1. Introduction
With growing concern for energy security and associated environmental benefits, alternative fuels are growing in popularity in the United States and abroad. One such fuel is bioenergy, which has been recognized a sustainable source of energy, and has become a staple in energy plans around the world. Biofuels using forested biomass has considerable potential as an energy source due to its sustainability and relative efficiency [
1,
2,
3,
4], but it is not without its issues; uncertainties associated with land-use change, inadequate volume for perennial crop cultivation for biomass feedstock, and a relative uneasiness among investors for producing feedstocks have combined to significantly slow the growth of bioenergy production in the United States [
5,
6,
7]. The feedstock requirements present perhaps the largest challenge; a 2005 US Department of Energy (DoE) and Department of Agriculture (USDA) joint estimate suggested a requirement of around 36,252 Mg of domestic perennial biomass for domestic bioethanol needs, of which little over 10,000 Mg has been produced as of 2016 [
8,
9]. Thus, there is a growing need for new biofuel feedstocks, especially those grown domestically.
Woody bioenergy feedstock has a number of environmental and economic benefits, including reduced risk of fire, reduced risk of disease outbreak, reduced greenhouse emissions, and a potential to increase wood revenue for landowners, all while being safely outside the food versus fuel debate that plagues many traditional biomass sources such as corn [
4,
10,
11,
12]. Loblolly pine (
Pinus taeda L.) is a potential woody bioenergy source, and is one of the most abundant and productive trees in the southern United States, largely due to its adaptability to a variety of environmental conditions [
4].
Virginia, in the American South, is a native biome for loblolly pine; Virginia has over 6.4 million hectares of forestland, of which around 1.17 hectares are loblolly pine, with much of its forest cover being under the stewardship of private landowners [
4,
13]. Due to a high density and availability of forested land, Virginia is also one of the top 10 states for biomass supply, making the state a key area for potential biofuel feedstock cultivation [
4,
14]. Unlike many other bioenergy feedstocks, loblolly pine is already a common product in the state, and therefore, owners and potential growers are already likely acclimated with its needs, yields, and requirements [
4,
15]. Loblolly pine is also known for its adaptability to a variety of climatic conditions and its relatively quick growth rates; due to this, the species is becoming of increased interest as a potential source for biomass and bioenergy.
However, despite the relative ease with which loblolly pine may be adapted as a biomass energy source, there remains uncertainty surrounding the likelihood of forest landowners adopting growth for bioenergy feedstock [
4]. Therefore, stakeholder opinions and perceptions surrounding bioenergy feedstock growth are crucial for improved understanding of this nascent industry. Further, stakeholder preferences in terms of growing conditions, ideal farmland, and other factors may be critical in matching landowner needs with beneficial policies, and may help to inform the future landscape of the bioenergy industry. However, currently, such preferences and perceptions are understudied, and despite Virginia’s considerable hold on the forestry industry, stakeholder opinions remain largely unknown. While best management practices for loblolly pine are well supported in the literature, the forest management practices that affect ecosystem services and are impacted by infrastructural networks are lesser known.
To this end, this study uses a forest industry stakeholder survey, utilizing both analytical hierarchy process (AHP) to understand the preferences of field experts in terms of geographic growing conditions for loblolly pine, in addition to exploring questions pertaining to recommended management practices such as final harvest year and stand density through regression analysis. This novel approach of combining AHP and regression analysis allows for an understanding of how stakeholder groups may act as a whole, and well as how individuals’ opinions may vary within those groups. Understanding these variations are essential in successful policy development, particularly in the circumstance of uncertainty of bioenergy industry development in the US. This survey targets experts including landowners, researchers and educators, state and federal employees, and consultants to cover a range of stakeholders involved in the forestry industry that hold opinions specifically for growing and managing loblolly pine. By understanding how geographic criteria are prioritized by these stakeholders through AHP, we can quantify stakeholder perception and visualize which areas may be prioritized for future bioenergy growth. These preferences can further be compared to current policies to analyze whether or not stakeholder needs are being addressed adequately. Additionally, growing and management practices between different stakeholder groups could better inform policy development to ensure best management practices specific for loblolly growth for biomass. Finally, this data may be used in future research to target areas that may be optimized for bioenergy feedstock harvesting.
