Fernando Martins

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.

Climate change already affects all inhabited regions of the world, with human influence contributing to many observed changes in climate extremes and to mitigate this trend, important decisions have been taken by different world organizations and countries to achieve global net-zero emissions. At the European Union level (EU27 countries), two of the main pillars for achieving carbon neutrality are: (1) The “Energy Efficiency First principle” in the formulation of energy policy and in taking relevant investment decisions and (2) the electrification of the energy sector, supported by generating electricity through endogenous renewable energy sources. In this context, a comprehensive review was carried out on what has been developed by the scientific community and main international energy organizations on the electrification of the energy sector. Additionally, with the purpose of better understanding the state of the art of a country regarding the decarbonization process, the Portuguese energy vectors were identified in terms of which ones can and should be electrified by 2050, based on the data available from Eurostat (2019). Portugal is a country highly dependent on imported energy (78%), where the largest energy vector is from oil and petroleum products (68%) mostly used in the transport sector, where the electrification of the sector will be increased gradually until 2050. However, other decarbonized solutions, such as biofuels and synthetic (green) fuels cannot be ruled out and should be the subject of future work and considered for the decarbonization goals to be achieved in 2050. The main conclusions reflect that there is still a long way to go, much like the rest of the world, as it is necessary to electrify the equivalent to almost all the energy presently imported by Portugal, a tough challenge considering the need for its generation to be decarbonized. In this context, energy efficiency must play an equivalently important role to significantly reduce current energy demand, leading to more cost-effective and resilient energy services.

Nowadays, modeling tools are a crucial part of best practice in the elaboration and implementation of a decarbonization plan in any organization, city, or country. The present review analyzes the different modeling tools available to assess energy systems in smart cities. It creates an updated overview of the modeling tools currently available, showing their capabilities and main potential outputs when considering the energy efficiency objective in the context of smart cities in Europe. A restricted set of 14 tools are identified which optimally fulfill the modeling mission of the energy sector, in a smart city context, for different time horizons. The selection considers the capability to include decarbonization assessments, namely, by considering the flexibility to use different external factors, energy policies, technologies, and mainly the implementation of Article 7 from the Energy Efficiency Directive and the “energy efficiency first” principle defined by the European Commission. The ELECTRE TRI method was used to implement a multi-criteria decision approach for sorting modeling tools, aiming at distributing the various alternatives by previously defined categories, and considering the performance criteria of each alternative modeling tool, the analysis suggests that the best options are the LEAP, MESSAGEix, and oemof tools.