Regionalized GHG emission factors and amounts based on local electricity consumption and energy source generation

Dowan Kim; Junbeum Kim

doi:10.4491/eer.2024.596

Environ Eng Res > Volume 30(6); 2025 > Article

Kim and Kim: Regionalized GHG emission factors and amounts based on local electricity consumption and energy source generation

Research

Environmental Engineering Research 2025; 30(6): 240596.

Published online: March 1, 2025

DOI: https://doi.org/10.4491/eer.2024.596

Regionalized GHG emission factors and amounts based on local electricity consumption and energy source generation

Dowan Kim^1,², Junbeum Kim^2,^†

¹Environmental Technology Institute, Seoul National University of Science and Technology, South Korea

²UR InSyTE Interdisciplinary research on Society-Technology-Environment Interactions, University of Technology of Troyes, France

^†Corresponding author: E-mail: junbeum.kim@utt.fr, Tel: +33-06-3420-6571, ORCID: 0000-0003-0665-7989

Received October 9, 2024 Revised January 14, 2025 Accepted February 28, 2025

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electricity is often generated in one region and consumed in another. The regions consuming electricity should bear the responsibility for the greenhouse gas (GHG) emissions associated with their consumption. A consumption-based calculation helps address the imbalance between generation regions (typically where fossil fuel power plants such as coal and LNG are located) and consumption regions. This approach can reduce the issue of disproportionately concentrating GHG emission responsibility on specific regions. In this context, we propose a regionalized approach that allocates GHG emissions based on electricity consumption rather than production. Utilizing data from the Korea Electric Power Corporation, the study estimates GHG emissions by energy source and considers electricity transfers between regions. The study results revealed that the responsibility for emissions from consumption is concentrated in cities that rely on coal-fired power generation. For example, Seoul, which supplied electricity from Chungnam where coal-fired power is the main source, has an emission factor of 633 kgCO₂eq/kWh. In contrast, Gyeongbuk, which primarily produces electricity through nuclear power, has an emission factor of only 12 kgCO₂eq/kWh. The findings reveal significant disparities in GHG emission factors across each region, highlighting the limitations of the current system and the need for more accurate regional emission assessments.

Keywords: Allocation, Consumption, Electricity, Greenhouse gas, Regionalized, Supply

Graphical Abstract

Keywords: Allocation, Consumption, Electricity, Greenhouse gas, Regionalized, Supply

1. Introduction

The greenhouse gas (GHG) is specific gas trapping the heat (include CO₂, CH₄, N₂O, SF₆, HFCs, PFCs, and CFCs, and cause modifying the Earth’s climate [1, 2]. Among the GHGs, CO₂ is primarily to blame for global climate change and global warming [3].

As one of the primary causes of GHG emissions globally, fossil fuels (FFs) currently provide around 82% of the energy supply worldwide, with oil comprising almost 32%, followed by coal (27%) and natural gas (24%) [4]. Countries continue to rely on FFs due to factors such as the unstable processes they have experienced, the volatility of energy resource prices, the inability to supply energy resources adequately and reliably, and other economic concerns. This dependence on FFs is what drives the majority of the world’s GHG emissions, especially CO₂ emissions [5]. Unfortunately, the total amount of GHG emissions associated with coal combustions in the world will increase to 19 BtCO_2eq by 2035 [6]

In most countries, electricity systems have prioritized economic efficiency by generating electricity in large-scale power plants located in rural areas and transmitting it to distant urban areas. While centralized electricity systems offer the benefit of stable electricity supply, they can also lead to environmental and social conflicts during generation and transmission [7]. In Korea, capital areas are major consumers of electricity, whereas non-capital areas are primarily responsible for electricity generation. This imbalance contributes to various social and economic conflicts, including disputes over infrastructure locations and transmission losses [8]. Additionally, the production of electricity from coal and LNG power plants is associated with the release of fine particulate matter (PM_2.5), PM and gas emissions have been known as direct and indirect reasons for more diseases, in addition to heart and strokes [9–10]. Although GHG emissions do not directly impact health, they exacerbate climate change, leading to events such as droughts, floods, and heat waves [11]. While nuclear power does not emit air pollutants or GHGs, it poses risks of explosions and the challenge of managing radioactive waste [12].

