New Approaches to Generate Data to Measure the Progress of Decarbonization of the Building Stock in Europe and Spain

Abstract

The European Commission has initiated efforts to establish indicator frameworks for monitoring building renovation policies and the decarbonization progress of the building stock, to be developed by EU Member States. The chapter suggests that the use of emerging technologies can improve the quality and availability of data for this purpose, focusing on two specific technologies whose potential is explored for the case of Spain. These technologies are, on one hand, georeferencing and automated cross-referencing of existing data, and, on the other, the Digital Building Logbook (DBL). The results of the study show that georeferencing and automated cross-referencing applications include calculating solar potential and estimating energy production, consumption, and emissions using Urban Energy Models (UBEMs). The UBEM, applied to Spain, enables the collection of previously unavailable indicators, improving data availability and quality mainly in the subjects of ‘An overview of the general characteristics of the national building stock’ and ‘An overview of the energy characteristics of the national building stock’. Examining the feasibility of collecting indicators through the DBL indicates that close to 69% of all necessary indicators for monitoring building renovation policies can be obtained. However, challenges in realizing DBL's full potential involve undefined tool models, owner awareness, funding uncertainties, regulatory alignment, and technical hurdles for large-scale implementation.

1 Introduction

While building renovation and decarbonization have been significant topics on the European political agenda since 2012, it is only recently that the European Commission (EC) has actively initiated efforts to create indicator frameworks for monitoring the effectiveness of building renovation policies and the decarbonization progress, as indicated in Chap. 1 of this book (López-Mesa et al. 2023). The utilization of indicators for assessing progress and policy effectiveness is a valuable instrument to ensure that building renovation policies are efficient, effective, and in harmony with overarching sustainability and climate objectives. It facilitates decision-making based on evidence, accountability, the attainment of long-term decarbonization goals, and it educates and involves the public in these significant initiatives (López-Mesa et al. 2023).

One of the key principles in the development of indicators is to ascertain the availability of existing data before initiating a costly data collection process (International Energy Agency 2014). Beltrán-Velamazán et al. (2023a), in Chap. 2 of this book, identified the subjects data collection should focus on first. These subjects follow next and have been developed in each of the chapters of this book:

• An overview of the general characteristics of the national building stock, in Chap. 3 (Gómez-Gil et al. 2023a).

• An overview of the energy characteristics of the national building stock, in Chap. 4 (Gómez-Gil et al. 2023b).

• An overview of deep renovation of buildings, in Chap. 5 (Arbulu-Dudagoitia et al. 2023a).

• An overview of worst performing segments of the national building stock, rented properties, and energy poverty, in Chap. 6 (Beltrán-Velamazán et al. 2023b).

• An overview of the capacities in the construction, energy efficiency and renewable energy sector, in Chap. 7 (Gómez-Gil et al. 2023c).

• An overview of actual energy savings and wider benefits of renovation of buildings, in Chap. 8 (Gómez-Gil et al. 2023d).

• Evidence-based estimate of expected energy savings and of reduction of costs for health systems from buildings renovation, in Chap. 9 (Gómez-Gil et al. 2023e).

• Policies and measures for the mobilization of investments into the renovation of buildings, in Chap. 10 (Arbulu-Dudagoitia et al. 2023b).

The assessment of data availability and quality in Spain for the development of indicators within these subjects to measure the effectiveness of building renovation policy and the progress of decarbonization of the building stock, conducted by (Gómez-Gil et al. 2023f) in Chap. 11 of this book, reveals that there is great room for improvement in terms of data quality and availability. Only around 17% of the indicators are fully available, providing a strong foundation for evaluating and monitoring progress in certain areas. Another 41% are partially accessible, which means they can be developed, but additional data sources or refinement are necessary. Finally, the remaining 42% are unavailable, underscoring a significant need for improvements in data availability. On the other hand, georeferencing, a critical component for spatial analysis and geographic-based indicators, was found in only 9% of the data sources, suggesting room for enhancement in this aspect (Gómez-Gil et al. 2023f). Furthermore, a significant segment (54%) of data sources is updated at intervals of a decade, consists of single publication documents, or lacks specific update frequencies, and this issue in data update frequency can affect the efficiency of policy monitoring and evaluation, underscoring the necessity for more frequent and timely data updates (Gómez-Gil et al. 2023f).

