Robust space–time modeling of solar photovoltaic deployment
Solar photovoltaic (PV) has established itself as a fairly promising, fast-growing renewable energy source. The main determinants of solar PV deployment are thought to be physical and climatic factors – such as latitude and solar irradiance, not to mention terrain and built environment features – as...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/3c5816c7fb504b5a927a97d675d7b09f |
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| Sumario: | Solar photovoltaic (PV) has established itself as a fairly promising, fast-growing renewable energy source. The main determinants of solar PV deployment are thought to be physical and climatic factors – such as latitude and solar irradiance, not to mention terrain and built environment features – as well as socio-economic drivers — such as population density, household size, and education level. Besides, peer effects and neighborhood effects are found to affect the willingness to adopt solar photovoltaic systems strongly. This study aims to set up robust space–time models, which enable us to investigate the drivers of solar PV deployment using fine-grained spatial and temporal data. We use space–time auto-regressive models (STAR) with several exogenous covariates that are expected to explain the installed solar PV capacity. STAR models require the specification of spatial weight matrices (W). As in regular lattice data, we select causal (lower triangular) W matrices so that the consistency of least-squares (LS) estimators is warranted. We show that they can be extended to robust LS estimators, which are necessary because of strong outlier contamination. Models are tested on the Italian municipal data of residential and industrial PV plants installed under the support schemes in force between 2006 and 2011. Empirical results confirm the important role played by the space–time dynamic components. Significant exogenous predictors are found in the domains of the physical features (elevation and land area), demography (population), built environment (residential buildings), and socio-economic aspects (income, employment rate, commuter workers). |
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