GHG emissions are often attributed to the generation area rather than where electricity is consumed. However, in reality, most electricity is produced for the consumption regions, so the responsibility lies with the regions where it is consumed rather than where it is generated. This underscores the importance of allocating GHG emissions based on actual electricity consumption. It requires quantifying GHG emissions during electricity consumption by region.

Ma and Ge [13] proposed a method for calculating CO₂ emissions based on detailed electricity consumption sources in each region, considering the amount supplied by region. However, this method relied on average emission factors from generation, which diluted the contribution effect of clean electricity production. This approach suits cases where the region's electricity supply data are known but isn’t applicable where electricity is centrally managed.

Mentel et al. [14] introduced a regional-level electricity pricing approach, suggesting regional differentiation in retail electricity tariffs, calculating environmental loads from consumption. However, it focused only on production, overlooking environmental loads from consumption. Similarly, Peyvandi et al. [15] measured GHG emissions (CO₂eq/kWh) from production and consumption by energy source in Iran but did so on a national scale without regional differentiation.

Research on GHG emissions related to regional energy supply and demand by energy source remains limited, highlighting a significant gap in understanding the localized impacts of energy consumption. This scarcity of research is primarily due to the limited focus on environmentally friendly electricity production and supply in many countries, where centralized management often overlooks regional variations. In countries with centrally managed systems like Korea, assessing the regional impacts of energy consumption becomes particularly challenging. The common practice is to use average national emission factors in life cycle assessments, which can obscure the true environmental burdens associated with electricity use in specific areas. This approach complicates the accurate calculation of actual environmental impacts and fails to account for local energy consumption. Consequently, even regions that utilize low-carbon electricity sources might be unjustly penalized by high national emission factors, undermining their contributions to reducing carbon emissions. Such discrepancies hinder efforts to transition to low-carbon energy systems, particularly in regions striving to produce renewable energy locally.

Therefore, there is a pressing need to develop methodologies that accurately reflect regional consumption emissions [16–18]. This study proposes a novel methodology to calculate GHG emissions from electricity consumption, disaggregated by both energy source and region, and supports these proposals with applicable case studies. By conducting a thorough analysis, the study is able able to establish fair and efficient principles for the distribution of environmental costs, ensuring accurate accountability for emissions. This approach not only enhances the understanding of the regional environmental impacts but also contributes to the development of policies aimed at promoting sustainable electricity consumption and protecting the environment. By advocating for methodologies that accurately capture regional emissions, this research supports more precise policymaking and encourages the creation of incentives that align with the goals of reducing carbon footprints and advancing the transition to low-carbon energy sources. The application of these methodologies can lead to more equitable solutions that reward regions for their sustainable practices and drive broader adoption of renewable energy initiatives, ultimately fostering a more sustainable and environmentally conscious society.

2. Data and Methods

2.1. Calculation of Electricity Demand and Supply

2.1.1. Data sources

The regional electricity generation and consumption based on 2023-year data were obtained from the Korea Electric Power Corporation [19]. This detailed dataset allows for a comprehensive analysis of how different regions generate and consume electricity, providing a nuanced understanding of the energy landscape in Korea. The data distinguish between regional electricity generation sectors and regional renewable energy sectors, enabling a granular approach to assessing energy trends and their environmental impacts.

In the regional generation sector, electricity generation sources are categorized into seven distinct types: nuclear, coal (including both anthracite and bituminous varieties), LNG (liquefied natural gas), renewables, oil, pumped storage, and others. This categorization helps in understanding the reliance on conventional energy sources and highlights areas where traditional fuels dominate the energy mix. Each category has unique environmental and economic implications, offering insights into regional dependencies and opportunities for transition towards cleaner energy sources. The renewable energy sector is further subdivided into five categories: hydro, solar, wind, bioenergy, and other renewables. This classification is critical, as it allows for the assessment of each renewable source's contribution towards the overall energy mix. Table 1 shows some examples of dataset for this study.

