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Valuing Water Purification by Forests: An Analysis of Malaysian Panel Data

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Abstract

Water purification might be the most frequently invoked example of an economically valuable ecosystem service, yet the impacts of upstream land use on downstream municipal water treatment costs remain poorly understood. This is especially true in developing countries, where rates of deforestation are highest and cost-effective expansion of safe water supplies is needed the most. We present the first econometric study to estimate directly the effect of tropical forests on water treatment cost. We exploit a rich panel dataset from Malaysia, which enables us to control for a wide range of potentially confounding factors. We find significant, robust evidence that protecting both virgin and logged forests against conversion to nonforest land uses reduced water treatment costs, with protection of virgin forests reducing costs more. The marginal value of this water purification service varied greatly across treatment plants, thus implying that the service offered a stronger rationale for forest protection in some locations than others. On average, the service value was large relative to treatment plants’ expenditures on priced inputs, but it was very small compared to producer surpluses for competing land uses. For various reasons, however, the latter comparison exaggerates the shortfall between the benefits and the costs of enhancing water purification by protecting forests. Moreover, forest protection decisions that appear to be economically unjustified when only water purification is considered might be justified when a broader range of services is taken into account.

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Notes

  1. The process-based model InVEST is increasingly being used to predict water treatment costs under different land-use scenarios (Conte et al. 2011).

  2. Treatment plants in Perak measure the concentration of suspended solids in source water as it enters the plant, and they adjust the amount of alum, the chief chemical used to remove suspended solids, accordingly. Alum reduces the pH of treated water, thus requiring the addition of lime before the water is distributed and causing a knock-on increase in treatment costs (Mohd Nordin et al. 2000). The third major treatment chemical is chlorine, a disinfectant, but treatment plants in Perak add it as a standard dose that does not vary with source water quality (personal communication, Perak Water Board, February 10, 2014).

  3. The proportion of the population with access to piped water or another improved water source is higher in Malaysia than in most developing countries: 88.2 % in 1990 and 99.6 % in 2010, according to the World Bank’s World Development Indicators database (http://data.worldbank.org/data-catalog/world-development-indicators). Currently, 98 % of Perak households have a piped water connection (personal communication, Perak Water Board, May 26, 2015).

  4. This double-log specification for treatment cost and water volume is common in the econometric literature on water treatment (Forster et al. 1987; Moore and McCarl 1987; Holmes 1988; Murray and Forster 2001; Piper 2003). We confirmed the appropriateness of the log-transform of treatment cost by estimating a Box-Cox model, which favored that specification over a linear specification: the estimated theta parameter in a left-hand-side Box-Cox model was 0.117, which is much closer to 0 than to 1. We confirmed the appropriateness of the log-transforms of the other explanatory variables by comparing the overall fit of models containing the transformed variables to ones containing untransformed variables.

  5. Some prior studies have used unit cost (C / Q) as the dependent variable (e.g., Dearmont et al. 1998; Piper 2003; Ernst 2004). Our double-log specification encompasses this as a special case \((\alpha = 1)\).

  6. In addition, data were incomplete for some TPs: 32 % of the TP-years had fewer than 12 months of data, with 19 % having fewer than 11 months. It is possible that some of the missing observations resulted from outages or failures caused by poor source water quality. We doubt that this explains most of the missing observations, however, which were typically missing for all or nearly all of the TPs in the state or a particular region of the state in a given month. This pattern implies problems with data recording and storage, not temporary closures of individual TPs.

  7. This problem can be avoided by estimating a profit function instead of a cost function. See Pattanayak and Kramer (2001) for an application to a different watershed service, drought mitigation. We were unable to estimate a profit function because the Perak Water Board did not provide data on revenue from water sales.

  8. A private company, the Malaysian Utilities Corporation, operates two additional TPs in Perak (Sultan Idris Shah II, Ulu Kinta). It operated a third until 2001 (Pekan Parit). A second company, GSL Water, operates two others (Gunung Semanggol, Taiping Headworks). We excluded these private TPs from the analysis due to their different management and because the companies did not report plant-level data in all time periods.

  9. Air Kuning, Gerik V, Jalan Baru, and Pulau Banding. The exclusion of TPs that used reservoirs probably biased our estimate of the effect of forests on treatment costs downward, as we have ignored dredging costs.

  10. Selama, which is one of the newest TPs (established in 2004).

  11. Grik, Sg. Jelintoh, and Ulu Soh, which closed in 2000, and Felda Bersia, which closed in 2001.

  12. BB Seri Iskandar (opened in 2001), Sg. Tapah (2002), Tg. Malim (2003), and Hilir Perak (2006).

  13. Perlop (1995), Terong (1999), Padang Rengas (2001), Teluk Kepayang (2001), Kota Lama Kiri (2002), Sg. Kampar (2004), Kg. Paloh (2005), Pengkalan Hulu (2005), Jelai (2007), Sumpitan (2007), and Manong (2008).

  14. A complete list is available at: http://kmam.moh.gov.my/public-user/drinking-water-quality-standard.html.

  15. A complete list is available at: http://water.epa.gov/drink/contaminants/#List.

  16. Data on other variables, such as the components of total operating cost (labor, energy, chemicals, maintenance) or quantities of specific treatment chemicals, were too incomplete to analyze.

