The influence of multi-target policies on the ranking of technologies - A bottom-up approach using OPERA optimisation model
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The increasing trends in GHG emissions over the last 40 years due to the economic growth are aggravating the effects of Climate Change. A way to stabilise and reverse these effects is through the decarbonisation of energy systems and the transition to a low carbon economy; however, this requires time and involves costly investments. The gradual transition to a low carbon economy demands the development and implementation of new technologies as well as policies that will exploit the potential of the technology mix in the least cost for society. Cost effectiveness has been used as an indicator for giving such insights to decision and policy makers. In planning such futures, models are being widely used providing input to assess the developments need to be undertaken taking into account policy and technology interactions. In this study we used OPERA optimisation model in order to study the Dutch energy system regarding the cost effectiveness of technologies under different policy pathways for 2030. Four scenarios were studied representing possible pathways with regard to the climate and energy targets. In particular renewable energy, primary energy savings and final energy savings targets were investigated along with the non-ETS emission reduction targets. Aim of the research was to provide a method to examine how the interactions among the scenario objectives and the assessed technologies affect the ranking of the latter using as indicator their cost effectiveness. The shadow prices of the assessed targets were used as a proxy to estimate the cost effectiveness of technologies as well as the interactions among the objectives of each scenario. The analysis suggests that the scoring of each technology is highly dependent on its influence on the scenario objectives. Providing such ranking can be proved really useful for policy makers because the alignment or misalignment of technologies with the policy objectives can be predicted. Hence, technological lock-ins can be avoided and more efficient and effective policies to be planned in the longer term time horizon.