Articles | Volume 17, issue 5
https://doi.org/10.5194/gmd-17-2015-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2015-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
07 Mar 2024
Model description paper |
| 07 Mar 2024
| 07 Mar 2024
Modelling long-term industry energy demand and CO2 emissions in the system context using REMIND (version 3.1.0)
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Felix Schreyer
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Gunnar Luderer
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
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Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
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This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Related subject area
Integrated assessment modeling
GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model
pathways-ensemble-analysis v1.0.0: an open-source library for systematic and robust analysis of pathways ensembles
CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests
Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation
Intercomparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1–CMAQ v5.3.1, WRF–Chem v4.1.1, and WRF v3.7.1–CHIMERE v2020r1) in eastern China
MESSAGEix-GLOBIOM nexus module: integrating water sector and climate impacts
MESSAGEix-Materials v1.0.0: Representation of Material Flows and Stocks in an Integrated Assessment Model
Minimum-variance-based outlier detection method using forward-search model error in geodetic networks
Bidirectional coupling of the long-term integrated assessment model REgional Model of INvestments and Development (REMIND) v3.0.0 with the hourly power sector model Dispatch and Investment Evaluation Tool with Endogenous Renewables (DIETER) v1.0.2
GCAM-CDR v1.0: enhancing the representation of carbon dioxide removal technologies and policies in an integrated assessment model
The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures
Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data
A tool for air pollution scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies
Improved CASA model based on satellite remote sensing data: simulating net primary productivity of Qinghai Lake basin alpine grassland
Pixel-level parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes with an efficient model–data fusion method and satellite-derived LAI and GPP data
Climate Services Toolbox (CSTools) v4.0: from climate forecasts to climate forecast information
TIM: modelling pathways to meet Ireland's long-term energy system challenges with the TIMES-Ireland Model (v1.0)
ANEMI_Yangtze v1.0: a coupled human–natural systems model for the Yangtze Economic Belt – model description
Nested leave-two-out cross-validation for the optimal crop yield model selection
GCAM-USA v5.3_water_dispatch: integrated modeling of subnational US energy, water, and land systems within a global framework
GOBLIN version 1.0: a land balance model to identify national agriculture and land use pathways to climate neutrality via backcasting
Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2
REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits
Parallel gridded simulation framework for DSSAT-CSM (version 4.7.5.21) using MPI and NetCDF
Estimating global land system impacts of timber plantations using MAgPIE 4.3.5
Mengqi Zhao, Thomas B. Wild, Neal T. Graham, Son H. Kim, Matthew Binsted, A. F. M. Kamal Chowdhury, Siwa Msangi, Pralit L. Patel, Chris R. Vernon, Hassan Niazi, Hong-Yi Li, and Guta W. Abeshu
Geosci. Model Dev., 17, 5587–5617, https://doi.org/10.5194/gmd-17-5587-2024, https://doi.org/10.5194/gmd-17-5587-2024, 2024
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The Global Change Analysis Model (GCAM) simulates the world’s climate–land–energy–water system interactions , but its reservoir representation is limited. We developed the GLObal Reservoir Yield (GLORY) model to provide GCAM with information on the cost of supplying water based on reservoir construction costs, climate and demand conditions, and reservoir expansion potential. GLORY enhances our understanding of future reservoir capacity needs to meet human demands in a changing climate.
Lara Welder, Neil Grant, and Matthew J. Gidden
EGUsphere, https://doi.org/10.5194/egusphere-2024-761, https://doi.org/10.5194/egusphere-2024-761, 2024
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Pathways investigating the link between emissions and global warming have been continuously used to inform climate policy. We have developed a tool that can facilitate the systematic and robust analysis of ensembles of such pathways. We describe the structure of this tool and then show an illustrative application of it. The application indicates the usefulness of the tool to the research community and shows how it can be used to establish best-practices.
Tommi Ekholm, Nadine-Cyra Freistetter, Aapo Rautiainen, and Laura Thölix
Geosci. Model Dev., 17, 3041–3062, https://doi.org/10.5194/gmd-17-3041-2024, https://doi.org/10.5194/gmd-17-3041-2024, 2024
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CLASH is a numerical model that portrays land allocation between different uses, land carbon stocks, and agricultural and forestry production globally. CLASH can help in examining the role of land use in mitigating climate change, providing food and biogenic raw materials for the economy, and conserving primary ecosystems. Our demonstration with CLASH confirms that reduction of animal-based food, shifting croplands and storing carbon in forests are effective ways to mitigate climate change.
