Angela Greco

Purpose: This study introduces an integrated decision-support framework to aid early-stage planning for building adaptation. It aims to support structured decision-making and priority-setting within organizations by combining scenario development, stakeholder evaluation and AI-enhanced communication. Design/methodology/approach: The framework integrates cross-impact balance (CIB) analysis, analytic hierarchy process (AHP), Fuzzy-TOPSIS and generative AI techniques for scenario communication and visualization. It was applied within a Paris-based social housing association through participatory workshops with internal stakeholders, including architects, sustainability officers and project managers. Findings: The integrated framework produced 21 internally consistent scenarios, prioritized them through stakeholder-weighted objectives and identified high-performing adaptation pathways. Results revealed that strong CIB-based scenario filtering substantially conditioned downstream MCDA behaviour, producing relatively robust but convergent ranking outcomes across structurally distinct scenarios. AI-generated narratives and visuals further supported communication of complex trade-offs and exploratory planning within the organizational context. Research limitations/implications: Application was limited to a single case and stakeholder group. Future research should test the method in broader multi-actor settings, incorporate participant validation and explore automation of CIB construction and weighting to improve scalability and reduce resource demands. Practical implications: The approach helps decision-makers co-develop and compare building adaptation pathways aligned with organizational goals. Its modular design supports integration into asset management and planning systems. Originality/value: This is the first study to combine CIB-based scenario planning with MCDA for building adaptation, enhanced with AI-supported scenario communication. Beyond methodological integration, the study contributes new insights into how upstream scenario-space conditioning influences downstream ranking behaviour, evaluative convergence and discriminatory capacity within exploratory decision-support systems.

Complex societal problems (CSPs) such as climate change, pandemics, and polarization are persistent, interdependent, and difficult to define, yet they are often reduced to binary labels like “wicked” or “tame” or high-level typologies that oversimplify their underlying complexity. This paper revisits these typologies and proposes a conceptual framework that treats complexity as a layered structure of interacting dimensions rather than discrete categories. Based on an integrative review of 25 typologies from policy science, systems science, and management, the framework organizes problem complexity into two families: system complexity (structure, dynamics, and scope) and stakeholder divergence (values, knowledge, and power). These dimensions can be visualized through multi-level representations such as radar diagrams that reveal a problem’s complexity profile. The paper outlines three modes of application: problem diagnosis to support sensemaking; frame comparison to monitor (temporal) problem perception differences; and method and governance alignment to connect salient complexity dimensions to appropriate governance instruments and systems methods. This approach reduces risks of misclassification, improves analytical precision, and provides a shared language that supports interdisciplinary collaboration. The framework offers a more nuanced characterization of CSPs and introduces concrete tools for governance and design.

The realization of sustainable plus energy neighborhoods (SPENs) is key to achieving a carbon neutral built environment, and meeting the objectives of the green deal. Financial schemes have demonstrated effectiveness in driving the energy transition of individual buildings. However, the role of financial schemes in initiating the development of SPENs remains unclear. This study aims to address this research gap by investigating the extent to which existing financial schemes support the technological advancements and stakeholders involved in SPEN realization. The focus is on four European countries: Austria, The Netherlands, Norway, and Spain, where noteworthy SPEN initiatives have been established. This study investigates whether and how financial schemes facilitate SPEN development and address the neighborhood-level dynamics. Our research used a mixed-methods approach comprising a literature review, case studies, and interviews. Our findings shed light on two significant barriers within current schemes. Firstly, the schemes rarely incentivize collective energy sharing and may inadvertently impede individuals’ prosumerism. Secondly, they primarily favor individuals capable of making upfront capital investments, rendering them inaccessible to a large portion of European citizens. By identifying these limitations, our study highlights the need for policy adjustments and innovative financial mechanisms to overcome the barriers hindering SPEN implementation. Moreover, our research contributes to the broader understanding of sustainable urban development and offers insights that extend beyond the examined countries, aiding policymakers and stakeholders in other regions facing similar challenges.