Scenario Analysis of Renewable Energy Development and Carbon Emission in the Beijing–Tianjin–Hebei Region

Abstract

The Beijing–Tianjin–Hebei region (BTH) is a key area with large carbon emissions in China and a demonstration area for renewable energy development, facing the dual test of energy structure transformation and the achievement of carbon peak and neutrality goals. This study analyzes the main influencing factors of carbon emissions based on Kaya’s identity, establishes a socio-economic-energy-carbon emission coupled with system dynamics (SD) model, and designs five scenarios to predict and compare the future trends of energy consumption, renewable energy development and carbon emissions in BTH, respectively. The results show that (1) under the baseline scenario, energy carbon emissions in BTH will peak around 2034, and the intermediate development scenario, the transition development scenario and the sustainable development scenario all show that the region can achieve the emission peak target around 2030. (2) The renewable energy output value of BTH will reach CNY 486.46 billion in 2050 under the baseline scenario, and the share of renewable energy consumption will exceed 50% under the sustainable development scenario. (3) Increasing energy tax regulation and scientific and technological investment and adopting more stringent policy constraints can guarantee the lowest emission intensity while maintaining the current social and economic development level. This study predicts the development of a renewable energy industry and carbon emissions in BTH under different scenarios and provides policy recommendations for the future energy transition in the region.

1. Introduction

Climate change has become an issue of concern to the international community. The excessive consumption of fossil energy is the main factor affecting global climate change. A low-carbon economy will curb greenhouse gas emissions caused by human activities and mitigate climate change [1]. The low-carbon economy takes technology and system innovation, renewable energy development and industrial transformation as the means of realizing the transition from using fossil energy to renewable energy, resulting in a win–win situation of economic and social development and ecological environmental protection. Optimizing the energy structure, improving the efficiency of energy use and reducing greenhouse gas emissions has become an inevitable choice to achieve low-carbon development [2].

China is under enormous pressure to reduce carbon emissions and transform its energy sources. The carbon emissions in China accounted for 27.2% of the world’s total in 2019 [3]. Against this background, China is actively undertaking the task of carbon emission reduction. The carbon peak and neutrality goals have been written into the Report on the Work of the Government for the first time, which proposed to promote the energy revolution, push forward the low-carbon energy transition, and transition from assessing the total amount and intensity of energy consumption to assessing the total amount and intensity of carbon emissions [4]. The 14th Five-Year Plan (2021–2025) for National Economic and Social Development and the Long-Range Objectives Through the Year 2035 stated that “ahead to 2035, carbon peak will be reached and carbon emissions will decrease, fundamental improvement of ecological environment, achieving the basic goal of beautiful China” “to enact specific action plans for the achievement of carbon peak by 2030. anchoring to strive to achieve carbon neutrality by 2060” [5]. Carbon neutrality means net-zero carbon emissions [6]. The carbon peak and neutrality goals will trigger a revolution in China’s energy system and promote a comprehensive green and low-carbon transformation in the economy. Therefore, by establishing different future development scenarios and forecasting future trends of renewable energy and carbon emissions, it is of great significance to better promote energy transition and achieve green development goals.

After the proposal of carbon peak and neutrality goals, scholars have conducted a series of related studies on its realization. Zhao et al. have put forward a three-stage strategy to achieve carbon neutrality in China, starting with the achievement of the carbon peak in 2021–2030, a deceleration of carbon emission growth in 2031–2040 and a gradual zero and complete net-zero emission in major industries in 2041–2060 [7]. Specifically, population growth will contribute to increased energy consumption and CO₂ emissions, and the negative effects of non-economic factors such as education and globalization on energy consumption and CO₂ emissions may also be evident in the future [8]. Under the new normal of slowing economic growth, promoting energy structure and industrial transformation will be necessary to achieve climate change mitigation goals [9]. Scholars have provided different insights regarding the timing of achieving the carbon peak in China [10,11]. Xu et al. argue that under normal conditions, CO₂ emissions in China will not peak in 2030; however, a lower growth rate of energy consumption and a more low-carbon energy structure will not only bring this peak earlier, but will also reduce the peak level [12]. Zhao et al. have modeled future scenarios of household carbon emissions in China [13]. Results show that at the national level, the carbon peak target will be achieved in all scenarios. However, at the provincial scale, Guangxi and Hainan provinces will maintain growth of CO₂ emissions in all scenarios until 2040. Fang et al. have modeled future emission scenarios for eight industrial sectors in China [14]. They found that emissions from agriculture, construction, manufacturing, other industries, and transportation are highly likely to peak by 2030, while emissions from the power and mining sectors are likely to peak after 2030. However, most of the above studies focus on a single perspective of economy, population and energy on carbon emissions, without considering the compound impact of these factors and the lack of dynamic studies on the complex system of the renewable energy industry.

