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Sustainable Business Models: An Empirical Analysis of Environmental Sustainability in Leading Manufacturing Companies

Author

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  • Patrizia Gazzola

    (Department of Economics, University of Insubria, 21100 Varese, Italy)

  • Carlo Drago

    (Department of Economics, Psychology and Communication Sciences, “Niccolò Cusano” University, 00166 Roma, Italy)

  • Enrica Pavione

    (Department of Economics, University of Insubria, 21100 Varese, Italy)

  • Noemi Pignoni

    (Department of Economics, University of Insubria, 21100 Varese, Italy)

Abstract

This study thoroughly investigates the role of sustainable business models in enhancing environmental sustainability in leading manufacturing companies. Guided by the United Nations Sustainable Development Goals (SDGs), we empirically analyse the integration of sustainability goals into corporate strategies. This study identifies sustainable business models based on an analysis of the sustainability reports published on the website, examining the strategies and action plans declared by 30 companies that are leaders in the sustainability industry, according to their Dow Jones Sustainability Index World (DJSI World) and S&P Global ESG Scores. The strategies considered are aligned with the following specific sustainability development goals: 6 (water security); 7 (renewable energy); 12 (responsible consumption and production); and 13 (climate action). The dataset contains several variables, each reflecting a particular facet of a company’s environmental sustainability, as follows: energy consumption; greenhouse gas emissions; waste management strategies; and water conservation initiatives. We use a multidimensional data analysis technique called multiple correspondence analysis (MCA). After using MCA, we use a hierarchical clustering algorithm with the aim of classifying the different companies. Our findings underscore the presence of seven clusters of companies. Compared to the well-established literature on the topic of sustainable business, the innovative contribution of this study is linked to the identification of reaction time as a strategic variable explaining the different sustainable business models. The study makes it clear that the different business models are linked to reaction time to strategic alignment with environmental objectives. The country in which the company is based is also important. This study provides practical insights for companies aiming to align their practices with SDGs. In fact, the time variable provides important information in this regard and makes it possible to identify different approaches to sustainability as well as strong and weak sustainable business models; the former are characterised by a medium long-term strategic orientation towards environmental sustainability, which can be interpreted as the desire to undertake more solid and structured environmental sustainability strategies.

Suggested Citation

  • Patrizia Gazzola & Carlo Drago & Enrica Pavione & Noemi Pignoni, 2024. "Sustainable Business Models: An Empirical Analysis of Environmental Sustainability in Leading Manufacturing Companies," Sustainability, MDPI, vol. 16(19), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8282-:d:1484089
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    References listed on IDEAS

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    Cited by:

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    2. Nadezhda Shmeleva & Tatyana Tolstykh & Tatiana Guseva & Arina Volosatova, 2025. "Open Environmental Collaborations as an Innovation Tool for Sustainable Development: Evidence from Russian Pulp and Paper Industry," Sustainability, MDPI, vol. 17(3), pages 1-27, January.
    3. Mazliza Azuana Abdullah & Arina Rus Zahira Rusli, 2025. "A Conceptual Analysis of the Relationship Between Environmental Strategy and Financial Performance," Information Management and Business Review, AMH International, vol. 17(1), pages 214-222.
    4. Patrizia Gazzola & Enrica Pavione & Stefano Amelio & Martina Mauri, 2024. "Sustainable Strategies and Value Creation in the Food and Beverage Sector: The Case of Large Listed European Companies," Sustainability, MDPI, vol. 16(22), pages 1-16, November.

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