Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work

How Consistent are Alternative Short-Term Climate Policies with Long-Term Goals?

Choosing long-term goals is a key issue in the climate policy agenda. Targets should be easily measurable and feasible, but also effective in damage control. Once goals are set globally, given the uncertainty affecting long-term strategies and region-specific preferences for different policy instruments, policies will be better represented by a diversified portfolio to be revised over time, rather than once and forever decisions. It therefore becomes crucial to understand to what extent different strategies (or policy portfolios) are consistent with long-term targets, that is, when they imply emission paths which do not irreversibly diverge from globally set goals. The present paper aims to investigate emission paths implied by plausible policy scenarios against those derived by imposing alternative long-term targets, comparing, for example, differences in peak periods. Plausible policy scenarios are for instance Kyoto-type targets with or without participation by the U.S. and/or by developing countries. Different long-term targets considered focus on stabilisation of CO2 concentrations, radiative forcing and the increase in atmospheric temperature relative to pre-industrial levels. In order to account for the uncertainty surrounding the climate cycle, for each long-term goal multiple paths of emission - the most probable, the optimistic and the pessimistic ones - are considered in the comparison exercise. Comparative analysis is performed using a newly developed version of the FEEM-RICE model, a regional economy-climate model of optimal economic growth which is based on Nordhaus and Boyers RICE model crucially extended in order to account for induced technical change. In particular, both carbon and energy intensity are affected by a new endogenous variable Technical Progress which captures both the role of Learning by Researching and of Learning by Doing. These are in turn determined by the optimal levels of Research and Development and of Emission Abatement

Risk hedging strategies under energy system and climate policy uncertainties

The future development of the energy sector is rife with uncertainties. They concern virtually the entire energy chain, from resource extraction to conversion technologies, energy demand, and the stringency of future environmental policies. Investment decisions today need thus not only to be cost-effective from the present perspective, but have to take into account also the imputed future risks of above uncertainties. This chapter introduces a newly developed modeling decision framework with endogenous representation of above uncertainties. We employ modeling techniques from finance and in particular modern portfolio theory to a systems engineering model of the global energy system and implement several alternative representations of risk. We aim to identify salient characteristics of least-cost risk hedging strategies that are adapted to considerably reduce future risks and are hence robust against a wide range of future uncertainties. These lead to significant changes in response to energy system and carbon price uncertainties, in particular (i) higher short- to medium-term investments into advanced technologies, (ii) pronounced emissions reductions, and (iii) diversification of the technology portfolio. From a methodological perspective, we find that there are strong interactions and synergies between different types of uncertainties. Cost-effective risk hedging strategies thus need to take a holistic view and comprehensively account for all uncertainties jointly. With respect to costs, relatively modest risk premiums (or hedging investments) can significantly reduce the vulnerability of the energy system against the associated uncertainties. The extent of early investments, diversification, and emissions reductions, however, depends on the risk premium that decision makers are willing to pay to respond to prevailing uncertainties and remains thus one of the key policy variables

Selected 'Starter kit' energy system modelling data for selected countries in Africa, East Asia, and South America (#CCG, 2021)

Energy system modeling can be used to develop internally-consistent quantified scenarios. These provide key insights needed to mobilise finance, understand market development, infrastructure deployment and the associated role of institutions, and generally support improved policymaking. However, access to data is often a barrier to starting energy system modeling, especially in developing countries, thereby causing delays to decision making. Therefore, this article provides data that can be used to create a simple zero-order energy system model for a range of developing countries in Africa, East Asia, and South America, which can act as a starting point for further model development and scenario analysis. The data are collected entirely from publicly available and accessible sources, including the websites and databases of international organisations, journal articles, and existing modeling studies. This means that the datasets can be easily updated based on the latest available information or more detailed and accurate local data. As an example, these data were also used to calibrate a simple energy system model for Kenya using the Open Source Energy Modeling System (OSeMOSYS) and three stylized scenarios (Fossil Future, Least Cost and Net Zero by 2050) for 2020–2050. The assumptions used and the results of these scenarios are presented in the appendix as an illustrative example of what can be done with these data. This simple model can be adapted and further developed by in-country analysts and academics, providing a platform for future work