2.1.2. Method for calculating external electricity demand and supply

Korea's electricity grid is managed at a national level, which means that electricity generated in one region is not directly tracked as being supplied to another. There is no publicly available data on regional electricity supply chains. In such cases, allocation methods based on consumption or demand are generally considered by Caro et al. [20]. However, according to the Korea Power Exchange (KPX) [21], specific regions, such as Jejudo (JJ), are known to supply electricity entirely or primarily from certain regions. Fig. 1 illustrates the regional electricity generation (blue) and consumption (yellow) provided by KPX. Referring to this, an allocation method considering the transmission network would provide a more accurate representation. For example, Incheon (ICN) supplies electricity to Seoul (SU) and Gyeonggido (GG), while Gangwondo (GW) supplies electricity to SU, GG, and Chungcheongbukdo (CB). Busan (BS) supplies Daegu (DG) and Ulsan (US), with the remainder going to CB. Jeollanamdo (JN) supplies all external electricity demand to JJ, with additional supply going to Gwangju (GJ), Jeollabukdo (JB), Daejeon (DJ), Sejong (SJ), and JB, with the remainder being supplied to CB. (other regions; Chungcheongnamdo (CN), Gyeongsangbukdo (GB), Gyeongsangnamdo (GN)).

A methodology that accounts for electricity lost during transmission must be applied to electricity demand and supply based on electricity generation and grid losses. This transmission loss creates a discrepancy between the electricity generated and the amount delivered to consumers. For instance, even if a power plant generates 100 MWh of electricity, due to losses in transmission, only about 93 MWh may reach the end user. Therefore, the consumption figures must be adjusted to account for these grid losses. To calculate the external demand (+) and supply (−), the sales volume (or electricity sold) is subtracted from the total generation to find the external demand/supply. I adjusted consumption using Eq. (1). E_c,i is the amount of electricity consumed in region i in a given year. E_s,i is the amount of electricity supplied from region i to other regions. E_g,t is the total amount of electricity generation, and E_s,t is the total amount of electricity sold in Korea.

(1)

E_{c, i} = E_{s, i} \times (E_{g, t} \div E_{s, t})

where,

E_c,i : the amount of electricity consumed in region i in a given year.
E_s,i : the amount of electricity supplied from region i to other regions.
E_g,t : the total amount of electricity generation
E_s,t : the total amount of electricity sold

The concept of External Demand Intensity (EDI) was introduced to effectively allocate electricity demand and supply between regions, as shown in Eq. (2). Here, EDI_i,j represents the amount of electricity supplied from region i to region j. ED_j denotes the total electricity supplied from external sources to region j, while ED_i,t is the total electricity supplied from external sources to all regions supplied electricity from region i. Of course, not all electricity produced by local low-carbon power sources will be used, but only by acknowledging this can low-carbon energy be promoted [22].

To illustrate this with a practical example, consider the case of GW. GW generated 36,429 GWh of electricity, but its internal consumption only amounted to 17,114 GWh. Gangwon initially utilizes its low-carbon energy sources, such as renewable energy (6,124 GWh) and pumped storage/other energy (669 GWh), to fulfill its internal demand. After meeting its own energy needs, GW allocates the surplus electricity generated, primarily from coal and LNG (10,321 GWh), to cover external demand. This allocation is based on the intensity of external demand.

The application of EDI not only ensures a fair distribution of electricity based on demand intensity across regions but also highlights the efficiency and flexibility inherent in the regional electricity trading system. This method approaches managing regional energy needs, helping to optimize resource allocation and reduce transmission losses. Moreover, such strategic allocation supports broader goals of enhancing grid reliability, promoting energy equity across different regions, and fostering sustainable energy practices by prioritizing low-carbon energy sources. Through understanding and utilizing EDI, policymakers can better address the imbalances and disparities in regional electricity demands and supplies, leading to more informed decisions that balance local generation capabilities with broader distribution requirements.

(2)

{E D I}_{i, j} = {E D}_{j} \div {E D}_{i, t}

where,

EDI_i,j : the amount of electricity supplied from region i to region j.
ED_j : total electricity supplied from external sources to region j
ED_i,t : the total electricity supplied from external sources to all regions supplied electricity from region i.

2.2. Estimation of GHG Emissions from Regional Electricity Generation and Consumption

2.2.1. GHG emission factors by power source

Table 2 presents the GHG emission factors for various power sources, highlighting the differences in emissions across energy production methods. According to the life cycle GHG emissions analysis conducted by the IPCC [23], nuclear and wind power have the lowest emissions, at 12 gCO₂eq/kWh. Hydropower follows closely behind, with emissions at 24 gCO₂eq/kWh, and solar power at 27 gCO₂eq/kWh. In stark contrast, LNG and coal exhibit significantly higher emissions, at 490 gCO₂eq/kWh and 820 gCO₂eq/kWh, respectively.

