Selection and Optimization of China’s Energy Transformation Pathway Under Carbon-Neutral Targets

Abstract

This paper uses a bottom-up national energy technology model to study the optimization of China’s energy transformation pathway. The model clarifies specific action plans for China’s energy transformation pathway from 2020 to 2060, total carbon emissions, industry emission reduction responsibilities, and other dimensions. The results show that: (1) The proportion of renewable energy consumption in China’s entire energy system from 2020 to 2060 will gradually exceed that of fossil energy under ideal circumstances, and the energy system will transition from traditional fossil energy to renewable energy. Meanwhile, the proportion of low-carbon energy sources, such as renewable energy, in primary energy demand will jump from 15.9% in 2020 to over 80% by 2060. (2) China’s CO₂ emissions will be approximately 3 billion tons, 2 billion tons, and 1 billion tons under three different socio-economic development scenarios of low, medium, and high speed in 2060. At that time, China will still need to absorb CO₂ through carbon sinks in forests, oceans, and wetlands. (3) The electricity industry has the highest CO₂ emissions compared to other industries. The electricity industry must bear significant responsibility for carbon reduction in future energy transformation and economic development.

1. Introduction

Global climate change is one of human society’s most significant challenges and is increasingly attracting widespread attention from the international community. To address the adverse effects of climate change on the environment, nearly 200 countries passed the Paris Agreement in 2015, which clearly stated the goal of controlling global temperature rise within 2 °C by this century and striving to control it within 1.5 °C. To achieve the targets, countries must peak their greenhouse gas emissions as soon as possible and achieve carbon neutrality by the mid-20th century. Against the backdrop of the intensified greenhouse effect, tightening energy resources, and deteriorating environmental pollution, climate change and energy security issues have attracted widespread attention from all sectors of society [1]. On 22 September 2020, China proposed a dual carbon target strategy, incorporating carbon peaking and carbon neutrality into the overall layout of ecological civilization construction, injecting strong impetus into the global response to climate change. The vision of carbon neutrality by 2060 is a new target and task for China to achieve green transformation and development in the latest stage of development, and promoting energy transformation is an inevitable choice to accomplish the dual carbon target. In the long run, building a clean, low-carbon, safe, and efficient energy system is of utmost importance in promoting China’s achievement of the carbon-neutral target [2]. China’s potential total energy demand and carbon emissions are still increasing. China increasingly needs to pay attention to the proportion of renewable energy in daily production, storage, and consumption processes because energy transformation is an important lever to ensure energy supply and security, regulate energy structure and production methods, promote energy conservation and environmental protection, and is also a key means to promoting energy consumption and low-carbon construction under the carbon-neutral target.

Reducing the energy intensity per unit of economic output, reducing emissions per unit of energy use, and gradually achieving decarbonization of the energy system will be important means for China to achieve its national independent contribution under the carbon-neutral target. As a world factory and energy demander, China plays a positive role in formulating the carbon neutrality target in multiple fields such as energy, development, ecology, and people’s livelihoods. China’s wind, photovoltaic, solar, and other industries have developed rapidly in recent years. Renewable energy storage has become increasingly important in the early 21st century as it plays a crucial role in ensuring sustainable and clean energy intensity. However, due to unclear technological and energy transformation routes, the abandonment rate of wind and solar power remains high, indicating that China’s energy transformation still faces a series of difficulties and challenges. As the world’s second-largest economy, the largest exporter, and the largest energy consumer, China urgently needs to identify the transformation pathway of its energy economy system, comprehensively promote energy transformation and development, and scientifically evaluate the policy intervention required for energy transformation. China’s formulation of carbon neutrality goals is a major strategic decision and deployment to actively address climate change, break energy, and resource constraints, and demonstrate the responsibility of a major country. This paper is based on the complexity and long-term nature of China’s energy transformation, using a bottom-up national energy technology model to simulate the energy transformation path under different scenarios. It describes the main energy consumption and CO₂ emissions in China from 2020 to 2060, analyzes the energy decarbonization strategies of key industries, and answers the scientific and rational nature of the transformation path from coal to natural gas and renewable energy. This can provide theoretical analysis models and framework references for the research field of energy transformation paths.

The remainder of this paper is organized as follows. Section 2 reviews relevant literature on energy transition pathways. Section 3 constructs a national energy technology model based on complex system theory. Section 4 explains the equilibrium conditions of the theoretical model, raw data processing, and scenario settings. Section 5 conducts a numerical simulation analysis on the optimization of the energy transformation pathway. Finally, conclusions and policy implications are presented.

2. Literature Review

Climate change caused by CO₂ emissions has become one of humanity’s significant challenges, posing an unprecedented threat to human survival and social development. As a participant, contributor, and leader in global ecological civilization construction, China’s proposal of a carbon-neutral target is of great significance for the harmonious coexistence between humans and nature. Promoting energy transformation and developing renewable energy are essential ways to promote the green and low-carbon transformation of the social economy. Low-carbon energy transformation or increased consumption of renewable energy has always been a significant development strategy for countries [3,4]. So far, human socio-economic development has undergone three major energy transformations: the era of firewood as the main energy source, the era of coal as the primary energy source, and the era of renewable energy as the main energy source. In recent years, China has made breakthroughs in energy transformation and renewable energy development that have attracted widespread attention from all sectors.

Since China is a country with high environmental pollution, researchers have intensively studied China’s environmental problems using various environmental indicators such as carbon emissions and ecological footprint [5]. To provide clean and sustainable energy, optimize clean resources, and structurally transform energy intensity into environmentally friendly energy, the use of renewable energy has increased [6,7]. In recent years, China’s carbon emissions have shown a clear downward trend, and the most important influencing factor is mainly due to the decrease in energy intensity. Improving energy efficiency and developing renewable energy are the two most important factors in energy transformation [8,9]. Developing renewable energy and constructing low-carbon cities can help achieve the “win-win” goals of environmental pollution prevention and economic development [10,11]. Yuan et al. (2014) pointed out that China is experiencing rapid industrialization and urbanization. Shortly after entering the high-income group, China’s per capita energy consumption and per capita CO₂ emissions are expected to peak at 4 tons and 6.8 tons, respectively, between 2020 and 2030 [12]. Cai et al. (2018) studied the relationship between clean energy consumption, economic growth, and CO₂ emissions. They found no consistency between the real per capita GDP, clean energy consumption, and CO₂ emissions of Canada, France, Italy, the United States, and the United Kingdom [13]. Liu et al. (2019) studied the impact of coal-fired power generation in China on carbon emissions, and the results showed that if stricter control is implemented on coal production capacity, the utilization rate of renewable energy power plants will be significantly improved, and the peak of carbon emissions is likely to be achieved between 2020 and 2025 [14]. Zhang et al. (2021) pointed out that the impact of renewable energy investment on carbon emissions varies at different stages, and in the early stages, renewable energy investment can increase carbon emissions. In the mid-term, renewable energy investment begins to play a role in reducing emissions. In the later stages, investment in renewable energy may once again be associated with an increase in carbon emissions [15]. Zhang & Chen (2022) used the China TIMES model, which covers all sectors of the energy system, to evaluate the impact of uncertainty in carbon peak time and carbon neutrality time on transformation. They elaborated and compared the technological choices, transformation costs, and synergistic effects on local air pollutant control in the energy system’s decarbonization process under different scenarios of achieving carbon neutrality [16].

From a practical perspective, the carbon neutrality target involves two dimensions: Carbon emissions and carbon sequestration. Most countries have yet to achieve carbon neutrality; some countries have even sustained carbon emissions and have yet to reach their peak. This requires further improvement of relevant technologies for carbon removal and sequestration. Carbon fixation can also be divided into natural and anthropogenic, with natural fixation being the primary method. The existing infrastructure construction related to decarbonization and carbon sequestration technologies determines a country’s energy transformation mode and CO₂ emission level, as well as the extent to which a country can capture CO₂ emissions, namely CO₂ capture, utilization, and storage [17]. Scholars have pointed out that carbon capture and storage (CCS) is another option. Still, due to its high cost and limited potential carbon sink capacity, it can only provide partial solutions [18]. In the past decade, China has made significant progress in CCUS technology [19]. Qi et al. (2014) studied 11 low-carbon, zero-carbon, and negative-carbon technologies, including wind, solar, and biomass power generation. They quantitatively analyzed the impact of renewable energy development on China’s energy structure adjustment and carbon emissions. The study showed that the development of CCUS technology and carbon removal technology reduced the CO₂ emissions of the economy [20]. Some scholars also believe that China has not yet promulgated any specific laws to encourage the deployment of CCUS technology, so it is necessary to introduce legal and policy frameworks and implement market incentive measures, including CO₂ pricing and subsidy mechanisms aimed at solving the high capital and operating costs of large-scale projects, to promote the rapid transformation of fossil fuels such as coal to renewable energy [21]. Pommeret & Schubert (2022) pointed out that in the short to medium term, renewable energy cannot replace coal and other fossil fuels for power generation on a large scale [22].

