Title: COVID-19 recovery packages can benefit climate targets and clean energy jobs, but scale of impacts and optimal investment portfolios differ among major economies

Date: 2022

Short description: To meet the Paris temperature targets and recover from the effects of the pandemic, many countries have launched economic recovery plans, including specific elements to promote clean energy technologies and green jobs. However, how to successfully manage investment portfolios of green recovery packages to optimize both climate mitigation and employment benefits remains unclear. Here, we use three energy-economic models, combined with a portfolio analysis approach, to find optimal low-carbon technology subsidy combinations in six major emitting regions: Canada, China, the European Union (EU), India, Japan, and the United States (US). We find that, although numerical estimates differ given different model structures, results consistently show that a >50% investment in solar photovoltaics is more likely to enable CO2 emissions reduction and green jobs, particularly in the EU and China. Our study illustrates the importance of strategically managing investment portfolios in recovery packages to enable optimal outcomes and foster a post-pandemic green economy.

Authors: van de Ven, D.-J., Nikas, A., Koasidis, K., Forouli, A., Cassetti, A., Chiodi, A., Gargiulo, M., Giarola, S., Köberle, A.C., Koutsellis, T., Mittal, S., Perdana, S., Vielle, M., Xexakis, G., Doukas, H., & Gambhir A.

Journal: One Earth

Links: https://doi.org/10.1016/j.oneear.2022.08.008

Title: Challenges in the harmonisation of global integrated assessment models: a comprehensive methodology to reduce model response heterogeneity

Date: 2022

Short description: To tackle the negative socioeconomic implications of the COVID-19 pandemic, the European Union (EU) introduced the Recovery and Resilience Facility, a financial instrument to help Member States recover, on the basis that minimum 37% of the recovery funds flow towards the green transition. This study contributes to the emerging modelling literature on assessing COVID-19 vis-à-vis decarbonisation efforts, with a particular focus on employment, by optimally allocating the green part of the EU recovery stimulus in selected low-carbon technologies and quantifying the trade-offs between resulting emissions reductions and employment gains in the energy sector. We couple an integrated assessment model with a multi-objective linear-programming model and an uncertainty analysis framework aiming to identify robust portfolio mixes. We find that it is possible to allocate recovery packages to align mitigation goals with both short- and long-term energy-sector employment, although over-emphasising the longer-term sustainability of new energy-sector jobs may be costlier and more vulnerable to uncertainties compared to prioritising environmental and near-term employment gains. Robust portfolios with balanced performance across objectives consistently feature small shares of offshore wind and nuclear investments, while the largest chunks are dominated by onshore wind and biofuels, two technologies with opposite impacts on near- and long-term employment gains.

Authors: Koasidis, K., Nikas, A., Van de Ven, D.J., Xexakis, G., Forouli, A., Mittal, S., Gambhir, A., Koutsellis, T., & Doukas, H.

Journal: Energy Policy

Links: https://doi.org/10.1016/j.enpol.2022.113301

Title: AUGMECON-Py: A Python framework for multi-objective linear optimisation under uncertainty.

Date: 2022

Short description: This paper presents AUGMECON-Py, a Python framework for solving large and complex multi-objective linear programming problems under uncertainty, optimally and robustly capturing all solutions. On the core of the AUGMECON-Py software lies the integration of a well-established optimisation algorithm (AUGMECON) with Monte Carlo analysis that helps maximise robustness against stochastic uncertainty, thereby avoiding the complexity of numerous cascading methods and code scripts. Using an object-oriented language, AUGMECON-Py overcomes limitations of its predecessors regarding memory requirements, and further extends the solution algorithm to ensure no efficient solution is left outside the solution grid. The framework is easily accessible, offering effortless data pre- and post-processing, management, and visualisation of results.

Authors: Forouli, A., Pagonis, A., Nikas, A., Koasidis, K., Xexakis, G., Koutsellis, T., Petkidis, C., & Doukas, H.

Journal: SoftwareX

Links: https://doi.org/10.1016/j.softx.2022.101220

Title: AUGMECON-Py: A Python framework for multi-objective linear optimisation under uncertainty.

