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Design Example: Sustainability in Concrete Building01:26

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As the construction industry moves towards more eco-friendly practices, concrete's adaptability and its ability to incorporate sustainable features make it a key material in the drive towards greener building solutions.
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Updated: Sep 28, 2025

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Visualizing Internal Sustainability Efforts in Big Companies.

Chiara Ceccarini, Edyta Paulina Bogucka, Indira Sen

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    This study introduces a deep-learning framework and interactive visualization to assess corporate internal sustainability efforts (ISE). The system helps users understand ISEs, showing potential for machine learning in enhancing engagement with sustainability data.

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    Area of Science:

    • Business Ethics
    • Computer Science
    • Data Visualization

    Background:

    • Internal sustainability efforts (ISE) encompass corporate policies for employee well-being, including work-life balance and equality.
    • Companies sometimes misrepresent or ignore employee feedback regarding ISE implementation, creating a transparency gap.
    • Assessing and communicating ISE effectiveness is crucial for corporate accountability and employee trust.

    Purpose of the Study:

    • To develop a deep-learning (DL) framework for evaluating corporate internal sustainability efforts (ISE) across S&P 500 companies.
    • To create an interactive web-based system utilizing DL-driven visualizations for user engagement and reflection on ISEs.
    • To assess the effectiveness of an interactive treemap visualization compared to traditional text for influencing user opinions on ISEs.

    Main Methods:

    • A deep-learning (DL) framework was designed to score companies on six key internal sustainability efforts (ISEs).
    • A web-based system was developed, featuring an interactive treemap visualization of ISE data.
    • Two crowdsourced studies involving 421 participants were conducted to evaluate the system's impact on user opinion change.

    Main Results:

    • The DL framework successfully scored a significant portion of S&P 500 companies on their internal sustainability efforts (ISEs).
    • The interactive treemap visualization led to an opinion change of up to 7% among participants compared to a textual representation.
    • The study demonstrated the potential of machine-learning (ML) driven visualizations in enhancing user understanding and engagement with corporate sustainability data.

    Conclusions:

    • The developed DL framework and visualization system offer a novel approach to assessing and communicating corporate internal sustainability efforts (ISEs).
    • Interactive treemap visualizations powered by machine learning can effectively influence user perceptions and promote reflection on sustainability practices.
    • This research highlights the value of integrating advanced computational methods with user-centered design to address corporate transparency and accountability in sustainability.