In addition to replacing traditional energy sources and compensating for energy shortages, renewable energy sources can also help reduce the environmental pollution and CO₂ emissions brought on by traditional energy use, thereby assisting in the achievement of carbon peak and neutrality goals. In the long run, investment in renewable energy will effectively reduce China’s carbon emission intensity [15]. Danish et al. use the dynamic auto-regressive distributive lag (DARDL) model to simulate the effects of technological innovation and renewable energy use on carbon emissions in China and the U.S. [16]. The study shows that the use of renewable energy will effectively reduce carbon emissions in both countries. Jia et al. argue that investment in renewable energy is a fundamental alternative to coal consumption and an essential means for achieving carbon emission reductions [17]. However, previous studies only explore the influencing factors that affect the achievement of carbon peak and neutrality goals, or they only simulate the future scenarios of carbon emissions under a single influencing factor, without taking into account the dynamic simulation of each influencing factor. The system dynamics (SD) methods can deeply study the information feedback behavior in complex systems and has greater advantages for solving nonlinear problems in complex systems [18]. It has been widely used in integrated research and decision support in social [19], economic [20], energy [21], and environment [22] domains, demonstrating its superiority in forecasting renewable energy development and future scenarios of carbon emission.

Moreover, the Beijing–Tianjin–Hebei region (BTH) is an important demonstration area for the construction of carbon neutrality in China. In the context of the synergistic development and deep integration of Beijing–Tianjin–Hebei, it is important to explore the impact path of the renewable energy industry’s development on society, economy and carbon emissions, and to predict the future development trend of the renewable energy industry to achieve regional and national sustainable development.

Therefore, this study adopted the SD model to reveal the reciprocal feedback between the economy, society, renewable energy industry and carbon emissions in BTH, and predict the development of the renewable energy industry and carbon emissions in the region under different scenarios, so as to guide the future low-carbon transition in the region.

2. Study Area

To accelerate the realization of low-carbon transition, carbon peak and neutrality goals, key metropolitan regions need to be piloted first. The BTH is the main zone responsible for the implementation of China’s carbon peak and neutrality goals. Currently, the metropolitan region is an important economic growth pole and a key energy consumption area in China [23]. The total energy consumption in BTH in 2018 was 474.28 million tons of coal equivalent (Mtce), accounting for 10% of the national energy consumption [24], and the region’s CO₂ emissions accounted for about 15% of China’s total CO₂ emissions [25], posing a serious challenge to the coordinated green development of the region. As one of the new industries, the renewable energy industry is the main direction of China’s future energy industry development [26]. The renewable energy industry will help BTH achieve carbon peak and neutrality goals, while carbon peak and neutrality goals will also promote the process of renewable energy revolution and diversification of energy structure in the region, and promote the leapfrog development of clean energy represented by wind power, hydropower and nuclear energy. Therefore, the study of the renewable energy industry’s development in BTH is referential for regional and even national low-carbon economic development. The geographical location of BTH is shown in Figure 1.

3. Materials and Methods

3.1. Kaya Identity

Carbon emissions from energy consumption are mainly influenced by factors such as population, economy and energy consumption [27]. Kaya identity decomposes carbon emissions as a product of society, economy and energy. Kaya identity has been widely used in the field of carbon emission research due to its advantages such as simple structure and logical clarity [28]. The standard form of Kaya identity is shown in Equation (1).

$$CE=POP\times \frac{GDP}{POP}\times \frac{E}{GDP}\times \frac{CE}{E}$$

(1)

In the above equation, CE represents carbon emissions, POP represents the total population, GDP represents the gross domestic product and E represents energy consumption. Referring to the study [29], the following decomposition of CE is performed.

$$CE={\displaystyle \sum _{i}POP\times GP\times EI\times {\eta }_i}$$

(2)

GP denotes GDP per capita, EI denotes energy intensity per unit of GDP, η denotes carbon emission factor and i denotes energy type. According to the above equation, the energy carbon emission is decomposed into four subsystems of economy, society, energy and carbon emission, and factors such as R&D investment, policy influence and renewable energy subsidies are introduced to establish the SD model of the renewable energy industry’s development and carbon emission in BTH.

3.2. SD Model

The SD model has been widely used to study energy consumption and greenhouse gas emissions at different scales, including national, regional and industry levels. The system dynamics model consists of a set of interlocking differential or algebraic equations [30]. It is a computer simulation method based on nonlinear dynamics and the feedback control theory. The method has the advantage of being able to enhance the understanding of complex feedback systems while supporting the policy-making process of decision makers [31]. SD simulates the complex behavior of different systems through stocks, flows and parameters. Stocks represent the accumulation of the inflows minus the outflows [32]. Based on the decomposition results of Kaya Identity, with reference to previous studies [33,34] and combined with the current situation of renewable energy industry’s development in the BTH region, the SD model is established. The structure of the model is shown in Figure 2. The four subsystems in the model are interconnected and form causal relationships. The specific parameters of each item are shown in the Supplementary Materials (Table S1).

