Spatial Autoregressive Panel Model for Analyzing the Environmental Quality Index

Alimatu Qurroti Ainina (Universitas Lambung Mangkurat, Banjarmasin, Indonesia)
Dewi Sri Susanti (Universitas Lambung Mangkurat, Banjarmasin, Indonesia)
Rony Riduan (Universitas Lambung Mangkurat, Banjarmasin, Indonesia)

Jurnal Varian
Vol. 9 No. 2 (2026)

A region’s general environmental conditions are reflected in the Environmental Quality Index (EQI). In South Kalimantan Province, differences in EQI between districts and cities indicate that the contributing factors do not operate consistently, requiring an analysis that accounts for both spatial and temporal dimensions. The aim of this research is to apply a spatial panel data regression model to analyze the effects of population density, regional gross domestic product (GRDP), human development index (HDI), sanitation access, poverty rate, and economic growth on the EQI across 13 districts and cities in South Kalimantan over the period 2016–2023. The research method used in this study involves spatial regression models, namely the Spatial Autoregressive Fixed Effect (SAR-FE) and Spatial Error Model Fixed Effect (SEM-FE) approaches with a queen contiguity spatial weight matrix. The results of the study show a spatial relationship in the EQI data, with a Moran’s I of 0.2606 in 2016 and 0.1668 in 2017. This emphasizes the importance of spatial panel data modeling. With an AIC of 1322.378 and a coefficient of determination of 0.8112, SAR-FE is the best-fitting model. The modeling results indicate a significant spatial effect, whereby the EQI of a district or city is influenced by the EQI of its neighboring regions, such that changes in environmental quality in one area may affect surrounding areas. Furthermore, access to sanitation and the poverty rate do not significantly affect the EQI, whereas population density, GRDP, HDI, and economic growth are significant, indicating that environmental quality dynamics in South Kalimantan are shaped by socioeconomic factors as well as spatial interdependencies among regions. In order to improve environmental quality and minimize negative spillover effects across nearby regions, these findings emphasize the necessity of spatially integrated environmental policies that support sustainable economic growth, human development, and interregional coordination.