Effect of Evolution of Electricity Emission Factor on Evaluation of Effectiveness of Decarbonization Measures in European Countries

Abstract

The evaluation of the effectiveness and sustainability of decarbonization measures, especially but not only through electrification, is not trivial. Combustion technologies improve by small amounts year to year and as the emission factors of fuels can be considered constant across European countries; however, the emission factors of electricity generation are not uniform among countries and do not share a common trend in their evolution over time. These two aspects, the different evolution trends of electricity emission factors and the non-uniformity of these factors among countries, can lead to unexpected results: under/overestimation of carbon savings and sustainability in the long-term forecast in the massive technologic transition in both the civil and industrial sectors. This paper, using trend observations and the Myopic model, highlights possible biases in the long-term projection of CO₂ emissions savings in the industrial sector. This study focuses on a comprehensive investigation of the integration of a high-temperature heat pump into a combined cooling, heating, and power plant. A detailed assessment of the system’s energy performance is conducted through steady-state simulations under both fixed boundary conditions and annual operational scenarios, addressing building heating, cooling, and electrical requirements across varying electricity emission factors for different European countries and evolution trends until 2050, with the results compared with conventional energy production systems.

1. Introduction

The energy sector is responsible for approximately 76.7% of the total global greenhouse gas (GHG) emissions, while industrial manufacturing and construction activities contribute an additional 12.2% through direct emissions. Their overall environmental impact rises substantially when indirect emissions associated with electricity consumption are taken into account [1]. From an economic standpoint, empirical evidence points to a positive and frequently linear relationship between gross domestic product growth and industrial CO₂ emissions, with non-renewable energy consumption identified as the primary determinant of this correlation. It is noteworthy, however, that industrial value added can also exert a partial mitigation effect on emission levels [2,3,4].

In Europe, the prioritization of energy efficiency has progressively emerged as a foundational pillar of energy policy. Initially codified in the Energy Efficiency Directive (EU/2018/2002) [5] and subsequently reinforced by Directive EU/2023/1791 [6], this principle has been further operationalized through a series of complementary recommendations issued by the European Commission [7]. In practice, this entails the adoption of cost–benefit methodological frameworks that extend beyond immediate financial considerations, explicitly accounting for broader societal benefits—including environmental sustainability, progress toward climate neutrality, and the stimulation of green economic growth [8].

The industrial sector offers several instructive examples of the complex interplay between improvements in energy efficiency, CO₂ emissions, and overall economic performance. Most of the studies reported in the literature treat key financial parameters—such as interest rate and inflation rate—as time-invariant quantities, maintained constant throughout the entire analysis period. An analogous assumption is commonly adopted with respect to energy conversion factors and energy-related emission factors. This methodological approach persists even in more recent contributions [9,10].

The primary objective of the present study is to introduce novel methodological approaches for the long-term quantification—with a time horizon extending to 2050—of CO₂ emission reductions generated by a high-temperature (vapor compression) heat pump (HTHP) integrated into a combined cooling, heating and power (CCHP) system, hereinafter referred to as CCHP+HTHP. The present contribution constitutes an additional advancement of a previously published work [11], which represents the main objective and original contribution of this study. In [11], an energy and exergy analysis of the system with different kinds of prime movers for combined electricity and heat production, a single-effect absorption chiller, and an HTHP was developed. The main scope was the optimization of the design of the primary components for a specific case study, a pharmaceutical manufacturing facility located in Tuscany, Italy, as referenced in [12]. The heat from the condenser and absorber of the absorption chiller was harvested and recovered as the heat source for the HTHP, allowing the upgrade of its thermal output to temperature levels suitable for heating applications. The performance of the system was compared with the benchmark energy production configurations—namely, separate generation, cogeneration, and conventional trigeneration—was carried out under fixed and annual operating conditions.

Scope and Novelty of This Study

The present study introduces additional novelty with a research objective of evaluating the CO₂ emissions of the integrated CCHP+HTHP system and comparing the emissions with those of the benchmark systems considering variable electricity emission factors during 2024–2050 for different European countries. Figure 1 presents the temporal evolution of the CO₂ emission factor associated with end-use electricity consumption, derived from an institutional data source [13]. During 1990–2024, this factor decreased from a maximum of 967 kg_CO2/MWh (relative to Poland, PL) to a minimum of 34.9 kg_CO2/MWh (relative to Sweden, SE), corresponding to a relative reduction of approximately 96.4%. The electricity-associated emission factors decreased in all European countries but with different trends (

Table 1. Variation in CO₂ electricity emission factors during 1990–2024 in percentage terms.

