Prediction of China’s Carbon Peak Attainment Pathway from Both Production-Side and Consumption-Side Perspectives

Abstract

To achieve global sustainable development and actively respond to climate change, China, as the world’s largest energy consumer and carbon emitter, needs to save energy and reduce emissions without delay. By constructing LEAP-China production-side and LEAP-China consumption-side models, we predict the carbon emissions of China’s production side and consumption side in different scenarios from 2020 to 2050, respectively. The results show that under the current policies, neither the production side nor consumption side can achieve targeted peak carbon emissions by 2030, which is contrary to China’s current carbon emission policy. Under the sustainable development scenario, China’s production-side CO₂ emissions would peak at 10,462.2 Mt in 2029, and China’s consumption-side CO₂ emissions would peak 3 years later compared to the production side at 9904.3 Mt in 2032. Therefore, to achieve the peak for both the production and consumption side, we need to coordinate various policies and actively promote industrial restructuring and energy structure optimization. In terms of trade structure, China’s existing import and export trade structure should be adjusted to reduce the export of industrially manufactured goods and increase the proportion of technology-intensive products in foreign trade to realize the transformation from a high-carbon trade structure to a low-carbon trade structure.

1. Introduction

Since China joined the WTO in 2001, its economy has continued to take off, creating a Chinese miracle that has caught global attention. In 2021, China’s GDP exceeded USD 17.73 trillion, becoming the world’s second-largest economy after the United States. At the same time, China is the only country in the world with a population of more than 1 billion and a per capita GDP of more than USD 10,000. In 2020, China became the only major economy in the world with a positive economic growth rate despite the COVID-19 pandemic. However, at the same time, China has also become the world’s largest carbon emitter. According to statistics, China’s carbon emissions reached 11.9 billion tons in 2021, accounting for 33% of global carbon emissions. To achieve global sustainable development and actively respond to future changes, China’s energy conservation and emission reduction should be treated as urgent.

As early as 2015, China signed the Paris Agreement, which aims to significantly reduce global greenhouse gas emissions, control the global temperature increase to 2 °C by two hundred years, and work toward the goal of limiting the temperature increase to 1.5 °C. In the joint Statement on Climate Change issued by China and the United States in 2014, China proposed that carbon dioxide emissions would peak approximately in 2030 and that it will strive to achieve the peak as early as possible. In 2020, in his address to the general debate of the 75th Session of the United Nations General Assembly, President Xi reiterated that China will increase its nationally determined contribution, adopt more powerful policies and measures, strive for a peak in carbon dioxide emissions by 2030, and strive for carbon neutrality by 2060. Subsequently, President Xi mentioned China’s carbon peak and carbon-neutral goals eight times on important international occasions, demonstrating China’s determination and importance in achieving these goals. There is no doubt that this will have a significant impact on China’s economic development and people’s lives, marking the transformation of China’s development model. In this context, it is of great practical significance to forecast the path of carbon emissions peaking in 2030 against the background of China’s macro-goal of carbon emissions peaking. The peak prediction of carbon emissions also received substantial attention in academic circles.

Data show that 90% of China’s CO₂ emissions come from fossil fuels. To a certain extent, China will achieve peak carbon emissions when fossil energy consumption peaks. However, the growth of China’s energy demand and carbon emissions is not only brought about by domestic economic growth and rising demands but is also partly due to foreign demand [1,2,3]. International trade is an important factor that affects a country’s CO₂ emissions. Via international trade, a country can separate its domestic production and consumption behavior from its carbon emissions to a certain extent. Since China acceded to the WTO, there has been a large surplus in import and export trade year-round. These articles show that China’s net export trade contains a large amount of hidden carbon transfer [1,2,4]. Therefore, in the study of China’s carbon emissions, we should pay attention to the impact of carbon implied by trade. However, existing studies on “carbon emissions in China” rarely consider the impact of carbon implied by trade, and almost all predict China’s carbon emissions trend from the perspective of the production side.

