Jun Wang
University of Michigan
17 Papers
29 Citations
Jun Wang is an academic researcher from University of Michigan. The author has contributed to research in topics: Medicine & Meta-analysis. The author has an hindex of 6, co-authored 7 publications. Previous affiliations of Jun Wang include Peking University.
Chat about Author
Papers
Climate adaptation, local institutions, and rural livelihoods: A comparative study of herder communities in Mongolia and Inner Mongolia, China
TL;DR: Li et al. as mentioned in this paper applied an analytical framework focused on adaptation, institutions, and livelihoods to study climate adaptation in the Mongolian grasslands and found that local institutions played important roles in shaping and facilitating livelihood adaptation strategies of herders.
213
Potential contributions of wind and solar power to China's carbon neutrality
Laibao Liu,Yang Wang,Zheng-you Wang,Shuangcheng Li,Jiangtao Li,He Gang,Yan Li,Yanxu Liu,Shilong Piao,Ziqi Gao,Rui Jun Chang,Wenbin Tang,Kejun Jiang,Shijin Wang,Jun Wang,Lin Zhao,Qing-Chen Chao +16 more
TL;DR: In this article , the authors demonstrate that deploying wind and solar capacity within flexible and optimized grids can meet ∼67% of electricity demands by all society sectors for 2050 (∼6.3% curtailment rate), even without other costly power sources or storage.
128
Drivers of the dynamics in net primary productivity across ecological zones on the Mongolian Plateau
TL;DR: In this article, the authors present a diagnostic analysis of the major drivers of the dynamics in grassland net primary productivity (NPP) across ecological zones on the Mongolian Plateau, which is a prerequisite for studying effective resource institutions and policies that can be used to govern grassland resources sustainably.
68
Exploratory analyses of local institutions for climate change adaptation in the Mongolian grasslands: An agent-based modeling approach
TL;DR: In this paper, an agent-based modeling approach was used to evaluate the performance of pasture rental markets and reciprocal pasture-use groups for climate change adaptation in the Mongolian grasslands.
39
Geostatistical inverse modeling for super-resolution mapping of continuous spatial processes
TL;DR: GIM is a practical solution for merging complementary coarse-resolution images and for super-resolution mapping of continuous spatial processes and a moving-window GIM approach was developed to accommodate spatial nonstationarity and reduce computational burden associated with large image data.
28