This web page was created programmatically, to learn the article in its authentic location you’ll be able to go to the hyperlink bellow:
https://www.nature.com/articles/s43016-025-01257-1
and if you wish to take away this text from our web site please contact us
Gaupp, F. et al. Food system growth pathways for wholesome, nature-positive and inclusive meals techniques. Nat. Food 2, 928–934 (2021).
Conti, C., Hall, A., Orr, A., Hambloch, C. & Mausch, Okay. Complexity-aware rules for agri-food system interventions: classes from undertaking encounters with complexity. Agric. Syst. 220, 104080 (2024).
Mausch, Okay. et al. Embracing uncertainty: foundations of a studying system for meals techniques transformation. Food Secur. (2025).
Allen, T. & Prosperi, P. Modeling sustainable meals techniques. Environ. Manage. 57, 956–975 (2016).
Frank, S. et al. Agricultural non-CO2 emission discount potential within the context of the 1.5 °C goal. Nat. Clim. Change 9, 66–72 (2019).
Willett, W. et al. Food within the Anthropocene: the EAT–Lancet Commission on wholesome diets from sustainable meals techniques. Lancet 393, 447–492 (2019).
Turner, S. W. D., Hejazi, M., Calvin, Okay., Kyle, P. & Kim, S. A pathway of world meals provide adaptation in a world with more and more constrained groundwater. Sci. Total Environ. 673, 165–176 (2019).
Yarlagadda, B. et al. Trade and local weather mitigation interactions create agro-economic alternatives with social and environmental trade-offs in Latin America and the Caribbean. Earths Future 11, e2022EF003063 (2023).
Reed, P. M. et al. Multisector dynamics: advancing the science of complicated adaptive human–Earth techniques. Earths Future 10, e2021EF002621 (2022).
van Dijk, M., Morley, T., Rau, M. L. & Saghai, Y. A meta-analysis of projected world meals demand and inhabitants susceptible to starvation for the interval 2010–2050. Nat. Food 2, 494–501 (2021).
Nelson, G. C. & Shively, G. E. Modeling local weather change and agriculture: an introduction to the particular concern. Agric. Econ. 45, 1–2 (2014).
Rogers, E. Diffusion and Adoption of Innovation (Free Press, 1962).
Britz, W. Estimating a world MAIDADS demand system contemplating demography, local weather and norms. Bio-Based Appl. Econ. 3, 219–238 (2021).
Nyborg, Okay. et al. Social norms as options. Science 354, 42–43 (2016).
Leblond, N. & Trottier, J. Performing an invisibility spell: world fashions, meals regimes and smallholders. Int. J. Sociol. Agric. Food 23, 21–40 (2017).
Saghai, Y. Subversive future seeks like-minded mannequin: on the mismatch between visions of meals sovereignty futures and quantified situations of world meals futures. Ethics Int. Aff. 35, 51–67 (2021).
Bodirsky, B. L. et al. The ongoing vitamin transition thwarts long-term targets for meals safety, public well being and environmental safety. Sci. Rep. 10, 19778 (2020).
Springmann, M. et al. Global and regional well being results of future meals manufacturing beneath local weather change: a modelling examine. Lancet 387, 1937–1946 (2016).
Hasegawa, T. et al. Risk of elevated meals insecurity beneath stringent world local weather change mitigation coverage. Nat. Clim. Change 8, 699–703 (2018).
Nelson, G. C. et al. Climate change results on agriculture: financial responses to biophysical shocks. Proc. Natl Acad. Sci. USA 111, 3274–3279 (2014).
Humpenöder, F. et al. Food issues: dietary shifts improve the feasibility of 1.5 °C pathways according to the Paris Agreement. Sci. Adv. 10, eadj3832 (2024).
Fricko, O. et al. The marker quantification of the Shared Socioeconomic Pathway 2: a middle-of-the-road situation for the twenty first century. Glob. Environ. Change 42, 251–267 (2017).
Ran, Y. et al. Information as an enabler of sustainable meals selections: a behavioural strategy to understanding client decision-making. Sustain. Prod. Consum. 31, 642–656 (2022).
Kukowski, C. A. et al. The perceived feasibility of habits change is positively related to help for domain-matched local weather insurance policies. One Earth 6, 1554–1563 (2023).
Creutzig, F. et al. Demand-side options to local weather change mitigation according to excessive ranges of well-being. Nat. Clim. Change 12, 36–46 (2022).
