# An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change
## 4 Discussion
These studies highlight NetLogo's utility in simulating diverse urban energy systems and informing sustainable policy design. NetLogo's capacity for integrating geographic data and decision-making processes is evident in a study on agricultural land use in Medellin, Colombia. By combining agent-based modelling with geographic analysis, the model identifies properties suitable for agricultural conversion, thus supporting policy adjustments to improve food security (2019). This methodology exemplifies how NetLogo can be used to prioritise land use policies and promote sustainable development in rural-urban interfaces. NetLogo also proved effective in assessing environmental risks. A spatio-temporal flood risk model for Shenzhen, China, was constructed using population mobility data derived from functional zones. The model's ability to dynamically simulate risk fluctuations during rainfall events demonstrates NetLogo's potential in enhancing urban resilience strategies (2019). NetLogo facilitates the exploration of behavioural responses to climate and energy challenges. One study simulated social learning effects on household participation in demand-side energy programmes in Portugal, demonstrating that socio-economic diversity influences the extent of behaviour changes (2019). Another study investigated environmentally friendly practises in educational buildings in Indonesia, highlighting how shared knowledge and green behaviours influence individuals' decisions to adopt sustainable practises, such as air conditioning control (2019). In the realm of low-carbon development, NetLogo was used to simulate the evolution of manufacturing enterprises' low-carbon innovation behaviours. The model, based on ecological succession theory, captured the dynamic interplay of social pressures, systemic factors, and path dependence (2019). Similarly, NetLogo was applied to analyse the effects of carbon emissions trading and renewable portfolio standards on electricity markets in China, demonstrating the software's robustness in evaluating policy interventions aimed at reducing carbon emissions (2019). Across these studies, NetLogo consistently serves as a powerful tool for modelling, visualising, and analysing the multifaceted challenges of climate change. Its accessibility and flexibility enable researchers to simulate interactions among diverse agents and integrate geographic, behavioural, and policy dimensions. By supporting dynamic scenario testing, NetLogo helps stakeholders make informed decisions regarding urban planning, resource management, and sustainability policy. These findings underline the potential of NetLogo in advancing climate change research. However, its broader adoption depends on addressing challenges such as the availability of high-quality input data and the scalability of models to larger or more heterogeneous systems. Nonetheless, the reviewed studies collectively demonstrate that NetLogo is an invaluable platform for tackling climate-related complexities and fostering sustainable development, and therefore also enhancing the quality of CBA. While this study highlights the potential of ABM tools for CBA, several critical areas require further exploration in order to enhance their applicability. First, although participatory modelling is essential for ensuring community relevance and the ownership of adaptation strategies, only NetLogo and GAMA explicitly support this feature. Incorporating more tools with participatory capabilities, such as interfaces for stakeholder engagement and collaborative decision-making, would strengthen the integration of community input into the modelling process. Second, uncertainty and risk assessment remain underdeveloped in many of the evaluated tools. Given the inherent uncertainties in climate adaptation, enhancing tools with mechanisms for sensitivity analysis, scenario testing, and probabilistic risk assessment would improve their robustness and decision-making utility.