Characteristics and Variations in Korea through the Lens of Net-Zero Carbon Transformation in Cities

Abstract

As climate issues become more severe, the necessity and importance of urban transformation are being widely recognized, and the breadth and depth of research in various disciplines of social sciences to promote net-zero carbon (NZC) transformation in cities is increasing. In this study, between 2015 and 2021, 17 major cities and administrative regions in Korea from were taken as the target and based on the driving force-pressure-state-impact-response (DPSIR) model, a framework of 23 indicators of energy, environment, and economic systems (3ES) was constructed through the coupling coordination degree (CCD) assessment system. The development level, development speed, coordination index of subsystems, and coupled coordination relationship were analyzed successively. Finally, a gray correlation model was adopted to extract the 3ES of each city and their key driving factors. The findings indicate that: (1) There is a phenomenon of high coupling and low coordination within the subsystem, with the environmental factors layer being the most critical concern. (2) The overall coupling of the system has improved continuously since 2015, but it is still at the moderate coupling stage, owing to the long-term nature of system contradictions and processing lags. (3) The driving causes of urban transformation have shifted from economic to environmental restrictions, resulting in noticeable regional differences later in the study period and a rise in the necessity for hierarchical zoning governance. Finally, based on the perspective of restricted subsystems and the consolidation of the 3ES coordination relationship, this study demonstrates the significant relationship between environmental protection, energy transition, and economic development, thus enriching the associated literature at the periphery. It also provides a theoretical foundation for investigating the transition path of NZC cities, thus enhancing research in this area.

1. Introduction

Cities are more than just locations for people to live in; they are also sites where integrated concerns are handled and global sustainability objectives and policies are advocated. The World Meteorological Organization’s “State of the Global Climate 2022” report, published in May 2023, indicated that greenhouse gas concentrations are continuing to gradually increase as heat-trapping greenhouse gases approach record levels and the land, seas, and atmosphere shift globally [1]. Meanwhile, NASA [2] reports that the present global average surface temperature is approximately 1.2 °C higher than it was in 1880, which is significantly outside the usual fluctuation range of the Earth’s average temperature during the past 10,000 years. Against this backdrop of environmental degradation and extreme weather, countries are gradually realizing that although earlier “carbon neutral” actions have a broad social base, they also have limitations: the low-carbon energy transition is proving to be a long-term, gradual, and complex process [3,4,5]. There is still a great deal of uncertainty about the global “carbon neutral” vision; more work is needed to find certainty among these uncertainties.

As new governance approaches are explored, the importance of cities in the governance process is emerging, and countries are beginning to expand their focus from energy transition to a more integrated approach to urban governance transformation and adaptive development [6,7,8,9]. Cities are increasingly becoming an important microcosm of the global sustainable development process [10,11,12]: they have bottlenecks and challenges [13], offer potential opportunities, and indicate the needs and importance of comprehensive transformation. Along with the rapid growth of urban populations, the share of energy consumption and greenhouse gas emissions is increasing, while the proportion of urban-dwelling populations is expected to grow by 29% to 5.8 billion by 2050 as the global urbanization process continues [14]. At the same time, the new urban buildings, regional planning transportation facilities, and residential consumption that accompany population growth will further lead to higher energy consumption and CO2 emissions. While cities generate more than 80% of the world’s GDP, they also consume 85% of the world’s total resources and energy consumption, and they emit greenhouse gases [15]. Cities are critical to the implementation of policies to decrease carbon emissions and ameliorate climate change, and they must reach net-zero emissions by mid-century if global temperature rises are to be kept to 1.5 degrees Celsius or less [16].

A growing number of countries and organizations are now taking active steps to achieve the goal of “net-zero carbon cities.” Cities were first proposed as a key point of success or failure for climate action in 2019 at the C40 World Mayors Summit [17,18,19,20], which proposed the need for policymakers to consciously integrate climate coping mechanisms with urban planning. The importance of cities was re-emphasized at the 2021 International Zero Carbon Cities Forum. In practice, the UN-backed “Race to Zero” global campaign was launched on World Environment Day 2020. Initially, 458 cities joined the effort; this number has now expanded to 1136 cities worldwide [21,22,23]. This campaign is an attempt to accelerate urban transformation in terms of data collection, governance, circular economy activities, energy efficiency, etc. The World Economic Forum (WEF) Climate Action Platform has since released a study entitled “Net-Zero Cities: An Integrated Approach,” which proposes a global framework for net-zero cities and an integrated approach to achieving systemic efficiency gains, thus providing solutions for increasing the resilience of cities to potential future climate and health crises [24].

South Korea set its development plan for 2020 to achieve carbon neutrality by 2050. In December 2021, the government submitted its latest 2030 National Determined Contribution (NDC) target with the overall goal of building a safe and sustainable carbon-neutral society and a short-term goal of reducing GHG emissions by 40% by 2030 compared to 2018. However, the latest 2023 Climate Change Performance Index (CCPI) results show that the policy’s effectiveness is unsatisfactory, with South Korea receiving very low CCPI scores (third from the bottom) in the four main categories of greenhouse gas emissions, renewable energy, energy use, and climate policy [25]. Evaluation experts point out that the program in Korea has low practical utility, lacks a basis for evaluation, and expects too many new technologies. Specifically, the key problem in Korea is that the electricity market structure favors fossil fuels over renewables, allowing the majority of state-owned utilities to continue to provide fossil fuel subsidies, etc. Therefore, CCPI experts emphasized that Korea needs to not only return to its previous target of 30% renewable energy by 2030 but also increase its policy commitment. Thus, although the Korean government has developed a legal system and a national-level emissions reduction route to achieve carbon neutrality, restructured its policies, and attempted to combine green and digital technologies with the New Deal to create synergies, the central government-centered program has not been effective in creating carbon-neutral cities. Korea’s response to the climate crisis has been insufficient in terms of actual results, with issues such as interregional development fault lines and policy feasibility [26,27]. Problems associated with regional development and policy feasibility are common [28,29]. Therefore, in June 2023, the Korea Net-Zero Cities initiative was proposed as a complementary practical solution to achieve the 2050 political goals, with the intention of enhancing the sustainability of urban areas through the progress of regionally-led projects to achieve true carbon neutrality.