Léna Gurriaran, Yannig Goude, Katsumasa Tanaka, Biqing Zhu, Zhu Deng, Xuanren Song, and Philippe Ciais
Geosci. Model Dev., 17, 2663–2682, https://doi.org/10.5194/gmd-17-2663-2024, https://doi.org/10.5194/gmd-17-2663-2024, 2024
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We developed a data-driven model simulating daily regional power demand based on climate and socioeconomic variables. Our model was applied to eight countries or regions (Australia, Brazil, China, EU, India, Russia, South Africa, US), identifying influential factors and their relationship with power demand. Our findings highlight the significance of economic indicators in addition to temperature, showcasing country-specific variations. This research aids energy planning and emission reduction.
Chao Gao, Xuelei Zhang, Aijun Xiu, Qingqing Tong, Hongmei Zhao, Shichun Zhang, Guangyi Yang, Mengduo Zhang, and Shengjin Xie
Geosci. Model Dev., 17, 2471–2492, https://doi.org/10.5194/gmd-17-2471-2024, https://doi.org/10.5194/gmd-17-2471-2024, 2024
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A comprehensive comparison study is conducted targeting the performances of three two-way coupled meteorology and air quality models (WRF-CMAQ, WRF-Chem, and WRF-CHIMERE) for eastern China during 2017. The impacts of aerosol–radiation–cloud interactions on these models’ results are evaluated against satellite and surface observations. Further improvements to the calculation of aerosol–cloud interactions in these models are crucial to ensure more accurate and timely air quality forecasts.
Muhammad Awais, Adriano Vinca, Edward Byers, Stefan Frank, Oliver Fricko, Esther Boere, Peter Burek, Miguel Poblete Cazenave, Paul Natsuo Kishimoto, Alessio Mastrucci, Yusuke Satoh, Amanda Palazzo, Madeleine McPherson, Keywan Riahi, and Volker Krey
Geosci. Model Dev., 17, 2447–2469, https://doi.org/10.5194/gmd-17-2447-2024, https://doi.org/10.5194/gmd-17-2447-2024, 2024
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Climate change, population growth, and depletion of natural resources all pose complex and interconnected challenges. Our research offers a novel model that can help in understanding the interplay of these aspects, providing policymakers with a more robust tool for making informed future decisions. The study highlights the significance of incorporating climate impacts within large-scale global integrated assessments, which can help us in generating more climate-resilient scenarios.
Gamze Ünlü, Florian Maczek, Jihoon Min, Stefan Frank, Fridolin Glatter, Paul Natsuo Kishimoto, Jan Streeck, Nina Eisenmenger, Volker Krey, and Dominik Wiedenhofer
EGUsphere, https://doi.org/10.5194/egusphere-2023-3035, https://doi.org/10.5194/egusphere-2023-3035, 2024
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Extraction and processing of raw materials is a significant source of CO2 emissions in industry and therefore contributor to climate change. We develop an open-source tool to assess different industry decarbonization pathways in Integrated Assessment Models (IAM) with a representation of material flows and stocks.Our research highlights the importance of expanding the scope of climate change mitigation options to include circular economy and material efficiency measures in IAM scenario analysis.
Utkan M. Durdağ
Geosci. Model Dev., 17, 2187–2196, https://doi.org/10.5194/gmd-17-2187-2024, https://doi.org/10.5194/gmd-17-2187-2024, 2024
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This study introduces a novel approach to outlier detection in geodetic networks, challenging conventional and robust methods. By treating outliers as unknown parameters within the Gauss–Markov model and exploring numerous outlier combinations, this approach prioritizes minimal variance and eliminates iteration dependencies. The mean success rate (MSR) comparisons highlight its effectiveness, improving the MSR by 40–45 % for multiple outliers.
Chen Chris Gong, Falko Ueckerdt, Robert Pietzcker, Adrian Odenweller, Wolf-Peter Schill, Martin Kittel, and Gunnar Luderer
Geosci. Model Dev., 16, 4977–5033, https://doi.org/10.5194/gmd-16-4977-2023, https://doi.org/10.5194/gmd-16-4977-2023, 2023
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To mitigate climate change, the global economy must drastically reduce its greenhouse gas emissions, for which the power sector plays a key role. Until now, long-term models which simulate this transformation cannot always accurately depict the power sector due to a lack of resolution. Our work bridges this gap by linking a long-term model to an hourly model. The result is an almost full harmonization of the models in generating a power sector mix until 2100 with hourly resolution.
David R. Morrow, Raphael Apeaning, and Garrett Guard
Geosci. Model Dev., 16, 1105–1118, https://doi.org/10.5194/gmd-16-1105-2023, https://doi.org/10.5194/gmd-16-1105-2023, 2023
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GCAM-CDR is a variant of the Global Change Analysis Model that makes it easier to study the roles that carbon dioxide removal (CDR) might play in climate policy. Building on GCAM 5.4, GCAM-CDR adds several extra technologies to permanently remove carbon dioxide from the air and enables users to simulate a wider range of CDR-related policies and controls.
