Regional energy-related carbon emission characteristics and potential mitigation in eco-industrial parks in South Korea: Logarithmic mean Divisia index analysis based on the Kaya identity

This study undertakes a decomposition analysis to identify the factors driving energy-related CO2 emissions in five regions of South Korea, where substantial eco-industrial parks (EIPs) are operational. CO2 emissions are decomposed into five effects: production, population, energy intensity, emission, and fuel mix. We also investigated promising technologies and networks as means to reduce CO2 emissions in the EIPs. Finally, based on comparison with CO2 emission trends of other developed and developing countries, we suggest implications for CO2 reduction policies. It is found that increases in carbon emissions were due mainly to the production effect of both EIPs and the surrounding regions. Reduced energy intensity, on the other hand, was the main factor mitigating carbon emissions. LMDI (logarithmic mean Divisia index) analysis also confirmed the role of EIPs in reducing CO2 emissions, which showed reduced energy intensity in all EIPs. Therefore, it is believed that EIPs have great potential to reduce CO2 emissions in South Korea.

Regions and countries worldwide are undergoing different stages of development and facing various environmental challenges. Rapid urbanization and anthropogenic disturbance have contributed to such challenges, especially the rise of greenhouse gas (GHG) emissions. Global warming caused by GHG emissions is associated not only with eco-crises such as meteorological disasters and ecological damage, but also represents a threat to global economic activity. Furthermore, it is predicted that future economic losses will increase several-fold due to the effects of climate change. Among GHGs, the main cause of climate-change is the emission of carbon dioxide from the consumption of fossil fuels. According to the Intergovernmental Panel on Climate Change (IPCC) [1], the average global temperature increase attributed to rising atmospheric carbon dioxide concentrations is predicted to cause not only flooding and water shortages, but also negative consequences for ecosystem structures.

Therefore, there has been global adoption of carbon dioxide mitigation strategies under the Kyoto Protocol in order to address the problem of global warming. The international community is discussing new, post-Kyoto GHG reduction systems, and is establishing long-term plans for a shared global vision to keep the atmospheric concentration of CO2 below 450 ppm by 2050, and to limit global temperature increases to less than 2 °C at the end of this century [1].

Each country's efforts are as follows: The European Union has a plan to reduce GHGs by 20% by 2020 compared with 1990 levels, the USA and Japan have set reduction targets of 17%, 15% from 2005 level by 2020, respectively [2]. The 37 Annex I countries under the Kyoto Protocol have a mandatory obligation to reduce their GHG emissions. In addition, non-Annex I countries have also pledged to reduce CO2 emissions. For example, South Korea, which does not belong to the Annex I countries, is the 9th biggest GHGs emitter in the world [3], which is attributed to its energy-intensive industrial and societal structures that are highly dependent on fossil fuels. Therefore, South Korea also decided on a 30% reduction in emissions by 2020, compared with business-as-usual (BAU) scenarios [2].

However, controlling and mitigating carbon dioxide emissions require effective analysis of the many factors that influence these emissions [4], such as economic and demographic developments, technological change, institutional frameworks, lifestyles and international trade [5]. Recently, the index decomposition method has primarily been used for analysis of energy and carbon dioxide issues. Many studies have investigated the CO2 emissions of each country over time. Decompositions of these time-series are helpful in understanding the contributing factors and the nature of changes in national CO2 emissions over time [6], [7]. Many researchers have applied decomposition analysis of time series for countries, such as APEC members [7], UK [8], Brazil [9], Greece [5], Turkey [10], China [4], [11], and South Korea [12], [13]. However, most studies analyzed CO2 emissions related to energy consumption at the national level. This is because national-level statistics such as those from OECD, IEA and IPCC are reliable and easy to compare between nations. However, it is necessary to reduce CO2 emissions on a local level in order to achieve overall national targets. Understanding regional CO2 characteristics will help in forming efficient and effective policies for local climate change strategies. To date, relatively few studies have examined regional CO2 characteristics, because there is insufficient availability of statistics on local CO2 emissions. Recently, China has been actively studying the characteristics of regional CO2 emissions. Zhang et al. investigated regional differences in the factors that influence China's energy-related carbon emissions, and potential mitigation strategies [4]. Zhao et al. decomposed the factors influencing industrial carbon emissions in Shanghai, using the logarithmic mean Divisia index (LMDI) method [14]. In addition, Meng et al. analyzed the characteristics of China's regional CO2 emissions and the effects of economic growth and energy intensity using panel data [15]. Yu et al. estimated regional characteristics of inter-provincial CO2 emissions in China using an improved PSO-FCM clustering algorithm [16]. However, there is relatively little research on regional CO2 emissions from other countries.

