Matthew B. Russell

US consumers' willingness to pay for green electricity

Abstract We analyze US consumers’ demand for environmental attributes of deregulated residential electricity services using results from a survey designed to elicit consumers’ willingness to pay for such attributes and using results from a hedonic analysis of actual price premiums charged for green electricity in several deregulated markets. Survey results suggest that many population segments are willing to pay for decreased air emissions even if there is no alteration in fuel source. Furthermore, several groups are willing to pay significantly more when emissions reductions stem from increased reliance upon renewable fuels. The hedonic analysis suggests that several product features not considered in the survey help explain real price premiums, including fuel mix from newly created renewable generation capacity, Green-e certification, brand name and state of offer. While survey and hedonic results are not easily compared due to limitations of the survey, both point to similar values for key environmental attributes, though the survey results are likely to overstate actual willingness to pay. In sum, the results suggest that consumer driven purchases can, in part, support the future of renewable generation capacity in the United States, though reliance upon other policy alternatives may be needed if energy prices spike.

Residence times and decay rates of downed woody debris biomass/carbon in eastern US forests

A key component in describing forest carbon (C) dynamics is the change in downed dead wood biomass through time. Specifically, there is a dearth of information regarding the residence time of downed woody debris (DWD), which may be reflected in the diversity of wood (for example, species, size, and stage of decay) and site attributes (for example, climate) across the study region of eastern US forests. The empirical assessment of DWD rate of decay and residence time is complicated by the decay process itself, as decomposing logs undergo not only a reduction in wood density over time but also reductions in biomass, shape, and size. Using DWD repeated measurements coupled with models to estimate durations in various stages of decay, estimates of DWD half-life (THALF), residence time (TRES), and decay rate (k constants) were developed for 36 tree species common to eastern US forests. Results indicate that estimates for THALF averaged 18 and 10 years for conifers and hardwoods, respectively. Species that exhibited shorter THALF tended to display a shorter TRES and larger k constants. Averages of TRES ranged from 57 to 124 years for conifers and from 46 to 71 years for hardwoods, depending on the species and methodology for estimating DWD decomposition considered. Decay rate constants (k) increased with increasing temperature of climate zones and ranged from 0.024 to 0.040 for conifers and from 0.043 to 0.064 for hardwoods. These estimates could be incorporated into dynamic global vegetation models to elucidate the role of DWD in forest C dynamics.

Biomass partitioning in a miniature-scale loblolly pine spacing trial

Stand conditions influence the partitioning of biomass to stem, needle, branch, and root components. Using data from 4- to 6-year-old loblolly pine (Pinus taeda L.) trees grown in a miniature-scale spacing trial, this study determined the effect of initial spacing on the biomass partitioning of loblolly pine. Multivariate analysis of variance procedures concluded that row and column spacing did not have a significant effect on the relative amount of biomass among tree components. Root/shoot and height/diameter ratios, however, differed across densities, indicating that allometric-based partitioning tradeoffs occurred. Results from the miniature-scale trees showed trends similar to those observed with mature-sized trees at operational spatial scales. Stem and woody roots were 70% and 14% of total mass, respectively. Since these trees were physiologically young at the time of harvest, the allocation of mass to needle continued to be a priority, accounting for 10% of the total mass. Initial planting spacing ...

Monitoring Network Confirms Land Use Change is a Substantial Component of the Forest Carbon Sink in the eastern United States

Quantifying forest carbon (C) stocks and stock change within a matrix of land use (LU) and LU change is a central component of large-scale forest C monitoring and reporting practices prescribed by the Intergovernmental Panel on Climate Change (IPCC). Using a region–wide, repeated forest inventory, forest C stocks and stock change by pool were examined by LU categories. In eastern US forests, LU change is a substantial component of C sink strength (~37% of forest sink strength) only secondary to that of C accumulation in forests remaining forest where their comingling with other LUs does not substantially reduce sink strength. The strongest sinks of forest C were study areas not completely dominated by forests, even when there was some loss of forest to agriculture/settlement/other LUs. Long-term LU planning exercises and policy development that seeks to maintain and/or enhance regional C sinks should explicitly recognize the importance of maximizing non-forest to forest LU changes and not overlook management and conservation of forests located in landscapes not currently dominated by forests.

Comparing strategies for modeling individual-tree height and height-to-crown base increment in mixed-species Acadian forests of northeastern North America

Various methods for predicting annual tree height increment (∆ht) and height-to-crown base increment (∆hcb) were developed and evaluated using remeasured data from permanent sample plots compiled across the Acadian Forest of northeastern North America. Across these plots, 25 species were represented upon which total height (ht) measurements were collected from mixed-species stands displaying both single- and multi-cohort structures. For modeling ∆ht, development of a unified equation form was found to result in higher accuracy and less bias compared to a maximum-modifier approach. Incorporating species as a random effect resulted in predictions that were not significantly different compared to predictions from species-specific equations for nine of the ten most abundant species examined. For ∆hcb, equations that modeled changes in hcb over two time periods (i.e., an incremental approach) were either not significantly different from or significantly closer to zero compared to predictions that estimated hcb at two time periods (i.e., a static approach). Results highlight the advantages of incorporating species as a random effect in individual-tree models and demonstrate the effectiveness of modeling tree crown recession directly for application in mixed-species forest growth and yield models.

