Cost degression in photovoltaics, wind power and battery storage has been faster than previously anticipated. In the future, climate policy to limit global warming to 1.5-2°C will make carbon-based fuels increasingly scarce and expensive. Here we show that further progress in solar and wind power technology along with carbon pricing to reach the Paris Climate targets could make electricity 25 cheaper than carbon-based fuels. In combination with demand-side innovation, for instance in emobility and heat pumps, this is likely to induce a fundamental transformation of energy systems towards a dominance of electricity-based end uses. In a 1.5°C-scenario with limited availability of bioenergy and carbon dioxide removal, electricity could account for 66% of final energy by midcentury, three times the current levels and substantially higher than in previous climate policy 30 scenarios assessed by the IPCC. The lower production of bioenergy in our high electrification scenarios markedly reduces energy-related land and water requirements.

Abstract. This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an integrated assessment model (IAM), provides an integrated view of the global energy–economy–emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multiregional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon from 2005 to 2100. The resulting solution corresponds to the decentralized market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables the analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input and output variables. Each module can be represented by different realizations, enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. Thus, the framework can be used for a variety of applications in a customized form, balancing requirements for detail and overall runtime and complexity.

Abstract. This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an Integrated Assessment Model (IAM), provides an integrated view on the global energy-economy-emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multi-regional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon 2005 to 2100. The resulting solution corresponds to the decentral market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input/output variables. Each module can be represented by different realizations enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. The framework can thus be used for a variety of applications in a customized form balancing requirements for detail and overall run-time and complexity.