The availability and quality of data depends on the subject of the indicator, as shown in Table 1, created from data in (Gómez-Gil et al. 2023f). The development of indicators associated with deep renovation and policies for the mobilization of investments were found to encounter the highest obstacles, as a great deal of these indicators are non-available. For other indicators, such as for those related to the general characteristics of the national building stock, there are more data available, however, many of their data sources have a very low frequency of updating given that they are published every ten years.

Table 1 Number of indicators on building stock renovation and decarbonization, classified by subjects, whose data are collectable nowadays in Spain from existing sources.

New emerging technologies have the potential to significantly enhance data availability and quality in various ways. For example, technologies such as Internet of Things (IoT) devices, sensors, and automated data collection tools can continuously gather real-time data, reducing the reliance on manual data entry and minimizing human error (e.g. Valinejadshoubi et al. 2021). Advanced analytics and big data tools can process large volumes of data quickly and efficiently, identify patterns and anomalies that might not be evident through traditional methods. The potential uses of these technologies in the context of reducing carbon footprints in buildings are extensive (Sandaruwan et al. 2023). Machine learning algorithms can improve data quality by detecting errors, inconsistencies, and outliers in datasets. They can also predict missing data values and impute them (e.g. Zhang et al. 2023). Technologies that facilitate the integration of data from diverse sources can enhance data quality by creating a more comprehensive and cohesive dataset (e.g. Anand and Deb 2023). Remote Sensing and Satellite Technology can provide valuable data for various applications, such as environmental monitoring and infrastructure assessment (e.g. Anand and Deb 2023). Government and industry-led open data initiatives can promote data sharing, increasing data availability for public and private sector use (e.g. Gómez-Gil et al. 2023g). Edge computing brings data processing closer to the data source, reducing latency and improving the availability of real-time data (e.g. Amadeo et al. 2023).

The objective of this chapter is to propose new approaches that can be useful to improve the availability and quality of data to develop indicators to measure the effectiveness of building renovation policies and the progress of decarbonization of the building stock in Europe, and to explore their potential.

2 Methodology

The methodology followed in this chapter started with the selection of two novel approaches for data enhancement. These approaches have been studied in the context of one of the research projects of which this book is the result, project LocalREGEN-A (LocalREGEN 2023). The first one is the georeferencing and automatized cross-referencing of open data as a tool to generate enriched data. The second one is a government-led information repository, the Digital Building Logbook (DBL), promoted by the European Commission that will collect all the information that is generated along the life cycle of each building and will be connected to existing data sources in the different countries of the European Union (EU), to emerging European data sources, and to new technologies, such as smart monitoring, which is also being studied in the mentioned project.

Once the two main approaches were identified, the following steps followed to study the potential of georeferencing and automatized cross-referencing in the improvement of data availability and quality of the indicators:

• We first studied the regulation and initiatives on georeferencing and automatized cross-referencing in Europe and Spain as well as the existing data sources in Spain with georeferenced data on buildings.

• Then we identified the indicators whose data could be enhanced by means of georeferencing and automatized cross-referencing. Afterwards we selected one of them as case study to show an example in this book.

• This indicator was developed with the support of an Urban Building Energy Model (UBEM) that has been developed in the context of the research project LocalREGEN-A.

To study the potential of the government-led information repository name the Digital Building Logbook (DBL) the following steps were undertaken for this chapter:

• We first described the characteristics of the DBL by means of a literature review.

• Then we identified the indicators whose data will be possible to collect from data collected in the DBL.

• Third, we identified the data fields that the DBL should contain to allow it.

Finally, in this chapter conclusions are drawn regarding the potential of these two technologies in improving data quality and availability.

3 New Approaches

3.1 Georeferencing and Automatized Cross-Referencing of Open Data as a Tool to Generate Enriched Data

In the digital age, data has become an invaluable resource that drives decision-making, innovation, and societal progress. Open data, in particular, has emerged as a catalyst for transparency, collaboration, and innovation. Georeferencing and automatized cross-referencing of open data are pivotal tools in enhancing the value of available information by adding spatial context and interconnecting disparate datasets, transforming them into a coherent and interrelated source of information.

