An Evaluation of Design Rule Spaces as Risk Containers

It is well understood that software development can be a risky enterprise and industrial projects often overrun budget and schedule. Effective risk management is, therefore, vital for a successful project outcome. Design Rule Spaces (DRSpaces) have been used by other researchers to understand why implemented software is error-prone. This industrial case study evaluates whether such spaces are durable, meaningful, and isolating risk containers. DRSpaces were created from UML class diagrams of architectural design artefacts. In our study, object orientated metrics were calculated from the UML diagrams, and compared to the error-proneness of the DRSpace implementation, to determine whether architectural coupling translated into implementation difficulties. A correlation between architectural coupling and error-proneness of DRSpaces was observed in the case study. Software developers were asked to identify DRSpaces they found difficult to implement, in order to understand which factors, other than architectural coupling, were also important. The qualitative results show agreement between the code areas developers found difficult to implement and the error-prone DRSpaces. However, the results also show that architectural coupling is just one risk factor of many. The case study suggests that architectural DRSpaces can be used to facilitate a targeted risk review prior to implementation and manage risk

"Twin Peaks" in Energy Prices: A Hotelling Model with Pollution Learning

We study how environmental regulation in the form of a cap on aggregate emissions from a fossil fuel (e.g., coal) affects the arrival of a clean substitute (e.g., solar energy). The cost of the substitute decreases with cumulative use because of learning-by-doing. We show that energy prices may initially increase but then decline upon attaining the targeted level of pollution, followed by another cycle of rising and falling prices. The surprising result is that with pollution and learning, the Hotelling model predicts the cyclical behavior of energy prices in the long run. The alternating trends in upward or downward price movements we show may at least partially explain recent empirical findings by Lee, List and Strazicich (2006) that long run resource prices are stationary around deterministic trends with structural breaks in intercept and trend slope. The main implication of our results is that testing for secular price trends as predicted by the textbook Hotelling model may lead to incorrect conclusions regarding the predictive power of the theory of nonrenewable resource economics.dynamic models; energy markets; environmental externalities; global warming; technological change

Monotonic Incompatibility Between Electing and Ranking

Borda a proposé une méthode qui attribue des points à chacun des m candidats. Condorcet a proposé une méthode qui attribue des points à chacun des différents m! classements des candidats. La première est plus appropriée pour élire. La seconde est plus appropriée pour classer. Chacune satisfait une certaine monotonie. Leurs monotonies sont incompatiblesChoix social, Borda, Condorcet, monotonie, incompatibilité, élire, classer.

Would Hotelling Kill the Electric Car?

In this paper, we show that the potential for endogenous technological change in alternative energy sources may alter the behaviour of resource-owning firms. When technological progress in an alternative energy source can occur through learning-by-doing, resource owners face competing incentives to extract rents from the resource and to prevent expansion of the new technology. We show that in such a context, it is not necessarily the case that scarcity-driven higher traditional energy prices over time will induce alternative energy supply as resources are exhausted. Rather, we show that as we increase the learning potential in the substitute technology, lower equilibrium energy prices prevail and there may be increased resource extraction and greenhouse gas emissions. We show that the effectiveness and the incidence of emissions reduction policies may be altered by increased potential for technological change. Our results suggest that treating finite resource rents as endogenous consequences of both technological progress and policy changes will be important for the accurate assessment of climate change policy.

Glaciochemistry of polar ice cores: A review

Human activities have already modified the chemical composition of the natural atmosphere even in very remote regions of the world. The study of chemical parameters stored in solid precipitation and accumulated on polar ice sheets over the last several hundred thousand years provides a unique tool for obtaining information on the composition of the preindustrial atmosphere and its natural variability over the past. This paper deals with the chemistry of polar ice focused on the soluble mineral (Na+, NH4+, K+, Ca++, Mg++, H+, F−, Cl−, NO3−, SO4−−, and H2O2) and organic (methanesulfonate (CH3SO3−), formate (HCOO−), acetate (CH3COO−), and formaldehyde (HCHO)) species and their interpretation in terms of past atmospheric composition (aerosols and water soluble gaseous species). We discuss ice core dating, the difficulties connected with trace measurements, and the significance of the ionic composition of snow. We examine temporal (from the last decades back to the last climatic cycle) and spatial (including examples from coastal as well as central areas of Greenland and Antarctica) variations in the ionic budget of the precipitation and evaluate ice core studies in terms of the chemical composition of our past atmosphere. We review (1) how Greenland and Antarctic ice cores that span the last few centuries have provided information on the impact of human activities and (2) how the chemistry of deep ice cores provides information on various past natural phenomena such as climatic variations (glacial-interglacial changes, El Niño), volcanic eruptions, and large boreal forest fires