Electricity-Related Emissions Factors in Carbon Footprinting—The Case of Poland

Abstract

Electricity is a significant factor in the life cycle of many products, so the reliability of greenhouse gas (GHG) emissions data is crucial. The article presents publicly available sources of emission factors representative of Poland. The aim of the study is to assess their strengths and weaknesses in the context of the calculation requirements of carbon footprint analysis in accordance with the GHG Protocol. The article presents the results of carbon footprint calculations for different ranges of emissions in the life cycle of 1 kWh of electricity delivered to a hypothetical organization. Next, a discussion on the quality of the emissions factors has been provided, taking account of data quality indicators. It was concluded that two of the emissions factors that are compared—those based on the national consumption mix and the residual mix for Poland—have been recognized as suitable for use in carbon footprint calculations. Beyond the calculation results, the research highlights the significance of the impact of the selection of emissions factors on the reliability of environmental analysis. The article identifies methodological challenges, including the risk of double counting, limited transparency, methodological inconsistency, and low correlation of data with specific locations and technologies. The insights presented contribute to improving the robustness of carbon footprint calculations.

1. Introduction

Environmental statements on the environmental performance of products and organizations are becoming particularly important in an era in which sustainable production and consumption models are being implemented. These statements include information on one or more environmental aspects or impacts [1]. The information they contain has the potential to play a significant role in decision-making processes on both the supply and demand sides of the market. From the perspective of producers, it increasingly forms the basis for greening supply chains and is one of the criteria for selecting suppliers [2,3,4,5]. From the consumer’s point of view, environmental performance is becoming increasingly valued as an attribute of goods and services. There are many studies in this area, covering various geographical areas, for example, consumers in the European Union [6] or in individual countries such as Portugal [7], the Dominican Republic [8], Malaysia [9], and Poland [10]. Given the growing importance of environmental statements in the economic sphere, particular attention should be paid to the quality of the information provided. This is all the more important as the phenomenon of greenwashing is becoming an increasing challenge and barrier to the green transition [11]. This issue is noted both in the area of consumer research [12,13,14] and in the context of environmental, social, and governance (ESG) disclosures [15,16,17,18].

The accuracy, verifiability, and repeatability of environmental information contained in environmental statements depend largely on how this information is generated (methodologies for identifying and assessing environmental aspects and calculating environmental impact) and on the sources and quality of the data. Environmental life cycle analyses such as Life Cycle Assessment (LCA), Carbon Footprint, and Water Footprint use large amounts of inventory data on the consumption of materials and raw materials, waste generated, and emissions to air, water, and soil. The quality of this data affects the uncertainty of the final results [19,20,21]. In practice, contractors performing such analyses typically use both primary and secondary data. Primary data refers to “directly measured or collected data from one or more facilities that are representative for the activities of the company” under study [22,23]. Secondary data refers to “data that is not directly collected, measured or estimated by the company, but rather sourced from a third party LCI database or other sources” [22]. Secondary data contains proxy and other generic data and can be taken from secondary sources like government statistics, industry average data, or the literature.

One of the key elements of broadly understood environmental analyses covering air pollutant emissions are emissions factors. They are more than just numbers [24]. They form the basis not only for micro-scale analyses but are also a key tool in developing national, regional, and local emission inventories for air quality management decision-making and the development of emission control strategies. Inconsistent data standards, methodologies, data gaps and boundaries, and limited availability complicate access to emissions factors, making it difficult for companies to select the right ones and ensure accurate and reliable greenhouse gas accounting [25]. The issue of emission factors is addressed in the literature. For example, in 2003, the European Topic Centre on Air and Climate Change published a report on emissions factors for European Union countries that analyzed the following issues: ranges of fuel-specific CO₂ emissions factors in EU member states; differences between country-specific emissions factors and the IPCC default CO₂ emissions factors; and possible differences between country-specific emissions factors and plant-specific emissions factors [26]. Global Climate Initiatives emphasizes the role of emissions factors in greenhouse gas accounting and points out that this accounting is not based on direct measurements but on a calculation using emissions factors [27]. Erfian et al. determined the emissions factors of Indonesian coal-fired power plants [28]. Lee et al. conducted a study to assess emissions factors and uncertainty ranges for methane and nitrous oxide from combined cycle power plants in Korea [29].

After reviewing the literature, it was decided that it would be useful to draw attention to the diversity of sources of information on greenhouse gas emissions factors associated with electricity production and distribution. This diversity is not necessarily an advantage, as it poses a problem for environmental analysts in choosing a data source. This article will present and discuss four publicly available sources of emission factors representative of Poland. The contribution of the paper is to be a voice in a discussion about data sources and emissions factors used in carbon footprint calculations [30]. Our intention is to show that a lack of transparency and consistency in emission factor reporting by authorities and other institutions may make the calculations challenging. Although the example presented concerns a single country, authors of environmental analyses performed for other geographical areas may face similar challenges. The question we seek to answer in our article is “what are the strengths and weaknesses of different emissions factors for electricity in Poland in the context of the calculations required for carbon footprint analysis in accordance with the GHG Protocol?” The main aim of the research is to assess their usefulness in carbon footprint studies and to identify calculation challenges.

The choice of energy was dictated by the fact that it is a key aspect of the activities of many organizations, as well as in the life cycle of various products [31,32]. For this reason, the correct modelling of energy in environmental calculations is important, as even small differences in emissions factors can affect the final result. Poland was chosen because it has one of the most carbon-intensive electricity sectors in the European Union [33,34]. It is important to note that since 2014 Poland has had a system and register of Guarantees of Origin operated by the Polish Power Exchange.

The article will first present the guidelines and types of data needed to calculate an organization’s carbon footprint related to energy consumption. Next, various sources of information on electricity-related GHG emission factors in Poland will be listed and discussed. Then, emissions for different ranges will be calculated using emissions factors from various sources, converted to 1 kWh of electricity supplied to a hypothetical organization. Finally, the strengths and weaknesses of individual factors will be discussed. At the time of writing the paper, the following main limitations were identified:

▪

There was no information about transmission and distribution losses in the residual electricity mix available for Poland;

▪

No information was available on the mix of renewable sources used to generate electricity tracked with GOOs in Poland;

▪

No detailed information is available about data sources and the national statistics used by the Association of Issuing Bodies (AIB) to calculate the residual mix for Poland;

▪

There was no emission factor based on the national electricity mix for Poland available for the year 2024.

The way in which these limitations were handled will be described later in the paper.

2. Materials and Methods

There are different common approaches to carbon accounting, like the emission factor method, the mass balance method, and LCA. The emission factor method is a way of estimating GHG emissions by multiplying activity data (like energy or fuel consumption) by an emission factor. The factors indicate the quantity of CO₂ emissions generated per unit of electricity produced or fuel consumed. As Bertolini et al. pointed out [30], the emissions factors vary according to several characteristics, such as fuel type and quality, because their different chemical properties and carbon content affect pollutant emissions. Three levels of sophistication may be distinguished in terms of emission factor calculations: a basic approach using default emissions factors with low accuracy and complexity (Tier 1), an intermediate approach using country-specific emissions factors with good accuracy (Tier 2), and an advanced approach using technology/plant-specific emissions factors with high accuracy and complexity (Tier 3) [30]. The mass balance approach deals with the quantitative relationships between inputs and outputs (stoichiometry). It is based on measuring the carbon exiting the process through products and entering the process through feedstocks and then calculating the difference between these two values, assuming that the carbon that is not accounted for is either directly released or oxidized and released as CO₂ [35]. LCA is a standardized and internationally recognized tool used to assess the potential environmental impact associated with the life cycle of products [36,37]. It is defined by the ISO 14040 standard as the compilation and evaluation of the inputs, outputs, and potential environmental impacts of a product system throughout its life cycle [37]. LCA is a sophisticated analysis to be carried out in four phases: definition of the goal and scope, life cycle inventory, life cycle impact assessment, and interpretation [36,37].

As part of the calculation of the carbon footprint, an inventory must be made of greenhouse gas emissions to the air, including those resulting from the organization’s energy needs. The emissions inventory concerns the quantity of emissions, i.e., it answers the question “how much and what greenhouse gases have been released into the air?”. The quantity of emissions is expressed in units of mass (e.g., kg) and reflects the amount of gas released. In LCA, it is possible to link the amount of emissions to their potential environmental consequences (impact) by using so-called characterization parameters. In the field of climate change, the Global Warming Potential (GWP) is most commonly used for this purpose. As part of the work of the Intergovernmental Panel on Climate Change (IPCC), a GWP value is calculated for each greenhouse gas [38], which represents integrated forcing over a certain time horizon (usually 100 years). The GWP uses the emission’s radiative forcing as an indicator, integrates it (the absolute GWP (AGWP)), and then divides the value at a specific point in time by that of CO₂ [39]. Thus, multiplying the quantity of GHG emissions by their corresponding GWP values provides information about the potential impact on the environment (Figure 1), which is characterized by radiative forcing (understood as an externally imposed perturbation in the radiative energy budget of the Earth’s climate system) [40]. The result is expressed in units of CO₂ equivalent mass (e.g., kg CO₂ eq).

The GHG Protocol (Corporate Accounting and Reporting Standard) [41] covers the accounting and reporting of different GHGs. The names of the gases and their GWP₁₀₀ values according to the Sixth Assessment Report (AR6) [42,43] are presented in

Table 1. The GHG gases and their GWP (AR6) [41,42].