On the one hand, the average cost of renewable energy is still higher than that of fossil fuels. On the other hand, renewable energy has unpredictable and intermittent characteristics due to its variability. Therefore, renewable energy development depends on the level of relevant energy utilization technology. With the rapid growth of low-carbon technology, the proportion of coal in the structure of power generation fuel will sharply decrease. Lv & Kang (2022) constructed a prediction model for carbon emissions and peak time in China’s industrial sector and four carbon-intensive industries based on the STIRPAT model. The study showed that carbon emissions could not peak by 2030 under the baseline scenario but could peak by 2030 under the low-carbon scenario [23]. Wang et al. (2023) pointed out that under a carbon-neutral target, upgrading the power system is crucial for accelerating the penetration of renewable energy in China. It is necessary to increase investment in photovoltaic and wind power generation to reduce the economic cost of achieving carbon neutrality, accelerate CCS transformation of existing power plants, upgrade the power system through building energy storage, expanding transmission capacity and adjusting demand side power loads to reduce the economic cost of deploying photovoltaic and wind power. As a result, the installed capacity of photovoltaic and wind power will reach 15PWh/year by 2060, and the average emission reduction cost per ton of CO₂ will be reduced from $97 to $6 [24]. Yang et al. (2024) conducted a comprehensive review of the latest research and reports on promoting energy transformation in China under the carbon neutrality goal using the Integrated Assessment Model (IAM) evaluation method, revealing the specific path of China’s energy transformation. The study helps policymakers understand the process of energy system transformation and provides diversified and multi-perspective decision-making solutions for the future transition to a net zero-emission energy system [25].

There has yet to be systematic research achievement in the academic community on the energy transformation pathway under the carbon neutrality target. Domestic and foreign scholars have provided theoretical and methodological guidance for this study on effective energy utilization, low-carbon energy transformation, and energy system development. However, there are also some imperfections in the existing research, specifically:

First, the research on China’s energy transformation pathway is a complex and systematic project that involves multiple disciplines, methods, and technologies. It requires formulating a scientific carbon reduction schedule and roadmap and handling social, economic, energy, and other issues. Therefore, this paper provides a new research perspective and analytical framework and combines the research on low-carbon energy transformation pathway with interdisciplinary knowledge and methods, revealing the theoretical mechanism of the impact of energy transformation on CO₂ emissions and analyzing the selection and optimization of energy transformation pathway under the dual carbon target, thus providing scientific theoretical guidance and empirical reference.

Second, in terms of pathway selection and optimization for energy system transformation, there is still room for further improvement in the theoretical model construction of energy transformation pathway research by domestic scholars, and there needs to be more theoretical models for energy transformation pathway research in China. Due to the inconsistency of research objectives, there may be differences in the theoretical models used. There is still a possibility for further deepening the relevant research on China’s energy transformation pathway and optimization models under the background of the dual carbon target in the existing literature. Therefore, this paper adopts a bottom-up national energy technology model, comprehensively considering factors such as China’s socio-economic development technology, and applies this model to relevant research on the selection and optimization of China’s energy transformation pathway. It fully considers China’s energy transformation pathway under different development scenarios and thus determines the specific pathway of China’s low-carbon energy transformation.

Third, due to the immaturity of China’s energy data statistics system, it is difficult to obtain the statistical data required for theoretical models related to energy transformation abroad, which makes it difficult for foreign theoretical models to predict China’s energy transformation pathway and CO₂ emissions. Therefore, based on the perspective of complex system theory, this paper adopts a bottom-up national energy technology model and applies this theoretical model to the optimization of the design of China’s energy transformation pathway. It scientifically predicts China’s future energy transformation pathway and carbon emissions, providing a scientific and effective theoretical modeling method and policy analysis tool for optimizing China’s energy transformation pathway.

Compared with existing research, the innovation of this paper is mainly reflected in three aspects. First, this study provides a new perspective for studying the path of energy transformation. Unlike existing literature, we are more interested in the specific energy consumption situation during the energy transition process, although reducing CO₂ emissions is the most important means and goal to achieve carbon neutrality. At the same time, the theoretical analysis framework we adopt can offer guidance and support for China’s transition toward net-zero energy systems, which will also provide some experience reference for other developing countries around the world. Second, from the perspective of complex system theory, we adopt the National Energy Technology Model (C³IAM/NET) developed by the Energy and Environmental Policy Research Center of the Beijing Institute of Technology and apply this model to the optimization of the design of China’s energy transformation path, rather than simply studying CO₂ emissions. The model provides an optimization pathway for China’s energy system to achieve the carbon neutrality target and depicts the scenario of reaching the emission peak before 2030 and achieving net zero CO₂ emissions in the energy system before 2060.The specific action plans for China’s energy transformation pathway, total carbon emissions, and industry emission reduction responsibilities from 2020 to 2060 have been clarified, providing effective modeling methods and analytical tools for optimizing China’s energy transformation pathway. Third, based on the complexity and long-term nature of China’s energy transformation, this paper simulates the impact of China’s energy transformation on energy consumption structure and CO₂ emissions under different scenarios. It theoretically analyzes the scientific and rational pathway of China’s transformation from high-carbon coal energy to low-carbon natural gas and renewable energy. It answers the internal reasons why China needs to transition from coal, oil, and natural gas to renewable energy. It clarifies how to choose appropriate energy transformation pathways under carbon-neutral targets.

3. Theoretical Model

3.1. Theoretical Framework

3.1.1. Model Introduction

China’s transition to a net zero emission energy system is not only reflected in the technological aspect, but also in the energy system configuration, which depends on unknown social changes, population growth, economic development, and other aspects. Therefore, accurately characterizing China’s energy transformation pathway under the carbon neutrality target is relatively tricky. The only thing that can be confirmed is that in the future, China’s energy transformation to achieve net zero CO₂ emissions will use fewer fossil fuels. At the same time, the application of renewable energy cannot be ignored, especially renewable energy such as hydropower, wind energy, and solar energy, which will play an important role in China’s energy transformation process. Therefore, this paper adopts a bottom-up national energy technology model (C³IAM/NET) to study the pathway selection of energy transformation under the carbon neutral target [1,26,27,28,29,30,31,32]. The C³IAM/NET model covers 20 sub-sectors, including primary energy supply, heat, electricity, cement, steel, chemical, agriculture, papermaking, construction (residential/commercial), transportation, and other industries, as well as over 800 key technologies.

3.1.2. Objective Function

According to [30,31,33], we set the total cost of energy transformation system construction as TC, which includes three major components: Energy equipment or technology investment cost (IC), energy equipment or technology operation or maintenance cost (MC), and energy consumption cost (OC). Thus, the total cost of constructing the energy transformation system can be expressed as follows:

$$min\hspace{0.17em}\hspace{0.17em}T{C}_t=I{C}_t+M{C}_t+O{C}_t$$

(1)

In Equation (1), TC_t is the total cost of constructing the energy transformation system. It is also a decision variable. IC_t refers to the investment cost of energy equipment or technology. MC_t represents the maintenance cost of energy equipment. OC_t is the cost of energy consumption.

When calculating the investment cost of energy equipment or technology in this study, factors such as government subsidy rates, internal rate of return, and equipment lifespan need to be considered, including:

$$I{C}_t={\displaystyle \sum _i^I{\displaystyle \sum _d^{D}i{c}_{i,d,t}\times (1-S{R}_{i,d,t})}}\times {\displaystyle \frac{I{R}_{i,d,t}\times {(1+I{R}_{i,d,t})}^{T_{i,d}}}{{(1+I{R}_{i,d,t})}^{T_{i,d}}-1}}$$

(2)

In Equation (2), ic_i,d,t represents the costs of equipment d invested in industry i in year t. SR_i,d,t represents the subsidy rates for equipment d invested by industry i in t-year. IR_i,d,t represents the internal rate of return for equipment d invested in industry i in year t. T_i,d represents the operational lifespan of equipment d invested in industry i.