When predicting China’s CO₂ emission trend, this paper added the factor of carbon implicit in international trade and predicted China’s CO₂ emission trend under different scenarios from the perspectives of production and consumption so as to study whether China can achieve the carbon peak before 2030. By comparing the periods of carbon peaks, this paper pointed out the direction of China’s carbon emission reduction policy and the change in import and export trade structure, which enrich carbon peak research in China.

2. Review of the Literature

2.1. A Review of Carbon Peak Projection Studies in China

The problem of China’s carbon peak projection has been the focus of research in related fields after the Joint Statement on Climate Change by China and the United States in 2014. Zhao et al. [5] applied the STIRPAT model and established three scenarios to analyze the trajectory and peak time of HCEs from the provincial perspective in the 30 provinces of China up until 2040. The results show that five provinces will not peak their HCEs by 2030 under any scenario. Wang et al. [6] used an extreme learning machine (ELM) prediction model based on manta ray foraging optimization (MRFO) and the scenario setting method to predict the peak of carbon dioxide emissions from transportation in China. The results show that under the baseline model scenario, China’s transportation CO₂ emissions will peak in 2039, while under the sustainable development model and the high growth model, China’s transportation CO₂ emissions will peak in 2035 and 2043. Xu et al. [7] used the dynamic nonlinear artificial neural network–nonlinear autoregressive model (NARX) to predict China’s carbon dioxide emissions. By constructing three scenarios, the results show that China’s carbon dioxide emissions will reach a peak of 100.8–116.3 billion tons between 2029 and 2035. Zhou et al. [8], by conducting a three-year joint research project to explore how to cost-effectively reshape the energy economy, found that if China adopts active carbon dioxide reduction technologies and efficient energy utilization and vigorously develops the development and utilization of nonfossil energy, carbon emissions in 2050 can be reduced by 42% than compared to emission levels in 2010. Li et al. [9] used the improved IPAT model [10] to calculate China’s carbon intensity for the 2005–2015 period, and the results show that China can achieve a CO₂ peak only when the carbon intensity decreases more than the GDP growth rate and that China will achieve a CO₂ peak if the GDP in 2030 is less than CNY 15,142.15 billion in 2030. Proposing an integrated economic and climate model (IMEC) based on the input–output model, Mi et al. [11] observed that China may reach the peak level of CO₂ emissions in 2026 with a peak of 1001.20 million tons and that China will reduce CO₂ emissions by approximately 2.2 billion tons from 2015 to 2035. Li et al. [12] simulated long-term CO₂ emissions and economic development in China by constructing an energy–environment–economy model. It was found that under the low-carbon scenario, China’s CO₂ emissions can peak by 2030 and that the negative impact of China’s fossil fuel public goods on GDP can be mitigated by 5.5% by 2050. Most prediction models are still based on traditional econometric methods. The impact of CO₂ emissions is a complex and variable nonlinear system, and traditional econometric models are affected by model selection, variable selection, and parameter estimation in predicting the peak of CO₂ emissions, resulting in poor prediction accuracy.

2.2. A Review of the Literature on Implicit Carbon Transfers in International Trade

Since the 1990s, there has been a large amount of literature on the study of embodied carbon in international trade. In recent years, almost all studies have shown that international trade has many implicit carbon emissions. Moreover, developed countries are net importers of embodied carbon. Using an improved multiregional input–output (MRIO) table, Jiang et al. [2] calculated the net transfer of CO₂ emissions in global international trade in 2007. The study shows that if processing exports are not properly differentiated, net CO₂ emissions from China to other regions will be distorted, with a relative deviation of 15%. The carbon emissions of China in 2006 are estimated based on consumption rather than production [4]. Kim et al. [13] applied a multiregional input–output approach to calculate China–Pakistan trade’s CO₂ emissions and value added. It was found that the CO₂ emissions and value added embodied in Chinese and Brazilian exports increased significantly from 2000 to 2014, while structural analysis shows that changes in consumption in China and Brazil, and changes in the structure of China’s intermediate exports to Brazil are important sources of the increase in implied CO₂ emissions. The results show that this approach could reduce the responsibility for CO₂ emissions in 2006 from 5500 Mt to 3840 Mt; therefore, special attention should be given to the amount of carbon transfer in trade when assigning responsibility for emission reduction. The analysis of the study by Weber et al. [1] revealed that approximately one-third of China’s carbon emissions in 2005 came from export production, and consumption in developed countries may make this trend increase; thus, it makes sense to make developed countries responsible for China’s export emissions, and the responsibility for emission reduction must be carefully designed in order to reach political consensus and equity.

