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Library Costs of land degradation and benefits of land restoration: a review of valuation methods and suggested frameworks for inclusion into policy-making.CAB Reviews

Costs of land degradation and benefits of land restoration: a review of valuation methods and suggested frameworks for inclusion into policy-making.CAB Reviews

Costs of land degradation and benefits of land restoration: a review of valuation methods and suggested frameworks for inclusion into policy-making.CAB Reviews

Resource information

Date of publication
December 2012
Resource Language
ISBN / Resource ID
AGRIS:GB2018800071
Pages
1-12

Land degradation has become a growing concern with the current increase in demand for arable land. Sustainable land management and land restoration practices are required in order to meet the demands to provide food and other services. Adoption of improved practices has, however, not been widespread partly because of a lack of clarity on the true economic value and setting of proper financial incentives. This review focuses on the economic costs of land degradation as a prelude to two ongoing initiatives involving the United Nations Convention to Combat Desertification (UNCCD). We review how ecosystem services derived from land have been economically valued to date. Economic valuation has mostly focused on the use value of provisioning services and cultural services, with limited valuation of non-use value of cultural services. Also, no unique valuation method has been applied following methodological developments, varying study objectives and data availability constraints. These factors impair coherent and consistent estimation of the total economic value of land degradation across countries. We identify a need to develop harmonized valuation methods to estimate total economic value under strong data and capacity constraints. We propose two alternative frameworks for harmonized total economic valuation of land degradation at country level to guide further research in making environmental valuation more relevant and practical under strong data and capacity constraints.

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