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
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Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
climate-assessmentworkflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement.
Théo Le Guenedal, Philippe Drobinski, and Peter Tankov
Geosci. Model Dev., 15, 8001–8039, https://doi.org/10.5194/gmd-15-8001-2022, https://doi.org/10.5194/gmd-15-8001-2022, 2022
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The CATHERINA model produces simulations of cyclone-related annualized damage costs at a country level from climate data and open-source socioeconomic indicators. The framework couples statistical and physical modeling of tropical cyclones to bridge the gap between general circulation and integrated assessment models providing a precise description of tropical-cyclone-related damages.
William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, and Noelle E. Selin
Geosci. Model Dev., 15, 7767–7789, https://doi.org/10.5194/gmd-15-7767-2022, https://doi.org/10.5194/gmd-15-7767-2022, 2022
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Understanding policy effects on human-caused air pollutant emissions is key for assessing related health impacts. We develop a flexible scenario tool that combines updated emissions data sets, long-term economic modeling, and comprehensive technology pathways to clarify the impacts of climate and air quality policies. Results show the importance of both policy levers in the future to prevent long-term emission increases from offsetting near-term air quality improvements from existing policies.
Chengyong Wu, Kelong Chen, Chongyi E, Xiaoni You, Dongcai He, Liangbai Hu, Baokang Liu, Runke Wang, Yaya Shi, Chengxiu Li, and Fumei Liu
Geosci. Model Dev., 15, 6919–6933, https://doi.org/10.5194/gmd-15-6919-2022, https://doi.org/10.5194/gmd-15-6919-2022, 2022
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The traditional Carnegie–Ames–Stanford Approach (CASA) model driven by multisource data such as meteorology, soil, and remote sensing (RS) has notable disadvantages. We drove the CASA using RS data and conducted a case study of the Qinghai Lake basin alpine grassland. The simulated result is similar to published and measured net primary productivity (NPP). It may provide a reference for simulating vegetation NPP to satisfy the requirements of accounting carbon stocks and other applications.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
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Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté, Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie
Geosci. Model Dev., 15, 6115–6142, https://doi.org/10.5194/gmd-15-6115-2022, https://doi.org/10.5194/gmd-15-6115-2022, 2022
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CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.
Olexandr Balyk, James Glynn, Vahid Aryanpur, Ankita Gaur, Jason McGuire, Andrew Smith, Xiufeng Yue, and Hannah Daly
Geosci. Model Dev., 15, 4991–5019, https://doi.org/10.5194/gmd-15-4991-2022, https://doi.org/10.5194/gmd-15-4991-2022, 2022
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Ireland has significantly increased its climate mitigation ambition, with a recent commitment to reduce greenhouse gases by an average of 7 % yr-1 in the period to 2030 and a net-zero target for 2050. This article describes the TIMES-Ireland model (TIM) developed to inform Ireland's energy system decarbonisation challenge. The paper also outlines a priority list of future model developments to better meet the challenge, taking into account equity, cost-effectiveness, and technical feasibility.
Haiyan Jiang, Slobodan P. Simonovic, and Zhongbo Yu
Geosci. Model Dev., 15, 4503–4528, https://doi.org/10.5194/gmd-15-4503-2022, https://doi.org/10.5194/gmd-15-4503-2022, 2022
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The Yangtze Economic Belt is one of the most dynamic regions of China. The fast urbanization and strong economic growth in the region pose severe challenges for its sustainable development. To improve our understanding of the interactions among coupled human–natural systems in the Belt and to provide the foundation for science-based policy-making for the sustainable development of the Belt, we developed an integrated system-dynamics-based simulation model (ANEMI_Yangtze) for the Belt.
Thi Lan Anh Dinh and Filipe Aires
Geosci. Model Dev., 15, 3519–3535, https://doi.org/10.5194/gmd-15-3519-2022, https://doi.org/10.5194/gmd-15-3519-2022, 2022
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We proposed the leave-two-out method (i.e. one particular implementation of the nested cross-validation) to determine the optimal statistical crop model (using the validation dataset) and estimate its true generalization ability (using the testing dataset). This approach is applied to two examples (robusta coffee in Cu M'gar and grain maize in France). The results suggested that the simple models are more suitable in crop modelling where a limited number of samples is available.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
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GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Colm Duffy, Remi Prudhomme, Brian Duffy, James Gibbons, Cathal O'Donoghue, Mary Ryan, and David Styles
Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, https://doi.org/10.5194/gmd-15-2239-2022, 2022
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The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
bottom-upbiophysical land use model capable of identifying broad pathways towards climate neutrality in the agriculture, forestry, and other land use (AFOLU) sector. The model is intended to bridge the gap between hindsight representations of national emissions and much larger globally integrated assessment models.
Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
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In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
Short summary
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This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
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This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
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Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Cited articles
Arias, P. A., Bellouin, N., Coppola, E., Jones, R. G., Krinner, G., Marotzke, J., Naik, V., Palmer, M. D., Plattner, G.-K., Rogelj, J., Rojas, M., Sillmann, J., Storelvmo, T., Thorne, P. W., Trewin, B., Achuta Rao, K., Adhikary, B., Allan, R. P., Armour, K., Bala, G., Barimalala, R., Berger, S., Canadell, J. G., Cassou, C., Cherchi, A., Collins, W., Collins, W. D., Connors, S. L., Corti, S., Cruz, F., Dentener, F. J., Dereczynski, C., Di Luca, A., Diongue Niang, A., Doblas-Reyes, F. J., Dosio, A., Douville, H., Engelbrecht, F., Eyring, V., Fischer, E., Forster, P., Fox-Kemper, B., Fuglestvedt, J. S., Fyfe, J. C., Gillett, N. P., Goldfarb, L., Gorodetskaya, I., Gutierrez, J. M., Hamdi, R., Hawkins, E., Hewitt, H. T., Hope, P., Islam, A. S., Jones, C., Kaufman, D. S., Kopp, R. E., Kosaka, Y., Kossin, J., Krakovska, S., Lee, J.-Y., Li, J., Mauritsen, T., Maycock, T. K., Meinshausen, M., Min, S.-K., Monteiro, P. M. S., Ngo-Duc, T., Otto, F., Pinto, I., Pirani, A., Raghavan, K., Ranasinghe, R., Ruane, A. C., Ruiz, L., Sallée, J.-B., Samset, B. H., Sathyendranath, S., Seneviratne, S. I., Sörensson, A. A., Szopa, S., Takayabu, I., Tréguier, A.-M., van den Hurk, B., Vautard, R., von Schuckmann, K., Zaehle, S., Zhang, X., and Zickfeld, K.: Technical Summary, in: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 33–144, Cambirdge University Press, Cambridge, United Kingdom and New York, NY, USA, ISBN 978-92-9169-160-9, 2021. a, b
Arujo, E., Bodirsky, B. L., Crawford, M. S., Leip, D., and Dietrich, J.: MissingIslands Dataset for Filling in Data Gaps from the WDI Datasets, Zenodo [data set], https://doi.org/10.5281/zenodo.4421504, 2021. a, b
Bashmakov, I., Nilsson, L., Acquaye, A., Bataille, C., Cullen, J., S. de la Rue du Can, Fischedick, M., Geng, Y., and Tanaka, K.: Industry, in: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1161–1243, Cambridge University Press, Cambridge, UK and New York, NY, US, ISBN 978-92-9169-160-9, 2022. a, b, c
Bauer, N., Calvin, K., Emmerling, J., Fricko, O., Fujimori, S., Hilaire, J., Eom, J., Krey, V., Kriegler, E., Mouratiadou, I., de Boer, H. S., van den Berg, M., Carrara, S., Daioglou, V., Drouet, L., Edmonds, J. E., Gernaat, D., Havlik, P., Johnson, N., Klein, D., Kyle, P., Marangoni, G., Masui, T., Pietzcker, R. C., Strubegger, M., Wise, M., Riahi, K., and van Vuuren, D. P.: Shared Socio-Economic Pathways of the Energy Sector – Quantifying the Narratives, Global Environ. Change, 42, 316–330, https://doi.org/10.1016/j.gloenvcha.2016.07.006, 2017. a
Bauer, N., Moreno, S., de Boer, H.-S., Fragkiadakis, D., Fragkos, P., Keramidas, K., van Sluisfeld, M., Unlu, G., Zotin, M., Baptista, L. B., Benke, F., Fragkiadakis, K., Krey, V., Luderer, G., Maczek, F., Madeddu, S., Min, J., Pehl, M., Rochedo, P., and van Vuuren, D.: Integrated Strategies Minimize Hard-to-Abate Industry Sector CO2 Emissions in Low-Emission Scenarios, in preparation, 2024. a, b, c
Baumstark, L., Bauer, N., Benke, F., Bertram, C., Bi, S., Gong, C. C., Dietrich, J. P., Dirnaichner, A., Giannousakis, A., Hilaire, J., Klein, D., Koch, J., Leimbach, M., Levesque, A., Madeddu, S., Malik, A., Merfort, A., Merfort, L., Odenweller, A., Pehl, M., Pietzcker, R. C., Piontek, F., Rauner, S., Rodrigues, R., Rottoli, M., Schreyer, F., Schultes, A., Soergel, B., Soergel, D., Strefler, J., Ueckerdt, F., Kriegler, E., and Luderer, G.: REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits, Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, 2021. a, b, c