Previous studies on carbon emission governance and low-carbon transition at the city level have been thoroughly discussed and mainly include the following aspects. First, in 2021, K. C. Seto demonstrated through an analysis of practical data and policies in various cities [30] that an NZC city transition is possible and that carbon capture and storage technologies and policies can be used to integrate and optimize energy, water, and environmental systems [31,32]. Meanwhile, demand will be adjusted, transportation will be improved, and green infrastructure will be expanded until a balance between industrial carbon emissions and carbon removal is established [33,34,35]. Second, governance and policy research related to the green transformation of government has shown that government policy support is an important driving force for project completion [36,37,38,39,40]; creating a favorable policy environment is an indispensable step in achieving a more adequate balance between environmental and economic activities. Finally, such policies are important for the risk management capacity of the economic system and financial market reform. Some scholars are also open and positive, arguing that climate change and the internationally agreed decarbonization of the global economy present opportunities to improve the risk-responsiveness of the financial system via expansion beyond niche markets and expansion of the scale and adaptability of green finance [41]. Such actions can also support energy or carbon market efficiency by addressing decentralized low-carbon incentives. Historically, national- and city-level managers made recommendations and policies from sectoral and functional viewpoints, whereas relevant practitioners often made suggestions and took action from an industry perspective [42,43,44]. While all stakeholders’ actions may assist cities in advancing toward an NZC future, complete solutions that incorporate various efforts will have a stronger beneficial influence on establishing a more sustainable, resilient, and inclusive city of the future [45]. However, several questions remain that need to be answered. Despite timely responses to problems and development goals and frameworks, why are current measures having little effect? How do economic development, energy transition, and the environment interact across systems? Which factors play a major role in economic development, low-carbon emission reduction, and environmental protection? At present, the NZC transition in cities needs to move from qualitative to quantitative research, examining the effects of previous carbon-neutral policies and analyzing them in the context of city-specific situations. As a result, more research into the coupling and coordination of economic growth, the carbon emission system, and the environmental system are needed to assist policymakers in developing sustainable development plans and informing planning and decision-making.

Based on the above analysis, this study focuses on identifying cost-effective decarbonization pathways at the city scale from a global perspective of urban transformation through the three dimensions of energy, environment, and economy, and analyzes the geospatial and temporal characteristics of the study object, 17 administrative-level provinces and cities in Korea, by formulating hypotheses and validating them. The specific hypotheses are presented below:

Hypothesis 1. 

The overall coupling coordination degree of the system is the basic coupling stage.

Hypothesis 2. 

The influence of the internal environment of the system is the greatest driving force.

Then, the implementation and effectiveness of decarbonization projects in the designated cities is evaluated based on specific panel data, and the NZC cities are comprehensively examined. The shortcomings and development gaps in the construction of NZC cities and the actual impact of policies at this stage are also examined. The results of the study can be used to optimize regional emission reduction development plans and urban transformation policies, which are crucial for helping NZC cities reach the 2050 carbon neutrality target. Compared with previous studies, the specific findings and contributions of this paper are as follows:

First, this study proposes a new indicator system for analyzing the linkages between economic, energy, and environmental systems according to the DPSIR framework combined with CCD analysis and applies a gray correlation model to quantify the synergies and trade-offs for Korea in the linkage framework, which is effectively evaluated.

Second, this study establishes a CCD model of the 3ES, investigates the scope and overall coordinated development of the 3ES in major cities and administrative regions throughout Korea using the two dimensions of time and space as entry points, analyzes the dynamic development characteristics and factors influencing the system, and proposes policy recommendations for pursuing the coordinated development of resource-based regions based on local conditions. This technique sheds light on how various cities might systematically control their intersystems and the synergistic link between these systems to increase government efficiency.

Third, this study focuses on how energy interacts with economic system transformation and environmental sustainability to build NZC cities and thus achieve sustainability; it also considers different mechanisms in three regions, north, central, and south, elucidating the profound relationship between environmental protection, energy transformation, and economic development. This study enriches the relevant literature, facilitates comprehensive and practical policy recommendations, and provides insights for establishing a balanced region. It provides a theoretical basis for the establishment of a balanced regional low-carbon development mechanism and enriches research in this field. The index system in this paper also provides a new perspective on how to identify the complex relationship between energy resource control, socioeconomic development, and resource environmental protection; due it its flexibility, it can feasibly be applied to other case studies.

Finally, the paper’s conclusions are topical and can serve as a valid reference for Korea’s current urban transformation, regional sustainable development, and government control of the entire model. Cities are more than just places where people live; they are also places where global sustainability goals and regulations are executed. Carbon-cutting efforts at the local level can improve national confidence and inspire more ambitious national ambitions. Furthermore, achieving net-zero carbon in cities will necessitate a shift from single-factor management to a whole-system strategy that incorporates climate change, biodiversity conservation, and human development. Overall, we want to think of cities as entire systems that can help us improve our future by adjusting development patterns to produce net-zero carbon cities.

