# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 4 Discussion
For instance, Sugarscape, developed in the late 1990s by [PERSON] and [PERSON] (1966), is tailored to simulate economic and social phenomena. TRANSIMS (Transportation Analysis and Simulation System) is specifically designed for simulating urban transportation systems. Recently, [PERSON] and [PERSON] (2017) combined Repast HPC and GIS to create Crop GIS-ABM which was used to examine the impact of milk market prices on changes in land use in Tasmania, Australia. In conclusion, NetLogo's standing as the quintessential ABM tool for CBA is not only supported by the detailed assessment but is well-grounded in the broader literature of related disciplines. Its unique combination of accessibility, participation options, and robust and ideally suited features positions it as the optimal choice for effective and inclusive community-based adaptation modelling. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Criteria** & **AOR Simulation** & **Ascape** & **Envision** & **GAMA (2D/3D)** & **JAS** & **LSD (2D/3D)** \\ \hline Adaptability to & TRUE & TRUE & TRUE & TRUE & TRUE & TRUE \\ Community Dynamics & & TRUE & TRUE & TRUE & TRUE & TRUE \\ Integration of & & TRUE & TRUE & TRUE & TRUE & TRUE & TRUE \\ Socio-Economic Factors & & FALSE & FALSE & False 1 & TRUE & False 1 & False 1 \\ Participatory Modelling Support & & False & FALSE & & TRUE & TRUE & TRUE \\ Handling Diverse Data Types & & False 1 & False 1 & TRUE & TRUE & TRUE & TRUE \\ Scalability to Different & & False 1 & TRUE & TRUE & TRUE & TRUE & TRUE \\ Community Sizes & & & TRUE & TRUE & TRUE & TRUE & TRUE \\ Risk and Uncertainty & & False 1 & False 1 & TRUE & False 1 & False 1 & False 1 \\ Assessment & & & & & & & \\ \hline \hline \end{tabular} \({}^{1}\) Limited information available to support or reject the statement in model documents and the existing literature.  
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Table 6: Assessment results of selected tools (Part 1). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Criteria** & **NetLogo (2D/3D)** & **Repast HPC** & **SeSAm** & **UrbanSim** & **TerraME** \\ \hline Adaptability to Community Dynamics & TRUE & TRUE & TRUE & TRUE & TRUE & TRUE \\ Integration of Socio-Economic Factors & TRUE & TRUE & TRUE & TRUE & TRUE & TRUE \\ Participatory Modelling Support & TRUE & False 1 & False 1 & TRUE & FALSE \\ Handling Diverse Data Types & TRUE & TRUE & TRUE & TRUE & TRUE & TRUE \\ Scalability to Different & & TRUE & TRUE & TRUE & TRUE & TRUE \\ Risk and Uncertainty & TRUE & TRUE & FALSE 1 & TRUE & False 1 \\ Assessment & & & & & & \\ \hline \hline \end{tabular} \({}^{1}\) Limited information available to support or reject the statement in model documents and the existing literature.  
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Table 7: Assessment results of selected tools (Part 2). The transparency of its code, active user community, real-time visualisation, educational value, and open-source nature further reinforce NetLogo's status as the choice for CBA modelling, aligning with the principles of open access and collaboration, as emphasised by [PERSON] et al. (2018) and [PERSON] (2019). NetLogo has proven to be a versatile and accessible platform for simulating complex systems and exploring climate change-related issues across diverse contexts. The following sections outline recent studies from late 2023 to 2024 which utilise NetLogo to model climate change issues, e.g., behaviours, policies, and environmental dynamics, and thus offer insights into its applications and contributions. Several studies leveraged NetLogo to explore energy consumption and its determinants. One model simulated household electricity usage in Indian cities, integrating factors such as population growth, income levels, and weather conditions. The findings underscore the limitations of widely advocated rooftop solar policies and propose a framework for emergent urbanism in energy planning (2019). Similarly, NetLogo was used to model energy use behaviours in university dormitories in China, identifying energy-efficient retrofitting strategies that reduced consumption by over 16% (2019).