Baumstark, L., Rodrigues, R., Levesque, A., Oeser, J., Bertram, C., Mouratiadou, I., Malik, A., Schreyer, F., Soergel, B., Rottoli, M., Mishra, A., Dirnaichner, A., Pehl, M., Giannousakis, A., Klein, D., Strefler, J., Feldhaus, L., Brecha, R., Rauner, S., Dietrich, J. P., Bi, S., Benke, F., Weigmann, P., Richters, O., Hasse, R., Fuchs, S., and Mandaroux, R.: mrremind: MadRat REMIND Input Data Package (v0.165.6), Zenodo [code], https://doi.org/10.5281/zenodo.10495588, 2024. a
Chung, J. W.: Utility and Production Functions: Theory and Applications, Blackwell, Oxford, UK, Cambridge, Mass., USA, ISBN 978-1-55786-417-8, 1994. a
Daehn, K. E., Cabrera Serrenho, A., and Allwood, J. M.: How Will Copper Contamination Constrain Future Global Steel Recycling?, Environ. Sci. Technol., 51, 6599–6606, https://doi.org/10.1021/acs.est.7b00997, 2017. a
Deason, J., Wei, M., Leventis, G., Smith, S., and Schwartz, L.: Electrification of Buildings and Industry in the United States: Drivers, Barriers, Prospects, and Policy Approaches, Tech. rep., Ernest Orlando Lawrence Berkeley National Laboratory, Berkeley, California, USA, 2018. a
Després, J., Keramidas, K., Schmitz, A., Kitous, A., Schade, B., Diaz Vazquez, A., Mima, S., Russ, H. P., and Wiesenthal, T.: POLES-JRC Model Documentation: 2018 Update, Tech. Rep. JRC113757, European Commission, Joint Research Centre, Luxembourg, 2018. a
Fischedick, M., Roy, J., Abdel-Aziz, A., Acquaye, A., Allwood, J., Ceron, J.-P., Geng, Y., Kheshgi, H., Lanza, A., Perczyk, D., Price, L., Santalla, E., Sheinbaum, C., and Tanaka, K.: Industry, in: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., and Minx, J. C., Cambirdge University Press, Cambridge, United Kingdom and New York, NY, USA, 2014. a, b, c, d
Fragkos, P. and Fragkiadakis, K.: Analyzing the Macro-Economic and Employment Implications of Ambitious Mitigation Pathways and Carbon Pricing, Front. Climate, 4, https://doi.org/10.3389/fclim.2022.785136, 2022. a
Fragkos, P., Kouvaritakis, N., and Capros, P.: Incorporating Uncertainty into World Energy Modelling: The PROMETHEUS Model, Environ. Model. A., 20, 549–569, https://doi.org/10.1007/s10666-015-9442-x, 2015. a
Fruehan, R., Fortini, O., Paxton, H., and Brindle, R.: Theoretical Minimum Energies to Produce Steel for Selected Conditions, Tech. Rep. NONE, 769470, https://doi.org/10.2172/769470, 2000. a, b, c
Fuss, S., Lamb, W. F., Callaghan, M. W., Hilaire, J., Creutzig, F., Amann, T., Tim Beringer, Garcia, W. d. O., Hartmann, J., Khanna, T., Luderer, G., Nemet, G. F., Joeri Rogelj, Smith, P., Vicente, J. L. V., Wilcox, J., Dominguez, M. d. M. Z., and Minx, J. C.: Negative Emissions – Part 2: Costs, Potentials and Side Effects, Environ. Res. Lett., 13, 063002, https://doi.org/10.1088/1748-9326/aabf9f, 2018. a
Grubler, A., Wilson, C., Bento, N., Boza-Kiss, B., Krey, V., McCollum, D. L., Rao, N. D., Riahi, K., Rogelj, J., Stercke, S. D., Cullen, J., Frank, S., Fricko, O., Guo, F., Gidden, M., Havlík, P., Huppmann, D., Kiesewetter, G., Rafaj, P., Schoepp, W., and Valin, H.: A Low Energy Demand Scenario for Meeting the 1.5 °C Target and Sustainable Development Goals without Negative Emission Technologies, Nature Energy, 3, 515–527, https://doi.org/10.1038/s41560-018-0172-6, 2018. a
International Energy Agency: Energy Technology Perspectives 2017: Catalyzing Energy Technology Transformations, Tech. Rep. 2079-259X, International Energy Agency, Paris, edited by: Justus, D. and French-Brooks, J., https://www.iea.org/reports/energy-technology-perspectives-2017 (last access: 23 January 2020), 2017. a, b, c, d, e
