Agent-based modeling of climate policy: An introduction to the ENGAGE multi-level model framework

Gerst, Michael D.; Wang, P.; Roventini, Andrea; Fagiolo, Giorgio; Dosi, Giovanni; Howarth, Richard B.; Borsuk, Mark E.

doi:10.1016/j.envsoft.2012.09.002

Cited by 101 publications

(63 citation statements)

References 50 publications

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“…Another well-known model for systems approach is ABM, which is a dynamic model consisting of agents with an ability for learning, adaptive capacity, heterogeneity, autonomy, local interaction, bounded rationality, and non-equilibrium dynamic characteristics (Fagiolo et al, 2007;Ringler et al, 2016). ABM also could possess bottom-up and top-down features at the same time (Ehlen and Scholand, 2005;Gerst et al, 2013). Therefore, ABM could model the heterogeneous agents from different approaches, for example, microeconomic approach for profit maximization problem, space theory for site selection problem, evolutionary programming for adaptive process problem, and experience-based decision models .…”

Section: System Thinking Approachmentioning

confidence: 99%

Selecting Tools for Renewable Energy Analysis in Developing Countries: An Expanded Review

et al. 2017

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Renewable energy planners in developing countries should be cautious in using analytical tools formulated in developed countries. Traditional energy consumption, economic and demography transitions, high-income inequality, and informal economy are some characteristics of developing countries that may contradict the assumptions of mainstream, widely used analytical tools. In this study, we synthesize the debate in previous review studies on energy models for developing countries and then extend the scope of the previous studies by highlighting emerging methods of system thinking, life cycle thinking, and decision support analysis. We then discuss how these tools have been used for renewable energy analysis in developing countries and found out that not all studies are aware of the emerging critical issues in developing countries. We offer here a guidance to select the most appropriate analytical tool, mainly when dealing with energy modeling and analysis for developing countries. We also suggest potential future improvements to the analytical tool for renewable energy modeling and analysis in the developing countries.

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Section: System Thinking Approachmentioning

confidence: 99%

Selecting Tools for Renewable Energy Analysis in Developing Countries: An Expanded Review

et al. 2017

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“…To this end, agent-based modeling (ABM) has been widely applied in recent works on environmental policy simulations [12][13][14][15][16]. Unlike the traditional top-down method, ABM takes a bottom-up approach and focuses on agents' heterogeneities and interactions that lead to the evolution of the system [17].…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Model to Project China’s Energy Consumption and Carbon Emission Peaks at Multiple Levels

Mohamed

Wang

2017

Sustainability

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Abstract:To assess whether China's emissions will peak around 2030, we forecast energy consumption and carbon emissions in China. We use an agent-based model driven by enterprises' innovation. Results show some differences in both energy consumption peaks and carbon emission peaks when we compare trends at different levels. We find that carbon emissions and energy consumption will peak in 2027 and 2028, respectively. However, the primary, secondary, and tertiary industries will reach energy consumption in different years: 2023, 2029, and 2022, respectively, and reach carbon emission peaks in 2022, 2028, and 2022, respectively. At the sectoral level, we find a wider range of energy consumption peaks and carbon emission peaks. Peak energy consumption occurs between 2020 and 2034, and peak carbon emissions between 2020 and 2032. Commercial and catering businesses, utilities and resident services, and finance and insurance achieve peak energy consumption and carbon emissions earliest in 2020, while building materials and other non-metallic mineral products manufacturing and metal products manufacturing are the two latest sectors to reach peak energy consumption and emissions, in 2034 and 2032, respectively.

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“…There appear to be a number of promising agent-based tools in development that could be applied for exploring decarbonisation pathways for climate stabilisation (e.g. [70] and [71]) but most published examples deal with sub-sectors of the energy economy and agent-based approaches have yet to scale fully to whole system analysis [72].…”

Section: Towards An Improved Representation Of Actors and Behaviour Imentioning

confidence: 99%

Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions

2017

Energy Strategy Reviews

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There are real political and social barriers to climate mitigation that arise from multiactor dynamics and micro-economic decisions. Exploratory analysis that captures key uncertainties in the energy system, including behaviour, is crucial for policy design aimed at achieving ambitious greenhouse gas (GHG) mitigation targets. This paper explores the case for developing policy assessments that include non-optimal behaviour in energy systems modelling. A stochastic system dynamic model of the energy system that features multiple actors with differentiated behaviours is used to investigate energy transition pathways that deviate from strict economic rationality. The results illustrate the risks of basing GHG reduction strategies on analysis that overlooks key insights into decision making from fields such as behavioural economics and political science. KeywordsEnergy systems modeling; climate policy; decarbonization; behavioral economics; uncertainty; Highlights• Dynamic stochastic model of energy transitions used to explore actor behaviour • Uncertainty analysis explores impact of sub-optimal actor choices and policies • Behaviour could be as significant as the techno-economic drivers of transitions • Climate targets difficult to achieve without idealised assumptions about behaviour • Energy systems modelling must expand consideration of socio-political factors

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Agent-based modeling of climate policy: An introduction to the ENGAGE multi-level model framework

Cited by 101 publications

References 50 publications

Selecting Tools for Renewable Energy Analysis in Developing Countries: An Expanded Review

Selecting Tools for Renewable Energy Analysis in Developing Countries: An Expanded Review

An Agent-Based Model to Project China’s Energy Consumption and Carbon Emission Peaks at Multiple Levels

Actors behaving badly: Exploring the modelling of non-optimal behaviour in energy transitions

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