Information Systems and Management
Industrial Demand Response (IDR) systems have emerged as a key enabler for enhancing grid flexibility, particularly as industries face increasing pressure to optimize energy consumption and integrate with renewable energy sources. However, despite their potential, the adoption and scalability of IDR solutions are limited by a range of technical, infrastructural, and organizational challenges. This dissertation investigates how emerging digital technologies—namely, Artificial Intelligence/Machine Learning (AI/ML) and the computing continuum (edge, fog, and cloud computing)—can be leveraged to overcome these limitations and enable scalable, intelligent, and interoperable IDR architectures.
The study addresses three core research questions. First, it develops a taxonomy of barriers to IDR adoption, distinguishing between technological and non-technological constraints. Second, it explores how distributed computing paradigms can mitigate these challenges by enabling real-time, privacy-aware, and latency-sensitive decision-making across industrial sites. Third, it examines the synergistic integration of AI/ML within the computing continuum, emphasizing methods such as federated learning, transfer learning, and multi-agent reinforcement learning to overcome issues related to data sparsity, system complexity, and semantic heterogeneity.
A reference architecture for IDR aggregators is proposed, combining layered intelligence, semantic interoperability, and orchestration mechanisms. This architecture is mapped to real-world cloud and open-source platforms to demonstrate its practical applicability. The findings confirm that the integration of AI/ML and distributed computing is not only feasible but essential for advancing the resilience, autonomy, and responsiveness of future industrial energy systems.
The transition toward sustainable urban mobility and industrial efficiency requires decision-making tools that go beyond correlation-based analysis to uncover true cause-and-effect relationships. Traditional machine learning models, while effective for prediction, often act as "black boxes," lacking interpretability and failing to reveal the mechanisms underlying complex systems. To address these limitations, this dissertation introduces a modular Causal AI framework for smart decision-making, integrating causal discovery and inference with structured domain knowledge to enhance sustainability outcomes.
The framework is validated across three key domains: (1) urban CO2 emissions, (2) shared mobility demand, and (3) SME energy use. The first case study analyzes over 500,000 vehicles to uncover how engine performance and maintenance drive urban emissions. The second study examines shared bike systems, identifying causal impacts of weather patterns, station topology, and temporal demand fluctuations, supporting more adaptive fleet operations. The third applies the framework in a manufacturing SME, identifying the root causes of energy inefficiency and enabling targeted interventions to improve operational performance without compromising productivity.
This research advances the interpretability and actionability of AI in sustainability contexts by replacing opaque predictive models with transparent, evidence-based causal reasoning. Algorithms such as PC, FCI, GES, and DirectLiNGAM are employed alongside domain ontologies to uncover valid causal relationships and support decision-making. A hybrid approach also addresses feature selection, dimensionality reduction, and model explainability, making the methodology broadly applicable across diverse sustainability challenges.
While the framework demonstrates strong applicability, future work may focus on enhancing real-time scalability, adaptive ontology integration, and broader validation across domains such as electric mobility and smart energy systems. Overall, this thesis contributes a generalizable, interpretable Causal AI framework that enhances systemic understanding and supports sustainable transformation in policy, planning, and industrial decision-making.
Environmental, Social, and Governance (ESG) reporting is increasingly critical for corporate transparency and accountability as demands from investors, regulators, and the public grow. Despite its importance, ESG reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global goals like the UN SDGs, and limited relevance for stakeholders. These issues weaken benchmarking, data reliability, and decision-making, raising the risk of greenwashing. Although previous studies have explored drivers of ESG performance, there is still a clear need for technical solutions that enhance the quality and usefulness of ESG disclosures through integrated approaches.
This dissertation addresses these critical gaps by developing and proposing a novel, integrated framework that leverages the strength of semantic technologies, MCDM methods, and ESG maturity models. The research is structured through several interconnected studies, exploring specific industry applications using multi-criteria analysis techniques to identify and prioritize relevant ESG KPIs. A systematic literature review (SLR) provides a foundational understanding of existing ontology-driven solutions and their limitations in addressing reporting challenges and integrating quantitative methods. Based on these insights, the core contribution is the design of an ontology-based framework called ESGOnt. This framework utilizes a modular ESG ontology to standardize terminology, integrate fragmented data sources, and explicitly map ESG metrics to SDG targets.
This research contributes by addressing critical challenges in ESG reporting through a novel, integrated framework. It advances the understanding of how semantic models can be combined with quantitative methods to create robust, transparent, and actionable sustainability reporting systems, strengthening corporate accountability and supporting more effective contributions to global sustainable development.
The existing Industry 4.0 maturity models (I4.0 MM) have mostly been built and tested in developed nations, making them less effective in developing countries with unique issues. Additionally, flexible updated models are needed to support the smooth integration of I4.0 adoption in rapid technological advancements and organizational matters. The research addresses the challenges by developing an adaptable I4.0 MM using approaches starting with structured literature reviews (SLRs), investigating the causal relationship and prioritization of I4.0 MMs key driving factors, aligning it with reputable reference architecture model (RAMs), and developing an ontology, named Ontomat 4.0, to facilitate interoperability of I4.0 MMs.
The research's general findings highlight the core gaps in existing I4.0 MMs, the need to prioritize and the interdependence of the key driving factor in I4.0 transformation, the importance of strategically enhancing I4.0 adoption by aligning the key factor of I4.0 MMs with RAMs, and the necessity of a framework with an approach that can bridge the theoretical foundation of I4.0 MMs with practical application.
The research concludes by describing contributions to the issues and challenges in the findings. However, while the research acknowledges the significant progress in its accomplishment, there are limitations to the study that need to be addressed in future research directions, including integrating sustainability metrics and increasingly essential factors, such as Customers, the potential integration of artificial intelligence (AI) within Ontomat 4.0, future exploration equipped with longitudinal studies, and the expansion of Ontomat 4.0 into a collaborative ecosystem where knowledge sharing and best practices can grow.