James, S. L., Gubbins, P., Murray, C. J., and Gakidou, E.: Developing a Comprehensive Time Series of GDP per Capita for 210 Countries from 1950 to 2015, Population Health Metrics, 10, 12, https://doi.org/10.1186/1478-7954-10-12, 2012. a, b
Kaya, Y., Yokobori, K., University, U. N., and Conference on Global Environment, Energy, and Economic Development, Environment, Energy, and Economy: Strategies for Sustainability, United Nations Univ. Press, Tokyo, ISBN 978-92-808-0911-4, 1997. a
KC, S. and Lutz, W.: The Human Core of the Shared Socioeconomic Pathways: Population Scenarios by Age, Sex and Level of Education for All Countries to 2100, Global Environ. Change, 42, 181–192, https://doi.org/10.1016/j.gloenvcha.2014.06.004, 2017. a, b, c
Kermeli, K., Graus, W. H. J., and Worrell, E.: Energy Efficiency Improvement Potentials and a Low Energy Demand Scenario for the Global Industrial Sector, Energy Efficiency, 7, 987–1011, https://doi.org/10.1007/s12053-014-9267-5, 2014. a
Kermeli, K., van Ruijven, B., Graus, W.-C., Edelenbosch, O., Worrell, E., and van Vuuren, D.: Enhancing the Representation of Energy Demand Developments in IAM Models – A Modeling Guide for the Cement Industry, Tech. rep., http://fp7-advance.eu/?q=content/industrial-sector-cement-guideline (last access: 7 July 2023), 2016. a
Koch, J. and Leimbach, M.: SSP Economic Growth Projections: Major Changes of Key Drivers in Integrated Assessment Modelling, Ecol. Econ., 206, 107751, https://doi.org/10.1016/j.ecolecon.2023.107751, 2023. a, b, c
Kriegler, E., Bauer, N., Popp, A., Humpenöder, F., Leimbach, M., Strefler, J., Baumstark, L., Bodirsky, B. L., Hilaire, J., Klein, D., Mouratiadou, I., Weindl, I., Bertram, C., Dietrich, J.-P., Luderer, G., Pehl, M., Pietzcker, R., Piontek, F., Lotze-Campen, H., Biewald, A., Bonsch, M., Giannousakis, A., Kreidenweis, U., Müller, C., Rolinski, S., Schultes, A., Schwanitz, J., Stevanovic, M., Calvin, K., Emmerling, J., Fujimori, S., and Edenhofer, O.: Fossil-Fueled Development (SSP5): An Energy and Resource Intensive Scenario for the 21st Century, Global Environ. Change, 42, 297–315, https://doi.org/10.1016/j.gloenvcha.2016.05.015, 2017. a
Kuramochi, T., Ramírez, A., Turkenburg, W., and Faaij, A.: Comparative Assessment of CO2 Capture Technologies for Carbon-Intensive Industrial Processes, Prog. Energ. Combust., 38, 87–112, https://doi.org/10.1016/j.pecs.2011.05.001, 2012. a, b
Luderer, G., Vrontisi, Z., Bertram, C., Edelenbosch, O. Y., Pietzcker, R. C., Rogelj, J., de Boer, H. S., Drouet, L., Emmerling, J., Fricko, O., Fujimori, S., Havlík, P., Iyer, G., Keramidas, K., Kitous, A., Pehl, M., Krey, V., Riahi, K., Saveyn, B., Tavoni, M., van Vuuren, D. P., and Kriegler, E.: Residual Fossil CO2 Emissions in 1.5–2 °C Pathways, Nat. Clim. Change, 8, 626–633, https://doi.org/10.1038/s41558-018-0198-6, 2018. a
Luderer, G., Madeddu, S., Merfort, L., Ueckerdt, F., Pehl, M., Pietzcker, R., Rottoli, M., Schreyer, F., Bauer, N., Baumstark, L., Bertram, C., Dirnaichner, A., Humpenöder, F., Levesque, A., Popp, A., Rodrigues, R., Strefler, J., and Kriegler, E.: Impact of Declining Renewable Energy Costs on Electrification in Low-Emission Scenarios, Nature Energy, 7, 32–42, https://doi.org/10.1038/s41560-021-00937-z, 2022. a, b