2.3. Literature Summary

In summary, there is a large body of literature that demonstrates that international trade contains implicit carbon, that China is a net transferor of implicit carbon, and that from a consumer responsibility perspective, the CO₂ produced by China to meet its consumption is lower than the published CO₂ emissions. At the same time, most of the literature on China’s carbon peak is considered from the production side, and little consideration is given to the implicit carbon factor in international trade. Therefore, this paper reviews the relevant literature in order to study China’s carbon peaking path and the problem of peaking from both the production and consumption side, which is of practical significance for the formulation of China’s current trade policy.

3. Materials and Methods

The long-range energy alternatives planning system (LEAP), developed by the Stockholm Environment Institution (SEI) and the Tellus Institute, Boston, USA [14], is favored by researchers in the field of energy and environment due to its characteristics of comprehensiveness, convenience, intuitiveness, and decision-making ability. The LEAP model has been used for energy policy analysis and carbon reduction assessment by hundreds of institutions in more than 150 countries around the world, including government agencies, academic institutions, NGOs, consulting firms, and the energy sector [15,16,17,18,19,20,21,22].

Therefore, based on LEAP and the scenario analysis method, this paper predicts China’s CO₂ emission trends under different scenarios on the production side and the consumption side. The energy consumption accounting and CO₂ emission prediction models based on the production side and consumption side are constructed in this study, including production-side energy accounting, the consumption-side energy accounting model, the LEAP model, and the integrated energy and CO₂ emission prediction model based on the socioeconomic conditions of China (LEAP-China production-side model, LEAP-China consumption-side model), which are considered from the production side and consumption side, respectively.

3.1. Accounting for Energy Consumption and Carbon Emissions on the Production Side

According to the emission reduction framework established by the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol, energy consumption/carbon emissions on the production side are accounted for based on the principle of “producer responsibility” or “geographical boundary”. Under this approach, the energy consumed or carbon emissions from a country’s production activities (whether final production to meet domestic or foreign demand) are counted as domestic energy consumption and carbon emissions if they occur in the country.

In this paper, production-side energy consumption and carbon emissions are mainly accounted for by the IPCC sectoral method developed in 2006, which accounts for CO₂ emissions from different sectors based on their energy consumption and sums up to obtain the overall national CO₂ emissions. China’s production-side energy consumption includes not only the energy consumed by the industrial and domestic sectors but also the energy consumed by the processing and transformation sectors.

The energy demand of the industrial sector or the domestic sector is

$$S{D}_t={\displaystyle \sum }_{i}A{L}_{ti}\ast E{L}_{ti}$$

(1)

where $A{L}_{ti}$ is the activity level of sub-department i of department t.$E{L}_{ti}$ is the energy intensity of sub-sector i of department t.$S{D}_t$ is the terminal energy demand of department t.

Therefore, the industrial sector or the domestic sector’s CO₂ emissions are described as follows:

$$S{E}_t={\displaystyle \sum }_i{\displaystyle \sum }_{k}S{D}_{tik}\ast E{F}_{tik}$$

(2)

where $S{D}_{tik}$ is the energy consumption of species k in subsector i of department t. $E{F}_{tik}$ is the carbon emission factor of species k in subsector i of department t. $S{E}_t$ is the total CO₂ emissions of the end-consumption sector t.

For the processing and transformation sector, the formula is as follows:

$$T{M}_S={\displaystyle \sum }_k{L}_{sk}\ast E{F}_{sk}$$

(3)

where $L_{sk}$ is the energy input of k species of department s. $E{F}_{sk}$ is the carbon emission factor of k species of department s. $T{M}_S$ is the total CO₂ emissions from processing and conversion sector s.

