Financial Risk, Innovation and Alternative Pathways to Decarbonising the Energy System in 2050

There is a lot of forward looking work attempting to envisage the decarbonised energy system of the future as reflected with current interest in 'smart grids'. A central tenet behind most visions of the 'smart grids' of the future are the price signals that financial and commodity markets will deliver to facilitate effective and efficient resource allocation. Most of these visions take stylised and static views of financial and commodity markets despite the fact that these markets are experiencing dramatic change due to innovation and regulation. Accordingly, the paper maps the risks associated in the fusion of financial innovation with innovation in the energy system through a theoretical framework that draws on evolutionary models of paradigm shift. Risks to both the financial and energy systems are characterised as either emanating from primary or secondary markets and these are explored in terms of alternative visions of the energy system in the long run

Eras of electric vehicles: electric mobility on the Verge. Focus Attention Scale

Daily or casual passenger vehicles in cities have negative burden on our finite world. Transport sector has been one of the main contributors to air pollution and energy depletion. Providing alternative means of transport is a promising strategy perceived by motor manufacturers and researchers. The paper presents the battery electric vehicles-BEVs bibliography that starts with the early eras of invention up till 2015 outlook. It gives a broad overview of BEV market and its technology in a chronological classification while sheds light on the stakeholders’ focus attentions in each stage, the so called, Focus-Attention-Scale-FAS. The attention given in each era is projected and parsed in a scale graph, which varies between micro, meso, and macro-scale. BEV-system is on the verge of experiencing massive growth; however, the system entails a variety of substantial challenges. Observations show the main issues of BEVsystem that require more attention followed by the authors’ recommendations towards an emerging market

Modelling Learning as Modelling

Economists tend to represent learning as a procedure for estimating the parameters of the "correct" econometric model. We extend this approach by assuming that agents specify as well as estimate models. Learning thus takes the form of a dynamic process of developing models using an internal language of representation where expectations are formed by forecasting with the best current model. This introduces a distinction between the form and content of the internal models which is particularly relevant for boundedly rational agents. We propose a framework for such model development which use a combination of measures: the error with respect to past data, the complexity of the model, the cost of finding the model and a measure of the model's specificity The agent has to make various trade-offs between them. A utility learning agent is given as an example

The KB paradigm and its application to interactive configuration

The knowledge base paradigm aims to express domain knowledge in a rich formal language, and to use this domain knowledge as a knowledge base to solve various problems and tasks that arise in the domain by applying multiple forms of inference. As such, the paradigm applies a strict separation of concerns between information and problem solving. In this paper, we analyze the principles and feasibility of the knowledge base paradigm in the context of an important class of applications: interactive configuration problems. In interactive configuration problems, a configuration of interrelated objects under constraints is searched, where the system assists the user in reaching an intended configuration. It is widely recognized in industry that good software solutions for these problems are very difficult to develop. We investigate such problems from the perspective of the KB paradigm. We show that multiple functionalities in this domain can be achieved by applying different forms of logical inferences on a formal specification of the configuration domain. We report on a proof of concept of this approach in a real-life application with a banking company. To appear in Theory and Practice of Logic Programming (TPLP).Comment: To appear in Theory and Practice of Logic Programming (TPLP

Equilibrium and non-equilibrium concepts in forest genetic modelling: population- and individually-based approaches

The environment is changing and so are forests, in their functioning, in species composition, and in the species’ genetic composition. Many empirical and process-based models exist to support forest management. However, most of these models do not consider the impact of environmental changes and forest management on genetic diversity nor on the rate of adaptation of critical plant processes. How genetic diversity and rates of adaptation depend on management actions is a crucial next step in model development. Modelling approaches of genetic and demographic processes that operate in forests are categorized here in two classes. One approach assumes equilibrium conditions in phenotype and tree density, and analyses the characteristics of the demography and the genetic system of the species that determine the rate at which that equilibrium is attained. The other modelling approach does not assume equilibrium conditions and describes both the ecological —and genetic processes to analyse how environmental changes result in selection pressures on functional traits of trees and the consequences of that selection for tree— and ecosystem functioning. The equilibrium approach allows analysing the recovery rate after a perturbation in stable environments, i.e. towards the same pre-perturbation stable state. The nonequilibrium approach allows, in addition to the equilibrium approach, analysing consequences of ongoing environmental changes and forest management, i.e. non-stationary environments, on tree functioning, species composition, and genetic composition of the trees in forest ecosystem. In this paper we describe these two modelling approaches and discuss advantages and disadvantages of them and current knowledge gaps