Exploring Urban Compactness and Greenhouse Gas Emissions in the Road Transport Sector: A Case Study of Big Cities in South Korea

By inergency On Feb 26, 2024

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1. Introduction

Home to over half the world’s population, cities are at the forefront of climate change challenges and solutions. Although cities are significant contributors to greenhouse gas (GHG) emissions, they also play a crucial role in climate change mitigation and efforts toward carbon neutrality [1,2,3,4,5,6]. A key factor in this urban dynamic is the concept of urban compactness, which influences energy consumption patterns and, consequently, GHG emissions. However, the relationship between urban compactness and GHG emissions remains a subject of debate in current research.

Previous studies have suggested that higher urban compactness, characterized by denser populations and more efficient land use, correlates with lower per capita GHG emissions [7,8,9,10,11,12,13,14,15,16]. This is often attributed to reduced vehicle usage in densely populated urban areas, where proximity to public facilities and efficient public transport systems diminish reliance on personal vehicles [17,18,19].

Contrastingly, some studies found no significant link between urban compactness and GHG emissions, with others arguing that more compact urban forms may increase GHG emissions [14,20,21]. These studies have suggested that factors including city size, policy measures such as fuel taxes, and advanced road transport infrastructure might be more effective in reducing energy use and GHG emissions. Furthermore, several studies have indicated that at the same level of compactness, polycentric cities emit less GHGs than monocentric ones [22,23,24,25].

Urban compactness is used to measure urban form and spatial structure, which are concepts that are simultaneously similar and different [12,25,26,27]. Moreover, urban compactness quantifies population distribution. Other indicators not reliant on population figures, including average floor area ratio, building floor area, and block size, indirectly capture the compactness of the built environment. Nevertheless, the more concentrated the population’s spatial distribution, the denser the built environment, and the stronger the correlations among compactness indicators [28]. Thus, urban compactness measures how people cluster within a city and whether the built environment permits economies of scale.

The concept of urban compactness is interpreted differently in various studies, depending on their approach. First, the concept of compact cities, emerging from the urban planning paradigms of new urbanism and smart growth, extends beyond high population densities or floor area ratios [29]. It encompasses compact development patterns, and a city is only considered compact if it fulfills certain requirements, including mixed land use, transit-oriented development (TOD), access to urban services, and street connectivity. Decision-makers can provide incentives to reduce pollution and improve walkability by mixing uses and TOD [30]. This divergence in perspective is exemplified in the contrasting viewpoints of Gordon and Richardson [21] and Ewing [8], who criticized monocentric compact development. Gordon and Richardson [21] critiqued the compact development pattern by focusing on total population density and floor area ratio within a single urban space. In contrast, Ewing [8] contended that a metropolitan area exhibits a compact development pattern if it comprises multiple nuclei rather than a single nucleus, with each nucleus exhibiting autonomy. Furthermore, Ewing [8] argued that addressing overcrowding by organizing spatial structures into compact development patterns with multiple nuclei, rather than allowing cities to expand based on free-market economy principles, is more pragmatic.

Various studies have adopted distinct criteria for constructing urban compactness indicators, leading to differing interpretations of the connection between GHG emissions and urban compactness. For instance, Kang [25] assessed urban compactness by focusing on indicators related to population distribution, including Moran’s I, entropy, and the Gini coefficient. In addition, Ha [12] evaluated urban compactness by considering factors such as development density and street connectivity, which are congruent with Ewing’s research [27,28]. However, Ha [12] introduced an urban compactness index (UCI) that standardized and combined these individual variables, making it challenging to pinpoint the specific variables that notably impacted GHG emission reduction. In addition to population, various socio-economic variables of a city, such as per capita vehicle registration, proportion of industrial areas, arterial road ratio, and elderly populations, can influence GHG emissions [31,32,33]. However, identifying the various components of a city for GHG reduction is not the objective of this study.

This study aimed to clarify the relationship between urban compactness and GHG emissions, considering the nuances and varying interpretations of urban compactness in the existing literature. In addition, the diversity in study scopes, units of analysis, and methodologies employed in previous research were acknowledged. Particular emphasis was placed on the often-overlooked aspect of city size, i.e., population, and its impact on GHG emissions. The distinctiveness of this study lies in proposing a new direction that considers city size in the methods used to determine the UCI, which has been indiscriminately used. UCI components, including Moran’s I, entropy, and the Gini coefficient with GHG emissions, were compared, with a specific focus on population distribution, while variables related to the built environment, including access to urban services and street connectivity, were excluded. If demographic indicators do not demonstrate a significant correlation with GHG emissions, it raises questions about the relevance of built environment indicators, which exclude population, in understanding the link between urban compactness and GHG emissions. Ultimately, this study contributes to the ongoing discourse by providing a nuanced understanding of the relationship between urban compactness and GHG emissions, considering different city sizes and contexts. The findings aim to inform urban planning and policy decisions, supporting efforts to mitigate climate change through smarter urban development strategies.