The IAEA [24] provides a slightly different perspective, reporting hydropower as having the lowest emissions at 7 gCO₂eq/kWh, which is followed by nuclear at 15 gCO₂eq/kWh, wind at 16 gCO₂eq/kWh, and solar at 27 gCO₂eq/kWh. For fossil fuels, LNG and coal emissions are calculated at 492 gCO₂eq/kWh and 1,025 gCO₂eq/kWh, respectively, indicating considerable environmental burdens associated with these sources.

In a 2021 study by UNECE [25], nuclear power's emissions ranged from 5.1–6.4 gCO₂eq/kWh, wind power varied from 7.8–23 gCO₂eq/kWh, and solar power had a broader range from 8 to 83 gCO₂eq/kWh. Emissions for LNG and coal were reported between 403–513 gCO₂eq/kWh and 751–1,095 gCO₂eq/kWh, respectively, emphasizing the substantial differences in emissions depending on specific technological and methodological applications.

Despite these variations in reported GHG emissions for power sources across different studies, this study uses the emission factors from the IPCC [23] as the basis for analysis. For other renewable energy sources(etc.) that are not specifically mentioned, average values of similar renewable sources are applied. The hydropower factor is used for pumped storage, considering its similarity in infrastructure and function. For sources classified under "other," the average GHG emission factor of all sources is applied as a general representation (as indicated in Table 2).

This approach aims to standardize the assessment of emissions for clearer comparisons and policy recommendations. It underscores the importance of selecting consistent and widely accepted data sources in environmental analysis to provide actionable insights. By clarifying these emission factors, the study contributes to a more informed discourse on energy choices and their implications for sustainable development. Furthermore, understanding the nuances of emission factors helps policymakers prioritize investments in low-emission technologies, encouraging a shift toward more sustainable energy systems worldwide.

2.2.2. Estimation of GHG emissions from regional consumption

The GHG emissions associated with consumption for each region were calculated by considering both the local electricity generation and the external electricity supply and demand. The emission factor for consumption was determined by subtracting the emissions from supply to other regions and adding the emissions from electricity demand from other regions (as shown in Fig. 2). The regional GHG emissions from electricity consumption were calculated according to Eq. (3).

Where, G_t,i represents total GHG emissions from electricity consumption of the region i. G_g,i represents the GHG emissions from electricity consumption in region i(only generated electricity in region i). G_j,i and G_k,i represent the GHG emissions resulting from electricity supply and demand between region i and regions (j or k), respectively. GE_i,a is the amount of electricity generated by source a in region i. In Eq. (4), CE_i,a represents the amount of electricity consumed from source a in region i. In Eq. (5), F_a denotes the emission factor per unit of electricity generated by source a. EPD_j,a refers to the amount of electricity from source a supplied from region j. In Eq. (6), while EPS_k,a refers to the amount of electricity from source a exported to other regions (k).

(3)

G_{t, i} = G_{c, i} + \sum_{j} G_{j, i} - \sum_{k} G_{k, i}

(4)

G_{c, i} = \sum_{i} ({C E}_{i, a} \times F_{i, a})

(5)

G_{j, i} = \sum_{j} ({E P D}_{j, a} \times F_{a})

(6)

G_{k, i} = \sum_{k} ({E P S}_{k, a} \times F_{a})

where,

G_t,i : total GHG emissions from electricity consumption of the region i
G_c,i : the GHG emissions from electricity consumption in region i(only generated electricity in region i).
G_j,i : the GHG emissions resulting from electricity that was supplied from region j to region i
G_k,i : the GHG emissions resulting from electricity supplied from region i to region k

3. Results and Discussion

3.1. Regional Electricity Generation, External Supply and Demand

Fig. 3 illustrates the electricity generation, consumption, and external supply/demand across different regions. The total generation and consumption are 588,047 GWh, with external supply and demand accounting for 188,724 GWh, which means approximately 32.2% of the national generation is transferred between regions. This suggests that around 9% of the generated electricity is lost due to regional transmission, assuming an electricity loss rate of 3%. As transmission distances increase, the rate of electricity loss could rise, potentially making this figure even larger when fully accounted for.

