Social status and air quality in Barcelona: A socio-ecological approach

Martori, Joan Carles; Lagonigro, Raymond; Iglesias‐Pascual, Ricardo

doi:10.1016/j.scs.2022.104210

Cited by 9 publications

(2 citation statements)

References 93 publications

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“…Recent literature has focused on the links between pollution and socio‐economic characteristics (Martori et al, 2022; Ren & Matsumoto, 2020). Therefore, socio‐economic and demographic variables have been included in the dataset for each European city.…”

Section: Datamentioning

confidence: 99%

Structural equation models for simultaneous modeling of air pollutants

Bottazzi Schenone,

Grimaccia,

Vichi

2024

Environmetrics

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This paper provides a new modeling for air pollution, simultaneously taking into account the six main pollutants (PM10 and PM2.5, Sulphate Dioxide, Nitrogen Dioxide, Carbon Monoxide, ground level Ozone concentrations) and their key determinants, employing Structural Equation Models (SEMs). The model is able to estimate the complex links among air pollutants, often neglected in literature, and identifies specific drivers of air pollution. In literature, indexes of air pollution achieved using a fully statistical methodology have not been proposed yet. Indeed, an added value of this proposal is the statistical procedure itself, which can be applied also to obtain indexes modeling different phenomena. In particular, in this study, the new Air Pollution Index (API) is based on a modeling approach that allows to assess, through statistical criteria, the goodness of fit of the SEM in modeling pollutants and the significance of their determinants. The performance of the new index is assessed using air quality data for municipal European areas, which are characterized by different socioeconomic, geographical, and meteorological features. SEMs are estimated and evaluated in terms of best fit and model complexity. The index resulting by the best SEM is compared with the well‐established Air Quality Index (AQI). The new API is validated by means of a sensitivity analysis, performed with a simulation study. Finally, to visualize the meaningfulness of the obtained results, a model‐based cluster analysis is estimated on the municipal areas. The proposed SEM contributes to a better understanding of the relationships between air pollutants and their determinants, and this knowledge can inform policy decisions aimed at reducing air pollution and improving public health.

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Section: Datamentioning

confidence: 99%

Structural equation models for simultaneous modeling of air pollutants

Bottazzi Schenone,

Grimaccia,

Vichi

2024

Environmetrics

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“…The GWR method has been previously used in empirical research concerning different application domains. The vast majority concern environmental issues to understand the spatial non-stationarity in factors influencing waste management [1,2], soil quality and land use [31,37], air quality [33,34,46], and, more in general, the management of resources [42,45]. Several scholars have focused on security issues by investigating spatial heterogeneity in crime [28,50] and traffic accident determinants [4,24,38].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the spatial heterogeneity of innovation drivers: a comparison between GWR and GWPR

Musella

Castellano

Bruno

2023

METRON

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In studies focusing on innovation activities, the potential spatial heterogeneity in the relationships between innovation and its triggering factors is an unexplored topic. On this ground, this paper aims to a twofold contribution. First, we verify the existence of spatial variability in the relationships. We evaluate the estimation gains due to local regressions, such as geographically weighted regression (GWR) and geographically weighted panel regression (GWPR), with respect to the classical global methods (e.g., OLS, Fixed Effects panel regression). Second, we compare the GWPR with GWR and global models to evaluate if the joint consideration of time and space dimensions allows for the rise of new insights. We resort to official data on 287 NUTS-2 European regions in 2014-2021. The results confirm that GWPR estimations significantly differ from GWR and global models, potentially producing new patterns and findings.

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