Towards Life Cycle Assessment for the Environmental Evaluation of District Heating and Cooling: A Critical Review

Abstract

District heating and cooling networks represent a compelling energy system solution due to their capacity to integrate renewable energies and leverage local surpluses of thermal resources. The meticulous design and optimization of network infrastructure are imperative to fully exploiting the potential of these energy systems. The Life Cycle Assessment of district heating and cooling networks for the purpose of environmental sustainability is a crucial and increasingly demanded aspect, particularly in light of the progressively stringent European regulations. The Life Cycle Assessment methodology could offer an evaluation throughout the entire life cycle of such networks. The proposed review scrutinizes the application of the Life Cycle Assessment methodology to evaluating the environmental profile of district heating and cooling systems. The methods, findings, and challenges are examined through a literature review and case study analysis. The results highlight variations in the climate profile influenced by the network generation type and multifunctionality approaches. The analysis revealed a range of emission factors, spanning from 11 gCO₂eq/kWh_th to 470 gCO₂eq/kWh_th for district heating and 6 gCO₂eq/kWh_th to 64 gCO₂eq/kWh_th for district cooling. The discussion emphasizes integrating district heating and cooling network management considerations and addressing methodological challenges. This study concludes by proposing future research directions for developing a universal LCA-based tool for district heating and cooling network analysis.

1. Introduction

Urban areas, covering just 2% of Earth’s landmass, wield a disproportionately significant influence, accounting for 60% of worldwide energy consumption, 70% of greenhouse gas (GHG) emissions, and 70% of waste output [1]. Global energy demands are projected to surge by 80% by 2050, contributing to a corresponding 50% increase in GHG emissions, primarily driven by a 70% growth in CO₂ emissions from energy [2,3]. In response to the challenge posed by increasingly stringent European energy and environmental regulations, designers and construction companies are continually embracing low-energy and decarbonization design strategies. This trend extends to the monitoring of building envelope conditions to prevent excessive energy consumption [4]. The scientific community underscores the urgency of mitigating urbanization repercussions. The Intergovernmental Panel on Climate Change (IPCC) elaborated on some decarbonization scenarios that represent potential future paths for reducing GHG emissions. These scenarios were created through modeling and simulations, considering various variables in the energy, industry, agriculture, and transportation sectors. They depict how societies could decrease emissions over time through actions like renewable energy adoption, energy efficiency, and sustainable practices [5]. These scenarios inform climate policy decisions and help assess the impact of climate actions on future climate change. Actually, they are the best predictive model for climate change present in the literature. The IPCC emphasizes the severe consequences of limiting global warming to just 2 °C above pre-industrial levels rather than 1.5 °C. Regional and industrial emission reduction goals are compelling nations and companies to reconsider production technologies and efficiency without compromising the conveniences of modern life [6]. The European Commission (EC) set ambitious objectives under the European Climate Law and Green Deal, aiming for a minimum 55% GHG emissions reduction by 2030 and climate neutrality by 2050. Plans like Fit for 55 and REPowerEU target a 49% increase in renewable energy sources for building consumption and a 40% reduction in primary energy consumption [7,8]. These initiatives aim for GHG reduction and various environmental objectives like climate change adaptation, water resource sustainability, circular economy promotion, pollution control, biodiversity conservation, and ecosystem restoration, as the European taxonomy outlines [9]. The EU’s Long-Term Strategy (LTS) advocates for energy transition pathways in the heating sector, emphasizing the electrification of heat and district heating and cooling (DHC) networks [10]. DHC networks play a crucial role in integrating local renewable energy sources and efficiently utilizing surplus heat or cold reservoirs. They deliver heat from single or multiple sources to buildings, offering space heating, domestic hot water, and other heating-related services through an insulated pipe network [11]. A comprehensive assessment of environmental impacts beyond GHG emissions reporting is imperative to achieve decarbonization targets. Life Cycle Assessment (LCA) emerges as a quantitative tool for this purpose, facilitating scrutiny of intricate energy systems and supporting policy development. The methodology is managed by worldwide recognized guidelines [12,13]. Despite widespread acceptance, challenges like data collection and processing complexity hinder reliable results, especially due to the limited access to company-specific primary data for complex industrial products. However, LCA remains pivotal for evaluating environmental profiles and supporting research and policy development, particularly in the construction sector [14,15].

A district heating network (DHN) is a centralized system that provides the distribution of heat energy from a single or multiple heat sources to residential, commercial, and industrial buildings in a specific geographic area. The heat is usually conveyed through a network of insulated pipes and is used for space heating, to provide domestic hot water, and for other heating-related purposes. DHNs are a sustainable and energy-efficient way to deliver heat, as they often use renewable energy sources or waste heat from industrial processes, thus reducing individual energy consumption and greenhouse gas emissions. The design of DHC networks has a quite long story. Within this scope, Werner (2017) wrote a comprehensive article discussing district heating worldwide [11]. Assessments and surveys have been conducted since the 1930s. These have covered pioneering countries such as the USA, Germany, and Russia, with renewed interest in the 1970s due to higher fuel prices. Recent surveys explore the use of renewables in district heating (DH). While European assessments date back to the late 1940s, a complete international review is yet to be published in a scientific energy journal. Various regions, including Russia and North America, have conducted surveys, while a global statistical survey and comprehensive international overview was also presented [11].

To the best of the authors’ knowledge, the recent scientific literature has not extensively addressed the evaluation of DHC networks through LCA analysis. Previous studies have not comprehensively covered all potential application scenarios, thereby hindering the attainment of a uniform and universally applicable assessment of the obtained results. Consequently, the authors proposed a literature review with the following objectives: (i) providing a state-of-the-art overview of the application of LCA methodology to DHC systems; (ii) critically analyzing the results reported in prior publications; (iii) examining potential limitations of the applied procedures; and (iv) establishing methodological foundations for developing a potential LCA analysis tool exclusively dedicated to DHC systems. The literature review comprises two main parts: identifying and analyzing publications with similar objectives to assess their scope of application and potential integration and focusing on case studies within the domain.

The paper was organized into several subsections. In the introduction, the authors explain why they wrote a review on this topic and highlight the main open themes. In the section dedicated to the research and methodology, the research process and the factors used to select the articles for analysis are explained. The results section is divided into four subsections: (i) analysis of the type of DHC, with the aim of identifying any factors determining which generation the DHC belongs to; (ii) results from the analysis of other literature reviews; and (iii) analysis of the results from other scientific contributions, mainly related to the case study. This work aims to derive and relate a DHC climate profile to the network generation typologies. The authors conclude the paper with a subsection dedicated to discussing the results, where some final considerations are presented based on the research findings, and the conclusion phase, where the results are summarized and solutions to emerging problems are proposed. To enhance the understanding of this review, the authors have presented a descriptive flowchart of the entire discussion in Figure 1.

2. Research Methodology

The required literature was gathered using a Systematic Literature Review (SLR), as well as the “snowball” approach [16]. The authors undertook the investigation utilizing the keywords reported in

Table 1. Keywords used for literature search.

Primary keywords	Life Cycle	District Heating
Secondary keywords	Environmental Impact LCA	District Cooling

.

The research employed principally the Scopus, Web of Science, and Google Scholar databases [17,18,19]. The search was limited to journal papers written in English. The initial search based on the keywords resulted in 815 documents, as reported in Figure 2. The authors initially curated articles spanning from 2013 to 2024, a time frame extending beyond the conventional 10-year publication window to encompass the current year, 2024. This extension allowed for the inclusion of scientific contributions written within the specified time frame but published subsequently due to administrative constraints. Initially, the authors focused their inquiry on openly accessible articles authored by research consortia affiliated with geographical regions akin to Europe, including extra-European Union (EU) contributions. This imperative underpinned this selection criterion to contextualize the issues and tailor the analyses to a predominantly European or analogous context.

In the initial stage, all the literature reviews were meticulously selected with the aim of furnishing an exhaustive overview of the state-of-the-art methodology concerning LCA as applied to DHC. After the initial literature search, the papers were first filtered based on an analysis of the title and the abstracts and subsequently based on an analysis of the introductions and conclusions. Additionally, the literature was filtered to meet specific criteria. The filtering process was performed conservatively, retaining papers where there was uncertainty regarding whether they met the criteria. The selected papers had to meet specific requirements. Firstly, they needed to exclusively pertain to analyses conducted using the LCA methodology or reference results reconstructed using the same methodology for applications to environmental profile assessment. Consequently, all papers that, while addressing environmental assessment, provided results only on the use phase were excluded. Secondly, the authors prioritized publications that specifically referred to analyses of district heating or cooling networks or alternatively that were related to district analyses that included data derived from this technology. The search operations described above were conducted independently on multiple search engines, resulting in the creation of three distinct databases of potential publications. In the subsequent phase, these databases were merged, and the “snowball approach” was applied. This process aimed to achieve two primary objectives, the first of which was to eliminate duplicates that inevitably arose during the searches across the three search engines. Additionally, for each identified paper, the bibliography was analyzed using Research Rabbit. However, the impact of these supplementary sources was limited, as the publications relevant to the purpose of this research had already been provided by Scopus. Research Rabbit, however, facilitated the reconstruction of a network of related works by leveraging bibliographic references from scholarly contributions. While the contribution of these additional databases was acknowledged, the authors had already planned to identify potential case studies within the Scopus-provided literature. Consequently, the integration primarily served to validate the selected articles.

