In this section, a literature review has been executed by examining relevant data/information sources. Data collection, processing, and validation phases are presented based on detailed relevant literature sources. Different aspects such as definition of the boundaries of the analysis to meet the comparability purposes of the evaluation, data inventory and collection, and data reliability have been considered in the current phase to ensure the quality of the gathered data/information.
2.2.1. Boundaries of the Evaluation (Data Comparability)
Even though most data providers adopt standardized data units and formats, this does not necessarily mean that data are fully comparable. Thus, attention attention has been given to data definition and comparability. Adjusting differences and inconsistencies among different measures, methods, assumptions, time references and specifications to improve data comparability is indeed one of the most meaningful aspects of the whole process of data elaboration. Data for the most recent year were collected for each country, with preference for data not older than 2018. The assembled data and information refer to the reference year by source. The developed datasets, including the documentation in this project, were expected to improve the data quality of existing data and provide the information required to track the development of space H&C technology utilization in various sectors and subsectors, respective energy carriers and sources, CO2 emissions, etc., moving towards the realization of the decarbonization goals depicted by the EC for 2030 and 2050 and mentioned in the introduction.
Regarding the methodology for environmental parameters, the functional units of this study coincide with the unit of measure of the environmental parameters described in
Table A2, and they are, respectively, gCO
2e/kWh and MJprim/kWh. It should be noted that “kWh” refers to the useful thermal energy generated for space H&C purposes. The definition of system boundaries has also been pivotal. The lifecycle phases included in this study are focused on the energy carriers used by the analyzed technologies. The extraction of raw energy carriers and their refining processes were included in the boundaries in order to consider the upstream supply chain. In addition to this, the use phase of the energy carriers in the space H&C technologies was also considered. The manufacturing, transportation and disposal of space H&C machineries and auxiliary components were thereby excluded by this study, together with biogenic carbon. Following the just-mentioned methodology, all the energy from renewable energy sources had zero CO
2 emissions. Waste heat, due to its “unavoidable” nature, was also considered to have zero emissions [
26].
Figure 3 below illustrates the lifecycle phases included within the aforementioned boundaries.
2.2.2. Data Collection
The production of an exhaustive list of all existing data providers was one of the major challenges concerning the development of an inventory of data and information for space heating and cooling technologies with the respective characteristics in different sectors and subsectors per country for each technology. In more broad terms, the advantage of using data coming from EU27+UK projects and EU27+UK-wide information providers is that these are available for a large territory. Concerning the latter, HotMaps [
27], Dittmann et al. [
28], Danish Energy Agency [
29], Invert/EE-Lab [
30], EU Building Stock Observatory [
31], Tabula [
32], Statistics Estonia [
33], Statistics Denmark [
34], Foresight [
35], INSEE [
36], Statistics Sweden [
37], Terna Driving Energy [
38] and European Statistical System [
39] were identified as exemplary sources. It is worth mentioning that the information inspected was never found to be fully detailed and complete. As a result, in order to increase data availability and coverage, it was necessary to scout data from national sources. In particular, in this section, we detailed the data utilized to fill the cells of
Table A2 for EU11 MSs, space H&C category, sector, building type, and technology. Moreover, to fill data gaps and complete the data inventory, it was crucial to assemble and extrapolate data from the large aforementioned data tools and also to research data source-by-source from single scientific literature studies such as project deliverables, journal papers and conference proceedings. These have been properly referenced below. Moreover, one essential element of the data inventory was to ensure that the information could be interpreted and grasped accurately by any user. Therefore, a compilation of understandable metadata descriptions, annotations, contextual documentation, and information was required. Following these principles, this data collection subsection presents source-by-source data and information concerning the technical and environmental parameters.