This subsection explores the significance of georeferencing and automatized cross-referencing as tools to generate enriched data. An application example is created to show the potential in the development of indicators to measure the effectiveness of building renovation policy and the decarbonization progress.

3.1.1 Georeferencing: Adding Spatial Context

Georeferencing is the process of associating data with geographic coordinates, allowing information to be placed on maps and analyzed in a spatial context. This spatial context allows the use of geographic information systems (GIS) helping to uncover patterns, relationships, and trends that might not be apparent when looking at data in isolation. Over time, these types of spatial analyzes have become crucial for a wide range of applications, from urban planning to environmental monitoring.

In the European context, there is a directive that is crucial in this topic, the Infrastructure for Spatial Information in the European Community (INSPIRE) Directive (European Parliament and the Council of the European Union 2007). It is based on the premise that geospatial information is fundamental for decision-making in a wide range of areas and was designed to establish a common framework for geospatial data at the European level with the aim of facilitating the exchange of geospatial information among EU Member States (MSs) and improving cooperation in the 34 spatial data themes which covers.

This directive establishes harmonized regulatory frameworks for the entire EU and establishes synchronized national Spatial Data Infrastructures (SDIs) that serve as centralized access points for geospatial data. These SDIs facilitate the search and access of geographic information, promoting cooperation and evidence-based decision-making through accurate and up-to-date geospatial data across the EU.

To achieve and measure the decarbonization objectives in Europe the INSPIRE Directive plays a crucial role in the European context of decarbonizing buildings for several reasons. On one hand, the standardization of the data and metadata across EU is essential for collecting, analyzing, and sharing data related to buildings, their energy performance, their carbon footprint and the renewable energy potential across borders. On the other hand, standardized data ensures consistency and comparability across different regions and countries, facilitating effective decarbonization strategies, supporting holistic approaches to decarbonization that consider regional and transnational factors.

Decarbonizing buildings requires detailed spatial analysis. Analyzing geospatial data related to buildings, such as their location, size, climate areas, and potential to generate renewable energy on-site is crucial for identifying areas with the greatest potential for energy efficiency improvements and renewable energy adoption.

Also mapping the energy performance of the building and the actual and potential energy efficiency supports the creation of maps and spatial visualizations, which can help policymakers, businesses, and citizens identify energy-efficient and energy-inefficient areas. This can lead to targeted decarbonization efforts where they are needed most.

In Spain, there are several databases containing georeferenced information about buildings. Some of the main databases georeferenced information in Spain include:

• The Spanish Cadaster. It is an administrative register with a fiscal origin, with an inventory of buildings and real estate properties. It contains physical, legal and economic information (General Directorate of the Cadaster 2023) covering the entire national territory except the Basque Country and Navarre. There are two main sets of information, georeferenced files and tabular files.

  • Tabular files are not georeferenced and contain a lot of useful information with higher level of detail than georeferenced files. Part of this information is protected and cannot be widely accessed. It is important to highlight that the tabular information from the Spanish Cadaster can be downloaded by province using some forms on the Electronic Headquarters of the Cadaster website. This means that there is no direct application programming interface (API) for automated download, what would facilitate to easily and quickly update data and make large-scale analysis.

  • INSPIRE Cadastral Mapping Services. A special mention deserves the INSPIRE service of the Spanish Cadaster. It offers the georeferenced Spanish Cadaster data in an automated way through the ATOM download service at the municipality level, with georeferenced information and with the structure indicated by the INSPIRE directive. It allows for automation of large-scale data analysis in a much more efficient way, however, the data they offer for each building is a summary of what is openly available in the cadaster.

• Spain’s National Centre for Geographic Information (CNIG). It is a government agency responsible for the management, development, and dissemination of geospatial information in Spain. CNIG is responsible for the collection, storage, and management of a wide range of geospatial data, including topographic maps, land use data, geographic databases, Digital Elevation Models and aerial imagery and offers them in open access from its Download Center (Spain’s National Geographic Institute 2023).

• Regional and municipal information sources. There is a wide variety of open information that is offered by regional or municipal entities, such as the portal ‘Aragon Open Data’ by the Government of Aragon or ‘GeoEuskadi’, an initiative of the Government of the Basque Country. However, the information is not always georeferenced and is not homogenized between different regions or municipalities, thus it cannot be analyzed on larger scales.

