Heterogeneity in Preferences for Woody Biomass Energy in the US Mountain West

Abstract Millions of acres of public forest in the US Mountain West are substantially degraded and are in need of restoration. Mechanized restoration treatments can improve forest health and reduce the likelihood of severe wildfire. These treatments produce some timber, and substantial amounts of forest residues that can be used to generate renewable energy and displace fossil fuels. Using the choice modeling method, this study investigates social preferences for generation of energy with woody biomass produced by restoration treatments on public forests in the Mountain West. Both multinomial logit and latent class logit (LCL) models are fit to the data and used to estimate marginal willingness to pay (MWTP) for increased amounts of woody biomass energy generation and important associated co-benefits and costs. Positive and statistically significant MWTP is found for the number of homes powered with wood, the extent of healthy forests, avoiding increases in the number of large wildfires, and local air quality. Significant heterogeneity was found in respondent preferences for the attributes. The heterogeneity can be explained in part by sociodemographic and attitudinal characteristics of respondents. The LCL revealed four classes of respondents with distinct preferences, revealing conflicting viewpoints toward forest management for woody biomass energy generation.

[1]  J. Hiers,et al.  Forest floor depth mediates understory vigor in xeric Pinus palustris ecosystems. , 2007, Ecological applications : a publication of the Ecological Society of America.

[2]  J. Scott Armstrong,et al.  Estimating nonresponse bias in mail surveys. , 1977 .

[3]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[4]  David A. Hensher,et al.  Non-attendance to attributes in environmental choice analysis: a latent class specification , 2011 .

[5]  Pankaj Lal,et al.  Assessing Public Preferences for Forest Biomass Based Energy in the Southern United States , 2010, Environmental management.

[6]  B. Roe,et al.  US consumers' willingness to pay for green electricity , 2001 .

[7]  Mikaela Huntzinger,et al.  Effects of fire management practices on butterfly diversity in the forested western United States , 2003 .

[8]  Robert L. Hicks,et al.  Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach , 2010 .

[9]  P. Howe,et al.  Geographic variation in opinions on climate change at state and local scales in the USA , 2015 .

[10]  Bryce J. Stokes,et al.  A strategic assessment of forest biomass and fuel reduction treatments in western states , 2003 .

[11]  David A. Hensher,et al.  Modelling attribute non-attendance in choice experiments for rural landscape valuation , 2009 .

[12]  D. Hensher How do respondents process stated choice experiments? Attribute consideration under varying information load , 2006 .

[13]  David A. Hensher,et al.  Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model , 2010 .

[14]  Marina Mura,et al.  Combining choice experiments with psychometric scales to assess the social acceptability of wind energy projects: A latent class approach , 2012 .

[15]  Teresa Del Giudice,et al.  Fossil energy versus nuclear, wind, solar and agricultural biomass: Insights from an Italian national survey , 2012 .

[16]  P. Lal,et al.  Random preferences towards bioenergy environmental externalities: A case study of woody biomass based electricity in the Southern United States , 2011 .

[17]  Jason J. Moghaddas,et al.  The National Fire and Fire Surrogate study: effects of fuel reduction methods on forest vegetation structure and fuels. , 2009, Ecological applications : a publication of the Ecological Society of America.

[18]  A. Bergmann,et al.  Rural versus urban preferences for renewable energy developments , 2008 .

[19]  Robert G. Bailey,et al.  Ecoregions of North America , 1981 .

[20]  Robert P. Berrens,et al.  Public support for reducing US reliance on fossil fuels: Investigating household willingness-to-pay for energy research and development , 2009 .

[21]  F. Johnson,et al.  Opt-out alternatives and anglers' stated preferences , 2000 .

[22]  Jianbang Gan,et al.  A comparative analysis of woody biomass and coal for electricity generation under various CO2 emission reductions and taxes. , 2006 .

[23]  Majid Ezzati,et al.  Fine-particulate air pollution and life expectancy in the United States. , 2009, The New England journal of medicine.

[24]  John I. Zerbe Thermal energy, electricity, and transportation fuels from wood. , 2006 .

[25]  Evelyne Thiffault,et al.  Effects of forest biomass harvesting on soil productivity in boreal and temperate forests - a review. , 2011 .

[26]  Alberto Longo,et al.  The Internalization of Externalities in the Production of Electricity: Willingness to Pay for the Attributes of a Policy for Renewable Energy , 2006 .

[27]  D. Dillman Mail and internet surveys: The tailored design method, 2nd ed. , 2007 .

[28]  Richard C. Ready,et al.  Preference heterogeneity for renewable energy technology , 2014 .

[29]  Robert Tibshirani,et al.  Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy , 1986 .

[30]  J. Swait A structural equation model of latent segmentation and product choice for cross-sectional revealed preference choice data☆ , 1994 .

[31]  K. Malmedal,et al.  Energy Policy Act of 2005 , 2007, IEEE Industry Applications Magazine.

[32]  Leif Gustavsson,et al.  Reducing CO2 emissions by substituting biomass for fossil fuels , 1995 .

[33]  G. McPherson,et al.  Evaluating the role of cutting treatments, fire and soil seed banks in an experimental framework in ponderosa pine forests of the Black Hills, South Dakota , 2004 .

[34]  D. Calkin,et al.  Forest treatment residues for thermal energy compared with disposal by onsite burning: Emissions and energy return , 2010 .

[35]  K. Train Recreation Demand Models with Taste Differences Over People , 1998 .

[36]  Kristina Ek Public and private attitudes towards “green” electricity: the case of Swedish wind power , 2005 .

[37]  S. Yoo,et al.  Willingness to pay for renewable energy investment in Korea: A choice experiment study , 2010 .

[38]  B. Solomon,et al.  Valuing climate protection through willingness to pay for biomass ethanol , 2009 .

[39]  Moshe Ben-Akiva,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1985 .

[40]  R. L. Hutto The ecological importance of severe wildfires: some like it hot. , 2008, Ecological applications : a publication of the Ecological Society of America.

[41]  Seung-Jun Kwak,et al.  Valuing environmental impacts of large dam construction in Korea: An application of choice experiments , 2008 .

[42]  Nick Hanley,et al.  Using conjoint analysis to quantify public preferences over the environmental impacts of wind farms. An example from Spain , 2002 .

[43]  K. Willis,et al.  Willingness-to-pay for renewable energy: Primary and discretionary choice of British households' for micro-generation technologies , 2010 .

[44]  K. Lancaster A New Approach to Consumer Theory , 1966, Journal of Political Economy.

[45]  Leif Gustavsson,et al.  Time-dependent climate benefits of using forest residues to substitute fossil fuels , 2011 .

[46]  Albino Prada,et al.  Generating electricity with forest biomass: Consistency and payment timeframe effects in choice experiments , 2012 .

[47]  A. Taylor IDENTIFYING FOREST REFERENCE CONDITIONS ON EARLY CUT‐OVER LANDS, LAKE TAHOE BASIN, USA , 2004 .

[48]  A. Bergmann,et al.  Valuing the attributes of renewable energy investments , 2006 .

[49]  Arne Risa Hole,et al.  A comparison of approaches to estimating confidence intervals for willingness to pay measures. , 2007, Health economics.

[50]  P. Boxall,et al.  Understanding Heterogeneous Preferences in Random Utility Models: A Latent Class Approach , 2002 .