Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution

Marvuglia, Antonino; Bayram, Alper; Baustert, Paul; Gutiérrez, Tomás Navarrete; Igos, Elorri

doi:10.1016/j.jclepro.2021.129847

Cited by 42 publications

(29 citation statements)

References 56 publications

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“…Since social networks are often characterized by “self-selection,” the adoption of GCT and social networks may show a reverse causal relationship, that is, in the process of understanding, learning, adopting, and exchanging GCT, farmers may enhance their social networks observations due to frequent interactions with their relatives and friends ( Marvuglia et al, 2022 ). Therefore, referring to relevant research ( Wei et al, 2018 ), we select “the number of farmers’ New Year’s greetings during the Spring Festival” as an instrumental variable of the social networks to deal with the above-mentioned endogeneity problem.…”

Section: Methodsmentioning

confidence: 99%

The influence of social capital on farmers’ green control technology adoption behavior

Ren

Zhong

2022

Front. Psychol.

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Relying on social capital to promote farmers’ adoption of green control technology is of great significance for the governance of rural environment and the realization of sustainable agricultural development. Based on the survey data of 754 farmers in Shandong Province, this paper uses the Probit model and the instrumental variable method to empirically analyze the impact of social capital on farmers’ green control technology adoption behavior. The results show that: social capital has a promoting influence on farmers’ green control technology adoption behavior; the influence of the three dimensions of social capital on farmers’ green control technology adoption behavior is in turn social norms, social networks, and social trust; social networks play an enhanced moderating role in the process of social trust and social norms promoting farmers’ green control technology adoption behavior; education level, the number of family labor force and annual family income level have a significant positive impact on farmers’ green control technology adoption behavior, while age has a significant negative impact. Therefore, the government should make full use of social capital to promote farmers to adopt green control technology.

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Section: Methodsmentioning

confidence: 99%

The influence of social capital on farmers’ green control technology adoption behavior

Ren

Zhong

2022

Front. Psychol.

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“…It will be necessary to re-evaluate the scenarios as we obtain consolidated data and as the system prototypes evolve through progressive adaptations. Another important aspect of model parameterization is the assumption of the stability of certain coefficients, such as farmers' risk aversion, which is likely to evolve progressively as the effects of climate change are felt (Bartkowski et al, 2018;Marvuglia et al, 2022). In order to go further, it would be useful to analyze transition pathways with dynamic modeling of scenarios.…”

Section: Limitations Of the Study And Scientific Challengesmentioning

confidence: 99%

Designing scenarios for upscaling climate-smart agriculture on a small tropical island

Selbonne¹,

Guindé²,

Belmadani³

et al. 2022

Agricultural Systems

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“…In the study, a simple virus transmission model was formulated based on the time a customer spends near infected customers. Marvuglia et al [62] presented an ABM-Life cycle assessment model of agricultural production in the Grand Duchy of Luxembourg. The goal of the study was to evaluate the effects of farmers' interactions in their social networks on agricultural activities.…”

Section: Agent-based Modelingmentioning

confidence: 99%

An agent-based modeling framework for the design of a dynamic closed-loop supply chain network

Bozdoğan

Aykut

Demirel

2022

Complex Intell. Syst.

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The supply chain is a dynamic and uncertain system consisting of material, information, and fund flows between different organizations, from the acquisition of the raw materials to the delivery of the finished products to the end customers. Closed-loop supply chains do not end with the delivery of the finished products to the end customers, the process continues until economic value is obtained from the returned products or they are disposed properly in landfills. Incorporating reverse flows in supply chains increases the uncertainty and complexity, as well as complicating the management of supply chains that are already composed of different actors and have a dynamic structure. Since agent-based modeling and simulation is a more efficient method of handling the dynamic and complex nature of supply chains than the traditional analytical methods, in this study agent-based modeling methodology has been used to model a generic closed-loop supply chain network design problem with the aims of integrating customer behavior into the network, coping with the dynamism, and obtaining a more realistic structure by eliminating the required assumptions for solving the model with analytical methods. The actors in the CLSC network have been defined as agents with goals, properties and behaviors. In the proposed model dynamic customer arrivals, the changing aspects of customers' purchasing preferences for new and refurbished products and the time, quantity and quality uncertainties of returns have been handled via the proposed agent-based architecture. To observe the behavior of the supply chain in several conditions various scenarios have been developed according to different parameter settings for the supplier capacities, the rate of customers being affected by advertising, the market incentive threshold values, and the environmental awareness of customers. From the scenarios, it has been concluded that the system should be fed in the right amounts for the new and refurbished products to increase the effectiveness of factors such as advertising, incentives, and environmental awareness for achieving the desired sales amounts and cost targets.

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Agent-based modelling to simulate farmers’ sustainable decisions: Farmers’ interaction and resulting green consciousness evolution

Cited by 42 publications

References 56 publications

The influence of social capital on farmers’ green control technology adoption behavior

The influence of social capital on farmers’ green control technology adoption behavior

Designing scenarios for upscaling climate-smart agriculture on a small tropical island

An agent-based modeling framework for the design of a dynamic closed-loop supply chain network

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