An agent-based model for analyzing land use dynamics in response to farmer behaviour and environmental change in the Pampanga delta (Philippines)

Mialhe, François; Bécu, Nicolas; Gunnell, Yanni

doi:10.1016/j.agee.2012.07.016

Cited by 40 publications

(16 citation statements)

References 22 publications

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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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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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“…Surveys have been used to understand the agricultural decision making process in many case studies (Below et al, 2012;Jara-Rojas et al, 2012), as well as for the parameterization of agent-based land change models (Mialhe et al, 2012;van Berkel and Verburg, 2012).…”

Section: Survey Approachmentioning

confidence: 99%

A survey-based exploration of land-system dynamics in an agricultural region of Northeast China

Verburg

et al. 2013

Agricultural Systems

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“…Concerning LULC modeling, even if some models propose to use a BDI agent architecture [3,18], most of the are still using simple architectures based on probabilities or on multi-criteria decision-making algorithms [8,14]. The main aims of this paper are thus to compare these three types of architectures on a case study concerning the coastal area of the Vietnamese Mekong Delta and establish recommendations for LULC modeling.…”

Section: Existing Agent Architectures To Model Land-use Changementioning

confidence: 99%

“…Although these models give good results for monitoring the past time, they show their limitations to predict the future due to complex behaviors of the social actors in the real world that is not captured by these models. Concerning the modeling of actors involved in LULC changes, Agent-Based Models (ABM) have been heavily used [8,14]. However, most of these models remain simple models based on probabilities.…”

Section: Modeling Of Land-use Changementioning

confidence: 99%

Exploring Agent Architectures for Farmer Behavior in Land-Use Change. A Case Study in Coastal Area of the Vietnamese Mekong Delta

Truong

Taillandier

Gaudou

et al. 2016

Lecture Notes in Computer Science

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 17129The contribution was presented at MABS 2015: https://www.openabm.org/event/mabs-2015-sixteenth-internationalworkshop-multi-agent-based-simulation Abstract. Farmers are the key actors of land-use change processes. It is thus essential to choose a suitable architecture for farmer behavior to model such processes. In this paper, we compared three models with different architectures to model the farmer behavior in the coastal areas of the Ben Tre province: (i) The first one is a probabilistic model that allows farmer to select the land-use pattern based on land change probability; (ii) The second model is based on multi-criteria decision making and takes into account the land suitability of the parcel and the farmer benefit; (iii) The third model used a BDI (Beliefs -Desires -Intentions) architecture. For each of these models, we have compared the difference between simulated data and real data by using the Fuzzy Kappa coefficient. The results show the suitability of the BDI architecture to build land-use change model and to support decision-making on land-use planning.

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An agent-based model for analyzing land use dynamics in response to farmer behaviour and environmental change in the Pampanga delta (Philippines)

Cited by 40 publications

References 22 publications

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

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

A survey-based exploration of land-system dynamics in an agricultural region of Northeast China

Exploring Agent Architectures for Farmer Behavior in Land-Use Change. A Case Study in Coastal Area of the Vietnamese Mekong Delta

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