Kuhn, M., Eker, S., Reiter, C. & Liu, Q. in Systems Analysis for Sustainable Wellbeing. 50 Years of IIASA Research, 40 Years after the Brundtland Commission, Contributing to the post-2030 Global Agenda (eds Lutz, W. & Pachauri, S.) 61–76 (IIASA, 2023).
Marrero, A. et al. Equity as a precedence in EAT–Lancet-aligned meals system transformations. Nat. Food 5, 811–817 (2024).
Wood, A. et al. Reframing the native–world meals techniques debate by a resilience lens. Nat. Food 4, 22–29 (2023).
Clark, M. A. et al. Global meals system emissions might preclude attaining the 1.5° and a couple of °C local weather change targets. Science 370, 705–708 (2020).
Stehfest, E. et al. Climate advantages of adjusting weight loss plan. Climatic Change 95, 83–102 (2009).
Springmann, M. et al. Options for protecting the meals system inside environmental limits. Nature 562, 519–525 (2018).
Obersteiner, M. et al. Assessing the land useful resource–meals value nexus of the Sustainable Development Goals. Sci. Adv. 2, e1501499 (2016).
Carducci, B. et al. Exploring girls’s dietary variety amidst local weather variability: a comparative evaluation throughout eight low- and middle-income international locations. Environ. Res. Lett. 20, 094005 (2025).
Creutzig, F. et al. in Climate Change 2022: Mitigation of Climate Change (eds Shukla, P. R. et al.) 752–943 (IPCC, Cambridge Univ. Press, 2022).
Sarkar, D., Walker-Swaney, J. & Shetty, Okay. Food variety and indigenous meals techniques to fight diet-linked power ailments. Curr. Dev. Nutr. 4, 3–11 (2020).
Brutschin, E. et al. A multidimensional feasibility analysis of low-carbon situations. Environ. Res. Lett. 16, 064069 (2021).
EAT–Lancet 2.0 Commissioners and contributing authors. EAT–Lancet Commission 2.0: securing a simply transition to wholesome, environmentally sustainable diets for all. Lancet 402, 352–354 (2023).
Singh, V. et al. A comparability of the consequences of native and EAT–Lancet dietary suggestions on chosen financial and environmental outcomes in India. Food Policy 134, 102898 (2025).
Stiglitz, J. E. Where trendy macroeconomics went mistaken. Oxford Rev. Econ. Policy 34, 70–106 (2018).
Tversky, A. & Kahneman, D. J. Judgment beneath uncertainty: heuristics and biases. Science 185, 1124–1131 (1974).
Bodirsky, B. L. et al. Integrating degrowth and effectivity views permits an emission-neutral meals system by 2100. Nat. Food 3, 341–348 (2022).
Lenton, T. M. et al. Operationalising constructive tipping factors in the direction of world sustainability. Glob. Sustain. 5, e1 (2022).
Centola, D., Becker, J., Brackbill, D. & Baronchelli, A. Experimental proof for tipping factors in social conference. Science 360, 1116–1119 (2018).
Peng, W. et al. Climate coverage fashions must get actual about folks—right here’s how. Nature 594, 174–176 (2021).
Banerjee, S. Meat taxes are inevitable, but we appear to shrink back from them. But why? Food Policy 130, 102787 (2025).
Sanga, U., Berrío-Martínez, J. & Schlüter, M. Modelling agricultural improvements as a social-ecological phenomenon. Socioenviron. Syst. Model. 5, 18562 (2023).
Arneth, A., Brown, C. & Rounsevell, M. D. A. Global fashions of human decision-making for land-based mitigation and adaptation evaluation. Nat. Clim. Change 4, 550–557 (2014).
Williams, T. G. et al. Power dynamics form sustainability transitions in a modeled meals system. One Earth 8, 101158 (2025).
Eker, S., Reese, G. & Obersteiner, M. Modelling the drivers of a widespread shift to sustainable diets. Nat. Sustain. 2, 725–735 (2019).
Sun, Z. et al. Simple or sophisticated agent-based fashions? An advanced concern. Environ. Model. Softw. 86, 56–67 (2016).
Mediavilla, M., Lifi, M., Ferreras-Alonso, N., Miguel, L. J. & de Blas, I. Analysis of the competitors between land, power and meals utilizing the TERRA module of WILIAM System Dynamics IAM. Renew. Sustain. Energy Rev. 216, 115651 (2025).
