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The increasing concern for the environmental impact of human activities stimulates the development of new methods for the analysis and design of industrial processes including energy systems. It is generally admitted that the considerations of environmental and other life cycle factors as early as possible in the design can contribute to a reduction of overall costs, favoring a cost effective evaluation of trade-offs between internal process improvements and post-treatments of effluents. For energy systems with significant time dependence of the major input and output parameters additional important questions for the designer are where and when to invest, in particular when staged or deferred cost investments are worth considering. Additionally advanced design methods have to be able to deal with availability (reliability) considerations, which are known to be of high relevance for energy systems. Modern energy systems are not only made of integrated technologies but they cannot be considered as fixed as they, like living bodies, tend to adapt to the decision making environment, which at present is mainly based on economic factors and regulatory issues. Ideally assessments at all levels (company, national, international) should be made in a coherent framework where all technology options can freely compete particularly when a major departure from present day economics can be envisaged, at least for sensitivity analyses. This paper intends to review a new approach meeting the above requirements and called environomic modeling and optimization, which has been extensively developed at LENI-EPFL with successful demonstration to the following applications : Ž Combined cycle power plant with or without cogeneration and with or without advanced features like CO2 separation, gas recirculation, oxygen enrichment, etc. Ž District heating network with centralized and/or decentralized heat pumps and cogeneration units, Ž Waste incineration plants with cogeneration and with or without toping cycle Results of the optimization of the above applications using genetic algorithms and including sensitivity analyses, in particular to the internalization of some pollution costs, will be presented. Further applications to other energy systems currently under study will be mentioned.