sustainability

ABSTRACT. Adding bioenergy to the U.S. energy portfolio requires long‐term profitability for bioenergy producers and long‐term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a regional scale. To quantify feedstock production, we compared lowland switchgrass yields simulated by SWAT with estimates from a model based on empirical data for the eastern U.S. The two produced similar geographic patterns. Average yields reported in field trials tended to be higher than average SWAT‐predicted yields, which may nevertheless be more
representative of production‐scale yields. As a preliminary step toward quantifying bioenergy‐related changes in water quality, we evaluated flow predictions by the SWAT model for the Arkansas‐White‐Red river basin. We compared monthly SWAT flow predictions to USGS measurements from 86 subbasins across the region. Although agreement was good, we conducted an analysis of residuals (functional validation) seeking patterns to guide future model improvements. The analysis indicated that differences between SWAT flow predictions and field data increased in downstream subbasins and in subbasins with higher percentage of water. Together, these analyses have moved us closer to our ultimate goal of identifying areas with high economic and environmental potential for sustainable feedstock production.

The sustainability of future bioenergy production rests on more than continual improvements in its environmental, economic, and social impacts. The emergence of new biomass feedstocks, an expanding array of conversion pathways, and expected increases in overall bioenergy production are connecting diverse technical, social, and policy communities. These stakeholder groups have different—and potentially conflicting—values and cultures, and therefore different goals and decision making processes. Our aim is to discuss the implications of this diversity for bioenergy researchers. The paper begins with a discussion of bioenergy stakeholder groups and their varied interests, and illustrates how this diversity complicates efforts to define and promote ‘‘sustainable’’ bioenergy production.  We then discuss what this diversity means for research practice. Researchers, we note, should be aware of stakeholder values, information needs, and the factors affecting stakeholder decision making if the knowledge they generate is to reach its widest potential use. We point out how stakeholder participation in research can increase the relevance of its products, and argue that stakeholder values should inform research questions and the choice of analytical assumptions. Finally, we make the case that additional natural science and technical research alone will not advance sustainable bioenergy production, and that important research gaps relate to understanding stakeholder decision making and the need, from a broader social science perspective, to develop processes to identify and accommodate different value systems. While sustainability requires more than improved scientific and technical understanding, the need to understand stakeholder values and manage diversity presents important research opportunities.

A framework for selecting and evaluating indicators of bioenergy sustainability is presented.
This framework is designed to facilitate decision-making about which indicators are useful for assessing
sustainability of bioenergy systems and supporting their deployment. Efforts to develop sustainability
indicators in the United States and Europe are reviewed. The fi rst steps of the framework for
indicator selection are defi ning the sustainability goals and other goals for a bioenergy project or program,
gaining an understanding of the context, and identifying the values of stakeholders. From the
goals, context, and stakeholders, the objectives for analysis and criteria for indicator selection can
be developed. The user of the framework identifi es and ranks indicators, applies them in an assessment,
and then evaluates their effectiveness, while identifying gaps that prevent goals from being met,
assessing lessons learned, and moving toward best practices. The framework approach emphasizes
that the selection of appropriate criteria and indicators is driven by the specifi c purpose of an analysis.
Realistic goals and measures of bioenergy sustainability can be developed systematically with the help
of the framework presented here. © 2015 Society of Chemical Industry and John Wiley & Sons, Ltd

For analyzing sustainability of algal biofuels, we identify 16 environmental indicators that fall into six categories: soil quality, water quality and quantity, air quality, greenhouse gas emissions, biodiversity, and productivity. Indicators are selected to be practical, widely applicable, predictable in response, anticipatory of future changes, independent of scale, and responsive to management. Major differences between algae and terrestrial plant feedstocks, as well as their supply chains for biofuel, are highlighted, for they influence the choice of appropriate sustainability indicators. Algae strain selection characteristics do not generally affect which indicators are selected. The use of water instead of soil as the growth medium for algae determines the higher priority of water- over soil-related indicators. The proposed set of environmental indicators provides an initial checklist for measures of algal biofuel sustainability but may need to be modified for particular contexts depending on data availability, goals of stakeholders, and financial constraints. Use of these indicators entails defining sustainability goals and targets in relation to stakeholder values in a particular context and can lead to improved management practices.

