Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-2239-2022
© Author(s) 2022. 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-15-2239-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
16 Mar 2022
Model description paper |
| 16 Mar 2022
| 16 Mar 2022
GOBLIN version 1.0: a land balance model to identify national agriculture and land use pathways to climate neutrality via backcasting
Colm Duffy
Bernal Institute, School of Engineering, University of Limerick, Limerick, Ireland
Ecological Sciences, The James Hutton Institute, Dundee, Scotland, UK
Remi Prudhomme
CIRAD Département Environnements et sociétés, Montpellier, Languedoc-Roussillon, France
Brian Duffy
independent researcher
James Gibbons
School of Natural Sciences, Bangor University, Bangor, Wales, UK
Cathal O'Donoghue
National University of Ireland Galway Policy Lab, University Road, Galway, Ireland
Mary Ryan
Rural Economy & Development Programme, Teagasc, Athenry, Co. Galway, Ireland
David Styles
Bernal Institute, School of Engineering, University of Limerick, Limerick, Ireland
Related subject area
Integrated assessment modeling
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
Modelling Long-Term Industry Energy Demand and CO2 Emissions in the System Context Using REMIND (Version 3.1.0)
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
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
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.
Michaja Pehl, Felix Schreyer, and Gunnar Luderer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-153, https://doi.org/10.5194/gmd-2023-153, 2023
Revised manuscript accepted for GMD
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We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemicals, steel, and other industry. We also present a method for deriving scenarios of industry subsector activity and energy demand, that are consistent with established socioeconomic scenarios. This allows us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of entire energy-economy-climate system.
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
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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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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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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.
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
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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
Allen, M. R., Shine, K. P., Fuglestvedt, J. S., Millar, R. J., Cain, M., Frame, D. J., and Macey, A. H.:
A solution to the misrepresentations of CO2-equivalent emissions of short-lived climate pollutants under ambitious mitigation,
npj Clim. Atmos. Sci.,
1, 16, https://doi.org/10.1038/s41612-018-0026-8, 2018.
Aznar-Sánchez, J. A., Piquer-Rodríguez, M., Velasco-Muñoz, J. F., and Manzano-Agugliaro, F.:
Worldwide research trends on sustainable land use in agriculture,
Land Use Policy,
87, 104069, https://doi.org/10.1016/j.landusepol.2019.104069, 2019.
Basco-Carrera, L., Warren, A., van Beek, E., Jonoski, A., and Giardino, A.:
Collaborative modelling or participatory modelling? A framework for water resources management,
Environ. Modell. Softw.,
91, 95–110, https://doi.org/10.1016/j.envsoft.2017.01.014, 2017.
Brunner, S. H., Huber, R., and Grêt-Regamey, A.:
A backcasting approach for matching regional ecosystem services supply and demand,
Environ. Modell. Softw.,
75, 439–458, https://doi.org/10.1016/j.envsoft.2015.10.018, 2016.
Cain, M., Lynch, J., Allen, M. R., Fuglestvedt, J. S., Frame, D. J., and Macey, A. H.:
Improved calculation of warming-equivalent emissions for short-lived climate pollutants,
npj Clim. Atmos. Sci.,
2, 29, https://doi.org/10.1038/s41612-019-0086-4, 2019.
CCAC:
Climate Change Advisory Council Carbon Budget Technical Report,
Climate Change Advisory Council, Dublin, Ireland, 2021.
Clarke L., Jiang, K., Akimoto, K., Babiker, M., Blanford, G., Fisher-Vanden, K., Hourcade, J.-C., Krey, V., Kriegler, E., Löschel, A., McCollum, D., Paltsev, S., Rose, S., Shukla, P. R., Tavoni, M., van der Zwaan, B. C. C., and van Vuuren, D. P.: Assessing Transformation Pathways, 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., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter6.pdf (last access: February 2022), 2014.
Cohn, A. S., Mosnier, A., Havlík, P., Valin, H., Herrero, M., Schmid, E., O'Hare, M., and Obersteiner, M.:
Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation,
P. Natl. Acad. Sci. USA,
111, 7236–7241, https://doi.org/10.1073/pnas.1307163111, 2014.
Creighton, P., Kennedy, E., Shalloo, L., Boland, T. M., and O' Donovan, M.:
A survey analysis of grassland dairy farming in Ireland, investigating grassland management, technology adoption and sward renewal,
Grass Forage Sci.,
66, 251–264, https://doi.org/10.1111/j.1365-2494.2011.00784.x, 2011.