Then, the total energy use related to CO₂ emissions TE is as follows.

$$TE={\displaystyle \sum }_{t}S{E}_t+{\displaystyle \sum }_{s}T{M}_s$$

(4)

3.2. Accounting for Energy Consumption and Carbon Emissions on the Consumption Side

Consumption-based energy/emissions differ from the production side in that it uses the production principle [23,24,25]. This calculation method considers the implicit carbon in trade and is more favorable for trade surplus countries such as China in sharing the responsibility for emission reductions. Most studies on the carbon content of trade are based on input–output models, which effectively combine carbon emissions and import/export issues. Input–output models are divided into single region input–output models (SRIOs), bilateral region input–output models (BRIOs), and multivariate region input–output models (MRIO). Based on the focus of this paper, the single region input–output model is adopted to calculate demand-side energy consumption in China, because this model is more suitable for studying the impact of trade on carbon emissions in a single region.

The SRIO model study examines trade-implied energy and carbon emissions, considers other countries, does not distinguish the origin of intermediate products, and adopts the principle of the same technology assumption at home and abroad. Based on the SRIO model that assesses demand-side energy consumption, scholars generally add production-side energy consumption to import-implicit energy and subtract export-implicit energy to obtain it.

If the world contains n countries, each with K sectors, the n-country production trade system can be written as the following chunking matrix:

$$X=AX+Y$$

(5)

where X is the total output matrix. $X_{ir}$ is the matrix of sectoral outputs in country i that directly or indirectly meet the demand of country r. Then, vector $X_i={\displaystyle \sum }rYir$ is the total output of country i; A is the matrix of intermediate input coefficients; $A_{ii}$ is the matrix of demand for country i’s intermediate goods per unit of output in country i. $A_{ir}$ is the matrix of demand for country i’s intermediate product per unit of output in country r, Y is the matrix of final demand, and$Y_{ii}$ is the matrix of the final demand for country i’s product in country i.$Y_{ir}$ is the final demand matrix of country r for country i’s product, and vector $Y_i={\displaystyle \sum }rYir$ is the total demand for the final product in country i.

Export energy matrix EEE and import energy matrix EEI for country 1 can be expressed as follows:

$$EE{E}_1=F_1{L}_{11}{\displaystyle \sum }_{i\ne 1}{E}_{1i}=F_1{L}_{11}{\displaystyle \sum }_{i\ne 1}\left(Y_{1i}+A_{1i}{X}_i\right)$$

(6)

$$EE{I}_1=F_1{L}_{11}{\displaystyle \sum }_{i\ne 1}{E}_{i1}=F_1{L}_{11}{\displaystyle \sum }_{i\ne 1}\left(Y_{i1}+A_{i1}{X}_1\right)$$

(7)

where $F_1$ is the energy intensity of country 1, $L_{11}$ is the Lyontief matrix for country 1 $L_{11}={\left(I-A_{11}\right)}^{-1}$, and $A_{11}$ is the matrix of demand for intermediate goods of country 1 per unit of output of country 1.

The consumption-side energy consumption matrix is as follows.

$$E{C}_1=T{S}_1-EE{E}_1+EE{I}_1$$

(8)

Consumption-side CO₂ emissions are as follows.

$$TD{E}_1={\displaystyle \sum }_{k}E{C}_{1k}\ast E{F}_{1k}$$

(9)

3.3. LEAP-Based CO₂ Emission Prediction Model

The base period is 2019, and the forecast period is 2020–2050. According to China’s energy balance table, China’s energy consumption departments are divided into the primary industry (agriculture, animal husbandry, forestry, water conservancy, and fishery), the secondary industry (construction and industry), the tertiary industry (storage, transportation, telecommunications, and post; retail trade and wholesale and catering; and others), and consumer life department. There are four main types of energy consumption: oil, coal, natural gas, and nonfossil energy. China’s energy processing and conversion sector are mainly composed of oil refining, thermal power generation, heating, cooking, and gas production.