GHG Gas	GWP100	Unit
1. Carbon dioxide (CO2)	1	kg CO2 eq
2. Methane (CH4):
Fossil	29.8	kg CO2 eq
Non-fossil	27	kg CO2 eq
3. Nitrous oxide (N2O)	273	kg CO2 eq
4. Hydrofluorocarbons (HFCs)	4.84—14,600	kg CO2 eq
5. Perfluorocarbons (PFCs)	0.004—12,400	kg CO2 eq
6. Sulphur hexafluoride (SF6)	24,300	kg CO2 eq
7. Nitrogen trifluoride (NF3)	17,400	kg CO2 eq

. Hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs) constitute two groups consisting of many gases with a broad range of GWP values. For example, in the group of HFCs, HFC-161 has the lowest GWP value, equal to 4.84 kg CO₂ eq/kg, and HFC-23 has the highest GWP value, equal to 14,600 kg CO₂ eq/kg.

According to the GHG Protocol guidelines, GHG emissions can be classified into scopes and categories. In relation to the energy, the following scopes can be indicated as follows:

▪

Scope 1—stationary combustion and mobile combustion emissions. Scope 1 includes emissions from the energy generation undertaken by the reporting company [41];

▪

Scope 2—emissions from purchased energy. Scope 2 includes emissions from the energy generation undertaken by an external supplier. The energy generated is consumed by the reporting company [41];

▪

Scope 3, category 3—fuel and energy-related activities not included in Scope 1 or Scope 2. This encompasses four subcategories: 3A—upstream emissions of purchased fuels; 3B—upstream emissions of purchased electricity; 3C—transmission and distribution (T&D) losses; and 3D—generation of purchased electricity that is sold [44].

The first issue is the supervision and control of the energy generation process. It can be generated on-site in facilities under the control and direct supervision of the reporting organization (on-site), or it can be produced in independent facilities (off-site). In the first case, the organization itself converts the carriers into energy, so the activity data required for calculation of the carbon footprint includes the amount of fuel consumed for each of the combustion sources and the fuel characteristics of each type of fuel used [41]. In the second case, the organization consumes energy generated by other entities, so the activity data required for calculation of the carbon footprint includes the amount of energy consumed and the characteristics of that energy. Another issue affecting the assignment to specific scopes is the stage of the life cycle to which the emissions relate. This may be the life cycle of energy carriers (fuels)—Well-to-Tank (WTT), energy generation itself—Tank-to-Wheel (TTW), or both of these issues related to losses in the transmission and distribution network (T&D losses).

Table 2. Energy-related emission sources classification based on the GHG Protocol [41,45].

Scope	Source	Description	Comment
Scope 1	Stationary combustion	Direct GHG emissions from stationary combustion. It typically includes devices like boilers, combustion turbines, incinerators, and process heaters that combust solid, liquid, or gaseous fuel, generally for the purposes of producing electricity or generating steam or heat.	“Tank-to-Wheel” —generation on-site, electricity/heat/steam generated and consumed by reporting company.
	Mobile combustion emissions	Direct GHG emissions from owned or leased mobile sources that are within the company’s inventory boundaries. It includes road and off-road vehicles such as company vehicles, forklifts, and construction equipment.	“Tank-to-Wheel” —generation on-site, energy for vehicles consumed by reporting company.
Scope 2	Emissions from purchased energy	Indirect emissions from the generation of energy that is purchased. They are a consequence of activities of the reporting organization but occur at sources owned and controlled by an outside entity, e.g., in a power plant or in a district heating plant.	“Tank-to-Wheel” —generation off-site, of energy consumed by the reporting company.
Scope 3	Category 3A Upstream emissions of purchased fuels	GHG emissions occurring in the life cycle of fuels purchased and consumed by the reporting company. This category includes life cycle stages like extraction, production, and transportation of fuels, e.g., mining of coal, refining of crude oil, etc.	“Well-to-Tank” —life cycle of fuels used in generation on-site, energy consumed by the reporting company.
	Category 3B Upstream emissions of purchased electricity	GHG emissions occurring in the life cycle of fuels consumed in the generation of energy (electricity, steam, heating, and cooling) that is purchased and consumed by the reporting company. This category includes stages like extraction, production, and transportation of fuels (e.g., mining and refining).	“Well-to-Tank” —life cycle of fuels used in generation off-site, energy consumed by the reporting company.
	Category 3C Transmission and distribution (T&D) losses	GHG emissions occurring: - in the life cycle of fuels used to generate the energy that is used and lost in a T&D system, - in the generation (combustion) of electricity, steam, heating, and cooling that is consumed and lost in the T&D system.	“Tank-to-Wheel” —energy generation off-site, energy lost in T&D. “Well-to-Tank” —fuels used in generation off-site, energy lost in T&D.
	Category 3D Generation of purchased electricity that is sold to end users	GHG emissions occurring in the generation (upstream activities and combustion) of electricity, steam, heating, and cooling that is purchased by the reporting company and sold to end users. It applies to utility companies and to energy retailers.	“Well-to -Tank” + “Tank-to-Wheel”—energy sold to end users.

summarizes the categorization of emission sources depending on the scope [41]. Figure 2 and Figure 3 graphically illustrate the stages of the energy life cycle and emission scopes [41,45].

Taking into account the information presented in Table 2 and Figure 2 and Figure 3, it is possible to compile the data needed to model the emissions in the energy life cycle in the carbon footprint calculation, as presented in

Table 3. Information needed to calculate GHG emissions in the life cycle of energy generation.

Generation	Information Needed for Calculations	Scope and Category
Energy generated on-site	▪ Type, characteristics and consumption of fuel	Scope 1
	▪ Amount of fuel consumed for each combustion source	Scope 1
	▪ Tank-to-Wheel emission factor for energy generation on-site for each combustion source and fuel used	Scope 1
	▪ Well-to-Tank emission factor for the life cycle of each fuel used	Scope 3, Category 3A
Purchased energy, generated off-site	▪ Type, characteristics, and consumption of energy	Scope 2 and 3
	▪ Tank-to-Wheel emission factor for energy generation off-site (production mix)	Scope 2
	▪ Fuel mix for generation off-site	Scope 3, Category 3B
	▪ Well-to-Tank emission factor for the life cycle of each fuel used in the fuel mix for energy generation off-site
	▪ Tank-to-Wheel emission factor for energy generation off-site (production mix)	Scope 3, Category 3C
	▪ Size of losses in the transmission and distribution network (T&D loss rate)
	▪ Fuel mix for energy generation off-site	Scope 3, Category 3C
	▪ Well-to-Tank emission factor for the life cycle of each fuel used in the fuel mix for energy generation off-site
	▪ Size of losses in the transmission and distribution network (T&D loss rate)

. As can be seen, in addition to activity data (i.e., information on the amount of fuel and energy consumed by the reporting organization), other data on fuel structure (the percentage of fuels used for energy production), transmission and distribution losses, and emissions factors in the Well-to-Tank and Tank-to-Wheel phases are needed. As already mentioned, according to the environmental life cycle assessment methodology [36], the emission factor for individual greenhouse gases is an inventory result and shows the mass of a given gas released into the air per unit of energy (e.g., kg of gas per kWh). However, after multiplying the gas emission amount by the corresponding GWP value (as shown in Table 1), the result reflects the environmental impact and is expressed in CO₂ equivalents per unit of energy (e.g., kg CO₂ eq per kWh), which in LCA analyses constitutes the life cycle impact assessment result (kg CO₂ eq/kWh). Therefore, from the point of view of LCA methodology, the results expressed in CO₂ equivalents do not directly answer the question of how much gas has been released into the air but indicate the radiative forcing potential resulting from these emissions.

At this point, it is worth noting that in the GHG Protocol [46], the definition of emissions factors is not fully consistent with this approach, as they are treated somewhat like the result for LCI-emission data. This is evidenced by the following definition from the GHG Protocol [46], which states that an emission factor is “a factor that converts activity data into GHG emissions data (e.g., kg CO₂e emitted per litre of fuel consumed, kg CO₂e emitted per kilometre travelled, etc.)”.

As already mentioned, an organization may consume energy from generation that it owns or operates (on-site generation) or energy that is generated by independent entities (off-site generation). In the latter case, energy may be supplied via a direct line (with no grid transfers) or as energy from the power grid (grid-distributed). In each of these cases, an emission factor is needed to calculate the carbon footprint. With regard to the calculation of Scope 2 emissions (emissions from purchased energy), the GHG Protocol [46] has established two distinct methods, each with its own hierarchy of emissions factors—a market-based and a location-based method.

The market-based method is “a method to quantify Scope 2 GHG emissions based on GHG emissions emitted by the generators from which the reporter contractually purchases electricity bundled with instruments, or unbundled instruments on their own” [46]. The market-based method reflects emissions from energy generation that companies have deliberately chosen. This method is based on emissions factors resulting from contractual instruments and the energy attributes specified in those instruments. Contractual instruments include agreements between parties for the sale and purchase of energy bundled with attributes about the energy generation or for unbundled attribute claims [46]. These may include renewable energy attribute certificates (e.g., Guarantees of Origin), direct contracts (low-carbon, renewable, or fossil fuel generation), and supplier-specific emissions factors. A key issue is the requirement in the GHG Protocol [46] that the contractual information needs to meet the Scope 2 Quality Criteria. These criteria indicate that all contractual instruments must include the following [46]:

▪

Include the energy attribute in terms of the amount of direct greenhouse gas emissions attributed to a unit of energy produced;

▪

Be the only instrument containing a statement of this attribute for that specific amount of energy produced;

▪

Be tracked and redeemed, retired, or cancelled by or on behalf of the reporting entity;

▪

Be issued and redeemed as close as possible to the period of energy consumption to which the instrument relates;

▪

Come from the same market in which the reporting entity’s electricity consumption operations are located and to which the instrument applies.

