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Modeling Uncertainty in Large Natural Resource Allocation Problems / Cai, Yongyang.
World Bank Open Knowledge Repository (formerly "World Bank E-Library Publications") Available online
View online- Format:
- Book
- Government document
- Author/Creator:
- Cai, Yongyang.
- Series:
- Policy research working papers.
- World Bank e-Library.
- Language:
- English
- Subjects (All):
- Agricultural Sector Economics.
- Agriculture.
- Climate Change and Agriculture.
- Crop Yield.
- Crops and Crop Management Systems.
- Dynamic Model.
- Environment.
- Land Use.
- Natural Resource Management.
- Natural Resources Management.
- Resource Allocation.
- Sustainable Land Management.
- Uncertainty.
- Local Subjects:
- Agricultural Sector Economics.
- Agriculture.
- Climate Change and Agriculture.
- Crop Yield.
- Crops and Crop Management Systems.
- Dynamic Model.
- Environment.
- Land Use.
- Natural Resource Management.
- Natural Resources Management.
- Resource Allocation.
- Sustainable Land Management.
- Uncertainty.
- Physical Description:
- 1 online resource (64 pages)
- Place of Publication:
- Washington, D.C. : The World Bank, 2020.
- System Details:
- data file
- Summary:
- The productivity of the world's natural resources is critically dependent on a variety of highly uncertain factors, which obscure individual investors and governments that seek to make long-term, sometimes irreversible investments in their exploration and utilization. These dynamic considerations are poorly represented in disaggregated resource models, as incorporating uncertainty into large-dimensional problems presents a challenging computational task. This study introduces a novel numerical method to solve large-scale dynamic stochastic natural resource allocation problems that cannot be addressed by conventional methods. The method is illustrated with an application focusing on the allocation of global land resource use under stochastic crop yields due to adverse climate impacts and limits on further technological progress. For the same model parameters, the range of land conversion is considerably smaller for the dynamic stochastic model as compared to deterministic scenario analysis. The scenario analysis can thus significantly overstate the magnitude of expected land conversion under uncertain crop yields.
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