Unveiling regional drivers of the blue economy in Sumatra: A multiscale space–time regression analysis with variable selection
DOI:
https://doi.org/10.58524/jgsa.v1i2.31Keywords:
Blue Economy, Kernel Function, Multiscale Geographically and Temporally Weighted Regression, Spatio-TemporalAbstract
Indonesia, as a maritime country, positions the blue economy as a cornerstone in the 2025–2045 National Long-Term Development Plan (RPJPN) to support sustainable economic growth, particularly through the development of the fisheries sector and marine tourism. This study aims to construct a Blue Economy Index (BEI) at the district/city level and identify its key determinants using the Regression Weighted Geographic and Temporal Multiscale (MGTWR) method. The BEI is based on environmental, social, and economic dimensions using data from 2020 to 2022 across 154 districts/cities on Sumatra Island. The analysis incorporates 14 predictor variables grouped into four dimensions and selected through Group LASSO and Elastic Net methods. The best-performing model is the RTGTM with Group LASSO variable selection and a Gaussian kernel function, yielding an of 30.71% and an AIC of 1377.52. The social dimension contributes most to the BEI, while Medan City and Subulussalam City consistently record the highest and lowest scores. Significant variables influencing BEI include population factors (number of sub-districts, total population, sex ratio), education factors (number of senior high schools and vocational schools), and environmental indicators (protected drinking water sources and sanitation access). These findings underscore the importance of multi-dimensional, spatial, and temporal approaches in evaluating and advancing blue economy policies at the regional level.
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