These criteria also indicate that, with regard to Scope 2 emissions, utility-specific emissions factors must be calculated based on the electricity supplied, taking into account certificates acquired and retired on behalf of customers. If the attributes have been sold (through contracts or certificates) or if the organization does not have contractual information that meets the Scope 2 Quality Criteria, then emission factors representing the untracked or unclaimed energy and emissions (defined as “residual mix”) should be used [46]. The residual mix is “the mix of energy generation resources and associated attributes such as GHG emissions in a defined geographic boundary left after contractual instruments have been claimed/retired/cancelled. The residual mix can provide an emission factor for companies without contractual instruments to use in a market-based method calculation” [46]. With regard to the market-based method in the GHG Protocol, the hierarchy of emissions factors is presented in

Table 4. Hierarchy of Scope 2 emissions factors—market-based method, the GHG Protocol [46].

Emissions Factors		Selected Examples of Emission Factor Sources	Comment
Higher precision	Energy attribute certificates or equivalent instruments	▪ Guarantees of Origin ▪ Renewable Energy Certificates ▪ Electricity contracts (e.g., PPAs) that also convey RECs or GOs ▪ Any other instrument meeting the Scope 2 Quality Criteria	Energy attribute certificates or equivalent instruments may be unbundled or bundled with electricity, conveyed in a contract for electricity, or delivered by a utility
	Contracts	▪ Contracts that convey attributes to the entity consuming the power where certificates do not exist ▪ Contracts for electricity from specified non-renewable sources like coal in regions other than NEPOOL and PJM ▪ Contracts for power that are silent on attributes, but where ▪ attributes are not otherwise tracked or claimed	Emission factors delivered from power purchase agreements (PPAs), contracts from specified sources, where electricity attribute certificates do not exist or are not required for a usage claim
Lower precision	Supplier/Utility emission rates	▪ Green energy tariffs ▪ Emission rate allocated and disclosed to retail electricity users, representing the entire energy product that is delivered ▪ Voluntary renewable electricity programme or product	A standard product offer or a different product (e.g., a renewable energy product or tariff), which is disclosed (preferably publicly) according to the best available information
	Residual mix	▪ Calculated by EU country under the RE-DISS project	Subnational or national mix that uses energy production data and factors out voluntary purchases
	Other grid-average emissions factors	▪ Defra annual grid average emission factor (UK) ▪ IEA national electricity emissions factors	Subnational or national

. It is worth noting that one of the proposed changes under the “Key Suggested Improvements from Scope 2 Proposals” is the introduction of hourly residual mix calculations to better reflect the temporal variability of electricity sources, which is particularly relevant for renewable energy. As the revised Scope 2 GHG Protocol Standard and Guidance is expected in 2027, the updated guidelines have not been taken into account in our calculations.

The location-based method is “a method to quantify scope 2 GHG emissions based on average energy generation emissions factors for defined locations, including local, subnational, or national boundaries” [46]. In this approach, emission factors reflect the average emission intensity of networks in which energy consumption occurs (grid-average emission factor data).

Table 5. Hierarchy of Scope 2 emissions factors—location-based method, the GHG Protocol [46].

Emissions Factors	Examples (Source of Emissions Factors)	Comment
Regional or subnational	▪ eGRID total output emission rates (the U.S.) ▪ Defra annual grid average emission factor (the U.K.)	Average emissions factors that represent all electricity production in a specific grid distribution region. They should reflect the net physical import/export of energy across the grid boundary.
National production emissions factors	▪ IEA national electricity emissions factors	Average emissions factors that represent all information on electricity production from geographic boundaries that are not necessarily related to the dispatch region, such as state or national borders. No adjustment for physical energy imports or exports, not representative of the energy consumption area.

presents the hierarchy of emissions factors indicated in the GHG Protocol [46] in relation to the location-based method.

In our calculations the Scope 2 emissions factors (Tank-to-Wheel) are derived directly from the following publications: KOBIZE-based factors from [47,48,49]; AIB-based factors from [50,51,52]. As the KOBIZE-sourced factor relates to the consumption mix (the factor for end users), the T&D losses have been subtracted to receive the factor directly related to the generation of 1 kWh of electricity consumption (the production-side emission factor was calculated by subtracting the proportion of transmission and distribution (T&D) losses from the consumption-based emission factor, using officially reported national data on electricity generation, end-use consumption, and balance differences). The Scope 3, category 3C (Tank-to-Wheel) factors have been calculated by multiplying the Scope 2 emissions factors (Tank-to-Wheel) by the percentage losses in the Transmission & Distribution network. As no information on T&D losses is included in the AIB reports, the same losses, taken from KOBIZE, have been used in the market- and location-based methods.

However, the energy life cycle includes not only Scope 1 and 2 emissions but also energy-related Scope 3 emissions (as shown in Table 2 and Table 3). Therefore, the methodology used in this article to calculate the following emissions factors will now be presented:

▪

Scope 3, category 3B—“Well-to-Tank”—the life cycle of fuels used for energy production outside the facility, energy consumed by the reporting company;

▪

Scope 3, category 3C—“Well-to-Tank”—fuels used for energy production outside the facility, energy lost during transmission and distribution;

▪

Scope 3, category 3C—“Tank-to-Wheel”—off-site energy generation, energy lost during transmission and distribution.

This article adopts the following procedure for determining the life cycle emissions (WTT) of fuels used to generate energy consumed by the organization (Scope 3, Category 3B—“Well-to-Tank”):

▪

Determining the emission factor for energy generation from Scope 2 (in Equation (1), this is the “direct” parameter);

▪

Identifying the fuel mix used to generate this energy;

▪

Multiplying the share of each fuel by its corresponding fuel life cycle emission factor [kg CO₂ eq/kWh];

▪

Determining the share of fuel life cycle emissions in energy generation emissions (in Equation 1, this is the “WTT/Direct” parameter);

▪

Calculating the WTT emission factor using the following formula taken from [53]. In accordance with [53], sample calculations for the United Kingdom in 2022 are presented below as Equation (1) [53]:

$$\mathrm{W}\mathrm{T}\mathrm{T}={\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}_{\mathrm{U}\mathrm{K}}\times \mathrm{U}\mathrm{K}{\displaystyle \frac{\mathrm{W}\mathrm{T}\mathrm{T}}{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}=61\times \mathrm{U}\mathrm{K}{\displaystyle \frac{24.19}{100}}=14.76 \mathrm{g}{\mathrm{C}\mathrm{O}}_2 \mathrm{e}\mathrm{q} \mathrm{p}\mathrm{e}\mathrm{r} \mathrm{k}\mathrm{W}\mathrm{h}$$

(1)

This article adopts the following procedure for determining the emissions intensity of fuels (WTT) used to generate energy lost in the T&D transmission and distribution network (Scope 3, Category 3C—“Well-to-Tank”):

▪

Determining the emission factor for energy generation from Scope 2 (in Equation (2), this is the “direct” parameter);

▪

Determining the level of losses in the T&D network (in Equation (2), this is the “losses” parameter);

▪

Identifying the mix of fuels used to generate this energy;

▪

multiplying the share of each fuel by its corresponding Well-to-Tank emission factor [kg CO₂ eq/kWh];

▪

Determining the share of fuel life cycle emissions in energy generation emissions (in Equation (2), this is the “WTT/direct” parameter);

▪

Calculating the WTT emission factor using the following formula taken from [53]. In accordance with [53], sample calculations for the United Kingdom in 2022 are presented below as Equation (2) [53]:

$$\mathrm{W}\mathrm{T}\mathrm{T} \mathrm{T}\&\mathrm{D}=\left({\displaystyle \frac{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}{1-\mathrm{L}\mathrm{o}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{s}}}-\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\right)\times {\displaystyle \frac{\mathrm{W}\mathrm{T}\mathrm{T}}{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}=\left({\displaystyle \frac{61}{1-\frac{8}{100}}}-61\right)\times {\displaystyle \frac{24.19}{100}}=1.28 \mathrm{g}{\mathrm{C}\mathrm{O}}_2 \mathrm{e}\mathrm{q} \mathrm{p}\mathrm{e}\mathrm{r} \mathrm{k}\mathrm{W}\mathrm{h}$$

(2)

The emission factor for energy lost in the transmission and distribution network (Scope 3, Category 3C—“Tank-to-Wheel”) was calculated by multiplying the emission factor from generation (‘direct’) by the share of losses in T&D (“losses”).

3. Results

The Section 3 presents and discusses publicly available emissions factors for electricity used in Poland. It analyzes them in terms of their potential use in calculating the carbon footprint of energy purchased via Scope 2 (off-site generation) and Scope 3 (categories 3B and 3C). Next, selected factors will be used to calculate the greenhouse gas emissions per kWh of electricity consumed.

Table 6. Characteristics of publicly disclosed emission factors for electricity in Poland.

