Logging and mill residues are currently the largest sources of woody biomass for bioenergy in the US, but short-rotation woody crops (SRWCs) are expected to become a larger contributor to biomass production, primarily on lands marginal for food production. However, there are very few studies on the environmental effects of SRWCs, and most have been conducted at stand rather than at watershed scales.
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Synthesis manuscript for an Ecology & Society Special Feature on Telecoupling: A New Frontier for Global Sustainability
The U.S. Department of Energy’s (DOE’s) Co-Optimization (Co-Optima) initiative is accelerating the introduction of affordable, scalable, and sustainable fuels and high-efficiency, low-emission engines with a first-of-its-kind effort to simultaneously tackle fuel and engine research and development (R&D).
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
Abstract: Cellulosic-based biofuels are needed to help meet energy needs and to strengthen rural investment and development in the midwestern United States (US). This analysis identifies 11 categories of indicators to measure progress toward sustainability that should be monitored to determine if ecosystem and social services are being maintained, enhanced, or disrupted by production, harvest, storage, and transport of cellulosic feedstock.
This report provides a status of the markets and technology development involved in growing a domestic bioenergy economy as it existed at the end of calendar year 2013. It compiles and integrates information to provide a snapshot of the current state and historical trends influencing the development of bioenergy markets. This information is intended for policy-makers as well as technology developers and investors tracking bioenergy developments. It also highlights some of the key energy and regulatory drivers of bioenergy markets.
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,
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.
Understanding the development of the biofuels industry in the United States is important to policymakers and industry. The Biomass Scenario Model (BSM) is a system dynamics model of the biomass-to-biofuels system that can be used to explore policy effects on biofuels development. Because of the complexity of the model, as well as the wide range of possible future conditions that affect biofuels industry development, we have not developed a single reference case but instead developed a set of specific scenarios that provide various contexts for our analyses.
The Biomass Scenario Model (BSM) is a system dynamics model that represents the entire biomass-to-biofuels supply chain, from feedstock to fuel use. The BSM is a complex model that has been used for extensive analyses; the model and its results can be better understood if input data used for initialization and calibration are well-characterized. It has been carefully validated and calibrated against the available data, with data gaps filled in using expert opinion and internally consistent assumed values.