For the current study, Fleiter et al. [
17] has been a noteworthy source; this work provided data at the country level on the different capacities installed for certain end-use technologies, DH technologies and cooling technologies. The latter author was also useful for understanding the net total efficiency for technologies such as biomass-fired DHP (non-CHP), coal-fired DHP (non-CHP), DH utilizing urban solid waste, DHP utilizing thermal storage, efficient DH using waste heat, efficient DHP (geothermal), gas-fired DHP (non CHP) and high-temperature DHN. Again, from Fleiter et al. [
17], other parameters such as energy efficiency, energy sources and energy carriers were identified. Dodds et al. [
40] provided efficiencies for hydrogen boilers, and Lorenzo et al. [
41] indicated the seasonal performance factor (SPF) for solar PV-driven heat pump installations in both the residential and service sectors. Olabarrieta et al. [
42] and Naicker et al. [
43] provided the efficiencies for machineries such as biomass boilers, combined solid fuel boilers, coal-fired boilers, liquid fuel boilers, gas-fired boilers, and hydrogen boilers. The Danish Energy Agency [
44] provided information on the energy carriers of hydrogen boilers, coal-fired boilers, gas-fired boilers, liquid fuel boilers, combined solid fuel boilers and biomass boilers, both for the residential and service sectors. In the guide provided by Goetzler et al. [
45], the energy carriers of all the space cooling systems were specified. The thermal efficiencies for liquid fuel boilers and gas-fired boilers were provided by Vakkilainen et al. [
46]. Furthermore, Zukowsky et al. [
47] and Redko et al. [
48] provided the efficiency values for solar thermal technology. Most of the missing parameters for DH technologies were obtained from: Danish Energy Agency [
29], Fan et al. [
49], and IPCC [
50]. Regarding the data collection for space cooling technologies, the most notable source was EC et al. [
51], which provided data at the country level. Demirel et al. [
52] provided the seasonal energy efficiency ratio (SEER) for the thermally driven heat pump (TDHP) technologies, while the remaining values were again provided by EC et al. [
51]. Finally, concerning the energy sources, Goetzler et al. [
53] and EUROVENT [
54] specified the input required.
Regarding the environmental parameters, the Research Center for Energy Economics [
55] was relevant for providing the input for the CO
2 calculation involving the residential and service sector for liquid fuel boilers, gas-fired boilers, coal-fired boilers, micro-CHP (natural gas), coal-fired district heating plant (non-CHP), gas-fired DHP (non-CHP), efficient DHP using CHP, DHP using thermal storage and high-temperature DHN. This source also specified the primary energy factors needed for the computation of the primary energy consumption for heat pumps, electric heating, space cooling systems (air conditioning—AC), district cooling (DC), TDHPs and efficient DHP (using heat pump). When considering the last-mentioned technologies, the AIB (Association of Issuing Bodies) [
56] provided the data needed for the calculation of the CO
2e emissions. Furthermore, Balcombe et al. [
57] revealed the CO
2 emissions of hydrogen boilers, while Dones et al. [
58] detailed those coming from biomass boilers, combined solid fuel boilers, low-temperature DHN and biomass-fired DHP (non-CHP). Concerning DHN utilizing urban solid waste, Hast et al. [
59] provided the data needed.
Expert interviews were carried out to complete missing data that were not available in the scientific literature. Ten experts in the fields of research, consultancy and public administration were consulted to integrate the missing information. The data that were retrieved using this methodology referred mainly to efficiencies and both input and output energy carriers at the country level for DC, low-temperature DHN, efficient DH using waste heat, micro-CHP (natural gas) and hydrogen boilers.
Lastly, to complete the data inventory and fill the data gaps, in the data processing subsection below we present how the calculations were performed in order to derive the data and any information that was deemed to be unavailable during the literature review.
2.2.3. Data Reliability
A literature review was crucial to obtain technical data: the sources cited in
Section 2.2.2. were able to meet expectations, and when specific values happened to not be available, expert interviews with national contact people fulfilled the missing information. In general, the aforementioned sources provided straight-to-use data for our research needs. It should be recalled that the gathered material, in particular the efficiencies of the machineries, provided the necessary information for the processing of environmental data.