Subsequently, in the ensuing phase, the authors directed their attention towards case studies. A more selective and comprehensive research approach was also undertaken among the scientific articles utilized to construct the selected papers. The authors rigorously categorized these case studies into 3rd, 4th, or 5th-generation DHNs. At the initial research stage, the authors conducted a scientific inquiry as delineated above, aiming to precisely characterize the distinguishing features of the DH or DC types within their generation. This undertaking sought to ascertain whether previous authors had provided a classification of the networks and, if so, the parameters employed for such classification. However, in this review, a rigorous scientific categorization of case studies became paramount. The emission factor of Global Warming Potential (GWP) for the energy emitted by each network was systematically reconstructed for each respective case study. The emission factors were reconstructed considering 1 kWh_th of energy delivered to the user as the functional unit, including losses. The decision to use kWh_th was also influenced by the Energy Performance of Buildings Directive (EPBD), which, in accordance with European and international regulations, employs this unit of measurement in place of MJ [20]. A concise description of the procedure used to select case studies from the bibliographic research is provided in Figure 2.

The authors cataloged the available documentation considering a series of technical evaluative aspects and methodological applications of LCA. The parameters considered are collected and displayed in

Table 2. Evaluation aspects.

Main Categories	Sub-Aspects
Technical aspects	Network typology
Methodological aspects	Functional unit
	Database adopted
	Characterization methodology
	Multifunctionality (if present)
	modeling approach (attributional or consequential)

. The aspects considered are both technical, such as the type of DHC, and methodological, related to LCA analysis. Based on these evaluative aspects, the authors constructed a matrix with all the information collected for the application case studies. From the matrix, the case studies were selected for which the environmental profile was either declared or could be reconstructed based on the unit of energy distributed to the user. The results obtained are reported and discussed in Section 3, while a complete list of the selected papers used for the analysis of the evaluation aspects is reported in

Table A1. List of documents consulted for methodological analysis.

Author	Year	Typology	fU	Database	Characterization Method	LCA Approach	Comparison	Multifunctionality
Tester et al. [63]	2019	-	1 kWhth produced	ecoinvent	-	-	Yes	-
Guarino et al. [49]	2020	4GDH	Not declared	-	ILCD 2011 midpoint impact assessment method	-	Yes	-
Neirotti et al. [42]	2020	3GDH	1 kWhth generated	ecoinvent	CML	Attributional	Yes	Energy method
Bartolozzi et al. [38]	2017	3GDH	1 kWhth distributed	ecoinvent	ILCD 2011 midpoint impact assessment method	Attributional	Yes	Exergy/energy method
Vauchez et al. [1]	2023	-	Not declared	ecoinvent	EF 3.1	Attributional	Yes	50/50
Kiehle et al. [50]	2023	3GDH	Not declared	-	-	-	No	-
Abu Rayash et al. [50]	2023	-	1 kWhele generated	-	TRACI	-	No	-
Urhmann et al. [43]	2023	4GDH	1 kWhth distributed	ecoinvent	ReCiPe midpoint (H)	Attributional	Yes	Energy method
Maione et al. [45]	2022	4GDH	Not declared	ecoinvent	ReCiPe midpoint (H)	Attributional	Yes	Energy method
Wang et al. [61]	2022	-	Unit of input energy	GaBi	-	Attributional	Yes	-
Temporim et al. [65]	2022	-	Unit of input energy	-	ReCiPe	Attributional	No	-
Hampoo et al. [73]	2021	-	Unit of input energy	ecoinvent	ReCiPe midpoint impact assessment technique			-
Guillen-Lambea et al. [2]	2019	4GDH/DC	Not declared	ecoinvent	ReCiPe and IPCC 2013 GWP 100 y	Attributional	Yes	-
Liu et al. [80]	2019	-	Not declared	GaBi	CML2001	Attributional	Yes	Energy method
Pratiwi et al. [57]	2021	4GDH/DC	1 kWhth generated	ecoinvent	ReCiPe midpoint nt 2016 H	Attributional	Yes	Exergy method
Roux et al. [81]	2017	-	Not declared	ecoinvent	ReCiPe midpoint 2016 H	Consequential	Yes	-
Kanematsu et al. [66]	2017	-	Not declared	ecoinvent	-	Attributional	No	Energy method
Karlsdottir et al. [46]	2020	4GDH	1 kWhth generated	ecoinvent	CML-IA baseline	Attributional	Yes	50/50
Olsson et al. [39]	2015	3GDH	1 kWhth generated	-	-	Attributional	Yes	Exergy method
Caserini et al. [82]	2010	-	Not declared	-	CML 2001	Attributional	No	Energy method
Nitzewitcz et al. [47]	2014	5GDH	Unit of input energy	-	eco-indicator 1999	Attributional	Yes	-
Ristimaki et al. [76]	2013	3GDH	1 kWhth distributed	-	-	-	No	-
Famiglietti et al. [40]	2022	3GDH	1 kWhth distributed	-	Environmental Footprint 3.0	Attributional	No	Different methods
Spirito et al. [69]	2021	3GDH/4GDH	1 kWhth distributed	-	-	-	--	-
Pozzi et al. [70]	2021	4GDH	1 kWhth distributed	-	-	-	--	-
Famiglietti et al. [75]	2021	3GDH/4GDH	1 kWhth distributed	-	Environmental Footprint 3.0	Attributional	Yes	Expansion (non-computed)
Menberg et al. [58]	2023	4GDH	1 kWhth generated	-	IMPACT 2002+	Attributional	Yes	Exergy method
Mainar-Toledo et al. [59]	2023	5GDH	1 kWhth generated	-	ReCiPe 2016 midpoint (H) v 1.04 method	Attributional	Yes	Exergy method
Solano et al. [83]	2023	5GDH	1 kWhth distributed	KBOB 2009	-	Attributional	Yes	-
Mahon et al. [52]	2022	3GDH	1 kWhth generated	-	CML 2001 methodology	Attributional	Yes	Efficiency
Livingstone et al. [84]	2022	-	Unit of input energy	-	CML 2015 method	Attributional	No	-
Abrahmsen et al. [64]	2022	3GDH	-	-	ReCiPe-a hierarchical (H) mid- and endpoint model	Attributional	No	Energy method
Nilsson et al. [85]	2021	-	1 kg of input material	-	Environmental Footprint 3.0	Consequential	Yes	Energy method
Bisinella et al. [54]	2022	-	1 kg of input material	-	Environmental Footprint 3.0	Consequential	No	-
Bjornson et al. [86]	2021	-	Unit of input energy	-	-	-	No	SPR
Aborekersh et al. [87]	2021	4GDH	Not declared	-	ReCiPe 2016 midpoint (H) v 1.04 method	-	No	Energy method
Jeandreau et al. [51]	2021	3GDH	1 kWhth generated	-	Environmental Footprint 3.0	Attributional	Yes	Energy method
Nordestam [88]	2021	-	1 kWhth generated	-	-	Consequential	Yes	-
Feofivlos et al. [89]	2019	3GDH/4GDH	Not declared	-	IMPACT 2002+	Attributional	Yes	-
Pericault et al. [53]	2018	4GDH	Not declared	-	-	Attributional	Yes	-
Sigurdson et al. [56]	2015	-	1 kWhele generated	-	IPCC 2013	Consequential	Yes	-

.

3. Results

This section aims to present and critically analyze the main results obtained from the research. An overall initial selection of applied case studies was discerned, and an initial dataset comprising 53 scientific papers and 5 literature reviews was defined. A more detailed examination of the references cited by the selected authors further augmented the overall corpus. Subsequently, all documents were meticulously chosen based on their declaration of the environmental profile pertinent to the energy output of the scrutinized network or the ability to infer such factors using the input data provided. This meticulous curation facilitated the establishment of a comprehensive database housing 66 case studies, delineated by (i) the source document’s provenance, (ii) a concise exposition of the studied DHC networks, (iii) the typology of the network, and (iv) the emission factor per unit of energy yielded by the energy system. The authors were able to extract 66 case studies from 54 publications because some scientific contributions included data and descriptions for more than one case study.

3.1. Preliminary Analysis of the Collected Documentation

This subsection aims to provide a preliminary analysis of the scientific contributions identified through the bibliographic research. As previously described, the articles were cataloged based on their characteristics and consolidated into a database, from which case studies were extracted for the analysis of the environmental profiles. The primary objective of the bibliographic research and the literature review conducted by the authors was to include only those articles that featured LCA analyses. The extant literature comprises significant contributions that discuss environmental assessments without necessarily utilizing the LCA methodology. For instance, many scientific contributions related to DHC systems perform a carbon footprint analysis (ISO 14067:2018) confined to the use phase, which is frequently the most impactful life cycle stage [21]. However, LCA offers a comprehensive evaluation covering a wider array of scenarios, particularly those case studies that are hypothetical or not yet fully operational, where the environmental impacts are more significant in other phases. Consequently, this research exclusively focused on contributions that were examined or provided information through LCA. The analysis of the evaluative aspects was conducted on a sample of documentation different from that of the case studies, as it included all scientific contributions that did not provide a direct analysis of DHC but revisited their technical aspects to support general concepts still related to the LCA application context. In total, 41 scientific contributions were cataloged and described. The evidence is reported in Table A1 (Appendix A), while Figure 3 and Figure 4 show the results obtained for each analyzed category for each evaluated aspect. It is important to mention that the results are presented as percentages. This is due to the fact that the necessary information was not complete and exhaustive within the 58 contributions. Therefore, for each evaluative methodological aspect, the number of articles from which the authors derived the necessary information is reported. The absence of information in the contributions is not essentially due to a lack on the part of the authors but is primarily related to the purpose for which the contribution was written. In cases focused on the LCA analysis of a DHC system, all information on the technical and methodological aspects was expressly reported by the authors. Conversely, this aspect was not always present in scientific contributions that simply used LCA data and results applied to the DHC context to support other theses.

