Modeling Uncertainty in Large Natural Resource Allocation Problems
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 exp...
| Main Authors: | , , , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
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| Subjects: | |
| Online Access: | http://documents.worldbank.org/curated/en/214641582232800623/Modeling-Uncertainty-in-Large-Natural-Resource-Allocation-Problems http://hdl.handle.net/10986/33391 |
| 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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