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Conference: CAS Climate: Synthesizing and assessing wholistic urban climate solutions in Texas

Conference: CAS Climate: Synthesizing and assessing wholistic urban climate solutions in Texas
会议:CAS 气候:综合和评估德克萨斯州的整体城市气候解决方案
批准号:
2232533
负责人:
Yangyang Xu
金额:
$4.27万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
已结题
起止时间:
2023-01-01 至 2023-12-31

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中文摘要
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英文摘要
Urban populations and associated infrastructure continue to grow globally amidst the increasing risk of extreme heat, rainfall, and flooding, especially in high hazard regions and vulnerable areas. A wholistic understanding and assessment of our current state of knowledge on urban climate is thus greatly needed. A multi-day workshop will be conducted in Summer 2023 at Texas A&M University, College Station, TX. The main theme of the workshop is to develop a research agenda for holistic urban solutions in response to climate change, using the fast-growing Texas as the case. A synthesis report on Texas urban climate will be produced and distributed to stakeholders to catalyze interdisciplinary efforts and facilitate real-world actions. The strong synthesis of knowledge and practices is crucial to guide future urban development strategies, environmental regulation, and adaptation recommendations to advance climate and environmental justice in both large cities and small towns. Developing resilient human settlements responding to climate change needs an integrated framework and interdisciplinary actions. The workshop will discuss the high-impact urban climate solutions in Texas, including adaptation and mitigation measures, and to plot a course forward for further research and application. The enhanced communication and collaboration among different research fields such as atmospheric sciences, urban planning, geography, public health, economics, civil engineering, computer science, etc., will enable the research community to efficiently and ethically tackle pressing climate and sustainability challenges facing people and urban system in coming decades.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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Accelerated distributed stochastic optimization methods and applications in machine learning
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    2208394
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    $25.0万
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Information-Based Complexity Analysis and Optimal Methods for Saddle-Point Structured Optimization
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Using Large Ensemble Simulations from Multiple Global Climate Models to Quantify the Internal Decadal Climate Variability
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Novel Numerical Approaches for Structured Optimization
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    2017
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国内基金
海外基金
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