82% of the regions, which were widely dispersed in plain and hilly areas, whereas significantly downward trends were observed in only 0.6% of the areas. As a representative of social development and population density, enhanced NTL indicated that social economic activities were intense during the study period.  
Figure 4f reveals sharp rises in CO2 concentration across the study area. Vegetation responds to CO2 by controlling the opening and closing of stomata. Elevated CO2 can accelerate photosynthetic rates, thereby improving vegetation productivity. As a result, the change in CO2 concentration, which is treated as a crucial factor, was considered in the attribution analysis conducted in our study.