Wuepper, D. et al. Public insurance policies and world forest conservation: empirical proof from nationwide borders. Glob. Environ. Change 84, 102770 (2024).
Matous, P. & Bodin, Ö. Hub-and-spoke social networks amongst Indonesian cocoa farmers homogenise farming practices. People Nat. 6, 598–609 (2024).
Davis, N., Dermody, B., Koetse, M. & van Voorn, G. Identifying private and social drivers of dietary patterns: an agent-based mannequin of Dutch client habits. J. Artif. Soc. Soc. Simul. 27, 4 (2024).
Langellier, B. A. et al. Potential impacts of insurance policies to scale back buying of ultra-processed meals in Mexico at completely different phases of the social transition: an agent-based modelling strategy. Public Health Nutr. 25, 1711–1719 (2022).
Turner, N. J., Cuerrier, A. & Joseph, L. Well grounded: indigenous peoples’ data, ethnobiology and sustainability. People Nat. 4, 627–651 (2022).
Moallemi, E. A. et al. Entry factors for driving systemic change towards a extra sustainable future. One Earth 8, 101287 (2025).
van Zanten, H. H. E. et al. Circularity in Europe strengthens the sustainability of the worldwide meals system. Nat. Food 4, 320–330 (2023).
Brown, Okay., Schirmer, J. & Upton, P. Can regenerative agriculture help profitable adaptation to local weather change and improved panorama well being by constructing farmer self-efficacy and wellbeing? Curr. Res. Environ. Sustain. 4, 100170 (2022).
Dietrich, J. P. et al. MAgPIE 4—a modular open-source framework for modeling world land techniques. Geosci Model Dev 12, 1299–1317 (2019).
Global Biosphere Management Model (GLOBIOM) Documentation 2023 Version 1.0 (IBF-IIASA, 2023); https://pure.iiasa.ac.at/18996
Leip, A. et al. Linking an financial mannequin for European agriculture with a mechanistic mannequin to estimate nitrogen and carbon losses from arable soils in Europe. Biogeosciences 5, 73–94 (2008).
Britz, W., Verburg, P. H. & Leip, A. Modelling of land cowl and agricultural change in Europe: combining the CLUE and CAPRI-Spat approaches. Scaling Methods Integr. Assess. Agric. Syst. 142, 40–50 (2011).
Le Page, Y., West, T. O., Link, R. & Patel, P. Downscaling land use and land cowl from the Global Change Assessment Model for coupling with Earth system fashions. Geosci. Model Dev. 9, 3055–3069 (2016).
Ramos, R. G. et al. A mathematical programming strategy for downscaling multi-layered multi-constraint land-use fashions. Int. J. Geogr. Inf. Sci. 37, 2020–2042 (2023).
Gocht, A. & Britz, W. EU-wide farm kind provide fashions in CAPRI— persistently disaggregate sector fashions into farm kind fashions. J. Policy Model. 33, 146–167 (2011).
Scoones, I. The politics of world assessments: the case of the International Assessment of Agricultural Knowledge, Science and Technology for Development (IAASTD). J. Peasant Stud. 36, 547–571 (2009).
Laborde Debucquet, D. & Martin, W. Implications of the worldwide progress slowdown for rural poverty. Agric. Econ. 49, 325–338 (2018).
Laborde, D., Martin, W. & Vos, R. Impacts of COVID-19 on world poverty, meals safety, and diets: insights from world mannequin situation evaluation. Agric. Econ. 52, 375–390 (2021).
Britz, W., van Ittersum, M. Okay., Oude Lansink, A. G. J. M. & Heckelei, T. Tools for built-in evaluation in agriculture. State of the artwork and challenges. Bio-Based Appl. Econ. J. 01, 125–150 (2012).
McDonald, C. Okay., MacLeod, N. D., Lisson, S. & Corfield, J. P. The Integrated Analysis Tool (IAT)-a mannequin for the analysis of crop-livestock and socio-economic interventions in smallholder farming techniques. Agric. Syst. 176, 102659 (2019).
Kuehne, G. et al. Predicting farmer uptake of latest agricultural practices: a software for analysis, extension and coverage. Agric. Syst. 156, 115–125 (2017).
Shang, L. et al. Surrogate modelling of an in depth farm-level mannequin utilizing deep studying. J. Agric. Econ. 75, 235–260 (2024).
