Link to the website with documentation and download instructions for the PNNL Global Change Assessment Model (GCAM), a community model or long-term, global energy, agriculture, land use, and emissions. BioEnergy production, transformation, and use is an integral part of GCAM modeling and scenarios.
This article connects the science of sustainability theory with applied aspects of sustainability deployment. A suite of 35 sustainability indicators spanning 12 environmental and socioeconomic categories has been proposed for comparing the sustainability of bioenergy production systems across different feedstock types and locations.
The Paris Agreement and the EU Climate and Energy Framework set ambitious but necessary targets. Reducing greenhouse gas (GHG) emissions by phasing out the technologies and infrastructures that cause fossil carbon emissions is one of today’s most important challenges. In the EU, bioenergy is currently the largest renewable energy source used. Most Member States have in absolute terms increased the use of forest biomass for energy to reach their 2020 renewable energy targets.
Social and economic indicators can be used to support design of sustainable energy systems. Indicators representing categories of social well-being, energy security, external trade, profitability, resource conservation, and social acceptability have not yet been measured in published sustainability assessments for commercial algal biofuel facilities.
Renewable, biomass-based energy options can reduce the climate impacts of fossil fuels.
This report is a collective effort of the Scientific Committee on Problems of the Environment (SCOPE), including contributions from 137 researchers of 82 institutions in 24 countries. It concludes that land availability is not a limiting factor to bioenergy production and that bioenergy can contribute to sustainable energy supplies even with increasing food demands, preservation of forests, protected lands, and rising urbanization.
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.
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
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.
Indicators are needed to assess environmental sustainability of bioenergy systems. Effective indicators
will help in the quantification of benefits and costs of bioenergy options and resource uses. We identify
19 measurable indicators for soil quality, water quality and quantity, greenhouse gases, biodiversity, air
quality, and productivity, building on existing knowledge and on national and international programs
that are seeking ways to assess sustainable bioenergy. Together, this suite of indicators is hypothesized
Developing scientific criteria and indicators should play a critical role in charting a sustainable path for the rapidly developing biofuel industry. The challenge ahead in developing such criteria and indicators is to address the limitations on data and modeling.
National interests in greater energy independence, concurrent with favorable market forces, have driven increased production of corn-based ethanol in the United States and research into the next generation of biofuels. The trend is changing the national agricultural landscape and has raised concerns about potential impacts on the nation?s water resources. This report examines some of the key issues and identifies opportunities for shaping policies that help to protect water resources.
The Emissions Prediction and Policy Analysis (EPPA) model is the part of the MIT Integrated Global Systems Model (IGSM) that represents the human systems. EPPA is a recursive-dynamic multi-regional general equilibrium model of the world economy, which is built on the GTAP dataset and additional data for the greenhouse gas and urban gas emissions. It is designed to develop projections of economic growth and anthropogenic emissions of greenhouse related gases and aerosols. The main purpose of this report is to provide documentation of a new version of EPPA, EPPA version 4.
Power generation emits significant amounts of greenhouse gases (GHGs), mainly carbon dioxide (CO2). Sequestering CO2 from the power plant flue gas can significantly reduce the GHGs from the power plant itself, but this is not the total picture. CO2 capture and sequestration consumes additional energy, thus lowering the plant's fuel-to-electricity efficiency. To compensate for this, more fossil fuel must be procured and consumed to make up for lost capacity.
This paper offers a graphical exposition of the GTAP model of global trade. Particular emphasis is placed on the accounting, or equilibrium, relationships in the model. It begins with a treatment of the a one region version of GTAP, thereafter adding a rest of world region to highlight the treatment of trade flows in the model. The implementation of policy instruments in GTAP is also explored, using simple supply-demand graphics. The material provided in this paper was first developed as an introduction to GTAP for participants taking the annual short course.
Biomass is a significant contributor to the US economy--agriculture, forest and paper products, food and related products account for 5% of our GDP. While the forest products industry self generates some of their energy, other sectors are importers. Bioenergy can contribute to economic development and to the environment. Examples of bioenergy routes suggest that atmospheric carbon can be cycled through biofuels in carefully designed systems for sustainability. Significant potential exists for these options.
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.
Biomass power offers utilities a potential pathway to increase their renewable generation portfolios for compliance with renewable energy standards and to reduce greenhouse gas (GHG) emissions relative to current fossil-based technologies. To date, a large body of life-cycle assessment (LCA) literature assessing biopower’s life-cycle GHG emissions has been published.
EXECUTIVE SUMMARY: Life cycle assessment (LCA) is a powerful tool that may be used to quantify the environmental impacts of products and services. It includes all processes, from cradle-to-grave, along the supply chain of the product. When analysing energy systems, greenhouse gas (GHG) emissions (primarily CO2, CH4 and N2O) are the impact of primary concern. In using LCA to determine the climate change mitigation benefits of bioenergy, the life cycle emissions of the bioenergy system are compared with the emissions for a reference energy system.