Reliable and accurate environmental state prediction can help in long-term sustainable planning and management. Enormous land-use/ land-cover (LULC) transformation has been increasing the carbon emissions (CEs) and land surface temperature (LST) around the world. The study aimed to (i) examine the influences of land specific CEs on LST dynamics and (ii) simulate future potential LULC, CEs and LST pattern of Khulna City Corporation. Landsat satellite images of the year 2000, 2010 and 2020 were used to derive LULC, LST and CEs pattern and change. The correlation between land-use indices (NDBI, NDVI, NDWI) and LST was examined to explore the impacts of LULC change on LST. Unplanned urbanization has increased 11.79 Km²(26.10%) buildup areas and 25,268 tons of CEs during 2000–2020. The calculated R² value indicates the strong positive correlation between CEs and LST. To simulate the future LULC, CEs and LST pattern for the year 2030 and 2040, multi-layer perceptron-Markov chain (MLP-MC)-based artificial neural network model was utilized with the accuracy rate of 94.12%, 99% and 98.48% for LULC, LST and CEs model, respectively. The simulation shows that by 2040, buildup area will increase to 87.33%, net CEs will increase by 19.82 × 10⁴tons, and carbon absorptions will decrease by 23. 55 × 10⁴tons and 69.54% of the total study area's LST will be above 39⁰C. Such predictions signify the necessity of implementing a sustainable urban development plan immediately for the sustainable, habitable and sound urban environment.

Rapid urbanization has induced land use and land cover change (LULC) that increases land surface temperature (LST). Analyzing seasonal variations of LULC and LST is a precondition for mitigating heat island effects and promoting a sustainable living environment. The objective of this study is to explore the association between the seasonal LST dynamics and LULC indices for the Dhaka district of Bangladesh. The LULC indices are comprised of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Bareness Index (NDBAI), and Modified Normalized Difference Water Index (MNDWI). The results show that the LULC effect on LST in Dhaka is significant, with an increase in summer season LST from 34.58 °C to 37.66 °C and in winter season LST from 24.710C to 26.24 °C. Predictably, the highest and lowest LST values were observed in the built-up and vegetation-covered areas, respectively. Secondly, the correlation values indicate a significant inverse correlation (R² > 0.50) between NDVI and LST, as well as MNDWI and LST. On the contrary, positive correlations were observed between NDBI and LST, and between NDBAI and LST for both the summer and winter seasons. Finally, subsequent vegetation decline (-69.34%) and increasing built-up area (+11.30%) between 2000 and 2020 in Dhaka district were found to be the most significant factors for the increasing trend and spatial heterogeneity of LST in Dhaka. The methodological approach of this study offers a low-cost efficient technique for monitoring LST hotspots, which can guide land use planners and urban managers for spatial intervention to ensure a livable environment.