Shang, L., Heckelei, T., Gerullis, M. Okay., Börner, J. & Rasch, S. Adoption and diffusion of digital farming applied sciences—integrating farm-level proof and system interplay. Agric. Syst. 190, 103074 (2021).
Johnson, J. A. et al. Investing in nature can enhance fairness and financial returns. Proc. Natl Acad. Sci. USA 120, e2220401120 (2023).
Valin, H. et al. The way forward for meals demand: understanding variations in world financial fashions. Agric. Econ. 45, 51–67 (2013).
Skea, J., Shukla, P., Al Khourdajie, A. & McCollum, D. Intergovernmental Panel on Climate Change: transparency and built-in evaluation modeling. Wiley Interdiscip. Rev. Clim. Change 12, e727 (2021).
Horridge, M., Meeraus, A., Pearson, Okay. & Rutherford, T. F. in Handbook of Computable General Equilibrium Modeling Vol. 1 (eds Dixon, P. B. & Jorgenson, D. W.) Ch. 20 (Elsevier, 2013).
Mosnier, A. et al. A decentralized strategy to mannequin nationwide and world meals and land use techniques. Environ. Res. Lett. 18, 045001 (2023).
Palazzo, A. et al. Linking regional stakeholder situations and shared socioeconomic pathways: quantified West African meals and local weather futures in a world context. Glob. Environ. Change 45, 227–242 (2017).
Mason-D’Croz, D. et al. Multi-factor, multi-state, multi-model situations: exploring meals and local weather futures for Southeast Asia. Environ. Model. Softw. 83, 255–270 (2016).
Zhao, H. et al. China’s future meals demand and its implications for commerce and setting. Nat. Sustain. 4, 1042–1051 (2021).
Soterroni, A. C. et al. Future environmental and agricultural impacts of Brazil’s Forest Code. Environ. Res. Lett. 13, 074021 (2018).
Wang, X. et al. Reforming China’s fertilizer insurance policies: implications for nitrogen air pollution discount and meals safety. Sustain. Sci. 18, 407–420 (2023).
Binsted, M. et al. GCAM-USA v5.3_water_dispatch: built-in modeling of subnational US power, water, and land techniques inside a world framework. Geosci. Model Dev. 15, 2533–2559 (2022).
Wentworth, C. et al. Navigating neighborhood engagement in participatory modeling of meals techniques. Environ. Sci. Policy 152, 103645 (2024).
Davis, J. T. M., Verburg, P. H. & May, J. D. Diverse actor views on African city meals techniques: classes from participatory meals system modeling in Worcester, South Africa. Ecol. Soc. 28, 26 (2023).
Inam, A. et al. Coupling of a distributed stakeholder-built system dynamics socio-economic mannequin with SAHYSMOD for sustainable soil salinity administration. Part 2: Model coupling and software. J. Hydrol. 551, 278–299 (2017).
Bandari, R. et al. Participatory modeling for analyzing interactions between high-priority sustainable growth objectives to advertise native sustainability. Earths Future 11, e2023EF003948 (2023).
Mangnus, A. C. et al. New pathways for governing meals system transformations: a pluralistic practice-based futures strategy utilizing visioning, back-casting, and critical gaming. Ecol. Soc. 24, 2 (2019).
Hebinck, A., Vervoort, J. M., Hebinck, P., Rutting, L. & Galli, F. Imagining transformative futures participatory foresight for meals techniques change. Ecol. Soc. 23, 16 (2018).
Iñiguez, N. Leveraging Indigenous Knowledge and Modern Science for Sustainable Food System Transformation (Rockefeller Foundation, 2024); https://coilink.org/20.500.12592/r2285rv
Voinov, A. et al. Tools and strategies in participatory modeling: deciding on the appropriate software for the job. Environ. Model. Softw. 109, 232–255 (2018).
Browne, J. et al. Food insurance policies for aboriginal and Torres Strait islander well being (FoodPATH): a techniques considering strategy. Food Policy 126, 102676 (2024).
Singh, D. R., Sah, R. Okay., Simkhada, B. & Darwin, Z. Potentials and challenges of utilizing co-design in well being providers analysis in low- and middle-income international locations. Glob. Health Res. Policy 8, 5 (2023).
Bankes, S. Exploratory modeling for coverage evaluation. Oper. Res. 41, 435–449 (1993).
Aston, L. M., Smith, J. N. & Powles, J. W. Impact of a diminished purple and processed meat dietary sample on illness dangers and greenhouse gasoline emissions within the UK: a modelling examine. BMJ Open 2, e001072 (2012).