  17. We dropped 13 observations that appeared to be affected by data-entry error, as indicated by water volumes or unit costs that were either more than twice as large or less than half as large as any other values for the same TPs. These observations came from 11 TPs. Dropping them had a negligible impact on the regression results.

  18. The NFIs contain information on the approximate time periods when logged forests were harvested. The different NFIs do not define all the time periods consistently, however. Furthermore, for time periods defined consistently between NFI 3 and NFI 4, the reported areas exhibit substantial logical inconsistencies in many catchments (e.g., the area classified as having been logged before a given year is reported as being larger in NFI 4 than in NFI 3). For these reasons, we did not analyze the effect of years since logging on treatment cost. This effect is likely negative: sediment loads decrease relatively rapidly after logging in tropical forests, though they remain higher than sediment loads from virgin forests for decades (Bruijnzeel 2004; Abdul Rahim and Zulkifli 2004).

  19. The sample for this calculation included only countries that reported a positive virgin forest area in 1990. The UN estimates refer to primary and secondary forests, which we assumed correspond to virgin and logged forests as defined in Malaysia. The endpoints, 1990 and 2005, are the UN reporting years closest to NFI 3 (1992) and NFI 4 (2004).

  20. Holmes (1988) reported a much lower turbidity elasticity, \(-\)0.07.

  21. Fixed effects were more appropriate than random effects because the sample included nearly all TPs in Perak, not a random selection of them (Wooldridge 2010, pp. 285–287; Kennedy 2008, p. 291). Moreover, Hausman tests rejected the null that the random effects were uncorrelated with the error term: \(\chi ^{2}(24) = 95.3\,(P = 0.000)\) if the test used the disturbance variance estimate from the random effects model and \(\chi ^{2}(24) = 97.0\,(P = 0.000)\) if it used the estimate from the fixed effects model. Despite these test results, the random effects coefficient estimates were not very different from the fixed effects estimates given by model (5) in Table 2: \(-\)1.41 \((P = 0.000)\) for virgin forest and \(-\)1.06 \((P = 0.000)\) for logged forest.

  22. Endogeneity of a different sort—a correlation between the forest cover variables and the error term—could occur if the four TPs that were established after 1994 were designed to cope with the effects of the land-use changes that occurred during the sample period. Reestimating model (5) with those TPs excluded from the sample did not change the results much: the coefficients on virgin forest and logged forest were \(-\)1.56 (\(P = 0.019\)) and \(-\)1.13 (\(P = 0.0.34\)), respectively.

  23. Results are very similar to those for model (6) if we use the first lag as an IV: the coefficients on virgin forest and logged forest are \(-\)1.62 \((P = 0.009)\) and \(-1.18\,(P = 0.020)\), respectively. The instrument is again very strong \((F = 1989)\).

  24. This is also the case if we use the first lag as an instrument \((P = 0.711)\).

  25. Air Ganda, BB Seri Iskandar, Kg. Gajah, Kg. Paloh, Kota Lama Kiri, and Teluk Kepayang.

  26. Table 1 in FAO and CIFOR (2005) indicates that land use affects sediment loads only in catchments up to 10,000 ha, which is smaller than 40 % of the observations in our sample. Ogden et al. (2013) note that the table is drawn from a source that provides no citations to support this threshold.

  27. An additional explanation could be that the US elasticities are biased upwards by a correlation between water volume and some omitted variable. A strong candidate for such a variable is catchment area. In our sample, water volume has a relatively large positive correlation with catchment area \((\rho = 0.310, \,P = 0.000)\). This correlation is not surprising; larger TPs require larger supplies of source water and thus have larger catchments. The US studies do not control for catchment area, but model (5) controls for it through the TP effects.

  28. The sign pattern on the linear and quadratic terms is the opposite (positive and negative) if TP effects are excluded, as in model (3). This demonstrates the importance of controlling for unobserved TP characteristics.

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Acknowledgments

This study was funded by the Global Environment Facility through the United Nations Development Programme (MAL/04/G31), with additional support from the Government of Malaysia (through the Ministry of Natural Resources and Environment and the Forest Research Institute Malaysia) and the Center for International Forestry Research (CIFOR). The cooperation of numerous Malaysian government agencies is gratefully acknowledged: the Forestry Department Peninsular Malaysia; the Departments of Agriculture, Drainage and Irrigation, and Statistics; the National Water Services Commission; and the Perak Water Board. We also thank David James and two anonymous reviewers for helpful comments.

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Authors and Affiliations

  1. Nicholas School of the Environment, Duke University, Durham, NC, 27708, USA

    Jeffrey R. Vincent, Walter B. Burwell III, Subhrendu K. Pattanayak, Jie-Sheng Tan-Soo & Kyle Thomas

  2. Forest Research Institute Malaysia, 52109, Kepong, Selangor, Malaysia

    Ismariah Ahmad & Norliyana Adnan

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  1. Jeffrey R. Vincent
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Correspondence to Jeffrey R. Vincent.

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Vincent, J.R., Ahmad, I., Adnan, N. et al. Valuing Water Purification by Forests: An Analysis of Malaysian Panel Data. Environ Resource Econ 64, 59–80 (2016). https://doi.org/10.1007/s10640-015-9934-9

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