CSO:
CSO AKA01 – Intake of Cows Milk by Creameries and Pasteurisers, PxSTAT, Central Statistics Office,
https://data.cso.ie/table/AKA01 (last access: June 2021), 2018.
DAFM:
Ireland's National Forest Inventory 2015–2017, Forest Service
Department of Agriculture, Food and
the Marine,
https://www.gov.ie/en/publication/823b8-irelands-national-forest-inventory/ (last access: February 2022), 2018.
DAFM:
Ag Climatise A Roadmap towards Climate Neutrality,
Department of Agriculture, Food and
the Marine, Dublin, Ireland, https://www.gov.ie/en/publication/07fbe-ag-climatise-a-roadmap-towards-climate-neutrality/ (last access: February 2022), 2020a.
DAFM:
Forest Statistics Ireland 2020,
Department of Agriculture, Food and
the Marine, Wexford, Ireland, https://www.gov.ie/en/collection/15b56-forest-statistics-and-mapping/#annual-forest-sector-statistics (last access: February 2022), 2020b.
Donnellan, T., Hanrahan, K., and Lanigan, G. J.:
Future Scenarios for Irish Agriculture: Implications for Greenhouse Gas and Ammonia Emissions, Teagasc, Galway, Ireland,
https://www.teagasc.ie/media/website/publications/2018/GHGscenarios2030final.pdf (last access: 26 November 2020), 2018.
Duffy, C., O'Donoghue, C., Ryan, M., Styles, D., and Spillane, C.:
Afforestation: Replacing livestock emissions with carbon sequestration,
J. Environ. Manage.,
264, 110523, https://doi.org/10.1016/j.jenvman.2020.110523, 2020a.
Duffy, C., O'Donoghue, C., Ryan, M., Kilcline, K., Upton, V., and Spillane, C.:
The impact of forestry as a land use on water quality outcomes: An integrated analysis,
Forest Policy Econ.,
116, 102185, https://doi.org/10.1016/j.forpol.2020.102185, 2020b.
Duffy, C., Pruhomme, R., Duffy, B., and Styles, D.:
General Overview for a Backcasting approach of Livestock Intensification (GOBLIN) (Version v1.0), Zenodo [code, data set],
https://doi.org/10.5281/zenodo.5047230, 2021.
Duffy, P., Black, K., Fahey, D., Hyde, B., Kehoe, A., Murphy, J., Quirke, B., Ryan, A. M., and Ponzi, J.:
Ireland's National Inventory Report 2020, Environmental Protection Agency,
https://www.epa.ie (last access: June 2021), 2020.
Duffy, P., Black, K., Fahey, D., Hyde, B., Kehoe, A., Murphy, J., Quirke, B., Ryan, A., and Ponzi, J.:
Ireland's National Inventory Report 2021, Environmental Protection Agency,
https://www.epa.ie (last access: June 2021), 2021.
Englund, O., Börjesson, P., Berndes, G., Scarlat, N., Dallemand, J. F., Grizzetti, B., Dimitriou, I., Mola-Yudego, B., and Fahl, F.:
Beneficial land use change: Strategic expansion of new biomass plantations can reduce environmental impacts from EU agriculture,
Global Environ. Chang.,
60, 101990, https://doi.org/10.1016/j.gloenvcha.2019.101990, 2020.
Finneran, E., Crosson, P., O'Kiely, P., Shalloo, L., Forristal, P. D., and Wallace, M.:
Stochastic modelling of the yield and input price risk affecting home produced ruminant feed cost,
J. Agr. Sci.,
150, 123–139, 2011.
Finneran, E., Crosson, P., O'Kiely, P., Shalloo, L., Forristal, D., and Wallace, M.:
Stochastic simulation of the cost of home-produced feeds for ruminant livestock systems,
J. Agr. Sci.,
150, 123–139, https://doi.org/10.1017/S002185961100061X, 2012.
Forster, E. J., Healey, J. R., Dymond, C., and Styles, D.:
Commercial afforestation can deliver effective climate change mitigation under multiple decarbonisation pathways,
Nat. Commun.,
12, 3831, https://doi.org/10.1038/s41467-021-24084-x, 2021.
Gardiner, M. J. and Radford, T.:
Soil associations and their land use potential Explanatory bulletin to accompany the general soil map of Ireland, Teagasc,
https://www.teagasc.ie/media/website/environment/soil/General.pdf (last access: March 2021), 1980.
Gordon, A.:
Implementing backcasting for conservation: Determining multiple policy pathways for retaining future targets of endangered woodlands in Sydney, Australia,
Biol. Conserv.,
181, 182–189, https://doi.org/10.1016/j.biocon.2014.10.025, 2015.
