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Description
Computational methods now achieve high accuracy rates, process vast document corpora, and generate actionable policy insights. Yet energy policy incoherence persists. Institutional fragmentation continues. Marginalized populations remain systematically excluded from the computational systems reshaping governance, a conceptual, not technical, failure. It is the perfect execution of biased algorithms encoding existing power structures. This book diagnoses why. The governance-data-inequality nexus demonstrates how institutional fragmentation produces biased data infrastructures, which train algorithms that legitimize and entrench the fragmentation they should overcome. Bridging complexity science (path dependence, structural inertia, co-evolution) with ethical frameworks (epistemic injustice, information ethics, responsible AI, energy justice), the analysis demonstrates why conventional computational approaches remain diagnostic instruments external to genuine governance. BRIDGE-E reframes what computational governance can be. Equity functions not as a post-hoc evaluation but as a constitutive logic governing algorithmic systems. Epistemic inclusion determines what counts as data. Energy justice defines which policy pathways the system can propose. Deliberation is a core feature of human-in-the-loop protocols that build endogenous institutional capacity rather than create external dependencies. The framework addresses precisely the conditions where conventional approaches fail institutional fragmentation, data scarcity, and contested values. BRIDGE-E is a framework for governing complexity through complexity itself.
A Computational Framework for Energy Policy Coherence serves policymakers, energy planners, AI researchers, and development practitioners understand how computational innovation can address governance fragmentation while ensuring equity.
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.
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.
