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...

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Main Authors:	Cai, Yongyang, Steinbuks, Jevgenijs, Judd, Kenneth L., Jaegermeyr, Jonas, Hertel, Thomas W.
Format:	Working Paper
Language:	English
Published:	World Bank, Washington, DC 2020
Subjects:	DYNAMIC MODEL EXTENDED NONLINEAR CERTAINTY EQUIVALENT APPROXIMATION METHOD CROP YIELD LAND USE NATURAL RESOURCE MANAGEMENT UNCERTAINTY RESOURCE ALLOCATION
Online Access:	http://documents.worldbank.org/curated/en/214641582232800623/Modeling-Uncertainty-in-Large-Natural-Resource-Allocation-Problems http://hdl.handle.net/10986/33391

id	okr-10986-33391
recordtype	oai_dc
spelling	okr-10986-333912022-09-20T00:13:39Z Modeling Uncertainty in Large Natural Resource Allocation Problems Cai, Yongyang Steinbuks, Jevgenijs Judd, Kenneth L. Jaegermeyr, Jonas Hertel, Thomas W. DYNAMIC MODEL EXTENDED NONLINEAR CERTAINTY EQUIVALENT APPROXIMATION METHOD CROP YIELD LAND USE NATURAL RESOURCE MANAGEMENT UNCERTAINTY RESOURCE ALLOCATION 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. 2020-02-26T15:42:30Z 2020-02-26T15:42:30Z 2020-02 Working Paper http://documents.worldbank.org/curated/en/214641582232800623/Modeling-Uncertainty-in-Large-Natural-Resource-Allocation-Problems http://hdl.handle.net/10986/33391 English Policy Research Working Paper;No. 9159 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper
repository_type	Digital Repository
institution_category	Foreign Institution
institution	Digital Repositories
building	World Bank Open Knowledge Repository
collection	World Bank
language	English
topic	DYNAMIC MODEL EXTENDED NONLINEAR CERTAINTY EQUIVALENT APPROXIMATION METHOD CROP YIELD LAND USE NATURAL RESOURCE MANAGEMENT UNCERTAINTY RESOURCE ALLOCATION
spellingShingle	DYNAMIC MODEL EXTENDED NONLINEAR CERTAINTY EQUIVALENT APPROXIMATION METHOD CROP YIELD LAND USE NATURAL RESOURCE MANAGEMENT UNCERTAINTY RESOURCE ALLOCATION Cai, Yongyang Steinbuks, Jevgenijs Judd, Kenneth L. Jaegermeyr, Jonas Hertel, Thomas W. Modeling Uncertainty in Large Natural Resource Allocation Problems
relation	Policy Research Working Paper;No. 9159
description	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.
format	Working Paper
author	Cai, Yongyang Steinbuks, Jevgenijs Judd, Kenneth L. Jaegermeyr, Jonas Hertel, Thomas W.
author_facet	Cai, Yongyang Steinbuks, Jevgenijs Judd, Kenneth L. Jaegermeyr, Jonas Hertel, Thomas W.
author_sort	Cai, Yongyang
title	Modeling Uncertainty in Large Natural Resource Allocation Problems
title_short	Modeling Uncertainty in Large Natural Resource Allocation Problems
title_full	Modeling Uncertainty in Large Natural Resource Allocation Problems
title_fullStr	Modeling Uncertainty in Large Natural Resource Allocation Problems
title_full_unstemmed	Modeling Uncertainty in Large Natural Resource Allocation Problems
title_sort	modeling uncertainty in large natural resource allocation problems
publisher	World Bank, Washington, DC
publishDate	2020
url	http://documents.worldbank.org/curated/en/214641582232800623/Modeling-Uncertainty-in-Large-Natural-Resource-Allocation-Problems http://hdl.handle.net/10986/33391
_version_	1764478658747039744

Modeling Uncertainty in Large Natural Resource Allocation Problems

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