Country	ΔCO2 Emission Factor 1990–2024	Country	ΔCO2 Emission Factor 1990–2024
AT, Austria	58.70%	IE, Ireland	60.70%
BE, Belgium	59.40%	IT, Italy	50.10%
BG, Bulgaria	50.10%	LU, Luxembourg	76.40%
HR, Croatia	52.20%	NL, The Netherlands	57.40%
CY, Cyprus	22.10%	PL, Poland	36.70%
CZ, Czechia	46.70%	PT, Portugal	79.50%
DK, Denmark	85.20%	RO, Romania	57.00%
EE, Estonia	46.80%	SVK, Slovakia	75.10%
FI, Finland	76.30%	SI, Slovenia	38.60%
FR, France	60.80%	ES-Spain	66.60%
DE, Germany	45.60%	SE, Sweden	−4.80%
GR, Greece	63.60%	EU, European Union	45.50%
HU, Hungary	55.50%

). This consistent and sustained downward trend in electricity-associated emission factors carries significant implications for the long-term environmental assessment of energy efficiency measures: the magnitude of avoided CO₂ emissions—and thus the environmental effectiveness of the intervention—tends to progressively diminish over time as the electricity generation mix becomes increasingly decarbonized. This dynamic highlights the critical importance of incorporating prospective scenarios of electricity mix evolution into the environmental impact assessment of energy efficiency interventions, particularly when long-term planning horizons are considered.

This paper is structured as follows: Section 2 provides a detailed description of the models developed for both the CCHP+HTHP configuration and the benchmark systems; Section 3 presents the simulation outcomes, first under fixed boundary conditions and subsequently in the context of annual operation for the selected industrial case study. Section 4 synthesizes the principal findings of the annual energy comparison and outlines prospective directions for future research.

2. Materials and Methods

2.1. Description of CCHP+HTHP System

The CCHP+HTHP system is composed of the following interconnected subsystems:

• The prime mover, which may consist of a fuel cell generator (phosphoric acid fuel cell (PAFC) or molten carbonate fuel cell (MCFC)), a gas turbine (GT) or an internal combustion engine (ICE). This component converts the primary chemical energy of natural gas (F_cog) into electricity (E_cog), a fraction of which is directed toward satisfying the electrical demand of the end user (E_user). Through an appropriate heat recovery system, the prime mover also yields useful thermal energy (Q_cog).
• A variable fraction (f) of the latter is allocated to drive the absorption chiller (Q_abs,gen), while the remaining portion (1 − f)Q_cog is supplied directly to the user for heating purposes. A minor fraction of the input energy is dissipated as non-recoverable thermal losses (Q_wasted).
• The absorption chiller (abs) is driven by a variable share of Q_cog, modulated by parameter f, and delivers cooling power at the evaporator (Q_abs,ev).
• The high-temperature heat pump (HTHP) takes advantage of the low-grade thermal energy recovered from the absorption chiller (Q_abs,cond) as the heat source in its evaporator, delivering hot water at 90 °C in its condenser (Q_HTHP,cond). The output of the HTHP contributes to meeting the user’s heating demand (Q_user).

Figure 2 shows a schematic representation of the proposed system, illustrating the principal energy flows. Auxiliary units may be required to supplement the system, including an auxiliary boiler (Q_{boiler,to,user}) and an auxiliary electric chiller (Q_chil,ev), deployed to cover residual heating and cooling demands, respectively.

Given the nominal electrical output of the prime mover, the nominal recovered heat is uniquely determined. Accordingly, the nominal cooling capacity of the absorption chiller is established to maximize the utilization of the recovered thermal energy. The nominal HTHP capacity is subsequently sized to fully harness the low-temperature heat discharged by the absorption chiller. The parameter f introduces operational flexibility into the system: it governs the fraction of heat recovered from the prime mover (Q_cog) that is directed to the absorption chiller generator (Q_abs,gen = f∙Q_cog), thus determining the quota available for direct heating supply. The limiting cases are defined as follows: when f = 0, no recovered heat feeds the absorption chiller; thus, no cooling power is produced; when f = 1, cooling power is maximized as all recovered heat is directed to the absorption chiller. As a consequence, the heating demand is met exclusively by the HTHP.

