Optimizing climate related global development pathways in the global calculator using Monte Carlo Markov chains and genetic algorithms

Abstract

Abstract Novel pathway optimization methods are presented using the 'Global Calculator’ model and webtool 1 to goal-seek within a set of optimization constraints. The Global Calculator (GC) is a model used to forecast climate-related develop pathways for the world’s energy, food and land systems to 2050. The optimization methods enable the GC’s user to specify optimization constraints and return a combination of input parameters that satisfy them. The optimization methods evaluated aim to address the challenge of efficiently navigating the GC's ample parameter space (8e70 parameter combinations) using Monte Carlo Markov Chains and genetic algorithms. The optimization methods are used to calculate an optimal input combination of the ‘lever’ settings in the GC that satisfy twelve input constraints while minimizing cumulative CO2 emissions and maximizing GDP output. This optimal development pathway yields a prediction to 2100 of 2,835 GtCO2 cumulative emissions and a 3.7% increase in GDP with respect to the “business as usual” pathway defined by the International Energy Agency, the IEA’s 6DS scenario, a likely extension of current trends. At a similar or lower ambition level as the benchmark scenarios considered to date (distributed effort, consumer reluctance, low action on forests and consumer activism), the optimal pathway shows a significant decrease in CO2 emissions and increased GDP. The chosen optimization method presented here enables the generation of optimal, user defined/constrained, bespoke pathways to sustainability, relying on the Global Calculator’s whole system approach and assumptions.