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• Spatiotemporal distribution of vegetation productivity and climate drivers are identified. • Water availability dominated the variability of vegetation productivity for grasslands in Mongolia. • Drought contributed to more vegetation decline than hot. • Vegetation productivity declined more in the northern typical steppe under more severe hot and drought events. Vegetation productivity is controlled by multiple climatic variables such as temperature, precipitation, and radiation, while the driving mechanism remains unclear in Inner Mongolia grasslands. Since climatic factors change geographically and seasonally, we need to know their spatiotemporal patterns and how they impact plant growth. Here, we investigated vegetation responses to climatic changes by analyzing both climatic data and NDVI (Normalized Difference Vegetation Index) for 34 years (1982 to 2015). Our results indicate that water availability dominated the variability of vegetation productivity, regardless of grassland types. The vegetation productivity was more sensitive to variations in water conditions than to variations in temperature, especially in the northern region. The results are of great significance for formulating adaptation and mitigation strategies against climate impacts on grassland productivity in Inner Mongolia and provide an invaluable comparison for grassland studies in other regions. [ABSTRACT FROM AUTHOR]

[Display omitted] • A bi-level joint-probabilistic programming approach is developed. • It can handle leader–follower issues and joint-probabilistic constraints. • The proposed approach is applied to China's multi-regional energy system model. • The optimal schemes during 2021–2050 under three mitigation scenarios are obtained. • Results are helpful to achieve low-carbon goals in multi-regional energy system. Climate change mitigation and renewable resources utilization are becoming particularly urgent for energy system management. In this study, a bi-level joint-probabilistic programming (BJPP) method is developed for planning multi-regional energy system under different mitigation policies and uncertainties. BJPP can handle leader–follower issues in decision-making process as well as examine the risk of violating joint-probabilistic constraints. Based on the BJPP method, a China's multi-regional energy system (named as BJPP_CMES) model is formulated to provide optimal scheme for energy system planning of China over a long-term horizon (2021–2050) by synergistically minimizing carbon dioxide (CO 2) emission and system cost. A series of scenarios associated with different carbon capture and storage (CCS) levels and violation risks of energy-demand constraints are examined. Results reveal that: (i) the share of non-fossil energy in China's energy supply would keep increasing in 2021–2050, and the highest growth of the renewable supply would occur in Ningxia (rising 47.7%); (ii) Sichuan, Inner Mongolia, and Gansu would be the top three suppliers of renewable electricity; (iii) the CO 2 emission of China would reach a peak of [44.3, 54.8] billion tonnes during the period of 2026–2030; Shandong, Inner Mongolia, and Shanxi would be main contributors of CO 2 emission in the future; (iv) compared with the single-level model, the CO 2 emission from the BJPP_CMES model would reduce by [2.7, 5.7]%; (v) among developed regions, the individual probability level of Jiangsu-Zhejiang-Shanghai is the most significant parameter for both CO 2 emission and system cost. The findings are helpful for decision makers to optimize multi-regional energy system (MES) with a low-carbon and cost-effective manner, as well as to provide useful information for renewable energy utilization and regional sustainable development. [ABSTRACT FROM AUTHOR]