	Energy Suppliers in Poland (Supplier General Mix)	Association of Issuing Bodies (AIB, Residual Mix)	Ministry of Climate and Environment in Poland (Regulation on the Greenhouse Gas Emission Factor for Electricity) (Regulation Based Mix)	The National Centre for Emissions Management in Poland (KOBIZE, Consumption Mix)
Calculation approach	Disclosures made in accordance with the Regulation of the Minister of Climate and Environment in Poland on the detailed conditions for electricity system operation	AIB calculates European Residual Mixes using shifted issuing-based methodology. This method is based on the RE-DISS (Reliable Disclosure Systems for Europe) approach. The data for the direct CO2 emissions is based on nationally reported information or the Ecoinvent database	The greenhouse gas emission factor for electricity is determined by taking into account data collected by The National Centre for Emissions Management in Poland in the National Emission Database, as well as other available scientific data. It is intended to support the implementation of the National Reduction Target in transport	Emission factor for end users of electricity. Data on emissions from fuel combustion installations entered into the national database. Next, the electricity balance is determined (the amount of electricity produced in combustion installations + energy from RES—losses and balance differences)
Specificity	Supplier general mix (production mix)	Country residual mix	Country mix	Country general mix (consumption mix)
GHG inventoried	CO2	CO2	No data	CO2
Biogenic CO2	Included and itemized	Not included	No data	Not included (emission factor for biomass is assumed to be zero)
Contractual instruments	Tracked and claimed + untracked and unclaimed energy	Untracked and unclaimed energy only	No data	Tracked and claimed + untracked and unclaimed energy
T&D losses	No data	T&D losses not included in the emission factor. No information about T&D losses provided	No data	T&D losses included in the emission factor. Information about T&D losses available
Import-export	No data	Included	No data	Not included
Fuel mix	Provided	Provided	No data	Provided
Considered for Scope 2 method	Market-based	Market-based	Location-based	Location-based
Scope 2 Quality Criteria	Not met	Met	Not applicable	Not applicable
Emission factor unit	g/kWh Mg/MWh	g/kWh	g CO2 eq/MJ	kg/MWh
Disclosure	Annually (usually at the end of March of the following year)	Annually (usually at the end of May of the following year)	Annually (usually in Autumn of the previous year)	Annually (usually at the end of December of the following year)

shows the following four publicly available potential sources of emission factors for electricity used in Poland:

▪

Supplier general mix—this emission factor is specific to the supplier, expressed in units of mass (g, kg) per unit of energy (kWh, MWh). The emissions factors are disclosed to the public by energy generators in Poland in accordance with the Regulation of the Minister of Climate and Environment in Poland on the detailed conditions for operating the electricity system. The statements that have been published contain information on the fuel structure and the amount of emissions of four pollutants (CO₂, SO₂, NO_x, and dust) into the air, as well as the radioactive waste generated. The emission factor covers emissions from energy generation (production mix). The statements contain information on emissions of only one greenhouse gas—CO₂. Biogenic CO₂ emissions from biomass combustion are also taken into account and itemized. The emissions factors published by suppliers could potentially be used in calculations of emissions from purchased energy included in Scope 2 (market-based method). However, the published data refer to the supplier’s total production and therefore include both energy covered by the guarantee of origin system and untracked/unclaimed energy. Apart from the overall mix, the published statements do not provide any information on the residual supplier mix. This means that the emissions factors provided in these statements should be recognized as not compliant with Scope 2 Quality Criteria for the market-based method. They do not guarantee compliance with the following quality criteria: “be the only instruments that carry the GHG emission rate attribute claim associated with that quantity of electricity generation”, “be tracked and redeemed, retired, or cancelled by or on behalf of the reporting entity”, and “be calculated based on delivered electricity, incorporating certificates sourced and retired on behalf of its customers” [46]. In our opinion, emission factors based on the supplier’s general mix are not suitable for use in calculating the Scope 2 carbon footprint in accordance with the GHG Protocol, and for this reason will not be taken into account in the calculations presented in this chapter;

▪

AIB, residual mix—a residual emission factor representative of a country, expressed in grams per kWh of energy. Determined by the Association of Issuing Bodies (AIB). The AIB calculates the residual mixes using shifted issuing-based methodology [54]. This method is based on the RE-DISS (Reliable Disclosure Systems for Europe) approach. Residual mixes are calculated for individual countries and include the fuel mix and CO₂ emissions from energy generation not covered by contractual instruments (untracked). The residual mixes calculated by the AIB take into account imports and exports but do not take into account losses in the transmission and distribution network [55]. Residual mixes contain information on emissions of only one greenhouse gas—CO₂. The data is based on nationally reported information or the Ecoinvent database [54]. Biogenic CO₂ emissions from biomass combustion are not included [55]. The residual emission factor covers CO₂ emissions from energy generation (production mix). In our opinion this meets Scope 2 Quality Criteria and can be used in the market-based method when energy purchases are not linked to contractual instruments that meet Scope 2 Quality Criteria. For this reason, it will be taken into account in further calculations (market-based method);

▪

Regulation-based mix—an emission factor representative for Poland, expressed in grams of CO₂ eq per MJ of energy. It is communicated by way of a regulation of the Minister of Climate and Environment in Poland on the greenhouse gas emission factor for electricity. It is used to calculate and monitor the implementation of the National Reduction Target in transport. It is determined taking into account data collected by the National Center for Emission Balancing and Management in the national database on greenhouse gas emissions and other substances, as well as other available scientific data [56]. Its calculation is based on the methodology used by the Institute of Environmental Protection—National Research Institute (IOŚ-PIB) in the project “Determination of the greenhouse gas emission factor (gCO₂ eq/MJ) per unit of energy in the life cycle of electricity” [57]. In accordance with the description of the objective and scope of this project [57], IOŚ-PIB used Eurostat data, GUS data, and the methodology set out in Council Directive (EU) 2015/652 of 20 April 2015 establishing calculation methods and reporting requirements pursuant to Directive 98/70/EC of the European Parliament and of the Council relating to the quality of petrol and diesel fuels. Using this as a basis, unit emissions factors were determined for the national energy mix, taking into account national and international reporting rules, i.e., for year n-2, broken down by fuel and stage (extraction/cultivation of energy raw materials; transport of energy raw materials; conversion of chemical energy of fuels into electrical energy) [57]. Subsequently, the IOŚ-PIB project determined a common emission factor for the end user, which includes the shares of individual fuels in electricity production [57]. This means that the factor investigated reflects the consumption mix. Due to the lack of a clear indication in the regulation of the possibility of universal use of this factor outside the context of implementing the National Reduction Target in transport, it is difficult to determine its usefulness and relevance for use in calculating the carbon footprint in accordance with the GHG Protocol guidelines. For this reason, this factor will not be taken into account in the calculations presented later in this chapter;

▪

KOBIZE, consumption mix—this is an emission factor representative of Poland, expressed in kilograms per MWh of energy. It is based on the consumption mix, taking into account losses in the transmission and distribution network (emission factor for end users of electricity). Published by The National Centre for Emissions Management in Poland (KOBIZE), KOBIZE reports contain information on emissions of CO₂, SO_x/SO₂, NO_x/NO₂, CO, and total dust into the air. The reports also contain information on fuel structure, losses, and balance differences. Two types of emissions factors are available: for electricity produced in fuel combustion installations (reflecting data reported to the National Emissions Database) and for end users of electricity (emissions after balancing, taking into account production in fuel combustion installations, production from renewable energy sources, and after deducting losses). The KOBIZE factor covers only CO₂, with biogenic CO₂ emissions from biomass combustion assumed to be zero. In further analyses, the factor for end users (consumption mix) was used, which, after adjustment (subtraction of losses in T&D), will be used in the location-based method.

The results of carbon footprint calculations for different ranges of emissions in the life cycle of 1 kWh of electricity delivered to a hypothetical organization will be presented below. Representative emission factors for Poland for 2021, 2022, and 2023 were used for the analyses. The following two scenarios were assumed:

▪

Scenario 1—the reporting organization purchases energy not linked to any contractual instruments meeting the Scope 2 Quality Criteria (0% contractual instruments);

▪

Scenario 2—the reporting organization purchases some (15% and 30%) of their energy from renewable sources tracked with Guarantees of Origin.

In both scenarios the calculations in the market-based method were based on the emission factor from the residual mix from the AIB reports [50,51,52], while the location-based method used the domestic consumption mix from the KOBIZE reports [47,48,49]. The renewable energy tracked with GOOs was only included in the market-based approach.

3.1. Scope 2 “Tank-to-Wheel”—Generation Off-Site, Energy Consumed by the Reporting Company

Table 7. Scenario 1—the carbon footprint of the generation of the electricity (Tank-to-Wheel) purchased by the reporting company, with no contractual instruments, using market- and location-based approaches, and the case of Poland in the years 2021–2023 [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Location-Based Method			Source/Comment
		100% AIB, Residual Mix, Poland			100% KOBIZE, Consumption Mix, Poland
		2021	2022	2023	2021	2022	2023
Electricity consumption	kWh/year	1	1	1	1	1	1
Emission factor (consumption mix)	kg/kWh	-	-	-	0.70800	0.68500	0.59700	KOBIZE [47,48,49]
T&D losses	%	-	-	-	5.88%	5.04%	6.31%	Based on KOBIZE [47,48,49]
Emission factor (production mix)	kg/kWh	0.85021	0.85812	0.78824	0.66638	0.65048	0.55936	AIB [50,51,52]
GWP100 for CO2	kg CO2 eq/kg	1	1	1	1	1	1	AR6 [42]
GWP100 of electricity generation off-site (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.85021	0.85812	0.78824	0.66638	0.65048	0.55936	Own calculation
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.85021	0.85812	0.78824	0.66638	0.65048	0.55936	Own calculation

presents the carbon footprint associated with generating (Scope 2—“Tank-to-Wheel”) 1 kWh of electricity for the reporting years 2021, 2022, and 2023 for Scenario 1. The results obtained with the market method were 0.85021 kg CO₂ eq/kWh, 0.85812 kg CO₂ eq/kWh, and 0.78824 kg CO₂ eq/kWh. In the location-based method, losses in T&D were subtracted from the emissions factors of the consumption mix to obtain emissions factors for the production mix. For the years analyzed, the following results were obtained: 0.66638 CO₂ eq/kWh, 0.65048 kg CO₂ eq/kWh, and 0.55936 kg CO₂ eq/kWh. As can be seen, the carbon footprint in the location-based approach is over 20% lower than in the market-based approach, which is primarily due to two reasons. Firstly, the assumption of 0% contractual instruments in energy purchases by the reporting organization. With the growth of the organization’s consumption of renewable energy related to contractual instruments, the emission factor in the market-based method would be lower. Secondly, the use of factors based on the residual mix, which do not include tracked and claimed renewable energy. In Poland, the market for guarantees of origin is still developing, and there is a clear increase in interest in this type of instrument. Despite this, the level of untracked energy in Poland is still very high [50,51,52]. In the location-based method, losses in T&D were subtracted from the emissions factors of the consumption mix to obtain emissions factors for the production mix. For the years analyzed, the results obtained were 0.66638 kg CO₂ eq/kWh, 0.65048 kg CO₂ eq/kWh, and 0.55936 kg CO₂ eq/kWh.

