ANNUAL CONGRESS 2025

Algorithmic Approaches to Prioritizing Urban Regeneration Scenarios Under NNLT Strategy: A Rule-Based Decision Support Model

Not scheduled

20m

Yildiz Technical University, Istanbul

Oral SS 13 | Enhancing Urban Decision-Making in the Digital Era: Tools, Methods, and Innovations

Speaker

Iuliia Kozlova (University of Bologna)

Description

1.Introduction and Research Gap

The European Union’s No Net Land Take (NNLT) strategy aims to halt further land consumption by 2050, compelling urban planners to focus on regeneration rather than expansion [1]. This policy shift, set in the framework of the EU Soils strategy for 2030 [2] and Nature Restoration Law [3], necessitates a systematic approach for prevention and restoration of soil degradation, prioritizing land conversion feasibility, ensuring that urban redevelopment decisions align with environmental, economic, and social sustainability goals. However, the lack of structured methods for assessing urban regeneration scenarios remains a critical challenge. Current approaches to land-use planning often rely on qualitative, partial expert assessments or static policy guidelines, which fail to adapt to dynamic urban conditions and real-time data inputs [4], [5], [6], [7], [8].

2.Research Questions

This study addresses the following key research questions:

1. How can land take hierarchy principles stated in the EU Soils strategy for 2030 (avoid, reuse, minimize, compensate) [2] be integrated into algorithmic rule-based models to enhance the feasibility assessment of urban regeneration scenarios? How European, national and local objectives could be integrated into these algorithmic rule-based models?
2. How can adaptive algorithmic frameworks optimize the prioritization of land-use interventions by incorporating dynamic policy constraints and urban transformation scenarios?

3.Methodology

The proposed study is based on the results of policy analysis which are encoded into an if-then rule matrix [9], [10] that automates feasibility assessments based on predefined policy criteria. The methodology is developed using a case study in the Emilia-Romagna Region, Italy, where regional policies on land take minimization and urban regeneration provide the basis for structuring decision rules. By encoding policy-driven decision rules into an adaptive algorithm, the model enhances objectivity, transparency, and efficiency in urban regeneration planning. The decision framework incorporates environmental (biodiversity restoration, carbon sequestration, climate resilience), economic (investment costs, projected revenue, long-term maintenance), and social (community acceptance, stakeholder priorities) dimensions. The if-then matrix dynamically adjusts the ranking of scenarios based on collected input variations, ensuring that feasibility assessments remain responsive to policy changes and urban dynamics.

4.Expected Outcomes and Contribution

This study proposes an approach for a scalable, algorithmic decision-support model that enhances real-time urban planning under NNLT strategies. By integrating a structured rule-based matrix into urban regeneration decision-making, the model ensures that abandoned land conversion feasibility assessments are systematic, transparent, and adaptable to policy shifts.

5.References

1. EU Roadmap to a Resource Efficient Europe, 20/09/2011
2. EU SOIL STRATEGY, 17/11/2021
3. NATURE RESTORATION LAW, 24/06/2024
4. Martellozzo, F., Amato, F., Murgante, B., & Clarke, K. (2018). Modelling the impact of urban growth on agriculture and natural land in italy to 2030, https://doi.org/10.1016/j.apgeog.2017.12.004
5. Ponzini, D. and Vani, M. (2014). Planning for military real estate conversion: collaborative practices and urban redevelopment projects in two italian cities. Urban Research & Practice, 7(1), 56-73.
6. Enoguanbhor, E., Gollnow, F., Walker, B., Nielsen, J., & Lakes, T. (2021). Key challenges for land use planning and its environmental assessments in the abuja city-region, nigeria. Land, 10(5), 443.
7. Cosentino, C., Amato, F., & Murgante, B. (2018). Population-based simulation of urban growth: the italian case study. Sustainability, 10(12), 4838
8. Wu, Y., Fan, P., Bo, L., Ouyang, Z., Liu, Y., & You, H. (2017). The effectiveness of planning control on urban growth: evidence from hangzhou, china. Sustainability, 9(5), 855.
9. Deng, Hepu & Wibowo, Santoso. (2008). A Rule-Based Decision Support System for Evaluating and Selecting IS Projects. Lecture Notes in Engineering and Computer Science, 1962-1968.
10. Artiemjew, P., Rudikova, L., & Myslivets, O. (2020). About Rule-Based Systems: Single Database Queries for Decision Making, https://doi.org/10.3390/fi12120212