Srikrishnan, V. et al. Uncertainty evaluation in multi-sector techniques: concerns for threat evaluation, projection, and planning for complicated techniques. Earths Future 10, e2021EF002644 (2022).
Hadjikakou, M. Ambitious meals system interventions required to mitigate the chance of exceeding Earth’s environmental limits. One Earth 8, e101351 (2025).
Doelman, J. C. et al. Exploring SSP land-use dynamics utilizing the IMAGE mannequin: regional and gridded situations of land-use change and land-based local weather change mitigation. Glob. Environ. Change 48, 119–135 (2018).
Mitter, H. et al. Shared socio-economic pathways for European agriculture and meals techniques: the Eur-Agri-SSPs. Glob. Environ. Change 65, 102159 (2020).
Valin, H., Hertel, T., Bodirsky, B. L., Hasegawa, T. & Stehfest, E. Achieving Zero Hunger by 2030 A Review of Quantitative Assessments of Synergies and Tradeoffs amongst the UN Sustainable Development Goals (Center for Development Research, 2021); https://doi.org/10.48565/scgr2021-2337
Prestele, R. et al. Hotspots of uncertainty in land-use and land-cover change projections: a global-scale mannequin comparability. Glob. Change Biol. 22, 3967–3983 (2016).
Stirling, A. Keep it complicated. Nature 468, 1029–1031 (2010).
Guivarch, C. et al. Using massive ensembles of local weather change mitigation situations for sturdy insights. Nat. Clim. Change 12, 428–435 (2022).
Rosenzweig, C. et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot research. Agric. For. Meteorol. 170, 166–182 (2013).
Pérez-Domínguez, I. et al. Short- and long-term warming results of methane could have an effect on the cost-effectiveness of mitigation insurance policies and advantages of low-meat diets. Nat. Food 2, 970–980 (2021).
Fujimori, S. et al. Land-based local weather change mitigation measures can have an effect on agricultural markets and meals safety. Nat. Food 3, 110–121 (2022).
Escobar, N. et al. Understanding uncertainty in market-mediated responses to US oilseed biodiesel demand: sensitivity of ILUC emission estimates to GLOBIOM parametric uncertainty. Environ. Sci. Technol. 59, 302–314 (2025).
Plevin, R. J. et al. Choices in land illustration materially have an effect on modeled biofuel carbon depth estimates. J. Clean. Prod. 349, 131477 (2022).
Tebaldi, C. & Knutti, R. The use of the multi-model ensemble in probabilistic local weather projections. Phil. Trans. R. Soc. A 365, 2053–2075 (2007).
Razavi, S., Tolson, B. A. & Burn, D. H. Review of surrogate modeling in water assets. Water Resour. Res. 48, W07401 (2012).
Holmes, A. et al. Emulating the worldwide change evaluation mannequin with deep studying. Preprint at (2024).
von Lampe, M. et al. Why do world long-term situations for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison. Agric. Econ. 45, 3–20 (2014).
Spiegelhalter, D., Pearson, M. & Short, I. Visualizing uncertainty concerning the future. Science 333, 1393–1400 (2011).
Wiebe, Okay. et al. Climate change impacts on agriculture in 2050 beneath a spread of believable socioeconomic and emissions situations. Environ. Res. Lett. 10, 085010 (2015).
Alexander, P. et al. Assessing uncertainties in land cowl projections. Glob. Change Biol. 23, 767–781 (2017).
Taleb, N. N. The Black Swan: The Impact of the Highly Improbable (Random House, 2007).
Haasnoot, M., Kwakkel, J. H., Walker, W. E. & ter Maat, J. Dynamic adaptive coverage pathways: a technique for crafting sturdy selections for a deeply unsure world. Glob. Environ. Change 23, 485–498 (2013).
Gibson, M. et al. Degrowth as a believable pathway for meals techniques transformation. Nat. Food (2025).
Zimm, C., Schinko, T. & Pachauri, S. Putting multidimensional inequalities in human wellbeing on the centre of transitions. Lancet Planet. Health 6, e641–e642 (2022).
Weber, E., Downward, G., Pinho, M. G. M. & Van Vuuren, D. P. Healthy lives and well-being for all in any respect ages: increasing representations of determinants of well being inside techniques dynamics and built-in evaluation fashions. Sustain. Earth Rev. 6, 15 (2023).