Table 8. Scenario 2—the carbon footprint of the generation of electricity (Tank-to-Wheel) purchased by the reporting company with 15% and 30% of contractual instruments, a market-based approach, and the case of Poland in the years 2021–2023 [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Market-Based Method			Source/Comment
		85% AIB, Residual Mix, Poland 15% RES power (GOOs)			70% AIB, Residual Mix, Poland 30% RES power (GOOs)
		2021	2022	2023	2021	2022	2023
Electricity consumption	kWh/year	1	1	1	1	1	1
Emission factor (production mix)	kg/kWh	0.85021	0.85812	0.78824	0.85021	0.85812	0.78824	Table 7
Emission factor (renewable electricity with GOOs)	kg/kWh	0.00	0.00	0.00	0.00	0.00	0.00	IEA [59]
GWP100 of electricity generation off-site (Scope 2 “Tank-to-Wheel”)	kg/kWh	0.72268	0.72940	0.67000	0.59515	0.60068	0.55177	Own calculation
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.72268	0.72940	0.67000	0.59515	0.60068	0.55177	Own calculation

presents the results for Scenario 2, where two shares of tracked renewable electricity in total electricity consumption were assumed: 15% and 30%. As contractual instruments are only taken into account in the market method, no results for the location-based approach were presented in Table 8 (in Scenario 2 the results for the location-based method are the same as presented in Table 7). In the case with 15% of renewable electricity with accompanying GOOs, the carbon footprints were 0.72268 kg CO₂ eq/kWh, 0.72940 kg CO₂ eq/kWh, and 0.67000 kg CO₂ eq/kWh for 2021, 2022, and 2023, respectively. If 30% of renewable electricity with accompanying GOOs is assumed, the carbon footprints were 0.59515 kg CO₂ eq/kWh, 0.60068 kg CO₂ eq/kWh, and 0.55177 kg CO₂ eq/kWh. It is worth emphasizing that 15% of tracked renewable energy is not sufficient to make the results of the market- and location-based methods equal. In this situation, the values obtained for the market approach are still higher than for the location-based one. To obtain a better environmental result in the market-based method, the share of tracked renewable electricity should, at a minimum, be between 25 and 30%. It seems that the reason for this difference goes beyond differences in the share of renewable sources assumed in both mixes. As no detailed information is available on which exact national statistics and data sources are used by the AIB to calculate the residual mixes, it is difficult to assess the methodological consistency between these two emission factors. Some differences can be observed in terms of the fuel mix (especially for the share of lignite) and of the inclusion of export and import. Another probable reason may be the fact that Poland has not so far been a member of the AIB, which potentially may hinder the exchange of information and the calculation of the Polish residual mix [58].

3.2. Scope 3, Category 3B—“Well-to-Tank”—Life Cycle of Fuels Used in Generation Off-Site, Energy Consumed by Reporting Company

Table 9. Scenario 1—the carbon footprint in the life cycle of fuels (Well-to-Tank) used to generate electricity purchased by the reporting company, no contractual instruments, market- and location-based approaches, and the case of Poland in 2021–2023 years [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Location-Based Method			Source/Comment
		100% AIB, Residual Mix, Poland			100% KOBIZE, Consumption Mix, Poland
		2021	2022	2023	2021	2022	2023
Electricity consumption	kWh/year	1	1	1	1	1	1
Hard coal	%	78.44%	81.78%	70.35%	53.6%	50.1%	46.8%	AIB [50,51,52] PSE [60,61,62]
WTT GWP100 for hard coal (WTT fuels, solid fuels—coal—electricity generation)	kg CO2 eq/kWh (Net CV)	0.05571	0.05571	0.05925	0.05571	0.05571	0.05925	DEFRA [63,64,65]
Lignite	%	0.03%	0.00%	0.00%	26.1%	26.8%	21.1%	AIB [50,51,52] PSE [60,61,62]
WTT GWP100 for lignite (WTT fuels, solid fuels—coal— electricity generation)	kg CO2 eq/kWh (Net CV)	0.05571	0.05571	0.05925	0.05571	0.05571	0.05925	DEFRA [63,64,65]
Gas	%	11.42%	8.02%	13.51%	7.7%	5.7%	8.3%	AIB [50,51,52] PSE [60,61,62]
WTT GWP100 for gas (WTT fuels, gaseous fuels—natural gas)	kg CO2 eq/kWh (Net CV)	0.03474	0.03446	0.03347	0.03474	0.03446	0.03347	DEFRA [63,64,65]
Oil	%	0.06%	0.00%	0.07%	-	-	-	AIB [50,51,52]
WTT GWP100 for oil (WTT fuels, liquid fuels —fuel oils)	kg CO2 eq/kWh (Net CV)	0.06264	0.06264	0.06291	-	-	-	DEFRA [63,64,65]
Nuclear	%	1.25%	0.49%	0.43%	-	-	-	AIB [50,51,52]
WTT GWP100 for nuclear (WTT nuclear fuel)	kg CO2 eq/kWh	0.00360	0.00360	0.00360	-	-	-	Climatiq [66]
Hydro	%	0.56%	0.47%	0.50%	1.6%	1.6%	2.2%	AIB [50,51,52] PSE [60,61,62]
WTT GWP100 for hydro	kg CO2 eq/kWh	0.0	0.0	0.0	0.0	0.0	0.0	IEA [59]
Biomass	%	2.44%	1.69%	2.00%	4.14%	4.02%	4.65%	AIB [50,51,52]
WTT GWP100 for biomass (WTT bioenergy, biomass—as average for different types)	kg CO2 eq/kWh	0.01854	0.01854	0.01854	0.01854	0.01854	0.01854	DEFRA [63,64,65]
Other renewables	%	4.95%	7.12%	12.09%	6.8%	11.7%	16.9%	AIB [50,51,52] PSE [60,61,62]
WTT GWP100 for other renewables	kg CO2 eq/kWh	0.0	0.0	0.0	0.0	0.0	0.0	IEA [59]
Other non-renewable	%	0.86%	0.42%	1.05%	-	-	-	AIB [50,51,52]
WTT GWP100 for hard coal (WTT fuels, solid fuels—coal—electricity generation)	kg CO2 eq/kWh (Net CV)	0.05571	0.05571	0.05925	-	-	-	DEFRA [63,64,65]
GWP100 of WTT fuels used for electricity generation off-site, direct (Scope 3, Category 3B—“Well-to-Tank”)	kg CO2 eq/kWh	0.04870	0.04889	0.04726	0.04786	0.04557	0.04391	“WTT”— result of calculation using Equation (1)
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.85021	0.85812	0.78824	0.66638	0.65048	0.55936	Table 7
${\displaystyle \frac{\mathrm{W}\mathrm{T}\mathrm{T}}{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}$	%	5.73%	5.70%	6.00%	7.18%	7.01%	7.85%	Parameter of “WTT/Direct” in Equations (1) and (2)

presents the results of the life cycle carbon footprint calculations of fuels used to generate 1 kWh of electricity consumed by a hypothetical organization (Scope 3, Category 3B—“Well-to-Tank”). The table presents the shares of individual fuels in both approaches [50,51,52,60,61,62] and the corresponding emissions factors: from DEFRA [63,64,65], for nuclear energy from Climatiq [66], and for renewable energy from IEA [59]. According to the IEA database [59], the fuel-cycle factors for renewables (hydro, wind, solar, geothermal, etc.) are assumed to be equal to zero. The market method yielded results of 0.04870 kg CO₂ eq/kWh, 0.04889 kg CO₂ eq/kWh, and 0.04726 kg CO₂ eq/kWh, respectively, for 2021, 2022 and 2023. The location-based method yielded the following values: 0.04786 kg CO₂ eq/kWh, 0.04557 kg CO₂ eq/kWh, and 0.04391 kg CO₂ eq/kWh. The results obtained in both methods are quite similar (a difference of several percent), which results from the fact that the shares of fossil fuels in the residual and domestic mix for Poland in the years analyzed are similar. This is also the effect of the low share of tracked and claimed renewable energy in the mix.