As an example of the method used in the previous study [11], the electrical, thermal and cooling outputs of the system are shown in Figure 3 as a function of f. They are reported in normalized terms, with a fuel input F_cog of 1 MW and given component efficiency values. In [11], four operational scenarios are presented with different electrical efficiency values of the cogenerator and the COP of the HTHP—0.35 and 0.45 for electrical efficiency and 3 and 4 for COP_HTHP—to account for the performance characteristics of the different available technologies. η_el,cog = 0.35 is representative of GT or PAFC systems, while η_el,cog = 0.45 is typical of ICE or MCFC configurations [14]. COP_HTHP = 3 is characteristic of systems employing low-GWP refrigerants such as R1234ze(E), while a value of 4 is representative of ammonia-based systems [15,16].

The thermodynamic performance of the proposed trigeneration system, along with all benchmark configurations outlined in Section 2.2 (Figure 2), was evaluated in [11] by means of two key indicators: the primary energy ratio of the overall system (PER_tot) and the exergy efficiency of the overall system (η_ex,tot). The former is defined as the ratio between the total useful energy output and the total non-renewable primary energy input, whereas the latter represents the ratio between the exergy associated with the system’s useful products and the exergy supplied at the system boundary. For a comprehensive discussion of the energy performance comparison, refer to [11].

As an example of the analysis reported in the previous study, Figure 3 reports the energy performance indexes and the useful power produced by the CCHP+HTHP system for the most efficient case (see the electric and thermal efficiency of the cogenerator, thermal efficiency of the boiler, EER of the absorption chiller and COP of the HTHP reported in the figure). The figure also shows the CO₂-specific emissions (i.e., per MWh of useful energy (electricity E, heating Q and cooling C) to the user) for various fractions of heat recovered from the prime mover directed to the absorption chiller generator. In fact, specific emissions decrease with increasing f as a result of increasing the PER_tot of the system.

In the present analysis, an electric-demand-following (E_{user-following}) operation mode is adopted for the operation of the cogenerator. In each time step of the simulation, any residual electrical deficit was compensated by electricity drawn from the grid (E_{from,grid,to,user}). In this configuration, the thermal energy recovered from the cogenerator—allocated partly to direct heating (Q_cog,to,user = (1 − f)∙Q_cog) and partly to the absorption chiller generator (Q_abs,gen = f∙Q_cog)—may prove insufficient to fully cover the respective demands; consequently, the integration of an auxiliary boiler and an auxiliary electric chiller may be required.

The thermal-demand-following (Q_{user-following}) operation mode was not considered because, in this case, the absorption chiller generator could not be supplied by the heat recovered from the prime mover, thereby precluding the realization of trigeneration operation.

2.2. Description of Benchmark Systems

As reference energy production configurations, four systems were considered:

• Separate production with boiler and electric (water–water) chiller (SP Boiler+Chiller) with η_boiler = 0.85 and EER_chil = 3, assumed constant;
• The same configuration as above with the addition of condenser heat recovery from the electric chiller to preheat the hot water supply;
• Cogeneration system (CHP) with the same prime mover as in the proposed CCHP+HTHP system, coupled with an electric water–water chiller (EER_chil = 3, assumed constant) and an auxiliary boiler (η_boiler = 0.85);
• Conventional trigeneration system (CCHP): identical to the CCHP+HTHP configuration but without the integration of the high-temperature heat pump.

Figure 2 shows a schematic of the proposed systems, illustrating the principal energy flows. Comprehensive information on the electrical and thermal efficiencies of the four prime mover technologies under part-load conditions; the hourly electrical, thermal, and cooling demand profiles employed in the annual simulation; and the nominal capacities of the main system components is reported in [11].

2.3. Assessment of Greenhouse Gas Emissions

The majority of the literature pertaining to this case study has implemented the emission factor of the national electricity system recorded in the year of implementation of the energy efficiency measure as a fixed reference. However, the trend illustrated in Figure 1 suggests that the progressive reduction in the carbon intensity of electricity consumption should be explicitly accounted for in long-term assessments.

Recent contributions have begun to provide a new perspective on the treatment of the variability in the carbon emissions associated with electricity generation [17], with particular attention devoted to the building sector [18]. A number of studies have further examined the temporal dynamics of carbon emission factors and electricity markets [19] along with transition pathways toward climate-neutral energy systems [20].

The methodological approach adopted in the present work is based on the Myopic Transition Path framework [21]. The Myopic modeling approach is particularly appropriate for analyzing progressive structural transformations in energy system networks, including those associated with decarbonization or energy transition pathways. Under this modeling framework, generation capacities installed at each time step continue to operate until the expiry of their respective technical lifetimes, thus allowing a dynamic representation of infrastructure evolution over time. This approach was originally formulated and implemented in [22] and later refined and extended in [23].

