Coupling climate and economic models in a cost-benefit framework: A convex optimisation approach

In this paper, we present a general method, based on a convex optimisation technique, that facilitates the coupling of climate and economic models in a cost-benefit framework. As a demonstration of the method, we couple an economic growth model à la Ramsey adapted from DICE-99 with an efficient intermediate complexity climate model, C-GOLDSTEIN, which has highly simplified physics, but fully 3-D ocean dynamics. As in DICE-99, we assume that an economic cost is associated with global temperature change: this change is obtained from the climate model, which is driven by the GHG concentrations computed from the economic growth path. The work extends a previous paper in which these models were coupled in cost-effectiveness mode. Here we consider the more intricate cost-benefit coupling in which the climate impact is not fixed a priori. We implement the coupled model using an oracle-based optimisation technique. Each model is contained in an oracle, which supplies model output and information on its sensitivity to a master program. The algorithm Proximal-ACCPM guarantees the convergence of the procedure under sufficient convexity assumptions. Our results demonstrate the possibility of a consistent, cost-benefit, climate-damage optimisation analysis with a 3-D climate mode

Thermo-physical properties of paraffin wax with iron oxide nanoparticles as phase change material for heat storage applications

Phase change materials (PCMs) are growing in importance in many thermal applications as heat storage or to smooth the energy peak demand in many technological fields in industrial as well as in civil applications. Conductive nanoparticles can be added to phase change material to improve their thermo-physical properties. In this work, Iron oxide nanoparticles (IOx-NPs) were synthesized using a simple and green synthesis method, free of toxic and harmful solvents, using the extract of a plant as a reducer and stabilizer at two different temperatures of calcination 500°C and 750°C. The metallic oxide was used as an additive with 2% wt. compositions to paraffin wax to prepare a nanocomposite. The variation in thermal properties of paraffin wax in the composite was experimentally investigated. The biosynthesized IOx-NPs were characterized by X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM) and Thermal Gravimetric Analysis (TGA) techniques. The thermal properties of the synthesized nanocomposites were characterized by a thermal conductivity analyzer and differential scanning calorimetry (DSC). The FTIR spectra showed a bond at 535 cm-1, which confirms the Fe-O vibration. The XRD powder analysis revealed the formation of the cubic phase of Fe3O4 with an average particle size of 11 nm at 500°C and the presence of the phase α-Fe2O3 with Fe3O4 at 750°C. Scanning Electron Microscopy (SEM) showed that the obtained oxide was made up of particles of nanoscale size. Experimental measurements showed that the presence of nanoparticles can improve the latent heat capacity by a maximum of 16.16 % and the thermal conductivity of the nanocomposites by a maximum of 16.99%

Coupling climate and economic models in a cost-benefit framework: a convex optimization approach

In this paper we present a general method, based on a convex optimisation technique, that facilitates the coupling of climate and economic models in a cost-benefit framework. As a demonstration of the method, we couple an economic growth model à la Ramsey adapted from DICE-99 with an efficient intermediate complexity climate model, C-GOLDSTEIN, which has highly simplified physics, but fully 3-D ocean dynamics. As in DICE-99 we assume that an economic cost is associated with global temperature change: this change is obtained from the climate model which is driven by the GHG concentrations computed from the economic growth path. The work extends a previous paper in which these models were coupled in cost-effectiveness mode. Here we consider the more intricate cost-benefit coupling in which the climate impact is not fixed a priori. We implement the coupled model using an oracle-based optimisation technique. Each model is contained in an oracle which supplies model output and information on its sensitivity to a master program. The algorithm Proximal-ACCPM guarantees the convergence of the procedure under sufficient convexity assumptions. Our results demonstrate the possibility of a consistent, cost-benefit, climate-damage optimisation analysis with a 3-D climate model

Resource Competition on Integral Polymatroids

We study competitive resource allocation problems in which players distribute their demands integrally on a set of resources subject to player-specific submodular capacity constraints. Each player has to pay for each unit of demand a cost that is a nondecreasing and convex function of the total allocation of that resource. This general model of resource allocation generalizes both singleton congestion games with integer-splittable demands and matroid congestion games with player-specific costs. As our main result, we show that in such general resource allocation problems a pure Nash equilibrium is guaranteed to exist by giving a pseudo-polynomial algorithm computing a pure Nash equilibrium.Comment: 17 page

A dynamic Game Model of Strategic RD&D Cooperation and GHG Emission Mitigation

This report describes the game structures implemented in the TOCSIN project to find self-enforcing and stable international environmental agreements. It presents the first results obtained with the use of these models. The document starts with a review of the different approaches that have been proposed in the literature to represent in a game theoretic framework the concept of self-enforcing or stable international environmental agreement

Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system

The Mathematics of Routing in Massively Dense Ad-Hoc Networks

International audienceComputing optimal routes in massively dense adhoc networks be-comes intractable as the number of nodes becomes very large. One recent ap-proach to solve this problem is to use a fluid type approximation in which the whole network is replaced by a continuum plain. Various paradigms from physics have been used recently in order to solve the continuum model. We propose in this paper an alternative modeling and solution approach similar to a model by Beckmann [3] developed more than fifty years ago from the area of road traffic