Optimization of Carbon Emission Reduction Path in the Beijing–Tianjin–Hebei Region Based on System Dynamics

Abstract

The Beijing–Tianjin–Hebei (BTH) region serves as a pivotal engine for China’s economic development and a gathering area for energy consumption and carbon emissions. Its early achievement of carbon peak is of great significance for promoting high-quality development and regional coordinated development. This study constructs a system dynamics model encompassing four primary subsystems, economy, energy, population, and environment, based on an in-depth analysis of the current situation and main characteristics of carbon emissions in the BTH region from 2010 to 2022. We explored the carbon emission reduction effects under different scenarios by simulating a baseline scenario, an industrial structure optimization scenario, an energy structure optimization scenario, an environmental protection scenario, and a coordinated development scenario. The results indicate the following: (1) From 2020 to 2030, carbon emissions from energy consumption in the BTH region is predicted to exhibit a fluctuating downward trend under all five scenarios, with the most rapid decline observed under the coordinated development scenario. (2) Under the single-variable regulation, Beijing achieves the best carbon emission reduction effect under the environmental protection scenario, while Tianjin and Hebei exhibit superior performance under the energy structure optimization scenario. (3) All three regions demonstrate optimal emission reductions under the coordinated development scenario. Finally, this study discusses the carbon emission reduction paths for Beijing, Tianjin, and Hebei, and provides targeted suggestions for their implementation.

1. Introduction

Global climate change poses profound and complex impacts on the natural environment, human society, and economic development, emerging as one of the foremost challenges confronting the world today. In response to climate change, the international community has reached a consensus, urging all nations to take effective actions. On 22 September 2020, at the 75th session of the United Nations General Assembly, China announced its commitment to peak carbon dioxide emissions by 2030 and its strive for carbon neutrality by 2060. To achieve these goals, China has accelerated its top-level design, with relevant departments formulating the Action Plan for Peak Carbon Dioxide Emissions by 2030, which focuses on the critical periods of peak carbon dioxide emissions during the 14th and 15th Five-Year Plans and proposes specific targets and action measures. In January 2022, the National Development and Reform Commission (NDRC) and the National Energy Administration (NEA) of China issued the Opinions on Improving the Institutional Mechanisms and Policy Measures for Green and Low-Carbon Energy Transition. The document proposes to ultimately establish a complete basic system and policy system for green and low-carbon energy development by the end of 2030 and form an energy production and consumption pattern where non-fossil energy sources can meet the incremental energy demand and replace fossil energy stocks on a large scale, while comprehensively strengthening energy security. However, as the world’s largest energy consumer and carbon emitter, China faces challenges such as a high proportion of fossil fuels, difficulties in industrial restructuring, insufficient research and development investment, and policy support. Therefore, there is still a long way to go in reducing carbon emissions.

The year 2024 marks the tenth anniversary of the implementation of the BTH region coordinated development strategy. Over the past decade, the region has witnessed significant green transformation, with a clearer direction towards green development. However, optimizing carbon emission reduction in the BTH region presents different challenges: Beijing has completed its industrialization process, gradually shifting towards a high-end industrial structure, yet the pace of improving carbon emission reduction is relatively slow due to limitations in energy resources; Tianjin, reliant on the manufacturing industry, experiences high energy consumption and carbon emission intensities, complicating industrial structure optimization and upgrading; Hebei Province, still in the industrialization stage, confronts the dual challenge of improving energy efficiency and eliminating outdated production capacity. The BTH region is a key region in China in terms of resource and environmental overload, energy consumption and air pollution. It is of great significance to explore the carbon emission reduction pathway in the BTH region in order to achieve the national carbon neutrality goal. Based on this, this study constructs a system dynamics model including four subsystems: Economy, energy, population and environment. It explores the time and peak value of the carbon peak in the BTH region, and simulates the impact of carbon emission reduction paths on the peak value under different scenarios. It aims to provide a theoretical basis for the formulation of practical, scientific, and reasonable carbon emission reduction policies, and help the BTH region to achieve the dual-carbon goal at an early date.

2. Literature Review

Given their varying economic structures, urban forms, and geographical locations, the challenges encountered by diverse cities in China in pursuing low-carbon development are markedly distinct [1]. To realize the global strategic goal of carbon neutrality, extensive and in-depth research has been conducted from both theoretical and practical perspectives, focusing on three core areas.

Firstly, research on carbon peak predictions has become a hot topic in academia, guided by the carbon peaking and neutrality goals. This research spans national levels and extends to individual provinces and key regions. At the national level, various models (e.g., the MBA-BP neural network model [2] and the system dynamics model [3,4,5]) have been utilized for prediction analysis [6], indicating that China is expected to reach its carbon peak target by 2030. Additionally, studies have employed other models to predict the peak and timing for specific provinces and the key regions [7,8,9], providing a scientific basis for differentiated carbon emission reduction strategies.

Secondly, academic discussions have delved into driving factors of carbon emission. Early studies focused on initial decomposition of carbon emission driving factors, categorizing them into energy scale effect, structural effect, and technology effect, which laid the foundation for subsequent research [10]. Subsequent scholars analyzed CO₂ emissions in the Netherlands and West Germany, finding limited strength in the technology and structural effects [11]. Research on carbon emission driving factors in China started late but has rapidly developed. Studies utilizing the Divisia index decomposition method examined carbon emissions from the power sector in twelve Asian countries, including China, revealing that energy intensity is the primary driving factor of carbon emission in China’s power sector [12]. Subsequent research using resource consumption as a mediating variable and the “three-level complete decomposition method” focused on carbon emission driving factors in China from 1985 to 1999, concluding that economic development is the most significant driving factor of carbon emissions [13]. This finding is supported by later studies, emphasizing that “economic development is the primary driving factor of rising carbon emission” [14,15,16,17,18,19]. In this context, optimizing and adjusting industrial and energy structures are seen as crucial paths to decoupling economic development and carbon emissions in China [20,21,22]. Additionally, population size, GDP per capita, energy intensity, and urbanization have also been proven to have a positive driving effect on carbon emissions [23,24,25].

Thirdly, some scholars have conducted research on optimizing carbon reduction pathways. Based on research in a certain industry, some scholars have developed a new carbon emission simulation model for the construction industry in Shaanxi Province, and found that structural adjustment of building energy consumption has the greatest potential for carbon reduction, playing a core role in achieving a green and low-carbon path [26]. At the urban level, scholars have studied the carbon emission characteristics of Wuxi, Jiangsu Province, China, as a low-carbon pilot city and found that the impact of carbon emissions is closely related to population growth rate, economic growth rate, energy consumption intensity, and energy structure [27]. The study introduces the KAYA model to set different scenarios for prediction, and finally proposes carbon reduction paths from multiple aspects such as urban spatial planning, economic structure, energy development, industry, housing, transportation, and low-carbon agricultural systems. Scholars have used the STIRPAT model to study the carbon emission characteristics of the Yangtze River Delta region and analyze the driving factors of carbon emissions, and have developed differentiated carbon emission reduction paths for six types of cities from multiple perspectives [28]. From the perspective of the BTH region, some scholars have analyzed and found that, except for a slight decrease in carbon emissions in Beijing compared to 2006, the carbon emissions of cities in the BTH region have increased by 2020. It is pointed out that the Beijing–Tianjin–Hebei urban agglomeration should focus on urban development positioning, and formulate and implement carbon reduction strategies in innovative development, industrial upgrading, ecological protection, and other aspects [29]. Scholars have also studied the key influencing factors of carbon emissions in the Beijing–Tianjin–Hebei urban agglomeration and optimized emission reduction measures. They have concluded that the key to achieving the carbon peak goal is to integrate various measures, such as improving energy efficiency, adjusting energy structure, increasing carbon sink quantity, guiding residents’ behavior, and optimizing industrial structure. They have put forward policy recommendations in these areas [30].

