Sökning: "Sustainability data model"

Visar resultat 1 - 5 av 611 uppsatser innehållade orden Sustainability data model.

  1. 1. Cooperative Sustainability : a study of Arla’s Sustainable Incentive Model

    Uppsats för yrkesexamina på avancerad nivå, SLU/Dept. of Economics

    Författare :Martin Söderlund; [2024]
    Nyckelord :Arla foods; Agricultural Cooperatives; Cooperative Sustainability; Incentive Models; Sustainable Incentive Models; Organizational Change; Agency Relationship;

    Sammanfattning : The agricultural industry is a large contributor to greenhouse emissions and therefore faces new challenges to implement sustainable practices. The rising societal demand for sustainability makes the agricultural sector dependent on working towards environmentally friendly practices. LÄS MER

  3. 2. Comparison of VADER and Pre-Trained RoBERTa: A Sentiment Analysis Application

    Kandidat-uppsats, Lunds universitet/Statistiska institutionen

    Författare :Linda Erwe; Xin Wang; [2024]
    Nyckelord :sentiment analysis; natural language processing; BERT; VADER; sustainability report; Mathematics and Statistics;

    Sammanfattning : Purpose: The purpose of this study is to examine how the overall sentiment results from VADER and a pre-trained RoBERTa model differ. The study investigates potential differences in terms of the median and shape of the two distributions. Data: The sustainability reports of 50 independent random companies are selected as the sample. LÄS MER

  4. 3. How to Draw a Circle: Investigating Relationship Management in Circular Business Companies

    C-uppsats, Handelshögskolan i Stockholm/Institutionen för företagande och ledning

    Författare :Louise Lee Hultberg; Aidan Luke Catterall Byrne; [2024]
    Nyckelord :Circular Business Models; Sustainability; Relationship Management; Circular Ecosystems; Constructivist Grounded Theory;

    Sammanfattning : Despite the recent surge in circular business model (CBM) research, the discipline is still in its infancy. The field is primarily driven by practitioners and lacks suggestive frameworks, which complicates current CBM trials. LÄS MER

  5. 4. Comparative Study of the Role of Digital Technologies in Implementing Circular Economy Practices in Product-Service Systems (PSS) : An Analysis Across Varying Organizational Scales

    Master-uppsats, KTH/Produktionsutveckling

    Författare :Ajay Surya Gnaneswaran; [2024]
    Nyckelord :Product Service Systems; PSS; Circular Economy; CE; Digital Technology; DT; Industry 4.0; Circular Strategies; Barriers and Opportunities; Qualitative; Product-tjänste system; PSS; Digitala Teknologier; DT; Cirkulär Ekonomi; CE; Cirkulära Strategier; Industri 4.0; Utmaningar och Möjligheter; Kvalitativ.;

    Sammanfattning : This thesis investigates the integration of digital technologies (DT) within Product-Service Systems (PSS) and their role in promoting Circular Economy (CE) practices. Despite increasing recognition of digitalization in industries with PSS offerings, research on DT as a catalyst for CE, especially across organizations of various sizes, remains limited. LÄS MER

  7. 5. Utilizing energy-saving techniques to reduce energy and memory consumption when training machine learning models : Sustainable Machine Learning

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Khalid El Yaacoub; [2024]
    Nyckelord :Sustainable AI; Machine learning; Quantization-Aware Training; Model Distillation; Quantized Distillation; Siamese Neural Networks; Continual Learning; Experience Replay; Data Efficient AI; Energy Consumption; Energy-Savings; Sustainable ML; Computation resources; Hållbar maskin inlärning; Hållbar AI; Maskininlärning; Quantization-Aware Training; Model Distillation; Quantized Distillation; siamesiska neurala nätverk; Continual Learning; Experience Replay; Dataeffektiv AI; Energiförbrukning; Energibesparingar; Beräkningsresurser;

    Sammanfattning : Emerging machine learning (ML) techniques are showing great potential in prediction performance. However, research and development is often conducted in an environment with extensive computational resources and blinded by prediction performance. LÄS MER