Painting art and sustainability: relationship from composite indices and a neural network

Abstract

Purpose

This work aims to establish the relationship between painting art and sustainability, which allows for highlighting implications likely to improve sustainability for humanity's welfare.

Design/methodology/approach

To achieve this objective, painting art is measured by a composite index aggregating the quantity and quality represented by the market value. As for sustainable development, it is represented by a composite index comprising three variables: the climate change performance index (ecological dimension), the wage index reflecting distributive justice (social dimension) and the gross domestic product (economic dimension). The composite indices were determined through adjusted data envelopment analysis. In addition, two other methods are used in this work: correlation analysis and a neural network method. These methods are applied to data from 2007 to 2021 across the world.

Findings

The correlation method highlighted a perfect positive correlation between painting art and sustainability. As for the neural network method, it revealed that the quality of painting has the greatest impact on sustainability. The neural network method also showed that the most positively impacted variable of sustainability by painting art is the social variable, with a pseudo-probability of 0.90.

Originality/value

The relationship between painting art and sustainability is underexplored, in particular in terms of statistical analysis. Therefore, this research intends to fill this gap. Moreover, analysis of the relationship between both using composite indices computed via an original method (adjusted data envelopment analysis) and a neural network method is nonexistent, which constitutes the novelty of this work.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0006

Citation

El Kadiri Boutchich, D. (2024), "Painting art and sustainability: relationship from composite indices and a neural network", International Journal of Social Economics, Vol. 51 No. 1, pp. 46-61. https://doi.org/10.1108/IJSE-01-2023-0006