To summarize, existing studies have mainly focused on specific industries such as power generation, construction, and logistics [12,26], and some scholars have analyzed them based on the national and inter-provincial levels. Studies on regional carbon emission reduction paths still need to be enriched and supplemented, especially for the Beijing–Tianjin–Hebei region and other major national strategic development areas; the analysis of carbon emissions and peak carbon paths is relatively weak. Secondly, most of the research on China’s carbon emissions focuses on analyzing historical data, and there are relatively few studies on the prediction of future carbon emission peaks. An accurate assessment of future carbon emission trends and peak points is needed to formulate targeted emission reduction strategies. In addition, existing studies on carbon peak prediction and scenario simulation mainly focus on the static or single level, and the realization of carbon emission reduction targets is jointly affected by a variety of factors, lacking a systematic dynamic analysis that integrates the consideration of various factors. Throughout the existing studies, we find that research on emission reduction pathways mostly focuses on the theoretical exploration of single-factor scenarios. However, any emission reduction policy formulation should be rooted in the joint influence of multiple factors such as economy, energy, environment, and population of a region [31]. There are complex interactions among these factors, and it is not enough to design an emission reduction pathway from a single dimension. Based on this, the possible contributions of this study are as follows: Firstly, this study takes full account of the fact that the peak carbon prediction is the result of the combined effect of multiple factors, such as economy, energy, environment, and population, and selects a system dynamics model to dynamically analyze the regional carbon emissions and peak carbon pathways. This approach helps to better understand the key drivers affecting carbon emissions. Secondly, this study takes into full consideration the important strategic position of the development of the Bohai Sea region in promoting the Bohai Sea Economic Circle and driving the development of China’s northern hinterland, and conducts research on this major national strategic development area. Thirdly, the regional emission reduction path is optimized. This study sets up corresponding development scenarios according to the actual situation of the BTH region, simulates the carbon emission reduction effects of single scenarios and coordinated development scenarios, and provides feasible suggestions for scientifically formulating carbon emission reduction policies in line with the characteristics of the BTH region.

3. Status of Carbon Emissions in the BTH Region

This study measures carbon dioxide emissions in the BTH region based solely on energy consumption. Data on the terminal consumption of the eight energy sources used to calculate carbon emissions are sourced from the China Energy Statistical Yearbook. The formula for calculating carbon emissions from fossil energy consumption in each province is as follows. The standard coal conversion factors and carbon emission factors for various energy sources are shown in

Table 1. Standard coal conversion factors and carbon emission factors for various energy sources.

Type of Energy	Standard Coal Factor	Carbon Emission Factor
Raw Coal	0.7143	0.7559
Coke	0.9714	0.8550
Crude Oil	1.4286	0.5857
Nature Gas	1.3300	0.4483
Fuel Oil	1.4286	0.6185
Gasoline	1.4714	0.5538
Kerosene	1.4714	0.5714
Diesel Fuel	1.4571	0.5921

.

$$\mathrm{C}={{\displaystyle \sum }}_{i=1}^n{E}_i\times e_i\times P_i\times 44/12$$

where C denotes the carbon emissions of a province; E_i represents the consumption of fossil energy in category i; e_i denotes the standard coal conversion factor of fossil energy in category i; P_i indicates the carbon emission factor of fossil energy in category i; n is the type of energy; and 44/12 represents the ratio of the molecular weights of CO₂ and carbon [32].

3.1. Total Carbon Emissions in the Beijing–Tianjin–Hebei Region

From the perspective of carbon sources, an in-depth analysis showed that more than 75% of CO₂ emissions in the BTH region originate from coal consumption. This grim fact not only highlights the urgent need for the region’s energy structure adjustment, but also foretells a significant potential for emission reduction. As the main fossil fuel, the massive use of coal is one of the main reasons for the increase in greenhouse gas emissions, so optimizing the energy structure and reducing the reliance on coal are crucial for the BTH region to achieve a low-carbon transition.

Figure 1 shows the details of carbon emissions from energy consumption in the BTH region between 2010 and 2022. Specifically, Beijing has achieved remarkable results during this period, with carbon emissions from energy consumption decreasing steadily from 142.33 Mt in 2010 to 94.20 Mt in 2022. The positive change reflects Beijing’s strong determination and effective initiatives to promote the use of clean energy and reduce carbon emissions. In contrast, Tianjin’s carbon emissions from energy consumption show a small fluctuating upward trend, increasing slightly from 190.45 Mt in 2010 to 194.57 Mt in 2022. Although the increase is not significant, this trend is still a cause for concern, indicating that Tianjin may face certain challenges in energy structure adjustment, and needs to further increase emission reduction efforts to avoid a continuous rise in carbon emissions. The situation is even more serious in Hebei Province, where carbon emissions from energy consumption continue to rise between 2010 and 2022, from 842.41 Mt to 923.98 Mt. This significant increase not only intensifies the carbon emission pressure on Hebei, but also highlights the huge challenges and potential that the province faces in terms of energy conservation and emission reduction. As an important part of the BTH region, the improvement of energy consumption and carbon emissions in Hebei Province has an important impact on the low-carbon development of the entire region.

3.2. Characteristics of Carbon Emissions from Energy Consumption in the BTH Region

3.2.1. Carbon Emission Characteristics of Energy Consumption in Beijing

The carbon emission characteristics of energy consumption within the BTH region, with a particular focus on Beijing, are illustrated in Figure 2.

From 2010 to 2022, Beijing’s coal consumption exhibited a steep decline, decreasing annually from 26.34 Mt in 2010 to 0.91 Mt in 2022. Concurrently, coal’s proportion in total energy consumption significantly dropped to below 5%. This trend underscores Beijing’s notable achievements in green and low-carbon transformation and clean energy substitution within its energy consumption structure. This pattern demonstrates Beijing’s proactive response to national environmental protection policies, promoting green economic transformation and high-quality development through industrial structure optimization, clean energy substitution, and enhanced energy utilization efficiency. Consequently, Beijing has effectively reduced its dependence on coal resources, aligning with the sustainable development trajectory of an international metropolis.

Similarly, Beijing’s coke consumption demonstrated a clear downward trend annually from 2010 to 2022. Coke, a traditional coal chemical product crucial for metallurgy, casting, fertilizer production, and other heavy industries, saw its consumption closely tied to the development of the iron and steel industry. In 2010, annual coke consumption accounted for 4.3% of Beijing’s energy consumption. However, following the suspension, relocation, and transformation of traditional heavy industrial enterprises, such as Shougang and coking plants, after 2011, coke consumption in Beijing drastically declined. By 2011 and 2012, annual consumption represented approximately 0.7% of energy use and nearly vanished from 2013 to 2022.