Table 10. Scenario 2—the carbon footprint in the life cycle of fuels (Well-to-Tank) used to generate electricity purchased by the reporting company, 15% and 30% of contractual instruments, a market-based approach, and the case of Poland in 2021–2023 years [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Market-Based Method			Source/Comment
		85% AIB, Residual Mix, Poland 15% RES power (GOOs)			70% AIB, Residual Mix, Poland 30% RES power (GOOs)
		2021	2022	2023	2021	2022	2023
GWP100 of WTT fuels used for electricity generation off-site, direct (production mix)	kg CO2 eq/kWh	0.04870	0.04889	0.04726	0.04870	0.04889	0.04726	Table 9
Biomass	%	23.51%	23.51%	23.51%	23.51%	23.51%	23.51%	Own estimate based on TGE [67,68,69] and NIK [70]
WTT GWP100 for biomass (WTT Bioenergy, Biomass—as average for different types)	kg CO2 eq/kWh	0.01854	0.01854	0.01854	0.01854	0.01854	0.01854	DEFRA [63,64,65]
Other renewable	%	76.49%	76.49%	76.49%	76.49%	76.49%	76.49%	Own estimate based on TGE [67,68,69] and NIK [70]
WTT GWP100 for other renewable	kg CO2 eq/kWh	0.0	0.0	0.0	0.0	0.0	0.0	IEA [59]
GWP100 of WTT fuels used for electricity generation off-site, direct (renewable energy with GOOs)	kg CO2 eq/kWh	0.00436	0.00436	0.00436	0.00436	0.00436	0.00436	Own calculation
GWP100 of WTT fuels used for electricity generation off-site, direct (Scope 3, Category 3B—“Well-to-Tank”)	kg CO2 eq/kWh	0.04205	0.04221	0.04082	0.03540	0.03553	0.03439	“WTT”—result of calculation using Equation (1)
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.85021	0.85812	0.78824	0.85021	0.85812	0.78824	Table 7
${\displaystyle \frac{\mathrm{W}\mathrm{T}\mathrm{T}}{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}$	%	4.95%	4.92%	5.18%	4.16%	4.14%	4.36%	Parameter of “WTT/Direct” in Equations (1) and (2)

presents the results of the life cycle carbon footprint calculations of fuels used to generate 1 kWh of electricity according to Scenario 2. The GWP₁₀₀ for the residual fuel mix has been used as calculated in Table 9. Shares of fuels used to generate the renewable energy tracked with GOOs have been estimated based on the annual reports of the Polish Energy Exchange TGE [67,68,69] and the report of the Polish Supreme Audit Office entitled “Use of biomass in energy production” [70]. Information on the amount of biomass-based electricity tracked in Poland with GOOs in 2018–2020 was found in the report of the Polish Supreme Audit Office [70]. Information on the total amount of renewable electricity tracked with GOOs for these years was taken from the reports of the Polish Energy Exchange [67,68,69]. With these references, it was estimated that in Poland, during the period of 2018–2020, the share of biomass in the fuel mix of renewable power tracked with GOOs was 23.51%. The same value was assumed for the years 2021, 2022, and 2023. In Scenario 2 with 15% of tracked renewable electricity, the life cycle carbon footprint of fuels used to generate 1 kWh of electricity (Scope 3, Category 3B—“Well-to-Tank”) obtained: 0.04205 kg CO₂ eq/kWh, 0.04221 kg CO₂ eq/kWh, and 0.04082 kg CO₂ eq/kWh for the individual years. If 30% of contractual instruments are assumed, the values are 0.03540 kg CO₂ eq/kWh, 0.03553 kg CO₂ eq/kWh, and 0.03439 kg CO₂ eq/kWh.

3.3. Scope 3, Category 3C—“Tank-to-Wheel”—Energy Generation Off-Site, Energy Lost in T&D

Table 11. Scenario 1—the carbon footprint of the generation of electricity (Tank-to-Wheel) lost in the T&D system, no contractual instruments, market- and location-based approaches, and the case of Poland in the years 2021–2023 [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Location-Based Method			Source/Comment
		100% AIB, Residual Mix, Poland			100% KOBIZE, Consumption Mix, Poland
		2021	2022	2023	2021	2022	2023
Electricity consumption	kWh/year	1	1	1	1	1	1
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.85021	0.85812	0.78824	0.66638	0.65048	0.55936	Table 7
T&D losses	%	5.88%	5.04%	6.31%	5.88%	5.04%	6.31%	Based on KOBIZE [47,48,49]
GWP100 of electricity generation off-site, energy lost in T&D (Scope 3, Category 3C—“Tank-to-Wheel”)	kg CO2 eq/kWh	0.04998	0.04324	0.04970	0.03918	0.03278	0.03527	Own calculation

and

Table 12. Scenario 2—the carbon footprint of the generation of electricity (Tank-to-Wheel) lost in T&D system, 15% and 30% contractual instruments, market-based approach, and the case of Poland in the years 2021–2023 [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Market-Based Method			Source/Comment
		85% AIB, Residual Mix, Poland 15% RES power (GOOs)			70% AIB, Residual Mix, Poland 30% RES power (GOOs)
		2021	2022	2023	2021	2022	2023
Electricity consumption	kWh/year	1	1	1	1	1	1
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.72268	0.72940	0.67000	0.59515	0.60068	0.55177	Table 8
T&D losses	%	5.88%	5.04%	6.31%	5.88%	5.04%	6.31%	Based on KOBIZE [47,48,49]
GWP100 of electricity generation off-site, energy lost in T&D (Scope 3, Category 3C—“Tank-to-Wheel”)	kg CO2 eq/kWh	0.04249	0.03676	0.04228	0.03499	0.03027	0.03482	Own calculation

show the carbon footprint for energy generation lost in the transmission and distribution system (Scope 3, Category 3C—“Tank-to-Wheel”) for Scenarios 1 and 2, respectively. This was calculated by multiplying the T&D losses (%) by the emission factor for energy generation in Scope 2 (Tank-to-Wheel). In Scenario 1, the market method yielded results of 0.04998 kg CO₂ eq/kWh, 0.04324 kg CO₂ eq/kWh, and 0.04970 kg CO₂ eq/kWh for 2021, 2022, and 2023, respectively. The location-based method yielded the following values: 0.03918 kg CO₂ eq/kWh, 0.03278 kg CO₂ eq/kWh, and 0.03527 kg CO₂ eq/kWh. Due to the lack of information on transmission and distribution losses in the AIB reports, values calculated on the basis of KOBIZE reports [47,48,49] were used. They amount to 5.88%, 5.04%, and 6.31% for 2021, 2022, and 2023, respectively. The comparable calculations for Scenario 2 are presented in Table 12.

3.4. Scope 3, Category 3C—“Well-to-Tank”—Fuels Used in Generation Off-Site, Energy Lost in T&D

Table 13. Scenario 1—the carbon footprint in the life cycle of fuels (Well-to-Tank) used to generate electricity lost in the T&D system, no contractual instruments, market- and location-based approaches, and the case of Poland in the years 2021–2023 [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Location-Based Method			Source/Comment
		100% AIB, Residual Mix, Poland			100% KOBIZE, Consumption Mix, Poland
		2021	2022	2023	2021	2022	2023
Electricity consumption	kWh/year	1	1	1	1	1	1
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.85021	0.85812	0.78824	0.66638	0.65048	0.55936	Table 7
T&D losses	%	5.88%	5.04%	6.31%	5.88%	5.04%	6.31%	Based on KOBIZE [47,48,49]
${\displaystyle \frac{\mathrm{W}\mathrm{T}\mathrm{T}}{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}$	%	5.73%	5.70%	6.00%	7.18%	7.01%	7.85%	Table 9
GWP100 of WTT T&D fuels used for electricity generation off-site, energy lost in T&D (Scope 3, Category 3C—“Well-to-Tank”)	kg CO2 eq/kWh	0.00304	0.00259	0.00318	0.00299	0.00242	0.00296	“WTT T&D”—result of calculation using Equation (2)

and

Table 14. Scenario 2—the carbon footprint in the life cycle of fuels (Well-to-Tank) used to generate electricity lost in the T&D system, 15% and 30% of contractual instruments, market-based approach, and the case of Poland in 2021–2023 years [kg CO₂ eq per 1 kWh].

Parameter	Unit	Market-Based Method			Market-Based Method			Source/Comment
		85% AIB, Residual Mix, Poland 15% RES power (GOOs)			70% AIB, Residual Mix, Poland 30% RES power (GOOs)
		2021	2022	2023	2021	2022	2023
Electricity consumption	kWh/year	1	1	1	1	1	1
GWP100 of electricity generation off-site, direct (Scope 2 “Tank-to-Wheel”)	kg CO2 eq/kWh	0.72268	0.72940	0.67000	0.59515	0.60068	0.55177	Table 8
T&D losses	%	5.88%	5.04%	6.31%	5.88%	5.04%	6.31%	Based on KOBIZE [47,48,49]
${\displaystyle \frac{\mathrm{W}\mathrm{T}\mathrm{T}}{\mathrm{D}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}}}\mathrm{R}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}$	%	4.95%	4.92%	5.18%	4.16%	4.14%	4.36%	Table 10
GWP100 of WTT T&D fuels used for electricity generation off-site, energy lost in T&D (Scope 3, Category 3C—“Well-to-Tank”)	kg CO2 eq/kWh	0.00223	0.00190	0.00234	0.00155	0.00132	0.00162	“WTT T&D”—result of calculation using Equation (2)

present the life cycle carbon footprint of fuels used to generate energy lost in the transmission and distribution system (Scope 3, Category 3C—“Well-to-Tank”) for Scenarios 1 and 2. This was calculated using the relationships presented in Equation (2). Due to the lack of information on transmission and distribution losses in the AIB reports, values calculated on the basis of the KOBIZE reports [47,48,49] were used. They amount to 5.88%, 5.04%, and 6.31%, respectively, for 2021, 2022, and 2023. In Scenario 1, the market method gave results of 0.04998 kg CO₂ eq/kWh, 0.04324 kg CO₂ eq/kWh, and 0.04970 kg CO₂ eq/kWh, respectively, for the years 2021, 2022, and 2023. The location-based method gave the following values: 0.03918 kg CO₂ eq/kWh, 0.03278 kg CO₂ eq/kWh, and 0.03527 kg CO₂ eq/kWh. The results for both situations in Scenario 2 (15% and 30% of contractual instruments) are presented in Table 14.

