A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators

Giuliano Poli; Stefano Cuntò; Eugenio Muccio

doi:10.1007/978-3-031-65285-1_22

A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators

Giuliano Poli, Stefano Cuntò, Eugenio Muccio^*

^*Corresponding author for this work

Real Estate Management

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

Monitoring SDG 11 targets is crucial for making informed decisions and supporting multidimensional transitions in European cities. Among all the goals, SDG 11 emerges as a cornerstone for cities, offering a comprehensive framework to tackle their multifaceted challenges. Composite indicators and indices, as suited evaluation tools to monitor city progress or decline, allow sustainability problems to be included in local agendas by aggregating multi-dimensional variables at different time spans through data-driven approaches. The primary concerns about using indicators as evaluation tools to compare performances are inherent to inconsistencies related to different assessment frameworks and methods, data downscaling from global to local levels, choice of aggregation rules to obtain synthetic results, and data gaps. This contribution, in particular, focuses on data gaps by elaborating on a testing case, while critically discussing related issues. The research was addressed to identify normative, assessment, and methodological gaps in monitoring progress towards SDG 11 at global, European, and Italian levels. Application of Machine Learning algorithms to predict null values within an SDG 11 regional dataset was implemented to compare three Italian regions according to 18 common indicators. The contribution is part of the Research Project of National Relevance “GLOSSA - GLOcal knowledge System for Sustainability Assessment of urban projects”, coordinated by Polytechnic of Turin (Italy), and it supports its first-step knowledge phase aimed at identifying gaps in SDG 11 indicators downscaling and monitoring.

Original language	English
Title of host publication	Computational Science and Its Applications – ICCSA 2024 Workshops
Subtitle of host publication	Hanoi, Vietnam, July 1–4, 2024, Proceedings, Part VI
Editors	Osvaldo Gervasi, Beniamino Murgante, Chiara Garau, David Taniar, Ana Maria A.C. Rocha, Maria Noelia Faginas Lago
Place of Publication	Cham
Publisher	Springer
Pages	337-355
Number of pages	19
ISBN (Electronic)	978-3-031-65285-1
ISBN (Print)	978-3-031-65284-4
DOIs	https://doi.org/10.1007/978-3-031-65285-1_22
Publication status	Published - 2024
Event	24th International Conference on Computational Science and Its Applications, ICCSA 2024 - Hanoi, Viet Nam Duration: 1 Jul 2024 → 4 Jul 2024

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	LNCS 14820
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Computational Science and Its Applications, ICCSA 2024
Country/Territory	Viet Nam
City	Hanoi
Period	1/07/24 → 4/07/24

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Data-driven Approach
Indicators
Machine Learning
SDG 11 Monitoring
Sustainable Development Strategies

Access to Document

10.1007/978-3-031-65285-1_22

Cite this

Poli, G., Cuntò, S., & Muccio, E. (2024). A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators. In O. Gervasi, B. Murgante, C. Garau, D. Taniar, A. M. A. C. Rocha, & M. N. Faginas Lago (Eds.), Computational Science and Its Applications – ICCSA 2024 Workshops: Hanoi, Vietnam, July 1–4, 2024, Proceedings, Part VI (pp. 337-355). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. LNCS 14820). Springer. https://doi.org/10.1007/978-3-031-65285-1_22

Poli, Giuliano ; Cuntò, Stefano ; Muccio, Eugenio. / A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators. Computational Science and Its Applications – ICCSA 2024 Workshops: Hanoi, Vietnam, July 1–4, 2024, Proceedings, Part VI. editor / Osvaldo Gervasi ; Beniamino Murgante ; Chiara Garau ; David Taniar ; Ana Maria A.C. Rocha ; Maria Noelia Faginas Lago. Cham : Springer, 2024. pp. 337-355 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators",

abstract = "Monitoring SDG 11 targets is crucial for making informed decisions and supporting multidimensional transitions in European cities. Among all the goals, SDG 11 emerges as a cornerstone for cities, offering a comprehensive framework to tackle their multifaceted challenges. Composite indicators and indices, as suited evaluation tools to monitor city progress or decline, allow sustainability problems to be included in local agendas by aggregating multi-dimensional variables at different time spans through data-driven approaches. The primary concerns about using indicators as evaluation tools to compare performances are inherent to inconsistencies related to different assessment frameworks and methods, data downscaling from global to local levels, choice of aggregation rules to obtain synthetic results, and data gaps. This contribution, in particular, focuses on data gaps by elaborating on a testing case, while critically discussing related issues. The research was addressed to identify normative, assessment, and methodological gaps in monitoring progress towards SDG 11 at global, European, and Italian levels. Application of Machine Learning algorithms to predict null values within an SDG 11 regional dataset was implemented to compare three Italian regions according to 18 common indicators. The contribution is part of the Research Project of National Relevance “GLOSSA - GLOcal knowledge System for Sustainability Assessment of urban projects”, coordinated by Polytechnic of Turin (Italy), and it supports its first-step knowledge phase aimed at identifying gaps in SDG 11 indicators downscaling and monitoring.",

