KDF Search Results

Highlights
• Opportunities to improve coproduction of wildlife and biomass-for-energy exist at multiple spatial scales.

• At the landscape scale, we review strategies for increasing biodiversity in biomass production systems, drawing examples from plantations, dedicated perennial grasses, and forest thinning systems in the Americas.

• At the scale of one land owner, we describe wildlife-friendly practices to promote land sharing for each production system.

The biobased economy is playing an increasingly important role in the American economy.

Through innovations in renewable energies and the emergence of a new generation of biobased products, the sectors that drive the biobased economy are providing job creation and economic growth. To further understand and analyze trends in the biobased economy, this report compares 2011 and 2016 report data.

Join the U.S. Department of Energy’s Bioenergy Technologies Office on Dec. 6, 2018, at 1 p.m. CST for a webinar on “Biomass Production and Water Quality in the Mississippi River Basin.” In this webinar, Argonne National Laboratory and Oak Ridge National Laboratory will jointly present modeling and analyses of potential implications of biomass production on nutrients and sediments in each of the six tributaries of the Mississippi River Basin.

Advanced biomass feedstocks tend to provide more non-fuel ecosystem goods and services (ES) than 1st-generation alternatives. We explore the idea that payment for non-fuel ES could facilitate market penetration of advanced biofuels by closing the profitability gap. As a specific example, we discuss the Mississippi-Atchafalaya River Basin (MARB), where 1st-generation bioenergy feedstocks (e.g., corn-grain) have been integrated into the agricultural landscape.

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.

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).

Bio-oil derived via fast pyrolysis is being developed as a renewable fuel option for petroleum distillates. The compatibility of neat bio-oil with 18 plastic types was evaluated using neat diesel fuel as the baseline.

Several crops have recently been identified as potential dedicated bioenergy feedstocks for the production of power, fuels, and bioproducts. Despite being identified as early as the 1980s, no systematic work has been undertaken to characterize the spatial distribution of their long‐term production potentials in the United states.

Energy market conditions have shifted dramatically since the USA renewable fuel standards (RFS1 in 2005; RFS2 in 2007) were enacted. The USA has transitioned from an increasing dependence on oil imports to abundant domestic oil production. In addition, increases in the use of ethanol, the main biofuel currently produced in the USA, is now limited by the blend wall constraint. Given this, the current study evaluates alternative biofuel deployment scenarios in the USA, accounting for changes in market conditions.

This analysis estimates the cost of selected oil and biomass supply shocks for producers and consumers in the light-duty vehicle fuel market under various supply chain configurations using a mathematical programing model, BioTrans. The supply chain configurations differ by whether they include selected flexibility levers: multi-feedstock biorefineries; advanced biomass logistics; and the ability to adjust ethanol content of low-ethanol fuel blends, from E10 to E15 or E05.