Regarding crude oil consumption, Beijing saw a decrease from 11.16 Mt in 2010 to 7.75 Mt in 2022, despite its proportion in energy consumption increasing from 21.8% to 40.8%. Notably, gasoline consumption exhibited an annual average growth rate of 11.59%, with its share in total energy consumption surging from 7.2% to 19.7%.

After 2008, diesel consumption in Beijing stabilized, with its growth rate slowing down. Given that nitrogen oxide (NOx) emissions from diesel vehicles account for over 50% of total motor vehicle emissions, controlling NOx emissions is pivotal for mitigating motor vehicle exhaust pollution, improving atmospheric quality, and addressing smog in Beijing. Consequently, Beijing has enforced a series of local regulations to strictly control air pollution from diesel vehicles. Since June 1, 2015, Beijing has adopted the National V emission standard for heavy-duty diesel vehicles, anticipated to reduce NOx emissions from individual vehicles by 40%. Due to these measures, the growth rate of diesel consumption in Beijing markedly slowed down, experiencing a 10.73% decrease in 2012.

Kerosene consumption in Beijing increased by 2.05 Mt from 2010 to 2019, with an average annual growth rate of 7.63%. However, from 2020 to 2022, kerosene consumption declined gradually. Kerosene’s proportion in the energy consumption structure increased annually, from 7.6% to 17.4%, reflecting Beijing’s booming air transport industry.

Conversely, fuel oil consumption in Beijing showed a yearly downward trend, decreasing from 666,900 tons in 2010 to 2000 tons in 2022. Its proportion in the energy structure declined from 1.3% in 2010 to 0.01% in 2022, indicating a reduction in both consumption and proportion. In contrast, natural gas consumption in Beijing has soared, growing from 7.45 billion cubic meters in 2010 to 19.79 billion cubic meters in 2022, with an average annual growth rate of 9.13%. Natural gas’s significance in energy consumption has increasingly become prominent, emerging as a vital component of Beijing’s energy mix.

3.2.2. Carbon Emission Characteristics of Energy Consumption in Tianjin

After 2010, China’s energy sector gradually exhibited a pattern characterized by “overall stable production, decelerating investment growth, fluctuating imports, and a moderation in consumption growth”, accompanied by a slowdown in the rate of energy consumption expansion. This trend can be attributed to various factors, including heightened awareness among energy-consuming industries regarding energy conservation and emission reduction, continuous optimization and adjustment of the energy structure, advancements in scientific and technological innovation, and market-oriented reforms in energy pricing. The trends observed in Tianjin serve as an illustrative example.

Currently, Tianjin’s primary energy consumption comprises coal and natural gas, with overall energy consumption demonstrating a fluctuating upward trajectory. Notably, coal consumption constitutes nearly 50% in the secondary industry, underscoring its pivotal role as an energy source. The rapid surge in crude oil consumption in Tianjin is primarily fueled by the rapid development of its oil and gas industry. This industry benefits from distinct geographical advantages, a robust industrial foundation, abundant scientific and technological human resources, as well as policy support stemming from the establishment of the Binhai New Area and the Beijing–Tianjin–Hebei coordinated development plan. Consequently, the oil and gas industry, along with the chemical industry, have emerged as cornerstone industries in Tianjin, driving a swift increase in crude oil consumption.

Regarding the various oil energy sources in Tianjin, consumption of diesel, gasoline, and kerosene has increased annually, albeit at a slower pace for diesel and gasoline. From 2010 to 2022, diesel consumption in Tianjin accounted for between 4.1% and 5.0% of total energy consumption. Gasoline consumption rose from 2.05 million tons in 2010 to 2.69 million tons in 2022, marking an average annual growth rate of 2.13%. Kerosene consumption is growing at an average annual rate of 9.13%, though it still constitutes a minor proportion of total energy consumption, accounting for less than 1%. Coke consumption initially increased but subsequently declined, with its share of total energy consumption ranging between 8.5% and 12.5%. Meanwhile, fuel oil consumption in Tianjin exhibited a pronounced downward trend, with an average annual change rate of −8.7%. Natural gas consumption has shown a significant increase, surging from 2.29 billion cubic meters in 2010 to 12.75 billion cubic meters in 2022. However, in 2022, natural gas accounted for only 1.8% of total energy consumption in Tianjin, suggesting considerable room for improvement in promoting clean fuel usage.

3.2.3. Carbon Emission Characteristics of Energy Consumption in Hebei

Due to the severe air pollution in the Beijing–Tianjin–Hebei region, unprecedented large-scale energy conservation and emission reduction policies have been implemented to eliminate outdated production capacities. As a result, the growth rate of energy consumption in Hebei Province has significantly decreased, although the total volume remains high. Among the primary types of energy consumed in Hebei Province, the usage of coke, crude oil, gasoline, kerosene, fuel oil, and natural gas has shown an increasing trend. However, coal consumption still accounts for approximately 70% of the total energy consumption in Hebei Province. In terms of the proportion of coal in energy consumption, there are notable differences in the energy consumption structure between Hebei Province and Beijing and Tianjin. This discrepancy is primarily attributed to the heavy reliance on coal by Hebei’s developed steel industry. It is evident that Hebei Province needs to accelerate the transformation of its energy structure and gradually reduce its dependence on coal and other polluting energy sources.

4. System Dynamics Modeling and Scenario Analysis

4.1. Construction of a System Dynamics Model

The carbon emission system is a highly intricate dynamic framework encompassing critical domains such as society, economy, and environment. This study delves into the impact of industrial structure, energy structure, and environmental regulations on carbon emissions, conducting an in-depth analysis of the interplay between these factors and carbon emissions. Consequently, a flow chart delineating the dynamics of the carbon emission system has been constructed (Figure 3), which clearly illustrates the structural relationships influencing each factor’s effect on carbon emissions, thereby providing a robust theoretical foundation for model construction.

In developing this carbon emission system dynamics model, this study extensively gathered and meticulously analyzed relevant research findings on regional carbon emission drivers. It comprehensively examined the interactions between the energy, population, economic, and environmental subsystems and the carbon emission subsystem. Vensim 7.3.5 software was selected as the tool for constructing the dynamics model of the carbon emission system. The spatial boundary of the system was defined as the entire Beijing–Tianjin–Hebei region, treated as a closed system without considering the transfer of energy, population, or other elements within and outside the region. The time boundary was set from 2010 to 2030 with a one-year time step. Specifically, the period from 2010 to 2020 was utilized for historical validation to assess the model’s simulation accuracy, while the period from 2020 to 2030 was employed to simulate carbon emission reduction scenarios under the national “dual carbon” target by 2030, predicting the carbon peak and analyzing emission reduction pathways in depth.

Regarding model design, this study drew extensively on existing research to develop a carbon emission system dynamics model that encompasses the economic, energy, population, and environmental subsystems. These four subsystems are interconnected and constrained by each other, forming a complex causal feedback network. For instance, industrial development and economic growth in the economic subsystem can increase energy consumption, leading to higher carbon emissions. Conversely, environmental regulation policies in the environmental subsystem can influence energy consumption patterns and industrial structures, creating a closed-loop feedback mechanism. This intricate causal feedback relationship renders the carbon emission system highly dynamic and uncertain.