Luderer, G., Bauer, N., Baumstark, L., Bertram, C., Leimbach, M., Pietzcker, R., Strefler, J., Aboumahboub, T., Abrahão, G., Auer, C., Benke, F., Bi, S., Dietrich, J., Dirnaichner, A., Führlich, P., Giannousakis, A., Gong, C. C., Haller, M., Hasse, R., Hilaire, J., Hoppe, J., Klein, D., Koch, J., Körner, A., Kowalczyk, K., Kriegler, E., Levesque, A., Lorenz, A., Ludig, S., Lüken, M., Malik, A., Manger, S., Merfort, A., Merfort, L., Moreno-Leiva, S., Mouratiadou, I., Odenweller, A., Pehl, M., Pflüger, M., Piontek, F., Popin, L., Rauner, S., Richters, O., Rodrigues, R., Roming, N., Rottoli, M., Schmidt, E., Schötz, C., Schreyer, F., Schultes, A., Sörgel, B., Ueckerdt, F., Verpoort, P., and Weigmann, P.: REMIND – REgional Model of INvestments and Development (3.1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7628336, 2023. a
Madeddu, S., Ueckerdt, F., Pehl, M., Peterseim, J., Lord, M., Kumar, K. A., Krüger, C., and Luderer, G.: The CO2 Reduction Potential for the European Industry via Direct Electrification of Heat Supply (Power-to-Heat), Environ. Res. Lett., 15, 124004, https://doi.org/10.1088/1748-9326/abbd02, 2020. a, b, c
Madlool, N. A., Saidur, R., Hossain, M. S., and Rahim, N. A.: A Critical Review on Energy Use and Savings in the Cement Industries, Renew. Sust. Energ. Rev., 15, 2042–2060, https://doi.org/10.1016/j.rser.2011.01.005, 2011. a, b
Matthews, H. D. and Caldeira, K.: Stabilizing Climate Requires Near-Zero Emissions, Geophys. Res. Lett., 35, 1–5, https://doi.org/10.1029/2007GL032388, 2008. a
Meinshausen, M., Meinshausen, N., Hare, W., Raper, S. C. B., Frieler, K., Knutti, R., Frame, D. J., and Allen, M. R.: Greenhouse-Gas Emission Targets for Limiting Global Warming to 2 °C, Nature, 458, 1158–1162, https://doi.org/10.1038/nature08017, 2009. a
Minx, J. C., Lamb, W. F., Callaghan, M. W., Fuss, S., Hilaire, J., Creutzig, F., Thorben Amann, Beringer, T., Garcia, W. d. O., Hartmann, J., Khanna, T., Lenzi, D., Gunnar Luderer, Nemet, G. F., Rogelj, J., Smith, P., Vicente, J. L. V., Wilcox, J., and Dominguez, M. D. M. Z.: Negative Emissions – Part 1: Research Landscape and Synthesis, Environ. Res. Lett., 13, 063001, https://doi.org/10.1088/1748-9326/aabf9b, 2018. a
Müller, D. B., Liu, G., Løvik, A. N., Modaresi, R., Pauliuk, S., Steinhoff, F. S., and Brattebø, H.: Carbon Emissions of Infrastructure Development, Environ. Sci. Technol., 47, 11739–11746, https://doi.org/10.1021/es402618m, 2013. a, b
Naims, H.: Economics of Carbon Dioxide Capture and Utilization – a Supply and Demand Perspective, Environ. Sci. Pollut. Res., 23, 22 226–22 241, https://doi.org/10.1007/s11356-016-6810-2, 2016. a
O'Neill, B. C., Kriegler, E., Ebi, K. L., Kemp-Benedict, E., Riahi, K., Rothman, D. S., van Ruijven, B. J., van Vuuren, D. P., Birkmann, J., Kok, K., Levy, M., and Solecki, W.: The Roads Ahead: Narratives for Shared Socioeconomic Pathways Describing World Futures in the 21st Century, Global Environ. Change, 42, 169–180, https://doi.org/10.1016/j.gloenvcha.2015.01.004, 2017. a, b
Pathak, M., Slade, R., Shukla, P., Skea, J., Pichs-Madruga, R., and Ürge-Vorsatz, D.: Technical Summary, in: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Cambirdge University Press, Cambridge, UK and New York, NY, US, ISBN 978-92-9169-160-9, 2022. a
Pauliuk, S., Milford, R. L., Müller, D. B., and Allwood, J. M.: The Steel Scrap Age, Environ. Sci. Technol., 47, 130307142353004, https://doi.org/10.1021/es303149z, 2013a. a
Pauliuk, S., Wang, T., and Müller, D. B.: Steel All over the World: Estimating in-Use Stocks of Iron for 200 Countries, Resour. Conserv. Recy., 71, 22–30, https://doi.org/10.1016/j.resconrec.2012.11.008, 2013b. a, b
Pehl, M.: Modelling-Long-Term-Industry-Energy-Demand, Zenodo [code], https://doi.org/10.5281/zenodo.8272197, 2024. a
Rochedo, P. R. R.: Development of a Global Integrated Energy Model to Evaluate the Brazilian Role in Climate Change Scenarios, PhD thesis, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil, 2016. a
Rogelj, J., Forster, P. M., Kriegler, E., Smith, C. J., and Séférian, R.: Estimating and Tracking the Remaining Carbon Budget for Stringent Climate Targets, Nature, 571, 335–342, https://doi.org/10.1038/s41586-019-1368-z, 2019. a
