The Economic Supply of Biodiversity in West Kalimantan
Apr 01, 1995

William Magrath, Charles Peters, Nalin Kishor, and Puneet Kishor. 1995. "The Economic Supply of Biodiversity in West Kalimantan." Ed. Susan Shen and Arnoldo Contreras-Hermosilla. Environmental and Economic Issues in Forestry. Technical Paper No. 281, The World Bank, Washington, DC.

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Problem Statement

Many sustainable agricultural and forestry landuses such as annual cropping, industrial tree crops, and pasture have been identified on West Kalimantan, the third largest province on the island of Borneo, Indonesia. The highly diverse ecosystems of West Kalimantan are disappearing rapidly, because these landuses are being pursued aggressively for their attractive financial returns. Since foregoing these landuses to preserve land in its natural state implies incurring an opportunity cost, preserving biodiversity becomes an economic proposition. Natural areas contain differing levels of biodiversity, hence an efficient landuse policy needs to recognize trade-offs between commercial values and overall biodiversity value.

West Kalimantan, Borneo, Indonesia


This study employs the concepts of opportunity costs, island biogeography, and geographic information systems (GIS) to estimate the economic supply of biodiversity. Data on financial returns of landuse were organized in the form of a schedule of marginal costs of habitat preservation, that is, a "biodiversity supply curve." Five steps were involved:

  1. modeling biodiversity quantity
  2. modeling opportunity costs
  3. spatially associating biodiversity with alternative opportunities to rank specific areas by both variables
  4. arraying the results in the form of a supply curve; and
  5. utilizing the results to evaluate selected policy problems

Modeling Biodiversity Quantity

This work develops the concept of biovalue to express a number of attributes of natural systems in a common unit. The biovalue of polygon i is the sum of its Biodiversity Index (BI) and its Conservation Priority Index (CPI), that is,

  1. Biovaluei = BIi + CPIi

Equations 1, 2, and 3 give BI where HD = Habitat Diversity, ND = Neighborhood Diversity, EAF = Endemism Adjustment Factor, SPRICH = Species Richness of the Habitat of polygon i, and A = area of i in hectares.

  1. BIi = (HDi i) x EAFi
  2. HDi = log10(SPRICHi, Ai)
  3. NDi = log(SSPRICHi)

CPI, calculated using the following three formulae, is the product of rarity, exhaustion rate (ER), and protection Status (PS) for a habitat.

  1. Rarity = 1 / log(Original Area)
  2. ER = log(10 x Original Area / Remaining Area)
  3. PS = log (Original Area / Protected Area)


Spatial Analysis

Landuse and land suitability maps of West Kalimantan were digitized and developed into a geographic information system (GIS) database. The GIS was then used to overlay these maps spatially.

The resulting composite map was used to generate an area matrix used for calculating the Biovalue.

The biovalue array was utilized to calculate the opportunity cost of conserving biodiversity.

Opportunity Cost

Marginal Costs of Conservation

The supply curve suggests that for a large part of West Kalimantan there is little trade off between economic development of land and protection of biodiversity. Nearly 3.7m ha of land in the province could be set aside for biodiversity conservation purposes at a cost of less than Rp 400/ha/yr. This is consistent with the aggregate results of the Indonesian Forest Land Use by Consensus planning process which allocated 3.71m ha to conservation. The curve also suggests that given a market for the biodiversity, a relatively small increase in the current willingness to pay for biodiversity could attract a significant amount of land away from alternative uses.

Average Cost

Policy Experiments

With an explicit estimate of biodiversity supply, it is possible to quantitatively explore a number of biodiversity policy issues. The graph below shows results of a sensitivity analysis to illustrate the impacts of relative prices and economy-wide policies (factors such as interest rates, wages and foreign exchange rates) on environmental concerns.

Sensitivity Analysis

Qualitatively, the results are highly intuitive. Changes in prices that make land development more profitable (decreases in wages) shift the supply curve up and to the left, while cost increases shift the curve down and to the right. Statistical tests are consistent with the theory that economy-wide variables have significant impact on biodiversity conservation.


Because of benefits that may accrue to the global community many tropical developing countries are being asked to undertake special efforts to conserve biodiversity. There are costs, however, to the land use restrictions that conservation implies. A conceptually sound approach to estimating these costs is needed to design land policy, and in negotiating compensation and side payments. The methodology discussed here can contribute to better understanding of the economic consequences of biodiversity policy and provides specific and quantitative insights into land use policy for West Kalimantan. Information structured in this format could therefore be very helpful in negotiating international compensation or other ways of financing conservation.

Besides the usual disclaimers and cautions, the results of this exercise should be interpreted with caution. In addition to the preliminary nature of these results, the data used in this study have not received adequate ground truthing and verification. The land resource maps, for example, are subject to significant margins of error. The Biodiversity Index also has important limitations and has some problematic properties of aggregation that will be evaluated in further research.

Future research will also incorporate transportation and opportunity costs for additional landuses. Budget estimates for various landuses will also be refined and a broader array of forestry options will be included.

Process and Acknowledgments

Spatial data from RePPProt Land Use maps at 1:250,000 were digitized in Arcinfo by P. Dhillon, P. Nyborg, and G. Saungweme. Spatial analysis was done in Arcinfo by P. Kishor, W. Luscombe, W. Magrath. Maps were made in Arcinfo and Mapinfo, and poster layout was done in Micrografx Designer by P. Kishor.

Work was partially supported by a grant from the Government of Norway. Findings and views expressed in this study are those of the authors and do not represent the official view of the World Bank.

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