Abstract
The primary purpose of this research is to assess the long-range energy demand assumption made in relevant Roadmaps for the transformation to a low-carbon energy system. A novel interdisciplinary approach is then implemented: a new model is estimated for the aggregated world primary energy demand with long historical time series for world energy, income, and population for the years 1900–2017. The model is used to forecast energy demand in 2050 and assess the uncertainty-derived risk based on the variances of the series and parameters analysed. The results show that large efficiency savings—up to 50% in some cases and never observed before—are assumed in the main Roadmaps. This discrepancy becomes significantly higher when even moderate uncertainty assumptions are taken into account. A discussion on possible future sources of breaks in current patterns of energy supply and demand is also presented, leading to a new conclusion requiring an active political stance to accelerate efficiency savings and lifestyle changes that reduce energy demand, even if energy consumption may be reduced significantly. This will likely include replacing the income-growth paradigm with other criteria based on prosperity or related measures.
Highlights
Forecasting long-range energy demand at the aggregated world level is the starting point for building general Roadmaps for the transformation to a low carbon energy system
Three series were analysed in a joint model for Total Primary Energy Supply (TPES) [23], world Gross Domestic Product (GDP) [24,25], and world population [26,27], an analysis that is missing in the literature
Plausible energy demand projections for the year 2050 were made and used as a yardstick to compare them with published results from a few selected relevant Roadmaps for a low carbon transformation
Summary
Forecasting long-range energy demand at the aggregated world level is the starting point for building general Roadmaps for the transformation to a low carbon energy system. The available long-range demand forecasts employed in Roadmaps are frequently based on ad hoc assumptions, mainly on population and GDP growth, and savings and efficiency. A further new contribution on the modelling of energy demand is proposed and implemented, based on a statistical analysis of the long-term historical time series that covers that gap. The results allow for comparing and assessing currently available future demand forecasts found in some relevant published Roadmaps [1,2,3]. A discussion on possible sources of future breaks in energy supply and demand is presented
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