Quantifying the Impact of Exogenous Non-Economic Factors on UK Transport Oil Demand

This paper attempts to quantify the impact of exogenous non-economic factors on UK transport oil demand (in addition to income, price, and fuel efficiency) by estimating the demand relationship for oil transport for 1960-2007 using the Structural Time Series Model. From this, the relative impact on UK transport oil demand from income, price, and efficiency are quantified. Moreover, the impact of the non-economic factors is also quantified, based on the premise that the estimated stochastic trend represents behavioural responses to changes in socio-economic factors and changes in lifestyles and attitudes. The estimated elasticities for income, price and efficiency are 0.6, -0.1, and -0.3 respectively and it is shown that for efficiency and price the overall contribution is relatively small, whereas the contribution from income and non-economic factors is relatively large. This has important implications for policy makers keen to reduce transport oil consumption and associated emissions, but not willing to reduce the trend rate of economic growth. Taxes and improved efficiency only have a limited impact; hence, a major thrust of policy should perhaps be on educating and informing consumers to persuade them to change their lifestyle and attitudes and thus reduce their consumption through the non-economic instruments route.Transport oil demand; Structural Time Series Model, STSM; Underlying Energy Demand Trend, UEDT; Exogenous Non-Economic Factors, ExNEF.

Set-based Multiobjective Fitness Landscapes: A Preliminary Study

Fitness landscape analysis aims to understand the geometry of a given optimization problem in order to design more efficient search algorithms. However, there is a very little knowledge on the landscape of multiobjective problems. In this work, following a recent proposal by Zitzler et al. (2010), we consider multiobjective optimization as a set problem. Then, we give a general definition of set-based multiobjective fitness landscapes. An experimental set-based fitness landscape analysis is conducted on the multiobjective NK-landscapes with objective correlation. The aim is to adapt and to enhance the comprehensive design of set-based multiobjective search approaches, motivated by an a priori analysis of the corresponding set problem properties

Clustering analysis of railway driving missions with niching

A wide number of applications requires classifying or grouping data into a set of categories or clusters. Most popular clustering techniques to achieve this objective are K-means clustering and hierarchical clustering. However, both of these methods necessitate the a priori setting of the cluster number. In this paper, a clustering method based on the use of a niching genetic algorithm is presented, with the aim of finding the best compromise between the inter-cluster distance maximization and the intra-cluster distance minimization. This method is applied to three clustering benchmarks and to the classification of driving missions for railway applications

Coordinating Local Adaptive Strategies through a Network-Based Approach

As the impacts of climate change become increasingly destructive and pervasive, climate adaptation has received greater political and academic attention. The traditional top-down model for mitigating climate change, however, is ill-suited to implementing effective adaptation strategies. Yet, local communities most impacted by climate change seldom have the tools and resources to develop effective adaptive strategies on their own. This note argues that a bottom-up, network-based approach could be a promising paradigm towards implementing effective adaptive strategies and empowering affected communities

Assessing the Consequences of Brood Parasitism and Nest Predation On Seasonal Fecundity in Passerine Birds

Brood parasites and nest predators reduce the seasonal fecundity and, hence, the population growth rates of their victims. However, most field studies do not measure directly how parasites and predators decrease seasonal fecundity, but instead measure the impact of these organisms on individual nesting attempts. Because a female may renest after losing a nest to predation, abandoning a parasitized nest, or successfully fledging a brood, knowing how brood parasites and nest predators reduce the number of offspring fledged from individual nesting attempts is not equivalent to knowing their impact on seasonal fecundity. We address this problem by developing a mathematical model that: estimates several parameters describing the natural history of this system, including the brood-parasitism rate, nest-predation rate, and probability of nest abandonment in response to a parasitism event; and extrapolates to seasonal fecundity from these parameters and others describing the length of the breeding season, the timing of events in the nesting cycle, and the productivity of parasitized and unparasitized nests. We also show how different researchers using different observational methodologies to study exactly the same population likely would arrive at noticeably different conclusions regarding the intensity of brood parasitism, and we provide mathematical formulas for comparing among several of these measures of parasitism. Our procedures extend Mayfield's method for calculating nest-success rates from nest-history data in that we simultaneously estimate parameters describing nest predation and brood parasitism, predict seasonal fecundity from these parameters, and provide confidence intervals on all parameter estimates. The model should make the design and interpretation of logistically difficult empirical studies more efficient. It also can be specialized to species affected by nest predators but not brood parasites. We use the model to analyze prairie Warbler (Dendroica discolor) and Black-capped Vireo (Vireo atricapillus) nesting data. We estimate the model's parameters for these species and use the resulting estimates to predict seasonal fecundity. For both species, the predicted seasonal fecundity closely matches the value measured directly.Integrative Biolog