Based on theoretical research on regional carbon emission drivers, this study categorizes the carbon emission system into energy, population, economic, and environmental subsystems to construct the system dynamics model. Complex interaction and influence mechanisms exist among these four subsystems:

(1)

Economic Subsystem

The economic subsystem constitutes a fundamental component of China’s carbon emission system, encompassing variables such as economic scale and economic structure. In this study, gross domestic product (GDP) serves as the metric for measuring economic scale, while the distribution among primary, secondary, and tertiary industries is utilized to assess economic structure. On one hand, population provides the labor force necessary for economic development; on the other hand, it also drives the consumption of goods produced through these economic activities. Economic growth necessitates a stable energy supply and typically results in increased energy consumption. Consequently, key variables within the economic subsystem include GDP; GDP growth rate; proportions of primary industry, secondary industry, and tertiary industry; energy consumption across these sectors; total population; per capita GDP; and overall living standards.

(2)

Population Subsystem

The population subsystem represents a critical element of China’s carbon emission system. An appropriate population size is essential for fostering a low-carbon economy; conversely, an excessive or insufficient population can hinder its development. The size of the population is influenced by factors including the total number of individuals, birth rates, and death rates. Variables incorporated into the population subsystem consist of total population count, birth rate, death rate, number of births, and deaths recorded annually, along with GDP per capita and overall energy consumption metrics.

(3)

Energy Subsystem

In the energy subsystem, energy consumption is a key link linking economic activities and environmental protection, and its components (such as raw coal consumption, coke consumption, crude oil consumption, natural gas consumption, fuel oil consumption, gasoline consumption, kerosene consumption, and diesel consumption) play a crucial role in the model. Eight kinds of energy consumption together constitute the core part of the energy system in the system dynamics model, and their changes not only reflect the activity of economic activities and the changing trend of energy demand, but also reflect the urgency of energy structure adjustment and environmental protection.

(4)

Environment Subsystem

Since this paper examines China’s carbon emission system, the environmental subsystem here only considers the environmental problems caused by carbon emissions. The large amount of energy consumption caused by China’s rapid economic development and the improvement of residents’ living standards is the main reason for the growth of carbon emissions and environmental damage. The adjustment of economic structure and the transformation of energy structure prevent the rapid growth of carbon emissions to a certain extent, thus protecting the environment. Environmental regulation refers to a series of policies, regulations, and standards formulated and implemented by the government to protect the environment and reduce pollution. In the system dynamics model, the environmental regulation variable is an important link connecting the environmental subsystem with the economic, energy, and population subsystems, and its change directly affects the operation state and interrelation of each subsystem. Environmental regulation variables play a crucial role in the system dynamics model. It not only directly affects the operating state of the environmental subsystem, but also has a profound impact on the economic subsystem through the cost effect and the innovation compensation effect. At the same time, environmental regulation also promotes the improvement of energy efficiency and the optimization of energy structure, as well as the change in population employment and income. Therefore, when formulating and implementing environmental regulation policies, it is necessary to fully consider their impacts and interactions on various subsystems to achieve the coordinated development of economy, energy, environment, and population. This comprehensive analytical perspective helps to provide scientific basis for government decision-making and promote the sustainable development of society.

4.2. System Parameterization and Data Sources

The state variables incorporated within the model encompass permanent population, GDP, and total carbon emissions, while the rate variables encompass birth rates, death rates, and GDP changes. Additionally, there are 35 auxiliary variables encompassing the proportion and energy consumption of the primary and secondary industries, environmental regulation per capita GDP, and carbon emissions stemming from energy consumption. The function description between the main parameters of the model is shown in

Table 2. Description of the functions between the main parameters of the model.

Key Variables	Main Parameter Setting of Beijing
Total GDP	INTEG (Change in GDP, 14,964)/CNY 100 million
Total population	INTEG (births—deaths, 1961.9)/10,000 persons
Birth rate	With Look Up ((2010, 0)–(2024, 0.01), (2010, 0.0073), (2011, 0.0083), (2012, 0.0090), (2013, 0.0089), (2014, 0.0097), (2015, 0.0079), (2016, 0.0092), (2017, 0.0089), (2018, 0.0081), (2019, 0.0079), (2020, 0.0069), (2021, 0.0064), (2022, 0.0057), (2023, 0.0047), (2025, 0.0045), (2030, 0.0043), (2040, 0.0040))
Death rate	With Look Up ((2010, 0)–(2024, 0.01), (2010, 0.0043), (2011, 0.0043), (2012, 0.0045), (2013, 0.0049), (2014, 0.0049), (2015, 0.0052), (2016, 00052), (2017, 0.0052), (2018, 0.0055), (2019, 0.0054), (2020, 0.0046), (2021, 0.0054), (2022, 0.0057), (2023, 0.0070), (2025, 0.0072), (2030, 0.0074), (2040, 0.0075))
Primary industry output	Share of primary sector × GDP/CNY 100 million
Secondary sector output	Share of secondary sector × GDP/CNY 100 million
Tertiary output	Share of tertiary sector × GDP/CNY 100 million
Primary energy consumption	EXP function based on primary industry output
Secondary energy consumption	EXP function based on secondary industry output
Tertiary energy consumption	EXP function based on tertiary sector output
Domestic energy consumption	EXP function based on GDP per capita
Total energy consumption	Sum of all energy consumption categories
Key Variables	Main Parameter Setting of Tianjin
Total GDP	INTEG (Change in GDP, 6830.76)/CNY 100 million
Total population	INTEG (births—deaths, 1299.29)/10,000 persons
Birth rate	With Look Up ((2010, 0)–(2024, 0.01), (2010, 0.0089), (2011, 0.0097), (2012, 0.0079), (2013, 0.0093), (2014, 0.0091), (2015, 0.0082), (2016, 0.0081), (2017, 0.0076), (2018, 0.0067), (2019, 0.0067), (2020, 0.0060), (2021, 0.0052), (2022, 0.0048), (2023, 0.0047), (2025, 0.0045), (2030, 0.0043), (2040, 0.0040))
Death rate	With Look Up ((2010, 0)–(2024, 0.01), (2010, 0.0049), (2011, 0.0049), (2012, 0.0049), (2013, 0.0052), (2014, 0.0053), (2015, 0.0056), (2016, 0.0055), (2017, 0.0046), (2018, 0.0054), (2019, 0.0053), (2020, 0.0059), (2021, 0.0062), (2022, 0.0064), (2023, 0.0070), (2025, 0.0072), (2030, 0.0074), (2040, 0.0075))
Primary industry output	Share of primary sector × GDP/CNY 100 million
Secondary sector output	Share of secondary sector × GDP/CNY 100 million
Tertiary output	Share of tertiary sector × GDP/CNY 100 million
Primary energy consumption	EXP function based on primary industry output
Secondary energy consumption	EXP function based on secondary industry output
Tertiary energy consumption	EXP function based on tertiary sector output
Domestic energy consumption	EXP function based on GDP per capita
Total energy consumption	Sum of all energy consumption categories
Key Variables	Main Parameter Setting of Hebei
Total GDP	INTEG (Change in GDP, 18,003.6)/ CNY 100 million
Total population	INTEG (births—deaths, 1961.9)/10,000 persons
Birth rate	With Look Up ((2010, −0.0005)–(2024, 0.02), (2010, 0.0132), (2011, 0.0130), (2012, 0.0128), (2013, 0.0130), (2014, 0.0132), (2015, 0.0114), (2016, 00114), (2017, 0.0124), (2018, 0.0112), (2019, 0.0108), (2020, 0.0104), (2021, 0.0102), (2022, 0.0098), (2023, 0.0091), (2025, 0.0080), (2030, 0.0075), (2040, 0.0070))
Death rate	With Look Up ((2010, 0.005)–(2024, 0.01), (2010, 0.0064), (2011, 0.0062), (2012, 0.0064), (2013, 0.0069), (2014, 0.0062), (2015, 0.0058), (2016, 00064), (2017, 0.0066), (2018, 0.0064), (2019, 0.0061), (2020, 0.0072), (2021, 0.0076), (2022, 0.0078), (2023, 0.0079), (2025, 0.0080), (2030, 0.0084), (2040, 0.0093))
Primary industry output	Share of primary sector × GDP/CNY 100 million
Secondary sector output	Share of secondary sector × GDP/CNY 100 million
Tertiary output	Share of tertiary sector × GDP/CNY 100 million
Primary energy consumption	EXP function based on primary industry output
Secondary energy consumption	EXP function based on secondary industry output
Tertiary energy consumption	EXP function based on tertiary sector output
Domestic energy consumption	EXP function based on GDP per capita
Total energy consumption	Sum of all energy consumption categories