Furthermore, significant effort has been put into complementing the missing data gaps through in-depth investigations and assessing the reliability of the gathered data. To ensure data reliability, the indications obtained per space H&C technology, building type, sector, and EU11 MS, were subject to a periodically made quality-check control of the constructed dataset. According to the latter, a draft of the data and information on building type, sector, space H&C technology and EU11 MSs was circulated inside the consortium for corrections and review. In addition, expert questioning was conducted to provide further sources and possible methodologies for data computations, to fill the remaining data gaps. The authors acknowledge the potential uncertainties inherent in the utilization of such a vast dataset. Nonetheless, they emphasize that the statistical significance of the final results, particularly with regard to the average values, remains robust, despite the possibility of limited uncertain data points. The precision of the analysis aligns with the scope and objectives of the project for which this research was conducted.
According to everything that has been stated above, the explored sources were classified per technology type. In particular, the data and information were collected from scientific papers, national datasets, reports, conference proceedings, etc., on the available technologies, for EU11 MSs, building categories and sector. Based on the aforementioned procedures, the template presented in
Table A2 in
Appendix A was filled for space H&C technology and EU11 MS.
2.2.4. Data Processing
This subsection presents the data processing. It should be noted that data were refined in a comparable manner. Attention was paid to the same units among equal parameters.
The analytical methodology for the primary energy and CO2 emissions parameters is described in the following paragraphs.
The procedure for the calculation of MJprim/kWh of useful energy is divided into two different methodologies, based on the type of energy directly used by the system.
It follows the equations applied to calculate the MJ of primary energy per kWh of useful thermal energy for machineries needing electric energy from the grid [
55].
In Equation (1), PEF represents an acronym that refers to the primary energy factor of each specific country [
55]. This factor is calculated as the inverse ratio between the amount of delivered energy and the primary energy required to provide it. PEFs can legitimately differ between MSs since the primary sources may vary, as well as the amount of energy required for transportation or processing.
When focusing on Equations (5) and (6), % RE and % NRE stand, respectively, for the percentages of renewable energy and non-renewable energy in the national electricity production mix [
55].
The values corresponding to the previously mentioned factors and percentages can be located in
Table 3 presented subsequently.
For energy carriers that differ from the electricity coming from the grid, the methodology to obtain the primary energy consumption is described as follows. Considering that the overall efficiency of the system is obtained as a ratio between the final thermal energy (that is, the useful effect) and the energy used (that is, the energy input (which could consist of combusted material)), the latter can be considered primary energy.
Considering that the technologies that do not use electricity coming from the grid only consume a single type of energy, whether fossil or renewable, a mathematical distinction of these two primary energy calculation methods is not needed.
As the primary energy consumption calculation, the quantitative calculation process for the CO2 equivalent per kWh of useful thermal energy is also divided into two different methodologies, based again on the type of energy used by the system.
In the case of electric energy from the grid, the CO
2 intensity of the national residual mix (rmix) is used to avoid double counting. The residual mix of a country depicts the shares of electricity generation attributes available for disclosure after the explicit tracking systems, as guarantees of origin (GOs), have been accounted for. Without a residual mix, renewable electricity sold with GOs would be double counted; this happens because the same electricity would be disclosed to consumers buying “regular” electricity [
56].
For coal, natural gas, heating oil, hydrogen and urban solid waste, values regarding the CO
2 intensity covering the generation and supply chain of these energy carriers can be found in [
55,
57], but are related to the lower heating value (LHV). Resultingly, a methodology akin to the above one has been adopted.
In the case of hydrogen [
57] and urban solid waste [
59], an average value has been utilized due to an extremely wide range of emissions.
Regarding biomass, source [
58] provided the information needed. For this energy carrier, net emissions were considered and an average value was adopted because investigating the immense variety of biomass fuels present in all the EU11 MSs exceeded the aim of this study.
Finally, as has already been mentioned in
Section 2.1, weighted results were computed based on each country’s specific population [
20] following Equation (11).
and refer, respectively, to the parameter and to the population of the specific country i of each of the EU11 MSs.