Recently, the central government of South Korea has supported local governmental attempts to address climate change by enacting relevant laws and appointing a climate-friendly model city. However, these policies have limits and constraints. Realistically, they are not helpful to local governments because local characteristics are not reflected in these policies [12].

To reduce CO2 emissions effectively, in this paper, we consider the eco-industrial park (EIP) concept, which is magnified for sustainable development. The South Korean government recognized the need for greater national-level support for environmental pollution initiatives, and announced an industrial development strategy including research on CO2 reduction and utilization under the slogan “Low Carbon, Green Growth” [2]. One of the core strategies, initiated in 2005, was to develop eco-industrial parks (EIPs) in order to reduce CO2emissions in local areas. An EIP has been defined as: “a community of businesses that cooperate with each other and with the local community to efficiently share resources such as information, materials, water, energy, infrastructure, and natural habitat, leading to economic and environmental quality gains, and equitable enhancement of human resources for the business and local community? [17]. We connect regional characteristics related to CO2 emissions with the implementation of EIPs. To date, there has been limited application of decomposition analysis to research the relationships between regions and EIPs. Maes et al. addressed the Flemish carbon neutrality initiative on new industrial parks. They emphasized, conceptually, the importance of industrial parks for carbon neutrality by focusing on energy efficient buildings, renewable energy production and energy integration through case studies [18]. Most of the EIPs initiated in different countries are modified industrial complexes. Since the 1960s, industrial complexes have played a pivotal role in the growth of the South Korean economy. However, urbanization and the downturn in the global economy starting in the 2000s resulted in the bankruptcy of many companies. In addition, poor governance within these companies significantly aggravated environmental problems such as GHG emissions caused by production, which has become a topic of conversation in local societies. Therefore, it is very important to recognize relationships between industrial complexes and the surrounding regions.

This paper analyzes regional CO2 emission characteristics, using the LMDI numerical analysis method, in order to determine the roles of EIPs in reducing CO2 emissions. The specific objectives are (1) to analyze regional CO2 emission characteristics before and after EIP implementation; (2) to analyze an EIP's CO2 emission characteristics as a key role in order to reduce CO2 emissions; (3) to suggest effective technologies or networks in EIPs in order to reduce CO2 emissions.

The remainder of the paper is organized as follows: In Section 2, we introduce the LMDI method, based on the Kaya identity, and explain the data set used to analyze the CO2emissions of regions and EIPs. Section 3 presents a time series and a period-wise analysis comprising local level (2002–2009), EIPs (2006–2009), and comparisons of the results. Finally, the conclusions are summarized in Section 4.

2. Decomposition methodology and data

2.1. LMDI methodology based on the Kaya identity

This study uses the LMDI method to analyze CO2 emission-change factors in EIPs and surrounding regions. The LMDI method is chosen because of its robust theoretical foundations, strong adaptability, and desirable properties, including the potential for perfect decomposition, in which no unexplained residual term appears in the results [19], [20], [21], [22], [23].

Our LMDI approach is based on the IPAT/Kaya identity. This approach has been widely discussed in analysis of energy-related carbon dioxide emissions. As shown in Eq. (1), it is a form of IPAT identity of I = P × A × T, in which population and per capita GDP can be regarded as population and affluence, and other factors as representing technology. The IPAT identity states that environmental impacts (e.g., CO2 emissions) are derived from the level of population, multiplied by affluence, multiplied by the level of technology deployed [24], [25], [26].