3. Methods
3.1. AHP Criteria
3.1.1. Distance to Water
Forests in the southern US frequently act as watershed areas that are able to collect, filter, and store water, which is later transported to communities for public use. Forest management can directly impact the quantity and quality of water within these watersheds, thereafter having an effect on the health of plants, wildlife, and humans [
25]. A forest watershed generally increases in the volume of available water after the removal of trees and vegetation, due to the increased precipitation landing directly on soil surfaces. While this varies depending on other climatic and geologic factors, this increase can be expected to last for at least one year after harvest [
26]. Water table management is critical for the productivity potential of the land as well as long-term groundwater availability, making forestland management an important and dynamic factor in developing the bioenergy industry. To negate some of these concerns, the Riparian Buffer Implementation Plan in Virginia that was initiated in 1996 was established to protect steams and shorelines by maintaining adequate riparian buffers to protect these ecosystem services [
27]. Buffer establishment and maintenance does fall to landowners, although the state provides funding to those who establish buffers as required: at least 35 feet on at least one side of a stream [
27]. While there are costs to the landowner associated with buffer establishment and maintenance, there are cost share and technical assistance programs to alleviate financial and utilitarian costs led by federal and state agencies such as the Natural Resource Conservation Service, Farm Service Agency, and VA Department of Conservation and Recreation, as well as non-profit conservation organizations such as the Chesapeake Bay Foundation and American Forests [
27,
28]. Meeting these standards for water quantity and quality can be a challenge for forest landowners, and incorporating best management practices is critical to ensure the sustainability of water resources within a growing industry for bioenergy production.
3.1.2. Distance from Protected Land
The manipulation of vegetation, soil, and water associated with forest management has a direct impact on biodiversity within a forested site. In addition to more direct impact through the harvesting of trees and removal of deadwood, increased road networks and land-use change can have a negative impact on biodiversity. Habitat management is also important to landowners, such that a lack of management could lead to issues with habitat connectivity leading to species overpopulation and the overuse of a habitat [
26]. There are several ways to manage tree stands to support biodiversity including longer rotations, thinning and harvesting for structural complexity, creating a habitat through man-made structures (i.e., nest boxes), and through the use of maintaining buffer zones for habitat support [
26]. There are also conservation lands managed through federal, local, and state municipalities as well as those managed privately and through non-profit organizations. Forestland adjacent to these conservation areas could cause a number of issues for both parties based on chosen land management practices. The Conserve Virginia conservation strategy has identified additional high-priority land that requires additional protection regarding concerns such as ecosystem diversity, natural habitats, potential rare species richness, cultural and historic preservation, and floodplains and landscape resilience [
28]. Meeting these environmental requirements, whether self-imposed or regulated, is an important factor for multiple stakeholders to recognize when attempting to support the development of the bioenergy industry.
3.1.3. Flat Land
Biomass harvesting systems exist to fell, collect, and transport biomass to a collection center or directly to a post-harvest utilization facility (e.g., sawmill). Depending on the scale of the landholding and the mechanical capabilities of equipment, the cost of this process can differ dramatically. Capital equipment, transportation, and operational costs all influence the cost-effectiveness of harvesting systems. Additionally, more level terrain allows for the use of less expensive feller equipment; the cost-effectiveness of feller equipment in a state with a varied topography such as Virginia can call for different best management practices for harvest and transportation processes [
26]. The more commonly used one-pass timber harvesting system allows for harvesting both roundwood and biomass simultaneously, and is generally considered to be more cost-efficient than the less common two-pass method [
2]. For this purpose, flat land may be a desirable trait when choosing to plant loblolly for woody biomass. While loblolly is known for being geographically resilient and able to grow on a variety of terrain settings, those who deal with the capital cost of equipment and the operational procedure of harvesting may find this feature particularly important to their willingness to participate in providing biomass for bioenergy production. Moreover, those who deal in legislative support in the renewable energy sector may need to consider the feasibility and cost-efficiency of harvesting and transportation ease on a varied topographic setting.
3.1.4. Distance to Road
The collection and transportation of biomass from forested land to the post-harvest use location is a major cost and varies based on the location and harvesting system used. In the US, trucks and trailers are used almost exclusively to transport biomass; roadways are favored, as opposed to railways, for accessing multiple forest sites for biomass pickup/delivery [
26]. Most products in the south are currently transported in tractor-trailers with 80,000-pound capacity, which are street legal for road networks without specified weight/height limits [
2]. Having access to major road networks is an important consideration for landowners and forest products manufacturers, particularly in the context of developing a feasible bioenergy production system. Biomass that has not been pre-processed (i.e., chipping or shredding) has low bulk density, which thereafter increases the cost of transportation. The rural nature of managed forestland may mean that ideal land for growing loblolly pine is further away from major road networks, making the possibility of a profitable bioenergy industry a true challenge [
1,
29,
30].