Havlík, P., Valin, H., Herrero, M., Obersteiner, M., Schmid, E., Rufino, M. C., Mosnier, A., Thornton, P. K., Böttcher, H., Conant, R. T., Frank, S., Fritz, S., Fuss, S., Kraxner, F., and Notenbaert, A.:
Climate change mitigation through livestock system transitions,
P. Natl. Acad. Sci. USA,
111, 3709–3714, https://doi.org/10.1073/pnas.1308044111, 2014.
Herrero, M., Henderson, B., Havlík, P., Thornton, P. K., Conant, R. T., Smith, P., Wirsenius, S., Hristov, A. N., Gerber, P., Gill, M., Butterbach-Bahl, K., Valin, H., Garnett, T., and Stehfest, E.:
Greenhouse gas mitigation potentials in the livestock sector,
Nat. Clim. Change,
6, 452–461, https://doi.org/10.1038/nclimate2925, 2016.
Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Baasansuren, J., Fukuda, M., and Troxler, T. G.:
2013 supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: Wetlands,
IPCC, Geneva, Switzerland, 2014.
Huppmann, D., Rogelj, J., Kriegler, E., Krey, V., and Riahi, K.:
A new scenario resource for integrated 1.5 ∘C research,
Nat. Clim. Change,
8, 1027–1030, https://doi.org/10.1038/s41558-018-0317-4, 2018.
IPCC:
2006 IPCC guidelines for national greenhouse gas inventories,
Intergovernmental Panel on Climate Change, Cambridge, UK, 2006.
IPCC:
Climate change 2013: The physical science basis,
edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Intergovernmental Panel on Climate Change, Working Group I Contribution to the IPCC Fifth Assessment Report (AR5),
Cambridge Univ Press, New York, 2013.
IPCC:
2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, IPCC,
https://www.ipcc.ch/report/2019-refinement-to-the-2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/ (last access: May 2021), 2019a.
IPCC:
Summary for Policymakers, in: Climate Change and Land: an IPCC special report on climate change,desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes interrestrial ecosystems, IPCC,
https://www.ipcc.ch/site/assets/uploads/sites/4/2020/02/SPM_Updated-Jan20.pdf (last access: 10 September 2020), 2019b.
Jones, A. K., Jones, D. L., and Cross, P.:
The carbon footprint of lamb: Sources of variation and opportunities for mitigation,
Agr. Syst.,
123, 97–107, https://doi.org/10.1016/j.agsy.2013.09.006, 2014.
Kalt, G., Mayer, A., Haberl, H., Kaufmann, L., Lauk, C., Matej, S., Röös, E., Theurl, M. C., and Erb, K. H.:
Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0,
Ecol. Model.,
459, 109729, https://doi.org/10.1016/j.ecolmodel.2021.109729, 2021.
Lóránt, A. and Allen, B.:
Net-zero agriculture in 2050: how to get there?, Institute for European Environmental Policy,
https://ieep.eu/uploads/articles/attachments/eeac4853-3629-4793-9e7b-2df5c156afd3/IEEP_NZ2050_Agriculture_report_screen.pdf?v=63718575577 (last access: May 2021), 2019.
Loucks, D. P. and Van Beek, E.:
Water resource systems planning and management: An introduction to methods, models, and applications,
Springer, Cham, ISBN 92-3-103998-9, 2017.
Lynch, J., Cain, M., Pierrehumbert, R., and Allen, M.:
Demonstrating GWP: A means of reporting warming-equivalent emissions that captures the contrasting impacts of short- and long-lived climate pollutants,
Environ. Res. Lett.,
15, 044023, https://doi.org/10.1088/1748-9326/ab6d7e, 2020.
Masson-Delmotte, V., Zhai, P., Pörtner, H.-O., Roberts, D., Skea, J., Shukla, P. R., Pirani, A., Moufouma-Okia, W., Péan, C., Pidcock, R., Connors, S., Matthews, J. B. R., Chen, Y., Zhou, X., Gomis, M. I., Lonnoy, E., Maycock, T., Tignor, M., and Waterfield, T.:
Global warming of 1.5 ∘C An IPCC Special Report on the impacts of global warming of 1.5 ∘C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change,
IPCC, https://www.ipcc.ch/site/assets/uploads/sites/2/2019/06/SR15_Full_Report_Low_Res.pdf (last access: February 2022), 2019.
McEniry, J., Crosson, P., Finneran, E., Mcgee, M., Keady, T. W. J., and O'kiely, P.:
How much grassland biomass is available in Ireland in excess of livestock requirements?, Teagasc,
https://www.jstor.org/stable/23631018 (last access: February 2022),
2013.