.

The historical data required for parameterizing the carbon emission system in the BTH region were primarily sourced from the statistical yearbooks of the respective provinces over several years. Specifically, the pertinent variables for the economic and population subsystems within the model are derived from the Statistical Yearbook of Hebei Province, Tianjin Statistical Yearbook, and Beijing Statistical Yearbook. Conversely, the relevant variables for the environmental and energy subsystems are obtained from the China Energy Statistical Yearbook. The parameters within the model are typically assigned using methods such as constant values, average values, and regression analysis. The system’s parameters are established through the following approaches:

(1)

Table Function Method: For variables that undergo non-linear changes, table functions provide a more precise description of parameter variations. Examples include the proportion of tertiary industries and the configuration of environmental regulation variables.

(2)

Literature Reference Method: The carbon emission coefficients for various energy sources are determined by consulting pertinent literature and referencing the China Energy Statistical Yearbook.

4.3. Evaluating Key Influcing Factors Based on Sensitivity Analysis

Sensitivity analysis involves altering the parameter value of a specific factor to observe the changes in model outcomes, while examining the temporal dynamics of other variables under the condition that all other factors remain constant. The primary factors influencing carbon emissions within this system encompass the intensity of environmental supervision, the proportion of tertiary industry, and energy structure. To systematically analyze and compare the impacts of these influencing factors on carbon emissions, sensitivity analysis was conducted by varying each factor independently at a consistent rate (±10%) [30], while holding other variables constant. This approach allows for the investigation of the impact of changes in each factor on carbon emissions, thereby identifying the most sensitive factors affecting energy consumption-related carbon emissions. The scenarios and key variable settings for the sensitivity analysis are shown in

Table 3. Scenarios and key variable settings of sensitivity analysis.

Scenario	Description
Base	-
S1	Environmental regulation + 10%
S2	Proportion of tertiary industry + 10%
S3	Proportion of energy structure + 10%

.

As illustrated in Figure 4, As illustrated in Figure 4, the selected variables exert varying degrees of influence on total carbon emissions. For Beijing, the intensity of environmental regulation, adjustments in energy structure, and optimization of industrial structure all have a discernible impact on carbon emissions. In Tianjin, while the intensity of environmental regulation also plays a role, it is the adjustment of energy structure and optimization of industrial structure that have a more pronounced effect on carbon emissions. In Hebei Province, the impact of industrial structure adjustment and environmental regulation on carbon emissions is relatively limited, whereas the adjustment of energy structure has a more significant influence.

4.4. Validity Testing

After constructing the carbon emission model for the BTH region, Vensim software was utilized to test the model’s validity for Beijing, Tianjin, and Hebei separately, with the simulation interval set from 2010 to 2020.

Table 4. Validity test of system dynamics model in Beijing.

Year	GDP			Carbon Emissions from Energy Consumption
	True Value /CNY 1 Billion	Simulated Value /CNY 1 Billion	Relative Error/%	True Value/Mt	Simulated Value/Mt	Relative Error/%
2010	1496.4	1496.4	0	142.33	140.64	−1.2
2011	1718.9	1701.4	−1.0	132.46	131.89	−0.4
2012	1902.5	1920.8	1.1	134.49	134.24	−0.2
2013	2113.5	2105.2	0.3	122.07	123.75	1.4
2014	2292.6	2313.7	0.9	126.18	121.93	−0.03
2015	2477.9	2494.1	0.6	122.12	122.95	0.6
2016	2704.1	2681.2	−0.8	115.43	117.14	1.5
2017	2988.3	2905.6	−2.8	113.17	117.19	3.4
2018	3310.6	3182.0	−4.0	116.25	117.39	0.9
2019	3544.5	3490.6	−1.5	115.36	111.02	−3.9
2020	3594.3	3720.6	3.4	98.31	98.61	0.3

,

Table 5. Validity test of system dynamics model in Tianjin.

Year	GDP			Carbon Emissions from Energy Consumption
	True Value /CNY 1 Billion	Simulated Value /CNY 1 Billion	Relative Error/%	True Value/Mt	Simulated Value/Mt	Relative Error/%
2010	683.1	683.1	0	190.45	188.76	−0.9
2011	811.2	795.1	−2.0	208.94	191.53	−9.09
2012	9.4.3	919.9	1.7	210.68	199.83	−5.4
2013	994.5	1013.7	1.9	216.98	203.96	−6.3
2014	1064.0	1106.0	3.7	209.44	198.56	−5.4
2015	1087.9	1177.9	7.6	206.40	202.15	−2.1
2016	1147.7	1203.8	4.7	195.00	200.01	2.5
2017	1245.0	1266.5	1.7	193.09	208.33	7.3
2018	1336.2	1365.5	2.1	200.19	203.17	1.5
2019	1405.5	1458.4	3.6	201.76	205.85	1.9
2020	1500.7	1529.9	1.9	193.92	207.63	6.6

and

Table 6. Validity test of system dynamics model in Hebei.

Year	GDP			Carbon Emissions from Energy Consumption
	True Value /CNY 1 Billion	Simulated Value /CNY 1 Billion	Relative Error/%	True Value/Mt	Simulated Value/Mt	Relative Error/%
2010	1800.4	1800.4	0	846.41	827.62	−2.3
2011	2138.4	2068.1	−3.4	957.21	936.42	−2.2
2012	2307.7	2395.0	3.6	970.58	956.01	−1.5
2013	2425.9	2570.8	5.6	971.64	965.71	−0.6
2014	2520.8	2696.0	6.5	924.02	933.72	1.0
2015	2639.8	2797.4	5.6	964.78	969.09	0.4
2016	2847.4	2923.6	2.6	966.38	965.38	−0.1
2017	3064.0	3136.7	2.3	960.71	972.48	1.2
2018	3249.4	3358.4	3.2	985.13	977.85	−0.7
2019	3497.8	3549.9	1.5	986.36	994.92	0.8
2020	3601.3	3801.9	5.3	973.98	996.65	2.3

present the real values, simulated values, and error rates for the BTH region during 2010–2020, including regional GDP and carbon emissions from energy consumption. GDP is a crucial indicator for measuring the overall economic output of a region, reflecting both the scale and growth rate of its economy. Energy consumption and carbon emissions are often closely related to the level of economic development. The total carbon emissions from energy consumption serve as a key metric for assessing a region’s energy use and environmental impact. In research focused on pathways for reducing carbon emissions from energy consumption, the accuracy of predicting total carbon emissions is paramount. Therefore, in the effectiveness assessment, GDP and total carbon emissions from energy consumption are selected as subjects for evaluation. By comparing the actual values of these two indicators with their predicted values generated by the model, this study aims to validate the rationality and accuracy of the model’s predictions.