2. Materials and Methods

2.1. Research Procedure

Figure 1 and Figure 2 depict the entire research process of this paper and the specific research methodological framework of this paper, respectively. (1) The DPSIR model was used to build an index framework to examine the development of the 3E subsystems completely. (2) After weighting and normalizing the initial index matrix, the level and rate of development of the energy, environment, and economy subsystems were assessed. By comparing the development stages and trends between the subsystem and the 3ES, the coordination index of the subsystem was defined. (3) Spatial autocorrelation analysis was performed on the 3ES to check the trend of the coordination degree, coupling degree, and CCD of each city, as well as the existence of spatial autocorrelation and spatial clustering. (4) The coupling coordination values of each city were combined with the gray correlation analysis model to determine their key drivers and elaborate on their impacts.

2.2. Data Source

The information for this study was taken from 8 major cities and 9 urban administrative districts in Korea, and the study covers the whole country. The study period was 2015–2021 and the data used related to energy, the environment, and economic systems. Data on industrial soot emissions for the energy system were obtained from the National Dust Information Center of Korea. Temperature data were obtained from the Climate Data Statistics Center of the Meteorological Agency. Data on industrial solid emissions for the environmental system came from the resource recycling system, and data on the economic system came from the Korea National Indicator System Center. The remaining data were obtained from the national database, and the missing data were filled in by the interpolation method to avoid affecting the analysis results.

2.3. Index System

The DPSIR model was used as a framework in this study to examine the intrinsic causation, trade-offs, and synergies across Korea’s energy, environmental, and economic systems using CCD assessment and econometric methodologies [46]. The OECD introduced the DPSIR model in 1993, and it has since been used in policy-making and research. The DPSIR model efficiently reflects system causality and integrates resources, development, environment, and other aspects, with the goal of establishing the chain of causation within the system [47,48,49]. The “driving force” is a socioeconomic or sociocultural component that raises or lowers system pressure; it is influenced by the potential force of the “main driving force,” such as population expansion, social views, technical changes, and technological change. To identify dangers from social, economic, and environmental issues, the DPSIR model can encompass social, economic, and environmental elements. CCD is a potentially essential supplement to the DPSIR framework since it allows for the investigation of synergies between complex systems during a full system analysis. The econometric approach allows for more quantitative analyses of the relationships between different subsystems and DPISR indices. Additionally, the DPISR framework’s combination of CCD assessment and econometric methods fully demonstrates its superiority in the field of quantitative relationship analysis [50,51]. Because the standards and classifications of Korean data statistics vary regularly and are not uniform, which makes later observation and indicator creation difficult, the indicators are adjusted based on the current circumstances in Korea and data accessibility. As shown in

Table 1. The indicator system of environmental, energy and economic.

System	Area	NO.	Index	Meaning	Type
Environmental X	Pressure X1	X11	Industrial fume emissions	Influence the atmospheric circulation and other aspects	−
		X12	Wastewater generation	Increases environmental pollution, soil damage	−
		X13	Energy industry PM10 emissions	Total emissions from sources in the energy sector	−
		X14	Non-industrial PM10 emissions	Total emissions from sources	−
		X15	Common industrial solids emissions	Other than industrial processes	−
	State X2	X21	Average temperature	Arithmetic mean of observed temperatures	−
	Response X3	X31	Waste disposal carbon monoxide emissions	Total exhaust emissions	−
	Management X4	X41	Environmental investment intensity	Level of government support for environmental protection	+
		X42	Urban green space	Level of environmental greening	+
Energy Y	Pressure Y1	Y11	Fuel consumption	Economic impacts in fuel consumption products	−
		Y12	Consumption of petroleum products	Energy mix transition impacts	−
	State Y2	Y21	Household electricity consumption	Impact on the overall energy efficiency of the city	−
	Response Y3	Y31	New energy production	Increased efforts to transform the energy supply system	+
		Y32	Renewable energy production	Increased efforts to transform the energy supply system	+
	Management Y4	Y41	Energy saving and emission reduction	Total energy consumption reduction	+
		Y42	Energy saving and emission reduction rate	Ratio of energy savings over the full cycle of the energy consumption system	+
		Y43	Capacity market	Marginal cost and value of resource abundance at point in time	+
	Impact Y5	Y51	Total energy consumption	Total amount of various energy sources consumed for production or living within a regional unit	+
Economic Z	State Z1	Z11	Population density	Number of people per unit of land area	+
		Z12	Value added of the secondary industry	Advanced level of industrial structure	-
	Impact Z2	Z21	GDP per capita	National economic level	+
		Z22	GDP growth rate	Level of macroeconomic development	+
		Z23	Fixed asset investment	Comprehensive indicators of size, speed, proportionality and direction of use	+

, this paper constructed 11 secondary indicators and 23 tertiary indicators (Figure 3) for 17 major urban areas based on the principles of objectivity, systematization, and applicability of data in accordance with the principles of index system construction and existing research.

3. Research Design

3.1. Research Subjects

The scope of this paper considers the regional representation and national influence of the selected cities, as well as data availability and operability; it is ultimately based on 17 administrative regions in Korea, including one special city (Seoul), one special autonomous city (Sejong), six metropolitan cities, seven provinces, and two special autonomous provinces (Jeju and Gangwon) known as “local self-governing bodies” (Figure 4). The research region was divided into three divisions based on geographical location and economic development level (

Table 2. Division of cities.

Area	City
North part	Incheon; Gyeonggi-do; Gangwon-do.
Central part	Chungcheongnam-do; Chungcheongbuk-do; Gyeongsangbuk-do; Daejeon; Sejong; Daegu.
Southern part	Jeollabuk-do; Jeollanam-do; Jeju-do; Gwangju; Ulsan; Busan; Gyeongsangnam-do.