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Poli, G, Cuntò, S & Muccio, E 2024, A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators. in O Gervasi, B Murgante, C Garau, D Taniar, AMAC Rocha & MN Faginas Lago (eds), Computational Science and Its Applications – ICCSA 2024 Workshops: Hanoi, Vietnam, July 1–4, 2024, Proceedings, Part VI. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. LNCS 14820, Springer, Cham, pp. 337-355, 24th International Conference on Computational Science and Its Applications, ICCSA 2024, Hanoi, Viet Nam, 1/07/24. https://doi.org/10.1007/978-3-031-65285-1_22

A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators. / Poli, Giuliano; Cuntò, Stefano; Muccio, Eugenio.
Computational Science and Its Applications – ICCSA 2024 Workshops: Hanoi, Vietnam, July 1–4, 2024, Proceedings, Part VI. ed. / Osvaldo Gervasi; Beniamino Murgante; Chiara Garau; David Taniar; Ana Maria A.C. Rocha; Maria Noelia Faginas Lago. Cham: Springer, 2024. p. 337-355 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. LNCS 14820).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Poli, Giuliano

AU - Cuntò, Stefano

AU - Muccio, Eugenio

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2024

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N2 - Monitoring SDG 11 targets is crucial for making informed decisions and supporting multidimensional transitions in European cities. Among all the goals, SDG 11 emerges as a cornerstone for cities, offering a comprehensive framework to tackle their multifaceted challenges. Composite indicators and indices, as suited evaluation tools to monitor city progress or decline, allow sustainability problems to be included in local agendas by aggregating multi-dimensional variables at different time spans through data-driven approaches. The primary concerns about using indicators as evaluation tools to compare performances are inherent to inconsistencies related to different assessment frameworks and methods, data downscaling from global to local levels, choice of aggregation rules to obtain synthetic results, and data gaps. This contribution, in particular, focuses on data gaps by elaborating on a testing case, while critically discussing related issues. The research was addressed to identify normative, assessment, and methodological gaps in monitoring progress towards SDG 11 at global, European, and Italian levels. Application of Machine Learning algorithms to predict null values within an SDG 11 regional dataset was implemented to compare three Italian regions according to 18 common indicators. The contribution is part of the Research Project of National Relevance “GLOSSA - GLOcal knowledge System for Sustainability Assessment of urban projects”, coordinated by Polytechnic of Turin (Italy), and it supports its first-step knowledge phase aimed at identifying gaps in SDG 11 indicators downscaling and monitoring.

AB - Monitoring SDG 11 targets is crucial for making informed decisions and supporting multidimensional transitions in European cities. Among all the goals, SDG 11 emerges as a cornerstone for cities, offering a comprehensive framework to tackle their multifaceted challenges. Composite indicators and indices, as suited evaluation tools to monitor city progress or decline, allow sustainability problems to be included in local agendas by aggregating multi-dimensional variables at different time spans through data-driven approaches. The primary concerns about using indicators as evaluation tools to compare performances are inherent to inconsistencies related to different assessment frameworks and methods, data downscaling from global to local levels, choice of aggregation rules to obtain synthetic results, and data gaps. This contribution, in particular, focuses on data gaps by elaborating on a testing case, while critically discussing related issues. The research was addressed to identify normative, assessment, and methodological gaps in monitoring progress towards SDG 11 at global, European, and Italian levels. Application of Machine Learning algorithms to predict null values within an SDG 11 regional dataset was implemented to compare three Italian regions according to 18 common indicators. The contribution is part of the Research Project of National Relevance “GLOSSA - GLOcal knowledge System for Sustainability Assessment of urban projects”, coordinated by Polytechnic of Turin (Italy), and it supports its first-step knowledge phase aimed at identifying gaps in SDG 11 indicators downscaling and monitoring.

KW - Data-driven Approach

KW - Indicators

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KW - SDG 11 Monitoring

KW - Sustainable Development Strategies

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DO - 10.1007/978-3-031-65285-1_22

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AN - SCOPUS:85200776914

SN - 978-3-031-65284-4

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Gervasi, Osvaldo

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A2 - Rocha, Ana Maria A.C.

A2 - Faginas Lago, Maria Noelia

PB - Springer

CY - Cham

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Y2 - 1 July 2024 through 4 July 2024

ER -

Poli G, Cuntò S, Muccio E. A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators. In Gervasi O, Murgante B, Garau C, Taniar D, Rocha AMAC, Faginas Lago MN, editors, Computational Science and Its Applications – ICCSA 2024 Workshops: Hanoi, Vietnam, July 1–4, 2024, Proceedings, Part VI. Cham: Springer. 2024. p. 337-355. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-65285-1_22

A Data-Driven Approach to Monitor Sustainable Development Transition in Italian Regions Through SDG 11 Indicators

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