The analyses and results presented in the reviewed articles were reported using various units of measurement, contingent upon the objectives of the analyses being discussed. In comparative analyses primarily concerning a thermal energy generation system, the LCA analyses were reported utilizing the thermal energy produced by the system itself as the functional unit (fU). The reviewers underscored that the majority of the analyses were conducted upstream of the facility; thus, the carbon footprint was calculated as the net distribution losses. However, in certain instances, some contributions accounted for this aspect and consequently presented the results in terms of the energy distributed to the user. This latter approach was particularly prevalent in scenarios where (i) the LCA results were integral to subsequent district-level considerations and (ii) the focus of the analyses was not on the optimization or comparison of a production facility but on the decarbonization of an entire district or neighborhood.

In case studies examining decarbonization scenarios that were related to the technical optimization of a system component’s efficiency directly, the emission factors were reported considering the unit of energy entering the generation system and necessary for its operation. In some scientific contributions, the study focused on a specific part of the generation system, particularly one capable of producing energy from waste (as in the case of waste-to-energy plants) or from a natural crop used as fuel instead of traditional fossil fuel. In this context, the environmental profile related to that specific part of generation was expressed considering the mass unit of the material subjected to combustion. In these cases, the interest of the scientific contribution was not in comparing an emission factor of the entire DHC system but studying its long-term benefit for the decarbonization scenario. The authors of some of the scientific contributions opted for reporting the amount of CO₂ actually saved over the entire life cycle without delving into the granular variations in the unit environmental profile of DHC systems or declaring a fU. This is primarily seen in scenarios depicting a global decarbonization strategy for a district which encompasses various decarbonization actions. Consequently, the contributions from DHC systems are presented as aggregate values over the entire operational lifespan of the production facility. Finally, in one scientific contribution, the results were presented only after the weighting operations. The weighting of indicators is used to aggregate and prioritize different environmental impacts based on their relative importance. This process helps decision-makers interpret LCA results by emphasizing more critical impacts, facilitating a clearer comparison of alternative products or processes [22]. Weighting thus aids in identifying the most significant environmental trade-offs. Figure 3 summarizes the results obtained by the authors from analyzing the aspects related to the use of functional units (fUs) in various scientific contributions. Excluding from the count all publications that did not mention the environmental profile of a DHC system based on an fU, in the scientific contributions studied, 36% present an environmental profile of a DHC system based on the unit of heat produced by the generation system. Conversely, 32% of the contributions report the results based on the unit of heat distributed. Furthermore, 21%—primarily in papers discussing direct efficiency improvements of DHC generation systems—present an environmental profile based on the unit of energy input into the system. Lastly, 11% of the environmental profiles of the studied DHC systems are presented based on the unit of combustible material input into the system. The reconstructed values all pertain to the unit of distributed thermal energy. Results based on the unit of produced energy should also account for hypothetical distribution losses to ensure sufficient accuracy. The literature provides indicative values for these losses based on the DH technology under examination.

The authors analyzed the methodological aspect of the retrieved scientific contributions by also examining from which background databases the information regarding the environmental profiles necessary for the analyses was collected. Most of the authors relied on the ecoinvent databases [23], followed by GaBi [24]. The geographic data coverage provided by ecoinvent ensures that the data can be used globally. Furthermore, the information provided is easily integrated into all LCA models or software available on the market or in the literature. The GaBi database, on the other hand, is particularly useful and reliable for reconstructing purely industrial processes where specific data for a particular production sector are needed. The use of one background database over another can result in significant variations in the obtained results [25]. In some cases, it is noteworthy that there is a lack of a declared background database in favor of primary data or secondary data derived from previous works published in the scientific literature. Primary data refer to information collected directly from specific processes, such as measurements or company records, whereas secondary data originate from indirect sources, such as databases, scientific literature, or general estimates. The latter are utilized when primary data are unavailable. Figure 4 shows the percentage distribution of the application of the adopted background databases in the scientific contributions.

The results reported by the scientific contributions were reconstructed based on a diverse selection of characterization methods, summarizing an analysis of the methodologies used and declared by the various scientific contributions. From the analysis, ReCiPe emerges as the most widely used family of methods [26]. None of the authors provide reasons for opting for one methodology over another, but the choice of ReCiPe could be attributed to its ability to provide comprehensive information and a balanced approach between detail and synthesis, even at a global scale, thanks to the proposed indicators. The family of characterization methods developed by the CML program is generally chosen for academic purposes and applications related to industrial research and development [27].

In most cases, the scientific contributions validated and presented the results by comparing them with a baseline scenario. Typically, in a case study where the benefit of applying DH to a district is examined, the comparison is made with a scenario where each individual user autonomously meets their thermal needs using fossil-fuel-fired heat generators or heat pumps powered by the national electrical grid for heating. When testing the environmental benefit of a generation system, the comparison is usually made with a plant for generation capable of meeting the same energy demands using fossil fuel technologies. Comparison with a baseline scenario is not performed only when the study related to DH supports a more general concept or the development of guidelines.

The authors continued their investigation into the methodological aspects of the scientific contributions by analyzing the methodological approach to applying LCA, i.e., an attributional or consequential approach. The difference between LCA’s attributional and consequential approaches lies in the respective goals (research questions). The attributional LCA approach focuses on describing the environmental impacts associated with a specific product system at a given point in time. This method aims to attribute environmental impacts to the various stages of the product’s life cycle as they currently exist. Conversely, the consequential LCA approach examines how changes in production and consumption systems affect environmental impacts. It considers the consequences of decisions and modifications within these systems, including indirect changes and market responses [28]. The results indicated that, as expected, most of the analyses are based on the attributional method, while only five publications employ a consequential approach. This discrepancy is primarily attributed to the complexity of the latter method.

Additionally, this study explored aspects related to the approach to multifunctionality. The findings suggest that authors favor allocation based on energy. However, recent publications have acknowledged the reliability of exergy-based allocation, recommending its use over the former method.

3.2. Preliminary Analysis of the DHC Typology

The subsection aims to provide a concise overview of the DH or DC typologies. The work presented in this subsection is preparatory for the analysis conducted on the environmental profiles collected for the 66 case studies. The findings from the review of the scientific literature necessitated a deeper investigation into the issues that emerged, followed by a position statement from the authors. As previously mentioned, the authors aimed to identify threshold parameters useful for categorizing different case studies when the network type was not explicitly stated. Historically, five main generations of DHCs can be identified and classified based on the supply temperature and pipe typology [29]. Another classification of networks is predicated not solely upon the supply temperature of the heat distribution fluid but also on the operational schema. Networks are dichotomously categorized as either high- or low-temperature, contingent upon whether the fluid operates at temperatures surpassing or falling below 60 °C. In instances of the latter, this classification is further stratified into three subgroups based on the requisite components for attaining the specified operational temperature. However, the authors of this review have deemed it imperative to maintain adherence to the historical classification. The first and second generations, which, respectively, utilized steam-based heat carriers or pressurized overheated water, have become obsolete and fallen out of use [30]. The emergence of the third generation of systems occurred in the 1970s, with significant expansion throughout the 1980s and beyond, coinciding with the peak of the oil crisis. This development arose from the increasingly urgent need to provide energy through a reliable and readily available alternative to oil. This generation is sometimes termed “Scandinavian district heating technology” due to the prevalence of Scandinavian manufacturers of these district heating components. Regarding the temperature of the heat distribution fluid in 3GDH systems, Guelpa et al. (2020) argue that they operate between 80 °C and 100 °C, while Caputo et al. (2021) characterize this generation as operating within a narrower range, specifically between 80 °C and 90 °C [29,31]. Typical components include prefabricated, pre-insulated pipes directly buried underground, compact substations featuring plate stainless steel heat exchangers, and materials optimized for efficiency [32]. Additionally, discussions around third-generation networks began to incorporate considerations for the integration of renewable sources. Fourth-generation networks (4GDHNs) adopt heat distribution fluid temperatures ranging from 80 °C to 50 °C, while Lund et al. (2018) declare an operating threshold temperature of around 80 °C to 30 °C [31,32]. The utilization of lower temperature levels facilitates reductions in district heating (DH) grid losses and enhances the efficiency of heat generation technologies such as combined heat and power (CHP), heat pumps, and solar collectors. The lower temperatures characteristic of 4GDH enable low-temperature heat sources to provide more heat with lower investments compared to other generations. Additionally, the reduced temperatures can decrease the reliance on heat pumps, favoring the direct utilization of waste heat sources through heat exchangers [30]. Finally, fifth-generation district heating and cooling (5GDHC) is characterized by networks operating at near-ground temperatures and employing bidirectional heat and cold exchange among interconnected buildings facilitated by seasonal storage. In order to achieve the appropriate temperature for domestic hot water, this network necessitates the use of heat pumps at the connected buildings. Regarding the classification of district cooling, Ostegaard et al. (2019) propose a typology classification of networks based on (i) the employed technologies and the integration of renewable resources, (ii) the difference between the supply and return temperatures of the heat distribution fluid, and (iii) system efficiencies [33].

Table 3. General identification of DHNs.

-	3GDHN	4GDHN	5GDHCN
General information about energy recovery	Possibility of recovering waste heat at high temperatures	Possibility of integrating renewable sources and waste heat at low temperatures	Increased efficiency by recovering waste heat from the evaporator of chillers in substations
Supply temperature of the fluid [°C]	80–100 °C [31] 80–90 °C [29] Over 80 °C (adopted)	80–50 °C [31] 80–30 °C [32] 80–40 °C (adopted)	Lower than 50 °C [31] Lower than 30 °C [32] Lower than 40 °C (adopted)
Pipe technology and grid losses	Utilizes prefabricated and insulated pipes	Reduces grid losses compared to 3GDH	Utilizes prefabricated and non-insulated pipes

outlines the primary differences among DHNs from the third to the fourth generation, while the first and second generations were omitted due to their disuse. The selection of network configurations is heavily influenced by the local conditions and specific heating and cooling requirements, particularly concerning the available heat source temperatures.