Table 15. Scenarios 1 and 2—the life cycle carbon footprint for 1 kWh of electricity, and the case of Poland in the years 2021–2023 [kg CO₂ eq per 1 kWh].

Year	Method	Scope 2 TTW Direct (Table 7 and Table 8)	Scope 3, Category 3B WTT Direct (Table 9 and Table 10)	Scope 3, Category 3C TTW T&D Losses (Table 11 and Table 12)	Scope 3, Category 3C WTT T&D Losses (Table 13 and Table 14)	Total	Unit
2021	Location-based, 0% RES GOOs	0.66638	0.04786	0.03918	0.00299	0.75640	kg CO2 eq/kWh
	Market-based, 0% RES GOOs	0.85021	0.04870	0.04998	0.00304	0.95193	kg CO2 eq/kWh
	Market-based, 15% RES GOOs	0.72268	0.04205	0.04249	0.00223	0.80945	kg CO2 eq/kWh
	Market-based, 30% RES GOOs	0.59515	0.03540	0.03499	0.00155	0.66709	kg CO2 eq/kWh
2022	Location-based, 0% RES GOOs	0.65048	0.04557	0.03278	0.00242	0.73125	kg CO2 eq/kWh
	Market-based, 0% RES GOOs	0.85812	0.04889	0.04324	0.00259	0.95284	kg CO2 eq/kWh
	Market-based, 15% RES GOOs	0.72940	0.04221	0.03676	0.00190	0.81027	kg CO2 eq/kWh
	Market-based, 30% RES GOOs	0.60068	0.03553	0.03027	0.00132	0.66780	kg CO2 eq/kWh
2023	Location-based, 0% RES GOOs	0.55936	0.04391	0.03527	0.00296	0.64149	kg CO2 eq/kWh
	Market-based, 0% RES GOOs	0.78824	0.04726	0.04970	0.00318	0.88838	kg CO2 eq/kWh
	Market-based, 15% RES GOOs	0.67000	0.04082	0.04228	0.00234	0.75544	kg CO2 eq/kWh
	Market-based, 30% RES GOOs	0.55177	0.03439	0.03482	0.00162	0.62260	kg CO2 eq/kWh
2021	Location-based, 0% RES GOOs	88.1%	6.3%	5.2%	0.4%	100.0%	%
	Market-based, 0% RES GOOs	89.3%	5.1%	5.3%	0.3%	100.0%	%
	Market-based, 15% RES GOOs	89.2%	5.2%	5.2%	0.3%	100.0%	%
	Market-based, 30% RES GOOs	89.2%	5.3%	5.2%	0.2%	100.0%	%
2022	Location-based, 0% RES GOOs	89.0%	6.2%	4.5%	0.3%	100.0%	%
	Market-based, 0% RES GOOs	90.1%	5.1%	4.5%	0.3%	100.0%	%
	Market-based, 15% RES GOOs	90.0%	5.2%	4.5%	0.2%	100.0%	%
	Market-based, 30% RES GOOs	89.9%	5.3%	4.5%	0.2%	100.0%	%
2023	Market-based, 0% RES GOOs	87.2%	6.8%	5.5%	0.5%	100.0%	%
	Market-based, 0% RES GOOs	88.7%	5.3%	5.6%	0.4%	100.0%	%
	Market-based, 15% RES GOOs	88.7%	5.4%	5.6%	0.3%	100.0%	%
	Market-based, 30% RES GOOs	88.6%	5.5%	5.6%	0.3%	100.0%	%

summarizes the calculation results for individual emission ranges and as a summary result for the entire life cycle of 1 kWh of supplied electricity (total). As can be seen, for the three years investigated, the difference in the results between the highest (market-based, 0% RES GOOs) and the lowest values (market-based, 30% RES GOOs) is about 30%. It reflects a change in the share of renewable energy tracked with GOOs. Emissions from power generation (Scope 2 TTW direct + Scope 3, Category 3C TTW T&D losses) cover together about 95% of the entire carbon footprint, which results from the still very high share of fossil fuels in Poland’s energy mix and the high emission intensity of the fuel combustion process. The fuel life cycle covers several percent (5–7%). Both factors, based on national mixes, have the potential to be reduced with the further greening of Poland’s energy mix. It should be noted that Category 3C emissions may be affected by the type of fuel used for electricity generation, as T&D losses associated with fossil and renewable sources may differ, even though current methods apply average values. In particular, decentralized renewable sources may contribute to lower grid losses, whereas centralized fossil-based generation often entails higher transmission distances and loss rates.

4. Discussion

4.1. Choosing Appropriate Emissions Factors

Due to the key importance of air pollution in environmental protection and environmental management (at various levels), emission factors are more than just numbers [24]. Meeting energy and transport needs is present at every stage of the life cycle of practically all products. In both cases, this is associated with generating energy needed to power machines, devices, and vehicles. The more energy-intensive the product life cycles are and the more this demand is met through the combustion of fossil fuels, the greater the role of energy-related emissions factors in environmental analyses. Companies calculating their carbon footprint should use the most appropriate, accurate, precise, and highest-quality emissions factors available for each method [46]. The most precise will be those that (1) constitute information obtained directly from the energy supplier; (2) are precisely assigned to the type, technology, and quantity of energy supplied; (3) have energy attributes—renewable and emission—that are reflected in contractual instruments that meet the Scope 2 Quality Criteria; (4) cover the time range covered by the inventory; and (5) have been calculated based on measurements using a repeatable, verified, and scientifically supported methodology.

4.2. Double Counting and Energy Attribute Claims

From the point of view of selecting emissions factors in carbon footprint analyses, it is crucial to avoid double counting of energy that demonstrates ecological attributes (renewable, low- or zero-emission) and for which the right to these attributes has been transferred to a specific entity. Energy is not a typical physical product to which a label can be attached and with which it goes to the market. Once introduced to the grid, it becomes indistinguishable, which gives even greater importance to contractual instruments allowing its “tracking”. This also means that the awareness of avoiding double counting by companies and experts performing environmental analyses in the field becomes particularly important. In this light, the emissions factors made public by energy suppliers in Poland (supplier general mix) create a real risk of double counting, which is the main argument for recognizing that they do not meet the Scope 2 Quality Criteria. It seems that a much better solution would be for energy suppliers in Poland to publish information on their environmental aspects (fuel structure, emissions, radioactive waste) broken down into a green energy product (renewable energy covered by contractual instruments) and the supplier’s residual mix (after excluding tracked renewable energy). In such a situation, the suppliers’ residual mix could be a good source of emissions factors for calculating the carbon footprint using the market method in the situation where no more product-specific information is available (as it is higher in the hierarchy) while being more precise than the factor from the national residual mix. This approach is also consistent with Poland’s 2030 targets set out in the National Energy and Climate Plan (NECP), which include a 7% reduction in GHG emissions from non-ETS (Emissions Trading System) sectors and a 23% share of renewable energy in final energy consumption. By integrating Guarantees of Origin and supplier-level data into emission factor selection, the calculation methodology can more accurately reflect national progress toward these climate goals.

4.3. Emission Factor Quality

As part of the discussion, it is worth analyzing the emissions factors investigated in the context of the Pedigree Matrix and Data Quality Indicators used in LCA analyses to assess data quality [71]. This approach takes into account five criteria: reliability, completeness, temporal correlation, geographical correlation, and technological correlation [71]. The final score of the data quality indicator depends strongly on the goal of the individual study. In different decision contexts the same emission data can reflect various quality levels. In our case study the assumed goal is to assess the GHG emissions related to the electricity purchased by the reporting company.

In terms of reliability, the highest quality would be emission factors based on verified data from measurements. This means that they should be the result of using special equipment to measure releases into the air. In practice, however, emission inventories are often carried out using default emissions factors centrally assigned to individual fuels and sources (installations). This is also a common practice in emission reporting. Therefore, it can be assumed that from the point of view of reliability, the emissions factors analyzed in this article are of moderate quality (score 3 → non-verified data partly based on assumptions). In the context of the market method, in the case of general supplier factors, the reliability is additionally reduced due to the risk of double counting. From a reliability perspective, it is also worth mentioning the unit in which the emissions factors are expressed, because it may affect the precision of the calculation. As mentioned earlier, in the understanding of the GHG Protocol, the emission factor is expressed in kg CO₂ eq, which is a certain inaccuracy, because de facto it does not mean emissions but the impact on the environment (characterized by radiative forcing). Of the four factors analyzed, only the one published under the regulation of the Ministry of Climate and Environment in Poland is expressed in CO₂ equivalents; all the others are in units of CO₂ mass per unit of energy. As long as the inventory covers only CO₂ emissions, this does not have a major significance, because the GWP for this gas is always 1. However, if the emissions factors analyzed also included other greenhouse gases, then communicating them in the kg CO₂ eq unit would limit this result to a specific Assessment Report from which the GWP values for individual gases were taken. Having the cumulative emission factor in kg CO₂ eq means that if the analysis contractor wanted to use the GWP value from another Assessment Report, he or she would not be able to disaggregate it.

