This deliverable presents the Improved Good Practice Guidance developed within the tunES project. The collection consolidates and updates good practices related to the design, deployment, and implementation of Energy Performance Certificates and the Smart Readiness Indicator across the European Union, supporting Member States in the implementation of the revised EPBD.
IntroductionCircular economy implementation in the construction sector is expected to increase opportunities for green entrepreneurship and significantly contribute to achieving carbon neutrality by 2050, in line with the European Union's ambitious target. However, nowadays circularity in the construction sector remains underdeveloped, with limited empirical research investigating individual stakeholders' perspectives. This paper, focusing on the case of Greece, examines the specifications for circular construction products identified as critical by stakeholders, as well as the barriers and drivers shaping current developments in the construction sector toward circularity.MethodsThe methodology includes the following steps: a critical literature review on the topics under investigation (specifications, barriers, drivers); development of a questionnaire to explore stakeholders' views; conduction of a quantitative empirical survey targeting Greek key stakeholders from the circular economy and/or the construction sectors. The survey was conducted from October 2024 to April 2025 and involved 100 stakeholders from the research, academic, industrial, policy, and construction sectors. Responses were collected via an online questionnaire or via a hard-copy version distributed at relevant conferences and events. The perceptions of stakeholders were revealed using descriptive statistics, while Kruskal-Wallis H tests were conducted to identify statistically significant differences in responses among stakeholders with varying levels of knowledge of circular economy practices in the construction sector.Results and discussionThe analysis showed that Greek stakeholders prioritize certain specifications of circular economy products, such as safety, energy efficiency, and impacts on human health, over others, such as aesthetics and delivery time. Cost-related aspects, lack of government support, regulatory framework and limited consumer awareness are perceived as the most significant barriers. In parallel, funding for innovation, raising awareness, and enforcement of related regulations are considered key drivers. Variations in perceptions based on stakeholders' knowledge levels suggest the need for targeted educational and policy interventions to bridge knowledge gaps. The paper's findings are considered significant for selecting appropriate strategies and policies to strengthen the implementation of the circular economy in the construction sector.
This study examines European citizens’ attitudes and willingness to pay for biobased materials in construction and renovation. A survey conducted across nine countries with over 4500 participants revealed a generally positive attitude toward using recycled materials, such as glass and wood, while skepticism persisted toward biobased options. Many respondents expressed willingness to pay more for biobased and innovative materials, though a notable proportion would pay less for recycled and reused components. The findings highlighted significant national differences in attitudes and preferences, underscoring the influence of socioeconomic and cultural factors on the adoption of circular economy practices in the construction sector.
Given the limited potential of conventional statistical models, machine learning (ML) techniques in the field of energy poverty have attracted growing interest, especially during the last five years. The present paper adds new insights to the existing literature by exploring the capacity of ML algorithms to successfully predict energy poverty, as defined by different indicators, for the case of the “Urban Region of Athens” in Greece. More specifically, five energy poverty indicators were predicted on the basis of socio-economic/technical variables through training different machine learning classifiers. The analysis showed that almost all classifiers managed to successfully predict three out of five energy poverty indicators with a remarkably good level of accuracy, i.e., 81–94% correct predictions of energy-poor households for the best models and an overall accuracy rate of over 94%. The most successful classifier in terms of energy poverty prediction proved to be the “Random Forest” classifier, closely followed by “Trees J48” and “Multilayer Perceptron” classifiers (decision tree and neural network approaches). The impressively high accuracy scores of the models confirmed that ML is a promising tool towards understanding energy poverty drivers and shaping appropriate energy policies.
Energy and environmental policies in the sector of buildings aim to achieve climate targets while ensuring affordable energy services for households. This study uses the Greek residential sector as a case study and focuses on energy poverty, examining both established and novel energy poverty indicators for its measurement, analyzing the key determinants of energy poverty, and developing statistical models to identify energy-poor households. The same models are also used for assessing the effectiveness of policies and measures implemented or planned to address energy poverty with a view to develop synergies with policies aiming to reduce greenhouse gas emissions. Energy poverty levels in Greece ranged from 8.4% to 19.6% in 2021, depending on the energy poverty measure used. The evaluation of the policies aiming at tackling energy poverty showed that deep energy renovations, combined with space heating system upgrades, can reduce energy poverty by 69–99%. Shallow energy renovations and upgrades of space heating systems, implemented either individually or in combination, are less effective. Finally, while the various subsidy schemes for vulnerable households do not significantly affect energy poverty levels, they play a critical role in alleviating the depth of energy poverty and improving the quality of energy services provided to households.