When calculating the operation or maintenance costs of energy equipment or technology, factors such as maintenance costs, management costs, labor costs, government subsidies, etc. need to be considered, including:

$$M{C}_t={\displaystyle \sum _i^I{\displaystyle \sum _d^{D}m{c}_{i,d,t}\times Q_{i,d,t}\times (1-S{R}_{i,d,t})}}$$

(3)

In Equation (3), mc_i,d,t represents the unit maintenance costs of equipment d invested by industry i in year t; Q_i,d,t represents the total number of equipment d invested by industry i in year t, and the symbols of the other variables have the same meaning as in Equation (2).

When calculating energy consumption costs, it is necessary to consider factors such as the price of different energy varieties over time, including:

$$O{C}_t={\displaystyle \sum _i^I{\displaystyle \sum _d^D{\displaystyle \sum _k^K{E}_{i,d,k,t}\times Q_{i,d,k,t}\times (1-T{E}_{i,d,k,t})\times p_{i,d,k,t}\times (1-S{R}_{i,d,t})}}}$$

(4)

In Equation (4), E_i,d,k,t represents the k-class energy consumed by equipment d invested by industry i in the t-th year. Q_i,d,k,t represents the amount of k-class energy consumed by equipment d invested by industry i in the t-th year. TE_i,d,k,t represents the k-class energy technology progress rates consumed by equipment d invested in by industry i in year t. p_i,d,k,t represents the k-class energy prices consumed by equipment d invested in by industry i in year t.

3.2. Energy Demand Constraint

Energy demand constraint refers to the actual process of demand-based production of a given industrial product, transportation, or construction service, where the product of the operating volume of all equipment and the output volume of a unit of equipment product or service must be greater than or equal to the demand for that product or service. The expression for energy demand constraints is:

$${\displaystyle \sum _d^D{Y}_{i,d,j,t}\times Q_{i,d,j,t}\times (1-T{E}_{i,d,j,t})\ge D{S}_{i,j,t}}$$

(5)

In Equation (5), Y_i,d,j,t represents the unit output of equipment d for the production of products or energy services j in industry i in year t. Q_i,d,j,t represents the operating quantities of equipment d for the production of products or energy services j in industry i in year t. TE_i,d,j,t represents the technological progress rates of equipment d for the production of products or energy services j in industry i in year t. DS_i,j,t is the total demand for products or energy services j in industry i in year t.

3.2.1. Energy Supply Constraint

The actual energy consumption cannot exceed or fall below a certain limit value, and the upper (lower) limit values are given by national and sectoral energy plans, see:

$$\hspace{0.17em}\{\begin{array}{l}{E}_t^{\min }\le E_t\le E_t^{\max }\\ E_{i,t}^{\min }\le E_{i,t}\le E_{i,t}^{\max }\\ E_{i,k,t}^{\min }\le E_{i,k,t}\le E_{i,k,t}^{\max }\\ {\displaystyle \sum _i^I{E}_t}\le E_t\\ {\displaystyle \sum _k^K{E}_{i,t}}\le E_{i,k,t}\\ {\displaystyle \sum _d^D{E}_{i,k,t}}\le E_{i,d,k,t}\end{array}$$

(6)

In Equation (6), E_t, E_i,t, E_i,k, and t are the total energy consumption, industry i, and category k energy products in year t, respectively; represents the lower limit of the total energy consumption of the country in year t; represents the upper limit of the total energy consumption of the country in year t; represents the lower limit of the total energy consumption of industry i in year t; represents the upper limit of the total energy consumption of industry i in year t; represents the lower limit of the total consumption of k-class energy products in industry i in year t; represents the upper limit of the total consumption of k-class energy products in industry i in year t.

3.2.2. Equipment Operation Quantity Constraint

The constraint on the number of devices in operation refers to the fact that the number of devices in operation in the energy transformation system cannot exceed the inventory of devices turned on. The expression for the constraint on the number of devices running is:

$$\hspace{0.17em}\{\begin{array}{l}S{Q}_{i,d,t}=S{Q}_{i,d,t-1}+N{Q}_{i,d,t}-R{Q}_{i,d,t}\\ O{Q}_{i,d,t}=S{Q}_{i,d,t}\times R{E}_{i,d,t},0<R{E}_{i,d,t}<1\end{array}$$

(7)

In Equation (7), SQ_i,d,t represents the inventory quantities of industry equipment d in year t. SQ_i,d,t-1 is the inventory quantity of industry equipment d in year t-1. NQ_i,d,t represents the newly added quantities of industry equipment d in year t. RQ_i,d,t is the number of retired equipment d in industry in year t. OQ_i,d,t is the operating quantity of industry equipment d in year t. RE_i,d,t represents the startup rates of industry equipment d in year t.

3.2.3. Technical Permeability Constraints

The constraint of technological penetration rate refers to the proportion of supply from certain equipment for a given service, which must not exceed or be lower than a certain constraint value in order to meet the policy needs of eliminating outdated production capacity or encouraging the development of advanced technology. The expression for the constraint conditions of technological permeability is:

$$SHAR{E}_{i,d,j,t}^{\min }\le SHAR{E}_{i,d,j,t}={\displaystyle \frac{Y_{i,d,j,t}\times Q_{i,d,j,t}\times (1-T{E}_{i,d,j,t})}{D{S}_{i,j,t}}}\le SHAR{E}_{i,d,j,t}^{\max }$$

(8)

In Equation (8), SHARE_i,d,j,t is the penetration rate of products or energy services produced by industry equipment d in the total production of products or energy services j in year t. Y_i,d,j,t is the unit output of equipment d for the production of products or energy services j in industry i in year t. Q_i,d,j,t is the operating quantities of equipment d for the production of products or energy services j in industry i in year t. TE_i,d,j,t is the technological progress rates of equipment d for the production of products or energy services j in industry i in year t. DS_i,j,t is the total demand for products or energy services j in industry i in year t. $SHAR{E}_{i,d,j,t}^{\min }$ is the minimum penetration rate of products or energy services produced by industry equipment d in year t in the total output of products or energy services j. $SHAR{E}_{i,d,j,t}^{\max }$ is the maximum penetration rate of products or energy services produced by industry equipment d in year t in the total production of products or energy services j.

3.2.4. Carbon Emission Constraints

The carbon emission constraint in this paper refers to the requirement that the total number of operating energy equipment multiplied by the unit equipment emissions cannot exceed the national or industry low-carbon development goal. From the perspective of carbon emission constraints, it includes not only constraining the total carbon emissions of the entire society, but also constraining the total carbon emissions of the energy system, and also constraining the total carbon emissions of a certain industry. The expression for the constraint conditions of technological permeability is:

$$\hspace{0.17em}\{\begin{array}{l}{E}_{n,g,t}^{ }\le E_{n,g,t}^{\max }\\ E_{s,g,t}^{ }\le E_{s,g,t}^{\max }\\ E_{i,g,t}^{ }\le E_{i,g,t}^{\max }\end{array}$$

(9)

In Equation (9), E_n,g,t represents the gas g emissions generated by the whole society in the t-th year. E_s,g,t is the gas g emissions generated by the energy system s in year t. E_i,g,t represents the gas emissions generated by industry i in year t. $E_{n,g,t}^{\max }$, $E_{s,g,t}^{\max }$, and $E_{i,g,t}^{\max }$, respectively, represent the upper limits of gas emissions generated by the entire society, energy system, and industry.

