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Simulation based optimization concept has been so far presented at local and national energy systems planning levels. This method represents a systematic way of dealing with uncertainties and combinatorial complexity when using modelling tools of very large size with significant number of possible policy inputs, and could be useful to analyse policy recommendations in broader models integrating more dimensions than energy. For the first time, the concept is successfully demonstrated using a working integrated assessment model (IAM), although some attempts have been mentioned in literature previously. IAMs aim to link main features of society, economy and energy with the biosphere and atmosphere into one modelling framework. The concept has been demonstrated for the IAM MEDEAS-World using system dynamics platform Vensim (version 9.3.4.) for both simulation and optimization. For an optimization problem formulation, 12 decision variables are selected from numerous input model parameters, the gross domestic product per capita has been selected for a goal function from simulation result as quantifiable policy objective, while for the constraint CO2 emissions is selected as subject to binding targets. It is possible to compile the simulation code so that optimization running time is around 10 times shorter. Preliminary results show that it is possible to implement richer optimization problems without significant increases in the running time using personal computers. The main contribution of this work is to prove the concept of simulation-based optimization by a demonstration within a working IAM. This opens the way to promising future work to inform about optimal combinations of available policies within different storylines, as well as providing additional general understanding of the IAM which could allow to improve the model. The original Vensim code has been provided in the Appendix.

Abstract The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO2) concentration scenarios used in key climate change modeling experiments. It computes the CO2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5‐8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments.

Achieving ambitious reductions in greenhouse gases (GHG) is particularly challenging for transportation due to the technical limitations of replacing oil-based fuels. We apply the integrated assessment model MEDEAS-World to study four global transportation decarbonization strategies for 2050. The results show that a massive replacement of oil-fueled individual vehicles to electric ones alone cannot deliver GHG reductions consistent with climate stabilization and could result in the scarcity of some key minerals, such as lithium and magnesium. In addition, energy-economy feedbacks within an economic growth system create a rebound effect that counters the benefits of substitution. The only strategy that can achieve the objectives globally follows the Degrowth paradigm, combining a quick and radical shift to lighter electric vehicles and non-motorized modes with a drastic reduction in total transportation demand.

The estimation of future energy demand is a key factor for the development of effective alternative policies towards a low carbon economy. This paper describes a novel method to estimate the energy demand in the new integrated assessment framework MEDEAS based on the projection of sectoral final energy intensities. The dynamic of each of the sectoral final energy intensity is broken down into (1) improvement in energy efficiency and (2) substitution of the final energy. The speed of changes in these factors depend on physical supply-demand unbalances in the market, climate mitigation and other energy saving policies and the perception of scarcity of the different economic agents. The simulated case studies in MEDEAS-World under the narrative of the Business-as-usual (BAU) scenario have allowed validating the model's robustness and showing the potentiality of its application. Keywords: Integrated assessment modelling, Energy intensity, Energy demand, Technological change, Electrification

A novel methodology is developed to dynamically assess the energy and material investments required over time to achieve the transition from fossil fuels to renewable energy sources in the electricity sector. The obtained results indicate that a fast transition achieving a 100% renewable electric system globally by 2060 consistent with the Green Growth narrative could decrease the EROI of the energy system from current ~12:1 to ~3:1 by the mid-century, stabilizing thereafter at ~5:1. These EROI levels are well below the thresholds identified in the literature required to sustain industrial complex societies. Moreover, this transition could drive a substantial re-materialization of the economy, exacerbating risk availability in the future for some minerals. Hence, the results obtained put into question the consistence and viability of the Green Growth narrative. Keywords: Energy return on energy investment, Transition to renewable energies, Energy trap, Green growth, Integrated assessment modelling

Whether renewable energy sources (RES) will provide sufficient energy surplus to entirely power complex modern societies is under discussion. We contribute to this debate by estimating the current global average energy return on energy invested (EROI) for the five RES technologies with the highest potential of electricity generation from the comprehensive and internally consistent estimations of their material requirements at three distinct energy system boundaries: standard farm-gate (EROIst), final at consumer point-of-use (EROIfinal), and extended (including indirect investments, EROIext). EROIst levels found fall within the respective literature ranges. Expanding the boundaries closer to the system level, we find that only large hydroelectricity would currently have a high EROIext ~ 6.5:1, while the rest of variable RES would be below 3:1: onshore wind (2.9:1), offshore wind (2.3:1), solar Photovoltaic (PV) (1.8:1), and solar Concentrated Solar Power (CSP) (ext levels of variable RES are currently below those of fossil fuel-fired electricity. It remains unknown if technological improvements will be able to compensate for factors, which will become increasingly important as the variable RES scale-up. Hence, without dynamically accounting for the evolution of the EROI of the system, the viability of sustainable energy systems cannot be ensured, especially for modern societies pursuing continuous economic growth.

Abstract Although the transition to renewable energies will intensify the global competition for land, the potential impacts driven by solar energy remain unexplored. In this work, the potential solar land requirements and related land use change emissions are computed for the EU, India, Japan and South Korea. A novel method is developed within an integrated assessment model which links socioeconomic, energy, land and climate systems. At 25–80% penetration in the electricity mix of those regions by 2050, we find that solar energy may occupy 0.5–5% of total land. The resulting land cover changes, including indirect effects, will likely cause a net release of carbon ranging from 0 to 50 gCO2/kWh, depending on the region, scale of expansion, solar technology efficiency and land management practices in solar parks. Hence, a coordinated planning and regulation of new solar energy infrastructures should be enforced to avoid a significant increase in their life cycle emissions through terrestrial carbon losses.