Secure access to energy and food are two of the challenges facing the Northeast region of the United States. Traditional biofuel feedstocks, such as corn and oil seed, are able to satisfy energy requirements. However, they compete with food production for desirable land and water resources and, in any case, are not likely to exploit the region's current comparative advantages. This study investigates a potential solution to the energy security problem in the Northeast: biofuel from advanced feedstock in the form of net forest growth and woody wastes, of which the region has abundant endowments. The federal government has committed to requiring 79.5 billion liters (BL) of advanced biofuel production annually by 2022. We evaluate both the physical capacity for its production and its cost competitiveness using an input-output model of consumption, production, and trade in the 13-state region. The model minimizes resource use required to satisfy given consumer demand using alternative technological options and subject to resource constraints. We compile data from the technical literature quantifying state-level biofuel feedstock endowments and the technological requirements for cellulosic ethanol production. We find that exploiting the region's endowment of cellulosic feedstock requires either making the price of biofuels competitive with gasoline through subsidies or restricting imports of gasoline. Based on this initial investigation, we conclude that the region can produce significant amounts of advanced biofuel, up to 20.28 BL of cellulosic ethanol per year, which could displace nearly 12.5% of the gasoline that is now devoted to motorized transport in the region. Keywords:cellulosic ethanol choice of technology industrial ecology inter-regional input-output model linear programming model Northeast United States Supporting information is available on the JIE Web site

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This study illustrates the benefits of data pre-processing through supervised data-mining techniques and utilizing those processed data in an artificial neural networks (ANNs) for streamflow prediction. Two major categories of physical parameters such as snowpack data and time-dependent trend indices were utilized as predictors of streamflow values. Correlation analysis of different models indicate that, for the period of January to June, using fewer predictors led to simpler modeling with equivalent accuracy on daily prediction models. This did not hold in all periods. For monthly prediction models, accuracy was improved compared to earlier works done to predict monthly streamflow for the same case of Elephant Butte Reservoir (EB), NM. Overall, superior prediction performance was achieved by utilizing datamining techniques for pre-processing historical data, extracting the most effective predictors, correlation analysis, extracting and utilizing combined climate variability indices, physical indices, and employing several developed ANNs for different prediction periods of the year.