Computational methods can achieve high accuracy, process large document collections, and generate actionable policy insights. Nevertheless, energy policy remains inconsistent, and institutional fragmentation persists, excluding marginalized populations from reshaping governance. This issue stems from conceptual failures rather than technical ones, as biased algorithms reinforce existing power structures. This book examines why these challenges arise. The governance-data-inequality nexus shows how institutional fragmentation creates biased data infrastructures that train algorithms, perpetuating fragmentation. By combining complexity science with ethical frameworks like energy justice, the analysis reveals why conventional computational approaches serve only as diagnostic tools rather than genuine governance solutions. The BRIDGE-E framework redefines computational governance. Equity is not merely an afterthought but a guiding principle for algorithmic systems. Epistemic inclusion shapes what counts as data, and energy justice determines policy pathways. Deliberation is central, promoting internal institutional capacity instead of reliance on external solutions. BRIDGE-E effectively addresses the challenges of institutional fragmentation, data scarcity, and contested values in governance.

A Computational Framework for Energy Policy Coherence serves policymakers, energy planners, AI researchers, and development practitioners by explaining how computational innovation can address governance fragmentation while ensuring equity.

Introduces how fragmented institutions and biased data promote cycles of incoherence and exclusion in policy systems Synthesizes complexity science and ethics to show that technical sophistication alone cannot drive structural change Proposes BRIDGE-E, a framework prioritizing epistemic inclusion and energy justice for resource-constrained environments

Autorentext

Rabaï Bouderhem is an Associate Professor of Law. His research investigates the regulation and governance of artificial intelligence and data science methodologies through an interdisciplinary lens, integrating legal analysis with complexity economics, epistemic injustice theory, information ethics, and responsible AI frameworks to develop adaptive and equitable regulatory approaches.

Inhalt

Part I: Foundations.- Bridging Computational Techniques and Energy Policy Coherence.- Theoretical Framework for Energy Policy Coherence.- Part II: Methodology and AI Applications.- Methodology for Replicability.- AI Applications in Measuring Policy Coherence for Energy Efficiency.- Challenges and Limitations of AI Application in Policy Coherence.- Initiatives and General Approaches Used to Apply AI in Measuring Policy Coherence for Energy Efficiency in Developing Countries.- Part III: Policy Implementation and Frameworks.-The Emergence of Computational Techniques for Policy Coherence.- Chapter 8. A Novel AI-Based Framework for Assessing Policy Coherence for Energy Efficiency: Bias-Resistant Integration of Data, Governance, and Equity for Energy (BRIDGE-E).- Conclusion.