(Table 1) Krill (Euphausia superba) biomass estimates at South Georgia Island from 2001-2005

The South Georgia region supports a large biomass of krill that is subject to high interannual variability. The apparent lack of a locally self-maintaining krill population at South Georgia means that understanding the mechanism underlying these observed population characteristics is essential to successful ecosystem-based management of krill fishery in the region. Krill acoustic-density data from surveys conducted in the early, middle and late period of the summers of 2001 to 2005, together with krill population size structure over the same period from predator diet data, were used with a krill population dynamics model to evaluate potential mechanisms behind the observed changes in krill biomass. Krill abundance was highest during the middle of the summer in 3 years and in the late period in 2 years; in the latter there was evidence that krill recruitment was delayed by several months. A model scenario that included empirically derived estimates of both the magnitude and timing of recruitment in each year showed the greatest correlation with the acoustic series. The results are consistent with a krill population with allochthonous recruitment entering a retained adult population; i.e. oceanic transport of adult krill does not appear to be the major factor determining the dynamics of the adult population. The results highlight the importance of the timing of recruitment, especially where this could introduce a mismatch between the peak of krill abundance and the peak demand from predators, which may exacerbate the effects of changes in krill populations arising from commercial harvesting and/or climate change.

Analysis of advanced European nuclear fuel cycle scenarios including transmutation and economic estimates

Four European fuel cycle scenarios involving transmutation options (in coherence with PATEROS and CPESFR EU projects) have been addressed from a point of view of resources utilization and economic estimates. Scenarios include: (i) the current fleet using Light Water Reactor (LWR) technology and open fuel cycle, (ii) full replacement of the initial fleet with Fast Reactors (FR) burning U?Pu MOX fuel, (iii) closed fuel cycle with Minor Actinide (MA) transmutation in a fraction of the FR fleet, and (iv) closed fuel cycle with MA transmutation in dedicated Accelerator Driven Systems (ADS). All scenarios consider an intermediate period of GEN-III+ LWR deployment and they extend for 200 years, looking for long term equilibrium mass flow achievement. The simulations were made using the TR_EVOL code, capable to assess the management of the nuclear mass streams in the scenario as well as economics for the estimation of the levelized cost of electricity (LCOE) and other costs. Results reveal that all scenarios are feasible according to nuclear resources demand (natural and depleted U, and Pu). Additionally, we have found as expected that the FR scenario reduces considerably the Pu inventory in repositories compared to the reference scenario. The elimination of the LWR MA legacy requires a maximum of 55% fraction (i.e., a peak value of 44 FR units) of the FR fleet dedicated to transmutation (MA in MOX fuel, homogeneous transmutation) or an average of 28 units of ADS plants (i.e., a peak value of 51 ADS units). Regarding the economic analysis, the main usefulness of the provided economic results is for relative comparison of scenarios and breakdown of LCOE contributors rather than provision of absolute values, as technological readiness levels are low for most of the advanced fuel cycle stages. The obtained estimations show an increase of LCOE ? averaged over the whole period ? with respect to the reference open cycle scenario of 20% for Pu management scenario and around 35% for both transmutation scenarios. The main contribution to LCOE is the capital costs of new facilities, quantified between 60% and 69% depending on the scenario. An uncertainty analysis is provided around assumed low and high values of processes and technologies.

Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain

Tolls have increasingly become a common mechanism to fund road projects in recent decades. Therefore, improving knowledge of demand behavior constitutes a key aspect for stakeholders dealing with the management of toll roads. However, the literature concerning demand elasticity estimates for interurban toll roads is still limited due to their relatively scarce number in the international context. Furthermore, existing research has left some aspects to be investigated, among others, the choice of GDP as the most common socioeconomic variable to explain traffic growth over time. This paper intends to determine the variables that better explain the evolution of light vehicle demand in toll roads throughout the years. To that end, we establish a dynamic panel data methodology aimed at identifying the key socioeconomic variables explaining changes in light vehicle demand over time. The results show that, despite some usefulness, GDP does not constitute the most appropriate explanatory variable, while other parameters such as employment or GDP per capita lead to more stable and consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period, which constitutes a very interesting case on variations in toll road use, as road demand has experienced a significant decrease since the beginning of the economic crisis in 2008.

Filling the gap: open economy considerations for more reliable potential output estimates. Bruegel Working Paper 2015/11, October 2015

This paper argues that the Phillips curve relationship is not sufficient to trace back the output gap, because the effect of excess demand is not symmetric across tradeable and non-tradeable sectors. In the non-tradeable sector, excess demand creates excess employment and inflation via the Phillips curve, while in the tradeable sector much of the excess demand is absorbed by the trade balance. We set up an unobserved-components model including both a Phillips curve and a current account equation to estimate ‘sustainable output’ for 45 countries. Our estimates for many countries differ substantially from the potential output estimates of the European Commission, IMF and OECD. We assemble a comprehensive real-time dataset to estimate our model on data which was available in each year from 2004-15. Our model was able to identify correctly the sign of pre-crisis output gaps using real time data for countries such as the United States, Spain and Ireland, in contrast to the estimates of the three institutions, which estimated negative output gaps real-time, while their current estimates for the pre-crisis period suggest positive gaps. In the past five years the annual output gap estimate revisions of our model, the European Commission, IMF, OECD and the Hodrick-Prescott filter were broadly similar in the range of 0.5-1.0 percent of GDP for advanced countries. Such large revisions are worrisome, because the European fiscal framework can translate the imprecision in output gap estimates into poorly grounded fiscal policymaking in the EU.