(1)${\text{CO}}_2\text{emissions}=\text{Population}\times \frac{\text{GDP}}{\text{Population}}\times \frac{\text{Energy}}{\text{GDP}}\times \frac{{\text{CO}}_2}{\text{Energy}}$

with Eq. (1), we present a model for a time series and a period-wise decomposition of CO2emissions. CO2 emissions can be expressed via five variables, as follows:

(2)$\text{C}=\sum _{ij}{P}_i\times \frac{{\text{GRDP}}_i}{P_i}\times \frac{{\text{TEC}}_i}{{\text{GRDP}}_i}\times \frac{{\text{EC}}_{ij}}{{\text{TEC}}_i}\times \frac{{\text{CO}}_{2ij}}{{\text{EC}}_{ij}}=P_i\times G_i\times I_i\times M_{ij}\times E_{ij}$

in which P is population size, GRDP is the gross regional domestic product, TEC is total energy consumption, and EC is fossil fuel energy consumption. In this equation, Eij(calculated as CO2/EC) is the CO2 emission coefficient arising from fuel j in year i, Mij(calculated as EC/TEC) is the portion of total energy consumption made up of fossil fuel consumption, Ii(calculated as TEC/GRDP) is energy intensity, Gi(calculated as GRDP/P) is per capita GRDP, and Pi is population size. Furthermore, regional CO2 emissions change by year i, between a base year 0 and a target year T.

Under the LMDI approach, the total change in carbon emissions (ΔCtot) through time (0 to T) is the sum of changes in production (ΔCpdn), energy intensity (ΔCint), fuel mix (ΔCmix), emissions (ΔCemf), and population (ΔCpop) [20]:

(3)$\Delta {\text{C}}_{\text{tot}}={\text{C}}^T{-\text{C}}^0{=\text{ΔC}}_{\text{pop}}{+\text{ΔC}}_{\text{pdn}}{+\text{ΔC}}_{\text{int}}{+\text{ΔC}}_{\text{mix}}{+\text{ΔC}}_{\text{emf}}$

Furthermore, the components of change in Eq. (3) can be calculated as:

(4)$\Delta {\text{C}}_{\text{pop}}=\sum _{ij}L\left({\text{C}}_{ij}^T,{\text{C}}_{ij}^0\right)\ln \left(\frac{P_i^T}{P_i^0}\right)$

$\Delta {\text{C}}_{\text{pdn}}=\sum _{ij}L\left({\text{C}}_{ij}^T,{\text{C}}_{ij}^0\right)\ln \left(\frac{G_i^T}{G_i^0}\right)$$\Delta {\text{C}}_{\mathrm{int}}=\sum _{ij}L\left({\text{C}}_{ij}^T,{\text{C}}_{ij}^0\right)\ln \left(\frac{I_i^T}{I_i^0}\right)$$\Delta {\text{C}}_{\text{mix}}=\sum _{ij}L\left({\text{C}}_{ij}^T,{\text{C}}_{ij}^0\right)\ln \left(\frac{M_{ij}^T}{M_{ij}^0}\right)$$\Delta {\text{C}}_{\text{emf}}=\sum _{ij}L\left({\text{C}}_{ij}^T,{\text{C}}_{ij}^0\right)\ln \left(\frac{E_{ij}^T}{E_{ij}^0}\right)$

where,

(9)$L\left({\text{C}}_{ij}^T,{\text{C}}_{ij}^0\right)=\frac{{\text{C}}_{ij}^T-{\text{C}}_{ij}^0}{\ln {\text{C}}_{ij}^T-\ln {\text{C}}_{ij}^0}$

Here, the CO2 emission effect (Cemf) is the proportion of CO2 emissions produced through energy conversion or technology development within the same energy source group (e.g., the conversion of light oil into gasoline). The fuel mix effect (Cmix) indicates the proportion of CO2 emissions produced through conversion of energy into other energy source groups (e.g., conversion of coal into natural gas). Finally, the energy intensity effect (Cint), production effect (Cpdn), and population effect (Cpop) represent energy consumption per GRDP, by GRDP per capita, and per change of population, respectively. A positive value for these effect coefficients indicates a net increase in emissions, whereas a negative value reflects a net reduction in emissions. Using the decomposition components defined in Eq. (2), we can determine the total change in CO2 emissions for the regions and EIPs during the investigation periods.

