Between Scylla and Charybdis: Delayed mitigation narrows the passage between large-scale CDR and high costs

There are major concerns about the sustainability of large-scale deployment of carbon dioxide removal (CDR) technologies. It is therefore an urgent question to what extent CDR will be needed to implement the long term ambition of the Paris Agreement. Here we show that ambitious near term mitigation significantly decreases CDR requirements to keep the Paris climate targets within reach. Following the nationally determined contributions (NDCs) until 2030 makes 2 °C unachievable without CDR. Reducing 2030 emissions by 20% below NDC levels alleviates the trade-off between high transitional challenges and high CDR deployment. Nevertheless, transitional challenges increase significantly if CDR is constrained to less than 5 Gt CO2 a−1 in any year. At least 8 Gt CO2 a−1 CDR are necessary in the long term to achieve 1.5 °C and more than 15 Gt CO2 a−1 to keep transitional challenges in bounds

Alternative carbon price trajectories can avoid excessive carbon removal

The large majority of climate change mitigation scenarios that hold warming below 2 °C show high deployment of carbon dioxide removal (CDR), resulting in a peak-and-decline behavior in global temperature. This is driven by the assumption of an exponentially increasing carbon price trajectory which is perceived to be economically optimal for meeting a carbon budget. However, this optimality relies on the assumption that a finite carbon budget associated with a temperature target is filled up steadily over time. The availability of net carbon removals invalidates this assumption and therefore a different carbon price trajectory should be chosen. We show how the optimal carbon price path for remaining well below 2 °C limits CDR demand and analyze requirements for constructing alternatives, which may be easier to implement in reality. We show that warming can be held at well below 2 °C at much lower long-term economic effort and lower CDR deployment and therefore lower risks if carbon prices are high enough in the beginning to ensure target compliance, but increase at a lower rate after carbon neutrality has been reached

Carbon leakage in a fragmented climate regime: The dynamic response of global energy markets

As a global climate agreement has not yet been achieved, a variety of national climate policy agendas are being pursued in different parts of the world. Regionally fragmented climate policy regimes are prone to carbon leakage between regions, which has given rise to concerns about the environmental effectiveness of this approach. This study investigates carbon leakage through energy markets and the resulting macro-economic effects by exploring the sensitivity of leakage to the size and composition of pioneering regions that adopt ambitious climate action early on. The study uses the multi-regional energy–economy–climate model REMIND 1.5 to analyze the implications of Europe, China and the United States taking unilateral or joint early action. We find that carbon leakage is the combined effect of fossil fuel and capital market re-allocation. Leakage is limited to 15% of the emission reductions in the pioneering regions, and depends on the size and composition of the pioneering coalition and the decarbonization strategy in the energy sector. There is an incentive to delay action to avoid near-term costs, but the immediate GDP losses after acceding to a global climate regime can be higher in the case of delayed action compared to early action. We conclude that carbon leakage is not a strong counter-argument against early action by pioneers to induce other regions to adopt more stringent mitigation

MAgPIE 4 – a modular open-source framework for modeling global land systems

The open-source modeling framework MAgPIE (Model of Agricultural Production and its Impact on the Environment) combines economic and biophysical approaches to simulate spatially explicit global scenarios of land use within the 21st century and the respective interactions with the environment. Besides various other projects, it was used to simulate marker scenarios of the Shared Socioeconomic Pathways (SSPs) and contributed substantially to multiple IPCC assessments. However, with growing scope and detail, the non-linear model has become increasingly complex, computationally intensive and non-transparent, requiring structured approaches to improve the development and evaluation of the model. Here, we provide an overview on version 4 of MAgPIE and how it addresses these issues of increasing complexity using new technical features: modular structure with exchangeable module implementations, flexible spatial resolution, in-code documentation, automatized code checking, model/output evaluation and open accessibility. Application examples provide insights into model evaluation, modular flexibility and region-specific analysis approaches. While this paper is focused on the general framework as such, the publication is accompanied by a detailed model documentation describing contents and equations, and by model evaluation documents giving insights into model performance for a broad range of variables. With the open-source release of the MAgPIE 4 framework, we hope to contribute to more transparent, reproducible and collaborative research in the field. Due to its modularity and spatial flexibility, it should provide a basis for a broad range of land-related research with economic or biophysical, global or regional focus