The classification of the case studies was informed by the typologies of the network generation, articulated as discussed before. The authors did not explicitly stipulate such categorization, which constituted the prevailing scenario. The determination pivoted on discerning factors such as the temperature differentials of the heat transfer fluid. In cases where these data were unavailable, an internal assessment was undertaken, factoring in integrating renewable energy sources (RESs) within the generation framework. This classification parameter remains fundamentally qualitative and is the least straightforward, as the literature lacks sufficiently validated and robust data and conditions to support its reconstruction. Additionally, reconstructing it from inventory data provided by DHCN operators would not be straightforward either, as these operators have integrated various alternative solutions to fossil fuels over time. Consequently, such integration could result in a classification parameter that does not correspond to the actual state of affairs. As observed, the threshold temperature for classification varies depending on the considerations put forth by the authors. The classification threshold between 4GDH and 5GDH was established at approximately 40 °C, considering the average value derived from the contributions of the two aforementioned authors. As for the threshold temperature between 3GDH and 4GDH, a value of 80 °C was determined based on the scientific literature. Figure 5 is a summary scheme of the parameters and the descriptions reported in this subsection.

3.3. Literature Review of Scientific Articles

The analyzed literature reviews provided an overview of the application of LCA methodology to DHC networks, each with specific objectives within each contribution. Bottero et al. (2021) aimed to investigate the scientific literature in the context of evaluation frameworks for supporting decision problems related to the energy transition and emerging trends and innovative research lines in the domain of energy planning and urban management [34]. In the process, they dedicated extensive research to the application of LCA without delving into the technical details of the DHC networks. However, they concluded that analyzing scenarios at the urban scale rather than at the individual building level is a trend that the research is strongly pursuing. Soares et al. (2017) presented a review of current advances in the energy and environmental performance of buildings toward a more sustainable built environment, considering various LCA contributions for their research but without detailing the application of DHC, while recognizing its potential benefits in terms of reducing GHG emissions compared to conventional technologies [35]. Gjoka et al. (2023) presented a review of the implementation and technical, environmental, and social aspects of 5GDHC, focusing part of the discussion on environmental performance aspects [36]. With this treatment, the authors briefly indicate the technical limitations to consider for 5GDHC to function as required. Milousi et al. (2022) provided a comprehensive understanding of the possible advantages and multiple uses of geothermal energy systems in the context of their technical and environmental evaluation through LCA, describing all the main technologies applied to DHC and the tools used for its modeling [37]. At the same time, they provided results from LCA analysis on heat production for various applications. Finally, Bartolozzi et al. (2017) proposed a contribution on applying DHC networks and integrating renewable energies, using a case study of LCA applied to a Tuscan case to demonstrate that these applications are not always a win-win solution. The authors also proposed interesting case studies that approach the objective of this review [38].

3.4. Literature Review of the Case Studies

This subsection aims to present the main scientific evidence gathered through the literature analysis by the authors. Various contributions have been categorized into macro-groups based on the topics addressed in the discussions, aiming to provide a comprehensive technical overview and highlight any limitations. The authors have noted that applying an environmental assessment to DHC has allowed previous studies to support various theses, ranging from the presentation of more or less complex decarbonization scenarios to the development of evaluative tools and energy mapping. A mind-map illustrating this concept is presented in Figure 6.

From the dataset of publications, 66 case studies were gathered for a subsequent technical analysis of the environmental profile used in the scientific contributions to support their discussions. The individual case studies examined and analyzed, which enabled the reconstruction of the emission factor for the studied DHC system, were compiled and are summarized in Table 4. Each study is characterized by the (i) author, (ii) generation system, (iii) application context, (iv) type of LCA application, (v) GWP source, and (iv) GWP. In most cases, the authors show the results of the analyses by normalizing the unit of energy produced by the system. The case studies primarily involve application contexts where various decarbonization solutions are presented. Generally, the contributions compare these decarbonization solutions to a base case, which typically involves district users operating their own generators powered by fossil fuels. As the solutions aim to reduce greenhouse gas emissions, the main findings consistently demonstrate that decarbonized solutions are environmentally superior to the comparative base case. In some instances, the base case is the existing district heating network in its “as-built” phase, in which the proposed solution involves replacing one or more generation units to reduce the overall environmental impact of the system. The authors noted a clear disparity in the presentation of the environmental profiles. Some contributions presented the results applied directly to the case study, normalized against the thermal energy produced or distributed by the DHC system. Conversely, a significant absence of explicit environmental profiles associated with DHC network systems was found in several LCA analyses across various scientific contributions. This gap was particularly evident when studies focused on alternative objectives or the DHC system served as a secondary element within the analysis. Consequently, the focal point of such scientific contributions predominantly revolved around mitigating the carbon footprint throughout the system’s life cycle. Emission factors were reconstructed by integrating the DHC system’s carbon footprint across its life cycle and correlating it with the energy output delivered to the serviced users, a practice commonly detailed during the system’s description in these studies.

3.4.1. DHC Environmental Profile for Optimization of Peak Loads and Decarbonization Scenarios

The reviewed scientific contributions predominantly focus on decarbonization scenarios for an existing, highly emissive generative system, proposing solutions aimed at mitigating the climate impact of the district heating and cooling (DHC) system considered. Notable among these cases are several key contributions. For instance, Olsson et al. (2015) presented a decarbonization scenario for a DH system in Sweden analyzed through LCA analysis, where oil fuel CHP was replaced with biomass CHP [39]. Bartolozzi et al. (2017) presented several decarbonization scenarios for a 4GDH network. The authors presented two decarbonization scenarios for the network, proposing the replacement of the natural gas boiler with a ground-source heat pump and biomass-fueled CHP [38]. Famiglietti et al. (2022) proposed a model that evaluates Milan’s 3GDH network using the attributional approach and exergy allocation to assess the environmental profile of the heat distributed to end users, which is subsequently used within a tool [40]. Also, Famiglietti et al. (2021) report a case study analyzing the implementation of a 4GDH network composed of solar thermal system, a ground-water heat pump, and thermal storage, with a backup system supported by an existing 3GDH composed of fossil fuel-based polygeneration systems, cogenerative systems, or systems utilizing industrial waste heat. The authors present the environmental profiles of the two DH typologies, calculated based on the unit of heat distributed by the system [15]. Famiglietti et al. (2023) presented an LCA analysis of the limits and constraints of Milanese social housing. This contribution presented an environmental assessment of a 5GDHC system, powered by heat pumps whose electrical consumption was compensated for by a photovoltaic system [41]. Another study worth mentioning, although it does not present a true decarbonization scenario but rather a comparison of LCA analyses of different multifunctionality approaches, is that by Neirotti et al. (2019), which proposed a comparative LCA study on an Italian 3GDH network fueled by a natural gas CHP system, varying the allocation approach to the products (exergetic or energetic) [42]. Finally, Uhrmann et al. (2023) proposed an LCA analysis on a case study in southern Bavaria, where a geothermal supply system with two production wells and an injection well, supplemented for peak load coverage by three 17 MW oil boilers, is installed for 1800 users of a DHN system. The base scenario was optimized by considering a scenario in which the fuel boilers are replaced by 90% natural gas coverage and the remaining 10% is replaced with biomass boilers. The percentage reductions in the carbon footprint are 57.8% for the first scenario and 20.4% in the second case. This research proved valuable, as it highlights the contribution to emissions from backup and integration generators. The authors directly calculated and reported the emission factors [43]. This scientific contribution is important not only due to its discussion of the emission factors but also because it emphasizes the impact of backup generators in supporting the overall system and considers the optimization of the peak load coverage for the system.

3.4.2. DHC Environmental Profile for Alternative Decarbonization Scenarios

Among the case studies presented, those that studied a comparison between an existing DHC scenario and one optimized through decarbonization using an alternative and unconventional system are noteworthy. Several contributions in this category aim to evaluate the potential positive effects of technologies such as polygenerative systems with renewable energy or thermal storage systems, starting from a centralized system. For instance, Ghafhazi et al. (2011) presented several decarbonization scenarios for a DH system where the centralized natural gas boiler was replaced with less impactful systems [44]. Meanwhile, Maione et al. (2022) investigated a polygenerative system where geothermal technology catered to domestic hot water and heat demands, complemented by heat pumps powered by a photovoltaic array to offset cooling needs [45]. Guillén-Lambea et al. (2021)’s case study showcased the utilization of three distinct types of thermal storage paired with a heat pump solely energized by photovoltaics [2]. Additionally, Karldottir et al. (2020) proposed a study featuring a high-temperature geothermal plant integrated with a CHP system to supply a 3GDH network alongside a carbon capture system [46]. Notably, Nitkiewicz and Sekret (2014) and Diaz et al. (2020) also contributed alternative decarbonization scenarios for DH systems within their respective studies [47,48]. Guarino et al. (2020) conducted a comparative LCA analysis to assess the climate footprint of a Canadian DH system [49]. Here, a solar thermal system with thermal storage addressed the heating and domestic hot water demands of a district, while its cooling needs were met by electric heat pumps fueled by photovoltaic panels. In another study by Kiehle et al. (2023), the carbon footprint calculation for a Finnish university campus relied entirely on a DH system with a peat-fired boiler, accounting for approximately 35% of its total carbon footprint [50]. Jeandreaux et al. (2021) conducted an analysis with energy-based allocation in five decarbonization scenarios for an existing DH system powered by natural gas boilers, where replacing them with cogeneration plants was hypothesized [51]. Mahon et al. (2022) presented a comparative LCA study on two scenarios for the decarbonization of a DH system powered by gas boilers. In one scenario, replacement of the generator was proposed [52]. Finally, Pericault et al. (2018) presented the scenario of using gravity sewers to power a high- or low-temperature DH system. The emission factors were derived using the distributed energy data from the presented case study [53]. Some scientific contributions have attempted to study the economic and environmental benefits of waste-to-energy systems serving heat production for neighboring districts, as in the case of Bisinella et al. (2022), which presented an environmental assessment of a waste incineration plant for electricity production at the district level in Copenhagen. The authors suggested a carbon offset scenario in which a carbon capture system and a storage system were also installed [54]. Several studies have explored the energy and environmental benefits of utilizing waste heat. Wahlroos et al. (2018) underlined how business models between district heating network operators and data center operators are often not transparent and analyzed the implications of economics and emissions for waste heat utilization in a DH system in Finland through LCA [55]. Finally, other studies have explored GWP reduction in wheat production and CHP generation via wheat straw thermal gasification. One such analysis was conducted considering the application of a DH system with CHP, and the results for the energy system were related to electricity production [56].

