sustainability standards

Ecological disturbances are occurring with greater frequency and intensity than in the past. Under projected shifts in disturbance regimes and patterns of recovery, societal and environmental impacts are expected to be more extreme and to span larger spatial extents. Moreover, preexisting conditions will require a longer time to re‐establish, if they do so at all. The word “unprecedented” is appearing more often in news reporting on droughts, fires, hurricanes, tsunamis, ice storms, and insect outbreaks. The causes and effects of these events are often exacerbated by human modifications of natural environments and influenced by technological developments.

Policy makers are interested in estimates of the potential economic impacts of oil price shocks, particularly during periods of rapid and large increases that accompany severe supply shocks. Literature estimates of the economic impacts of oil price shocks, summarized by the oil price elasticity of GDP, span a very wide range due to both fundamental economic and methodological factors. This paper presents a quantitative meta-analysis of the oil price elasticity of GDP for net oil importing countries, with a focus on the United States (US). The full range of estimates of the oil price elasticity of GDP for the US in the data is − 0.124 to + 0.017, accounting for different methodologies, data and other factors. We employ a meta-regression model that controls for key determinant factors to estimate the mean and variance of the GDP elasticity across studies. We use a robust estimation technique to deal with heterogeneity of the data and well-known econometric issues that confront meta-analysis. The resulting regression model is used to simulate the oil price elasticity of GDP for the US, with a mean of − 0.020% and 68% confidence interval of − 0.035 to − 0.006, four quarters after a shock.

Understanding the environmental effects of alternative fuel production is critical to characterizing the sustainability of energy resources to inform policy and regulatory decisions. The magnitudes of these environmental effects vary according to the intensity and scale of fuel production along each step of the supply chain. We compare the spatial extent and temporal duration of ethanol and gasoline production processes and environmental effects based on a literature review and then synthesize the scale differences on space-time diagrams. Comprehensive assessment of any fuel-production system is a moving target, and our analysis shows that decisions regarding the selection of spatial and temporal boundaries of analysis have tremendous influences on the comparisons. Effects that strongly differentiate gasoline and ethanol-supply chains in terms of scale are associated with when and where energy resources are formed and how they are extracted. Although both gasoline and ethanol production may result in negative environmental effects, this study indicates that ethanol production traced through a supply chain may impact less area and result in more easily reversed effects of a shorter duration than gasoline production.

To date, feedstock resource assessments have evaluated cellulosic and algal feedstocks independently, without consideration of demands for, and resource allocation to, each other. We assess potential land competition between algal and terrestrial feedstocks in the United States, and evaluate a scenario in which 41.5 × 109 L yr−1 of second-generation biofuels are produced on pastureland, the most likely land base where both feedstock types may be deployed. Under this scenario, open-pond microalgae production is projected to use 1.2 × 106 ha of private pastureland, while terrestrial biomass feedstocks would use 14.0 × 106 ha of private pastureland. A spatial meta-analysis indicates that potential competition for land under this scenario would be concentrated in 110 counties, containing 1.0 and 1.7 × 106 ha of algal and terrestrial dedicated feedstock production, respectively. A land competition index applied to these 110 counties suggests that 38 to 59 counties could experience competition for upwards of 40% of a county's pastureland, representing 2%–5% of total pastureland in the U.S.; therefore suggesting little overall competition between algae production, terrestrial energy feedstocks and alternative uses for existing agricultural production such as livestock grazing.

We propose a causal analysis framework to increase understanding of land-use change (LUC) and the reliability of LUC models. This health-sciences-inspired framework can be applied to determine probable causes of LUC in the context of bioenergy. Calculations of net greenhouse gas (GHG) emissions for LUC associated with biofuel production are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under regional (EU), national (US, UK), and state (California) regulations. Biofuel policymakers and scientists continue to discuss to what extent presumed indirect land-use change (ILUC) estimates should be included in GHG accounting for biofuel pathways. Current estimates of ILUC for bioenergy rely largely on economic simulation models that focus on causal pathways involving global commodity trade and use coarse land-cover data with simple land classification systems. This paper challenges the application of such models to estimate global areas of LUC in the absence of causal analysis. The proposed causal analysis framework begins with a definition of the change that has occurred and proceeds to a strength-of-evidence approach that includes plausibility of relationship, completeness of causal pathway, spatial co-occurrence, time order, analogous agents, simulation model results, and quantitative agent–response relationships. We discuss how LUC may be allocated among probable causes for policy purposes and how the application of the framework has the potential to increase the validity of LUC models and resolve controversies about ILUC, such as deforestation, and biofuels.

The ongoing debate about costs and benefits of wood‐pellet based bioenergy production in the southeastern United States (SE USA) requires an understanding of the science and context influencing market decisions associated with its sustainability. Production of pellets has garnered much attention as US exports have grown from negligible amounts in the early 2000s to 4.6 million metric tonnes in 2015. Currently, 98% of these pellet exports are shipped to Europe to displace coal in power plants. We ask, ‘How is the production of wood pellets in the SE USA affecting forest systems and the ecosystem services they provide?’ To address this question, we review current forest conditions and the status of the wood products industry, how pellet production affects ecosystem services and biodiversity, and what methods are in place to monitor changes and protect vulnerable systems. Scientific studies provide evidence that wood pellets in the SE USA are a fraction of total forestry operations and can be produced while maintaining or improving forest ecosystem services. Ecosystem services are protected by the requirement to utilize loggers trained to apply scientifically based best management practices in planning and implementing harvest for the export market. Bioenergy markets supplement incomes to private rural landholders and provide an incentive for forest management practices that simultaneously benefit water quality and wildlife and reduce risk of fire and insect outbreaks. Bioenergy also increases the value of forest land to landowners, thereby decreasing likelihood of conversion to nonforest uses. Monitoring and evaluation are essential to verify that regulations and good practices are achieving goals and to enable timely responses if problems arise. Conducting rigorous research to understand how conditions change in response to management choices requires baseline data, monitoring, and appropriate reference scenarios. Long‐term monitoring data on forest conditions should be publicly accessible and utilized to inform adaptive management.

