2016, Kriegler, Elmar, Bauer, Nico, Popp, Alexander, Humpenöder, Florian, Leimbach, Marian, Strefler, Jessica, Baumstark, Lavinia, Bodirsky, Benjamin Leon, Hilaire, Jérôme, Klein, David, Mouratiadou, Ioanna, Weindl, Isabelle, Bertram, Christoph, Dietrich, Jan-Philipp, Luderer, Gunnar, Pehl, Michaja, Pietzcker, Robert, Piontek, Franziska, Lotze-Campen, Hermann, Biewald, Anne, Bonsch, Markus, Giannousakis, Anastasis, Kreidenweis, Ulrich, Müller, Christoph, Rolinski, Susanne, Schultes, Anselm, Schwanitz, Jana, Stevanovic, Miodrag, Calvin, Katherine, Emmerling, Johannes, Fujimori, Shinichiro, Edenhofer, Ottmar

This paper presents a set of energy and resource intensive scenarios based on the concept of Shared Socio-Economic Pathways (SSPs). The scenario family is characterized by rapid and fossil-fueled development with high socio-economic challenges to mitigation and low socio-economic challenges to adaptation (SSP5). A special focus is placed on the SSP5 marker scenario developed by the REMIND-MAgPIE integrated assessment modeling framework. The SSP5 baseline scenarios exhibit very high levels of fossil fuel use, up to a doubling of global food demand, and up to a tripling of energy demand and greenhouse gas emissions over the course of the century, marking the upper end of the scenario literature in several dimensions. These scenarios are currently the only SSP scenarios that result in a radiative forcing pathway as high as the highest Representative Concentration Pathway (RCP8.5). This paper further investigates the direct impact of mitigation policies on the SSP5 energy, land and emissions dynamics confirming high socio-economic challenges to mitigation in SSP5. Nonetheless, mitigation policies reaching climate forcing levels as low as in the lowest Representative Concentration Pathway (RCP2.6) are accessible in SSP5. The SSP5 scenarios presented in this paper aim to provide useful reference points for future climate change, climate impact, adaption and mitigation analysis, and broader questions of sustainable development.

2021, Baumstark, Lavinia, Bauer, Nico, Benke, Falk, Bertram, Christoph, Bi, Stephen, Gong, Chen Chris, Dietrich, Jan Philipp, Dirnaichner, Alois, Giannousakis, Anastasis, Hilaire, Jerome, Klein, David, Koch, Johannes, Leimbach, Marian, Levesque, Antoine, Madeddu, Silvia, Malik, Aman, Merfort, Anne, Merfort, Leon, Odenweller, Adrian, Pehl, Michaja, Pietzcker, Robert C., Piontek, Franziska, Rauner, Sebastian, Rodrigues, Renato, Rottoli, Marianna, Schreyer, Felix, Schultes, Anselm, Soergel, Bjoern, Soergel, Dominika, Strefler, Jessica, Ueckerdt, Falko, Kriegler, Elmar, Luderer, Gunnar

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.