3.3. Model Balance Analysis

3.3.1. Balance of Primary Energy Supply and Demand

First, from a supply perspective, the total amount of primary energy supply is equal to the sum of the supply of k (k = 1, 2, 3,…, K) primary energy varieties. So, the expression for the total amount of primary energy supply is:

$$E_t^s={\displaystyle \sum _{k=1}^K{E}_{k,t}^s}$$

(10)

In Equation (10), $E_t^s$ represents the total amount of primary energy supply, including k-class primary energy varieties such as coal, oil, natural gas, renewable energy, primary electricity, etc. Other energy sources besides primary energy, such as coal, oil, natural gas, and renewable energy, have a supply equal to the amount of primary energy consumed in the secondary energy processing and conversion process, the direct consumption of primary energy in the terminal industry, net exports, losses, and inventory. Thus, the final expression for the total amount of primary energy supply is:

$$E_{k,t}^s={\displaystyle \sum _s^S{\displaystyle \sum _d^D{E}_{k,s,d,t}^c+}}{\displaystyle \sum _f^F{\displaystyle \sum _d^D{E}_{k,s,d,t}^f-I{M}_{k,t}}}+E{X}_{k,t}+LOS{S}_{k,t}+STOC{K}_{k,t}$$

(11)

In Equation (11), s (s = 1, 2, 3, …, S) refers to various energy processing and conversion links for producing secondary energy; f represents different terminal energy consumption industries; $E_{k,s,d,t}^c$ represents the consumption of primary energy k in the processing and conversion process s equipment d in year t; $E_{k,s,d,t}^f$ represents the consumption of primary energy k in terminal industry f equipment d in year t; IM_k,t is the import volume of primary energy k in year t; LOSS_k,t is the export volume of primary energy k in year t; EX_k,t represents the loss of primary energy k in year t; and STOCK_k,t is the inventory of primary energy k in year t.

Second, from the perspective of demand,once the energy enters the terminal energy-consuming sector, its demand is equal to the product of the demand for products or energy services in the industry and the energy consumption per unit product, that is:

$$E_{k,f,d,t}^{f\_c}=E_{k,f,d,t}^c\times Q_{k,f,d,t}^{ }\times (1-T{E}_{k,f,d,t}^{ })$$

(12)

In Equation (12), $E_{k,f,d,t}^{f\_c}$, $E_{k,f,d,t}^c$, Q_k,f,d,t, and TE_k,f,d,t, respectively, represent the total consumption, unit consumption, operating quantity, and technological progress rate of equipment d for energy type k consumed by terminal industry f in year t.

3.3.2. Balance of Secondary Energy Supply and Demand

Once primary energy enters the processing and conversion stage, it will be converted into secondary energy. The supply of secondary energy is equal to the sum of the production of secondary energy in other processing and conversion processes, the supply of secondary energy in the terminal industry, and the losses incurred during the conversion of primary energy to secondary energy (including transportation, distribution, storage, etc.), that is:

$$E_{m,t}^s={\displaystyle \sum _s^S{\displaystyle \sum _d^D{E}_{m,s,d,t}^s+}}{\displaystyle \sum _f^F{\displaystyle \sum _d^D{E}_{m,f,d,t}^f}}+LOS{S}_{m,t}^{ }$$

(13)

In Equation (13), $E_{m,t}^s$ represents the total supply of secondary energy m in year t.$E_{m,s,d,t}^s$ is the production volume of equipment d in the processing and conversion process of secondary energy m in year t. $E_{m,f,d,t}^f$ represents the supply of secondary energy m in the terminal industry f processing and conversion process s equipment d in year t. $LOS{S}_{m,t}^{ }$ represents the amount of loss during the conversion process from primary energy to secondary energy in year t.

Furthermore, we assume that the primary energy consumption in each link is equal to the ratio of secondary energy output to efficiency. From the perspective of secondary energy consumption, there are:

$$E_{k,s,d,t}^c=E_{m,s,d,t}^{s\_p}\times {\rho }_{m,s,d,t}^{ }\times (1-T{E}_{m,s,d,t}^{ })$$

(14)

where m (m = 1, 2, 3,…, M) refers to various types of secondary energies, including fuel products such as coke, gasoline, diesel, blast furnace gas, etc. $E_{k,s,d,t}^c$ represents the consumption of primary energy k in the processing and conversion process s equipment d in year t. $E_{m,s,d,t}^{s\_p}$ is the production volume of equipment d in the processing and conversion process of secondary energy m in year t. ${\rho }_{m,s,d,t}^{ }$ is the energy efficiency of equipment d in the t-th year processing and conversion process, which converts primary energy k to secondary energy m. TE_m,s,d,t is the technological progress rate of equipment d for the production of secondary energy m in year t and conversion process s.

3.3.3. Energy Generation Balance

Next, we explain the expression of power generation for various types of energy use. Specifically, it includes thermal power generation and renewable energy generation. If the total power generation is equal to the sum of thermal power generation and renewable energy generation, then:

$$E_{ele,t}^s=E_{ele,t}^{h\_s}+E_{ele,t}^{r\_s}$$

(15)

where $E_{ele,t}^s$ represents the total electricity generation in year t; $E_{ele,t}^{h\_s}$ is the thermal power generation in the t-th year; and $E_{ele,t}^{r\_s}$ represents the t-th year of renewable energy generation.

In Equation (15), the expression for the thermal power generation in year t is:

$$E_{ele,t}^{h\_s}={\displaystyle \sum _h^H{\displaystyle \sum _d^{D}O{T}_{h,d,t}^{ }\times H_{h,d,t}^{ }}\times {\rho }_{h,d,t}^{ }}\times (1-T{E}_{h,d,t}^{ })$$

(16)

where h (h = 1, 2, 3, …, H) represents the h-th type of thermal power technology; OT_h,d,t represents the installed capacity of thermal power technology h equipment d in year t; H_h,d,t represents the power generation hours of thermal power technology h equipment d in year t; ρ_h,d,t represents the power generation efficiency of thermal power technology h equipment d in year t; TE_h,d,t represents the progress rate of power generation technology for thermal power technology h equipment d in year t.

In Equation (15), the expression for the renewable energy generation $E_{ele,t}^{r\_s}$ in year t is:

$$E_{ele,t}^{r\_s}={\displaystyle \sum _r^R{\displaystyle \sum _d^{D}O{T}_{r,d,t}^{ }\times H_{r,d,t}^{ }}\times {\rho }_{r,d,t}^{ }}\times (1-T{E}_{r,d,t}^{ })$$

(17)

where r (r = 1, 2, 3, …, R) represents the r-th renewable energy technology; OT_r,d,t represents the installed capacity of renewable energy technology r equipment d in year t; H_r,d,t represent the power generation hours of renewable energy technology r equipment d in yeart; ρ_r,d,t represents the power generation efficiency of renewable energy technology r equipment d in year t;TE_r,d,t represents the progress rate of renewable energy technology r and equipment d in power generation technology in year t.

In addition, thermal power generation and renewable energy generation are equal to the sum of electricity consumption, energy storage, losses, and net exports in the terminal industry. Thus, the expression for the supply–demand balance of energy generation is the sum of terminal industry electricity consumption, electricity reserves, electricity losses, and net electricity exports, that is:

$$E_{ele,t}^s=E_{ele,t}^c+E_{ele,t}^s+E_{ele,t}^l-E_{ele,t}^i+E_{ele,t}^e$$

(18)

where $E_{ele,t}^c$ represents the electricity consumption of the terminal industry in year t; $E_{ele,t}^s$ represents the terminal industry electricity reserve in year t; $E_{ele,t}^l$ represents the power loss of the terminal industry in year t; $E_{ele,t}^i$ represents the electricity import volume of the terminal industry in year t; and $E_{ele,t}^e$ represents the electricity export volume of the terminal industry in year t.

Finally, when electricity is applied to the terminal sector, the electricity consumption in each sector has the following expression:

$$E_{ele,t}^c={\displaystyle \sum _f^F{\displaystyle \sum _d^D{E}_{ele,f,d,t}^{ }\times Q_{ele,f,d,t}^{ }}}\times (1-T{E}_{ele,f,d,t}^{ })$$

(19)

where E_ele,f,d,t represents the unit power consumption of power-consuming device d in the terminal industry f; Q_ele,f,d,t represents the operating quantity of power-consuming equipment d in the terminal industry f in year t; and TE_ele,f,d,t represents the technological progress rate of power-consuming equipment d in the terminal industry f in year t.

4. Methodology and Data

4.1. Data Source

The time observation period for the numerical simulation analysis in this paper is from 2020 to 2060. Therefore, 2020 is used as the benchmark year for research, and the national energy technology model is used to predict the energy transformation pathway, energy consumption structure, and total carbon emissions from 2021 to 2060, striving to achieve a carbon peak by 2030 and carbon neutrality by 2060. The raw data used in the numerical simulation analysis in this paper mainly come from historical sources such as China Statistical Yearbook, China Electric Power Yearbook, China Energy Statistical Yearbook, China Energy Development Report, China Urban Statistical Yearbook, China Energy Big Data Report, China Renewable Energy Development Report, China Population and Employment Statistical Yearbook, and China Carbon Emissions Database (CEADs).