). A first-tier administrative region is useful for analyzing the coupling and synergistic relationship between carbon emissions from economic development and environmental quality because of its representation in terms of regional economic strength, city size, and radiation power.

3.2. System Construction

Before performing the evaluation, the data must be rendered dimensionless, and the indicators must be allocated weights. The objective weights of indicators are derived using the entropy value approach, which is an objective assignment method that, to some extent, overcomes the bias brought about by subjective considerations. The indicator weights are determined by assessing the degree of correlation between the indicators, and the overall level of the data is assessed using the indicator weights. The following is the calculation concept for the entropy value approach. First, because the scale and unit of each indicator are different and cannot be directly compared and calculated, each indicator weight must be normalized before it can be computed. The standardization formula is:

$$x_{ij}^{\prime }=\frac{x_{ij}-x_j^{\min }}{x_j^{\max }-x_j^{\min }}$$

(1)

$$x_{ij}^{\prime }=\frac{x_j^{\max }-x_{ij}}{x_j^{\max }-x_j^{\min }}$$

(2)

When the indicator is moderate, its normalization formula is as follows:

$$x_{ij}^{\prime }=1-\frac{\left|x_{ij}-d_i\right|}{\max \left|x_{ij}-d_i\right|}$$

(3)

where $d_i$ is the determined standard value, $x_j^{\max }$ represents the maximum value of the jth indicator, $x_{ij}$ represents the data of the $i$th indicator of the $j$th sample in the original data, and $x_{ij}^{\prime }$ represents the data of the $i$th indicator of the $j$th sample in the standardized data. Second, after the standardization of some indicator values, there may be a situation in which the value is zero or negative. For the unity and convenience of calculation, the standardized values are panned to eliminate the above situation.

$$x_{ij}^{″}=H+x_{ij}^{\prime }$$

(4)

where H is the magnitude of indicator panning, generally taken as 0.01. $x_{ij}^{″}$ indicates the data of the $i$th indicator of the $j$th sample in the data after panning. The data become dimensionless via the specific gravity method; the formula is shown below.

$$y_{ij}=\frac{x_{ij}^{″}}{{\displaystyle \sum _{i=1}^n{x}_{ij}^{″}}}$$

(5)

where $y_{ij}$ denotes the data of the $i$th indicator of the $j$th sample in the dimensionless data. Subsequently, the entropy value of the $j$th indicator is calculated with the following formula.

$$e_j=-\frac{1}{\ln n}{\displaystyle \sum _{i=1}^n{y}_{ij}}\ln y_{ij}$$

(6)

In the formula, the coefficient of variation of the $j$th indicator is calculated as follows.

$$g_j=1-e_j$$

(7)

where $j=1,2,\cdots \cdots p$, the formula for calculating the weight of the $j$th indicator is as follows.

$${\omega }_j=\frac{g_j}{{\displaystyle \sum _{j=1}^p{g}_j}}$$

(8)

Finally, the standardized data were multiplied by the weights to obtain the composite score calculation formula shown below.

$$Z_i={\displaystyle \sum _{j=1}^p{\omega }_j}{x}_{ij}^{\prime }$$

(9)

3.3. Calculation of the Coupling Degree

The coupling degree is an essential assessment measure that reflects the degree of interaction across systems and is appropriate for assessing the state of coordination among different elements. The coupling degree model was created to compute the coupling status among the three subsystems with reference to existing research methodologies in order to further examine the development state and connection type of the coupled energy, environment, and economic systems in Korea. The following is the coupling formula for the Korean triadic system.

$$C={\left\{\frac{f(x)\times g(y)\times h(z)}{{\left[\frac{f(x)+g(y)+h(z)}{3}\right]}^3}\right\}}^{\frac{1}{3}}$$

(10)

When the value of C tends towards one, the degree of association between the systems is stronger and shows the development of an orderly direction; the best coupling coordination state is when it is equal to one. In contrast, when the value of C moves toward zero, the degree of association between the systems is weaker and develops in the direction of disorder; the best coupling coordination state is when it is equal to zero.

3.4. Calculation of Coupling Coordination Degree

In general, the coupling degree can reflect only the strength of the interaction between the systems, and it is difficult to reflect the status of the overall function and comprehensive benefits, i.e., the level of coupled and coordinated development. Therefore, to better evaluate the level of coupled and coordinated development between the three subsystems, a CCD model is introduced for calculation, which can be used to understand the degree of coordination between energy, the environment, and economic systems and can also reflect the stage of the level of coordinated development. The calculation formula is:

$$D=\sqrt{C\times T}$$

(11)

$$T=af(x)+\beta g(y)+\delta h(z)$$

(12)

where D is the CCD, D ∈ (0, 1), the greater the value of D tends to one, the better the effect, and the more it tends to zero, the worse the effect. t is the comprehensive evaluation index of the three subsystems, and α, β, and δ denote the weights to be determined. Here, α = β = γ = 1/3.

