Payments for Carbon Sequestration to Alleviate Development Pressure in a Rapidly Urbanizing Region

Smith, Jordan W.; Dorning, Monica A.; Shoemaker, Douglas A.; Méley, Andréanne; Dupéy, Lauren Nicole; Meentemeyer, Ross K.

doi:10.5849/forsci.2016-084r1

Cited by 1 publication

(1 citation statement)

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“…We also populated the matrix with the age and income of the forest owner, the sense_of_place construct and the total size of the wooded areas on their property. Age and income were included as controls, given previous analyses of these data has revealed age to be a significant predictor of both forest owners' willingness to allow woody biomass to be harvested from their properties (Dorning, Smith, Shoemaker, & Meentemeyer, 2015) and their willingness to opt into a voluntary payments for carbon storage program (Smith et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Bayesian methods to estimate urban growth potential

Smith

Smart

Dorning

et al. 2017

Landscape and Urban Planning

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Urban growth often influences the production of ecosystem services. The impacts of urbanization on landscapes can subsequently affect landowners' perceptions, values and decisions regarding their land. Within land-use and land-change research, very few models of dynamic landscape-scale processes like urbanization incorporate empirically-grounded landowner decision-making processes. Very little attention has focused on the heterogeneous decision-making processes that aggregate to influence broader-scale patterns of urbanization. We examine the land-use tradeoffs faced by individual landowners' in one of the United States' most rapidly urbanizing regionsthe urban area surrounding Charlotte, North Carolina. We focus on the land-use decisions of non-industrial private forest owners located across the region's development gradient. A discrete choice experiment is used to determine the critical factors influencing individual forest owners' intent to sell their undeveloped properties across a series of experimentally varied scenarios of urban growth. Data are analyzed using a hierarchical Bayesian approach. The estimates derived from the survey data are used to modify a spatiallyexplicit trend-based urban development potential model, derived from remotely-sensed imagery and observed changes in the region's socioeconomic and infrastructural characteristics between 2000 and 2011. This modeling approach combines the theoretical underpinnings of behavioral economics with spatiotemporal data describing a region's historical development patterns. By integrating empirical social preference data into spatially-explicit urban growth models, we begin to more realistically capture processes as well as patterns that drive the location, magnitude and rates of urban growth.

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Section: Discussionmentioning

confidence: 99%