4.2. Scenario and Parameter Setting

4.2.1. Scenario Setting

This paper designs corresponding scenarios around the uncertainty of the energy transformation pathway and explains the main parameter settings of the national energy technology model. It conducts in-depth research on the CO₂ emission process (including industrial production process emissions) of energy transformation (including processing and conversion, transportation and distribution, terminal use, and terminal treatment processes). Considering social, economic, and behavioral uncertainties, this paper predicts and analyzes the demand for products and services in various industries, as well as the CO₂ emissions related to the national energy system in 2020. It sets up development scenarios for the energy transformation pathway:

(1)

Low-speed development scenario. In the low-speed development scenario, the economic growth rate is relatively slow and the number and scale of deployment of CO₂ capture and storage technology facilities are relatively small. Under a feasible technological pathway, China’s energy system still needs to bear the significant task of reducing CO₂ emissions.

(2)

Medium-speed development scenario. Under the scenario of medium-speed development, the economic growth rate is relatively fast, and the deployment and scale of CO₂ capture and storage technology facilities have expanded. Under a feasible technological pathway, the CO₂ emission reduction task that China’s energy system needs to bear has been reduced.

(3)

High-speed development scenario. In the context of high-speed development, the economic growth rate is at its fastest, and the number and scale of deployment of CO₂ capture and storage technology facilities have further expanded. At this point, under feasible technological path conditions, the CO₂ emission reduction task that China’s energy system needs to bear is lightest, and theoretically, it is also the most beneficial for China to achieve its dual carbon goals.

4.2.2. Core Parameter Settings

Given the numerous industries involved in the National Energy Technology Model (C3IAM/NET), there are significant differences in the factors and processes considered in predicting product or service demand for each industry. Therefore, this paper only explains the common parameter settings for demand forecasting in various industries. The energy balance sheet is the basic database. Due to limitations in statistical methods, industry classification, and data refinement, the current China Energy Balance Sheet released by the China Bureau of Statistics cannot reflect the true situation of various industries. In this article, we draw on the method of Yu et al. (2019) to improve China’s energy balance sheet and obtain national and industry energy data [34].

Table 1. Core parameter settings.

System	Classification	Technical Characteristics	Data Sources
Energy supply	Coal, oil, natural gas, biomass, hydro, wind, solar, nuclear	Device lifetime, initial investment cost, O and M cost, service supply to energy consumption ratio, stock quantity, newly recruited quantity, operating quantity, subsidy rate, discount rate, energy price, carbon emissions coefficient, dynamic change in cost, technological progress rate	Xing et al. (2015) [35], Zhang and Gu (2017) [36], Zhang (2018) [37], An et al.(2018) [26], Li and Yu (2019) [27], Tang et al. (2020) [38], Zhang (2020) [39], Wang (2020) [40], China National Bureau of Statistics
Energy processingand conversion	Power
	Heat
Energy consumption	Steel
	Aluminum
	Cement
	Ethylene
	Intercity
	Freight
	Residential
	Commercial
	Other

Note: “Other” refers to other industries, i.e., the collection of non-energy-intensive industries with relatively small energy consumption, including agriculture, forestry, animal husbandry, fishery, mining, etc.

shows the settings of core parameters and the data sources.

4.2.3. Other Parameter Settings

This paper set the total CO₂ emissions related to China’s energy system in 2020 to 11.3 billion tons (including industrial process emissions). The corresponding CO₂ emissions from coal, oil, natural gas, and industrial processes in China account for 75.00%, 18.10%, 6.99%, and 13%, respectively. At the same time, electricity, steel, transportation, cement, and other industries are still key industries for CO₂ emissions. In 2020, the corresponding CO₂ emissions of the four industries were 3.931 billion tons, 1.73 billion tons, 1.217 billion tons, and 1.125 billion tons, respectively. If the current development trend continues, China’s CO₂ emissions will remain above 10 billion tons in the long term, which is obviously not conducive to achieving the carbon neutrality target. Therefore, it is necessary to further promote energy system optimization and transformation based on existing emission reduction efforts.

Second, this paper needs to set the future population of China. Referring to the 2019 World Population Outlook released by the United Nations Department of Economic and Social Affairs, the population of China is expected to peak at 1.46 billion by 2030 and decrease to 1.33 billion by 2060. According to the statistics of the National Population Development Plan (2016–2030), the predicted data in the 2019 World Population Outlook is consistent with this. Of course, the year when the total population of the Chinese Mainland reached its peak and the peak population varied under different prediction schemes. Therefore, this paper conducts slight corrections based on the seventh national population census, and

Table 2. Population forecast for China from 2020 to 2060 (Unit: 100 million people).

Year	2020	2025	2030	2035	2040	2045	2050	2055	2060
Population	14.10	14.30	14.40	14.30	14.20	14.00	13.80	13.40	13.10

reports the basic situation of China’s population size prediction from 2020 to 2060 (unit: 100 million people).

Furthermore, with the continuous improvement of people’s living standards and income levels, domestic demand will further expand, and residents’ consumption choices will shift towards consumption dominated by services and leisure. The proportion of the service industry will gradually increase, while the proportion of the first and second industries will gradually decrease. Therefore, based on the predicted results of relevant research, this paper sets the results of China’s industrial structure changes from 2020 to 2060 as shown in

Table 3. Prediction of industrial structure changes in China from 2020 to 2060 (Unit: %).

Year	2020	2025	2030	2035	2040	2045	2050	2055	2060
Primary industry	8.0	7.0	6.0	6.0	5.0	4.0	4.0	4.0	4.0
Secondary industry	38.0	34.0	31.0	28.0	26.0	26.0	25.0	24.0	23.0
Tertiary industry	55.0	59.0	63.0	67.0	69.0	70.0	71.0	72.0	73.0

.

Finally, this study needs to predict and set the macroeconomic development environment. Specifically, China’s GDP growth rate from 2020 to 2060 is shown in

Table 4. Forecast of China’s economic growth rate from 2020 to 2060 (Unit: %).

Year	2020–2025	2026–2030	2031–2035	2036–2040	2041–2045	2046–2050	2051–2055	2056–2060
Low	5.0	4.5	3.5	3.5	2.5	2.5	2.5	2.5
Medium	5.6	5.5	4.5	4.5	3.5	3.5	3.5	3.5
High	6.0	5.5	5.0	5.0	4.5	4.5	4.5	4.5

. According to China’s strategic arrangement and target tasks of building a modern, strong country in all respects, the total GDP by 2035 will double compared to 2020, and by 2060, it will double compared to 2020. The GDP growth rate can still be maintained at 2.60% by 2060. At the same time, the per capita GDP by 2035 will double to 140,000 yuan (in 2020, the Chinese yuan remains unchanged), about 20,000 US dollars, compared to 2020. It will further increase to approximately 230,000 yuan, close to 35,000 US dollars, by 2050. It will more than double to reach 310,000 yuan, approximately 45,000 US dollars in 2060 compared to 2020.

5. Numerical Simulation Results and Analysis

This paper uses a self-designed and constructed national energy technology model to comprehensively optimize the energy transformation pathway. It fully considers the impact of social, economic, behavioral, and technological uncertainties on terminal energy products (such as steel, cement, chemical products, aluminum, papermaking, and other industrial products) and service demands (heating, cooling, lighting, passenger/cargo transportation, and other services). From the perspective of minimizing the total cost of the energy supply and demand system, it dynamically optimizes the industry-wide technological layout from 2020 to 2060. Based on the optimization of the national energy technology model, this study analyzes the overall energy transformation pathway, carbon emissions, and key industry responsibility allocation in China. It proposes an energy transformation pathway that balances economics and safety under multiple scenarios and clear action plans at multiple levels, including China’s overall carbon emission pathway, industry emission reduction responsibilities, and key technology planning.

5.1. Optimization Analysis of Energy Transformation Pathway under Low-Speed Development Scenario

Based on the scenario setting, we first analyze the optimization results of China’s energy transformation pathway under the low-speed development scenario. The optimization results of China’s energy transformation pathway, carbon emission pathway, and carbon emissions of major industries are shown in Figure 1 and Figure 2 and

Table 5. Energy consumption structure of different types of terminal energies under low-speed development scenarios.

