A Spatio-Temporal Framework Based on Geographic Information System (GIS) to Evaluate the $\mathbf{CO}_{2}$ Emissions

Carbon Dioxide (CO
2
) emission and its associated negative effects on the environment and quality of life present a significant challenge to scientists, environmental authorities, and politicians. Lowering the released CO
2
emission is vital to preserve the natural environmental conditions and achieve sustainability in the long run. Recent literature has emphasized the importance of studying and analyzing CO
2
emissions generated by the building construction sector. However, limited research considered the Spatio-temporal analysis and its relevance to the CO
2
emissions at an urban scale. This research aims to develop a Geographic Information System (GIS) based framework to assess CO
2
emissions with urbanization over time for the United Arab Emirates (UAE). The UAE has witnessed a significant evolution in terms of urban development, demographic distribution, and lifestyle, in the last few decades. The developed framework offers a visualized CO
2
assessment of both construction carbon and operational carbon to provide concerned authorities with better insights into the environmental effect of urban development. The framework will be implemented and validated on a case study of one of the Emirates in the UAE to study the expansion and carbon emission over a specific analysis period. In this research, Ajman city was chosen to implement the methodology. In the case of construction carbon, the results illustrated that residential buildings have the highest CO
2
emissions factor with about 1,358 kg
$\mathbf{CO}_{2}/\mathbf{m}^{2}$
associated with building construction, while governmental buildings have the lowest CO
2
emissions factor with about 445 kg
$\mathbf{CO}_{2}\mathbf{m}^{2}$
. The results also indicated that hospitals have the highest CO
2
emissions factor associated with building operation with about 375 kg
$\mathbf{CO}_{2}/\mathbf{m}^{2}/\mathbf{year}$
, while educational institutes have the lowest CO
2
emissions factor of about 102 kg
$\mathbf{CO}_{2}/\mathbf{m}^{2}/\mathbf{year}$
. Further, the results will be used to test the impact of performing different retrofitting scenarios on the CO
2
emissions behavior. It would be interesting for future work to analyze the impact of using smart and sustainable energy fixtures on CO
2
emissions.