Based on the current literature [26,27,28,29], it can be observed that there are many driving factors affecting long-term energy demands and CO₂ emissions in China, which can be divided into macroeconomic factors and policy factors. In addition, according to the requirements of the model framework and scenario setting, the following factors are selected as the main influencing factors of the carbon emission model: the proportion of different types of energy consumption, carbon emission coefficient, industrial structure, energy intensity, urbanization rate, population size, and gross national product (i.e., GDP). The end consumption sector can be further divided into the industrial sector and the living sector after considering the above factors.

The industry sector’s CO₂ emission projection model is as follows:

$$\begin{gathered} C_{1t}={\displaystyle \sum }_i{\displaystyle \sum }_j{C}_{ijt}={\displaystyle \sum }_i{\displaystyle \sum }_j\left(\frac{C_{ijt}}{E_{ijt}}\ast \frac{E_{ijt}}{E_{it}}\ast \frac{E_{it}}{Y_{it}}\ast \frac{Y_{it}}{Y_t}\ast Y_t\right) \\ ={\displaystyle \sum }_i{\displaystyle \sum }_{j}C{I}_{ijt}\ast ET{S}_{ijt}\ast E{I}_{it}\ast E{S}_{it}\ast Y_t \end{gathered}$$

(10)

where i represents industry type, j represents energy type, and t represents time. $C_{1t}$ is the total carbon dioxide emissions from the industry sector in year t, and $C_{ijt}$ denotes the carbon dioxide emissions generated by the jth fuel of industry i in year t, and $E_{ijt}$ represents the consumption of jth fuel of industry i in year t, and $E_{it}$ stands for the total energy consumption of industry i in year t. $Y_{it}$ stands for the value added of industrial production of industry i in year t, and $Y_t$ stands for China’s gross domestic product in year t. $C{I}_{ijt}$ represents the carbon emission coefficient of the j fuel of i industry in year t, $ET{S}_{ijt}$ represents the energy consumption ratio of the j fuel of i industry, $E{I}_{it}$ denotes the production-side energy intensity/consumption-side energy intensity of industry i, and $E{S}_{it}$ stands for the proportion of value-added production of industry i in year t.

The living sector consists of two parts, including the urban living sector and the rural living sector. The total CO₂ prediction model is shown as follows:

$$C_{2t}={\displaystyle \sum }_u{\displaystyle \sum }_v{C}_{uvt}={\displaystyle \sum }_u{\displaystyle \sum }_{v}C{I}_{uvt}\ast ET{S}_{uvt}\ast P{I}_{ut}\ast T{P}_{ut}$$

(11)

where u represents the rural living or urban department, v stands for the energy type, $C_{2t}$ represents the total carbon dioxide emissions generated by the living department in year t, $C_{uvt}$ represents the carbon dioxide emissions generated by fuel v in u living department in year t, and $C{I}_{uvt}$ stands for the carbon emission coefficient of fuel v in u living department in year t. $ET{S}_{uvt}$ represents the share of energy consumption of fuel v in the total energy consumption in department u in year t. $P{I}_{ut}$ represents the energy intensity per capita of the u living department in year t, and $T{P}_{ut}$ represents the total population of the u living department in year t.

The frameworks of LEAP-China production-side and LEAP-china consumption-side models in this paper are shown in Figure 1 and Figure 2.

3.4. Scenario and Variables Setting

To discuss, compare, and analyze the peaking time of China’s production-side and consumption-side peak models under different low-carbon development paths, as well as the impact of different low-carbon development paths on China’s energy consumption and carbon-peaking time, this paper refers to related articles [8,30,31,32]. To discuss and compare the impact of different low-carbon development paths on China’s energy consumption and carbon-peaking time, three scenarios were established, including the current policy scenario, carbon constraint scenario, and sustainable development scenario.