	Low-Speed Development Scenarios
Year	2020	2025	2030	2035	2040	2045	2050	2055	2060
Coal	56.9%	50.6%	44.6%	41.2%	36.2%	31.6	27.4	21.4	16.2%
Oil	18.8%	19.1%	17.8%	17.4%	16.0%	14.6	12.8	11.6	9.9%
Gas	8.4%	9.9%	11.4%	12.3%	13.5%	14.1	14.5	14.3	13.3%
Renewable energy	15.9%	20.4%	26.2%	29.1%	34.3%	39.7	45.3	52.7	60.6%

. This paper combines Figure 1 and Table 5 to estimate that the estimated proportions of coal, oil, natural gas, and clean energy in China in 2030 are 44.60%, 17.80%, 11.40%, and 26.20%, respectively; the estimated proportions of coal, oil, natural gas, and clean energy in China in 2040 are 36.20%, 16.00%, 13.50%, and 34.30%, respectively; the estimated proportions of coal, oil, natural gas, and clean energy in China in 2050 are 27.40%, 12.80%, 14.50%, and 45.30%, respectively; and the estimated proportions of coal, oil, natural gas, and clean energy in China in 2060 are 16.20%, 9.90%, 13.30%, and 60.60%, respectively. This means that by 2060, the consumption of clean energy in China’s entire energy system will exceed 60%, while the consumption of fossil fuels such as coal, oil, and natural gas will remain below 40%. The energy system will gradually transition from traditional fossil energy to clean energy.

However, if deployed and arranged according to China’s carbon neutrality target, the proportion of non-fossil fuels in primary energy consumption needs to exceed 80% by 2060. Therefore, under the scenario of low-speed development, China’s energy consumption structure needs to accelerate transformation under the carbon-neutral target, transforming the traditional fossil energy-dominated energy system into a renewable energy-dominated and multi-energy complementary energy system so that non-fossil energy can approach 80% as much as possible in order to achieve the expected goals proposed for the country.

Furthermore, this paper analyzes the CO₂ emission and per capita CO₂ emission pathway in China from 2020 to 2060 under the scenario of low-speed development (see Figure 2). In the process of depicting China’s CO₂ emission path from 2020 to 2060, this paper sets China’s 2020 (benchmark year) carbon emission index to 100. China’s 2021 carbon emission index is divided by the 2021 CO₂ emissions, divided by the 2020 CO₂ emissions, and multiplied by 100. The method for calculating carbon emission indices in other years is similar. If the index is greater than 100, it indicates that the carbon emissions in the observation year are higher than those in 2020. If it is lower than 100, it indicates that the carbon emissions are lower than those in 2020. It can be seen that in the context of relatively slow economic growth and small deployment and scale of CO₂ capture and storage technology facilities, China’s CO₂ emissions need to go through a plateau period before 2030, during which the scale of CO₂ emissions is still relatively large. After 2030, China’s CO₂ emissions will show a rapid decline. Thus, the period from 2020 to 2030 is a crucial stage for China to achieve modernization and a crucial stage for China to achieve its carbon peak target. Against the backdrop of accelerating the transformation of energy systems and the deployment of technological facilities for CO₂ capture and storage, China is expected to achieve its carbon peak target by 2030. In 2060, China’s CO₂ emission index will be 26.68, equivalent to a CO₂ emission level of 3.018 billion tons. This means that if China’s energy system transformation is not in place and the deployment of CO₂ capture and storage technology facilities is slow, it will be difficult to achieve a carbon-neutral target. At that time, there will still be about 3 billion tons of CO₂ that need to be absorbed through forests, oceans, wetland carbon sinks, and other means. Therefore, China’s action plan to achieve the carbon neutrality target must be coordinated and planned under the guidance of deep energy transformation and industry carbon emission constraints. It is necessary to accelerate the transformation of the energy consumption structure, promote the development of green and low-carbon industries, and expand the scale of CCUS technology.

Finally, this paper developed CO₂ emission pathways for various industries from 2020 to 2060 under the low-speed development scenario through numerical simulation. The following analysis mainly focuses on representative industries such as electricity, steel, cement, and transportation. Figure 3 shows that under the low-speed development scenario, China’s CO₂ emissions from 2020 to 2060 were mainly concentrated in the electricity, steel, cement, and transportation industries. The CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation in 2020 were 3.931 billion tons, 1.73 billion tons, 1.125 billion tons, 191 million tons, 301 million tons, and 726 million tons, respectively. Compared with other industries, the electricity industry still faces significant pressure for low-carbon transformation at this time. The CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation in 2030 will be 4.032 billion tons, 1.528 billion tons, 746 million tons, 273 million tons, 422 million tons, and 863 million tons, respectively. The CO₂ emissions from various industries will decrease compared to 2020. In 2040, the CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation will be 3.273 billion tons, 1.153 billion tons, 511 million tons, 247 million tons, 413 million tons, and 778 million tons, respectively. After the carbon peak in 2030, the CO₂ emissions from various industries will show a rapid decline, with CO₂ emission ratios of 51.34%, 18.09%, 8.02%, 3.87%, 6.48%, and 12.20%, respectively. In 2050, the CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation will be 2.081 billion tons, 781 million tons, 288 million tons, 181 million tons, 351 million tons, and 622 million tons, respectively. The CO₂ emissions from various industries will have further decreased compared to 2040. In 2060, CO₂ emissions from various industries will gradually approach the level of CO₂ emissions under the carbon neutrality target. It can be seen that CO₂ emissions in the power industry still account for a large proportion of the entire industry, even exceeding 50% at one point. The reason is that the power sector is mainly composed of coal-fired power plants, which account for more than 60% of the current total power generation. Their emissions account for more than 95% of the potential cumulative emissions of existing power plants in the power sector by 2060. Therefore, the power sector needs to bear significant responsibility for carbon reduction in the future energy transformation and production development process in China.

Based on the above analysis, from 2020 to 2030, China needs to promote different industries and sectors to achieve tiered peak emissions, especially for industries and sectors with high CO₂ emissions, such as electricity, steel, cement, and transportation. It is also necessary to further promote and expand the utilization range of carbon capture and storage technologies, promote various industries to achieve a carbon peak by 2030, and gradually achieve a stable decline thereafter in order to ensure that China achieves a carbon peak by 2030 and carbon neutrality by 2060. For the transportation industry, Chinese policymakers have been striving to introduce anti-pollution measures at a fast enough pace, and the emission restrictions on various types of vehicles are the strictest in the world. Therefore, the implementation of air pollution standards and the electrification of vehicles will significantly reduce the emissions of various pollutants from the transportation sector under the carbon-neutral target.

5.2. Optimization Analysis of Energy Transformation Pathwaysunder the Medium-Speed Development Scenario

The optimization results of China’s energy transformation pathway, carbon emission pathway, and carbon emissions of major industries under the scenario of medium-speed development are shown in Figure 4, Figure 5 and Figure 6 and

Table 6. Energy consumption structure of end users by category under the medium-speed development scenario.

	Medium-Speed Development Scenarios
Year	2020	2025	2030	2035	2040	2045	2050	2055	2060
Coal	56.9%	50.1%	43.6%	39.1%	33.7%	27.4%	21.3%	16.4%	11.9%
Oil	18.8%	18.9%	17.6%	16.8%	14.4%	12.9%	11%	9.8%	8.2%
Gas	8.4%	9.9%	11.2%	12.3%	13.6%	13.8%	13.4%	12.2%	10.1%
Renewable energy	15.9%	21.1%	27.6%	31.8%	38.3%	45.9%	54.3%	61.6%	69.8%

. In the context of relatively fast economic growth and an expansion in the number and scale of CO₂ capture and storage technology facilities, this paper analyzes, in combination with Figure 4 and Table 6, and finds that the estimated proportions of coal, oil, natural gas, and clean energy in China in 2030 are 43.60%, 17.60%, 11.20%, and 27.60%, respectively. Compared with the low-speed development scenario, the proportion of clean energy is 1.40 percentage points higher. In 2040, the estimated proportions of coal, oil, natural gas, and clean energy in China are 33.70%, 14.40%, 13.60%, and 38.30%, respectively. At this time, the proportion of clean energy in the entire energy consumption structure will have increased; In 2050, the estimated proportions of coal, oil, natural gas, and clean energy in China are 21.30%, 11.00%, 13.40%, and 54.30%, respectively. The proportion of clean energy in the entire energy consumption structure will exceed that of petrochemical energy, further increasing the depth and breadth of energy system transformation. The estimated proportions of coal, oil, natural gas, and clean energy in China in 2060 are11.90%, 8.20%, 10.10%, and 69.80%, respectively. According to the results of the numerical simulation analysis in this paper, by 2060, the proportion of consumption of clean energy in China’s entire energy system will be close to 70%, while the proportion of consumption of fossil fuels such as coal, oil, and natural gas in the entire energy system will be maintained at around 30%. The transformation of the energy system from traditional fossil energy to clean energy is more obvious.

