Modelling Spatial Spillovers of regional economic growth in East Java: an empirical analysis based on Spatial Durbin Model

Atikah, Nur; Rahardjo, Swasono; Afifah, Dyah Laillyzatul; Kholifia, Nadia

doi:10.1088/1742-6596/1872/1/012029

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given the spatial dependence of PM 2.5 concentrations, spatial effects were incorporated into the regression simulation and the spatial regression models were established in place of ordinary least squares (OLS) regression. Spatial regression models can show the interaction effects of endogenous and exogenous variables [45]. The first were the endogenous interactions between dependent variables, which could be described as the PM 2.5 pollution in neighboring areas that caused the change of PM 2.5 concentrations in a specific location through the migration effect and reflect the pollution externality of PM 2.5 contaminants through the spatial interaction mechanism.…”

Section: Spatial Regression Modelmentioning

confidence: 99%

The Direct and Spillover Effect of Multi-Dimensional Urbanization on PM2.5 Concentrations: A Case Study from the Chengdu-Chongqing Urban Agglomeration in China

Wang

Sun

et al. 2021

IJERPH

View full text Add to dashboard Cite

The Chengdu-Chongqing urban agglomeration (CUA) faces considerable air quality concerns, although the situation has improved in the past 15 years. The driving effects of population, land, and economic urbanization on PM2.5 concentrations in the CUA have largely been overlooked in previous studies. The contributions of natural and socio-economic factors to PM2.5 concentrations have been ignored, and the spillover effects of multi-dimensional urbanization on PM2.5 concentrations have been underestimated. This study explores the spatial dependence and trend evolution of PM2.5 concentrations in the CUA at the grid and county level, analyzing the direct and spillover effects of multi-dimensional urbanization on PM2.5 concentrations. The results show that the mean PM2.5 concentrations in CUA dropped to 48.05 μg/m3 at an average annual rate of 4.6% from 2000 to 2015; however, in 2015, there were still 91% of areas exposed to pollution risk (> 35 μg/m3). The PM2.5 concentrations in 92.98% of the area have slowly decreased but are rising in some areas, such as Shimian County, Xuyong County, and Gulin County. The PM2.5 concentrations in this region presented a spatial dependence pattern of “cold spots in the east and hot spots in the west”. Urbanization was not the only factor contributing to PM2.5 concentrations. Commercial trade, building development, and atmospheric pressure were found to have significant contributions. The spillover effect of multi-dimensional urbanization was found to be generally stronger than the direct effects, and the positive impact of land urbanization on PM2.5 concentrations was stronger than population and economic urbanization. The findings provide support for urban agglomerations such as CUA that are still being cultivated to carry out cross-city joint control strategies of PM2.5 concentrations, also proving that PM2.5 pollution control should not only focus on urban socio-economic development strategies but should be an integration of work optimization in various areas such as population agglomeration, land expansion, economic construction, natural adaptation, and socio-economic adjustment.

show abstract

Section: Spatial Regression Modelmentioning

confidence: 99%