It should be acknowledged that the Myopic model is subject to certain inherent limitations, as it is unable to account for energy crises or abrupt policy changes. Nevertheless, a rigorous and carefully calibrated application of this model can provide a valuable new perspective for interpreting current emission saving data and projecting them into future scenarios. Drawing from this well-established modeling framework, the temporal evolution of the emission factor e(t) can be expressed as follows:

$$e\left(t\right)=e_0\left(1+\left(r+m\right)t\right)·e^{-mt}$$

(1)

where

• e₀ is the emission factor in the first year of operation;
• r is the initial linear growth rate, assumed herein to be r = 0;
• m is the decay parameter.

The independent variable of the Myopic model is t (time), while the dependent variable is e(t), representing the emission factor at time t. The parameters r and m are determined as follows: the initial growth rate r is set to zero, consistent with both the modeling assumption and the observed trends in the statistical data; the decay parameter m is estimated by fitting nonlinear regression. In this work, the Myopic model was implemented in the electricity system of different EU countries, following a straightforward nonlinear regression procedure. The data source was that of Ember agency, provided by the website OurWorldInData [24]. The dataset downloaded from the website was complete and ready to use. Though it may not to be the best way to fit the model to the data, least-squares optimization was performed on both the initial emission factor e₀ and the decay parameter m. During the data analysis process, some problems arose in the cases of three countries (LA, Latvia; LI, Lithuania; MT, Malta), where the historic data series show multiple variation (ascending/descending); therefore, the Myopic model proved unfit for the cases. That is why only 24 out of the 27 EU countries are finally listed.

Table 2. R² values of fitting curve (Myopic) toward recoded data.

Country	R2 Value	Country	R2 Value
AT, Austria	52.3%	IE, Ireland	95.5%
BE, Belgium	96.4%	IT, Italy	89.2%
BG, Bulgaria	30.8%	LU, Luxembourg	72.0%
HR, Croatia	0.9%	NL, The Netherlands	66.1%
CY, Cyprus	82.5%	PL, Poland	87.4%
CZ, Czechia	96.7%	PT, Portugal	73.2%
DK, Denmark	90.5%	RO, Romania	77.8%
EE, Estonia	66.6%	SVK, Slovakia	92.9%
FI, Finland	53.7%	SI, Slovenia	69.5%
FR, France	44.1%	ES, Spain	73.8%
DE, Germany	85.3%	SE, Sweden	21.9%
GR, Greece	82.8%	EU, European Union	92.1%
HU, Hungary	86.9%

presents the R² values of the fitting curve for each country. As it can be seen, only 4 countries out of the 24 present a weak correlation (R² < 0.5), but over the whole 24 EU countries, the correlation is excellent (R² > 0.9).

The corresponding results are presented in Figure 4.

3. Results and Discussion

3.1. Energy Analysis Under Fixed Operating Conditions

A preliminary comparative assessment of the described energy systems was conducted at a single steady-state operating point. All configurations were evaluated under identical end-user demand conditions, specifically, E_user = 1 MW, Q_user = 2 MW, and C_user = 0.5 MW.

As an example of the results of a previous work [11], Figure 5 presents the non-renewable primary energy consumption of the examined plants (F), together with their respective PER_tot and exergy efficiency values, as well as the primary energy saving (PES) that the proposed CCHP+HTHP system allows with respect to the benchmark configurations. The figure reports the results for the best configuration in terms of prime mover technology (MCFC), considering the optimal value of parameter f (defined as the value that simultaneously maximizes both PER_tot and η_ex,tot) and a higher value of COP for the HTHP.

As reported in Figure 5, the optimal value of f is very high (0.8): for electrically more efficient systems like MCFC, and due to the high COP value of the HTHP, it is thermodynamically more advantageous to allocate a larger fraction of the recovered heat to the absorption chiller and, consequently, to the HTHP. As the latter is powered by electrical energy generated by the cogenerator, it is energetically more favorable to generate heating by the HTHP rather than through direct heat recovery from the prime mover.

Among all configurations examined, the greatest positive PES value—associated with the superior energy and exergy performance of the CCHP+HTHP system—is attained with respect to the separate production benchmark (SP Boiler+Chiller). In decreasing order of energy benefit, this is followed by the SP (Boiler+HP/Chiller), the conventional trigeneration, and the cogeneration systems.

