Modeling decision making as a support tool for policy making on renewable energy development
Modeling decision making as a support tool for policy making on renewable energy development
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DOI：
10.1016/j.enpol.2013.12.011
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发表时间：
2014-04-01
期刊：
影响因子：
9
通讯作者：
Gomez-Navarro, Tomas
中科院分区：
文献类型：
--
作者：
Cannemi, Marco;Garcia-Melon, Monica;Gomez-Navarro, Tomas
本文介绍了一个研究的结果，决策模型的分析资本风险投资者！生物质发电厂项目的优惠。研究的目的是为可再生能源发展领域的政策制定者提供更好的支持工具，网络分析法（ANP）有助于更好地了解资本风险投资者对不同类型生物质燃料发电厂的偏好。研究结果使公共行政部门能够更好地预测投资者对奖励制度的反应，或修改奖励制度，以更好地推动投资者的决定，改变奖励制度被投资者视为主要风险。因此，公共行政必须设计更好和更长期的奖励制度，预测市场反应。为此，设计了两种方案，一种是典型的决策过程，另一种是改进的决策过程。在意大利进行的案例研究表明，ANP可以帮助理解资本风险投资者如何解释情况，并在投资生物质发电厂时做出决策：公共行政部门和促进者利益之间的差异，增加新的决策标准如何影响决策，以及根据决策模型哪种情况将被排序为最佳。(C)2013爱思唯尔有限公司保留所有权利。
This paper presents the findings of a study on decision making models for the analysis of capital-risk investors! preferences on biomass power plants projects. The aim of the work is to improve the support tools for policy makers in the field of renewable energy development.Analytic Network Process (ANP) helps to better understand capital-risk investors preferences towards different kinds of biomass fueled power plants. The results of the research allow public administration to better foresee the investors' reaction to the incentive system, or to modify the incentive system to better drive investors' decisions.Changing the incentive system is seen as major risk by investors. Therefore, public administration must design better and longer-term incentive systems, forecasting market reactions. For that, two scenarios have been designed, one showing a typical decision making process and another proposing an improved decision making scenario.A case study conducted in Italy has revealed that ANP allows understanding how capital-risk investors interpret the situation and make decisions when investing on biomass power plants: the differences between the interests of public administrations's and promoters', how decision making could be influenced by adding new decision criteria, and which case would be ranked best according to the decision models. (C) 2013 Elsevier Ltd. All rights reserved.