Design and Analysis of Bioenergy Networks

Kempener, Ruud; Beck, Jessica; Petrie, Jim

doi:10.1111/j.1530-9290.2009.00120.x

Cited by 36 publications

(14 citation statements)

References 40 publications

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“…) or comparing the bottom‐up modeling results with a top‐down optimization scenario (Kempener et al. ). Validation is not performed in this hypothetical example.…”

Section: Discussionmentioning

confidence: 99%

Agent‐Based Modeling of Temporal and Spatial Dynamics in Life Cycle Sustainability Assessment

Apul

et al. 2017

J of Industrial Ecology

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SummaryCurrent aggregate and top-down approaches in life cycle sustainability assessment (LCSA) generally fail to account for spatial, temporal, and emergent behavioral dynamics simultaneously during the inventory stage. We discuss the key characteristics captured by complex system approaches (agent-based modeling [ABM] in particular) in the context of LCSA. It is understood that by integrating ABM, temporal, spatial, and behavioral dynamics can be addressed during the life cycle inventory stage. We propose a general concept to integrate ABM into current building life cycle assessment standards. We then use a hypothetical example of green building development to compare the ABM approach with a predefined static policy model. Simulation results from the agent-based model confirm that there are temporal and spatial variations caused by behavioral dynamics. The results are integrated into the calculation of temporally dynamic LCSA indicators on an annual basis. Spatially distributed simulation results can also be used in spatially dynamic LCSA. Keywords:agent-based modeling complexity life cycle sustainability assessment life cycle inventory (LCI) green buildings industrial ecology Supporting information is linked to this article on the JIE website

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“…) or comparing the bottom‐up modeling results with a top‐down optimization scenario (Kempener et al. ). Validation is not performed in this hypothetical example.…”

Section: Discussionmentioning

confidence: 99%

Agent‐Based Modeling of Temporal and Spatial Dynamics in Life Cycle Sustainability Assessment

Apul

et al. 2017

J of Industrial Ecology

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“…Its combination with top-down approaches such as global dynamic optimization or dynamic MFA allows for additional insights into e.g. what can be achieved with particular measures and what could create barriers to an implementation of such plans (Beck et al, 2008;Kempener et al, 2009). Until now, such a combination has mostly been applied to energy systems (Andrews et al, 2011;Axtell et al, 2001;Davis et al, 2010;Kempener et al, 2009).…”

Section: Agent-based Model and Dynamic Materials Flow Modelmentioning

confidence: 99%

“…what can be achieved with particular measures and what could create barriers to an implementation of such plans (Beck et al, 2008;Kempener et al, 2009). Until now, such a combination has mostly been applied to energy systems (Andrews et al, 2011;Axtell et al, 2001;Davis et al, 2010;Kempener et al, 2009). Just recently metal flows were explicitly addressed as Bollinger et al (2011) contrasted MFA with an agent-based model including material entities (resulting in flows) for analysing different recycling schemes.…”

Section: Agent-based Model and Dynamic Materials Flow Modelmentioning

confidence: 99%

Towards a dynamic assessment of raw materials criticality: Linking agent-based demand — With material flow supply modelling approaches

Knoeri

Wäger

Stamp

et al. 2013

Science of The Total Environment

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Abstract:Emerging technologies such as information and communication-, photovoltaic-or battery technologies are expected to increase significantly the demand for scarce metals in the near future. The recently developed methods to evaluate the criticality of mineral raw materials typically provide a 'snapshot' of the criticality of a certain material at one point in time by using static indicators both for supply risk and for the impacts of supply restrictions. While allowing for insights into the mechanisms behind the criticality of raw materials, these methods cannot account for dynamic changes in products and/or activities over time. In this paper we propose a conceptual framework intended to overcome these limitations by including the dynamic interactions between different possible demand and supply configurations. The framework integrates an agent-based behaviour model, where demand emerges from individual agent decisions and interaction, into a dynamic material flow model, representing the materials' stocks and flows. Within the framework, the environmental implications of substitution decisions are evaluated by applying life-cycle assessment methodology. The approach makes a first step towards a dynamic criticality assessment and will enhance the understanding of industrial substitution decisions and environmental implications related to critical metals. We discuss the potential and limitation of such an approach in contrast to state-of-the-art methods and how it might lead to criticality assessments tailored to the specific circumstances of single industrial sectors or individual companies.

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“…Bioenergy in the power sector [59] Hydrogen in the road transport sector [60] Models use agent-based or dynamic simulation approaches to capture both inter-actor dynamics and also changes to the selection environment as transitions unfold (e.g. technology performance, prices, behavioural preferences).…”

Section: Technology or Product Diffusion Simulationsmentioning

confidence: 99%

Modelling energy transitions for climate targets under landscape and actor inertia

Strachan

2017

Environmental Innovation and Societal Transitions

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The speed at which established socioeconomic and technological systems can be adapted to alternatives that are compatible with a climate stabilised, 2°C world remains unknown. Quantitative models used for assessing this challenge typically make a number of arguably optimistic assumptions regarding human behaviour and decision making. This often restricts the insights produced to futures approximating a so-called first-best policy landscape. However, empirical studies of socio-technical change have shown that technological diffusion is often influenced by actors and institutions interacting under less ideal, second-best conditions. This paper quantifies these factors in a formal energy model as landscape and actor inertia and employs them for the first time in BLUE, a dynamic stochastic socio-technical simulation of technology diffusion, energy and emissions inspired by the multi-level perspective. Using the UK energy system as an example, the results illustrate how socio-technical inertia may significantly blunt future efforts to achieve climate targets. KeywordsSocio-technical change; transitions; energy systems; modelling; decarbonisation; climate policy; Highlights We present a quantitative socio-technical energy transitions model inspired by the Multi-Level Perspective  BLUE is a stochastic system dynamic model that features multiple actors with detailed behavioural parameters  The feasibility of UK climate targets is explored under landscape and actor inertia  The results show that actor inertia may significantly increase the difficulty of achieving climate targets  The results also show the importance of taking socio-technical perspectives on energy transitions

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Design and Analysis of Bioenergy Networks

Cited by 36 publications

References 40 publications

Agent‐Based Modeling of Temporal and Spatial Dynamics in Life Cycle Sustainability Assessment

Agent‐Based Modeling of Temporal and Spatial Dynamics in Life Cycle Sustainability Assessment

Towards a dynamic assessment of raw materials criticality: Linking agent-based demand — With material flow supply modelling approaches

Modelling energy transitions for climate targets under landscape and actor inertia

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