# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 5 Conclusions  
Dryland areas are undergoing changes under the influence of global warming. These ecosystems are fragile and extremely sensitive to external forces such as climate change and deforestation, which can significantly impact social and economic development. While there are many factors contributing to dryland ecosystem change, discovering the dominant factor that impacts each region and quantifying the effects of each factor remain worldwide hotspot issues.  
In our study, through statistical methods, we disentangled the state of the ecosystem in Chinese and American dryland areas over the past 40 years. We attributed the vegetation change to CO\({}_{2}\), land use (LU), climate change (CC), and climatic variability (CV) and quantified the effects of each factor on regional analysis. Additionally, we discovered the drivers for green and desertification regionally.  
Our results showed that most of the Chinese and American dryland areas exhibit a greening trend, suggesting that the states of ecosystems in dryland areas are improving. However, desertification exists in some regions, such as Xinjiang, Qinghai, and Inner Mongolia in China and Colorado, Kansas, and California in the USA.  
Furthermore, we found that the most dominant driver for vegetation changes in Chinese dryland areas is CC (48.3%), while CO\({}_{2}\) is the most dominant driver for the USA (47.9%). The primary factor of positive trends in America is the CO\({}_{2}\) fertilization effect (42.9%), while LU is the primary factor for desertification. For China, CC is the main driver for positive trends (35.3%), while LU is the primary driver for desertification. Our analysis for greening and desertification in each dryland area is summarized in Table 1.  
All in all, our study highlights the importance of understanding the driving factors behind the green and land degradation of Chinese and American dryland areas. While our approach quantifies the impact of CO\({}_{2}\), LU, CC, and CV on vegetation change, we acknowledge that there may be other factors at play, such as nitrogen deposition. Understanding the dominant factors can help prevent desertification and promote sustainable development in these areas.  
[PERSON] and [PERSON] designed the study. [PERSON], [PERSON] and [PERSON] conducted the data and wrote the paper. [PERSON] and [PERSON] contributed to writing. [PERSON], [PERSON] and [PERSON] helped with validations. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China under Grant nos. 41975062. This work was funded by the National Natural Science Foundation of China under Grant nos. 42130610, 41905053, 42275034, 42075029, 41675050, and 11801398 and the Outstanding Young Talents Support Plan of Shanxi province.  
The NDVI dataset is from NOAA and can be accessed from [[https://www.ncei.noaa.gov/products/climate-data-records](https://www.ncei.noaa.gov/products/climate-data-records)]([https://www.ncei.noaa.gov/products/climate-data-records](https://www.ncei.noaa.gov/products/climate-data-records)) (accessed on 20 May 2023). We use a combination of a 9-member ensemble that consists of three precipitation and three temperature datasets. They are daily records of TerraClimate [[[http://www.climatologylab.org/terracclimate.html](http://www.climatologylab.org/terracclimate.html)]([http://www.climatologylab.org/terracclimate.html](http://www.climatologylab.org/terracclimate.html)) (accessed on 20 May 2023)], CRU4 [[[http://data.ceda.ac.uk/badc/cru/data/cru_ts/](http://data.ceda.ac.uk/badc/cru/data/cru_ts/)]([http://data.ceda.ac.uk/badc/cru/data/cru_ts/](http://data.ceda.ac.uk/badc/cru/data/cru_ts/)) (accessed on 20 May 2023)] and ERA5 [[[https://cds.climate.copemicus.eu/](https://cds.climate.copemicus.eu/)]([https://cds.climate.copemicus.eu/](https://cds.climate.copemicus.eu/)) (accessed on 20 May 2023)].  
The authors declare no conflict of interest.