The potential impact of Brexit on the energy, water and food nexus in the UK: A fuzzy cognitive mapping approach

Ziv, Guy; Watson, Elizabeth E.; Young, Dylan M.; Howard, D.C.; Larcom, Shaun; Tanentzap, Andrew J.

doi:10.1016/j.apenergy.2017.08.033

Cited by 59 publications

(40 citation statements)

References 34 publications

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“…These FCM techniques are quite general and have led to numerous applications [3]- [6]. A recent application developed an FCM representation of how 'Brexit' scenarios could affect energy demand in the United Kingdom [7]. Another application used FCM techniques to represent the numerous social factors involved in homelessness [8].…”

Section: Fuzzy Cognitive Maps For Causal Modelingmentioning

confidence: 99%

Causal Modeling with Feedback Fuzzy Cognitive Maps

Osoba

Kosko

2019

Social‐Behavioral Modeling for Complex Systems

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Fuzzy cognitive maps (FCMs) model feedback causal relations in interwoven webs of causality and policy variables. FCMs are fuzzy signed directed graphs that allow degrees of causal influence and event occurrence. Such causal models can simulate a wide range of policy scenarios and decision processes. Their directed loops or cycles directly model causal feedback. Their nonlinear dynamics permit forward-chaining inference from input causes and policy options to output effects. Users can add detailed dynamics and feedback links directly to the causal model or infer them with statistical learning laws. Users can fuse or combine FCMs from multiple experts by weighting and adding the underlying fuzzy edge matrices and do so recursively if needed. The combined FCM tends to better represent domain knowledge as the expert sample size increases if the expert sample approximates a random sample. Many causal models use more restrictive directed acyclic graphs (DAGs) and Bayesian probabilities. DAGs do not model causal feedback because they do not contain closed loops. Combining DAGs also tends to produce cycles and thus tends not to produce a new DAG. Combining DAGs tends to produce a FCM. FCM causal influence is also transitive whereas probabilistic causal influence is not transitive in general. Overall: FCMs trade the numerical precision of probabilistic DAGs for pattern prediction, faster and scalable computation, ease of combination, and richer feedback representation. We show how FCMs can apply to problems of public support for insurgency and terrorism and to US-China conflict relations in Graham Allison's Thucydides-trap framework. The Thucydides-trap FCM predicts war-like conflict for nearly 80% of input scenarios where we treat all such scenarios

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Section: Fuzzy Cognitive Maps For Causal Modelingmentioning

confidence: 99%

Causal Modeling with Feedback Fuzzy Cognitive Maps

Osoba

Kosko

2019

Social‐Behavioral Modeling for Complex Systems

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“…In this context, Fuzzy Cognitive Map is a method that can illustrate the uncertainty causality [14] and becomes a powerful tool to model such complex systems that involve many factors [2]. Due to this fact, FCM has been recently deployed in a renewable energy domain with great perspectives.Taking one step further, FCMs have recently implemented management and decision-making tasks in the Energy sector [9,15]. In particular, in [15], an FCM approach was applied to predict and analyze the consequences of Brexit for the Energy-Water-Food (EWF) demand, examining different Brexit scenarios.…”

mentioning

confidence: 99%

“…Due to this fact, FCM has been recently deployed in a renewable energy domain with great perspectives.Taking one step further, FCMs have recently implemented management and decision-making tasks in the Energy sector [9,15]. In particular, in [15], an FCM approach was applied to predict and analyze the consequences of Brexit for the Energy-Water-Food (EWF) demand, examining different Brexit scenarios. FCMs can deal with complex problems, vast domains of inherent uncertainties [16] and processes like decision making that are based on human reasoning process [2,17].…”