It can be seen from the following table that the error rate between the real value and the simulated value is within 10%, passing the validity test. This indicates that the optimized carbon emission model for the BTH region can accurately reflect the energy consumption and carbon emission situations in the region, suitable for predicting future carbon emissions.

4.5. Scenario Setting and Scenario Analysis

4.5.1. Scenario Setting and Parameter Setting

After validating the carbon emission system dynamics model for the BTH region, actual data from 2020 were used as initial variable values, and the shares of the tertiary industry in GDP, energy structure, and environmental regulation were selected as regulating variables. The reasons for selecting them as control variables are as follows:

1.

Proportion of the Tertiary Industry in GDP

The proportion of the tertiary industry within GDP serves as a crucial indicator for assessing the degree of industrial structure optimization in a given region. A higher share of the tertiary sector typically signifies a more rational economic structure, enhanced energy efficiency, and comparatively lower carbon emission intensity. Research indicates that optimizing industrial structures significantly influences carbon emissions; specifically, an increased proportion of the tertiary industry can effectively diminish carbon emission intensity. Consequently, utilizing the proportion of the tertiary industry in GDP as a regulatory variable allows for an improved simulation of how industrial restructuring impacts carbon emission reduction.

2.

Energy Structure

Energy structure constitutes one of the key determinants influencing carbon emissions. Optimizing this structure by elevating the share of clean energy while reducing reliance on fossil fuels is essential for achieving reductions in carbon emissions. Studies demonstrate that adjustments to energy structures have substantial effects on overall carbon emissions; notably, an increase in clean energy’s contribution can lead to significant decreases in total carbon output. Therefore, considering energy structure as a regulatory variable enhances our ability to model how changes in consumption patterns affect efforts toward reducing carbon emissions.

3.

Environmental Regulation

The enforcement of environmental regulation policies exerts direct constraints on both enterprise energy consumption behaviors and associated carbon emissions. Stringent environmental regulations encourage businesses to adopt more efficient technologies for energy utilization and implement effective measures for emission reduction, thereby contributing to lower levels of carbon output. Evidence suggests that heightened intensity in environmental regulation correlates with marked reductions in carbon emissions. Thus, incorporating environmental regulation as a regulatory variable facilitates better modeling outcomes regarding its impact on mitigating greenhouse gas outputs.

These regulating variables were combined according to their actual meanings to establish five carbon emission scenarios, as shown in

Table 7. Carbon emission scenario setting in the BTH region.

Scenario	Tertiary Sector Share	Energy Structure	Environmental Regulation
Baseline (A1)	Center	Center	Center
Industrial Structure Optimization (A2)	High	Center	Center
Energy Mix Optimization (A3)	Center	Low	Center
Environmental Protection (A4)	Center	Center	High
Coordinated Development (A5)	High	Low	High

, with parameter settings for variables at low, medium, and high rates.

Base Scenario (A1): This scenario projects the future trajectory of carbon emissions in the BTH region under the continuation of current systematic behaviors. It maintains the existing economic development model and selects the median values for each regulatory variable and its rate of change.

Industrial Structure Optimization Scenario (A2): In this scenario, the growth rate of the tertiary industry’s share is set to a high value, while other regulatory variables and their rates of change are kept at median levels. By optimizing the industrial structure, this scenario aims to investigate the impact of such optimization on future carbon emissions within the framework of the current economic development model. Compared with the base scenario, there is an increased proportion of the tertiary industry across all regions.

Energy Conservation and Emission Reduction Scenario (A3): This scenario selects a low value for the energy structure, indicating an accelerated decline in the reliance on traditional energy sources, while other regulatory variables and their rates of change are set to median values. The objective is to examine the effects of intensified energy policies, active implementation of conservation and emission reduction measures, and reduced coal consumption on future carbon emissions within the existing development trajectory of the Beijing–Tianjin–Hebei region. Relative to the base scenario, this scenario shows a downward trend in the region’s energy structure.

Environmental Regulation Scenario (A4): This scenario explores the influence of enhanced environmental regulations and increased investment in industrial pollution control on future carbon emissions in the Beijing–Tianjin–Hebei region under the current economic development model. It sets the environmental regulation variable to a high value while maintaining other regulatory variables and their rates of change at median levels.

Coordinated Development Scenario (A5): This scenario sets high values for the proportion of the tertiary industry and environmental regulation, and a low value for the energy structure. It aims to analyze the future carbon emission trends under a multi-dimensional comprehensive optimization strategy that includes industrial structure, energy consumption, and environmental regulation, thereby evaluating the carbon emission reduction effects of regional coordinated development.

Calculating the average rate of change based on data from the past five years (2015–2020) to determine the medium rate. Specific parameter settings are shown in

Table 8. Parameterization of carbon emission scenarios in the BTH region.

Scenario	Tertiary Sector Share Rate of Change	Energy Mix Rate of Change	Environmental Regulation
Baseline (A1)	0/0.2/0.5	−4.0/−4.5/1.5	0.040/0.075/0.150
Industrial Structure Optimization (A2)	1.2/1.2/1.5	−4.0/−4.5/1.5	0.040/0.075/0.150
Energy Mix Optimization (A3)	0/0.2/0.5	−6.0/5.0/4.3	0.040/0.075/0.150
Environmental Protection (A4)	1.2/1.2/1.5	−4.0/−4.5/1.5	0.050/0.100/0.200
Coordinated Development (A5)	1.2/1.2/1.5	−6.0/5.0/4.3	0.050/0.100/0.200

.

4.5.2. Forecast Results and Analysis

1.

Baseline Scenario

By integrating historical carbon emission data for Beijing with the outcomes of model simulations, Figure 5 reveals a notable achievement in Beijing’s carbon emission journey. The city successfully reached its carbon peak in 2010, with a peak of 142.33 Mt; the findings consistently align with the results reported in other studies [3].The data presented indicate that Beijing has achieved a preliminary equilibrium between economic development and environmental conservation. Under the baseline scenario, Beijing’s energy consumption-related carbon emissions are projected to be 60.76 million tons in 2030. The system simulation results indicate that Tianjin is anticipated to reach its carbon peak at 193.77 Mt in 2022, aligning with the relevant provisions outlined in the Tianjin Carbon Peaking Implementation Plan. In contrast, Hebei Province, following its existing development trajectory, exhibits a continuous upward trend in carbon emissions, with no signs of peaking before 2035. Given this persistent growth, it is prudent to extend the Peak Carbon Forecast period for Hebei Province to 2035, allowing for a more comprehensive review and assessment of its carbon emission situation over an extended timeframe. Upon analyzing this extended forecast, the model projections reveal that, even under the baseline scenario, Hebei Province’s total carbon emissions in 2035 remain unchecked and do not reach a peak.