2. Materials and Methods

2.1. Study Location and Emissions Data

The analysis focused on metropolitan areas in South Korea with populations of 500,000 or more. The study’s temporal scope covers a single year—2019—encompassing population and road transport emissions data. In 2022, South Korea amended its Local Government Act, designating cities with a population of 500,000 or more as metropolitan areas. By 2023, 27 cities, including those that had already been designated, had earned this recognition. Considering that GHG emission calculations vary by country, this study was limited to Korean cities, preventing cross-country comparisons.

This study primarily focused on observing GHG emissions per capita according to city size to establish a clearer relationship between compactness and GHG emissions. Planners should consider upgrading the thermal efficiency of buildings to reduce both the cost of energy use in residential and commercial sectors and GHG emissions from the construction sector [34]. While residential energy use shows significant variability between temperate and cold climates [35], its correlation with urban compactness is not as strong as with energy use in the road transport sector [17,25,36,37]. Therefore, this study focused its analysis on GHG emissions from the road transport sector. A higher population often correlates with increased energy consumption and, consequently, greater GHG emissions.

This study directly constructed data on per capita GHG emissions in the road transport sector, referencing the guidelines of the Intergovernmental Panel on Climate Change (IPCC). The correlation between urban sprawl, compactness, and gasoline consumption has been extensively explored in prior research [7,8,21,27,28]. This study focused on road transport GHG emissions to investigate the direct association between urban compactness and GHG emissions. These road transport GHG emissions were calculated at the Tier 2 level using conversion Equation (1).

$E_{i, j} = Q_{i, j, k, l} \times {E C}_{i} \times {E F}_{i, j, k, l} \times 10^{- 6}$

(1)

where $E_{i, j}$ represents GHG emissions (j) from the combustion of fuel (i) [in ${t C O}_{2} e q$ .]; $Q_{i, j, k, l}$ represents fuel (i) usage according to vehicle type (k) and control technology (l) [in KL]; ${E C}_{i}$ represents the calorific value (net calorific value) coefficient of fuel [in MJ/L]; $E_{i, j, k, l}$ is the emission factor of GHG (j) according to fuel (i), vehicle type (k), and control technology (l) [in ${k g C O}_{2} e q$ ./TJ].

GHG emissions in the road transport sector can be calculated differently depending on the level of data granularity, ranging from Tier 1 to Tier 3. Tier 1 estimates emissions based on average emission factors for each fuel type and fuel consumption, and currently, South Korea’s GHG emissions statistics are provided at the Tier 1 level. Tier 2 is more detailed, with vehicle and fuel types being further subdivided. Furthermore, emissions are estimated based on the number of vehicles and annual mileage. Tier 3 involves activity data-based analysis, estimating emissions based on detailed emission factors and vehicle mileage, but it includes challenging variables to derive, such as annual vehicle usage time. Owing to limitations in data acquisition, this study constructed and used GHG emissions data for the road transport sector at the Tier 2 level. To address this, the Ministry of Land, Infrastructure and Transport (MLIT) recently developed a Carbon Spatial Map based on these data [38,39].

2.2. Determination of UCIs

Moran’s I, entropy index, and Gini coefficient are UCIs based on population distribution and were computed for each city. The unit of analysis for these calculations was a grid cell measuring 500 m × 500 m. The equations for each indicator have been published by Tsai and Lai et al. [26,40] and are as follows:

$E n t r o p y i n d e x = \ln n - \sum_{i = 1}^{n} P_{i} \ln (\frac{1}{P_{i}})$

(2)

where $n$ , is the number of sub-areas $; P_{i}$ is the population ratio of the sub-area of the ith jurisdiction.

$G i n i C o e f f i c i e n t = 0.5 \sum_{i = 1}^{n} |X_{i} - Y_{i}|$

(3)

where $n$ is the number of sub-areas; $X_{i}$ is the proportion of land area in sub-area i; $Y_{i}$ is the proportion of the population in sub-area i.

$G l o b a l M o r a n ’ s I i n d e x = \frac{n}{\sum_{i = 1}^{n}} (X_{i} - \bar{X}) (X_{j} - \bar{X}) \sum_{i = 1}^{n} \sum_{j = 1}^{n} W_{i j} [\sum_{i = 1}^{n} {(X_{i} - \bar{X})}^{2}]$

(4)

where $n$ is the number of sub-areas; $W_{i j}$ is the weight of I in sub-area j; $X_{i}$ is the population of sub-area j; $X_{j}$ is the population of sub-area j; $\bar{X}$ is the average population.

When analyzing these indicators, the sub-area under consideration is an individual grid cell. For instance, if the city of Seoul has an area of 600 km², there would be approximately 1200 grid cells of 500 m $\times$ 500 m each. When analyzing the UCI of Seoul, each of these 1200 grid cells become a sub-area of Seoul.

This study constructed the UCI (entropy, Gini coefficient, and Moran’s I) for the 84 cities under study and presented the relationship between each index and per capita road transport GHG emissions in scatter plots. Among the UCIs, the one that best represented compactness was selected, and a correlation analysis was conducted to assess the relationship between this selected index and GHG emissions. After examining the relationship between each UCI indicator and per capita GHG emissions in the road transport sector, the UCI was reconstructed by multiplying it with the min–max normalized city size, allowing the relative size of each city to act as a weighted factor. This enabled a re-exploration of the relationship between the original and improved UCIs and per capita GHG emissions in the road transport sector.