3.4.3. DHC Environmental Profile for Geothermal Plant Evaluation

Several studies have addressed the potential benefits brought about by implementing a geothermal plant connected to a DHC system. For instance, Pratiwi et al. (2019) presented an LCA analysis of a district utilizing six different geothermal plant configurations, which varied depending on the installation type. The analysis revealed that in the case of the DHC application, connecting the geothermal system to a heat pump resulted in more detrimental emissions into the environment compared to a scenario in which the system was directly connected to the network, considering all the limitations in both the heating and cooling phases [57]. Memberg et al. (2020) proposed an LCA analysis applied to different typologies for CHP. Considering an exergy-based allocation, the results demonstrate that the emission factor for heat distribution does not vary with the applications [58]. Mainar-Toledo et al. (2023) presented a comparative LCA considering exergy-based allocation of products in a case study with DH powered by a geothermal system with CHP for electricity and heat production [59]. Maione et al. (2023) studied the implementation of a geothermal plant in southern Italy alongside a DH system, offering insights into GHG emissions through Emergy Analysis, alongside results from a parallel LCA analysis [60]. Regarding the integration of geothermal systems, the most researched and studied scenario concerns the possible configuration and type of plant itself. In addition, Wang et al. (2021) and Liu et al. (2021) propose LCA analyses where the integration of a geothermal system with a DHC system applied to existing districts is studied, replacing configurations with boilers and absorption chillers. These analyses are carried out with a different approach from those adopted by previous authors, making it difficult to compare the results as transparently and functionally as possible for the main purpose of this review [61,62]. Finally, Tester et al. (2021) conducted a technical and economic review of the application of geothermal production systems, discussing aspects related to GHG emissions and with evaluation through LCA analysis. They provided case studies on high-temperature geothermal plants for electricity production [63]. Since the results presented by these authors exclusively pertained to electricity generation, they are not included among the case studies considered in Figure 7.

3.4.4. Environmental Profile of DHC Systems Using Biomass as an Alternative Energy Source

Several scientific contributions have focused on analyzing the impact that could be achieved on greenhouse gas emissions by replacing fossil fuel combustion generators with biomass-operated generators. Abrahmsen et al. (2023) report an analysis on the applications of biomass as an energy source to CHP. The authors assessed the efficiency and environmental performance of a nearly zero-energy university building’s energy system in Norway. In this case, the results were derived from the consumption of the studied case, while the LCA evaluations were performed considering energy-based allocation [64]. For instance, Temporim et al. (2022) advocate for the utilization of biomass extract sourced from a specific arboreal species deemed to thrive effortlessly within the Italian landscape, requiring minimal maintenance due to its inherent adaptability to the prevailing climate conditions. The comparative efficacy of this biomass is evaluated against a baseline scenario where the heat generator operates using natural gas, employing LCA methodologies encompassing all life cycle stages. The findings illustrate a potential reduction of up to 81% in CO₂ emissions across the entire life cycle through the adoption of this biomass-based technology [65]. Furthermore, Kanematsu et al. (2017) undertake the reconstruction of an operational DHC system situated on a Japanese island, with heat provision facilitated by a biomass-fueled CHP plant. Here, the authors elucidate the nexus between the CO₂ reduction benefits derived from biomass utilization and factors such as the raw material consumption dynamics and the imperative of sustainable forest management practices [66]. These insights are further echoed by Ericsson et al. (2013), who underscore the necessity of embracing a dynamic approach to biomass assessment, encompassing the entirety of its life cycle in order to accurately gauge its environmental impact and efficacy as an alternative energy source [67]. Other contributions have focused on assessing the techno-economical potential of biomass as an alternative energy source, employing comprehensive LCA analyses of hypothetical scenarios derived from existing network configurations. LCA analyses on DH systems prove valuable as a support system for economic analyses, as demonstrated by Bjornebo et al. (2018). This study served to introduce an economic evaluation tool and guide for investments necessary for reducing climate-altering emissions. The case study concerned a DH system where biomass was hypothetically utilized to replace fossil fuels in powering a CHP plant. However, from the obtained results, reconstructing the emission factor of the DH system was not feasible, as the outcomes were presented solely to support economic analyses [68].

3.4.5. DHC Environmental Profile for Validation and Energy Mapping

Some scientific contributions have utilized an environmental profile analysis applied to a DHC system to support studies and validation of district-level energy mapping models. In this regard, Spirito et al. (2021) presented a case study in northern Italy with the aim of validating an urban and district heating energy mapping tool. This contribution aimed to analyze the actual decarbonization potential of the technology. This study also included an LCA analysis with the emission factors of the analyzed district heating system [69]. The same modus operandi was presented by Pozzi et al. (2021) in analyzing the role of DH in building retrofit scenarios [70]. Another interesting case was provided by Gustavsonn et al. (2016), who presented a techno-economic analysis of energy renovation measures for a district-heated multi-family house. The analyses were conducted considering the average emission factor of a typical Swedish DH system [71].

3.4.6. DHC Environmental Profile for Innovative Operation Configurations

In some cases, the evaluation of the environmental profile applied to DHC has been used to assess potential scenarios for the configuration, optimization, and operation of the energy systems within the DHC system itself. For instance, Abu-Rayash and Dincer (2023) presented an LCA analysis of a polygenerative plant in Egypt, focusing on electricity production [72]. Some authors utilized LCA methodology for DHC systems to examine innovative operating configurations for existing generation systems, comparing them with known base cases. For instance, Hampo et al. (2021) analyzed the optimization of an absorption chiller coupled with thermal energy storage, finding nighttime production more advantageous for GHG emissions [73]. Akbar and Mokhtar (2017) reviewed the applications of LCA and LCC methodology to centralized chilled water generation at the district level [74].

Table 4. Case study summary.

Author	Generation System	Context of Application	Type of LCA Application	GWP Source	Network Type	GWP [gCO2eq/kWhth]
Uhrmann et al. (2023) [43]	Implementation of a geothermal plant in DH in Germany with various boiler backup decarbonization solutions	Germany	Attributional	Declared by the authors	4GDH	78.60
					4GDH	74.10
					4GDH	62.20
					4GDH	73.00
Pratiwi and Trutnevyte, (2021) [57]	Study on various geothermal plant configurations	Switzerland	Attributional	Declared by the authors	4GDH	18.10
					4GDH	14.90
					4GDH	18.90
					4GDH	17.30
					DC	6.30
					DC	7.50
					DC	10.20
					DC	7.80
Olsson et al. (2015) [39]	Comparative study of DH with fuel oil and biomass for CHP	Sweden	Attributional	Declared in the contribution	4GDH	160.00
					3GDH	210.00
Neirotti et al. (2020) [42]	Study on the application of two different allocation methods to natural gas CHP in DHC in Italy	Italy	Attributional	Declared in the contribution	3GDH	100.00
					3GDH	470.00
Bartolozzi et al. (2017) [38]	Analysis of DH in Italy applied to 3GDH with various decarbonization solutions	Italy	Attributional	Declared in the contribution	3GDH	217.00
					3GDH	175.00
					3GDH	142.00
Famiglietti et al. (2022) [40]	Results for a 3GDHN for the presentation of an LCA tool for the evaluation of Milan’s real estate	Italy	Attributional	Declared in the contribution	3GDH	161.00
Famiglietti et al. (2021) [75]	DH system with heat recovery and waste-to-energy, 4 CHP, and boilers for 3GDH in Italy	Italy	Attributional	Declared in the contribution	3GDH	208.00
					4GDH	89.00
Famiglietti et al. (2023) [15]	DH system with Solar thermal system and Global Warming Potential (GWP) of 4GDH in Italy	Italy	Attributional	Declared in the contribution	4GDH	89.00
Famiglietti et al. (2023a) [41]	Analysis with DHC supplied by heat pumps powered by photovoltaic modules	Italy	Attributional	Declared in the contribution	5GDH	157.00
					DC	64.00
Spirito et al. (2021) [69]	Decarbonization scenarios of 3GDH and 4GDH in Italy	Italy	Attributional	Declared in the contribution	3GDH	290.00
					4GDH	190.00
					4GDH	140.00
					4GDH	130.00
Pozzi et al. (2021) [70]	Polygeneration system for 4GDH in Italy	Italy	Attributional	Declared in the contribution	4GDH	98.00
Ghafghazi et al. (2011) [44]	Analysis of various decarbonization solutions in order to substitute boilers in DH	Europe	Attributional	Declared in the contribution	3GDH	240.00
					4GDH	39.40
					5GDH	15.80
					5GDH	24.60
Guarino et al. (2020) [49]	Comparative study of DH in Canada, covered by heat pumps and a BTES with Solar thermal system	Canada	Attributional	Calculated by the authors	4GDH	54.43
					4GDH	61.27
Kiehle et al. (2023) [50]	Life cycle emissions analysis of peat-powered DH in Finland	Finland	Attributional	Calculated by the authors	3GDH	221.00
Maione et al. (2022) [45]	Analysis of a geothermal plant with 4GDH connected to ORC system for heating and a heat pump system for cooling	Italy	Attributional	Calculated by the authors	4GDH	64.74
					DC	14.89
Guillén-Lambea et al. (2021) [2]	LCA analysis of various thermal storage configurations	Spain	Attributional	Calculated by the authors	4GDH	57.00
					4GDH	52.46
					4GDH	61.82
					DC	17.12
					DC	15.56
					DC	18.45
Karlsdottir et al. (2020) [46]	Study with high-temperature geothermal technology with carbon capture in Iceland for 4GDH	Iceland	Attributional	Calculated by the authors	4GDH	15.80
					4GDH	11.20
Nitkiewicz and Sekret, (2014) [47]	Study in Poland of DH with various decarbonization technologies applied to 5GDH	Poland	Attributional	Calculated by the authors	5GDH	50.00
					5GDH	45.00
					5GDH	65.00
Diaz et al. (2020) [48]	Analysis of DH with a biomass generator and backup boiler for 3GDH and 4GDH	Europe	Consequential	Calculated by the authors	3GDH	95.00
					4GDH	83.00
Ristimaki et al. (2013) [76]	Climate profile of 3GDH in Finland	Finland	Attributional	Calculated by the authors	3GDH	142.00
Mahon et al. (2020) [52]	Analysis of various decarbonization solutions in order to substitute boilers into DH	Ireland	Attributional	Calculated by the authors	3GDH	268.00
					4GDH	200.00
					4GDH	186.00
Abrahmsen et al. (2023) [64]	Analysis of the environmental performance of a nearly zero-energy university building’s energy system	Norway	Attributional	Calculated by the authors	3GDH	229.00
Jeandreau et al. (2021) [51]	Analysis and general recommendations on decarbonization scenario for DH systems	Europe	Attributional	Calculated by the authors	3GDH	270.00
					4GDH	50.00
					4GDH	160.00
					4GDH	100.00
					4GDH	200.00
Pericault et al. (2018) [53]	Analysis of the use of sewer water for DH decarbonization scenarios	Sweden	Attributional	Calculated by the authors	3GDH	56.00
					4GDH	53.00
					4GDH	52.00
Gustafsson et al. (2022) [71]	Climate profile of 4GDH in Sweden	Sweden	Attributional	Declared in the contribution	4GDH	89.00