A global energy crop productivity model that provides geospatially explicit quantitative details on biomass
potential and factors affecting sustainability would be useful, but does not exist now. This study describes a
modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling.
We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized
natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and
management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation,
and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate
(HPC-EPIC) model simulated a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock
production potentials and effects across the globe. This modeling platform can assess soil C sequestration,
net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy
exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus,
energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass
field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding
metrics of sustainability.

Agroecosystem models that can incorporate management practices and quantify environmental effects
are necessary to assess sustainability-associated food and bioenergy production across spatial scales.
However, most agroecosystem models are designed for a plot scale. Tremendous computational capacity
on simulations and datasets is needed when large scales of high-resolution spatial simulations are conducted.
We used the message passing interface (MPI) parallel technique and developed a master–slave
scheme for an agroecosystem model, EPIC on global food and bioenergy studies. Simulation performance
was further enhanced by applying the Vampir framework. On a Linux-based supercomputer, Cray XT7
Titan, we used 2048 cores and successfully shortened the running time from days to 30 min for a global
30 years of modeling of a bioenergy crop at the resolution of half-degree (62,482 grids) with the message
passing interface based EPIC (mpi_EPIC). The results illustrate that mpi_EPIC using parallel design can
balance simulation workloads and facilitate large-scale, high-resolution analyses of agricultural production
systems, management alternatives and environmental effects.

The US Congress passed the Renewable Fuels Standard (RFS) seven years ago. Since then, biofuels have gone from darling to scapegoat for many environmentalists, policy makers, and the general public. The reasons for this shift are complex and include concerns about environmental degradation, uncertainties about impact on food security, new access to fossil fuels, and overly optimistic timetables. As a result, many people have written off biofuels. However, numerous studies indicate that biofuels, if managed sustainably, can help solve pressing environmental, social and economic problems (Figure 1). The scientific and policy communities should take a closer look by reviewing the key assumptions underlying opposition to biofuels and carefully consider the probable alternatives. Liquid fuels based on fossil raw materials are likely to come at increasing environmental cost. Sustainable futures require energy conservation, increased efficiency, and alternatives to fossil fuels, including biofuels.

The production of biobased feedstocks (i.e., plant– or algal-based material use for transportation fuels, heat, power and bioproducts) for energy consumption has been expanding rapidly in recent years. Biomass now accounts for 4.1% of total U.S. primary energy production. Unfortunately, there are considerable knowledge gaps relative to implications of this industry expansion for wildlife.

The Wildlife Society convened an expert committee to analyze the latest scientific literature on the effects of growing, managing, and harvesting feedstocks for bioenergy on wildlife and wildlife habitat, and provide answers to questions and variables affecting bioenergy development and wildlife so that site managers might better predict consequences of managing bioenergy feedstocks.

This Technical Review is organized with respect to an ecosystems approach and tries to identify key biomass management practices within those systems, including agricultural lands and croplands; grassland ecosystems and Conservation Reserve Program (CRP) grasslands; forest ecosystems; and algae and aquatic feedstocks. A PDF of this review can be downloaded for free at the link below.

We present a system dynamics global LUC model intended to examine LUC attributed to biofuel production. The model has major global land system stocks and flows and can be exercised under different food and biofuel demand assumptions. This model provides insights into the drivers and dynamic interactions of LUC, population, dietary choices, and biofuel policy rather than a precise number generator.

The Department of Energy (DOE) Bioenergy Technologies Office held a workshop on "Social Aspects of Bioenergy" on April 24, 2012, in Washington, D.C., and convened a webinar on this topic on May 8, 2012. The workshop addressed questions about how to measure and understand the social impacts of bioenergy production based on a set of social sustainability indicators for bioenergy that were developed by Oak Ridge National Laboratory. The workshop was attended by representatives from DOE, national labs, the Environmental Protection Agency, United States Department of Agriculture, and several universities.