Non-Linear Regression Modelling to Estimate the Global Warming Potential of a Newspaper

Abstract

Technological innovations are not enough by themselves to achieve social and environmental sustainability in companies. Sustainable development aims to determine the environmental impact of a product and the hidden price of products and services through the concept of radical transparency. This means that companies should show and disclose the impact on the environment of any good or service. This way, the consumer can choose in a transparent manner, not only for the price. The use of the eco-label as a European eco-label, which bases its criteria on life cycle assessment, could provide an indicator of corporate social responsibility for a given product. However, it does not give a full guarantee that the product was obtained in a sustainable manner. The aim of this work is to provide a way of calculating the value of the environmental impacts of an industrial product, under different operating conditions, so that each company can provide detailed information on the impacts of its products, information that can form part of its "green product sheet". As a case study, the daily production of a newspaper, printed by coldset, has been chosen. Each process involved in production was configured with raw material and energy consumption information from production plants, manufacturer data and existing databases. Four non-linear regression models have been trained to estimate the impact of a newspaper’s circulation from five input variables (pages, grammage, height, paper type, and print run) with 5508 data samples each. These non-linear regression models were trained using the Levenberg–Marquardt nonlinear least squares algorithm. The mean absolute percentage errors (MAPE) obtained by all the non-linear regression models tested were less than 5%. Through the proposed correlations, it is possible to obtain a score that reports on the impact of the product for different operating conditions and several types of raw materials. Ecolabelling can be further developed by incorporating a scoring system for the impact caused by the product or process, using a standardised impact methodology.

LearnCarbon - A tool for machine learning prediction of global warming potential from abstract designs

“…Mendecka et al (2019) used nonlinear regression to predict GWP along with other impact metrics for the wind energy industry. Additionally, methods for EIA of an industrial product under different operating conditions were formulated using the same ML tool (Lozano, Cabrera, and Blanco-Marigorta, 2020).…”

Artificial Intelligence and Computational Methods in the Modeling of Complex Systems

“…The aim of the work by Lozano et al [ 5 ] is to provide a way of calculating the value of the environmental impacts of an industrial product under different operating conditions. As a case study, the daily production of a newspaper has been chosen.…”