3.1.2 Automatized Cross-Referencing: Creating Data Synergy.

Cross-referencing open data from multiple sources is a crucial strategy to generate enriched information. By merging related datasets, the resulting information becomes more comprehensive and valuable. Automatized cross-referencing simplifies this process and ensures data synergy.

This cross-referencing is particularly relevant when dealing with open data, as it transforms disparate datasets into a coherent and interrelated source of information. This integration of datasets allows for a more holistic view of the subject matter, filter inconsistencies and errors, leading to higher data quality, enhancing the reliability and accuracy of the information. This combined information allows more elaborate analyzes to be carried out, revealing insights that were previously hidden and promoting transparency and accountability, giving rise to data-based actions for both public authorities and citizens.

In the EU, the leadership in promoting open data is a collaboration between the EU institutions, MSs and a wide range of stakeholders working together to foster transparency, innovation, and access to information. The European Commission, through its various directorates-general and expert groups, plays a central role in coordinating and guiding these efforts at EU level.

In this context, there are several directives that play a key role in the development of this open data infrastructure. The aforementioned INSPIRE directive (European Parliament and the Council of the European Union 2007) focuses on geographic information related to the environment and spatial planning, the Public Sector Information (PSI) Directive (European Parliament and the Council of the European Union 2013), repealed by the Directive 2019/1024 on open data and the re-use of public sector information (European Parliament and the Council of the European Union 2019), encourages the re-use of public sector information, including open data. The European strategy for data (European Commission 2020b) is currently being developed with the Data Act (European Commission 2022) a key pillar of the European strategy for data laying down harmonized rules on fair access to and use of data.

The advance of these European regulations and their transposition into the laws of the member countries has led to significant progress in the quantity and accessibility of data. For example, the EU Open Data Portal and national open data portals initiatives offer a one-stop shop for discovering and accessing open data sets that greatly facilitates access to information.

In the Spanish context, the Open Data Initiative of the Government of Spain (Ministry of economic affairs and Digital Transformation 2023) provides a broad repository with access to open data provided by multiple public entities of different levels, greatly facilitating access to the data.

3.1.3 Applications and Benefits

The utilization and advantages of georeferencing and automated cross-referencing of open data are extensive and have wide-ranging implications. These techniques find various applications, in the particular context of assessing the effectiveness of building renovation policies and monitoring progress in decarbonization, which are presented next.

One of these applications is in the field of renewable energy, particularly in the calculation of solar potential on building roofs. This application is especially significant as it directly contributes to one of the mandatory indicators within the Amendments adopted by the European Parliament on 14 March 2023 on the proposal for a directive of the European Parliament and of the Council on the energy performance of buildings (recast) (European Parliament 2023), the ‘Share of renewable energy in the building sector: on-site’. This indicator holds key importance in assessing a building’s capacity to autonomously generate electricity from renewable sources. This calculation is key for the EU as it strives to advance its renewable energy goals and georeferencing and data integration are relevant tools in addressing this challenge. As discussed in Beltran-Velamazan et al. (2021), it is possible to generate 3D models of neighborhoods and cities automatically and use them to calculate the photovoltaic potential on roofs, capitalizing on the georeferencing capabilities enabled by openly available data and Geographic Information System (GIS) tools. These models offer a multitude of benefits. Firstly, they allow for the accurate assessment of a building’s solar energy generation potential, taking into account factors such as building orientation, shading, and local climate conditions. Additionally, the use of 3D models offers a higher degree of precision and realism, as they consider the three-dimensional aspects of buildings and their surroundings, resulting in more reliable estimates of solar energy potential for smaller areas than the 2D models. Additionally, 3D models offer accurate analysis with low computing costs and times, being suitable for urban scales.

Another possible application of georeferencing and automatized cross-referencing is the estimation of energy production, consumption and associated emissions. According to Beltran-Velamazan et al. (under review) it is possible, for example, to develop Urban Energy Models (UBEMs) on a national scale in Spain using openly available data sources. UBEMs are computational energy models used to analyze and simulate energy production, consumption and emissions within urban areas for multiple energy-driven applications, for instance, energy planning, energy supply–demand calculation, retrofit analysis, forecasting, renewable energy impact, emission reduction, and optimization (Ali et al. 2021).

