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Climate Change

4000 Scenarios For A Climate Turnaround

Using an extensive computer simulation of the climate, the global economy and the global energy system, researchers at PSI have been analyzing different ways of mitigating climate change, together with colleagues from the US, China, Ireland, Finland, and Sweden.

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Human activities currently contribute approximately 42 billion tonnes of CO2 emissions per year. The Intergovernmental Panel on Climate Change (IPCC) has determined that only an additional 300 to 600 billion tonnes can be emitted from 2020 onwards, if we are to have a realistic chance of limiting global warming to 1.5 degrees Celsius. According to Evangelos Panos from the PSI’s Laboratory for Energy Systems Analysis, there is a slim margin for success, as 70% of the projected scenarios suggest that the world will surpass the 1.5° C threshold within the next five years.

Determining The Most Effective Climate Measures:

Addressing climate change necessitates making numerous political, economic, and social decisions. However, these decisions are fraught with uncertainties. Understandably, decision-makers seek robust evidence, particularly when addressing the central question of which measures can have the greatest impact while also being economically advantageous in achieving the net-zero emissions target set by countries like Switzerland. A comprehensive computer simulation has been developed to shed light on this matter. It combines climate models, economic models, and 1200 energy-related technologies to analyze and reduce greenhouse gas emissions. The study employed a supercomputer to calculate 4,000 scenarios for 15 world regions, considering possible developments in ten-year intervals until the year 2100. “This requires sophisticated data analysis and visualization techniques,” explains co-author James Glynn, who heads the Energy Systems Modelling Program at Columbia University in the US. The resulting data amounts to 700 gigabytes, and the research paper has now been published in the academic journal Energy Policy.

The work conducted by Evangelos Panos and his co-authors is noteworthy due to their integration of assessment models that consider the inherent uncertainties within the models. Previous scenarios often assumed that all future parameters were known, including technology availability, costs, renewable energy potential, and more. Additionally, the IPCC calculations focused solely on the impact of technology choices on the climate, disregarding uncertainties in climate models, the relationship between climate and economic growth, population trends, and policy measures. “The significant contribution of our research is enabling policymakers to make informed decisions on climate action while fully understanding the existing uncertainties,” explains co-author Brian Ó Gallachóir from University College Cork.

Incorporating 18 Uncertainty Factors & Analyzing 72,000 Variables

When researchers aim to calculate scenarios involving a multitude of variables and uncertainties, they often employ the Monte Carlo method. This method does not predict the future but creates a data map of possible decision pathways. In the current study, the team adjusted 72,000 variables for each scenario, considering 18 uncertainty factors such as population and economic growth, climate sensitivity, resource potential, changes in agriculture and forestry, energy technology costs, and decoupling energy demand from economic development, as explained by James Glynn of Columbia University.

Providing A Solid Foundation For National Energy System Transformations

To develop specific national pathways for energy system transformations, it is essential to consider additional parameters specific to each country while examining political and economic aspects. Panos emphasizes that transitioning to a zero-carbon economy requires a capital-intensive energy system and the mobilization of resources from all stakeholders. Therefore, customized analyses at the national level are necessary, and the study provides a reliable basis for conducting such analyses.

By Paul Scherrer Institute

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