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Paris, France

Paris Dauphine University , often referred to as Paris Dauphine or Dauphine is a grande école and an established public research and higher education institution in Paris, France.Dauphine was founded as a faculty of economics and management in 1968 in the former NATO headquarters in Western Paris, in the XVIth arrondissement. Wikipedia.

In this paper we investigate the co-movement and the causality relationship between energy consumption as well as electricity consumption and the HDI (human development index) using as a proxy of human well-being and by including energy prices as an additional variable, in fifteen developing countries for the period 1988 to 2008. Recently developed tests for the panel unit root, heterogeneous panel cointegration, and panel-based error correction models are employed.The empirical results support the neutrality hypothesis in the short-term, regards total energy or electricity consumption, implying an absence of causality running in either direction. In the short term, energy as well as electricity consumption has a neutral effect on the HDI.In the long-term the findings provide a clear support of a negative cointegration relationship between energy consumption and the HDI. While a positive cointegration relationship exists between electricity consumption and HDI.A 1% increase in per capita energy consumption reduces the HDI by 0.8% and, a 1% increase in per capita electricity consumption increases the HDI by 0.22%. Moreover, a 1% increase in energy price reduces the HDI by around 0.11%.This study thus provides empirical evidence of long-run and causal relationships between energy consumption and the HDI for our sample of countries; supporting the assertion that lack or limited access to modern energy services could hamper economic and human development prospects of countries and underpins all the MDGs (millennium development goals). © 2013 Elsevier Ltd. Source

Ouedraogo N.S.,University of Paris Dauphine
Energy Economics | Year: 2013

Access to modern energy is believed to be a prerequisite for sustainable development, poverty alleviation and the achievement of the Millennium Development Goals.However, theoretical models and empirical results offer conflicting evidence on the relationship between energy consumption and economic growth that we remain largely unsure of the cause-and-effect nature of this relationship, if indeed a relationship exists at all.This paper tests, in a panel context, the long-run relationship between energy access, and economic growth for fifteen African countries from 1980 to 2008 by using recently developed panel cointegration techniques.We adopt a three-stage approach, consisting of panel unit root, panel cointegration and Granger causality tests to study the dynamic causal relationships between energy consumption, energy prices and growth as well as relationship between electricity consumption, prices and growth.Results show that GDP and energy consumption as well as GDP and electricity move together in the long-run. By estimating these long-run relationships and testing for causality using panel-based error correction models, we found unidirectional long-run and short-run causality. The causality is running from GDP to energy consumption in the short-run, and from energy consumption to GDP in the long-run. There is also evidence of unidirectional causality running from electricity consumption to GDP in the long-run.This study thus provides empirical evidence of long-run and causal relationships between energy consumption and economic growth for our sample of fifteen countries; suggesting that lack or limited access to modern energy services could hamper economic growth and compromise the development prospects of these countries. © 2012 Elsevier B.V. Source

Fondja Wandji Y.D.,University of Paris Dauphine
Energy Policy | Year: 2013

The aim of this paper is to study the nature of the relationship between energy consumption and economic growth in Cameroon through a three-step approach: (i) Study the stationarity of the chronic, (ii) test of causality between variables and (iii) estimate the appropriate model. The study concludes in a non-stationarity of the series. Using the data in first difference, the Granger causality test yields a strong evidence for unidirectional causality running from OIL to GDP. Cointegration tests also show that these two series are co-integrated and the Error Correction Model (ECM) reveals that every percentage increase in Oil products consumption increases economic growth by around 1.1%. This result confirms the intuition that an economic policy aimed at improving energy supply will necessarily have a positive impact on economic growth. On the other side, a lack of energy is a major bottleneck for further economic development in Cameroon. © 2013 Elsevier Ltd. Source

Peyre G.,University of Paris Dauphine
IEEE Transactions on Signal Processing | Year: 2010

This paper proposes a best basis extension of compressed sensing recovery. Instead of regularizing the compressed sensing inverse problem with a sparsity prior in a fixed basis, our framework makes use of sparsity in a tree-structured dictionary of orthogonal bases. A new iterative thresholding algorithm performs both the recovery of the signal and the estimation of the best basis. The resulting reconstruction from compressive measurements optimizes the basis to the structure of the sensed signal. Adaptivity is crucial to capture the regularity of complex natural signals. Numerical experiments on sounds and geometrical images indeed show that this best basis search improves the recovery with respect to fixed sparsity priors. © 2006 IEEE. Source

Peyre G.,University of Paris Dauphine
IEEE Transactions on Pattern Analysis and Machine Intelligence | Year: 2010

This paper proposes a new method to synthesize and inpaint geometric textures. The texture model is composed of a geometric layer that drives the computation of a new grouplet transform. The geometry is an orientation flow that follows the patterns of the texture to analyze or synthesize. The grouplet transform extends the original construction of Mallat [1] and is adapted to the modeling of natural textures. Each grouplet atoms is an elongated stroke located along the geometric flow. These atoms exhibit a wide range of lengths and widths, which is important to match the variety of structures present in natural images. Statistical modeling and sparsity optimization over these grouplet coefficients enable the synthesis of texture patterns along the flow. This paper explores texture inpainting and texture synthesis, which both require the joint optimization of the geometric flow and the grouplet coefficients. © 2010 IEEE. Source

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