The massive increase in production and consumption of fossil fuels during the 20 th century was accompanied by several problems in economic, social and environmental levels. Thus, the energy as it is produced, distributed and consumed currently does not meet the requirements of sustainable development. Hence it is necessary to use RE that do not emit GHG in order to move toward the sustainable development. Since mid-1980s, Tunisia has implemented its national strategy in the field of RE. To revive the market of solar water heating, the government decided to establish, in 2005, an ambitious program called PROSOL. With the help of case study (PROSOL project), the paper shows that the contribution of RE to the economic, social and environmental dimensions of sustainable development is significant.
International audienceThe world today finds itself in the worst economic and environmental crises in generations. Hence, we need policies that can stimulate recovery and at the same time reach the sustainability of the global economy. United Nations Environment Programme (UNEP and ILO, 2008; UNEP 2009a; 2009b; 2011) and many economists (i.e. Barbier, 2009a; 2009b; 2009c; Edenhofer and Stern, 2009; Robins et al., 2009) advocate the need for a "Green Recovery". The concept of "Green Recovery" means that the current economic crisis should be grasped by governments as an opportunity to reduce carbon dependency and put economies on a path of Green Growth. This needs the use of fiscal stimulus packages to provide green infrastructures necessary to reach a significant "greening" of the global economy. Since renewable energy is a key to a future without dangerous climate change, many papers and international reports advocate that the renewable energy sector is an obvious choice in the path of Green Recovery and Green Growth. (UNEP SEFI, 2009; 2010; 2011). The main aim of this paper is to review a selection of responses to the double crisis by international institutions and to focus on the achievements made in the renewable energy sector since the use of fiscal stimulus packages
The main difficulty for natural disaster insurance derives from the uncertainty of an event's damages. Insurers cannot precisely appreciate the weight of natural hazards because of risk dependences. Insurability under uncertainty first requires an accurate assessment of entire damages. Insured and insurers both win when premiums calculate risk properly. In such cases, coverage will be available and affordable. Using the artificial neural network - a technique rooted in artificial intelligence - insurers can predict annual natural disaster losses. There are many types of artificial neural network models. In this paper we use the multilayer perceptron neural network, the most accommodated to the prediction task. In fact, if we provide the natural disaster explanatory variables to the developed neural network, it calculates perfectly the potential annual losses for the studied country
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