McKay, M. D., Beckman, R. J., and Conover, W. J.:
A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,
Technometrics,
42, 55–61, https://doi.org/10.1080/00401706.2000.10485979, 2000.
Misselbrook, T. and Gilhespy, S.:
Report: Inventory of Ammonia Emissions from UK Agriculture 2017, DEFRA,
https://uk-air.defra.gov.uk/assets/documents/reports/cat09/1903141332_UK_Agriculture_Ammonia_Emission_Report_1990-2017.pdf (last access: November 2021), 2019.
Misselbrook, T., Chadwick, D., Gilhespy, S., Chambers, B. J., Smith, K. A., Williams, J., and Dragosits, U.:
Inventory of Ammonia Emissions from UK Agriculture 2009 DEFRA Contract AC0112, DEFRA,
http://nora.nerc.ac.uk/id/eprint/13234/2/N013234CR.pdf (last access: 30 November 2020), 2010.
Noszczyk, T.:
A review of approaches to land use changes modeling,
Hum. Ecol. Risk Assess.,
25, 1377–1405, https://doi.org/10.1080/10807039.2018.1468994, 2019.
NZ-MftE:
New Zealand's Greenhouse Gas Inventory 1990–2019,
Ministry for the Environment, Wellington, 2021.
O'Donovan, M., Hennessy, D., and Creighton, P.:
Ruminant grassland production systems in Ireland,
Irish J. Agr. Food Res.,
59, 225–232, https://doi.org/10.15212/ijafr-2020-0118, 2021.
O'Mara, F. P.:
Development of emission factors for the Irish cattle herd (2000-LS-5.1.1-M1): special report,
Environmental Protection Agency, Wexford, Ireland, 2007.
Oireachtas:
Climate Action and Low Carbon Development (Amendment) Bill 2021, Oireachtas, Dublin, Ireland,
https://www.gov.ie/en/publication/984d2-climate-action-and-low-carbon-development-amendment-bill-2020/ (last access: 4 May 2021), 2021.
Pressman, R.:
Software Engineering: A Practitioner's Approach, 7th edn., McGraw Hill,
http://www.mhhe.com/pressman (last access: October 2021), 2010.
Prudhomme, R., O'Donoghue, C., Ryan, M., and Styles, D.:
Defining national biogenic methane targets: Implications for national food production and climate neutrality objectives,
J. Environ. Manage.,
295, 113058, https://doi.org/10.1016/j.jenvman.2021.113058, 2021.
Reisinger, A. and Leahy, S.: Scientific aspects of New Zealand’s 2050 emission targets, NZAGRC, https://www.nzagrc.org.nz/publications/scientific-aspects-of-new-zealands-2050-emission-targets/ (last access: February 2022), 2019
Rogelj, J. and Schleussner, C.-F.:
Unintentional unfairness when applying new greenhouse gas emissions metrics at country level,
Environ. Res. Lett.,
14, 114039, https://doi.org/10.1088/1748-9326/ab4928, 2019.
Rogelj, J., Popp, A., Calvin, K. V., Luderer, G., Emmerling, J., Gernaat, D., Fujimori, S., Strefler, J., Hasegawa, T., Marangoni, G., Krey, V., Kriegler, E., Riahi, K., Vuuren, D. P. van, Doelman, J., Drouet, L., Edmonds, J., Fricko, O., Harmsen, M., Havlík, P., Humpenöder, F., Stehfest, E., and Tavoni, M.:
Scenarios towards limiting global mean temperature increase below 1.5 ∘C,
Nat. Clim. Change, 8, 325–332, https://doi.org/10.1038/s41558-018-0091-3, 2018.
Rounsevell, M. D. A. and Metzger, M. J.:
Developing qualitative scenario storylines for environmental change assessment,
WIREs Clim. Change,
1, 606–619, https://doi.org/10.1002/wcc.63, 2010.
Saltelli, A., Chan, E. and Scott, E. M.:
Sensitivity Analysis, Wiley Series in Probability and Statistics, Wiley,
https://www.wiley.com/en-us/Sensitivity+Analysis-p-9780470743829 (last access: October 2021), 2009.
Schaldach, R., Alcamo, J., Koch, J., Kölking, C., Lapola, D. M., Schüngel, J., and Priess, J. A.:
An integrated approach to modelling land-use change on continental and global scales,
Environ. Modell. Softw.,
26, 1041–1051, https://doi.org/10.1016/j.envsoft.2011.02.013, 2011.
