Lessons from a large‐scale systems dynamics modeling project: the example of the biomass scenario model

Peterson, Steve; Bush, Brian; Inman, Daniel; Newes, Emily; Schwab, Amy; Stright, Dana; Vimmerstedt, Laura

doi:10.1002/sdr.1620

Cited by 16 publications

(16 citation statements)

References 9 publications

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“…They are linked through three major balancing feedback loops (B1a and B1b for production adjustment and B2 for demand adjustment). The processes driving production are grounded in structures from related modelling studies (for livestock, e.g., Muller et al, 2017, Schader et al, 2015 and Zimmermann, Nemecek, & Waldvogel, 2017; for plant products, e.g., Gerber, 2016 and OECD, 2013; for land use, e.g., Peterson et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…Both loops are balancing because more land allocation under normal conditions leads to more production (Schilling, 2000) and a higher supply. A higher supply itself has a decreasing effect on the food price causing reduced farm revenue and profitability, which leads to less allocation of land to the corresponding product category (Peterson et al, 2019; Varian, 2010). The balancing production feedback loops are complemented by a balancing demand adjustment loop (B2) .…”

Section: Methodsmentioning

confidence: 99%

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Sustainable and healthy diets: Synergies and trade‐offs in Switzerland

2020

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Food systems have increasingly strong impacts on the environment, and they influence our human well‐being. In Switzerland, food consumption accounts for one‐third of the environmental impact caused by total final consumption. At the same time, non‐communicable diseases have been linked to a number of dietary aspects. In Switzerland, all non‐communicable diseases together are responsible for 80% of total public health care costs annually. Current assessments that link environmental sustainability and human health‐oriented diets for Switzerland lack a transparent representation of the dynamic effects caused by large‐scale conversions of the food system. In this study, therefore, a system dynamics model is employed to investigate intended and unintended changes on the food system structure and on environmental impacts. Several human health‐oriented scenarios are implemented and tested with different production‐ and consumption‐side intervention strategies. Because all scenarios assuming an increase in the consumption of plant‐based products also involve higher consumption of dairy products, consequences for bovine meat need to be considered. The biological link between milk and bovine meat production leads to an unintended increase in bovine meat production as milk production increases. Intervention strategies at the consumption level thus need to be accompanied by intervention strategies at the agricultural production level. Similarly, intervention strategies that aim at improving health outcomes at the production level need to be accompanied by strategies that affect diets and thus consumption preferences. Avoiding instances of policy resistance requires integrated policy design and implementation across agriculture, the environment and human health. This integration is a challenge for farmers, the food industry and consumers alike.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Sustainable and healthy diets: Synergies and trade‐offs in Switzerland

2020

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“…Model validation process. SD model validation occurs through an iterative process of model conceptualization, calibration, and simulation, via cycles of deliberation, data input, review, and decision-making by key stakeholders [24,45,[48][49][50][51][52][53]. Our iterative model development and revision process focused on four types of model validity.…”

Section: Model Conceptualization Calibration and Validationmentioning

confidence: 99%

Simulating system dynamics of the HIV care continuum to achieve treatment as prevention

Weeks

Lounsbury

et al. 2020

PLoS ONE

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The continuing HIV pandemic calls for broad, multi-sectoral responses that foster community control of local prevention and care services, with the goal of leveraging high quality treatment as a means of reducing HIV incidence. Service system improvements require stakeholder input from across the care continuum to identify gaps and to inform strategic plans that improve HIV service integration and delivery. System dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and about ways to achieve sustainable system-level improvements. Via an intensive group model building process with a task force of community stakeholders with diverse roles and responsibilities for HIV service implementation, delivery and surveillance, we designed and validated a multi-module system dynamics model of the HIV care continuum, in relation to local prevention and care service capacities. Multiple sources of data were used to calibrate the model for a three-county catchment area of central Connecticut. We feature a core module of the model for the purpose of illustrating its utility in understanding the dynamics of treatment as prevention at the community level. We also describe the methods used to validate the model and support its underlying assumptions to improve confidence in its use by stakeholders for systems understanding and decision making. The model's generalizability and implications of using it for future community-driven strategic planning and implementation efforts are discussed.

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“…Together with the U.S. Department of Energy (DOE), the National Renewable Energy Laboratory (NREL) developed the BSM to explore scenarios for development of the biofuels industry. The BSM incorporates thousands of input values which, due to the presence of myriad feedback dynamics and relationships in the model, have the potential to result in complex and nonlinear behaviors (Peterson et al ., ). Variance in the inputs, coupled with the potential for interactions among inputs, complicates interpretation of the model output.…”

Section: Introductionmentioning

confidence: 97%

Application of a variance‐based sensitivity analysis method to the Biomass Scenario Learning Model

Jadun

Vimmerstedt

Bush

et al. 2017

System Dynamics Review

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Variance-based sensitivity analysis can provide a comprehensive understanding of the input factors that drive model behavior, complementing more traditional system dynamics methods with quantitative metrics. This paper presents the methodology of a variance-based sensitivity analysis of the Biomass Scenario Learning Model, a published STELLA model of interactions among investment, production, and learning in an emerging competitive industry. We document the methodology requirements, interpretations, and constraints, and compute estimated sensitivity indices and their uncertainties. We show that application of variance-based sensitivity analysis to the model allows us to test for non-additivity, identify influential and interactive variables, and confirm model formulation. To enable use of this type of sensitivity analysis in other system dynamics models, we provide this study's R code, annotated to facilitate adaptation to other studies. A related paper describes application of these techniques to the much larger Biomass Scenario Model. has twenty five years of experience with infrastructure, energy, and transportation modeling, simulation, and analysis.Daniel Inman, a PhD soil scientist, has over ten years of experience in bioenergy feedstocks, modeling, and advanced statistics.Steve Peterson is an independent consultant and a Senior Lecturer at the Thayer School of Engineering at Dartmouth College. Steve has been involved in the teaching and application of dynamic modeling approaches since the early 1980s.Additional supporting information may be found online in the Supporting Information section at the end of the article.Appendix S1 Model documentation and annotated R code. P. Jadun et al.: Variance-based Sensitivity Analysis of the Biomass Scenario Learning Model 335

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Lessons from a large‐scale systems dynamics modeling project: the example of the biomass scenario model

Cited by 16 publications

References 9 publications

Sustainable and healthy diets: Synergies and trade‐offs in Switzerland

Sustainable and healthy diets: Synergies and trade‐offs in Switzerland

Simulating system dynamics of the HIV care continuum to achieve treatment as prevention

Application of a variance‐based sensitivity analysis method to the Biomass Scenario Learning Model

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