|Timeline:||April 2015 - 31 March 2019|
Socioeconomic development driving greenhouse gas emissions is affecting human and natural systems through anthropogenic climate change. Urban areas are the largest emitters of greenhouse gases and are sensitive to climate change. Integrated assessments are useful tools that help study the drivers of climate change, risks, and opportunities for human and natural systems. They are also helpful in testing the effectiveness of policies in mitigating climate change, and adapting human and natural systems. However, there is limited experience at the city scale in developing and least developed countries, with current practices largely concentrated on indicator based methodologies that rely on the researcher’s subjective selection of variables and their relative weights. These studies are limited in the application, as they do not include the dynamics of a complex system such as non-linearity and feedbacks, neither do they contribute in developing scenarios to help decision-making processes. To overcome these limitations at the city scale, I intend on developing a fuzzy cognitive mapping approach based integrated assessment that can i) model complex human-biophysical interactions whilst include time relations and ii) generate scenarios that analyse synergies and trade-offs between mitigation and adaptation strategies while being robust to the uncertainty of climate change and alternative trends in the evolution of the society. The fuzzy cognitive mapping approach is useful in integrated assessments as they can elicit knowledge from several domains of expertise. The current state of the art does not properly incorporate time relations, which is crucial in generating knowledge on highly dynamic systems such as climate change and urbanisation, despite existing methodological contributions to the conceptualisation of time in fuzzy cognitive maps. To overcome this limitation with fuzzy cognitive mapping I intend on explaining the specific representation of time in FCMs for relationships within a system and testing the role of time relations in modelling the coupled human-biophysical system and in semi-quantitative scenario generation.