Estimating litter carbon stocks on forest land in the United States

Forest ecosystems are the largest terrestrial carbon sink on earth, with more than half of their net primary production moving to the soil via the decomposition of litter biomass. Therefore, changes in the litter carbon (C) pool have important implications for global carbon budgets and carbon emissions reduction targets and negotiations. Litter accounts for an estimated 5% of all forest ecosystem carbon stocks worldwide. Given the cost and time required to measure litter attributes, many of the signatory nations to the United Nations Framework Convention on Climate Change report estimates of litter carbon stocks and stock changes using default values from the Intergovernmental Panel on Climate Change or country-specific models. In the United States, the country-specific model used to predict litter C stocks is sensitive to attributes on each plot in the national forest inventory, but these predictions are not associated with the litter samples collected over the last decade in the national forest inventory. Here we present, for the first time, estimates of litter carbon obtained using more than 5000 field measurements from the national forest inventory of the United States. The field-based estimates mark a 44% reduction (2081±77Tg) in litter carbon stocks nationally when compared to country-specific model predictions reported in previous United Framework Convention on Climate Change submissions. Our work suggests that Intergovernmental Panel on Climate Change defaults and country-specific models used to estimate litter carbon in temperate forest ecosystems may grossly overestimate the contribution of this pool in national carbon budgets.

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA—Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)—in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R2) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

A Tale of Two Forest Carbon Assessments in the Eastern United States: Forest Use Versus Cover as a Metric of Change

The dynamics of land-use practices (for example, forest versus settlements) is often a major driver of changes in terrestrial carbon (C). As the management and conservation of forest land uses are considered a means of reducing future atmospheric CO2 concentrations, the monitoring of forest C stocks and stock change by categories of land-use change (for example, croplands converted to forest) is often a requirement of C monitoring protocols such as those espoused by the Intergovernmental Panel on Climate Change (that is, Good Practice Guidance and Guidelines). The identification of land use is often along a spectrum ranging from direct observation (for example, interpretation of owner intent via field visits) to interpretation of remotely sensed imagery (for example, land cover mapping) or some combination thereof. Given the potential for substantial differences across this spectrum of monitoring techniques, a region-wide, repeated forest inventory across the eastern U.S. was used to evaluate relationships between forest land-use change (derived from a forest inventory) and forest cover change (derived from Landsat modeling) in the context of forest C monitoring strategies. It was found that the correlation between forest land-use change and cover change was minimal (<0.08), with an increase in forest land use but a net decrease in forest cover being the most frequent observation. Cover assessments may be more sensitive to active forest management and/or conversion activities that can lead to confounded conclusions regarding the forest C sink (for example, decreasing forest cover but increasing C stocks in industrial timberlands). In contrast, the categorical nature of direct land-use field observations reduces their sensitivity to forest management activities (for example, clearcutting versus thinning) and recent disturbance events (for example, floods or wildfire) that may obscure interpretation of C dynamics over short time steps. While using direct land-use observations or cover mapping in forest C assessments, they should not be considered interchangeable as both approaches possess idiosyncratic qualities that should be considered when developing conclusions regarding forest C attributes and dynamics across large scales.

Forest production dynamics along a wood density spectrum in eastern US forests

Key messageEmerging plant economics spectrum theories were confirmed across temperate forest systems of the eastern US where the use of a forest stand’s mean wood density elucidated forest volume and biomass production dynamics integrating aspects of climate, tree mortality/growth, and rates of site occupancy.AbstractAs a tree’s functional trait of wood density has been used to refine models of tree competition, it may also aid in evaluating hypotheses of forest production such as declining growth and mortality across a spectrum of increasing wood density. The goal of this study was to examine trends in aboveground live tree production as related to mean wood density using a region-wide repeated forest inventory across eastern US forests. Using quantile regression, the 90th percentile of volume and biomass accretion was negatively related to the mean wood density of a stand’s constituent tree species. This relationship was strongest on forest sites with the highest number of growing season degree days, as growing season length influences the rates of stand development. For these sites, variations in volume and biomass accretion were most pronounced in stands with low mean tree wood density, which also demonstrated the highest rates of site occupancy and mortality. This study confirmed aspects of the emerging theory of “fast–slow” plant economics spectrums across temperate forest ecosystems. Stands with relatively low wood density appear to occupy sites more rapidly leading to a concomitantly higher rate of tree mortality, but with less biomass accretion relative to volume due to allocating biomass or carbon to a greater tree volume. In contrast, stands with higher wood density exhibited slower site occupancy due to high wood density construction costs, but with increased biomass relative to volume accretion. These findings highlight the potential application of the plant economics spectrum theory in refining our understanding of general patterns of forest stand production, the role of plant traits in forest management, and knowledge gaps such a shifts in tree allometry during stand development.

Influence of prior distributions and random effects on count regression models: implications for estimating standing dead tree abundance

The presence and abundance of standing dead trees (SDTs) in forests is typically characterized by an excess number of zeros and high variation. The variability inherent in SDT data naturally leads to the assessment of novel quantitative methods to represent SDT populations and their role in contributing to forest ecosystem structure. This analysis assessed the performance of count regression methods fit with Bayesian mixed-effects models that estimate SDTs (all dead trees with a diameter at breast height