3.4.1. Scenario Description

Current policy scenario: This scenario continues the policies of the 13th Five-Year Plan and its predecessors, does not consider industrial structure upgrading and energy structure optimization, and sets relevant parameters based on the production patterns of industrial sectors and energy consumption of end-use sectors in the base year, with the improvement in energy efficiency and clean energy generation mainly relying on socioeconomic development. The improvement in energy efficiency and clean energy generation is mainly driven by socioeconomic developments. Therefore, this scenario can basically reflect nature-led economic growth, energy consumption, and CO₂ emission processes.

Carbon emission constraint scenario: Based on the current policy scenario, this scenario takes into account the upgrading of industrial structure and the commitments made in the U.S.–China Joint Statement on Climate Change and the Paris Agreement, as well as the expected targets in special programs such as China’s Policies and Actions to Address Climate Change and the 14th Five-Year Plan to Control Greenhouse Gas Emissions. The goal is to set targets for energy consumption and carbon emissions.

Sustainable Development Scenario: This scenario is a comprehensive regulatory scenario. Compared with the current policy scenario, the industrial structure, regional structure, energy efficiency level, and energy consumption structure in this scenario will be further optimized and require major initiatives in macroeconomic policies, energy planning, and climate policies. Therefore, this scenario can basically reflect the dynamic evolution path of economic growth, energy consumption, and climate change under the effect of integrated regulation.

3.4.2. Variables Settings

In summary, the key node parameters for 2020–2045 in each scenario set in this paper are shown in

Table 1. Variable settings for different scenarios.

Variables		Base Year	Current Policy Scenario		Carbon Emission Constraint Scenario		Sustainable Development Scenario
		2019	2030	2050	2030	2050	2030	2050
Industry Structure/%	Primary Industry	7.5	7.5	7.5	6	4	6	4
	Secondary Industry	38.77	38.77	38.77	38.77	38.77	32	26
	Tertiary Industry	53.77	53.77	53.77	53.77	53.77	62	70
Nonfossil energy consumption share/%		15.9	15.9	15.9	25	33	30	38
Share of clean energy generation/%		32.7	32.7	32.7	42.4	57.8	42.4	57.8
Percentage decrease in energy intensity kj/CNY	Primary Industry	265.19	2	1.5	2.5	2	2.5	2
	Secondary Industry	1802.67	4	3.6	5.4	4.1	5.4	4.1
	Tertiary Industry	359.189	2	1.5	2.5	2	2.5	2
Energy consumption per capita tce/person	Cities and towns	0.31	0.35	0.39	0.35	0.39	0.35	0.39
	Rural	0.30	0.28	0.25	0.28	0.25	0.28	0.25

.

3.5. Data Source

Based on data availability and simulation study needs, the macroeconomic data required for this study, such as GDP, population, and import and export volume by industry in the base period, are from the National Bureau of Statistics of China; the required energy data are from the China Energy Balance Sheet and China Energy Statistical Yearbook; the carbon emission coefficients are from the CEAD’s database; the future Chinese GDP growth rate is set concerning articles [30,33]. The urbanization rate is set regarding “THE URBAN BLUE BOOK: CHINA URBAN DEVELOPMENT REPORT NO. 12”, published by the Institute of Urban Development and Environment and Chinese Academy of Social Sciences and Social Science Literature Press, and “CHINA URBANIZATION 2.0: SUPER METROPOLITAN AREAS”, published by Morgan Stanley; the data of future population projections are obtained from the “Population Development Plan” and the “Compilation of Population Projections”. The future macroeconomic parameters of China are shown in

Table 2. Setting of key node variables of the future macroeconomy.

	2030	2050
Average GDP growth rate/%	5.3	3.0
Population size/million	1442	1364
Urbanization rate/%	70	80

.

4. Results

Models were run in LEAP, and the prediction results of China’s CO₂ emission trends were obtained under different scenarios on the production side and the consumption side.

As shown in Figure 3 and Figure 4, the CO₂ emissions in all scenarios rise first and then fall in both the production-side model and consumption-side model, and the emission trends show an inverted U-shape. From the figure, it can be observed that the peak time and peak size of China’s carbon emissions vary in different scenarios, with the sustainable development scenario being the earliest to reach the peak and exhibiting the smallest peak in the production-side model and consumption-side model. Meanwhile, although the overall trend is the same, same-year emission values of the consumption-side carbon emission model, considering implied carbon, were lower than those of production-side carbon emissions in all scenarios. The overall peak time for the different scenarios is roughly distributed between 2028 and 2039.