3.2. Annual Energy Performance

Figure 6 illustrates the annual non-renewable primary energy consumption of the examined systems divided by that consumed by the cogenerator (F_cog) and the boiler (F_boiler) and for the electricity from the grid (F_grid). The CCHP+HTHP system demonstrates superior performance relative to all benchmark configurations, exhibiting the lowest total non-renewable primary energy consumption (and the lowest for the boiler). Consequently, the CCHP+HTHP system achieves PES values of 3.5%, 5.8%, 14.1%, and 22.2% with respect to the SP (Boiler+HP/Chiller), cogeneration, trigeneration, and SP (Boiler+Chiller) configurations, respectively.

3.3. Annual and Specific CO₂ Emissions

Concerning CO₂ emissions, Figure 7 presents the annual values for the first (2024) and last (2050) years of the period considered by the Myopic model. In this study, the carbon emission accounting covered only the operational stage, and no other stages (upstream fuel emissions, refrigerants, and equipment manufacturing) were considered. The most effective solution varied as a function of the electricity emission factor considered. As a first result, the emissions were calculated with respect to the specific minimum, median and maximum values of the electricity emission factors among those of each EU country, as reported in Figure 4. This choice allows us to show all the results for all the countries without resulting in extremely dense graphs of lines and data, which are very difficult to read and to understand. With this choice, the authors think that the results for all the countries can be represented with an acceptable degree of approximation. The Italian value is also reported. Considering the minimum of the specific electricity emission factors, the best solution is the SP (Boiler+HP/Chiller) system, i.e., that maximizing the non-renewable primary energy consumption from the grid (Figure 6). Considering the maximum of the electricity emission factors, the best solution is the CCHP+ HTHP system, with the 2050 value (16,125 t_CO2/y). Instead, with the 2024 value, traditional cogeneration and trigeneration allow for the lowest annual CO₂ emissions (20,302 t_CO2/y and 19,273 t_CO2/y, respectively) as they consume less non-renewable primary energy from the grid with respect to CCHP+ HTHP. When considering the median value (or the Italian one, which is quite close), the CCHP+HTHP system allows for the lowest emissions only if the 2024 electricity factor is considered (12,474 and 13,717 t_CO2/y, respectively). If the 2050 value is adopted, again, the SP (Boiler+HP/Chiller) system allows for the best performance.

In terms of specific emissions per unit of useful energy produced by the different systems and considering the minimum, median, maximum, and Italian electricity emission factors, Figure 8 reports the trends during 2024–2050. For all systems, the curves present a decreasing trend. Separate production with boiler and chiller (Figure 8b,c) produces the largest difference between the maximum and minimum values of the electricity emission factor, consuming the most non-renewable primary energy from the grid (Figure 6). In this case, in countries with very low electricity emission factors (such as Sweden or France), separate production is competitive with the CCHP+HTHP system, with specific CO₂ emissions per unit of useful energy produced varying between 0.17 and 0.14 t_CO2/MWh. Instead, cogeneration and trigeneration (Figure 8d,e) produce the smallest difference between the maximum and minimum values of the electricity emission factors, as they are the solution consuming the least non-renewable primary energy from the grid. In this case, in countries with a very high electricity emission factor (such as Poland or Cyprus), cogeneration and trigeneration are also competitive with the CCHP+HTHP system, with specific CO₂ emissions per unit of useful energy produced varying between 0.30 and 0.24 t_CO2/MWh.

3.4. Annual and Cumulative CO₂ Emissions

Figure 9 allows the assessment of the best solution in terms of CO₂ emissions during the whole period of 2024–2050, taking into account the decrease in the electricity factor. When considering the minimum value, the best solutions are separate production systems for the entire period, which allow CO₂ emissions of less than 5 kt_CO2 per year (Figure 9a). In contrast, when considering the maximum electricity emission factor, the cogeneration and trigeneration systems are the best during the first part of the period, with emissions of less than 21 kt_CO2/y, while CCHP+HTTP is also competitive in the last part of the period, with emissions between 16 and 17 kt_CO2/y (Figure 9c). In countries with electricity emission factors close to the median European value, the CCHP+HTHP system is the best solution until 2039 (Figure 9b), whereas in Italy, this is true for almost the whole period (Figure 9d).

Finally, Figure 10 reports the total cumulative CO₂ emissions for the different cases. In countries with low electricity emission factors, separate production of energy with boilers and HP/Chiller results in the lowest emissions (around 120 kt_CO2, orange curve in Figure 10). Instead, in countries with average electricity emission factors, the proposed system (CCHP+HTHP) performs better (blue and green curves, with 290 kt_CO2 and 315 kt_CO2, respectively). Finally, in countries with high electricity emission factors, the cogeneration and trigeneration systems result in minimum emissions (red curve in Figure 10, with values around 490 kt_CO2).

