UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Perpustakaan Tuanku Bainun |
| The consumer services industry plays a crucial role in supporting regional economies and improving the quality of urban life. In Shandong Province, however, its development has shown clear disparities between coastal and inland cities, reflecting uneven resource allocation and infrastructure conditions. Although these differences are widely recognized, there has been a lack of systematic spatial modeling to explain the mechanisms shaping such heterogeneity. This study applies a spatial modeling approach to examine the regional consumer services industry in Shandong Province. The analysis extends the Cobb_Douglas production function by incorporating energy input and embeds a spatial distance matrix to capture spatial dependence. Methodologically, the research combines the Modified Delphi_ Hierarchical Grey_Fuzzy (MDHGF) algorithm with econometric estimators, including Ordinary Least Squares (OLS), the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and Geographically Weighted Regression (GWR). The empirical investigation uses panel data derived from the Shandong Statistical Yearbooks and the yearbooks of 16 prefecture-level cities, with the final-demand rate employed to identify major industry subsectors. The results reveal significant spatial heterogeneity in industry output. GWR estimates show that capital investment is the most decisive factor, followed by labor input, while the effect of energy consumption varies considerably across locations of 16 prefecture-level cities. Coastal cities such as Qingdao and Yantai record stronger performance due to geographic advantages, robust infrastructure, and diversified economic bases. In contrast, inland cities including Heze and Liaocheng lag behind because of limited infrastructure and weaker development foundations. Moran_s I confirms moderate positive spatial autocorrelation, highlighting clustering of high-output and low-output areas. The findings carry important policy implications. Inland regions should focus on strengthening infrastructure and attracting resources to reduce disparities, while coastal areas need to emphasize innovation and technological upgrading. Overall, the study demonstrates the value of spatial modeling in revealing local differences and provides empirical evidence to inform regional strategies aimed at balanced and sustainable development in Shandong Province and comparable contexts. |
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