Abstract
This paper reviews different approaches to modelling the energy transition towards a zero carbon economy. It identifies a number of limitations in current approaches such as a lack of consideration of out-of-equilibrium situations (like an energy transition) and non-linear feedbacks. To tackle those issues, the new open source integrated assessment model pymedeas is introduced, which allows the exploration of the design and planning of appropriate strategies and policies for decarbonizing the energy sector at World and EU level. The main novelty of the new open-source model is that it addresses the energy transition by considering biophysical limits, availability of raw materials, and climate change impacts. This paper showcases the model capabilities through several simulation experiments to explore alternative pathways for the renewable transition. In the selected scenarios of this work, future shortage of fossil fuels is found to be the most influential factor of the simulations system evolution. Changes in efficiency and climate change damages are also important determinants influencing model outcomes.
Highlights
Today’s societal challenges require new tools and models that consider, in an integrative way, aspects such as climate change impacts [1], impacts and vulnerabilities due to resource limitations [2], bio physical resources management [3] and human impacts on ecosystems [4].Models can be classified following the fields/areas they analyse or the features they can represent
The World model shows systemic changes when the system achieves the maximum of fossil fuel production (Peak Oil or ‘Hubbert peak’) [59] in the Business as Usual (BAU) scenario
The pymedeas models are useful tools that can be adapted, by modifying the input scenarios, and changing the model equations and relationships based on the particular needs of the user
Summary
Today’s societal challenges require new tools and models that consider, in an integrative way, aspects such as climate change impacts [1], impacts and vulnerabilities due to resource limitations [2], bio physical resources management [3] and human impacts on ecosystems [4].Models can be classified following the fields/areas they analyse or the features they can represent. In the field of energy market modelling, bottom-up dynamic partial equilib rium models like the MARket ALlocation models and TIMES, are widely used, and combine technical engineering and economic approaches [5] If the features they represent are analysed, eight aspects could be considered [6]: (i) complexity, non-linearity, non-ergodicity and deep uncertainty, (ii) the importance of time, (iii) agents’ heterogeneity and behavioural elements, (iv) interdisciplinary aspects (v) role of in stitutions and social context, (vi) ethical and philosophical aspects, (vii) finance and (vii) multiple equilibria/disequilibrium. In the case of CGE or the DSGE for instance, they do not have the possibility to represent out-of-equilibrium situations (like an economic crisis or energy transi tion), non-linear feedbacks or other system characteristics related to complexity In this line [6], classified eleven models following these eight aspects and four general types: econometric, system dynamics, agent-based and Stock-Flow Consistent (SFC) models. In the econo metric type they distinguish those that are Keynesian and post-Keynesian [6]. identified eleven models that met such criteria and in the case of pymedeas, it is a system dynamics, econometric (using an input-output approach) model framed in a post-Keynesian approach
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