3.5. Gray Relational Analysis Model

Prof. Deng Julong suggested the gray system theory [52,53]. The gray relational analysis model is a quantitative model that represents the correlation between two sequences, and this correlation shows the relative changes in the components over the system’s growth. If the patterns of two factors are constant, the correlation between them is strong. In the opposite instance, the correlation is low. This approach, which is appropriate for the study of subjects with small samples, quantifies the degree of correlation or approximation between parent and child data columns primarily based on these changes. First, the changes in the meaning, substance, and value criteria of each indicator have an influence on the initialization of the original data in this article. As a result, the data are often of varying magnitudes, making uniform comparison difficult. To make the data comparable, dimensionless processing is used to remove the valid elements of each data point and turn them into standardized quantitative dimensionless data on a consistent measurement scale, allowing for easier comparison of each indication. The data in this research are standardized using the homogenization technique.

$$x_i^{\prime }(k)=\frac{x_i(k)}{\overline{x_i}}$$

(13)

where $x_i(k)$ represents the data of the ith indicator and the data of the kth sample, and $x_i^{\prime }(k)$ represents the mean of the data of the ith indicator, followed by the calculation of the absolute difference between the comparison series and the reference series, as shown below.

$${\Delta }_i(k)=\left|x_0^{\prime }(k)-x_i^{\prime }(k)\right|$$

(14)

where ${\Delta }_i(k)$ represents the absolute difference, and the comparison series is a data series consisting of factors that affect the behavior of the system, which is constructed with values from the evaluation indices of each evaluated object. After construction, the difference in value is used to calculate the gray coefficient of the index system. The gray correlation coefficient is the expression of correlation in gray theory, and correlation essentially refers to the degree of difference in geometry between curves; thus, the size of the difference between curves can be used as the dimension to measure the degree of correlation. In the gray correlation analysis method, the correlation coefficient is the geometric distance between the reference series and the comparison series at each point in time: the larger its value is, the greater the degree of correlation between the two indicator series on the corresponding indicators. Its calculation formula is as follows:

$$\gamma (x_0(k),x_i(k))=\frac{\underset{j}{\min } \underset{k}{\min }{\Delta }_i(k)+\xi \underset{j}{\max } \underset{k}{\max }{\Delta }_i(k)}{{\Delta }_i(k)+\xi \underset{j}{\max } \underset{k}{\max }{\Delta }_i(k)}\hspace{1em}\hspace{1em}0<\xi <1$$

(15)

where $\xi$ is a constant, usually; $\xi$ is taken as 0.5; and $\gamma (x_0(k),x_i(k))$ is the gray correlation coefficient.

Finally, the gray correlation degree is calculated by arranging the correlation sequence because the correlation coefficient is the degree of correlation between the reference series and the comparison series. Furthermore, it is the degree of correlation at different time points; thus, there is more than one correlation coefficient, and the distribution is scattered. Therefore, it is impossible to make a uniform comparison. The gray correlation degree is the value obtained by pooling these correlation coefficients via a certain method, which can reflect the correlation degree between the reference series and other indicators in general. The larger the value of the gray correlation degree, the stronger the correlation. The formula for calculating the integrated gray correlation is as follows.

$x_i(k)$ represents the data of the ith indicator and the data of the kth sample, and $x_i^{\prime }(k)$ represents the mean of the data of the ith indicator, followed by the calculation of the absolute difference between the comparison series and the reference series, as shown below.

$$\gamma (x_0,x_i)=\frac{1}{n}{\displaystyle \sum _{k=1}^n\gamma (x_0(k),x_i(k))}$$

(16)

The formula $\gamma (x_0,x_i)$ is the gray correlation between the reference sequence and the ith indicator, which is a combination of the CCD of this paper as the reference sequence and each indicator as the comparison sequence. Finally, the gray correlation degree of each indicator with the CCD is calculated.

4. Analysis of Results

4.1. Evaluation Index of the Subsystem

To explore the development level, development rate, and coordination indices of subsystems in urban agglomerations, this paper classifies and summarizes the full development pattern as well as continuous data quantification based on 23 indices for each region from 2015 to 2021. The average coordination index of each subsystem is presented in the Supplementary Materials.

From the perspective of the energy factor layer, most of the cities show a wave-like upward trend in energy development of different magnitudes, with a significant increase in the city of Seoul, which rises from 0.0878 in 2015 to 0.185 in 2021. Some cities, such as Gyeongsangbuk-do, Daejeon, Ulsan, and Jeollanam-do, show a downward trend in the range of 0.1 to 0.3. On the whole, the regional energy system of the northern urban agglomeration is well developed, with no cities in a declining trend or a steady rise; however, the central and southern parts developed more slowly, with more cities in a declining stage and room for further improvement.

From the perspective of the environmental subsystem, except for Seoul, Chungcheongnam-do, and Gyeongsangnam-do, which experienced a large decline, the values of the remaining city systems were in the normal fluctuation range, showing a bifurcated structure. Among them, Seoul’s environmental system index fell from 0.3219 in 2015 to 0.1979 in 2021, while Chungcheongnam-do’s fell from 0.6385 to 0.3918. The range is between 0.1240 and 0.2467, which requires attention. From the perspective of the economic subsystem, all cities in the country, except Sejong, showed an upward trend with a range of 0.1403–0.3392.

The harmonized indices of the three subsystems were combined to derive the 3ES for each region; the overall composite index for all cities is within the normal fluctuation range, except for Gyeongsangbuk-do and Gyeongsangnam-do, which are both on the rise. Furthermore, all regions have witnessed a continuous improvement in the 3EC. From the perspective of geographic division, the numbers of cities with slightly higher overall development levels in the north and central areas are also higher than those in the south. The system is relatively balanced, with the north being more prominent in terms of development and having a larger increase. These indices, combined with the 3E subsystem data, show that the overall composite index of Korean cities is on the rise at this stage, but problems between internal systems still need to be solved. The overall development of the environmental and economic systems is better than that of the energy system. However, although it shows relatively upward growth in this stage, the environmental system is polarized among cities, which indicates more serious issues. The unbalanced development of the system within the cities and the large differences between regions appear to hinder the sustainable development and transformation of the cities.