The allocation-based calculation may slightly differ from reality, with around 60% of the national generation occurring in four regions: CN (18%), GB (16%), GG (15%), and JN (11%). However, there is a notable imbalance in regional electricity consumption, heavily concentrated in SU and GG. Regions like GG (62%), SU (10%), and CB (11%) exhibit very low electricity self-sufficiency rates, whereas CN (214%), GB (216%), and GW (213%) exceed 200%, indicating significant disparities in production versus consumption across areas.

Regarding external supply and demand, regions such as BU, ICN, GW, CN, JN, GB, and GN not only generate enough electricity to meet their own needs but also supply other regions. Conversely, regions like SU, GG, DG, and GJ depend on electricity supplied from other areas. This underscores the challenge of electricity concentration in capital areas, where large power plants are built in non-capital regions, and electricity is transmitted via the grid to urban centers. Generally, regions with high levels of economic growth have higher levels of consumption, and these regions supplied more electricity from other regions to meet the demand [26]. From this perspective, concentrated power consumption in the capital area is natural.

This uneven distribution highlights the inefficiencies and potential vulnerabilities in the current energy infrastructure. Ignoring the impact of distribution will lead to CO₂ emissions leakage [27], which will then lead to regional carbon inequality between production and consumption [28].

It emphasizes the need for strategic planning and investment in local renewable energy projects to enhance self-sufficiency and reduce transmission losses. Addressing these imbalances could mitigate the environmental impact associated with long-distance electricity transmission and contribute to more equitable and sustainable energy distribution across regions. Additionally, fostering local energy initiatives can empower regions to better manage their energy resources, ensuring reliability while supporting national goals for energy security and sustainability.

3.2. GHG Emission Factors by Regional Electricity Use

The GHG emission factors derived from regional electricity generation and consumption are depicted in Fig. 4. We found a significant difference between the emission factors due to production and those due to consumption, which is similar to the results of previous studies [29]. We see a decrease in intensity as the share of non-fossil generation increases. When the share of non-fossil generation is high, consumption intensity tends to be higher than production intensity due to imports from countries with high production intensity. Conversely, most areas where this is not the case are areas where electricity is generated using FFs.

The average GHG emission factor for electricity generation is 368 kgCO₂eq/MWh. Regions such as SU, DG, ICN, GW, CN, and GN exceed this average, mainly due to their reliance on fossil fuels in electricity generation. For instance, the proportion of coal-fired power generation is significant in these regions, with ICN at 46.0%, GW at 73.9%, CN at 81.7%, and GN at 90.4%. Additionally, LNG constitutes a large part of the energy mix in SU (88.4%) and DG (80.8%). In contrast, regions like BS, US, GB, and JN, where nuclear power is the dominant source, and CB, where renewable energy plays a major role, have much lower GHG emission factors. Notably, GB, with nuclear power constituting 90.3% of its energy mix, the lowest emission factor.

The average GHG emission factor for external electricity supply is 557 kgCO₂eq/MWh, about 1.5 times higher than the generation average. However, this varies by region; for example, in regions supplied electricity generated from fossil fuels, the factor may increase significantly. In CB, for instance, the emission factor at the generation phase is 78 kgCO₂eq/MWh, but when it was supplied electricity from GW, which relies entirely on coal generation, the factor doubles to 156 kgCO₂eq/MWh.

The GHG emission factor in the consumption phase ranges from a minimum of 12 kgCO₂eq/MWh to a maximum of 740 kgCO₂eq/MWh, a startling variance of about 61.6 times. This may be determined by the GHG intensity of the power source used in the region, but it seems to be mostly influenced by whether highly GHG intensive energy is supplied from other regions and use low GHG intensity [30–33].

Furthermore, adopting region-specific emission factors can promote more accurate reporting and accountability, encouraging industries and regions to transition towards cleaner energy sources [36]. This targeted approach not only aligns with global sustainability goals but also helps to address the specific environmental challenges faced by high-emitting regions. Consequently, implementing region-specific strategies and policies could be pivotal in driving national efforts towards reducing GHG emissions and advancing the transition to a sustainable, low-carbon economy. By the calculation of emission factors, policymakers can more effectively allocate resources and investments to areas with the highest potential impact, thereby optimizing efforts to combat climate change.