Whitten, S. et al. Exploring Climate Risk in Australia: The Economic Implications of a Delayed Transition to Net Zero Emissions (CSIRO, 2021); https://ecos.csiro.au/wp-content/uploads/2022/02/Technical-Report__Exploring-Climate-Risk-in-Australia__20220204.pdf
Land Use Futures—Natural Capital Roadmap (Climateworks Centre, 2019); https://www.climateworksaustralia.org/resource/land-use-futures-natural-capital-roadmap/
Saltelli, A. et al. Five methods to make sure that fashions serve society: a manifesto. Nature (2020).
Saltelli, A. et al. Bring digital twins again to Earth. Climatic Change 15, e915 (2024).
Conti, C. et al. Top-down vs bottom-up processes: a scientific evaluation clarifying roles and patterns of interactions in meals system transformation. Glob. Food Secur. 44, 100833 (2025).
Grimm, V. et al. A regular protocol for describing individual-based and agent-based fashions. Ecol. Model. 198, 115–126 (2006).
Rahmandad, H. & Sterman, J. D. Reporting tips for simulation-based analysis in social sciences. Syst. Dyn. Rev. 28, 396–411 (2012).
DeCarolis, J. et al. Formalizing finest observe for power system optimization modelling. Appl. Energy 194, 184–198 (2017).
Stehfest, E., van Vuuren, D., Bouwman, L. & Kram, T. Integrated Assessment of Global Environmental Change with IMAGE 3.0: Model Description and Policy Applications (Netherlands Environmental Assessment Agency, 2014).
Ye, Q. et al. FeliX 2.0: An built-in mannequin of local weather, economic system, setting, and society interactions. Environ. Model. Softw. 179, 106121 (2024).
GCAM Documentation (Version 7.0) (JGCRI, 2023); https://github.com/JGCRI/gcam-doc
Robinson, S. et al. The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Documentation for Version 3.6. Modeling Systems Technical Paper 1 (International Food Policy Research Institute, 2024).
van Vuuren, D. P. et al. Exploring pathways for world growth inside planetary boundaries. Nature 641, 910–916 (2025).
Hackbarth, T. X., May, J. & Verburg, P. H. Food system interventions in city environments: Integrating simulation fashions and stakeholder options. Food Policy 134, 102878 (2025).
Plagányi, É. et al. Multispecies fisheries administration and conservation: tactical functions utilizing fashions of intermediate complexity. Fish Fish. 15, 1–22 (2014).
Bailey, R. L. et al. Artificial intelligence in meals and vitamin proof: the challenges and alternatives. PNAS Nexus 3, 461 (2024).
Krupitzer, C. Generative synthetic intelligence within the agri-food worth chain-overview, potential, and analysis challenges. Front. Food Sci. Technol. 4, 1473357 (2024).
Fanzo, J. et al. The Food Systems Dashboard is a brand new software to tell higher meals coverage. Nat. Food 1, 243–246 (2020).
Puy, A. et al. Models with greater efficient dimensions have a tendency to supply extra unsure estimates. Sci. Adv. 8, eabn9450 (2022).
Green, Okay. C. & Armstrong, J. S. Simple versus complicated forecasting: the proof. J. Bus. Res. 68, 1678–1685 (2015).
Collins, N. et al. Design and implementation of elements within the Earth system modeling framework. Int. J. High Perform. Comput. Appl. 19, 341–350 (2005).
Friedenthal, S., Moore, A. & Steiner, R. A Practical Guide to SysML: The Systems Modeling Language (Morgan Kaufmann, 2014).
Castonguay, A. C. et al. Navigating sustainability trade-offs in world beef manufacturing. Nat. Sustain. 6, 284–294 (2023).
Castonguay, A. C. & Moallemi, E. A. Interactive determine for ‘Strategies for addressing transformation complexity and uncertainty in models of future food systems’. Zenodo (2025).
Moallemi, E. A. et al. Structuring and evaluating resolution help processes to reinforce the robustness of complicated human–pure techniques. Environ. Model. Softw. 123, 1045–1051 (2020).
Morgan, J. S. et al. A toolkit of designs for mixing discrete occasion simulation and system dynamics. Eur. J. Oper. Res. 257, 907–918 (2017).
This web page was created programmatically, to learn the article in its authentic location you’ll be able to go to the hyperlink bellow:
https://www.nature.com/articles/s43016-025-01257-1
and if you wish to take away this text from our web site please contact us