Within the context of the research project of which this book is a result, we developed an UBEM of the country of Spain following the methodology described in (Beltran-Velamazan et al. under review). The model is named EPC-based national-scale UBEM. It combines building information from various sources: the Spanish tabular cadaster, the INSPIRE cadaster, the regional registries of Energy Performance Certificates and others, and leverages georeferencing capabilities. This UBEM has multiple applications useful for the energy diagnosis of national building stocks and to establish national renovation trajectories in Spain and its different regions. For this chapter, we studied what indicators, among those proposed in this book, can be developed with the EPC-based national-scale UBEM developed for Spain. The results can be found in detail in Table 4 in Appendix A, and are summarized in Table 2. As can be observed, all the indicators under categories ‘An overview of the general characteristics of the national building stock’, ‘An overview of the energy characteristics of the national building stock’ can be obtained. Furthermore, certain additional indicators from other categories are obtainable, including the indicators ‘Number, total floor area, percentage of buildings in lowest energy classes’, ‘Reduction of emissions’, and ‘Expected energy saving per building sector and type’.

Table 2 Number of indicators on building stock renovation and decarbonization, classified by subjects, that are collectable using the EPC-based national-scale UBEM for Spain (Beltran-Velamazan et al. under review).

As an example of the enhanced data this model is capable of generating, we developed for this chapter one of the indicators that could only be partially developed using the open data sources currently available in Spain, as discussed in Chap. 6 of this book (Beltrán-Velamazán et al. 2023b). The indicator is ‘Number, total floor area, and percentage of buildings in lowest energy classes’. In Chap. 6, we found that the only information available is the percentage of certified new and existing buildings in the lowest energy classes. This percentage information is interesting, but it does not allow us to define with great precision the real size of the decarbonization challenge, since for this, we would need to know the square meters that should be decarbonized as a priority. However, with the georeferenced EPC-based national-scale UBEM, it becomes feasible to generate all the total floor area of certified buildings and building units in the worst performing segments in Spain and its regions (corresponding to the energy letter E, F, and G) and extrapolate this data to estimate the total floor area to renovate in Spain. Furthermore, the data are not contingent on periodic ministry publications, as the construction of UBEMs can be automated taking real time data from the open sources.

Figure 1 shows the total floor area of certified buildings and the complete buildings certified in Spain and in the region of Aragon in each of the energy classes, and the percentage they represent. The obtained data reveals a notable concentration of building stock in the least favorable energy classes (E, F, and G) at present. These categories encompass 64% of Spain’s certified surface area and 75% of fully certified buildings. In Aragon, a comparable scenario exists, with 61% of the certified surface area and 77% of fully certified buildings falling into these energy classes.

Fig. 1

Source own creation from data in (Beltran-Velamazan et al. under review)

Distribution of square meters (m²) and complete buildings certified by energy class in Spain and Aragon.

The UBEM model allows us to obtain the quantity of buildings and their corresponding square footage in Spain and its regions, excluding those of Navarra and the Basque Country, as they are not accounted for in the Spanish Cadaster. This analysis reveals a total of 9,301,130 buildings in Spain, encompassing a built-up area of 3,851,560,898 square meters, and 300,554 buildings in Aragon, accounting for a total area of 132,161,106 m².

We can make an estimation of worst-performing buildings in Spain and Aragon by extrapolating the data from certified buildings to the entire building stock. By doing so, we estimated that 6,975,847 buildings and 2,464,998,974 square meters are in the worst energy classes in Spain. In Aragon, the estimation is that a total of 231,426 buildings and 80,008,274 square meters are in the worst energy classes. These findings underscore the considerable challenge of decarbonization of the building stock in Spain.

In summary, the development of UBEMs through georeferencing and automated cross-referencing of open data presents a transformative approach with wide-ranging applications, making it a powerful tool for addressing urban energy efficiency, sustainability, and the pursuit of a low-carbon future.

3.2 The Digital Building Logbook as a Tool to Increase Data Availability for the Development of Measurable Progress Indicators

In addition to improving data availability and quality using state-of-the-art technologies that leverage open data, we can further enhance data through government-led information repositories, like the Digital Building Logbook (DBL) promoted by the European Commission. To frame and understand the DBL, it is necessary, first and foremost, to introduce the Building Renovation Passport (BRP).