Beijing achieved its carbon peak at an earlier stage, primarily due to the optimization of its economic structure and stringent enforcement of environmental protection policies. While Tianjin has also reached its carbon peak, the peak value is comparatively higher, resulting in significant future emission reduction pressures. As a major industrial province, Hebei’s heavy industry-dominated economic structure and irrational energy consumption patterns have led to a continuous increase in carbon emissions. Consequently, Hebei Province must intensify efforts to adjust its industrial structure and optimize its energy consumption structure to meet its carbon emission reduction targets.

2.

Industrial Structure Optimization Scenario

Amidst the global focus on environmental protection and sustainable development, Beijing is actively pursuing green and low-carbon development and accelerating the optimization and upgrading of its industrial structure. It is anticipated that the tertiary industry will account for 92.6% of the economy by 2030, with energy-related carbon emissions projected at 47.31 million tons. Under this industrial structure optimization scenario, Tianjin is expected to reach its carbon peak in 2022, at 212.47 million tons, and decrease to 192.83 million tons in 2030. Hebei, still in the midst of industrialization with a high proportion of the secondary industry, would benefit significantly from increasing the share of the tertiary industry in reducing carbon emissions. However, even under this scenario, Hebei is not expected to reach its carbon peak by 2030, with emissions forecasted at 1370.13 million tons, representing a 1.4% reduction compared to the baseline scenario.

Industrial structure optimization represents one of the most effective strategies for reducing carbon emissions. By vigorously promoting the development of the tertiary sector, Beijing has successfully decreased the share of high-energy-consuming and high-emission industries, thereby achieving a significant reduction in carbon emissions. Although Tianjin has also implemented industrial restructuring, its peak carbon emissions have risen due to its robust industrial foundation and relatively slower growth in the proportion of the tertiary sector. While the increase in the proportion of the tertiary sector in Hebei Province has contributed to some extent to carbon reduction, the province’s heavy industrial structure and irrational energy consumption pattern have limited the effectiveness of these efforts.

3.

Energy Structure Optimization Scenario

During the 13th Five-Year Plan period, Beijing underwent substantial energy structure adjustments, resulting in a significant decrease in coal consumption. The proportion of coal consumption has decreased from 13.1% to 1.5%. Based on this trend, Beijing’s energy-related carbon emissions are forecasted to be 59.68 million tons in 2030. In the context of energy structure optimization, Tianjin’s energy-related carbon emissions in 2030 are projected to be 191.16 million tons, a 1.5% reduction compared to the baseline scenario. Similarly, Hebei Province’s energy-related carbon emissions in 2035 are expected to be 138.15 million tons, marking an 11.3% decrease from the baseline scenario.

In the context of energy structure optimization, Beijing has realized a significant decrement in carbon emissions by diminishing the proportion of coal consumption and augmenting the utilization of clean energy. Although Tianjin has also undergone energy structure adjustments, the carbon reduction effect is not pronounced due to its relatively monolithic energy consumption structure and the slow pace of clean energy substitution. Meanwhile, Hebei Province, with an energy consumption framework heavily reliant on coal and other fossil fuels, exhibits a comparatively favorable outcome in terms of carbon reduction through energy structural adjustments; however, further enhancement in the transition towards clean energy is imperative.

4.

Environmental Protection Scenario

Under the environmental protection scenario, Beijing demonstrates the most significant carbon emission reduction effect, with energy-related carbon emissions projected at 47.029 million tons in 2030, a 29.2% reduction compared to the baseline scenario. During the prediction period, the average cumulative carbon emissions of Tianjin and Hebei are anticipated to decrease by 0.12% and 0.15%, respectively, compared to the baseline scenario. The industrial structures of Tianjin and Hebei still have considerable room for optimization, and urgent improvements in energy consumption practices are necessary. While investments in environmental protection and governance may be relatively modest compared to other measures in terms of carbon emission reduction, prioritizing prevention over cure is crucial.

The findings indicate that while environmental regulations can improve environmental quality, their direct effect on carbon emission reduction in Tianjin and Hebei Province is limited. Consequently, the Beijing–Tianjin–Hebei region should focus more on fundamental measures such as optimizing industrial structures and adjusting energy structures in its efforts to reduce carbon emissions. At the same time, it is necessary to avert the scenario of “polluting first and then cleaning up later”, and to foster a positive cycle of economic growth and environmental protection.

5.

Coordinated Development Scenario

Considering the concurrent adjustments in industrial and energy structures, the carbon emission system of the BTH region undergo optimization and regulation. Under the coordinated development scenario, Beijing’s energy-related carbon emissions in 2030 are projected to be 47.2376 million tons, with a 15.3% reduction in average cumulative emissions from 2020 to 2030 compared to the baseline scenario. Tianjin is expected to reach its carbon peak in 2022, at 206.638 million tons, and its energy-related carbon emissions in 2030 are forecasted at 175.350 million tons, a 10.8% decrease from the baseline scenario. This coordinated development scenario is particularly conducive to Hebei achieving its carbon peaking target, with energy-related carbon emissions expected to peak in 2025 at 888.359 million tons.

The synergetic development strategy represents a pivotal approach for the Beijing–Tianjin–Hebei region to attain its objectives for carbon emission reduction. By adopting a holistic perspective that encompasses the optimization and enhancement of the industrial framework, as well as the shift towards a more sustainable energy infrastructure, the region is poised to execute a cohesive optimization and governance of its carbon emissions network. Within the paradigm of synergetic development, both Beijing and Tianjin have witnessed a marked decrement in carbon emissions, while Hebei Province is well positioned to accomplish the milestone of carbon emission peaking within a notably concise timeframe. This trend unequivocally illustrates that regional synergetic progression serves as an efficacious methodology for the proficient management and mitigation of carbon emissions.

5. Optimization Path and Response Strategy for Carbon Emission Reduction in the BTH Region

5.1. Path Selection

In the baseline scenario, Beijing has already attained its carbon peak in 2010. When considering single-variable regulation, the effectiveness of carbon emission reduction measures in Beijing decreases in the following order: Environmental regulation, industrial structure adjustment, and energy structure optimization. Under the coordinated development scenario, Beijing exhibits the most significant carbon emission reduction, with a 6.5% decrease in energy-related carbon emissions by 2030 compared to the baseline scenario. Dominated by natural gas and reliant on external power transfer, Beijing’s energy sector offers limited potential for carbon emission reduction. However, the advanced nature of its industrial structure serves as an effective inhibitor of carbon emissions, which is consistent with other research results [33,34]. Thus, alongside optimizing the industrial structure, a strategic increase in investment in industrial pollution control could further enhance carbon emission reduction. Simulation results highlight environmental regulation as the most impactful single measure for carbon emission reduction in Beijing. The findings of pertinent studies affirm that a measured increase in investment in industrial pollution control is beneficial for advancing carbon emission reduction efforts in Beijing.

