A Road-Segment-Level Energy Classification Framework for Public Lighting: From Algorithmic Assessment to Voluntary Energy Labels for Municipal Action

Public lighting can account for nearly 40% of municipal energy consumption in some European cities and plays a vital role in road safety, mobility, and the quality of public spaces. Despite notable efficiency gains from the widespread adoption of light-emitting diode (LED) technologies, the technical outputs of standards-based and installation-level assessment methods are not usually simple and communicable energy-performance labels for municipal decision-making. This study addresses this issue by introducing an algorithm-based framework for classifying energy performance in public lighting at the road-segment level. This approach translates existing lighting standards and efficiency indicators into a straightforward and understandable energy label, adapting the energy labelling concept, commonly used for buildings and appliances, to public space infrastructure. This framework is implemented through a national digital platform for public lighting classification, which has already attracted formal interest from more than 100 municipalities, indicating strong institutional uptake. The results indicate that road-segment-level energy classification is feasible and scalable as a voluntary tool to enhance municipal accountability and support informed decision-making. This study concludes that algorithmic energy labels for public lighting can support sustainable urban governance transparency, comparability and decision-making capacity, with future research aimed at building capacity for large-scale implementation and incorporating environmental, human health, and ecological impact considerations into the classification system.

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Publication Details

Journal
Electricity
Published
2026-07-02
DOI
https://doi.org/10.3390/electricity7030066
Primary Topic
Impact of Light on Environment and Health
Type
article
Field-Weighted Citation Impact
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article

A Road-Segment-Level Energy Classification Framework for Public Lighting: From Algorithmic Assessment to Voluntary Energy Labels for Municipal Action

Fernando Martins, Pedro Moura, Anı́bal T. de Almeida, Alberto Van Zeller et al.
Electricity
Impact of Light on Environment and Health
article

A Road-Segment-Level Energy Classification Framework for Public Lighting: From Algorithmic Assessment to Voluntary Energy Labels for Municipal Action

Fernando Martins, Pedro Moura, Anı́bal T. de Almeida, Alberto Van Zeller, Sara Fradique
article en

Abstract

Public lighting can account for nearly 40% of municipal energy consumption in some European cities and plays a vital role in road safety, mobility, and the quality of public spaces. Despite notable efficiency gains from the widespread adoption of light-emitting diode (LED) technologies, the technical outputs of standards-based and installation-level assessment methods are not usually simple and communicable energy-performance labels for municipal decision-making. This study addresses this issue by introducing an algorithm-based framework for classifying energy performance in public lighting at the road-segment level. This approach translates existing lighting standards and efficiency indicators into a straightforward and understandable energy label, adapting the energy labelling concept, commonly used for buildings and appliances, to public space infrastructure. This framework is implemented through a national digital platform for public lighting classification, which has already attracted formal interest from more than 100 municipalities, indicating strong institutional uptake. The results indicate that road-segment-level energy classification is feasible and scalable as a voluntary tool to enhance municipal accountability and support informed decision-making. This study concludes that algorithmic energy labels for public lighting can support sustainable urban governance transparency, comparability and decision-making capacity, with future research aimed at building capacity for large-scale implementation and incorporating environmental, human health, and ecological impact considerations into the classification system.

ElectricityVol. 7(3)
National Laboratory of Energy and Geology (PT), Institute for Systems Engineering and Computers (PT), Aurora Sinai Medical Center (US), University of Coimbra (PT)
Openalex Percentile: Top 9%
Impact of Light on Environment and Health
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