ABSTRACT: A growing number of countries are implementing greenhouse gas (GHG) emissions trading schemes. As these schemes impose a cost for GHG emissions they should increase the competitiveness of low carbon fuels. Bioenergy from biomass is regarded as carbon neutral in most of the schemes, therefore incurring no emission costs. Emissions trading schemes may therefore encourage increased use of biomass for energy, and under certain conditions may also incentivize the construction of new bioenergy plants.
Bioenergy in a Changing Climate: Key Findings of the IPCC Special Report on Renewable Energy Sources (SRREN) and Climate Change Mitigation
Provides a summary of the key findings of the IPCC Special Report on Renewable Energy Sources (SRREN) and Climate Change Mitigation.
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.
The IPCC SRREN report addresses information needs of policymakers, the private sector and civil society on the potential of renewable energy sources for the mitigation of climate change, providing a comprehensive assessment of renewable energy technologies and related policy and financial instruments. The IPCC report was a multinational collaboration and synthesis of peer reviewed information: Reviewed, analyzed, coordinated, and integrated current high quality information.
The United States shares with many other countries the goal of the United Nations Framework Convention on Climate Change “to achieve . . . stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.”1 The critical role of new technologies in achieving this goal is underscored by the fact that most anthropogenic greenhouse gases (GHGs) emitted over the next century will come from equipment and infrastructure that has not yet been built.
There is a strong societal need to evaluate and understand the sustainability of biofuels, especially because of the significant increases in production mandated by many countries, including the United States. Sustainability will be a strong factor in the regulatory environment and investments in biofuels. Biomass feedstock production is an important contributor to environmental, social, and economic impacts from biofuels.
This paper describes the GTAP land use data base designed to support integrated assessments of the potential for greenhouse gas mitigation. It disaggregates land use by agro-ecological zone (AEZ). To do so, it draws upon global land cover data bases, as well as state-of-the-art definition of AEZs from the FAO and IIASA. Agro-ecological zoning segments a parcel of land into smaller units according to agro-ecological characteristics, including: precipitation, temperature, soil type, terrain conditions, etc. Each zone has a similar combination of constraints and potential for land use.
In this paper we investigate the potential production and implications of a global biofuels industry. We develop alternative approaches to the introduction of land as an economic factor input, in value and physical terms, into a computable general equilibrium framework. Both approach allows us to parameterize biomass production in a manner consistent with agro-engineering information on yields and a ?second generation? cellulosic biomass conversion technology.
The preceding two chapters of this volume have discussed physical and economic data bases for global agriculture and forestry, respectively. These form the foundation for the integrated, global land use data base discussed in this chapter. However, in order to utilize these data for global CGE analysis, it is first necessary to integrate them into a global, general equilibrium data base. This integration is the subject of the present chapter