In addition, similar to the energy transformation goal under low-speed development scenarios, the proportion of non-fossil fuels in primary energy consumption must exceed 80% by 2060 in order to fully meet the expected carbon-neutral target. However, under the scenario of moderate development, China’s energy consumption structure will still not meet the expected results of the carbon-neutral target. Therefore, China still needs to pay attention to the transformation of its energy system, establish the important position of low-carbon development in the national energy and industrial development system, significantly improve the promotion and application level of renewable energy, build an energy system that balances clean, low-carbon, safe and efficient, continuously track the development process of cutting-edge carbon reduction technologies at home and abroad, promote the deep application of advanced carbon reduction technologies, effectively reduce the total carbon emissions while improving carbon emission efficiency, and smoothly promote the achievement of the carbon-neutral target.

Furthermore, based on the analysis of China’s CO₂ emission and per capita CO₂ emission pathway from 2020 to 2060 under the medium-speed development scenario (see Figure 5), it is found that from 2020 to 2030, China’s CO₂ emissions experienced a development trend of first increasing and then decreasing, with carbon peaking before 2030, despite relatively fast economic growth and an expansion in the number and scale of CO₂ capture and storage technology facilities. Thus, against the backdrop of accelerating energy system transformation and the deployment of CO₂ capture and storage technology facilities, China will achieve a carbon peak before 2030. After 2030, China’s CO₂ emissions will show a rapid downward trend; the CO₂ emission index in 2040 will be 81.35, equivalent to a CO₂ emission level of 9.201 billion tons; the CO₂ emission index in 2050 will be 49.41, equivalent to a CO₂ emission level of 5.588 billion tons; and in 2060, China’s CO₂ emission index will be 21.03, equivalent to a CO₂ emission level of 2.379 billion tons. That is to say, according to the energy transformation, economic development, and carbon capture and storage technology deployment under the medium-speed development scenario, China still needs to absorb about 2 billion tons of CO₂ through forests, oceans, wetland carbon sinks, and other means by 2060. Therefore, under the scenario of medium-speed development, if China can absorb about 2 billion tons of CO₂ by 2060, it can fully achieve the carbon-neutral target. Therefore, the Chinese government needs to further guide the technology sector to accelerate the large-scale application of breakthrough technologies, significantly improve the efficiency level of electrification operation, and provide scientific support for safe and low-cost carbon emissions reduction by improving the top-level design of energy system transformation.

Finally, this paper showed the CO₂ emission pathways of various industries from 2020 to 2060 under the scenario of medium-speed development through numerical simulation. Figure 6 shows that under the medium-speed development scenario, China’s CO₂ emissions from 2020 to 2060 are mainly concentrated in the electricity, steel, cement, and transportation industries. Among them, the CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation in 2025 will be 4.178 billion tons, 1.753 billion tons, 936 million tons, 286 million tons, 400 million tons, and 863 million tons, respectively. Compared with other industries, the electricity industry still faces significant pressure for low-carbon transformation at this time. The CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation in 2030 will be 4.212 billion tons, 1.532 billion tons, 794 million tons, 255 million tons, 409 million tons, and 941 million tons, respectively. The proportions of CO₂ emissions from various industries are 51.73%, 18.81%, 9.75%, 3.13%, 5.02%, and 11.56%, respectively. After reaching its peak, CO₂ emissions from various industries have shown a certain degree of stability or decline. In 2040, the CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation will be 3.35 billion tons, 1.086 billion tons, 517 million tons, 205 million tons, 383 million tons, and 838 million tons, respectively. Compared with 2030, the CO₂ emissions from various industries show a rapid decline. In 2050, the CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation will be 1.77 billion tons, 647 million tons, 306 million tons, 116 million tons, 319 million tons, and 625 million tons, respectively. The CO₂ emissions from various industries will further decrease compared to 2040. In 2060, it can be clearly seen that CO₂ emissions from various industries will gradually approach the level of CO₂ emissions under the carbon neutrality target, with total CO₂ emissions of 1.43 billion tons from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation. It can be seen that China’s transportation industry will have relatively small CO₂ emissions in the future. The main reason for this is that over the past two decades, China has invested a large amount of funds in the field of transportation infrastructure and achieved remarkable results in green transportation construction. As of the end of 2023, the operating mileage of China’s high-speed railways reached 45,000 km, accounting for more than 70% of the world’s total high-speed railway mileage. It is the country with the longest operating mileage, the largest under-construction scale, the highest commercial operation speed, the most comprehensive high-speed railway technology, and the most diverse operating scenarios of high-speed railways in the world. In addition, public transportation can transport more passengers in a more efficient way and occupy much less space than private cars. Therefore, the energy intensity of China’s car transportation is much lower than that of the United States, where cars dominate. China’s total passenger volume (measured in passenger kilometers) is higher than that of the United States, but its energy consumption is less than half of that of the United States.

5.3. Optimization Analysis of Energy Transformation Pathway under High-Speed Development Scenario

The optimization results of China’s energy transformation pathway, carbon emission pathway, and carbon emissions of major industries in the context of rapid development are shown in Figure 7, Figure 8 and Figure 9 and

Table 7. Energy consumption structure of end users by product under the high-speed development scenario.

	High-Speed Development Scenario
Year	2020	2025	2030	2035	2040	2045	2050	2055	2060
Coal	56.9%	50.3%	43.7%	38.6%	31.5%	23.6%	16.4%	11.4%	7.3%
Oil	18.8%	18.9%	17.7%	16.7%	14.3%	11.9%	8.6%	7.3%	5.4%
Gas	8.4%	9.8%	11.3%	11.6%	11.7%	11.7%	10.8%	9.5%	6.9%
Renewable energy	15.9%	21%	27.3%	33.1%	42.5%	52.8%	64.2%	71.8%	80.4%

. In the context of the fastest economic growth and further expansion of the deployment and scale of CO₂ capture and storage technology facilities, this paper shows, in combination with Figure 7 and Table 7, that the estimated proportions of coal, oil, natural gas, and clean energy in China in 2030 are 43.70%, 17.70%, 11.30%, and 27.30%, respectively. Compared with the medium-speed development scenario, the proportion of clean energy is 0.30 percentage points higher. The estimated proportions of coal, oil, natural gas, and clean energy in China in 2040 are 31.50%, 14.30%, 11.70%, and 42.50%, respectively. At this time, the proportion of clean energy in the entire energy consumption structure has increased significantly, an increase of 15.20 percentage points compared to 2030. The proportion of clean energy in the entire energy consumption structure has exceeded that of petrochemical energy; In 2050, the estimated proportions of coal, oil, natural gas, and clean energy in China are 16.40%, 8.60%, 10.80%, and 64.20%, respectively. The estimated proportion of clean energy in the entire energy consumption structure exceeds 60%, indicating that China’s energy system is transitioning rapidly from traditional fossil fuels to clean energy. The estimated proportions of coal, oil, natural gas, and clean energy in China in 2060 are 7.30%, 5.40%, 6.90%, and 80.40%, respectively. This means that, based on the requirement of non-fossil fuels accounting for over 80% of primary energy consumption under the carbon neutrality target, in the context of rapid development, China’s energy consumption structure by 2060 clearly meets the expected target.

In the context of rapid development, China’s energy consumption structure will be able to achieve the expected final result of carbon neutrality. By 2030, the proportions of coal, oil, natural gas, and clean energy in China will be 43.70%, 17.70%, 11.30%, and 27.30%, respectively. By 2060, the proportion of coal, oil, natural gas, and clean energy in China will be 7.30%, 5.40%, 6.90%, and 80.40%, respectively. This means that the proportions of low-carbon energy sources such as solar energy, wind energy, hydropower, bioenergy, and other renewable energy sources in primary energy demand will jump from 15.9% in 2020 to around 80% in 2060. In the coming period, relevant departments in China need to focus on the clean and low-carbon utilization of nonrenewable energy, actively promote distributed energy, significantly improve the level of clean energy application, build an energy system that balances clean, low-carbon, safe, and efficient, and ultimately ensure the effective achievement of China’s carbon-neutral target, based on the regulation of total carbon emissions and carbon intensity. In addition, China’s achievement of carbon neutrality goals will also bring other important environmental benefits, especially significant improvements in air quality. Although air quality has significantly improved, the current air pollution is still worrying due to the rapid growth of China’s automobile ownership and the use of coal in heavy industry and power generation.

