Regional Environmental Change

, Volume 19, Issue 2, pp 441–450 | Cite as

How spatial targeting of incentive payments for forest carbon storage can be adjusted for competing land uses

  • Yoomi KimEmail author
  • Seong-Hoon Cho
Original Article


Spatial consideration of costs and benefits plays a critical role in assessing the effectiveness of payments for ecosystem services (PES). While spatial assessment has received much attention, few, if any, studies have explicitly considered spatial variations in the benefits and landowners’ opportunity costs of competing land uses as targeting criteria for PES. The objective of our research is to identify different spatial targets for PES based on spatial variations in ecosystem benefits and opportunity costs for competing land uses. As a case study, we use incentive payments for forest carbon storage in the Central and Southern Appalachian Regions of the eastern United States. We find, on average, supplying forest carbon storage by converting pasture to forest is approximately five times more cost effective than mitigating deforestation for urban use because of its lower opportunity cost and its higher per hectare gain in carbon storage. We also find that the targeted areas that have positive net social benefits in supplying forest carbon represent 9.32% of the case-study region’s pasture land, while zero pixels are identified with positive net social benefits when urban use is the competing land use. These findings imply that the spatial targeting of the region’s areas that have positive net social benefits should focus on afforesting pasture instead of preventing forestland from being converted to urban use. The results also help target cost-effective areas for afforestation of pasture for carbon storage.


Competing land uses Forest carbon storage Payment for ecosystem services (PES) Spatial targets for PES 



We gratefully acknowledge the Agriculture and Food Research Initiative Competitive Grant no. 11401442 and Multistate Project no. TEN00507 (Multistate no. W4133) from the USDA National Institute of Food and Agriculture through the project “Developing a Cost-Effective Payment System for Forest Carbon Sequestration” and “Costs and Benefits of Natural Resources on Public and Private Lands: Management, Economic, Valuation, and Integrated Decision-Making” respectively. We also gratefully acknowledge D.J. Hayes and G. Chen for generating carbon outputs and B. Wilson, J. Menard, L. Lambert, T. Kim, S. Kwon, and S. Moon for helpful discussion and data support.