4.2. Spatiotemporal Characteristics of Coordination

The harmonized indices of the three subsystems were combined to derive the 3ES for each region; the overall composite index for all cities is within the normal fluctuation range, except for Gyeongsangbuk-do and Gyeongsangnam-do, which are both on the rise. Furthermore, all regions have witnessed continuous improvement in the 3EC. From the perspective of geographic division, the numbers of cities with slightly higher overall development levels in the north and central areas are also higher than those in the south. The system is relatively balanced, with the north being more prominent in terms of development and having a larger increase. These indices, combined with the 3E subsystem data, show that the overall composite index of Korean cities is on the rise at this stage, but problems between internal systems still need to be solved. The overall development of the environmental and economic systems is better than that of the energy system. However, although it shows relatively upward growth in this stage, the environmental system is polarized among cities, which indicates more serious issues. The unbalanced development of the system within cities and the large differences between regions appear to hinder the sustainable development and transformation of cities.

The value range of CCD, according to the coupling analysis formula, is mostly between zero and one. When the value is near one, the CCD of the region’s two subsystems is good. The CCD is split into 10 stages on this basis, as indicated in

Table 3. Evaluation criteria for coupling coordination.

Coupling Stage	Degree of Coordination	Grade	Value
Extremely uncoupled	Low coordination	Extreme imbalance	[0.0, 0.1]
		Severe imbalance	[0.1, 0.2]
Moderate coupling	Moderate coordination	Moderate imbalance	[0.2, 0.3]
		Slight imbalance	[0.3, 0.4]
		Close to imbalance	[0.4, 0.5]
Basic coupling	Basic coordination	Barely imbalanced	[0.5, 0.6]
		Primary disorder	[0.6, 0.7]
		Moderate disorder	[0.7, 0.8]
Highly coupled	High coordination	Good coordination	[0.8, 0.9]
		Superior coordination	[0.9, 1.0]

. The coupling degree may be used to quantify and elaborate the degree of mutual effect between systems, as well as to express the degree of dependency and mutual restrictions between systems. When a system reaches a critical stage, the coupling role and degree define what sort of order and structure it takes on; these parameters dictate the system’s trend from disorder to order. The development trend of the coupling degree indicates that the level of coupling degree between the 3ESs in each city fluctuates upward year by year and over the long term. At the advanced coupling stage, we can also see that the independence of each system is relatively poor and needs to be unified and controlled. The magnitude of benign coupling in the coupling interaction relationship is a measure of the degree of harmony and consistency between systems or between elements within the system, whereas the significantly lower subsystem among the three can be defined as a regionally restrictive subsystem. The development trend of the regional coordination degree clearly shows that between 2015 and 2021, the coordination and coordination relationships of each city’s subsystem range from extreme to moderate dissonance. Although the interaction and correlation between the systems are strong, the coordination relationship is relatively poor: this situation of high coupling and low coordination shows that the subsystems cannot easily work together and are prone to conflicts and problems. Therefore, timely control from within the system is needed to avoid further negative effects from stagnation, decline, and extinction of the factor layer.

The CCD of the study area was then combined with GIS software 10.8 to obtain the spatial distribution and trend distribution as shown in Figure 5. The CCD of 3ES in Korean cities shows a slow increasing trend in the study timeframe (Figure 6). Overall, the coupling values in Figure 6a show an increasing trend in the south, shifting from low coupling to moderate in the north region in 2015, while the coupling in the central region is basically unchanged. Secondly, from Figure 6b, we can understand that the degree of urban system coordination is in a declining trend, the process of coordinated development is relatively slow, and the system coordination relationship needs to be further improved. Finally, the average value in 2021 is 0.5268, while the average value in 2015 is 0.4693, which indicates that these cities are still in the stage of barely imbalanced, and barely imbalanced belongs to the basic coupling stage, so hypothesis 1 is established.

This level of development indicates that the cities are still in the barely imbalanced stage, this stage belongs to the basic coupling stage, therefore hypothesis 1 is valid. Cities with relatively good coupling coordination that are in the primary disorder stage (0.6, 0.7) include Gyeonggi-do (0.6392), Ulsan (0.6622), Chungcheongnam-do (0.6878), and Jeollanam-do (0.6068). In contrast, the cities that lag behind in the index are Jeju Island (0.4299), Gwangju Metropolitan City (0.4332), and Daejeon City (0.4400). In terms of spatial distribution, the northern urban areas are better than the central and southern cities, with the central coupling coordination index ranking the lowest and developing the slowest. Specifically, among the northern cities, Gyeonggi-do has the best development and coordination index for each system, with an average value of 0.6015 in the primary disorder stage from 2015 to 2021, and Incheon has the largest development increase from 0.4433 to 0.5632. Second, among the central cities, except for Gyeongsangbuk-do and Gyeongsangnam-do, the average value is 0.4941–0.6728. Additionally, the remaining cities are within the range of 0.4186–0.4351, which is close to the imbalance stage. Finally, the development of the southern cities shows that the coupling coordination values of all cities are in a significant rising stage except for Jeju and Gwangju, which are slightly rising. These results indicate that the long-term sustainable development of the region requires the interactive development of energy, the environment, and economic systems to reach a stable and common state. Whereas resource reserves, environmental conditions, and socioeconomic levels directly contribute to the variability of the regional systems in Korea, there is still much room for improvement in the degree of coupling coordination among Korean cities at this stage.

5. Analysis of Influencing Factors

The degree of coupling and coordination among urban energy, economic development, and environmental protection systems is influenced by a variety of factors, and its spatial and temporal variation characteristics are influenced by the dual roles of economic development pressure and environmental quality constraints. The correlation degree among subsystems in the research region is calculated using Equations (13)–(16). The correlation degree matrix of components in various years is generated to clarify the primary elements impacting the function of 3E on the overall coupling and coordination degree of the city and to identify the significant restrictions on its growth. To increase computation accuracy, the weight percentage of the three systems is computed independently, as each system is located in a different city and is at a distinct stage of development.