3.1.5. Distance to a Mill
The transportation of woody biomass to a manufacturing site accounts for 25–50% of the total delivery costs based on haul distance, fuel cost, vehicle capacity, and biomass density [
26]. Further, emissions associated with the transportation of biomass can potentially negate the environmental benefits of manufacturing bioenergy as opposed to fossil fuels. Access to mills is critical for the feasibility of developing a sustainable bioenergy production system and supporting rural development [
31]. Much of the land in Virginia, with preferable climatic and environmental growing conditions, lays in the Southern Piedmont and Coastal Plain regions, while many pre-existing mills reside on the northwestern edge of the Piedmont region, closer to the Blue Mountain Ridge [
32]. While this distance may be preferable to Virginia residents, so that housing and communities are further from industry, it acts as a challenge toward environmental and economic sustainability. The physical distance between the biomass source and post-harvest systems is a major disparity that could affect the sustainability of a bioenergy production system [
26,
31].
3.2. Survey Design
For our survey development, we undertook an existing literature review to delineate the factors that were relatively important for woody biomass for energy development. As such, we did not include biological factors, such as climate or water needs, as these tend to be well studied. We also reached out to a number of researchers in the forestry field to guide our design and suggest concepts for the survey. The five AHP criteria as described in
Section 3.1 include distance to water, distance from protected land, flat land, distance to a road, and distance to a mill, and the survey asked: “Which attribute is more important for growing loblolly pine in Virginia?” We solicited the opinions of experts, and used the AHP questions for a number of different surveys permutations, but changed the phrasing of the auxiliary questions, such as harvest year and stand density, to best pertain to specific stakeholder groups. This was done both to avoid bias by any single group, as well as to understand the differences amongst the stakeholder groups and how preferences may vary depending on needs. To expedite the surveying process, the survey was coded into the “Survey 123 Connect for ArcGIS” program on a number of tablets for rapid responses. Once completed, each survey was pretested by researchers and graduate students at Montclair State University for clarity and readability.
In order to solicit responses from experts without specifically targeting individuals, the survey was administered during the annual Virginia Forestry Summit hosted by the Virginia Forestry Association in May 2019. This summit is well attended by a mix of landowners, researchers, and industry stakeholders, and thus was an ideal setting for processing survey responses. Responses were voluntary, and respondents could participate in the survey by visiting our research area at the conference. Surveys were completed via tablets loaned to respondents by our researchers, who then remained nearby for any questions or technical assistance that was required. The survey was carried out in the conference hall over two days. Responses that were incomplete were removed from the final analysis.
3.3. Analytical Hierarchy Process (AHP) Method
The analytical hierarchy process (AHP) method is a framework used to approach complex decision making and rank factors by priority. The method works by reducing complex decision making down to a number of pairwise comparisons, and then using the resulting comparisons to rank individual factors in terms of importance. These pairwise comparisons are given to experts in the given subject, allowing for subjective opinion to be turned, in aggregate, to a more objective ranking. The technique can also check the consistency of responses to reduce bias in decision maker evaluations [
33]. Therefore, AHP allows the incorporation of both subjective and objective observations while also providing a framework for complexity, enabling the weighting of different factors in a structured method [
34].
We performed the AHP method as laid out by Saaty (1980) and by following the use of the method in the environmental fields, as per Dos Santos et al. (2019), Ananda and Herath (2003), and Dwivedi and Alavalapati (2009) [
33,
35,
36,
37]. In order to prepare our survey for the pairwise criteria to be considered (
Figure 1), we carried out an extensive literature review in addition to conducting informal interviews with a number of researchers in the forestry field. To produce the hierarchy structure that characterizes the AHP method, we followed the basic method for creating the numerical scale; the pairwise criteria were each given a ranking from 1 to 9, where 1 indicates that two activities or factors are of equal importance, and 9 indicates that one factor is considerably more important than the other [
33,
35].
The AHP method was then broken down into four steps [
33,
35]: (1) Define the problem and identify the necessary information; (2) structure the decision hierarchy from the goal, to the broader objectives, throughout the intermediate levels into the lower levels; (3) construct a set of pairwise comparison matrices, wherein the upper level is used to compare the elements in the level immediately below it, and; (4) use the results of the comparisons to weight the priorities for each element, continuing the process by adding to find the overall global priority of each level. In order to analyze the judgments, AHP uses decision matrices in
n order with the eigenvectors related to them [
32].
Error calculation, eigenvector method:
We analyzed each respondent within their stakeholder group: forest landowners, private forest consultants, forest management researchers or educators, and federal or state agency foresters [
38]. The result table displayed all five criteria (distance to a water body, distance from protected land, distance to a road, distance to a mill, and flat land) with calculated weights and errors using the eigenvector method [
38,
39]. Consistency ratios (CR) were used to find inconsistent comparisons in responses; a CR ≤0.1 or 10% is considered to be within the acceptable limit. Using the Alonson/Lamata linear fit, the consistency ratio is calculated based on the principal eigenvalue [
40].