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confidence: 99%

Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique

et al. 2020

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Photovoltaic Solar Energy (PSE) sector has sparked great interest from governments over the last decade towards diminution of world's dependency to fossil fuels, greenhouse gas emissions reduction and global warming mitigation. Photovoltaic solar energy also holds a significant role in the transition to sustainable energy systems. These systems and their optimal exploitation require an effective supply chain management system, such as design of the network, collection, storage, or transportation of this energy resource, without disregarding a country's certain socio-economic and political conditions. In Brazil, the adoption of photovoltaic solar energy has been motivated not only by the energy matrix diversification but also from the shortages, problems, and barriers that the Brazilian energy sector has faced, lately. However, PSE development is affected by various factors with high uncertainty, such as political, social, economic, and environmental, that include critical operational sustainability issues. Thus, an elaborate modelling of energy management and a well-structured decision support process are needed to enhance the performance efficiency of Brazilian PSE supply chain management. This study focuses on the investigation of certain factors and their influence on the development of the Brazilian PSE with the help of Fuzzy Cognitive Maps. Fuzzy Cognitive Map is an established methodology for scenario analysis and management in diverse domains, inheriting the advancements of fuzzy logic and neural networks. In this context, a semi-quantitative model was designed with the help of various stakeholders from the specific energy domain and three plausible scenarios were conducted in order to support a decision-making process on PSE sector development and the country's economic potential. The outcome of this analysis reveals that the development of the PSE sector in Brazil is mainly affected by economic and political factors.Energies 2020, 13, 1427 2 of 23 represents the system's behavior using the accumulated knowledge, perceptions, experiences or beliefs from experts, stakeholders or both [3,4], usually expressed in natural language.Over the last years, FCMs have attained great attention and popularity since they are able to implement modelling, analyzing and simulating tasks, as well as test the influence of parameters and predict the behavior of the examined system [2]. Furthermore, they include learning capabilities and characteristics as they can learn from historical data and previous knowledge to overcome the subjectivity of experts' opinions [5][6][7]. In other words, it is a semi-quantitative method that uses qualitative knowledge from experts and stakeholders to form FCM-based models and further increase system understanding and reduce uncertainties.The task of simulation is incorporated by FCMs and can help managers to increase system understanding and reduce uncertainties about how the studied phenomenon may respond to changes in the main system drivers [8].Among other fields, FCMs are also used for ...

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“…FCMs have been also applied to assess climate change impacts and effects of adaptation measures [42,43]. Nevertheless, examples of the implementation of FCMs to the WEF nexus are scarce [44].…”

Section: Introductionmentioning

confidence: 99%

The Water–Energy–Food Nexus: A Fuzzy-Cognitive Mapping Approach to Support Nexus-Compliant Policies in Andalusia (Spain)

Martínez

Blanco

Castro-Campos

2018

Water

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Water, energy and food are essential resources for economic development and social well-being. Framing integrated policies that improve their efficient use requires understanding the interdependencies in the water-energy-food (WEF) nexus. Stakeholder involvement in this process is crucial to represent multiple perspectives, ensure political legitimacy and promote dialogue. In this research, we develop and apply a participatory modelling approach to identify the main interlinkages within the WEF nexus in Andalusia, as a starting point to developing a system dynamic model at a later stage. The application of fuzzy cognitive mapping enabled us to gain knowledge on the WEF nexus according to opinions from 14 decision-makers, as well as contributing to raising awareness and building consensus among stakeholders. Results show that climate change and water availability are key drivers in the WEF nexus in Andalusia. Other variables with significant interlinkages within the WEF nexus are food production, irrigated agriculture, energy cost, socio-economic factors, irrigation water use, environmental conservation, and farm performance indicators. The scenario analysis reveals the interdependencies among nexus sectors and the existence of unanticipated effects when changing variables in the system, which need to be considered to design integrated policies.

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The potential impact of Brexit on the energy, water and food nexus in the UK: A fuzzy cognitive mapping approach

Cited by 59 publications

References 34 publications

Causal Modeling with Feedback Fuzzy Cognitive Maps

Causal Modeling with Feedback Fuzzy Cognitive Maps

Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique

The Water–Energy–Food Nexus: A Fuzzy-Cognitive Mapping Approach to Support Nexus-Compliant Policies in Andalusia (Spain)

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