The concept of a building passport is not new, since it has been used in Europe for decades (Blum 2001, 2008) as a tool for collecting and exchanging information about buildings (Gómez-Gil et al. 2022a). However, it is in recent years that it has been increasingly focused on renovation (Fabbri et al. 2016, Sesana and Salvalai, 2018, Sesana et al. 2020), giving rise to the concept of the BRP. This is due to the increasing concern about accelerating building renovation pace. Despite the widely acknowledged benefits of renovation, especially of energy renovation, at various levels such as energetic, environmental, social, and economic, the annual renovation rate in Europe falls well below the recommended level by the European Commission, an annual rate of 3% (European Parliament and the Council of the European Union 2018). The BRP is positioned within this context as a tool that can assist in raising both the rate and quality of renovations.

The BRP was first introduced in European legislation through Directive (EU) 2018/844 (European Parliament and the Council of the European Union 2018), and although its structure was not specified in that text, there is sufficient consensus in the research field that this passport should consist of two parts: a DBL and a renovation roadmap.

Recently, the European Commission has recognized the potential of the passport if its model was unified at the scale of the entire EU. This aligns with the goal of creating a collaborative European environment where data is shared and compared among MSs to establish common improvement targets.

However, in parallel, the potential of the logbook to operate independently was also identified. In this regard, the European-scale DBL first emerged as a standalone tool through the Renovation Wave strategy (European Commission 2020a) published in October 2020. Subsequently, in 2023, the European DBL was officially defined in the Proposal for a Directive of the European Parliament and of the Council on the energy performance of buildings (recast) as a “common repository for all relevant building data, including data related to energy performance such as energy performance certificates, renovation passports and smart readiness indicators, as well as on the life-cycle GWP and indoor environmental quality, which facilitates informed decision making and information sharing within the construction sector, among building owners and occupants, financial institutions and public authorities” (European Parliament 2023).

A great amount of data is generated during a building’s life cycle, at the design and construction phases, during its use stage, when it is renovated and when its life comes to an end. The 2021 Proposal for a Directive of the European Parliament and of the Council on the Energy Performance of Buildings (Recast) (European Commission 2021) highlights the importance of collecting and managing these data in an effective way. In this sense, Gómez-Gil et al. (2023g) propose that the DBL should be linked to other existing data sources, such as national cadasters, Energy Performance Certificate (EPC) registries, technical inspections reports, or building renovation passports, as well as to new emerging sources in development, such as the Smart Readiness Indicator (SRI), the Environmental Product Declaration (EPD) or the Level(s) assessment framework. These will all be valuable sources of information that will feed data to the DBL in order to develop an interoperable, homogeneous, and coherent national database on energy efficiency. Additionally, the DBL can benefit from new technologies, such as the Internet of Things (IoT), Artificial Intelligence (AI), Building Information Modelling (BIM) or smart monitoring, what could help to considerably increase real-time data gathering (Gómez-Gil et al. 2022b).

After the DBL was defined, efforts are currently underway through initiatives such as European research projects and individual endeavors in the development of a DBL model. Despite the absence of a standardized model being adopted yet, there are similarities among the initiatives that have already emerged, particularly in the categories of data that the tool should gather. After examining four of the leading DBL initiatives to date—iBRoad-Log, ALDRAN BuildLog, X-Tendo Logbook, and the Study on the Development of a European Union Framework for Buildings’ Digital Logbook- Gómez-Gil et al. (2022a) categorized them into: ‘general and administrative information’, ‘construction information and materials’, ‘building technical systems and smart readiness’, ‘energy performance, operation and use’ and ‘finance’. Under these categories, data should be grouped to address the problem of data loss and unavailability due to the current asymmetric and outdated way of gathering data practiced by numerous stakeholders of the construction sector (Davidson and Skibniewski 1995).

According to the most known functionality identified for the DBL, those data would be mainly used to design the building's renovation roadmap, tailored to both the building and its users’ needs. This way, by streamlining and customizing the renovation processes, it is expected to significantly increase the pace of these endeavors. However, Gómez-Gil et al. (2022a, 2022b, 2023a) point out new uses for the tool, such as gathering information to create building maintenance plans or to assess the progress of decarbonization in the building sector.