  1. Abolina E, Luzadis VA (2013) Forest sustainability and social policy: the role of ecosystem services. In: Wallimann I (ed) Environmental policy is social policy–social policy is environmental policy. Springer, New York, pp 63–78CrossRefGoogle Scholar
  2. Ando A, Camm J, Polasky S, Solow A (1998) Species distributions, land values, and efficient conservation. Science 279:2126–2128. CrossRefGoogle Scholar
  3. Armsworth PR, Acs S, Dallimer M, Gaston KJ, Hanley N, Wilson P (2012) The cost of policy simplification in conservation incentive programs. Ecol Lett 15:406–414. CrossRefGoogle Scholar
  4. Babcock BA, Lakshminarayan PG, Wu J, Zilberman D (1996) The economics of a public fund for environmental amenities: a study of CRP contracts. Am J Agric Econ 78:961–971. CrossRefGoogle Scholar
  5. Babcock BA, Lakshminarayan PG, Wu J, Zilberman D (1997) Targeting tools for the purchase of environmental amenities. Land Econ 73:325–339. CrossRefGoogle Scholar
  6. Barton DN, Faith D, Rusch G, Gjershaug JO, Castro M, Vega M, Vega E (2003) Spatial prioritisation of environmental service payments for biodiversity protection. NIVA (Norwegian Institute for Water Research). NorwayGoogle Scholar
  7. Cho S, Lee J, Roberts RK, English BC, Yu ET, Kim T, Armsworth PR (2017) Evaluating a tax-based subsidy approach for forest carbon sequestration. Environ Conserv 44:244–243. CrossRefGoogle Scholar
  8. Claassen R, Cattaneo A, Johansson R (2008) Cost-effective design of agri-environmental payment programs: U.S. experience in theory and practice. Ecol Econ 65:737–752. CrossRefGoogle Scholar
  9. Crossman ND, Connor JD, Bryan BA, Summers DM, Ginnivan J (2010) Reconfiguring an irrigation landscape to improve provision of ecosystem services. Ecol Econ 69:1031–1042. CrossRefGoogle Scholar
  10. Engel S, Pagiola S, Wunder S (2008) Designing payments for environmental services in theory and practice: an overview of the issues. Ecol Econ 65:663–674. CrossRefGoogle Scholar
  11. ESRI (2012) ArcGIS help 10.1: Spatial analyst toolsets, ArcGIS resources. Accessed 30 Jan 2018
  12. Faustmann M (1849) Calculation of the value which forest land and immature stands possess for forestry. In: Gane M (ed) Martin Faustmann and the evolution of discounted cash flow: two articles from the original German of 1849. Commonwealth Forestry Institute, Oxford, pp 18–34Google Scholar
  13. Ferraro PJ (2004) Targeting conservation investments in heterogeneous landscapes — a distance function approach and application to watershed management. Am J Agric Econ 86:905–918. CrossRefGoogle Scholar
  14. Foldvary EF (1997) The business cycle: a geo-Austrian synthesis. Am J Econ Sociol 56:521–541. CrossRefGoogle Scholar
  15. Goldman-Benner RL, Benitez S, Boucher T, Calvache A, Daily G, Kareiva P, Kroeger T, Ramos A (2012) Water funds and payments for ecosystem services: practice learns from theory and theory can learn from practice. Oryx 46:55–63. CrossRefGoogle Scholar
  16. Hayes DJ, McGuire AD, Kicklighter DW, Gurney KR, Burnside TJ, Melillo JM (2011) Is the northern high-latitude land-based CO2 sink weakening? Glob Biogeochem Cycles 25.
  17. Hellerstein D, Higgins N (2010) The effective use of limited information: do bid maximums reduce procurement cost in asymmetric auctions? J Agric Resour Econ 39:288–304. CrossRefGoogle Scholar
  18. Hoyt H (1933) One hundred years of land values in Chicago. Rpt Amo Press & The New York Times, 1970, New YorkGoogle Scholar
  19. Hoyt H (2000) One hundred years of land values in Chicago: The Relationship of the Growth of Chicago to the Rise of Its Land Values, 1830-1933. Rpt Amo Press & The New York Times, 1970, New YorkGoogle Scholar
  20. Hsiao C (2014) Analysis of panel data. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  21. Huang H-H (2016) Three essays on applied environmental economics. Dissertation, University of MichiganGoogle Scholar
  22. IDEAS (2018) XTABOND2: stata module to extend xtabond dynamic panel data estimator. Accessed 30 Jan 2018
  23. Marine Biology Laboratory (2018) Terrestrial Ecosystem Model (TEM). Accessed 30 Jan 2018
  24. Mason C, Plantinga A (2011) Contracting for impure public goods: carbon offsets and additionality. NBER Working Paper No. 16963.
  25. Multi-Resolution Land Characteristics Consortium (MRLC) (2011) National land cover database 2011 (NLCD2011). Accessed 30 Jan 2018
  26. Myronidis D, Arabatzis G (2009) Evaluation of Greek post-fire erosion mitigation policy through spatial analysis. Pol J Environ Stud 18:865–872Google Scholar
  27. National Agricultural Statistics Service (NASS) (2014) Quick stats. Accessed 30 Jan 2018