3.3. Regional GHG Emissions by Electricity Use

Table 3 and Fig. 5 illustrates the change in GHG emissions based on regional electricity generation and consumption. The total GHG emissions in Table 3 show a difference of approximately 2% based on generation and consumption. Considering this discrepancy is due to allocation ratios, it can be concluded that the allocation has been appropriately conducted. The capital region (comprising SU, ICN, and GG) is responsible for 30.8% of the emissions from electricity generation. However, when taking into account emissions from electricity consumption, this figure rises to 55.5%. For instance, SU, which initially represents only about 1% (2,294 ktCO₂eq) of total generation emissions, sees its share soar to 14.2% (33,541 ktCO₂eq) when external demand is factored in a dramatic increase of approximately 14 times. This shift is because SU does not generate electricity from coal power but instead purchases most of its electricity from CN, which primarily produces power through FF generation.

Conversely, CN, contributing 41.7% (77,507 ktCO₂eq) to total emissions during electricity generation, experiences a reduction to 14.5% (34,431 ktCO₂eq) when considering consumption emissions, primarily because much of its generated electricity is supplied externally. This is show how based on generation emissions are higher in non-capital regions, while based on consumption emissions high in the capital region. The reasons for this can be attributed to the fact that power plants in Korea are primarily located in coastal areas where industrial complexes are situated, and cooling water supply is readily available. Additionally, the NIMBY phenomenon makes it difficult for power plants to be established in residential areas. It underscores the notion that emissions are transferred to the capital region the electricity grid. Consequently, based on generation may obscure true accountability for GHG emissions at the national level.

These results reveal the urgent need to reassess the environmental impacts originating from electricity generation and consumption. From a policy perspective, it is important that regions with high electricity consumption remain accountable for GHG emissions arising from externally supplied electricity when establishing emission reduction targets and managing performance. Regions such as SU should formulate climate action policies that not only address direct emissions but also incorporate those resulting from electricity consumption.

Moreover, this approach calls for a paradigm shift in regional policy-making, advocating for integrated strategies that couple local renewable energy development with comprehensive emissions accounting. This would encourage more equitable distribution of environmental responsibilities and facilitate more informed decision-making, promoting nationwide efforts toward reducing carbon footprints. By implementing policies that reflect the nuances of electricity consumption emissions, policymakers could incentivize regions to enhance their energy efficiency and invest in low-carbon technologies, ultimately fostering a transition toward a more sustainable and resilient energy future. Additionally, such insights could guide international climate negotiations, offering a more nuanced understanding of regional responsibilities and capabilities in contributing to global GHG reduction targets. If inter-regional power source production and supply can be monitored in real time, more accurate calculations may be possible [29].

4. Conclusion

This study introduced the concept that “emissions are where production originates in consumption,” calculating regional GHG emission factors based on electricity generation and consumption and analyzing the resulting disparities in emissions. Notably, the study highlighted the limitations inherent in centralized electricity supply systems, which often fail to accurately reflect the actual carbon emissions attributable at the consumption stage. This methodology also introduced applicable case studies that provide practical solutions to these discrepancies, aiming to influence both policy and practice.

The study’s key findings demonstrated that certain regions accounted for 32.3% of national emissions when calculated based on generation. However, this percentage dropped significantly to 14.7% when assessed based on consumption. This shift reveals a disconnect between where electricity is produced and where its environmental impact is felt. The GHG emission factors, which varied substantially from 12 kgCO₂eq/MWh to 739.6 kgCO₂eq/MWh, highlight the importance of developing more detailed emission estimates that consider the diverse energy generation sources and the role of external electricity supply.

GHG emissions management of based on consumption is suggested as a pathway to reducing emissions from electricity generation by optimizing generation structures, increasing the use of renewable energy sources, and improving efficiency in power production. Adopting a consumer responsibility model emphasizes reducing GHG emissions through enhanced energy conservation measures, improved efficiency, and other reduction strategies during electricity consumption processes. This approach recognizes that consumers drive demand, which in turn influences upstream emissions.

Based on consumption method offers a more objective framework for designing emission reduction policies, as opposed to the traditional based on generation. By factoring in emissions tied to electricity demand and consumption, as well as supply, policymakers can craft more effective and targeted strategies that align with actual use patterns and carbon footprints. Such considerations are crucial for setting realistic and impactful GHG emission reduction targets.

