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Abstract

Sustainable shrimp aquaculture requires reliable and affordable energy, particularly for coastal pond aeration. However, identifying suitable locations for photovoltaic (PV) deployment remains challenging because ground-based solar-resource measurement is expensive, spatially limited, and difficult to maintain across large coastal regions. This study develops a village-level spatial framework to evaluate PV potential for shrimp aquaculture using open geospatial datasets. The analysis covered villages across Central Java, Special Region of Yogyakarta, and East Java. PV power potential (PVout), Global Horizontal Irradiation (GHI), and Direct Normal Irradiation (DNI) were obtained from the Global Solar Atlas; land surface temperature (LST) and precipitation were derived from Google Earth Engine; and slope from digital elevation data. Three datasets were analyzed: all villages (n = 17,672), coastal villages (n = 3,215), and shrimp pond villages (n = 989). Methods included village-level aggregation, correlation analysis, hierarchical regression, residualized modeling, spatial cross-validation, Moran’s I, and spatial regression using k-nearest-neighbor weights. Hierarchical regression achieved R² values of 0.965, 0.974, and 0.983, respectively, reflecting strong relationship between PVout and solar-resource variables. Significant PVout clustering occurred in all villages (Moran’s I = 0.14–0.16) and coastal villages (0.03–0.05), but not in pond villages. OLS residuals remained spatially autocorrelated, and the spatial error model outperformed the spatial lag model for coastal and pond-village datasets, indicating omitted spatial processes. The framework supports preliminary screening of candidate shrimp-farming villages for technical validation and feasibility assessment. It integrates solar-resource availability, environmental conditions, and spatial dependence to guide decentralized PV planning for energy-intensive aquaculture in coastal regions.

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