Table 4. Case study summary.

Author	Generation System	Context of Application	Type of LCA Application	GWP Source	Network Type	GWP [gCO2eq/kWhth]
Uhrmann et al. (2023) [43]	Implementation of a geothermal plant in DH in Germany with various boiler backup decarbonization solutions	Germany	Attributional	Declared by the authors	4GDH	78.60
					4GDH	74.10
					4GDH	62.20
					4GDH	73.00
Pratiwi and Trutnevyte, (2021) [57]	Study on various geothermal plant configurations	Switzerland	Attributional	Declared by the authors	4GDH	18.10
					4GDH	14.90
					4GDH	18.90
					4GDH	17.30
					DC	6.30
					DC	7.50
					DC	10.20
					DC	7.80
Olsson et al. (2015) [39]	Comparative study of DH with fuel oil and biomass for CHP	Sweden	Attributional	Declared in the contribution	4GDH	160.00
					3GDH	210.00
Neirotti et al. (2020) [42]	Study on the application of two different allocation methods to natural gas CHP in DHC in Italy	Italy	Attributional	Declared in the contribution	3GDH	100.00
					3GDH	470.00
Bartolozzi et al. (2017) [38]	Analysis of DH in Italy applied to 3GDH with various decarbonization solutions	Italy	Attributional	Declared in the contribution	3GDH	217.00
					3GDH	175.00
					3GDH	142.00
Famiglietti et al. (2022) [40]	Results for a 3GDHN for the presentation of an LCA tool for the evaluation of Milan’s real estate	Italy	Attributional	Declared in the contribution	3GDH	161.00
Famiglietti et al. (2021) [75]	DH system with heat recovery and waste-to-energy, 4 CHP, and boilers for 3GDH in Italy	Italy	Attributional	Declared in the contribution	3GDH	208.00
					4GDH	89.00
Famiglietti et al. (2023) [15]	DH system with Solar thermal system and Global Warming Potential (GWP) of 4GDH in Italy	Italy	Attributional	Declared in the contribution	4GDH	89.00
Famiglietti et al. (2023a) [41]	Analysis with DHC supplied by heat pumps powered by photovoltaic modules	Italy	Attributional	Declared in the contribution	5GDH	157.00
					DC	64.00
Spirito et al. (2021) [69]	Decarbonization scenarios of 3GDH and 4GDH in Italy	Italy	Attributional	Declared in the contribution	3GDH	290.00
					4GDH	190.00
					4GDH	140.00
					4GDH	130.00
Pozzi et al. (2021) [70]	Polygeneration system for 4GDH in Italy	Italy	Attributional	Declared in the contribution	4GDH	98.00
Ghafghazi et al. (2011) [44]	Analysis of various decarbonization solutions in order to substitute boilers in DH	Europe	Attributional	Declared in the contribution	3GDH	240.00
					4GDH	39.40
					5GDH	15.80
					5GDH	24.60
Guarino et al. (2020) [49]	Comparative study of DH in Canada, covered by heat pumps and a BTES with Solar thermal system	Canada	Attributional	Calculated by the authors	4GDH	54.43
					4GDH	61.27
Kiehle et al. (2023) [50]	Life cycle emissions analysis of peat-powered DH in Finland	Finland	Attributional	Calculated by the authors	3GDH	221.00
Maione et al. (2022) [45]	Analysis of a geothermal plant with 4GDH connected to ORC system for heating and a heat pump system for cooling	Italy	Attributional	Calculated by the authors	4GDH	64.74
					DC	14.89
Guillén-Lambea et al. (2021) [2]	LCA analysis of various thermal storage configurations	Spain	Attributional	Calculated by the authors	4GDH	57.00
					4GDH	52.46
					4GDH	61.82
					DC	17.12
					DC	15.56
					DC	18.45
Karlsdottir et al. (2020) [46]	Study with high-temperature geothermal technology with carbon capture in Iceland for 4GDH	Iceland	Attributional	Calculated by the authors	4GDH	15.80
					4GDH	11.20
Nitkiewicz and Sekret, (2014) [47]	Study in Poland of DH with various decarbonization technologies applied to 5GDH	Poland	Attributional	Calculated by the authors	5GDH	50.00
					5GDH	45.00
					5GDH	65.00
Diaz et al. (2020) [48]	Analysis of DH with a biomass generator and backup boiler for 3GDH and 4GDH	Europe	Consequential	Calculated by the authors	3GDH	95.00
					4GDH	83.00
Ristimaki et al. (2013) [76]	Climate profile of 3GDH in Finland	Finland	Attributional	Calculated by the authors	3GDH	142.00
Mahon et al. (2020) [52]	Analysis of various decarbonization solutions in order to substitute boilers into DH	Ireland	Attributional	Calculated by the authors	3GDH	268.00
					4GDH	200.00
					4GDH	186.00
Abrahmsen et al. (2023) [64]	Analysis of the environmental performance of a nearly zero-energy university building’s energy system	Norway	Attributional	Calculated by the authors	3GDH	229.00
Jeandreau et al. (2021) [51]	Analysis and general recommendations on decarbonization scenario for DH systems	Europe	Attributional	Calculated by the authors	3GDH	270.00
					4GDH	50.00
					4GDH	160.00
					4GDH	100.00
					4GDH	200.00
Pericault et al. (2018) [53]	Analysis of the use of sewer water for DH decarbonization scenarios	Sweden	Attributional	Calculated by the authors	3GDH	56.00
					4GDH	53.00
					4GDH	52.00
Gustafsson et al. (2022) [71]	Climate profile of 4GDH in Sweden	Sweden	Attributional	Declared in the contribution	4GDH	89.00

Figure 7 presents the authors’ climate profile, retrieved or reconstructed based on the information provided in the case studies. The case studies were separated according to the type of DH regarding the heating and domestic hot water production systems, while the reconstructed environmental profiles for cooling were treated separately and without dividing the cases by network type, as they can be attributed to a common cooling production system. In total, the emission factors were reported for (i) No. 17—3GDH; (ii) No. 34—4GDH; (iii) No. 6—5GDH; and (iv) No. 9—DC (no distinction was made here). The range of emission factors related to 3GDH varies from 56 gCO₂eq/kWh_th, obtained in a DH system with an energy mix clearly influenced by the presence of a biomass boiler [48], to 470 gCO₂eq/kWh_th, as reported in the study on CHP-powered DH fueled by natural gas with energy-based allocation [42]. All the results reported by the authors were calculated using a time horizon of 100 years. It is noteworthy that few authors have cited IPCC scenarios, which were subsequently used to derive the characterization factors for the analyses. These factors adhere to the guidelines established for calculating GWP. Over time, the IPCC has revised its methodologies for GWP calculation, incorporating additional greenhouse gasses not included in previous report editions. Consequently, these updates may lead to varying degrees of change in the GWP values, thereby complicating direct comparisons among the environmental profiles reported in the publications. This justification partly elucidates the significant variability observed in the presented and collected results. The range is significantly wide, indicating considerable potential for network decarbonization. On the other hand, the reconstructed emission factors for 4GDH reported a range of variation ranging from 11 gCO₂eq/kWh_th, in the case proposed by Karlsdottir et al. (2020) [46], studying the operation of a DH system entirely covered by a high-temperature geothermal plant with carbon capture technology, to 200 gCO₂eq/kWh_th, proposed by Jeandreau et al. (2021) [51], where 69% of the energy demand was covered by electric heat pumps and the remaining demand by CHP and natural gas boilers. The reconstructed results for 5GDH range from 16 gCO₂eq/kWh_th, considering a scenario in which all production is based on heat recovery from a sewage plant [44], to 157 gCO₂eq/kWh_th, where production relies on a boiler but the supply temperature of the heat transfer fluid is set to low temperatures [48]. Finally, the reconstructed results for DC vary within a range between 6 gCO₂eq/kWh_th and 64 gCO₂eq/kWh_th. The first case is related to the application of a very shallow geothermal grid coupled with a heat pump [57]. In the second case, the results pertain to the application of a refrigeration system consisting of heat pumps powered by a photovoltaic system [41]. Table 5 illustrates, for exemplary purposes, the ranges identified for each type, indicating, in order, (i) a minimum value; (ii) an average value; and (iii) a maximum value.