Tianjin, under the baseline scenario, is projected to reach its carbon peak in 2022, with emissions peaking at 210.79 million tons. In the single-variable control, the effectiveness of carbon emission reduction measures decreases as follows: Energy structure optimization, industrial structure adjustment, and environmental regulation. Under the coordinated development scenario, Tianjin is anticipated to achieve its carbon peak a year earlier, in 2021, with emissions topping at 206.86 million tons. Given Tianjin’s heavy reliance on coal-based industries, its carbon emission reduction potential lies primarily in optimizing the energy structure by reducing dependence on traditional energy sources and intensifying the development and utilization of clean energy. Numerous subsequent investigations have similarly highlighted the criticality of diminishing reliance on conventional energy sources [35,36,37].

Hebei faces a longer journey to achieve its carbon peak. Under the current economic development mode, reaching the carbon peak before 2035 remains unfeasible. In the single-variable regulation, the effectiveness of carbon emission reduction measures in Hebei decreases as follows: Energy structure optimization, industrial structure adjustment, and environmental regulation—which is consistent with the findings of another study [37]. The coordinated development scenario presents the most favorable conditions for Hebei to reach its carbon peak by 2030, with emissions peaking at 121.804 million tons. In light of the findings from model simulations and pertinent scholarly inquiries [38], Hebei Province is placing particular emphasis on enhancing environmental governance, optimizing industrial synergy, and improving energy synchronization. It is imperative for the province to employ a variety of regulatory strategies and integrate an assortment of measures to facilitate the efficacious mitigation of carbon emissions.

5.2. Countermeasures for Implementation

1. In compliance with the extant economic development legislation, it is ascertainable that environmental regulation plays a pivotal role in fostering carbon emission mitigation within the municipal boundaries of Beijing. Consequently, it is imperative for Beijing to prioritize the enhancement of environmental regulatory frameworks and thoroughly deliberate upon the practicality thereof. More specifically, it is requisite to intensify the endeavors directed towards industrial pollution mitigation, while concurrently assimilating exemplary practices from domestic and international jurisdictions to iteratively refine pertinent policies and statutory instruments. To guarantee the harmonization and unity of policy initiatives, thereby circumventing potential conflicts and discrepancies, it is advisable to implement more rigorous mandatory policy instruments and to bolster the legal foundation for preemptive control. Moreover, in the ongoing process of optimizing and enhancing incentive-based policy instruments, including the environmental protection tax, pollutant discharge permits, and the carbon emission trading market, it is essential to take into account the pragmatic considerations of policy enforcement, such as corporate affordability and regulatory enforcement capabilities, to facilitate the seamless execution and efficacy of such policies.

2. In light of Tianjin’s developmental trajectory, it is imperative to enhance energy efficiency and foster green, low-carbon industries. When advancing the construction of natural gas pipeline infrastructure, such as the double line of the Tianjin LNG external transmission pipeline within the national network, it is critical to fully consider capital investment, technical support, and the coordination capabilities of local governments. Concurrently, during the expansion of photovoltaic power generation and the promotion of wind power projects, it is essential to assess factors such as land resource availability, project economics, and grid connection capacity to ensure the projects’ successful and profitable execution. Moreover, in an endeavor to reinforce wind power technology research and development and equipment manufacturing, a focus on the integration of technological breakthroughs with market demand is crucial to enhance the economic viability and reliability of wind power initiatives.

3. Given the prevailing economic development paradigm, Hebei Province must adopt a holistic approach to deploying a diverse array of regulatory measures aimed at curbing carbon emissions. On the one hand, it is essential to foster the high-quality advancement of the industrial economy by optimizing the internal composition of the manufacturing sector and elevating the tier of industrial low-carbon practices. In pursuit of GDP growth objectives and an augmented share of value-added manufacturing, it is critical to remain vigilant about the practicality of policies and to steer clear of an over-dependence on sectors with substantial energy consumption footprints. For instance, over the course of the 14th Five-Year Plan period, Shijiazhuang and other cities aim to achieve the following objectives: Achieving a regional GDP of CNY 1 trillion, increasing the proportion of value-added manufacturing to 30%, reducing industrial value-added energy consumption per unit by 15 percentage points, and cutting carbon dioxide emissions per unit of industrial value-added by 19 percentage points. To accomplish these goals, they will accelerate the growth of the proportion of investment in the service sector, strengthen industry access and standardize management in high-energy-consuming industries, and further dissolve excess capacity. The dichotomy between regional economic expansion and the “dual control of carbon emissions” objective can only be effectively addressed by emphasizing the development of five hundred billion industrial clusters and guiding growth through the high-end, intelligent, and eco-friendly progression of these clusters.

On the other hand, as Hebei Province vigorously fosters the development of renewable energies such as wind and solar power, it is incumbent to consider the stability of the power grid, the construction of energy storage facilities, and renewable energy subsidy policies to guarantee the consistent expansion of renewable energy generation.

4. The Beijing–Tianjin–Hebei region ought to meticulously consider the pragmatic circumstances of regional economic integration and industrial transformation when endeavoring to mitigate carbon emissions through collaborative efforts. On the one hand, it is imperative to enhance regional economic integration, elevate the industrialization quotient in less-developed areas, raise the entry threshold for industries, and phase out obsolete production capacities. Guided by governmental directives, the acceleration of industrialization via corporate innovation should underscore the harmonious fusion of policy direction and market dynamics. On the other hand, in the joint crafting of medium- to long-term strategies for low-carbon progression, it is essential to delineate distinct roles and developmental objectives, with a focus on the synergistic interplay and practicality of policies. In the pursuit of energy infrastructure interconnectivity, there should be an emphasis on the collaborative development and sharing of electricity, oil and gas, and renewable energy infrastructure, while taking into account the equilibrium between regional energy supply and demand, energy security, and environmental stewardship. To guarantee the viability of the aforementioned strategic recommendations and the efficacy of policy enforcement, it is proposed that the Beijing–Tianjin–Hebei governments enhance communication and synergy, establish an enduring collaborative framework, and jointly advance the attainment of carbon emission reduction objectives.

6. Discussion

6.1. Uncertainty Analysis

Due to the potential inaccuracies or variations in emission factors at the data aggregation phase, the computed outcomes may deviate from the actual carbon emissions figures. For example, in the course of examining carbon emissions resultant from energy utilization, the present investigation primarily focused on eight principal energy resources, encompassing raw coal, coke, crude oil, natural gas, fuel oil, gasoline, kerosene, and diesel. The analysis failed to incorporate the carbon emissions occurring during the processes of power conversion and transmission. Despite the power and heat sectors being major contributors to greenhouse gas emissions, the quantification of their emission factors is challenging due to the absence of a standardized reference framework for inter-regional power emission accounting, as well as the divergent primary energy compositions employed in power generation. Consequently, the carbon emissions calculated within the scope of this study may underestimate the true emissions.

6.2. Limitation

In the initial development of the system dynamics model, our focus was on analyzing the direct correlation between traditional energy consumption and economic and environmental factors, as well as the potential impact of these variables on carbon emission reduction targets. Although clean energy plays an increasingly important role in carbon emission reduction, its impact on carbon emissions is typically long-term and gradual. Therefore, the effect of changes in clean energy utilization may not be as immediately significant in the short term as the direct impact of traditional energy consumption on carbon emissions. The share of clean energy utilization encompasses the aggregation and analysis of various energy types (such as solar, wind, and hydropower), and these data can exhibit significant variability and uncertainty across different regions and time periods. Therefore, future work will focus on conducting in-depth research in this area.