This latter issue is of particular relevance for collecting measurable indicators on the decarbonization of the construction sector, as indicated in Gómez-Gil et al. (2024). In said study, the main decarbonization objectives related to the building sector in Europe were identified and prioritized. Measurable progress indicators were assigned to these objectives, derived from various sources in the literature, including the 2023 proposal for the EPBD recast and European Recommendation (EU) 2019/786. Within those progress indicators, a selection and prioritization were made to establish the most relevant ones for the EU and Spain. For the EU, those stemming from the 2023 proposal for the EPBD recast were considered, and for Spain, they were identified through a survey conveyed among sector experts. Once the framework of the most relevant indicators for both cases was defined, an analysis was conducted to determine which of them could be obtained using the DBL. For those that could be collected through this tool, a study was carried out on what data the DBL should gather to make it possible.

In this chapter, we analyzed whether the indicators selected for development in this book (Beltrán-Velamazán et al. 2023a, 2023b) (Gómez-Gil et al. 2023a, 2023b, 2023c, 2023d, 2023e) (Arbulu et al. 2023a, 2023b) can be or not obtained using data collected in the DBL. Table 5 in Appendix A shows this information in detail. Table 3 shows the same information summarized, deploying the number of indicators that are collectable and non-collectable organized by subjects. This study is based on a previous analysis in which part of the indicators had already been examined (Gómez-Gil et al. 2024).

Table 3 Number of indicators on building stock renovation and decarbonization, organized by subjects, which would be collectable using data stored in the DBL classified by subjects.

The analysis of the whole set of indicators proposed in this book (Table 3) shows that nearly 68% of all the indicators can be collected using data sourced from the DBL. As can be seen in the table, 100% of the indicators in subjects ‘An overview of the general characteristics of the national building stock’, ‘An overview of the energy characteristics of the national building stock’, and ‘An overview of deep renovation of buildings’ can be collected by means of the DBL. The high percentage is due to the fact that these categories gather indicators that solely refer to building characteristics. In subjects ‘An overview of actual energy savings and wider benefits of renovation of buildings’, ‘Evidence based estimate of expected energy savings and of reduction of costs for health systems from buildings renovation’, and ‘Policies and measures for the mobilization of investments into the renovation of buildings’ the percentage is around 50%. In these categories, indicators related to buildings are mixed with others related to other aspects of the construction sector, which cannot be captured by the logbook. Finally, in categories ‘An overview of worst performing segments of the national building stock, rented properties and energy poverty’ and ‘An overview of the capacities in the construction, energy efficiency and renewable energy sector’ only 33% of the indicators could be collected using data from the DBL. This is due not only to the fact that some indicators do not pertain to building characteristics, but also because there may be data protection conflicts that hinder the collection and processing of data, as is the case with indicators related to energy poverty.

With the aim that the DBL contains the necessary information for the 68% of the indicators, it will be required that it includes the following data fields:

• Date of construction

• Location of the building.

• Climate zone where the building is located

• Main use of the building

• Area of the building

• Number of dwellings

• Occupancy status

• Energy consumption (pre and post renovation) by source

• GHG emissions (pre and post renovation)

• Amount of on-site renewable energy generation and type

• Energy performance certificate

• Energy performance class

• Is this building renovated? If yes, year, type, and depth of the renovation

• Was the renovation process aggregated with that of other buildings to make the operation feasible?

• Is this building a nZEB?

• LCA Report (pre and post renovation)

• Is this building equipped with smart systems? If yes, which ones?

• Is this building fully accessible? If not, which barriers does it have?

• Total budget of the renovation

• Were public funds used in the renovation?

• District heating access

• Saving water strategies implemented in the renovation

• Monitoring data (Temperature, CO₂, RH)

It should be emphasized that the collection of these indicators through data from the DBL, in most cases, will not be carried out directly. Instead, mathematical operations or even the combination of different data fields from the tool's database are necessary to obtain the desired progress indicator. However, this involves a straightforward data processing task that can be swiftly accomplished through automatable computational processes or by using tools like Geographic Information Systems (GIS), which also enable the georeferencing of all data.