  28. National Sustainable Agriculture Coalition (2018) Conservation reserve program. Accessed 30 Jan 2018
  29. Newburn D, Reed S, Berck P, Merenlender A (2005) Economics and land-use change in prioritizing private land conservation. Conserv Biol 19:1411–1420. CrossRefGoogle Scholar
  30. Polasky S, Camm JD, Garber-Yonts B (2001) Selecting biological reserves cost-effectively: an application to terrestrial vertebrate conservation in Oregon. Land Econ 77:68–78. CrossRefGoogle Scholar
  31. Porras I, Greig-Gran M, Neves N (2008) All that glitters: a review of payments for watershed services in developing countries. International Institute for Environment and Development, UKGoogle Scholar
  32. Roodman D (2009) How to do xtabond2: an introduction to difference and system GMM in stata. Stata J 9:86–136CrossRefGoogle Scholar
  33. Schomers S, Matzdorf B (2013) Payments for ecosystem services: a review and comparison of developing and industrialized countries. Ecosyst Serv 6:16–30. CrossRefGoogle Scholar
  34. Schomers S, Sattler C, Matzdorf B (2015) An analytical framework for assessing the potential of intermediaries to improve the performance of payments for ecosystem services. Land Use Policy 42:58–70. CrossRefGoogle Scholar
  35. Sommerville MM, Jones JPG, Milner-Gulland EJ (2009) A revised conceptual framework for payments for environmental services. Ecol Soc 14:34. CrossRefGoogle Scholar
  36. Timber Mart-South (TMS) (2011) Product and services. Accessed 30 Jan 2018
  37. U.S. Census Bureau (2000) Census 2000 gateway. Accessed 30 January 2018
  38. U.S. Census Bureau (2009) American community survey (ACS) 2009. Accessed 30 Jan 2018
  39. U.S. Census Bureau (2010) American community survey (ACS) 2010. Accessed 30 Jan 2018
  40. U.S. Census Bureau (2012) American community survey (ACS) 2012. Accessed 30 Jan 2018
  41. U.S. Department of Agriculture Forest Service (2017) FIA data and tools. Accessed 30 Jan 2018
  42. U.S. Geological Survey (2013) Protected Areas Data. Accessed 30 Jan 2018
  43. U.S. Geological Survey, Gap Analysis Program (GAP) (2016) Protected areas database of the United States (PAD-US), version 1.4. Combined feature classGoogle Scholar
  44. U.S. Interagency Working Group on Social Cost of Greenhouse Gases (2016) Technical support document: technical update of the social cost of carbon for regulatory impact analysis under executive order. In: Keck JM (ed) Social cost of carbon estimates for regulatory impact analysis: development and technical assessment. U.S. Government, Washington D.C, p 12866Google Scholar
  45. Upadhyay TP, Solberg B, Sankhayan PL (2006) Use of models to analyse land-use changes, forest/soil degradation and carbon sequestration with special reference to Himalayan region: a review and analysis. For Policy Econ 9:349–371. CrossRefGoogle Scholar
  46. Uthes S, Matzdorf B, Müller K, Kaechele H (2010) Spatial targeting of agri-environmental measures: cost-effectiveness and distributional consequences. Environ Manag 46:494–509. CrossRefGoogle Scholar
  47. van der Horst D (2006) Spatial cost–benefit thinking in multi-functional forestry; towards a framework for spatial targeting of policy interventions. Ecol Econ 59:171–180. CrossRefGoogle Scholar
  48. Wendland KJ, Honzák M, Portela R, Vitale B, Rubinoff S, Randrianarisoa J (2010) Targeting and implementing payments for ecosystem services: opportunities for bundling biodiversity conservation with carbon and water services in Madagascar. Ecol Econ 69:2093–2107. CrossRefGoogle Scholar
  49. Windmeijer F (2005) A finite sample correction for the variance of linear efficient two-step GMM estimators. J Econom 126:25–51. CrossRefGoogle Scholar
  50. Wunder S (2005) Payments for environmental services: some nuts and bolts. Center for International Forestry Research. Accessed 30 Jan 2018
  51. Wunder S (2007) The efficiency of payments for environmental services in tropical conservation. Conserv Biol 21:48–58. CrossRefGoogle Scholar
  52. Wunder S, Wertz-Kanounnikoff S (2009) Payments for ecosystem services: a new way of conserving biodiversity in forests. J Sustain For 28:576–596. CrossRefGoogle Scholar
  53. Wünscher T, Engel S, Wunder S (2008) Spatial targeting of payments for environmental services: a tool for boosting conservation benefits. Ecol Econ 65:822–833. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Public AdministrationEwha Womans UniversitySeoulRepublic of Korea
  2. 2.Department of Agricultural & Resource EconomicsUniversity of TennesseeKnoxvilleUSA

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