The GSA performed in this study helps in identifying the input factors that need to be carefully parameterized in the model to implement ABMs that well reproduce observed patterns at multiple space-time scales. Additionally, the results highlight the time-dependent sensitivity to parameter values in spatially explicit ABMs. The results show parameters related to interactions between agents and the environment have great impact on the ability of a model to reproduce observed patterns, although the magnitudes of such impacts vary by space-time scales. Input factors used in GSA include one related to the environment (introduction rates), two related to interactions between agents and environment (level of herd immunity, mosquito population density), and one that defines agent state transition (mosquito extrinsic incubation period). A vector-borne disease transmission model was used as the case study. For instance, it is unclear how to introduce a reduction rate of CO2 allowances to ensure a steady decrease in CO2 emissions from households. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. In this paper, we propose the use of multiple scale space-time patterns in variance-based global sensitivity analysis (GSA). A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist.

Nevertheless, less attention has been paid to understanding the extent to which parameter values in ABMs are responsible for mismatch between model outcomes and observations. For spatially explicit ABMs, the comparison of spatial or spatio-temporal patterns has been advocated to evaluate models.

Sensitivity analysis (SA) in spatially explicit agent-based models (ABMs) has emerged to address some of the challenges associated with model specification and parameterization.

Anylogic sensitivity analysis