Implementing FAIR through a distributed data infrastructure

Within the research project LOD-GOESS (https://lod-geoss.gitub.io ) we are developing a distributed data architecture for sharing and improved discovery of research data in the domain of energy systems analysis. A central element is the databus (https://databus.dbpedia.org ) which acts as a central searchable metadata catalog. Research data can be registered to the databus. The metadata improves the findability of the data, direct links to the data sources accessibility. If the metadata is annotated with an ontology (e.g. the open energy ontology), semantic searches can be performed to find suitable research data. This improves interoperability and reusability of the data. Currently we are developing several demonstrators which show the benefit of open and transparent data handling for the publication of scenario data, model coupling and shared technology data bases. The infrastructure can also be used to track the provenance of data which is used in energy systems analysis. With our presentation we want to show how this infrastructure can be used to improve transparency and traceability of the analysis of future energy systems

REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits

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

Decarbonizing global power supply under region-specific consideration of challenges and options of integrating variable renewables in the REMIND model

Abstract We present two advances in representing variable renewables (VRE) in global energy-economy-climate models: accounting for region-specific integration challenges for eight world regions and considering short-term storage. Both advances refine the approach of implementing residual load duration curves (RLDCs) to capture integration challenges. In this paper we derive RLDCs for eight world regions (based on region-specific time series for load, wind and solar) and implement them into the REMIND model. Therein we parameterize the impact of short-term storage using the highly-resolved model DIMES. All RLDCs and the underlying region-specific VRE time series are made available to the research community. We find that the more accurate accounting of integration challenges in REMIND does not reduce the prominent role of wind and solar in scenarios that cost-efficiently achieve the 2°C target. Until 2030, VRE shares increase to about 15-40% in most regions with limited deployment of short-term storage capacities (below 2% of peak load). The REMIND model's default assumption of large-scale transmission grid expansion allows smoothening variability such that VRE capacity credits are moderate and curtailment is low. In the long run, VRE become the backbone of electricity supply and provide more than 70% of global electricity demand from 2070 on. Integration options ease this transformation: storage on diurnal and seasonal scales (via flow batteries and hydrogen electrolysis) and a shift in the non-VRE capacity mix from baseload towards more peaking power plants. The refined RLDC approach allows for a more accurate consideration of system-level impacts of VRE, and hence more robust insights on the nature of power sector decarbonization and related economic impacts

Exploring Model-Based Decarbonization and Energy Efficiency Scenarios with PROMETHEUS and TIAM-ECN

This study provides a quantitative analysis of future energy–climate developments at the global level using two well-established integrated assessment models (IAMs), PROMETHEUS and TIAM-ECN. The research aims to explore the results of these IAMs and identify avenues for improvement to achieve the goals of the Paris Agreement. The study focuses on the effects of varying assumptions for key model drivers, including carbon prices, technology costs, and global energy prices, within the context of stringent decarbonization policies. Diagnostic scenarios are utilized to assess the behavior of the models under varying exogenous assumptions for key drivers, aiming to verify the accuracy and reliability of the models and identify areas for optimization. The findings of this research demonstrate that both PROMETHEUS and TIAM-ECN exhibit similar responses to carbon pricing, with PROMETHEUS being more sensitive to this parameter than TIAM-ECN. The results highlight the importance of carbon pricing as an effective policy tool to drive decarbonization efforts. Additionally, the study reveals that variations in technology costs and global energy prices significantly impact the outcomes of the models. The identified sensitivities and responses of the IAMs to key model drivers offer guidance for policymakers to refine their policy decisions and develop effective strategies aligned with the objectives of the Paris Agreement. By understanding the behavior of the models under different assumptions, policymakers can make informed decisions to optimize decarbonization pathways and enhance the likelihood of meeting global climate goals