Table 5. Range of environmental profiles provided per DHC typology.

	No. of Samples	Minimum	Mean	Maximum
-	-	[gCO2eq/kWhth]	[gCO2eq/kWhth]	[gCO2eq/kWhth]
3GDH	17	56	204	470
4GDH	34	11	63	200
5GDH	6	16	60	157
DC	9	6	18	64

Table 5. Range of environmental profiles provided per DHC typology.

	No. of Samples	Minimum	Mean	Maximum
-	-	[gCO2eq/kWhth]	[gCO2eq/kWhth]	[gCO2eq/kWhth]
3GDH	17	56	204	470
4GDH	34	11	63	200
5GDH	6	16	60	157
DC	9	6	18	64

Figure 7. Summary of the climate profiles obtained in the case studies analyzed.

Figure 7. Summary of the climate profiles obtained in the case studies analyzed.

3.5. Results Concerning a Multifunctionality Approach in the Case Studies

This subsection presents and discusses the main approaches and methods reported in the scientific literature for dealing with multifunctionality in LCA analysis. The ISO 14044 [13] guidelines outline various approaches to addressing the issue of multifunctionality. From a purely hierarchical point of view, ISO 14044 considers three approaches: (i) dividing the unit process, (ii) expanding the product system, and (iii) partitioning. Figure 8 presents a schematic overview of some options for dealing with multifunctionalities.

The first approach involves system substitution. In this scenario, the impact of the primary product is determined by the difference between the emission factor associated with that specific product and the emission factor associated with the marginal production of the by-product. The latter represents the impact that the by-product would incur on the external system if it were produced by an independent activity outside the context of the study.

The second approach necessitates the partition (allocation) of impacts from a multi-output process to the respective products based on an allocation factor, which is derived from the physical or economic characteristics of the generating system. This method is applicable exclusively within the framework of the attributional approach. Regarding the selection of the appropriate allocation factor, several authors have stated that this issue remains open and debated. An unresolved question pertains to the allocation of portions of the fuel consumption (and the associated environmental emissions) by an energy conversion plant to various products, such as electricity, hot water, and steam. While several approaches and methods have been proposed, none have attained global acceptance. Various case studies in the literature, particularly focusing on CHP and waste-to-energy (WtE) plants, validate some of the primary allocation methods. Famiglietti et al. (2021) identified six such cases, categorizing them based on the utilized relationships [77]:

-

No. 1 cases incorporate relationships beyond system boundaries (Separate Production Reference (SPR)).

-

No. 2 cases adopt physical relationships (exergy and energy content).

-

The No. 1 method adopts considerations based on the energy selling price.

Although outdated, two additional allocation methods are also mentioned: the Incremental Electricity-Centered Reference (IECR) method and the Incremental Heat-Centered Reference (IHCR) method. The first allocates products by considering the emission factor of electricity for systems with multiple production as if there were a separate production system. While utilized in the early stages of DH development, it is now considered obsolete and inequitable because it allocates the entire benefit of cogeneration savings to heat production, creating the impression of a minimal primary energy factor for cogenerated heat production. The second, on the contrary, assumes the primary energy consumption to be attributed to the production of cogenerated heat, equating it to the primary energy required for heat production in a separate facility. While it was used in the early stages of industrial cogeneration for waste heat, it is now obsolete and unfair, assigning the entire benefit of cogeneration savings to electricity production. The Separate Production Reference (SPR) method, mentioned by Beretta et al. (2012), Karlsdottir et al. (2020), and Olsson et al. (2015) [39,46,78], allocates the emission factors of cogenerated electricity and heat based on the relative proportions of the emission factors they would require in separate production facilities. However, it faces a drawback where the prescribed reference emission factors differ from the actual average primary energy factors of electricity and heat in the local area. In this method, the products are represented as in Equations (1) and (2):

$${\mathrm{A}}_{\mathrm{w}}={\displaystyle \frac{{EF}_{\mathrm{w},\mathrm{sep}}\times \mathrm{W}}{({EF}_{\mathrm{W},\mathrm{sep}}\times \mathrm{W}+{EF}_{\mathrm{Q},\mathrm{sep}}\times \mathrm{Q})}}$$

(1)

$${\mathrm{A}}_{\mathrm{Q}}={\displaystyle \frac{{EF}_{\mathrm{Q},\mathrm{sep}}\times \mathrm{Q}}{({EF}_{\mathrm{W},\mathrm{sep}}\times \mathrm{W}+{ EF}_{\mathrm{Q},\mathrm{sep}}\times \mathrm{Q})}}$$

(2)

The SPR method is a straightforward allocation approach suitable for the context discussed, relying on emission factors tied to national production, which are readily available data. However, its limited adaptation to local contexts and temporal fluctuations may render it unreliable in application scenarios requiring specific evaluations tailored to the geographic and temporal aspects of the generation system used.

The exergy-based allocation method allocates cogenerated electricity and heat emission factors based on the second law of thermodynamics. In this method, the products are represented as in Equations (3) and (4):

$${\mathrm{A}}_{\mathrm{w}}={\displaystyle \frac{{\mathrm{Ex}}_{\mathrm{W}}}{({\mathrm{Ex}}_{\mathrm{W}}+{\mathrm{Ex}}_{\mathrm{Q}})}}$$

(3)

$${\mathrm{A}}_{\mathrm{Q}}={\displaystyle \frac{{\mathrm{Ex}}_{\mathrm{Q}}}{({\mathrm{Ex}}_{\mathrm{W}}+{\mathrm{Ex}}_{\mathrm{Q}})}}$$

(4)

where ${\mathrm{Ex}}_{\mathrm{W}}$ and ${\mathrm{Ex}}_{\mathrm{Q}}$ represent, respectively, the exergy associated with thermal and electrical production.

While this method relies on energy quality, its main disadvantage lies in its association with complex thermodynamic processes, which demand a high level of user knowledge in this regard.

Finally, the energy-based allocation method relies on the first law of thermodynamics for allocation purposes [75]. In this method, the products are represented as in Equations (5) and (6):

$${\mathrm{A}}_{\mathrm{w}}={\displaystyle \frac{\mathrm{W}}{(\mathrm{W}+\mathrm{Q})}}$$

(5)

$${\mathrm{A}}_{\mathrm{Q}}={\displaystyle \frac{\mathrm{Q}}{(\mathrm{W}+\mathrm{Q})}}$$

(6)

where W and Q represent, respectively, the net electricity and heat production in the CHP plant.

In the literature, there are other approaches to addressing multifunctionality, which consider an arbitrary allocation of emissions to one product rather than another [39,77,79] or consider a 50/50 split between products. Another option presented by various authors is based on allocation according to the selling price of energy. Multifunctionality is thus linked to an economic system associated with the specific country of production. In this method, the products are represented as in Equations (7) and (8):

$${\mathrm{A}}_{\mathrm{w}}={\displaystyle \frac{{\mathrm{p}}_{\mathrm{W}}\times \mathrm{W}}{({\mathrm{p}}_{\mathrm{W}}\times \mathrm{W}+{\mathrm{p}}_{\mathrm{Q}}\times \mathrm{Q})}}$$

(7)

$${\mathrm{A}}_{\mathrm{Q}}={\displaystyle \frac{{\mathrm{p}}_{\mathrm{Q}}\times \mathrm{Q}}{({\mathrm{p}}_{\mathrm{W}}\times \mathrm{W}+{\mathrm{p}}_{\mathrm{Q}}\times \mathrm{Q})}}$$

(8)

where ${\mathrm{p}}_{\mathrm{W}}$ and ${\mathrm{p}}_{\mathrm{Q}}$ are the unit prices for purchased electricity and heat.

This method is primarily tied to factors of an economic nature. On the one hand, these factors enable geographical localization of information, while on the other hand, they entail the risk that economic fluctuations due to geopolitical causes may invalidate behaviors of a physical nature.

The choice of allocation method significantly influences the emission factor for the heat emitted by the studied network [42,77,78]. Famiglietti et al. (2021) propose a review of the main allocation methods found in the literature and investigate their application to a natural gas cogenerator that simultaneously produces electricity, heat in the form of steam, and domestic hot water. The results reveal significant variations among the different allocation methods, which, in turn, influence the emission factors associated with the generator [77]. Beretta et al. (2012) present their allocation method, the Self-Tuned Average-Local-Productions Reference (STALPR) method. This method operates similarly to the SPR, with the distinction that the emission factors should not be fixed by authorities but rather self-determined according to the energy production scenario in the area of interest [78]. In this method, the products are represented as in Equations (9) and (10):

$${\mathrm{A}}_{\mathrm{w}}={\displaystyle \frac{\overline{{\mathrm{E}\mathrm{F}}_{\mathrm{w},\mathrm{s}\mathrm{e}\mathrm{p}}}\times \mathrm{W}}{(\overline{{\mathrm{E}\mathrm{F}}_{\mathrm{w},\mathrm{s}\mathrm{e}\mathrm{p}}}\times \mathrm{W}+\overline{{\mathrm{E}\mathrm{F}}_{\mathrm{w},\mathrm{s}\mathrm{e}\mathrm{p}}}\times \mathrm{Q})}}$$

(9)

$${\mathrm{A}}_{\mathrm{Q}}={\displaystyle \frac{\overline{{EF}_{\mathrm{Q},\mathrm{sep}}} \times \mathrm{Q}}{(\overline{{EF}_{\mathrm{Q},\mathrm{sep}}}\times \mathrm{W}+\overline{{ EF}_{\mathrm{Q},\mathrm{sep}}}\times \mathrm{Q})}}$$

(10)

Lastly, Neirotti et al. (2021) propose an applied case study on a natural gas cogenerator applied to 3GDH in Turin, in which they compare the results obtained by allocating using exergetic and energetic methods. The authors report the emission factors, demonstrating that when the second method is applied to a comparative case study with a natural gas boiler, it could lead to the district heating network being considered more emissive in terms of CO₂ [42]. Analyses conducted using an exergy-based method tend to underestimate the emission factor associated with heat production, as they allocate the majority of emissions to electricity generation. Conversely, assessments conducted using a different allocation method could render the concept of the greater environmental sustainability of DHC networks compared to a baseline scenario with individual boilers installed in residential units inconsistent.