In this paper, the starting point and focus of the study years are selected as the key observation years in order to analyze trends while observing the influencing factors, and the information on the rest of the years will be explained in the Supplementary Materials. Figure 7a depicts the study’s findings. First, the key variables influencing urban coupling coordination in the indicators in 2015 are concentrated in the economic factor layer, followed by the energy and environmental factor layers. Specific variables, such as gross per capita product, GDP growth rate, total energy consumption, and secondary sector value added, are significantly connected with integrated indicators and have a significant influence on coordinated urban development. Furthermore, the net-zero carbon transition has a strong coercive effect. In 2021 (Figure 7b), the combined importance of the aforementioned three component levels to the study region switched from focusing primarily on the economic factor layer to the environmental factor layer, followed by the energy layer. In terms of specific indicators, GDP per capita remains the most influential factor, followed by environmental investment intensity, average temperature, total energy consumption, and common industrial solid emissions, which are highly correlated with the composite indicators and have a strong coercive effect on the NZC transition in cities. From the combination of changes in correlations, the 3ES has an increasing scope and impact on cities from 2015 to 2021, increasing the need for comprehensive management solutions. Meanwhile, the main influence relationship of carbon-neutral policy shifts from the economy to the environment. Therefore, hypothesis 2 is established. However, structural transformation, decarbonization, and energy use efficiency still need to be continuously improved, among which increases in energy savings and emission reductions, renewable energy production, household electricity consumption, and the efficiency of the population spatial agglomeration subsystem will improve the comprehensive index of urban energy systems and contribute on to coordinated and sustainable urban development.

Second, from the perspective of the geographical distribution of cities between 2015 and 2021, the northern cities are subject to greater economic constraints at the beginning of the period and uneven development at the end of it. Gyeonggi-do and Gangwon-do are more affected by environmental factors, and Seoul and Incheon are more subject to economic constraints. The development of Gyeonggi-do should be taken seriously, and environmental problems such as PM10 emissions from nonindustrial industries, carbon monoxide emissions from waste treatment and industrial smoke emissions are becoming increasingly serious and need to be addressed in a timely manner. The distribution of influencing factors in the central cities shows that the environment is highly correlated with the city composite index from early 2015 and has a strong constraining effect on energy development. Among them, Chungcheongbuk-do, Chungcheongnam-do, and Gyeongsangbuk-do in the south-central region should pay attention to industrial soot emissions, wastewater generation, and household electricity consumption; increase the share of the construction (capacity market) electricity capacity market; and comprehensively improve energy saving and emission reduction rates. The remaining cities should pay attention to the total amount of energy consumption as well as energy savings and emission reduction. As the demand for energy in cities continues to increase, the total amount of energy consumption continues to rise, and energy savings and emission reduction should be continuously strengthened to promote the development of a healthy balance between economic development and energy systems. In the southern region, the influential constraints between regions and within regions vary greatly depending on the city’s development orientation. Transformations in the southwest regional cities of Jeju Island, Jeolla Namdo, and Gwangju City are mainly influenced by GDP per capita, GDP growth rate and population distribution. The southeastern region cities represented by Ulsan, Busan, and Gyeongsangnam-do should pay more attention to the energy and economic factors layer, among which fuel consumption, new energy production, and urban green space areas should be given priority. In summary, with the change in time and the change in policy focus, the degree of influence of intersystem correlation on the city composite index in some cities is on the rise, while the problems that emerged in 2015 in most cities have not yet been solved, indicating the need to strengthen the control of economic development, environmental protection, and energy transition from a global perspective.

6. Future Policy Directions and Conclusions

6.1. Conclusions

In this paper, 17 provinces and cities in Korea were studied, and a coupled coordination evaluation index system of the 3ES was constructed based on the DPSIR model to analyze the impact of the urban 3ES on the NZC transition of cities. Then, surface mapping and the CCD model were used to quantitatively analyze the internal and overall coupling coordination development levels of the three subsystems. Based on this, the final gray correlation analysis method was used to calculate the main driving forces and influence relationships between the systems of Korean provinces and cities. The highlights of the findings of the study are as follows.

First, from the perspective of research and analysis, the study comprehensively examines the issue of net-zero carbon transition from an urban perspective. The results of the study indicate that the current systemic internal problems in Korean cities are characterized by chronicity and lagging governance. Second, the research methodology uses DPSIR theory to analyze the coupling and coordination mechanisms between the systems and the CCD, combined with GIS analysis software to analyze its characteristics in the time and space dimensions. Finally, from the research results, the overall subsystems at the element level are rising in a wave-like manner, but there is a serious polarization problem within the subsystems, and the research results show that the overall coupling and coordination of the city are strongly coupled and at a low level of coordination, respectively, and the overall state is between close to disequilibrium (0.4, 0.5) and barely disequilibrated (0.5, 0.6). The main conclusions of the study are as follows.

For the subsystems in the 3E factor layer, the systems as a whole are rising in waves, but the problem of polarization within each subsystem is serious, with the environmental and economic systems experiencing high-quality development and the energy subsystem, in general, developing poorly in each city. In particular, the city of Seoul and the Chungcheongnam-do region within the environmental system show a continuous decline with poor internal stability and a large decline, which needs to be taken seriously.

