The urban sprawl index is typically regarded as the most precise indicator of a city's potential to measure the level of sustainable urban growth. Using panel data, we examine the causes of urban sprawl over a twenty-year period in Abomey-Calavi, Benin, and Guangzhou, China. A panel data set might contain group effects, time effects, or both. This study analyzes the typical least squares approach with fixed effects and random effects for panel data analysis. Multiple specification tests, such as the Breusch--Pagan Lagrange Multiplier and Hausman specification tests, are employed to identify the optimal strategy for least square, fixed effect, or random effect models. Using a random effect model to address this scenario appears to be the most successful method for producing statistically significant results. These results could help city planners find ways to make up for the bad effects of urban sprawl on land use.

Sustainable city growth necessitates sound political decisions. Owing to ineffective policies, the land price increments have had a significant impact on immigration settlements. Scholars have studied city immigrant settlements without mentioning rising land prices as a major driver of illegal settlements. This study explores the influence of the long-term increase in land prices on immigrant settlement, by employing a panel cointegration model and the city-level panel data of Abomey-Calavi from 1992 to 2012. Increments in land prices are found to be correlated with both internal and external immigrants. Rising land prices hamper formal immigrant settlement. City growth may suffer significantly as the number of immigrants may double by 2030. However, implementing a novel land banking policy in urban planning, may help control land price spikes and limit illegal settlements. The findings advocate for a discussion on affordable land prices at the Sustainable Development Goals (SDG11) summit.