The value of travel time: new estimates developed using a speed-choice model. Final report.

Mode of access: Internet.

Improved Annual Average Daily Traffic (AADT) estimation for local roads using parcel-level travel demand modeling

Annual Average Daily Traffic (AADT) is a critical input to many transportation analyses. By definition, AADT is the average 24-hour volume at a highway location over a full year. Traditionally, AADT is estimated using a mix of permanent and temporary traffic counts. Because field collection of traffic counts is expensive, it is usually done for only the major roads, thus leaving most of the local roads without any AADT information. However, AADTs are needed for local roads for many applications. For example, AADTs are used by state Departments of Transportation (DOTs) to calculate the crash rates of all local roads in order to identify the top five percent of hazardous locations for annual reporting to the U.S. DOT. ^ This dissertation develops a new method for estimating AADTs for local roads using travel demand modeling. A major component of the new method involves a parcel-level trip generation model that estimates the trips generated by each parcel. The model uses the tax parcel data together with the trip generation rates and equations provided by the ITE Trip Generation Report. The generated trips are then distributed to existing traffic count sites using a parcel-level trip distribution gravity model. The all-or-nothing assignment method is then used to assign the trips onto the roadway network to estimate the final AADTs. The entire process was implemented in the Cube demand modeling system with extensive spatial data processing using ArcGIS. ^ To evaluate the performance of the new method, data from several study areas in Broward County in Florida were used. The estimated AADTs were compared with those from two existing methods using actual traffic counts as the ground truths. The results show that the new method performs better than both existing methods. One limitation with the new method is that it relies on Cube which limits the number of zones to 32,000. Accordingly, a study area exceeding this limit must be partitioned into smaller areas. Because AADT estimates for roads near the boundary areas were found to be less accurate, further research could examine the best way to partition a study area to minimize the impact.^

THE PRICE SENSITIVITY OF DEMAND FOR HIGHER EDUCATION AMONG NON-TRADITIONAL STUDENTS

This dissertation examines the price sensitivity of demand for higher education among non-traditional students in the United States. Chapter 1 discusses the issues related to the demand for higher education. It presents the recent trends and reviews the literature addressing these issues. A major conclusion that emerges from this chapter is that the price sensitivity of demand for higher education appears to depend on the source of the variation in price and the characteristics of the students who face the price change. The baseline estimate for the price sensitivity of demand is that a $1,000 (in year 2000 dollars) decrease in tuition costs should result in a 4 percentage-point increase in enrollment for the traditional 18- to 24-year-old student. Chapter 2 examines the price sensitivity of demand for higher education for military spouses resulting from variation in tuition due to military-mandated moves across states. The data suggest that a $1,000 (in year 2000 dollars) decrease in the cost of 2-year schools is associated with a 1--1.5 percentage-point increase in the probability of attending college. This estimate is less than half the previous estimates due to in-state tuition price differences faced by the civilian 18- to 24-year-old population on a percentage-point basis. However, this represents a 7--10 percent increase for this population, and the magnitude of this metric is in line with previous estimates. This suggests tuition assistance can be an effective means of increasing enrollment for military spouses, but other barriers to education for this population may also need to be addressed. Chapter 3 examines the impact of a change in the tax treatment of savings set aside for higher education by those who decide to suspend their education and enter the workforce. The taxation of these funds appears to have increased the rate at which these funds are included in an employee's initial contract and the quantity of funds allocated. These results are counterintuitive if the tax preference was the primary reason for the savings plan. However, these results suggest the rationale for the savings plan was to offer targeted additional compensation to recruits with greater negotiating power. Taxation of funds previously set aside did not appear to have a statistically significant impact on their utilization. Point estimates of the price sensitivity of demand from changes in the out-of-pocket costs for higher education induced by the taxation of these funds were small and often not statistically significant. The results from this dissertation show responses to changes in the net cost of college that differ by the source of price variation and the population experiencing them. This is consistent with the previous literature. This dissertation contributes to the literature by providing estimates for the price sensitivity of demand for higher education to previously understudied non-traditional students.

Estimating the demand curve for sustainable use of pesticides from contingent-valuation data

Stated-preference valuation techniques are often used to assess consumers' willingness-to-pay for food items produced in farming systems that adopt a sustainable use of pesticides (SUP). We propose an innovative valuation methodology in which dichotomous-choice contingent valuation is used to estimate the demand curve (price-quantity relationship) for such food items where price means price premium for the SUP output, quantity is the probability of choosing SUP and the conventional food product is kept available in the market at the current market price. This methodology can be used to evaluate market differentiation as a policy option to promote the SUP. The methodology is tested with data from a sample of urban consumers of fruits and vegetables in Portugal. The estimated demand curve is used to define the price level maximizing the total premium revenue for the SUP sector as a whole. This optimal level of the price premium is €77.55 (or 163% of the value of the monthly basket of fruits and vegetables at current prices). Adopting the optimal price premium will decrease the number of consumers of SUP food by 54%. The reduction is even higher for low income consumers (80%) leaving them more exposed to the risks of pesticide use.