Nevertheless, for the logbook to fully develop the functionality of allowing the collection of decarbonization progress indicators, it would be necessary for all buildings to have a DBL. However, achieving this in the short term seems challenging due to a series of barriers. These include the current lack of a defined model of the tool, owners’ lack of awareness about its benefits, debates about who should fund it, its alignment with international and national regulations, and the technical challenges of large-scale implementation.

4 Conclusions

The European Commission has proactively launched initiatives to establish indicator frameworks for monitoring the effectiveness of building renovation policies and the progress of decarbonization of the building stock (López-Mesa et al., 2023), which are to be developed by the EU MSs. Utilizing indicators to assess progress and policy effectiveness in building renovations is crucial for ensuring efficiency, effectiveness, alignment with sustainability goals, facilitating evidence-based decision-making, promoting accountability, supporting long-term decarbonization objectives, and engaging the public in these initiatives (López-Mesa et al., 2023). However, the development of these indicators poses a significant data collection challenge for member countries, for which they may not be adequately prepared.

In this chapter, we propose that emerging technologies have the capacity to notably improve data availability and quality, thereby aiding MSs in facing this challenging task, and we explored this through two technologies in particular, applied to the case of Spain, one of the MSs of the EU. One of these technologies is georeferencing and automated cross-referencing of open data. The second technology is the Digital Building Logbook (DBL), a government-led information repository endorsed by the European Commission.

On one hand, the chapter explored the applications and advantages of georeferencing and automated cross-referencing of open data in assessing building renovation policies and monitoring decarbonization progress through the use of indicators, and identified two applications: the calculation of solar potential on building roofs, and the estimation of energy production, consumption, and emission reductions using an EPC-based national-scale Urban Energy Model (UBEM) built from open data. This second application was further explored in the chapter. By employing this UBEM, it becomes possible to gather data to develop sixteen indicators that were only partially available and two indicators that were previously unavailable. This implies an important improvement in the availability of data, particularly significant in the areas of ‘An overview of the general characteristics of the national building stock’ and ‘An overview of the energy characteristics of the national building stock’.

On the other hand, an analysis was conducted to determine the feasibility of obtaining the indicators selected for development in this book using data from the DBL. The comprehensive examination showcased that nearly 68% of all indicators can be collected from DBL data. By employing the DBL, it would be possible to generate data to develop 22 indicators that are nowadays only partially available and 20 indicators that are unavailable. Notably, subjects such as ‘An overview of the general characteristics of the national building stock,’ ‘An overview of the energy characteristics of the national building stock,’ and ‘An overview of deep renovation of buildings’ demonstrated a 100% collectability rate, given their focus on building characteristics. In subjects like ‘An overview of actual energy savings and wider benefits of renovation of buildings,’ ‘Evidence-based estimate of expected energy savings and of reduction of costs for health systems from buildings renovation’ and ‘Policies and measures for the mobilization of investments into the renovation of buildings,’ the collectability rate hovers around 50%. These categories incorporate indicators related to both buildings and other aspects of the construction sector, limiting their capture by the DBL. In categories such as ‘An overview of worst-performing segments of the national building stock, rented properties, and energy poverty’ and ‘An overview of the capacities in the construction, energy efficiency, and renewable energy sector,’ only 33% of indicators could be collected due to data protection conflicts and non-building-related indicators.

The EPC-based national-scale UBEM holds immediate potential as it enriches existing data sources through georeferencing and automated cross-referencing. However, realizing the full potential of the DBL in collecting indicators for evaluating the effectiveness of building renovation policies and the decarbonization of the building stock encounters diverse challenges. These challenges encompass the absence of a clearly defined tool model, owners’ lack of awareness regarding its benefits, uncertainty on who should fund the deployment of the tool, alignment issues with regulations, and technical obstacles in implementing it on a large scale. Achieving comprehensive DBL coverage in the short term is considered to be a challenging endeavor.

Appendix A

See Tables 4 and 5.

Table 4 Analysis of the indicators on building stock renovation and decarbonization, organized by subject, with a focus on their feasibility for collection through the use of the ECP-based national-scale UBEM developed for the country of Spain (Beltran-Velamazan et al. under review).

Table 5 Analysis of the indicators on building stock renovation and decarbonization, organized by subject, with a focus on their feasibility for collection through the use the DBL.