Robust Downscaling Approaches to Disaggregation of Data and Projections Under Uncertainties: Case of Land Cover and Land Use Change Systems^*

详细信息    查看全文

• 作者：Y. M. Ermoliev ; T. Y. Ermolieva ; P. Havlík ; A. Mosnier…
• 关键词：sustainable development planning ; uncertainties ; robust downscaling ; local ; global interdependencies
• 刊名：Cybernetics and Systems Analysis
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：53
• 期：1
• 页码：26-33
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Systems Theory, Control; Artificial Intelligence (incl. Robotics); Processor Architectures; Software Engineering/Programming and Operating Systems;
• 出版者：Springer US
• ISSN：1573-8337
• 卷排序：53

文摘

The interdependencies among land use systems at national and global levels motivate the development of advanced systems analysis approaches for integration of land use models operating at different weights. The paper develops novel general approaches based on cross entropy principle for downscaling aggregate data and projections, which are robust with respect to feasible priors. Robust downscaling methods account for so-called non-Bayesian uncertainties, i.e., incomplete, unobservable, or erroneous information or data. In numerous case studies in China, African countries, Brazil, and Ukraine, the approaches allowed deriving local development projections of land use and land use change consistently with existing trends and expectations.