We also investigated each EIP's characteristics using both period-wise and time series analysis. Generally, decomposition analysis has the advantage of being less data intensive, since a period-wise analysis can be conducted using only a base year and a target year. However, if the index value has a large discontinuity, it can lead to grave errors in interpretation [5]. Thus, we present not only period-wise analysis but also all intervening years, through a time series analysis, in order to reduce errors.

2.2. Research area and data set

The research area covers five regions of South Korea: Ulsan, Kyunggi, Chungbuk, Jeonnam, and Kyungbuk, all of which include EIPs (Ulsan EIP, Banwol EIP, Chungju EIP, Yeosu EIP, Pohang EIP), as shown in Fig. 1. The data used in this study were sourced from the ‘Local energy statistics yearbook’ by the Korea Energy Economics Institute (KEEI) [27]and the ‘Industrial complexes statistics yearbook’ by the Korea Industrial Complex Corporation (KICOX) [28]. The CO2 emissions from different fuel types for each region were estimated using the Tier 1 method from the IPCC, and energy consumption data. The IPCC Tier 1 methodology calculates carbon dioxide emissions from fuel combustion, and generally involves five stages [1].

1. Download full-size image

Fig. 1

Step 1. Fuel consumption data for 5 regions are retrieved from KEEI and KICOX statistics in tons of oil equivalent (TOE) units.

Step 2. Carbon content is derived by multiplying carbon emission factors (CEF) for each type of fuel (C = TOE × CEF (ton C/TOE)).

Step 3. To get the actual carbon emissions (ACE), IPCC manual suggests using global default values (GDV) of fractions of carbon oxidized if more specific information is not available locally. IPCC GDV of fractions of carbon oxidized is 0.98 for coal, 0.99 for oil and oil products and 0.995 for gas (ACE = C × GDV).

Step 4. ACE calculated from step 3 is converted to CO2 emitted from fuel combustion (CO2 = ACE × 44/12).

Step 5. CO2 emissions of each fuel combusted are summed to get the CO2 emissions of each region.

CO2 emissions from power plants are reflected in the region in which the energy is finally used rather than the region in which the plant is located. We used 2150 kcal instead of 860 kcal as an electricity equivalent when converting to tons of oil equivalent. This produces realistic reduction of CO2 emissions of the regions [12]. If these CO2 emissions are included in the areas where electricity is produced, electricity consumption can be contradictory to CO2 reduction outcomes of the areas of production, not consumption regions. Furthermore, the carbon emission factor for electricity is applied across all regions because Korea uses a centralized supply system.

3. Results

To begin with, the 5 regions shown in Fig. 1 account for 54% of energy consumption and 56% of CO2 emissions of the annual average in South Korea from 2002 to 2009 [27]. Fig. 2shows changes in CO2 emissions within each region. CO2 emissions gradually increased in all regions, to differing extents, due to the strong development of the Korean economy, and associated increases in energy consumption within the manufacturing sector. The calculated CO2 emissions differ between the IEA report [3] and the present research. This is because we use 2150 kcal for the electricity-ton of oil equivalent, whereas the IEA used 860 kcal. As explained in Section 2.2, this conversion factor was chosen to represent a more realistic reduction of CO2 emissions of the regions.

1. Download full-size image

Fig. 2

To be more specific, elementary change trends within the LMDI analysis are shown in Table 1. The greatest increase in population (16.7%) between 2002 and 2009 was recorded in the Kyunggi region. Over the last ten years, the population of Kyunggi region increased 2.3 million when compared with 1.7 million of the whole country. This was because the huge influx of population occurred in the Kyunggi region intensively. Contrary to this, other regions showed slight population decrease. GRDP increased in all regions, with the greatest increase in Kyunggi region. Energy consumption also increased in all regions, with the highest rates of change in Kyunggi and Jeonnam. Energy intensity decreased in all regions except for Jeonnam. Jeonnam showed an elasticity factor of more than 1, meaning that energy consumption increased by more than 1% for a corresponding 1% increase in production. The findings suggest that Jeonnam region has seen growth in energy-intensive industries.

If you are interested in this research article, you can download the full length article through the download page using the link below, both PDF file and Word file are provided.

https://www.sciencedirect.com/search/advanced?tak=LMDI&show=25&sortBy=relevance