The latest approach is the surplus method. In this specific case, the impact of the system is entirely assigned to the determining product, while the by-product is considered zero-impact. Famiglietti et al. (2021) discuss this methodology in their analysis of CHP systems, illustrating two applications of the surplus method [77]:

-

One case where the entire emissions are allocated to electricity production;

-

One case where the emissions are allocated to heat production.

4. Discussion

This section aims to provide commentary on the scientific evidence arising from the analysis of the aforementioned results. The authors acknowledge that the number of scientific contributions utilized is limited compared to what a typical literature review would require. This limitation primarily stems from the scarcity of analyses focusing on the environmental impact of DHC networks conducted through LCA. The number of scientific contributions available for analysis would have increased if different keywords had been used for the literature review. However, this would likely have led to more articles not aligning with the review’s objectives. It is essential to highlight that the purpose of this study was to evaluate the application of LCA methodology to DHC networks. Through prior experience, research solely focused on the footprint of these networks would yield results related to studies evaluating only the operational phase of the energy system. This approach would exclude life cycle phases or components with significant environmental impacts. Such findings would be challenging to compare with those obtained from LCA studies and therefore would not be in line with the objectives of this literature review. The authors initially underscored a considerable numerical discrepancy between the case studies enabling the reconstruction of an emission factor associated with heating and domestic hot water production and those encompassing cooling aspects. This observation is also intertwined with geographic considerations, as many case studies originate from Northern Europe or regions where cooling dynamics are relatively neglected. However, given evolving climate patterns and the persistent rise in temperatures, it will be imperative to incorporate cooling-related considerations into future analyses. Primarily, the generation of cooling is intricately linked to energy sources predominantly reliant on electricity. Consequently, the decarbonization pathway for district cooling predominantly hinges upon the decarbonization efforts within the national or localized electricity sector, particularly if the system is interconnected with an autonomous generation setup. The majority of the analyses were conducted using attributional modeling.

Only in one case was a consequential LCA conducted, but the results are not comparable, as it was applied to a specific, non-replicated case study. Conducting a comparative study using both approaches and comparing the results could be interesting. The authors have noted that in all studies, the issue of the vectorization of the energy distribution at the neighborhood level has been neglected. Without regulation systems, the network continues to distribute heat to substations even when dwellings are unoccupied and thus does not require thermal comfort regulation. In spite of this, the distribution of the heat transfer fluid is subject to thermal losses that must be considered. A study on optimizing this process could yield different results in terms of environmental sustainability. Finally, the authors highlight how each study was conducted by reconstructing a specific model for each case under consideration. Such models would be difficult to apply if the conditions were to vary. Essentially, no LCA-based model is capable of conducting analyses on district heating and cooling networks.

4.1. Discussion Concerning Network Categorization

As elucidated in the preceding sections, the authors were compelled to categorize the DHC systems of the various case studies considered into generational typologies. This classification was contingent upon the descriptions provided in the scientific contributions, particularly emphasizing technical data such as the heat transfer fluid supply temperatures. Such classification became requisite due to most of the authors’ lack of explicit generational attributions regarding the studied DHC systems. Notably, some descriptions of the system operations were cursory, lacking the requisite details for straightforward determination of the case study affiliations. While the omission of system classification and operational details may ostensibly be deemed of peripheral import vis à vis the authors’ primary objectives, advocating for their inclusion is imperative given the potential utility of the presented case studies as reference points for stakeholders and industry practitioners. Indeed, comprehensive descriptions, coupled with systematic classification, could facilitate the construction of benchmark background databases for industry optimization endeavors. System classification hinges upon the specific operational parameters of DHC systems. However, as extensively deliberated heretofore, a universally acknowledged and singular classification methodology remains elusive. Discrepancies in classification methods, notably pertaining to the threshold supply temperature considerations across generational delineations, engender uncertainty in classification endeavors, potentially confounding authors during descriptive phases. The identified deficiencies inadvertently impinge upon the presented outcomes, augmenting the variability therein. To mitigate such variance, the authors of this review cataloged the DHC systems within the selected case studies, employing a singular methodology with predefined thresholds. Exploring the variations in the results contingent upon divergent classification methodologies could thus serve as fertile ground for scholarly discourse. In summation, the establishment of a universally recognized and singular classification methodology would render the presented outcomes replicable sans superfluous discursive tangents on the applied classification methodologies. Furthermore, the attenuated variability in the result distributions, compared to extant portrayals, would ideally culminate in a standardized emission factor benchmark contingent upon the generational affiliation of the scrutinized network, thereby advancing the horizons of the scholarly research.

4.2. Discussion Concerning the Multifunctionality Approach

As per the guidelines set forth by ISO 14044 [13], the allocation of emissions to multiple outputs is necessary for product systems featuring two or more outputs. The challenge of addressing multifunctionality arises due to the absence of a universally acknowledged approach among industry stakeholders. This issue is particularly crucial in conducting LCA analyses within energy sector case studies, especially when the examined energy systems generate both heat and electricity from a single source. An unresolved question pertains to the allocation of portions of fuel consumption (and the associated environmental emissions) by an energy conversion plant to various products, such as electricity, hot water, and steam. While several approaches and methods have been proposed, none have attained global acceptance.

An alternative system for allocation methods for managing multifunctionality is the adoption of system expansion. From a hierarchical perspective, ISO 14040 places this solution as the first to be addressed and considered when conducting an LCA involving production systems with two or more output products. Through expansion, each co-product is identified and assigned an alternative substitute product or service, thereby subtracting the environmental impacts from those of the original system, reflecting the environmental benefit derived from the substitution of the co-product itself. This process is realized by extending the system boundaries. As highlighted by Famiglietti et al. (2021), this approach could potentially enhance the benefits of DHC systems, as it would easily include the advantages brought by technologies, such as the utilization of waste heat from industrial plants adjacent to the district or urban waste disposal facilities [75]. Additionally, this system is useful and frequently adopted when a consequential approach is employed for the analysis.

5. Conclusions and Future Directions

The contribution aimed to provide a literature review of the current state of the art regarding the application of LCA methodology to evaluating the environmental profile of district heating and cooling networks. The authors commenced with a study of literature reviews published in indexed journals and then proceeded to search for applicable case studies that aligned with the purpose of the work. The collected documentation was analyzed with the aim of framing the technical aspects of the DHC systems under study and the methodological aspects related to the application of the LCA methodology required for the analyses. The search for the articles was primarily conducted using well-known search engines in the field, such as Scopus, Web of Science, Google Scholar, and Research Rabbit. Additionally, for each identified paper, its bibliography was analyzed to create a comprehensive background database of publications. The results obtained on the units of heat produced by the systems were collected and analyzed to study the differences in the methodologies applied among different authors. Based on the articles retrieved from the research, it emerged that over the last 10 years, research trends have predominantly focused on seeking applicative solutions involving the use of renewable sources such as geothermal systems, biomass applications, and other interesting decarbonization scenarios. Conversely, some authors have challenged the notion that district heating and cooling is more efficient than an individual system fueled by fossil fuels. They highlight how certain environmental indicators (other than GWP) or the choice of the multifunctionality approach to by-products in some application cases may prove disadvantageous. The authors attempted to reconstruct a range of emission factors per 1 kWh of thermal energy distributed for various technologies based on the data collected from the literature review. The analysis revealed a range of emission factors spanning from 11 gCO₂eq/kWh_th to 470 gCO₂eq/kWh_th for district heating and from 6 gCO₂eq/kWh_th to 64 gCO₂eq/kWh_th for district cooling. The review highlighted how each analysis is closely linked to the descriptive inputs of each applicable case study. Furthermore, the authors have shown how the application of various methodological conditions to LCA analysis, predetermined by the authors of the scientific contributions, can critically influence comparisons among different case studies. This variability undermines the use of averaged data as predefined benchmarks for preliminary analyses. Therefore, the results presented cannot be universally applied to other contexts but, at most, used as benchmarks for qualitative comparison in a subsequent validation phase. Finally, based on the authors’ extensive knowledge, no universal LCA-based calculation tool is exclusively dedicated to district heating and cooling analyses. This kind of tool could be reconstructed following the indications presented in the literature, thereby creating an empirically based tool capable of scaling various input quantities to adapt them to any application context. The authors’ objective is to develop such a tool following this line of research. The availability of a user-friendly tool, as previously proposed, would facilitate methodological analyses akin to those introduced in the review. Applied to a case study, it would assess sensitivity to varying parameters such as the adopted multifunctionality approach or the selected LCA methodology. Technically, it would enable a comparative analysis based on the environmental profiles computed within predefined decarbonization scenarios tailored to the specific case study.