Furthermore, this paper analyzes the CO₂ emission and per capita CO₂ emission pathway in China from 2020 to 2060 under the scenario of rapid development (see Figure 8). It can be seen that with the fastest economic growth rate and further expansion of the number and scale of CO₂ capture and storage technology facilities, China’s CO₂ emissions will experience a development trend of first increasing and then decreasing, with CO₂ emissions reaching a peak of 12.425 billion tons in 2029. Therefore, against the backdrop of accelerating energy system transformation and the deployment of CO₂ capture and storage technology facilities, China is expected to achieve its carbon peak target before 2030. Meanwhile, after 2030, China’s CO₂ emissions will show a rapid downward trend. The estimated CO₂ emission index in 2040 is 79.79, equivalent to a CO₂ emission level of 9.025 billion tons. The CO₂ emission index in 2050 will be 38.05, which is equivalent to a CO₂ emission level of 4.304 billion tons. In 2060, China’s CO₂ emission index will be 10.08, equivalent to a CO₂ emission level of 1.14 billion tons. That is to say, according to the energy transformation, economic development, and carbon capture and storage technology deployment under the high-speed development scenario, China still needs to absorb about 1 billion tons of CO₂ through forests, oceans, wetland carbon sinks, and other means by 2060. According to relevant research, the annual carbon sequestration of terrestrial ecosystems in China from 2010 to 2020 was 1–1.3 billion tons of CO₂ (Wang et al., 2020) [41]. Therefore, in the context of rapid development, if China can consume about 1 billion tons of CO₂ by 2060, then it can smoothly implement its carbon-neutral target.

In addition, this paper identified the CO₂ emission pathways of various industries from 2020 to 2060 under high-speed development scenarios through numerical simulations. Figure 9 depicts the CO₂ emission pathways of the electricity, steel, cement, and transportation industries from 2020 to 2060. It can be seen that under the high-speed development scenario, the CO₂ emissions of electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation in 2025 are 4.278 billion tons, 1.789 billion tons, 988 million tons, 286 million tons, 401 million tons, and 889 million tons, respectively. Compared to other industries, the electricity industry still faces significant pressure of low-carbon transformation at this time. The CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation in 2030 are estimated to be 4.387 billion tons, 1.595 billion tons, 849 million tons, 255 million tons, 409 million tons, and 1.006 billion tons, respectively. The proportions of CO₂ emissions from various industries are 51.61%, 18.76%, 9.99%, 3.00%, 4.81%, and 11.83%, respectively. In 2040, the estimated CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation are 3.15 billion tons, 1.072 billion tons, 558 million tons, 181 million tons, 378 million tons, and 916 million tons, respectively. Compared with 2030, the CO₂ emissions from various industries will have stabilized or decreased to a certain extent after reaching their peak. In 2050, the estimated CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation will be 1.349 billion tons, 514 million tons, 240 million tons, 1.044 billion tons, 230 million tons, and 576 million tons, respectively. The CO₂ emissions from various industries will have further decreased compared to 2040. In 2060, the CO₂ emissions from electricity, steel, cement, urban passenger transportation, intercity passenger transportation, and freight transportation will be 0 million tons, 310 million tons, 65 million tons, 11 million tons, 89 million tons, and 162 million tons, respectively. It can be clearly seen that the CO₂ emissions from various industries are gradually approaching the CO₂ emission level under the carbon neutrality target.

This paper provides a detailed simulation of China’s energy transformation pathway, carbon emission pathway, and major industry carbon emissions from 2020 to 2060 under low-speed, medium-speed, and high-speed development scenarios based on the national energy technology model. Overall, regardless of the development scenario, based on the proportion of clean energy in China’s entire energy consumption structure in 2020, it can be seen that in the future, by significantly increasing the proportion of clean energy consumption, gradually reducing the proportion of fossil energy consumption, developing distributed energy according to local conditions, promoting the gradual transformation of the energy system from traditional fossil energy to clean energy, and improving the carbon emission efficiency of industries such as electricity, steel, cement, and transportation, it is entirely possible to achieve carbon neutral target.

6. Conclusions and Policy Implications

To achieve the carbon-neutral target, we should vigorously promote the basic requirements of energy transformation, accelerate the innovation of new formats, promote the popularization of new energy, and deploy low-carbon technologies. China’s energy transformation path is still facing a tight deadline and heavy tasks. Formulating a scientific carbon reduction schedule and roadmap and handling social, economic, energy, and other issues is necessary. Therefore, this paper uses a bottom-up national energy technology model to study the optimization problem of China’s energy transformation path that considers both economic and security aspects. It clarifies specific action plans for China’s energy transformation path from 2020 to 2060, total carbon emissions, industry emission reduction responsibilities, and other dimensions. The result shows that first, the proportion of renewable energy consumption in China’s entire energy system from 2020 to 2060 will gradually exceed that of fossil energy, and the energy system will transition from traditional fossil energy to renewable energy under ideal circumstances. Meanwhile, the proportion of low-carbon energy sources, such as renewable energy, in primary energy demand will jump from 15.9% in 2020 to over 80% by 2060. Second, under three different socio-economic development scenarios of low speed, medium speed, and high speed, China’s CO₂ emissions (including industrial process emissions) in 2060 were approximately 3 billion tons, 2 billion tons, and 1 billion tons. At that time, China still needs to absorb CO₂ through carbon sinks in forests, oceans, and wetlands. Thirdly, electricity remains the industry with the highest CO₂ emissions compared to other industries. The electricity industry must bear significant responsibility for carbon reduction in the future energy transformation and economic development process. It needs to accelerate the decarbonization process of the electricity industry.

Energy transformation is a long-term process and a complex and arduous task that requires the joint efforts of the government, enterprises, and individuals to increase the proportion of renewable energy, strengthen energy technology innovation, and improve energy utilization efficiency. Only by doing so can we ensure the smooth progress of energy transformation and achieve the carbon neutrality target. This paper proposes the following policy recommendations based on research findings:

First, strengthen the guiding and constraining role of energy strategy and planning, clarify the goals and tasks of green and low-carbon energy transformation, strengthen the synergy and mutual aid between various energy varieties, upstream and downstream of the industrial chain, and between regions, consolidate and promote the clean and efficient utilization of fossil fuels, fully integrate fossil fuels and renewable energy, accelerate the construction of large-scale wind and photovoltaic power generation bases, upgrade and transform existing coal-fired power units in the region, promote the cleanliness of power sources and the electrification of terminal energy consumption, and promote the low-carbon, efficient, and clean development of energy utilization.

Second, accelerate the promotion of energy-saving and carbon reduction transformation and upgrading in key areas such as steel, petrochemical, chemical, non-ferrous, building materials, and data centers, guide industrial enterprises to carry out clean energy substitution, reduce unit product carbon emissions, encourage eligible enterprises to take the lead in forming low-carbon and zero-carbon energy consumption models, support the development and utilization of clean low-carbon energy in their own places, build distributed clean energy and smart energy systems, encourage emerging key energy consumption areas to mainly meet energy demand with low-carbon energy, and fully utilize waste heat, residual pressure, and residual gas.

Third, establish a major technology collaborative innovation system for clean and low-carbon energy, accelerate breakthroughs in a number of key technologies for clean and low-carbon energy, strengthen research and innovation in CCUS technology, increase investment in CCUS technology research and development, increase the proportion of research and development funds, encourage enterprises and research institutions to carry out innovative technology research and development, support breakthroughs in key CCUS technology challenges, build a number of CCUS technology demonstration projects, promote the scale and commercialization of technology applications, and deepen the integration and development of energy transformation in new energy development, clean technology, green manufacturing, and other fields.

It is worth noting that energy transformation is a very complex and systematic project, coupled with limitations in the author’s research ability and knowledge structure, inevitably leading to shortcomings in the paper. For example, due to limitations in the availability of consumption data for renewable energy sources such as wind, hydro, and solar, future research needs to combine official renewable energy data further for precise empirical analysis. In addition, there has yet to be a comparison between different models of the subsidy settings in the model, which may lead to imprecise research conclusions; these are directions for further research in the future.