As a final result,

Table 3. Cumulative CO₂ emissions during 2024–2050 considering variable and fixed (those of 2024) values of electricity emission factor for EU countries.

	Cumulative 2024–2050 (ktCO2)				Cumulative 2024–2050 with Fixed 2024 CO2 Emission Factor (ktCO2)				Delta%
	Min	Med	Max	IT	Min	Med	Max	IT	Min	Med	Max	IT
CCHP-HTHP (following Euser)	211.7	292.8	504.7	322.6	215.4	336.8	579.4	370.4	1.7%	13.1%	12.9%	12.9%
SP (BOILER+CHILLER)	202.3	381.4	849.2	447.3	210.6	478.5	1014.1	552.6	3.9%	20.3%	16.3%	19.1%
SP (BOILER+HP/CHILLER)	123.6	302.7	770.5	368.6	131.9	399.8	935.4	473.9	6.3%	24.3%	17.6%	22.2%
COGENERATION	263.8	326.5	490.4	349.6	266.7	360.5	548.2	386.5	1.1%	9.4%	10.5%	9.5%
TRIGENERATION	316.4	361.4	478.9	378.0	318.5	385.8	520.4	404.4	0.7%	6.3%	8.0%	6.5%

reports the cumulative CO₂ emissions during 2024–2050 considering variable and fixed values (those of 2024) of the electricity emission factor for the EU countries. The technology with the largest overestimation of CO₂ emissions with the fixed electricity emission factor is SP (Boiler+HP/Chiller), as this is the solution that needs the most electricity from the grid. In this case, the overestimation is between 24.3% (calculated with the mean electricity emission factor) and 6.3% (using the minimum of the electricity emission factors). The systems consuming the least electricity from the grid (cogeneration and trigeneration) produce the maximum overestimation of CO₂ emissions using the maximum electricity emission factor (10.5% and 8.0%, respectively), whereas the systems with separate production produce the maximum overestimation using the median electricity emission factor (20.3% and 24.3%).

4. Conclusions

The present work examined the integration of an ammonia-based high-temperature heat pump within a trigeneration system designed to deliver hot water at 90 °C for industrial end uses. Steady-state simulations were performed under both fixed operating conditions and over an annual time horizon. The energy and exergy analyses demonstrated that the CCHP+HTHP configuration exhibits superior performance relative to the benchmark systems exclusively when the prime mover achieves sufficiently high electrical efficiency, during which the utilization of cogeneration-generated electricity to operate the HTHP is advantageous. Moreover, the fraction of cogenerator-recovered thermal energy allocated to drive the absorption chiller requires careful optimization to maximize the overall energy performance of the integrated system. The key finding suggests that higher cogenerator electrical efficiency and an elevated coefficient of performance of the high-temperature heat pump are associated with a greater optimal value of the parameter f, allowing the exergy performance of the system to exceed that of conventional configurations.

Regarding CO₂ emissions, the most effective solution varies as a function of the electricity emission factor. The best solution is the SP (Boiler+HP/Chiller) system if the minimum value is considered. When considering the median value, the best solution is the proposed CCHP+HTHP system with the 2024 value and SP (Boiler+HP/Chiller) with the 2050 value. The technology that features the largest overestimation in CO₂ emissions with fixed electricity emission factor is SP (Boiler+HP/Chiller), as this is the solution that needs the most electricity from the grid. The gradual reduction in electricity emission factor, if not properly accounted for, can ultimately result in a significant overestimation of carbon dioxide savings, which, in the case studies analyzed here, ranges between 1% and 23% depending on the scenario under consideration. Addressing the potential impact of future changes in natural gas emission factors due to future methane leakage regulations, blending with hydrogen, or carbon capture deployment could be an interesting future extension of this study, as would be the applicability of the results to other industrial sectors with different load profiles. Also, considering the influence of variable high-temperature heat pump COP and cogeneration unit power generation efficiency could be an interesting option. Finally, in the industrial sector, separate energy production (boiler+a/w chiller or heat pump/chiller), cogeneration and trigeneration are the most common configurations for energy production. Energy storage is an interesting option but it is limited, while considering biomass heating and photovoltaics could be an option for new low-carbon facilities and could be considered as a future extension of this study.