<?xml version='1.0' encoding='UTF-8'?><xml><records><record><source-app name="HighWire" version="7.x">Drupal-HighWire</source-app><ref-type name="Journal Article">17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Becker, Oliver</style></author><author><style face="normal" font="default" size="100%">Börger, Tobias</style></author><author><style face="normal" font="default" size="100%">Meyerhoff, Jürgen</style></author></authors><secondary-authors></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Applying the contingent behavior method at the regional level</style></title><secondary-title><style face="normal" font="default" size="100%">Land Economics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2026-04-13 10:38:59</style></date></pub-dates></dates><elocation-id><style  face="normal" font="default" size="100%">le.102.4.052425-0039R1</style></elocation-id><doi><style  face="normal" font="default" size="100%">10.3368/le.102.4.052425-0039R1</style></doi><volume><style face="normal" font="default" size="100%"></style></volume><issue><style face="normal" font="default" size="100%"></style></issue><abstract><style  face="normal" font="default" size="100%">Contingent behavior (CB) studies typically analyze changes in recreation demand at single sites by presenting identical scenarios to all respondents. This prevents assessing demand responses under diverse future conditions and across broader spatial scales. The present study applies the CB method using a general population survey, integrating self-reported site characteristics with an experimental design to define CB scenarios. This allows recreation demand to be modeled as a function of multiple environmental attributes and analyzed regionally but requires adjusting the prediction procedure. Using data on forest recreation in Mecklenburg-Western Pomerania, Germany, we propose approaches for site- and region-level predictions.</style></abstract></record></records></xml>