Subsequently, this study categorized 84 cities into ordinal ranks using natural breaks by city size in GIS for comparative analysis. During this process, the average per capita GHG emissions in the road transport sector for each city size category were calculated, and the standard deviation for each group was determined. This comprehensive approach allowed for a thorough examination of the relationship between urban compactness and GHG emissions in the context of South Korean metropolitan areas.

4. Discussion

The UCI, which assesses a city’s physical form, should be adjusted to account for city size. Failing to consider city size poses the risk of comparing cities with vastly different population sizes on the same scale. This may lead to unrealistic interpretations and overlook the true level of compactness. Particularly, owing to the mathematical characteristics of entropy and the Gini coefficient, smaller cities often appear more compact because they have more sparsely populated areas. Initially, the concept of compact cities emerged as a response to urban sprawl, driven by the increasing size of cities. However, imposing skyscrapers and public transit-oriented land use plans in a city such as Taebaek (with a population of approximately 44,000), whose urban area is mostly covered by mountains, would be impractical. Therefore, considering city size is essential.

Above a certain scale, a more compact urban spatial structure tends to be associated with lower GHG emissions. Low-density cities have been proposed to be developed as a sustainable development strategy for a low-carbon society [41], but this is considered inappropriate when considering GHG emissions per capita. The specific threshold for a ‘certain scale’ may vary by country, and in Korea, this appears to be approximately 1 million in terms of city population. Accordingly, when examining the 27 major cities with a population of at least 670,000, excluding Jeju Island, observing the linear relationship between compactness and emissions becomes challenging. This trend suggests that in this group, the more compact the city, the higher the emissions. As populations grow to a range of 1–1.5 million people, emissions tend to increase. However, the UCIs of the 10 cities with populations ranging from 1 to 10 million indicated that a city’s per capita emissions tend to decrease as its physical form becomes increasingly compact. Wang et al. [42] analyzed the relationship between urban size and per capita GHG emissions across 259 cities in China. They found that in small- and medium-sized cities (with populations under 1 million), the relationship between city size and per capita GHG emissions formed an inverse U-shape, while in larger cities (with populations over 1 million), it exhibited a U-shape [43]. Because they did not consider the compactness of the cities, caution should be exercised when directly comparing their findings with those of the present study. Nevertheless, they suggested that in large cities that are not overpopulated, per capita GHG emissions tend to decrease as the city size increases, consistent with the findings of the current study.

Large- and small-to-medium-sized cities differ in their GHG emission mechanisms [19]. Larger cities tend to use energy more efficiently, resulting in lower per capita transport GHG emissions. This becomes observable only when cities are categorized by size, owing to the presence of spatial structure factors such as urban railway systems, which directly impact energy use. Thus, merely assessing the population and cohabitation patterns within a city does not indirectly indicate the efficient utilization of energy. Therefore, we categorized cities by size rather than treating them as a continuous variable and verified that increased city compactness does not translate to higher GHG emissions.

5. Conclusions

Cities should aim to adopt a compact urban form and spatial structure to achieve carbon neutrality effectively [12,17,18,19,25]. Several cities worldwide are aiming for carbon neutrality, but transforming cities while addressing the substantial burden of energy costs and improving local government capacity has not been discussed [44,45]. In this context, planners need to establish future-oriented urban planning for carbon-neutral sustainable development [46]. Long-term strategies for reducing GHG emissions can be implemented through rational land use planning and spatial structuring [3,47]. However, the higher the UCI in smaller metropolitan cities, the higher the probability for GHG emissions to be higher. This may be attributed to the absence of urban railway systems in cities within this group. Small populations tend to have higher reliance on cars since urban railway construction is deemed inefficient. In such cases, although the spatial structure of the city is compact, GHG emissions increase with the population.

This study has some limitations, particularly related to explaining the entire urban spatial structure. We explored the relationship between the UCI and GHG emissions by emphasizing city size rather than addressing various components of the spatial structure. Consequently, insights into the specific influence of each spatial structure element are lacking. This study directly derived more sophisticated emission data at a resolution of 500 × 500. Due to constraints in obtaining data on energy consumption in South Korea, there was only constructed a dataset for the year 2019, which is a limitation. Additionally, owing to significant differences in the sample sizes of city size groupings, linear regression and multi-level analyses could not be conducted, limiting the ability to test for a clear causal relationship. Finally, the study was constrained in its capacity to track changes in compactness over time because of its inability to calculate historical estimates of transport sector GHG emissions for individual cities.

Future studies can focus on gaining a clearer understanding of this relationship. Based on various urban factors that can influence GHG emissions, categorizing cities and determining the optimal model for a carbon-neutral city within the context of the relationship between these factors and urban compactness is required. Longitudinal data analyses that track changes in the spatial structure of cities served by urban rail and changes in GHG emissions may provide a more comprehensive explanation of the relationship between GHG emissions and urban compactness across different city sizes.

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