Date: 2022

Short description: Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multi-objective linear programming problems, by drawing from the weaknesses of AUGMECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

Authors: Nikas, A., Fountoulakis, A., Forouli, A., & Doukas, H.

Journal: Operational Research

Links: https://doi.org/10.1007/s12351-020-00574-6

Title: Where is the EU headed given its current climate policy? A stakeholder-driven model inter-comparison

Date: November 2021

Short description: Recent calls to do climate policy research with, rather than for, stakeholders have been answered in non-modelling science. Notwithstanding progress in modelling literature, however, very little of the scenario space traces back to what stakeholders are ultimately concerned about. With a suite of eleven integrated assessment, energy system and sectoral models, we carry out a model inter-comparison for the EU, the scenario logic and research questions of which have been formulated based on stakeholders' concerns. The output of this process is a scenario framework exploring where the region is headed rather than how to achieve its goals, extrapolating its current policy efforts into the future. We find that Europe is currently on track to overperforming its pre-2020 40% target yet far from its newest ambition of 55% emissions cuts by 2030, as well as looking at a 1.0–2.35 GtCO2 emissions range in 2050. Aside from the importance of transport electrification, deployment levels of carbon capture and storage are found intertwined with deeper emissions cuts and with hydrogen diffusion, with most hydrogen produced post-2040 being blue. Finally, the multi-model exercise has highlighted benefits from deeper decarbonisation in terms of energy security and jobs, and moderate to high renewables-dominated investment needs.

Authors: Nikas, A., Elia, A., Boitier, B., Koasidis, K., Doukas, H., Cassetti, G., Anger-Kraavi, A., Bui, H., Campagnolo, L., De Miglio, R., Delpiazzo, E., Fougeyrollas, A., Gambhir, A., Gargiulo, M., Giarola, S., Grant, N., Hawkes, A., Herbst, A., Köberle, A.C., Kolpakov, A., Le Mouël, P., McWilliams, B., Mittal, S., Moreno, J., Neuner, F., Perdana, S., Peter, G.P., Plötz, P., Rogelj, J., Sognnæs, I., Van de Ven, D.J., Vielle, M., Zachmann, G., Zagamé, P., Chiodi, A.

Journal: Science of the Total Environment

Links: https://doi.org/10.1016/j.scitotenv.2021.148549

Title: Challenges in the harmonisation of global integrated assessment models: a comprehensive methodology to reduce model response heterogeneity

Date: August 2021

Short description: Harmonisation sets the ground to a solid inter-comparison of integrated assessment models. A clear and transparent harmonisation process promotes a consistent interpretation of the modelling outcomes divergences and, reducing the model variance, is instrumental to the use of integrated assessment models to support policy decision-making. Despite its crucial role for climate economic policies, the definition of a comprehensive harmonisation methodology for integrated assessment modelling remains an open challenge for the scientific community. This paper proposes a framework for a harmonisation methodology with the definition of indispensable steps and recommendations to overcome stumbling blocks in order to reduce the variance of the outcomes which depends on controllable modelling assumptions. The harmonisation approach of the PARIS REINFORCE project is presented here to layout such a framework. A decomposition analysis of the harmonisation process is shown through 6 integrated assessment models (GCAM, ICES-XPS, MUSE, E3ME, GEMINI-E3, and TIAM). Results prove the potentials of the proposed framework to reduce the model variance and present a powerful diagnostic tool to feedback on the quality of the harmonisation itself.

Authors: Giarola, S., Mittal, S., Vielle, M., Perdana, S., Campagnolo, L., Delpiazzo, E., Bui, H., Anger-Kraavi, A., Kolpakov, A., Sognnaes, I., Peters, G.P., Hawkes, A., Koberle, A., Grant, N., Gambhir, A., Nikas, A., Doukas, H., Moreno, J., & Van de Ven, D.J.

Journal: Science of the Total Environment

Links: https://www.sciencedirect.com/science/article/pii/S0048969721019318