Comparison inconsistency (CI) equation:
Consistency ratio equation:
Consistency ratio using Alonson/Lamata linear fit:
After all the surveys were completed, the AHP data was then transcribed into a pre-generated AHP Excel template, which included worksheets for pairwise comparisons, a judgment consolidation sheet, a summary sheet of results, and a sheet for solving the eigenvalue problem when using the eigenvector method [
38].
3.4. Survey Regression Model
The AHP criteria weight for each individual respondent were then transcribed into JMP®, along with the other coded responses from the survey, allowing us to compare the stakeholder groups as a whole as well as compare the individual responses within each group. As landowners have the most direct interaction with forestland and were our sample’s largest stakeholder group, we explored what factors, such as landholding size and management practices, may have influenced their weighted decisions for the AHP criteria. To do this, we built a regression model from a set of predictor variables by entering and removing predictors in a stepwise manner to justify the use of certain variables associated with the AHP criteria of interest. In our stepwise model, we ran a forward direction using a p-value threshold of probability to enter α = 0.25 and probability to leave α = 0.1. We made our least squares model based on the stepwise output, which identified the predictor variables that had t-test p-values less than p = 0.1 in order to see how those predictor variables affect the AHP criteria of interest. We did this through the fit model platform, choosing the standard least squares personality based on the continuous-response nature of the dependent variable to construct a linear model.
Similarly, comparing recommended management practices between stakeholder groups, such as the recommended stand density and rotation age, is important to gauge whether what is recommended by industry experts is actually being performed by landowners and managers. To explore this, we performed a one-way analysis of these recommended management practices by the stakeholder groups to compare trends. A one-way analysis allows one to explore the distribution of a continuous variable (AHP criteria) across a group defined by a categorical variable to test for variability within a single stakeholder group and between multiple stakeholder groups. Finally, understanding how self-indicated experience levels with loblolly pine management influences stakeholders’ weighted criteria decision is important to see how increased experience may lead to different geographic criteria recommendations. To explore this, we performed a contingency analysis to explore the distribution of a categorical variable (experience levels) within a single stakeholder group and between multiple stakeholder groups.
5. Conclusions, Limitations, and Future Work
This study utilized an in-person survey to assess the varying opinions both within and between four forestry stakeholder groups in Virginia (forest landowners, private forest consultants, forest management researchers or educators, and federal or state agency foresters) to assess the importance of different factors in growing and managing loblolly pine for bioenergy. The survey followed a standard AHP method, which used pairwise criteria to rank factors by their overall importance and weight across all stakeholders. Our results found that distance to a mill was considered to be the most important factor by all but one group, who instead identified distance to a road as most important, while the distance to protected land was considered to be the least important by all stakeholder groups. With a considerably higher number of respondents, the forest landowner stakeholder group’s responses for distance to a mill and distance to protected land were subjected to regression analyses. These analyses found that the recommended rotation age significantly increased the preference for proximity to a mill, while experience managing loblolly pine significantly increased the preference for proximity from protected land. These findings indicate the need for infrastructural support, and stronger consensus on best management practices in order to support the growth of the biofuel industry.
Although we are confident in our methods and approach, our study did suffer from some limitations. While AHP relies on expert opinions to make an unbiased judgment, our survey relied on self-reporting for levels of experience, and thus, the surveys may not represent the peak of experience that many AHP surveys aim for. However, the varying levels of experience with loblolly pine within each stakeholder group did allow additional insight into management practices, which could be important for future work on transitioning to growth for bioenergy feedstock. We utilized an extensive literature review to identify criteria that were understudied or poorly understood for the survey. While we may have excluded factors that we were unaware of, interaction with the stakeholders allowed us to correct this to some degree. Finally, doing this survey at a forester’s conference may provide some bias due to economic, spatial, and temporal limitations, though we feel that the results provide a reasonably unbiased analysis of the stakeholders in question.
In future work, we aim to use these findings as a factor to better understand suitable growing regions and preferential infrastructural requirements in order to support an economically viable bioenergy industry in Virginia which could utilize loblolly pine as a potential feedstock. We aim to utilize the weighted criterion results from this study to further be exploited as an input of a Geographic Information System (GIS)-based fuzzy logic model. This model will be used to locate suitable regions for developing loblolly pine growth and management in the southern US. The model will involve multiple additional processes for data acquisition and transformation to produce gridded images with fuzzy membership functions for identified factors. Then, these comparable GIS layers will be aggregated with individual criteria weight in order to create a suitable map of the pine cultivation as a potential biomass for clean energy.