Consumer demand for urban forest ecosystem services and disservices: Examining trade-offs using choice experiments and best-worst scaling

Many studies value urban ecosystem service benefits using residents’ willingness to pay and supply-side analyses of ecosystem attributes. But, few studies account for consumer demand and ecosystem disservices. To address this gap we surveyed 1052 homeowners eliciting consumer demand for key urban forest ecosystem attributes and service-disservice levels in both their properties and surrounding neighborhood. We use an approach integrating focus group, field data, and surveys to identify consumer preferences and trade-offs between urban forest ecosystem structure-functional attributes and their level of services and disservices. This method, called best worst choice, produces more estimates of utility while reducing the likelihood of introducing biases associated with human cognitive tendencies. Results indicate that consumer choices for property value were highest followed by tree condition, a structural proxy for minimizing disservices, and tree shade, a functional proxy for temperature regulation. We also found evidence of trade-offs in demand for different ecosystem services, significant scale effects, and that willingness to pay for ecosystem disservices was negative. Findings suggest that management, and studies that value and map ecosystem services, using fixed scales should account for end-user demand and functional traits, as consumers can discern trade-offs in benefits and disservices across different cognitive and spatial scales.

[1]  F. Escobedo,et al.  Community leader perceptions and attitudes toward coastal urban forests and hurricanes in Florida , 2012 .

[2]  N. Timilsina,et al.  Urban forest structure effects on property value , 2015 .

[3]  Timothy C. Haab,et al.  Referendum Models and Negative Willingness to Pay: Alternative Solutions , 1997 .

[4]  J. E. Wagner,et al.  Urban forests and pollution mitigation: analyzing ecosystem services and disservices. , 2011, Environmental pollution.

[5]  T. Peters,et al.  Best--worst scaling: What it can do for health care research and how to do it. , 2007, Journal of health economics.

[6]  Damian C. Adams,et al.  Does policy process influence public values for forest-water resource protection in Florida? , 2016 .

[7]  N. Timilsina,et al.  Tree biomass, wood waste yield, and carbon storage changes in an urban forest. , 2014 .

[8]  C.Y. Jim,et al.  Protest response and willingness to pay for culturally significant urban trees: Implications for Contingent Valuation Method , 2015 .

[9]  Kevin J. Boyle,et al.  The implicit value of tree cover in the U.S.: A meta-analysis of hedonic property value studies , 2016 .

[10]  John M. Rose,et al.  Applied Choice Analysis , 2015 .

[11]  L. Nahuelhual,et al.  Valuing cultural ecosystem services: Agricultural heritage in Chiloé island, southern Chile , 2014 .

[12]  N. Timilsina,et al.  The Role of Composition, Invasives, and Maintenance Emissions on Urban Forest Carbon Stocks , 2015, Environmental Management.

[13]  D. Laband,et al.  Energy savings from tree shade. , 2010 .

[14]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[15]  Christina L. Staudhammer,et al.  Hurricane Debris and Damage Assessment for Florida Urban Forests , 2009, Arboriculture & Urban Forestry.

[16]  Jakub Kronenberg,et al.  From Valuation to Governance: Using Choice Experiment to Value Street Trees , 2014, AMBIO.

[17]  Benjamin L. Campbell,et al.  The effects of individual environmental concerns on willingness to pay for sustainable plant attributes , 2014 .

[18]  Francisco J. Escobedo,et al.  A framework for developing urban forest ecosystem services and goods indicators , 2011 .

[19]  Dionysios Latinopoulos,et al.  Valuing the benefits of an urban park project: A contingent valuation study in Thessaloniki, Greece , 2016 .

[20]  Andrew K. Koeser,et al.  Factors driving professional and public urban tree risk perception , 2015 .

[21]  F. Escobedo,et al.  Spatial patterns of a subtropical, coastal urban forest: implications for land tenure, hurricanes, and invasives. , 2010 .

[22]  Damian C. Adams,et al.  Landowner attitudes and willingness to accept compensation from forest carbon offsets: Application of best–worst choice modeling in Florida USA , 2016 .

[23]  J. Louviere,et al.  Discrete Choice Experiments Are Not Conjoint Analysis , 2010 .

[24]  Wendy Y. Chen,et al.  Citizens' distrust of government and their protest responses in a contingent valuation study of urban heritage trees in Guangzhou, China. , 2015, Journal of environmental management.

[25]  Joanna Coast,et al.  Estimating preferences for a dermatology consultation using Best-Worst Scaling: Comparison of various methods of analysis , 2008, BMC medical research methodology.

[26]  Rebecca L. Moore,et al.  Moving beyond the exchange value in the nonmarket valuation of ecosystem services , 2016 .

[27]  Christine Bertram,et al.  The role of urban green space for human well-being , 2015 .

[28]  E. Corbera,et al.  Payments for ecosystem services as commodity fetishism , 2010 .

[29]  E. Gómez‐Baggethun,et al.  Classifying and valuing ecosystem services for urban planning , 2013 .

[30]  K. Train Discrete Choice Methods with Simulation , 2003 .

[31]  Donna J. Lee,et al.  Public preferences for controlling upland invasive plants in state parks: Application of a choice model , 2011 .

[32]  Marc Tadaki,et al.  Revealing ecological processes or imposing social rationalities? The politics of bounding and measuring ecosystem services , 2015 .

[33]  J Coast,et al.  Preferences for aspects of a dermatology consultation , 2006, The British journal of dermatology.

[34]  J. Louviere,et al.  A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling , 2008 .

[35]  Danny Campbell,et al.  Position Bias in Best‐Worst Scaling Surveys: A Case Study on Trust in Institutions , 2015 .