The overall coupling and coordination index of each city shows a trend of high coupling and low coordination among the systems, and the system as a whole is still in between the close to imbalance and primary disorder stages, although there has been a slow increase during the study period. Specifically, the northern city has the best development degree, and the central city is relatively poor. It is necessary to focus on the internal development of the 3ES in each city and to conduct in-depth research on the implied energy pattern of each regional industry, environmental governance, and economic constraints and their intensity.

The major driving force impacting the connection transitions progressively from economic considerations in early 2015 to environmental ones in 2021, according to the city coupling coordination index. Due to the spatially heterogeneous distribution of influencing factors, the northern region should pay more attention to environmental issues such as PM10 emissions from nonindustrial sectors, carbon monoxide emissions from waste treatment, and industrial soot emissions. The central region should pay attention to total energy consumption, energy savings, and emission reduction in the energy system layer, while the southern cities should focus on the internal relationship between the energy and economic system layers.

Notably, the current phase of the systemic internal problems of Korean cities is long-term in nature and lagging in treatments. Most of the problems have existed since 2015, which represents the early stage of the study statistics, but they have not been managed in an effective and timely manner. In 2021, these problems either continued at their current levels or even worsened. For example, cities have increased economic development and energy production while continuing to ignore environmental issues, which in turn has led to a decline in overall system coordination, with Seoul and Chungcheongnam-do having the most serious environmental problems.

6.2. Policy Recommendations

Based on the study findings presented above, the following policy suggestions are offered.

Improve the organizational system and promote the synchronized development of the urban system. Emphasis should be placed on institutional innovation and the reform of institutional mechanisms, strengthening capacity building, and improving supporting policies and measures to ensure the sustainability, monitoring, and evaluation of NZC construction. In terms of the current situation, there is a large gap in the coupling degree between regions, and there are still large barriers between policies and concrete implementation effects. For example, the value of the system in Seoul increased from 0.4779 to 0.517 during the study period, while the coupling degree of the system in the southern cities, such as Jeju Island (0.3919–0.4298) and Gyeongnam-do (0.481–0.4976), was poorer for reasons of weaker urban infrastructure construction and a lack of facilities. Reasons such as poor intersystem coupling and relatively weak policy implementation. Therefore, it is necessary to establish an environmental protection system suitable for each region through macrocontrol, according to the shortcomings of the development of each region and the existing conditions, and to incorporate the key development indicators into the performance appraisal system for local government to reach a perfect city in terms of energy, environment, and economy in the total amount and intensity of two-way regulation.

Tailor policies to local conditions and cities. Existing policies cannot effectively solve the problem of urban differentiation, so to strengthen the integrated development of urban agglomerations and reduce the differences in internal development, it is necessary to start from the actual internal problems and achieve a city-by-city policy. Korea implemented a carbon-neutral development plan in 2020, and from the viewpoint of specific development indicators, most of the cities have shown a significant increase in the coupling harmonization value between 2020 and 2021, but there are still a small number of cities, such as Seoul, which decreased from 0.5175 to 0.5045, Chungcheongnam-do, which decreased by 0.0238, etc., showing a decreasing trend. Meanwhile, from the perspective of interregional gap development, the value of the coupling degree of the northern cities is still between 0.5357 and 0.5632, the central part is between 0.4399 and 0.6877, and the value of the southern cities is between 0.4298 and 0.6622, which shows that although the policy at this stage is obviously effective in the development of the overall regional system, it also enlarges the problem of regional imbalance in the development of the region. Therefore, it is necessary to strengthen the strategic understanding of the overall development of the 3Es, implement a development concept based on multisystem coupling and coordination, and formulate plans based on the actual development of the city to narrow the regional gap. The dismantling of noncapital functions in cities is a whole industry chain project rather than the independent operation of each link, component, and region, and intersystem cooperation must be strengthened with a holistic view.

Improve the spatial layout and promote industrial restructuring. Supporting industry transformation through an integrated approach helps cities rethink urban ecosystems to ensure their sustainability and resilience while bridging the gap between the energy, environmental, economic, and other systems to achieve balanced internal development. On the energy side, first, the findings of this paper should be combined with policies to promote system transformation, reconfigure the energy system, save energy, and improve energy use efficiency. Furthermore, these policies should shift from a production-oriented model focusing only on industries with higher energy consumption and lower energy use efficiency on the production side to an integrated multisectoral governance model that considers both the production and demand sides. Other methods that can help reduce energy consumption and energy intensity should be actively explored, such as adjusting and optimizing the energy consumption structure, developing and utilizing new and renewable energy sources with higher efficiency, and promoting the transformation and upgrading of energy systems. On the environmental side, a green and low-carbon recycling system should be clearly established and improved, and efforts to protect the environment and ecological restoration should be increased. Economic and energy construction should not be improved at the expense of the environment, which, if not taken seriously, will incur counterproductive environmental degradation in the long run. Finally, economically, through the “government-enterprise” cooperation model, financial tools should be extensively used to develop market-oriented environmental protection funds and energy industries via the green economic transformation and low-carbon development of the environment and energy systems, thus effectively promoting their long-term benefits.

6.3. Limitations

The following are the limitations of this paper. First, because the indices of the three subsystems are not completely covered and the study period can be updated only through 2021 due to data availability, we did not include carbon emission or energy consumption data after 2021 in this analysis. Second, because cities were chosen as the research object in this study, other dimensions and related indicators, such as public engagement and business efficiency, should be added, leaving some opportunity for future research. Finally, if reliable data are available, our next focus will be to extend the spatial and temporal characteristics of coordination among systems and participating players at the city level, explore in depth the construction of 3ES indicators, improve the indicator system, and provide more city-specific insights to advance the NZC transition and carbon neutrality goals of cities.