|
Sort Order |
|
|
|
Items / Page
|
|
|
|
|
|
|
Srl | Item |
1 |
ID:
072722
|
|
|
2 |
ID:
079342
|
|
|
Publication |
Dordrecht, Springer, 2007.
|
Description |
x, 371p.
|
Standard Number |
9781402055638
|
|
|
|
|
|
|
Copies: C:1/I:0,R:0,Q:0
Circulation
Accession# | Call# | Current Location | Status | Policy | Location |
052627 | 333.790973/SOV 052627 | Main | On Shelf | General | |
|
|
|
|
3 |
ID:
101460
|
|
|
Publication |
2010.
|
Summary/Abstract |
Transformational energy and climate policies are being debated worldwide that could have significant impact upon the future of the forest products industry. Because woody biomass can produce alternative transportation fuels, low-carbon electricity, and numerous other "green" products in addition to traditional paper and lumber commodities, the future use of forest resources is highly uncertain. Using the National Energy Modeling System (NEMS), this paper assesses the future of the forest products industry under three possible U.S. policy scenarios: (1) a national renewable electricity standard, (2) a national policy of carbon constraints, and (3) incentives for industrial energy efficiency. In addition, we discuss how these policy scenarios might interface with the recently strengthened U.S. renewable fuels standards. The principal focus is on how forest products including residues might be utilized under different policy scenarios, and what such market shifts might mean for electricity and biomass prices, as well as energy consumption and carbon emissions. The results underscore the value of incentivizing energy efficiency in a portfolio of energy and climate policies in order to moderate electricity and biomass price escalation while strengthening energy security and reducing CO2 emissions.
|
|
|
|
|
|
|
|
|
|
4 |
ID:
111076
|
|
|
Publication |
2012.
|
Summary/Abstract |
This paper identifies six myths about clean electricity in the southern U.S. These myths are either propagated by the public at-large, shared within the environmental advocacy culture, or spread imperceptibly between policymakers. Using a widely accepted energy-economic modeling tool, we expose these myths as half-truths and the kind of conventional wisdom that constrains productive debate. In so doing, we identify new starting points for energy policy development. Climate change activists may be surprised to learn that it will take more than a national Renewable Electricity Standard or supportive energy efficiency policies to retire coal plants. Low-cost fossil generation enthusiasts may be surprised to learn that clean generation can save consumers money, even while meeting most demand growth over the next 20 years. This work surfaces the myths concealed in public perceptions and illustrates the positions of various stakeholders in this large U.S. region.
|
|
|
|
|
|
|
|
|
|
5 |
ID:
117242
|
|
|
Publication |
2013.
|
Summary/Abstract |
Improving the energy economics of manufacturing is essential to revitalizing the industrial base of advanced economies. This paper evaluates ex-ante a federal policy option aimed at promoting industrial cogeneration-the production of heat and electricity in a single energy-efficient process. Detailed analysis using the National Energy Modeling System (NEMS) and spreadsheet calculations suggest that industrial cogeneration could meet 18% of U.S. electricity requirements by 2035, compared with its current 8.9% market share. Substituting less efficient utility-scale power plants with cogeneration systems would produce numerous economic and environmental benefits, but would also create an assortment of losers and winners. Multiple perspectives to benefit/cost analysis are therefore valuable. Our results indicate that the federal cogeneration policy would be highly favorable to manufacturers and the public sector, cutting energy bills, generating billions of dollars in electricity sales, making producers more competitive, and reducing pollution. Most traditional utilities, on the other hand, would lose revenues unless their rate recovery procedures are adjusted to prevent the loss of profits due to customer owned generation and the erosion of utility sales. From a public policy perspective, deadweight losses would be introduced by market-distorting federal incentives (ranging annually from $30 to $150 million), but these losses are much smaller than the estimated net social benefits of the federal cogeneration policy.
|
|
|
|
|
|
|
|
|
|
6 |
ID:
181428
|
|
|
Summary/Abstract |
Our analysis of policy options was motivated by an inexplicable under-investment in demand response (DR) in the U.S. state of Georgia. In addition to estimating the size of the DR gap, we identify its causes and consequences. By modifying parameters of the U.S. flagship National Energy Modeling System (NEMS), we generate a baseline DR forecast with a default 4% maximum on-peak demand reduction, an achievable case with a DR limit of 20%, and a technical scenario that also halved the cost of storage. The results document many benefits of DR including a demand-reduction induced price effect (DRIPE), which makes DR more equitable than many other clean-energy policies that shift costs to non-participants. Our modeling results, literature review, and focus group analysis enable identification of DR barriers and motivators related to financial costs, electricity rates, consumer bills, pollution emissions, public health, energy equity, and inclusion. Our results suggest that the DR gap is caused less by technology limitations than by the need for financing initiatives, market innovations, infrastructure modernization, and enablers of socio-economic inclusion. By studying a state that lags in DR implementation, other countries and sub-national entities where DR is under-utilized can learn from our findings and methods.
|
|
|
|
|
|
|
|
|
|
7 |
ID:
098254
|
|
|
Publication |
2010.
|
Summary/Abstract |
A dearth of available data on carbon emissions and comparative analysis between metropolitan areas make it difficult to confirm or refute best practices and policies. To help provide benchmarks and expand our understanding of urban centers and climate change, this article offers a preliminary comparison of the carbon footprints of 12 metropolitan areas. It does this by examining emissions related to vehicles, energy used in buildings, industry, agriculture, and waste. The carbon emissions from these sources-discussed here as the metro area's partial carbon footprint-provide a foundation for identifying the pricing, land use, help metropolitan areas throughout the world respond to climate change. The article begins by exploring a sample of the existing literature on urban morphology and climate change and explaining the methodology used to calculate each area's carbon footprint. The article then depicts the specific carbon footprints for Beijing, Jakarta, London, Los Angeles, Manila, Mexico City, New Delhi, New York, São Paulo, Seoul, Singapore, and Tokyo and compares these to respective national averages. It concludes by offering suggestions for how city planners and policymakers can reduce the carbon footprint of these and possibly other large urban areas.
|
|
|
|
|
|
|
|
|
|
8 |
ID:
098550
|
|
|
Publication |
2010.
|
Summary/Abstract |
A dearth of available data on carbon emissions and comparative analysis between metropolitan areas make it difficult to confirm or refute best practices and policies. To help provide benchmarks and expand our understanding of urban centers and climate change, this article offers a preliminary comparison of the carbon footprints of 12 metropolitan areas. It does this by examining emissions related to vehicles, energy used in buildings, industry, agriculture, and waste. The carbon emissions from these sources-discussed here as the metro area's partial carbon footprint-provide a foundation for identifying the pricing, land use, help metropolitan areas throughout the world respond to climate change. The article begins by exploring a sample of the existing literature on urban morphology and climate change and explaining the methodology used to calculate each area's carbon footprint. The article then depicts the specific carbon footprints for Beijing, Jakarta, London, Los Angeles, Manila, Mexico City, New Delhi, New York, São Paulo, Seoul, Singapore, and Tokyo and compares these to respective national averages. It concludes by offering suggestions for how city planners and policymakers can reduce the carbon footprint of these and possibly other large urban areas.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|