4.1. CO₂ Emission Trends from Different Perspectives

4.1.1. Production-Side Stand CO₂ Emission Trend

As shown in Figure 3, production-side carbon dioxide emissions in different scenarios show an upward and then downward trend. In the current policy scenario, production-side carbon emissions peak at 12,208.3 Mt, and the peak year is 2039, which obviously cannot achieve the requirement of reaching the peak before 2030. Under the carbon emission constraint scenario, the carbon emission peak on the production side is 10,949.4 Mt, which is 10.3% lower than the peak of the current policy scenario, and the peak year is 2036; moreover, it is pushed forward by 3 years, which indicates that the upgrade and transformation of industrial structure will have a significant positive impact on the peak’s advancement and peak reduction. Compared with the carbon constraint scenario, the carbon peak only reduced by 487.2, but the peak year was significantly earlier by 6 years. This result indicates that the carbon emission reduction brought by the energy conversion of consumption is not as great as that brought by the optimization of industrial structure, but it can achieve the carbon peak year significantly earlier.

4.1.2. Consumption-Side Carbon Dioxide Emission Trend

As shown in Figure 4, consumption-side CO₂ emission values are lower than production-side CO₂ emission values in all scenarios. In the current policy scenario, consumption-side carbon emissions considered the implicit carbon peak at 11,508.7 Mt, with a peak year of 2039. The peak consumption-side carbon emissions are 5.7% lower than the peak production-side carbon emissions, which is within a reasonable range considering the share of net exports in China’s GDP in recent years. In the carbon emission constraint scenario, the consumption-side carbon emissions peak is 10,378.7 Mt, and the peak year is also 2036, similarly to the production-side carbon emissions, which is 5.2% lower than the production-side carbon emissions’ peak and 9.8% lower than the peak of the current policy scenario for consumption-side carbon emissions considering implicit carbon. In the sustainable development scenario, the peak year of the consumption-side carbon emissions considering implicit carbon is 2032, which is 3 years later than the peak year of production-side carbon emissions in this scenario, with a peak of 9904.3

4.2. CO₂ Emission Trends from Different Scenarios

As mentioned in

Table 3. Comparison of peak time and peak value of carbon measurements between the production side and consumption side.

	Production Side Peak Model		Consumption-Side Carbon Peak Model
	Peak Time	Peak	Peak Time	Peak
Current policy scenario	2039	12,208.3	2039	11,508.7
Carbon emission constraint scenario	2035	10,949.4	2035	10,378.7
Sustainable development Scenario	2029	10,462.2	2032	9904.3

, under the current policy scenario, both the production side and consumption side have the highest CO₂ emission values, and the peak time is also the latest, with a peak time of 2039. Under this scenario, it is obvious that the 2030 carbon emission peak requirement cannot be achieved; under the carbon emission constraint scenario, the peak time of both the production side and the consumption side is earlier than the current policy scenario, and the peak is also reduced, which increases the carbon emission constraint under the current policy scenario, indicating that the decrease in energy intensity has a significant positive impact on the peak advance and peak reduction.