Schreyer, F., Ueckerdt, F., Pietzcker, R., Rodrigues, R., Rottoli, M., Madeddu, S., Pehl, M., Hasse, R., and Luderer, G.: Distinct roles of direct and indirect electrification in pathways to a renewables-dominated European energy system, One Earth, 7, 226–241, https://doi.org/10.1016/j.oneear.2024.01.015, 2024. a, b
Smith, P., Davis, S. J., Creutzig, F., Fuss, S., Minx, J., Gabrielle, B., Kato, E., Jackson, R. B., Cowie, A., Kriegler, E., van Vuuren, D. P., Rogelj, J., Ciais, P., Milne, J., Canadell, J. G., McCollum, D., Peters, G., Andrew, R., Krey, V., Shrestha, G., Friedlingstein, P., Gasser, T., Grübler, A., Heidug, W. K., Jonas, M., Jones, C. D., Kraxner, F., Littleton, E., Lowe, J., Moreira, J. R., Nakicenovic, N., Obersteiner, M., Patwardhan, A., Rogner, M., Rubin, E., Sharifi, A., Torvanger, A., Yamagata, Y., Edmonds, J., and Yongsung, C.: Biophysical and Economic Limits to Negative CO2 Emissions, Nat. Clim. Change, 6, 42–50, https://doi.org/10.1038/nclimate2870, 2015. a, b
The WITCH team: WITCH Documentation, https://doc.witchmodel.org/witch_documentation.pdf (last access: 12 December 2023), 2017. a
Uekert, T., Singh, A., DesVeaux, J. S., Ghosh, T., Bhatt, A., Yadav, G., Afzal, S., Walzberg, J., Knauer, K. M., Nicholson, S. R., Beckham, G. T., and Carpenter, A. C.: Technical, Economic, and Environmental Comparison of Closed-Loop Recycling Technologies for Common Plastics, ACS Sustain. Chem. Eng., 11, 965–978, https://doi.org/10.1021/acssuschemeng.2c05497, 2023. a
United Nations: Paris Agreement (Dec. 13, 2015), in: UNFCCC, COP Report No. 21, Addenum, at 21, U.N. Doc. FCCC/CP/2015/10/Add, 1 (Jan. 29, 2016), https://unfccc.int/sites/default/files/resource/docs/2015/cop21/eng/10a01.pdf (last access: 22 May 2023), 2015. a
United Nations Department of Economic and Social Affairs Population Division: World Population Prospects: The 2015 Revision, Volume I: Comprehensive Tables (ST/ESA/SER.A/379), Tech. Rep. ST/ESA/SER.A/379, United Nations, Department of Economic and Social Affairs, Population Division, 2015. a, b, c, d
United Nations Environment Programme: Emissions Gap Report 2022: The Closing Window – Climate Crisis Calls for Rapid Transformation of Societies, Nairobi, Kenya, edited by: Olhoff, A., Christensen, J., and Maxwell, S., United Nations Environment Programme, https://www.unep.org/emissions-gap-report-2022 (last access: 1 March 2024), 2022. a
van Ruijven, B. J., van Vuuren, D. P., Boskaljon, W., Neelis, M. L., Saygin, D., and Patel, M. K.: Long-Term Model-Based Projections of Energy Use and CO2 Emissions from the Global Steel and Cement Industries, Resour. Conserv. Recy., 112, 15–36, https://doi.org/10.1016/j.resconrec.2016.04.016, 2016. a, b, c, d
van Sluisveld, M. A., de Boer, H. S., Daioglou, V., Hof, A. F., and van Vuuren, D. P.: A Race to Zero – Assessing the Position of Heavy Industry in a Global Net-Zero CO2 Emissions Context, Energy and Climate Change, 2, 100051, https://doi.org/10.1016/j.egycc.2021.100051, 2021. a
World Steel Association: Steel Statistical Yearbook, Tech. rep., World Steel Association, Belgium, https://www.worldsteel.org/en/dam/jcr:e5a8eda5-4b46-4892-856b-00908b5ab492/SSY_2018.pdf (last access: 23 January 2020, report no longer available), 2018. a
Short summary
We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemical, steel, and other industries. We also present a method for deriving scenarios of industry subsector activity and energy demand, consistent with established socioeconomic scenarios, allowing us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of the entire energy–economy–climate system.
We extend the REMIND model (used to investigate climate mitigation strategies) by an industry...