Schulte, R. P. O., Donnellan, T., Black, K. G., Crosson, P., Farrelly, N., Fealy, R. M., Finnan, J., Lannigan, G., O'Brien, D., O'Kiely, P., Shalloo, L., and O'Mara, F.:
Carbon-Neutrality as a horizon point for Irish Agriculture,
Teagasc, Dublin, 2013.
Searchinger, T., Zionts, J., Wirsenius, S., Peng, L., Beringer, T., and Dumas, P.:
A Pathway to Carbon Neutral Agriculture in Denmark,
https://doi.org/10.46830/wrirpt.20.00006,
World Resources Institute, 2021.
Smith, P., Bustamante, M., Ahammad, H., Clark, H., Dong, H., and Elsiddig, E. A.:
Agriculture, Forestry and Other Land Use (AFOLU),
in: Climate Change 2014: Mitigation of Climate Change. Contribution ofWorking 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.,
Cambridge University Press, Cambridge, UK and New York, NY, USA, 2014.
Soteriades, A. D., Gonzalez-Mejia, A. M., Styles, D., Foskolos, A., Moorby, J. M., and Gibbons, J. M.:
Effects of high-sugar grasses and improved manure management on the environmental footprint of milk production at the farm level,
J. Clean. Prod.,
202, 1241–1252, https://doi.org/10.1016/j.jclepro.2018.08.206, 2018.
Soteriades, A. D., Foskolos, A., Styles, D., and Gibbons, J. M.:
Diversification not specialization reduces global and local environmental burdens from livestock production,
Environ. Int.,
132, 104837, https://doi.org/10.1016/j.envint.2019.05.031, 2019.
Styles, D., Gibbons, J., Williams, A. P., Stichnothe, H., Chadwick, D. R., and Healey, J. R.:
Cattle feed or bioenergy? Consequential life cycle assessment of biogas feedstock options on dairy farms,
GCB Bioenergy,
7, 1034–1049, https://doi.org/10.1111/gcbb.12189, 2015.
Styles, D., Dominguez, E. M., and Chadwick, D.:
Environmental balance of the of the UK biogas sector: An evaluation by consequential life cycle assessment,
Sci. Total Environ.,
560–561, 241–253, https://doi.org/10.1016/j.scitotenv.2016.03.236, 2016.
Styles, D., Gonzalez-Mejia, A., Moorby, J., Foskolos, A., and Gibbons, J.:
Climate mitigation by dairy intensification depends on intensive use of spared grassland,
Glob. Change Biol.,
24, 681–693, https://doi.org/10.1111/gcb.13868, 2018.
Teagasc:
2027 SECTORAL ROAD MAP: BEEF Road Map for 2027, Teagasc,
https://www.teagasc.ie (last access: November 2021), 2020a.
Teagasc:
2027 SECTORAL ROAD MAP: DAIRY Road Map for 2027, Teagasc,
https://www.teagasc.ie/publications/2020/2027-sectoral-road-map-dairy.php (last access: 21 January 2021), 2020b.
Tilman, D. and Clark, M.:
Global diets link environmental sustainability and human health,
Nature,
515, 518–522, https://doi.org/10.1038/nature13959, 2014.
UFCCC:
Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23 November 2013, Addendum Part two: Action taken by the Conference of the Parties at its nineteenth session Contents,
United Nations Framework Convention on Climate Change, Geneva, 2014.
UK CCC:
Net Zero: The UK's contribution to stopping global warming,
UK Climate Change Committee, London, 2019.
UN:
UNcomtrade, United Nations Commod. Trade Stat. Database, United Nations,
https://comtrade.un.org/db/default.aspx, (last access: 10 December 2020), 2016.
UNFCCC:
Paris Agreement on Climate Change,
United Nations Framework Convention on Climate Change, UNFCC: Adoption of the Paris Agreement,
21st Conference of the Parties, Paris, United Nations, 12 December 2015, https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf (last access: February 2022), 2015.
Van Asselen, S. and Verburg, P. H.:
Land cover change or land-use intensification: Simulating land system change with a global-scale land change model,
Glob. Change Biol.,
19, 3648–3667, https://doi.org/10.1111/gcb.12331, 2013.
Van Meerbeek, K., Muys, B., and Hermy, M.:
Lignocellulosic biomass for bioenergy beyond intensive cropland and forests,
Renew. Sust. Energ. Rev.,
102, 139–149, https://doi.org/10.1016/J.RSER.2018.12.009, 2019.
Webb, J. and Misselbrook, T. H.:
A mass-flow model of ammonia emissions from UK livestock production,
Atmos. Environ.,
38, 2163–2176, https://doi.org/10.1016/j.atmosenv.2004.01.023, 2004.
Short summary
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.
The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a...
