Optimizing urban morphology can help city managers deploy internal carbon reduction strategies. However, due to the numerous factors that influence urban carbon emissions and the complex paths of their effects, existing research findings are diverse …
The influence of urban spatial form on the environment is complex and lengthy. The spatial analysis for the urban form and residential-related CO$_2$ emissions at the city scale is challenging due to the lack of extensive urban form data and …
Estimating the aboveground biomass (AGB) at the plot level plays a major role in connecting accurate single-tree AGB measurements to relatively difficult regional AGB estimates. The goal of this study is to determine whether combining machine learning with spatial statistics reduces the uncertainty of plot-level AGB estimates.
Cities play an essential role in low-carbon development. However, Estimating CO$_2$ emissions at the urban scale, including both un-gridded (i.e., administrative unit maps) and gridded maps, cannot avoid the propagation of uncertainties from input to result, which highlights the importance of being aware of uncertainty estimation, especially in gridded maps due to its implications for the precision mitigation of CO$_2$ emissions. We proposed an analytic workflow to analyze the propagated uncertainties caused by the gridded model and the input for gridded CO$_2$ emission maps.