The sustainable development scenario is the most likely development model for China in the future, where an optimized industrial structure and a shift in the energy consumption structure can lead to further advancements with respect to the peak year and a further reduction in the peak. As shown in Figure 5, in this scenario, the peak time on the production side is 2029, and the peak value is 10,462.2 million tons. It can be calculated from Figure 6 that the contribution of CO₂ emissions from the primary sector is 1.9%, the contribution of secondary sector CO₂ emissions is 64.6%, the contribution of tertiary sector CO₂ emissions is 24.4%, and the contribution rate of carbon dioxide emissions from the consumer life department is 9.1%. Considering that the peak time of the production side of international trade implied carbon is 2032 and the peak is 9904.3 million tons, at this time, the contribution rate of carbon dioxide emissions from the primary industry is 2.14%, the contribution rate of carbon dioxide emissions from the secondary industry is 60.6%, the contribution rate of carbon dioxide emissions from the tertiary industry is 27.96%, and the contribution of the consumer life department’s CO₂ emissions is 9.3%, as calculated from Figure 7. It can be observed that the contribution of the secondary industry to CO₂ emissions is the largest, and the relevant departments can introduce corresponding policies to suppress CO₂ emissions from the secondary industry in order to achieve a peak reduction in carbon emissions and reach the peak as early as possible. At the same time, the peak time on the consumption side is 3 years later than that on the production side, indicating that the existing import and export trade structure will delay the carbon peak time, which is not conducive to China’s current development. If we want to achieve the peak on the production side and the consumption side at the same time in the year 2030, in addition to the coordinated efforts of various policies, such as the implementation of the “double control” of energy consumption intensity and total amount and the active promotion of industrial restructuring and energy structure optimization, we also need to adjust the existing trade structure of China’s existing import and export trade, reduce the export of industrially manufactured goods, and increase the proportion of technology-intensive production in foreign trade to realize the transformation from a high-carbon trade structure to a low-carbon trade structure.

5. Discussion

In recent years, China has been emphasizing green development and green transformation. Whether China can achieve the carbon peak before 2030 and fulfill its commitment has been a hot topic of research. According to the research results of this paper, under the scenario of sustainable development on the production side, China’s carbon emission can reach its peak before 2030, which is similar to the research results of many scholars [8,32,34]; that is, the carbon peak can be achieved only with the joint efforts of a variety of policies. From the perspective of carbon emissions on the consumption side, under the baseline scenario and the carbon emission constraint scenario, the peak time is the same as that on the production side, but under the sustainable development scenario, the peak time is different from that on the production side, which is a problem worth discussing. If the production side and consumption side reach the peak, this means that China’s import and export trade structure needs to be adjusted.

In addition, the study had some limitations. To some extent, the forecast results depend on the settings of indicators, which are determined from relevant data, and the economy is dynamic, which leads to indicators that do not accurately reflect the state of the economy. Thus, the next step is to use dynamic forecasting, taking into account the responses of the economic system, to make the results more accurate.

6. Conclusions and Recommendations

This paper simulates and predicts the trend of China’s CO₂ emissions in different scenarios from the production side and the consumption side, and it draws the following conclusions based on the analysis.

In terms of individual prediction models, industrial structure upgrading, different energy intensities, and energy mix optimization have a significant impact on the trend of CO₂; here, the more the industrial structure is skewed toward the tertiary sector, the more effective the energy intensity policy and the energy structure transition policy, and the carbon emissions peak is attained earlier, and the peak target becomes lower.

Based on both the production side and consumption side, China’s continued development will inevitably lead to an increase and then a decrease in CO₂ emissions. Under different scenarios, CO₂ emission values followed the following order: baseline scenario > carbon constraint scenario > sustainable development scenario; peak years followed the following order: baseline scenario > carbon constraint scenario > sustainable development scenario; combined with the implications of different scenarios, this suggests that China’s carbon peak path will be influenced by a combination of economic structure, technological progress, energy intensity, and energy structure. Therefore, it is necessary to break the “lock-in” effect of China’s current high-carbon development model, industrial structure system, energy technology path, and energy consumption behavior and realize the structural optimization of industry and energy as well as paradigm change in production and consumption.

Under the projection model of carbon emissions on the consumption side considering implicit carbon, although CO₂ emissions are lower than carbon emissions on the production side under the same conditions, the peak of carbon emissions on the consumption side is delayed by 3 years compared with the peak on the production side under the sustainable development scenario, indicating that the existing trade structure is not conducive to the future development of China. To achieve the peak of both the consumption side and production side, China’s current import and export trade structure needs to be adjusted by reducing the export of high-carbon products in industrial manufactures and increasing the proportion of technology-intensive products in foreign trade to achieve a shift from a high-carbon trade structure to a low-carbon trade structure.