Wood pellet exports from the Southeastern United States (SE US) to Europe have been increasing in response to European Union member state policies to displace coal with renewable biomass for electricity generation. An understanding of the interactions among SE US forest markets, forest management, and forest ecosystem services is required to quantify the effects of pellet production compared to what would be expected under a reference case or ‘counterfactual scenario’ without pellet production. Inconsistent methods to define and justify the counterfactual scenario result in conflicting estimates and large uncertainties about the impacts of pellet production on SE US forests. Guidelines to support more consistent and transparent counterfactual scenarios are proposed. The guidelines include identifying major influences on current SE US forest conditions, developing potential futures that clearly document underlying assumptions and associated uncertainties, identifying the most likely alternative feedstock fates, and estimating the effects of no pellet demand on future forest conditions. The guidelines can help modelers to more accurately reflect the past and current forest dynamics and to consider the implications for SE US forest landscapes of future scenarios with and without pellet production. WIREs Energy Environ 2017, 6:e259. doi: 10.1002/wene.259

Understanding the complex interactions among food security, bioenergy sustainability, and resource management requires a focus on specific contextual problems and opportunities. The United Nations’ 2030 Sustainable Development Goals place a high priority on food and energy security; bioenergy plays an important role in achieving both goals. Effective food security programs begin by clearly defining the problem and asking, ‘What can be done to assist people at high risk?’ Simplistic global analyses, headlines, and cartoons that blame biofuels for food insecurity may reflect good intentions but mislead the public and policymakers because they obscure the main drivers of local food insecurity and ignore opportunities for bioenergy to contribute to solutions. Applying sustainability guidelines to bioenergy will help achieve near‐ and long‐term goals to eradicate hunger. Priorities for achieving successful synergies between bioenergy and food security include the following: (1) clarifying communications with clear and consistent terms, (2) recognizing that food and bioenergy need not compete for land and, instead, should be integrated to improve resource management, (3) investing in technology, rural extension, and innovations to build capacity and infrastructure, (4) promoting stable prices that incentivize local production, (5) adopting flex crops that can provide food along with other products and services to society, and (6) engaging stakeholders to identify and assess specific opportunities for biofuels to improve food security. Systematic monitoring and analysis to support adaptive management and continual improvement are essential elements to build synergies and help society equitably meet growing demands for both food and energy.

In order to understand the climate effects of a bioenergy system, a comparison between the bioenergy system and a reference system is required. The reference system describes the situation that occurs in the absence of the bioenergy system with respect to the use of land, energy, and materials. The importance of reference systems is discussed in the literature but guidance on choosing suitable reference systems for assessing climate effects of bioenergy is limited. The reference system should align with the purpose of the study. Transparency of reference system assumptions is essential for proper interpretation of bioenergy assessments. This paper presents guidance for selecting suitable reference systems. Particular attention is given to choosing the land reference. If the goal is to study the climate effects of bioenergy as a part of total anthropogenic activity the reference system should illustrate what is expected in the absence of human activities. In such a case the suitable land reference is natural regeneration, and energy or material reference systems are not relevant. If the goal is to assess the effect of a change in bioenergy use, the reference system should incorporate human activities. In this case suitable reference systems describe the most likely alternative uses of the land, energy and materials in the absence of the change in bioenergy use. The definition of the reference system is furthermore subject to the temporal scope of the study. In practice, selecting and characterizing reference systems will involve various choices and uncertainties which should be considered carefully. It can be instructive to consider how alternative reference systems influence the results and conclusions drawn from bioenergy assessments.

Published in Bioenergy and Land Use Change (pp. 141–153). John Wiley & Sons, Inc.

While many data sets are increasingly available to describe land cover characteristics, these data require careful analysis and supplemental research before conclusions can be drawn about the scope, magnitude, and drivers of change. Land cover data, typically derived from remote sensing, are frequently analyzed to estimate land use change (LUC) that may be attributable to policies such as the U.S. Renewable Fuel Standard, which encourages bioenergy. However, land cover classifications such as grassland do not clearly differentiate among multiple land uses (pasture, fodder, crop, yard, wildlife corridor, decorative cover, and erosion control strip), many of which may occur on a single parcel simultaneously. A land cover class in one data set rarely corresponds exactly to the same land cover class in another data set. Further, persistent improvements in remote‐sensing systems result in changes in spatial and temporal resolutions that lead to changes in classification methodology, which limit the ability to use those data sets to accurately measure changes occurring on the ground. Most LUC estimates are derived by comparing land cover across a few points in time and using aggregate land cover classes such as “forest,” “cropland” or “urban.” Analyses that cover short time spans or rely on just a few points in time are likely to generate spurious results if dynamic interacting classes such as U.S. cropland and grassland are considered. We review land cover dynamics in the Western Corn Belt (WCB) region, which comprises the U.S. states of Iowa, Minnesota, Nebraska, North Dakota, and South Dakota, to illustrate how the selection and manipulation of data can result in estimates of change associated with the cropland‐grassland transition that vary by over 100%.