Another criterion for assessing data quality is completeness. The highest level of quality in this criterion occurs when we deal with “representative data from a sufficient sample of sites over an adequate period to even out normal fluctuations” [71]. Here it can be assumed that each of the factors analyzed demonstrates good quality in relation to the scale it represents. Data published by suppliers should include all emission sources that are under their supervision (score 1). In the case of KOBIZE, the factor is calculated on the basis of national reporting within the framework of the National Emission Balancing and Forecasting System and the existing National Database on Emissions of Greenhouse Gases and Other Substances [72]. In Poland, all entities using the environment whose activities cause emissions of greenhouse gases and other substances are obliged to report to the system [72]. It can therefore be assumed that the KOBIZE factor reflects the national situation well. It is difficult to assess the completeness of the emission factor based on the residual mix for Poland. The AIB methodological report [54] mentions that the data is based on nationally reported information or the Ecoinvent database, but there is no indication of the exact national sources. Unlike the consumption-based KOBIZE factor, the indicator based on the residual mix also includes fuels from imported energy (an example of which is nuclear fuel in the Polish residual mix), but there is no information on the level of losses in the transmission and distribution network. From the perspective of assessing the completeness of the values, it should be noted that all four factors analyzed showed emissions of only one greenhouse gas—CO₂.

In the context of temporal correlation, the highest quality will be demonstrated by emissions factors referring to the reporting period. In Weidema’s Pedigree Matrix [71], the highest quality score is in the situation where the temporal coverage is less than three years of difference to the year of study [71]. This does not seem to be restrictive enough from the perspective of carbon footprint calculations, where using data representative of the reporting year is crucial. In our case study, emission factors with a very good temporal correlation were used (score 1), as all of the factors were representative for the years that were analyzed.

Therefore, from this point of view, the time of publication of information on indicators is important. The regulations of the Ministry of Climate and Environment in Poland on the greenhouse gas emission factor for electricity are announced the most rapidly, as early as in the autumn of the previous year (e.g., the emission factor for 2025 was published on 24 September 2024). Suppliers usually publish information on general emissions factors in the first quarter of the following year (by the end of March). Residual mixes and the emissions factors based on them are usually published by AIB at the end of May of the following year. The greatest time shift occurs in relation to KOBIZE factors, as their publication can take place with a delay of up to one year. For this reason, this article does not show calculations for 2024, due to the unavailability of the KOBIZE factor for that year.

Another criterion for assessing the quality of data is geographical correlation. The highest quality will be demonstrated by emissions factors calculated for the process in the area subject to analysis, i.e., from the supplier’s installation in which the energy supplied to the reporting organization was generated. In the market method, the highest quality score (score 1) will be assigned to the product-specific emissions factors delivered by the supplier from the power installation. The remaining national factors analyzed, covering all emission sources in Poland, can be scored as a 2, as they represent average data from a larger area in which the area under study is included. However, they will undoubtedly have a higher quality than regional (EU average), continental (European average), or global factors.

The last criterion is technological correlation. The highest quality occurs when the data has a precise relationship to the company, processes, and materials subject to analysis [71]. This would be the case if the energy supplier provided a product-specific emission factor corresponding to the place and technology in which it was generated (score 1). In the case of national factors, the quality falls to a moderate level (score 3–4), as it includes energy generated using technologies used by all domestic suppliers.

4.4. Poland’s Energy System and the Methodological Implications

The Polish energy sector is one of the largest in the European Union and ranks in the top 10, taking into account the main macro-energy indicators [73]. It constitutes an important backbone of the Polish economy, generating over 8% of GDP [73]. Poland’s economy is still strongly dependent on fossil fuels, but the situation is improving. On 2 February 2021, the Council of Ministers adopted the Energy Policy of Poland until 2040 (EPP2040) [74]. Among other considerations, the policy takes into account changes in the energy mix. The diversification of sources has been selected as a way to decarbonize the energy mix with an emphasis on the optimal use of Poland’s own energy sources. Investments in renewable energy, nuclear energy, and a transitional role for natural gas have been planned. Besides the greening of the fuel mix, other important goals have been assumed in the policy, such as the development of electricity generation and network infrastructure, the development of energy markets, the implementation of smart power grids, the diversification of supplies, and the expansion of the network infrastructure for natural gas, crude oil, and liquid fuels [74]. The investments in infrastructure have the potential to reduce T&D losses, which should also make the emissions factors lower. In the calculations presented, the national emissions factors for three years have been used: 2021, 2022, and 2023. As presented in Table 7, in the case of the KOBIZE factors, the values for the consumption mix (factors for end users) are 0.708 kg CO₂/kWh, 0.685 kg CO₂/kWh, and 0.597 kg CO₂/kWh. The reduction resulted from changes in the Polish energy mix. According to the Polish Power System reports [60,61,62], the share of hard and brown coal decreased from 79.7% in 2021 to 67.9% in 2023, and the share of renewable sources increased from 12.54% in 2021 to 23.75% in 2023. The implementation of the goals of the Energy Policy of Poland [74] will be reflected in lower values of emission factors based on the production/consumption mix. The realization of the planned goals will impact the residual mix too. If the development of renewable sources were to be accompanied by increasing interest in the Guarantees of Origin (GOOs), then more renewable energy will be tracked. The more energy is tracked, the less renewable energy will be in the residual mix and the higher the emissions factors will be. Therefore, it can be predicted that the difference in values of emission factors between the Polish national consumption/production mix and the Polish residual mix will become greater and greater.

4.5. Role of Carbon Capture and Storage (CCS)

Given that Poland remains one of the most carbon-intensive countries in the European Union in terms of electricity generation—primarily due to the dominant share of hard coal and lignite in its energy mix—CCS is increasingly recognized as a critical transitional technology, particularly for hard-to-abate sectors, which are inherently more resistant to rapid decarbonization.

Among the various carbon dioxide sequestration technologies, geological sequestration appears to be the most feasible and near-term deployable option in the Polish context. This is attributable to Poland’s well-established heavy industrial base and coal-dependent power sector, both of which provide suitable large-point emission sources and potential subsurface storage sites such as depleted hydrocarbon reservoirs or deep saline aquifers [75]. This strategy is consistent with the European Union’s climate and energy framework, which identifies CCS as a transitional instrument [76], especially within sectors where full decarbonization is technologically or economically challenging.

Biological and mineral carbonation-based sequestration methods may serve as complementary solutions, particularly in the context of land-use change, forestry, and select industrial processes. However, their overall sequestration capacity and long-term permanence are generally lower when compared to geological methods.

Technologies such as bioenergy with carbon capture and storage (BECCS) and direct air carbon capture and storage (DACCS) offer the potential for durable negative emissions. Nevertheless, they require substantial capital investment, advanced technological infrastructure, and robust policy support, making them more suitable for long-term strategic development or cross-border climate cooperation frameworks.

Finally, hydrate-based and oceanic sequestration techniques, although scientifically promising in controlled experimental settings, remain at the conceptual or early development stage [77]. These approaches are not currently viable for near-term deployment in Poland due to geographical limitations (e.g., lack of access to deep-sea storage environments) and unresolved environmental safety concerns, including potential impacts on marine ecosystems.

5. Conclusions

The analyses carried out show the need for a fundamental change in approach to the generation and use of emissions factors in environmental analyses—both in calculating the carbon footprint and in the broader context of managing the impact of the organization on the climate. The current practice, based mainly on general average values, turns out to be insufficient in the face of increasing requirements for precision, detail, and compliance with global standards such as the GHG Protocol.

The challenges identified in the study—the risk of double counting, limited transparency, methodological inconsistency, and low correlation of data with specific locations and technologies—show that the model based on aggregated data needs to be replaced with a more tailored, contextual approach. Product-specific factors, assigned to a specific place, method, and time of energy generation and transmission, are gaining increasing importance. In the light of this, the conclusions contained in the “Scope 2 Proposal Summary” published by the GHG Protocol in 2023 are important [78]. This document indicates that future reporting of electricity emissions should take into account greater time resolution (moving from annual to hourly data) and more precise geographical assignment (in line with actual supply boundaries), as well as the use of consumer load profiles as a temporary solution in situations where detailed data are not available. In addition, it is proposed to treat market instruments—such as guarantees of origin—as tools requiring strict time and space consistency, and not just “formal” certificates.

Implementing these guidelines means moving from retrospective reporting to a system based on dynamic data that better reflects actual energy production and consumption. This approach not only eliminates double counting of emissions but also increases the credibility and transparency of reports—a key factor in stakeholder trust. It is important to note that the use of more accurate, contextually embedded emission data can also directly affect the quantitative assessment of the carbon footprint of products. Differences in energy emissions depending on the time of day, season, or source of supply can lead to significant differences in attributed unit emissions, which in turn translate into opportunities for process optimization, production planning, and environmental management.

Further activities should focus on (1) developing tools to integrate actual measurement data with Scope 2 calculations; (2) supporting the availability of hourly and local data from operators and suppliers; and (3) standardizing the rules for the allocation of environmental attributes in line with the requirements of the GHG Protocol.

In the long term, implementing these principles should contribute to changing the way electricity is managed from passive emission recording to an active tool supporting energy transformation and adapting to increasingly stringent regulatory and market requirements. In conditions where decarbonization is becoming not only a challenge but also a measure of organizational maturity, the quality of emission data will be a key resource for every responsible organization.