Additionally, to promote sustainable development principles, this study suggests the introduction of regional differentiation in electricity pricing, aligned with social justice principles. By doing so, regions could be incentivized to manage their carbon emissions more effectively and equitably. This method reinforces the idea of regional accountability and responsibility, encouraging local efforts to embrace sustainable practices and reduce ecological impacts.

While this study makes significant strides in rethinking emissions accountability, it acknowledges certain limitations. It primarily focuses on GHG emissions, overlooking other environmental burdens such as particulate matter or the management of radioactive waste from nuclear power. Future research should aim to incorporate more granular data and broader environmental impacts to cultivate a comprehensive understanding of electricity's environmental burdens. Addressing these complexities will enable the development of more informed decision-making frameworks and policies designed to mitigate a range of environmental impacts and sustainable growth.

The adoption of the methodologies outlined in this study could see regions better balance their production and consumption demands, potentially leading to more innovative energy policies that prioritize low-emission technologies and equitable resource distribution. Encouraging regions to act locally could drive national efforts, accelerating the transition to a low-carbon economy and fostering resilience against the multifaceted challenges posed by climate change.

Notes

Conflict-of-Interest Statement

The authors declare that they have no conflict of interest.

Author Contributions

D.K. (Post-doc) conducted all the analysis and wrote the manuscript. J.K. (Professor) revised the manuscript.

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Fig. 1

National electricity transmission network operation diagram.

Fig. 2

This figure is present the method for calculating GHG emission factors and emissions based on regional electricity consumption. The electricity generated in region A but not consumed there is added to the actual GHG emissions from electricity consumption in regions B and C, which consume this electricity.

Fig. 3

Sankey diagram of regional electricity generation and consumption considering external Supply/Demand. The left side represents power generation by region, while the right side represents consumption considering external supply/demand (Unit: GWh).

Fig. 4

GHG Emission Factors for regional electricity generation and consumption. The blue bars represent regional GHG emissions from electricity generation.

Fig. 5

Regional GHG Emissions based on generation (a) and consumption (b) in Korea.

Table 1

Example of dataset in regional electricity generation and consumption

Category			Seoul (SU)	Gyeonggi (GG)	Chungnam (CN)
Generation (6MWh)	Nuclear		-	-	-
	Coal		-	1,705,207	-
	LNG		4,522,395	80,695,136	-
	New& Renewable energy	Hydro Power	334	742,495	1,002,139
		Solar Energy	52,175	1,706,473	1,584,469
		Wind Power	-	3,340	-
		Bio Energy	23,423	250,463	5,539
		Etc.	384,558	2,048,145	338,620
	Oil		-	44,861	84,479
	Pumping		-	286,495	-
	Others		132,259	164,469	176,302
	Total		5,115,143	87,647,084	3,191,549
Consumption (Sales to the region) (MWh)	Total		49,218,956	140,312,047	29,450,797

Table 2

The GHG emission factors for each Power source applied in this study (Source: IPCC [17])

Category	Nuclear	Coal	LNG	New & Renewable energy					oil	Pumping	Others

				Hydro Power	Solar Energy	Wind Power	Bio Energy	etc.
GHG (gCO₂eq/kwh)	12	820	490	24	48	12	230	78.5	800	24	305

Table 3

GHG Emissions and rate of change by region.

Regions	GHG emission(tCO₂eq/year)		Rate of change [B-A]/[A]
Regions	Based on generation [A]	Based on consumption [B]	Rate of change [B-A]/[A]
SU	2,294,400	33,541,524	1362%
BS	3,532,017	278,605	−92%
DG	923,649	7,724,246	736%
ICN	30,254,027	13,587,654	−55%
GJ	195,423	3,278,159	1577%
DJ	87,040	584,243	571%
US	3,739,601	4,706,470	26%
SJ	1,860,811	1,876,560	1%
GG	41,349,971	82,762,546	100%
GW	23,995,434	9,239,737	−61%
CB	249,317	4,937,218	1880%
CN	77,507,060	34,430,765	−56%
JB	5,254,713	5,254,713	0%
JN	11,008,946	439,251	−96%
GB	2,858,859	567,372	−80%
GN	33,513,803	28,958,748	−14%
JJ	1,449,762	2,067,119	43%
Total	240,074,832	234,234,931	−2%