(1)

Economic subsystem. Economic growth is the main factor affecting carbon emissions [35]. The economic subsystem mainly represents the change in GDP and the change in the intensity of renewable energy subsidies. On the one hand, economic growth will lead to an increase in carbon emissions. On the other hand, there is also a positive relationship between economic development and investment in environmental protection and renewable energy industries because carbon emissions can be reduced by increasing the investment in environmental protection and renewable energy industries to advance the energy transition.

(2)

Social subsystem. The growth of population size will lead to an increasing demand for energy consumption, which in turn will lead to more carbon emissions. The population subsystem is mainly reflected by the population size, including variables such as the total population of the region and the natural population growth rate.

(3)

Energy subsystem. Energy structure and energy consumption directly affect carbon emissions, and the energy subsystem is the core part of the SD model. The energy subsystem is mainly divided into two parts: conventional energy and renewable energy, among which the renewable energy output is influenced by technological progress, energy tax, renewable energy subsidies, emission reduction policies and other factors.

(4)

Carbon emission subsystem. Carbon emissions are closely related to social, economic and energy development, which in turn will stimulate the development of renewable energy industry. The carbon emission subsystem mainly involves variables such as carbon emission, carbon reduction by policy influence, carbon reduction by environmental protection investment and carbon emission per capita.

The combination of the cause–effect relationships and feedback loops of the above subsystems are integrated according to the proposed model framework. After clarifying the internal structure of each subsystem and the influence relationships between the main variables, the cause–effect loop diagram of the economic-socio-energy system is established, and the total feedback structure diagram of the energy-saving and emission reduction system is made using VENSIM PLE 9.0.0 software. The model operation ranges from the years 2000 to 2050, and the simulation step is 1 year, with 2017–2019 being the years to test the model operation and the actual situation, which can be used for model debugging and the determination of relevant parameters and variables. The years 2000–2050 are the predicted years of the system policy simulation, and the simulation at this stage is designed to forecast future trends in energy efficiency and emission reduction for policy analysis. The model converts GDP time-series data from current year prices to real values corrected for 2005 constant prices to remove the effect of price factor changes.

The model parameters mainly include three types—constants, initial values and table functions. According to the characteristics of the of energy-saving and emission reduction system structure, and considering the stability of data changes of the main variables and characteristics of inter-variable dependence, this study defines some individual parameters based on the relevant literature. For constants with large fluctuations, the effectiveness of using table functions to deal with nonlinear problems allows the model to simulate the real system more accurately. Such table functions include GDP growth rate (GGR), population growth rate (PGR), conventional energy growth rate (CEGR), renewable energy growth rate (NEGR) and renewable energy tax factor (NETF), with the parameters listed in the Supplementary Material. For some data with missing statistics, in order to retrieve the more realistic data required for the operation of the system, the data are generated by regression analysis, the expert estimation method and empirical prediction method. Descriptive statistics for some of the key parameters used in the model are shown in

Table 1. Descriptive statistics.

Variable	Unit	Obs.	Max	Min	Mean	Std. Dev.
GDP	CNY 108	20	84,605.1	9497.7	40,735.41	24,402.5
Total population (POP)	104 people	20	9745.57	8037.6	8955.34	622.82
Conventional energy consumption (CEC)	104 tce	20	48,136.45	18,133.42	36,249.36	10,340.34
Renewable energy consumption (NEC)	108 kWh	20	528.85	33.74	156.89	137.43

.

3.3. Model Validation

After establishing and quantifying the SD model, validity checks are conducted to ensure the credibility of the simulation results. The data from 2000–2016 are imported into the SD model, and the model outputs of key indicators such as total population, GDP, conventional energy consumption and renewable energy consumption for 2017–2019 are compared with the actual values to calculate the relative error (

Table 2. Validity check of the SD model.

Variable		2017	2018	2019
Total population (POP/104 people)	Actual value	9690.22	9710.50	9745.57
	Simulation value	9628.53	9676.68	9696.03
	Relative error	−0.0064	−0.0035	−0.0051
GDP (CNY 108)	Actual value	72,974.4	78,963.5	84,605.1
	Simulation value	73,001.3	78,987.4	84,595.5
	Relative error	0.0004	0.0003	−0.0001
Conventional energy consumption (CEC/104 t)	Actual value	47,002.6	47,428.3	48,136.5
	Simulation value	47,014.9	47,438.1	48,149.6
	Relative error	0.0003	0.0002	0.0002
Renewable energy consumption (NEC/108 kWh)	Actual value	339.31	432.62	528.85
	Simulation value	352.73	415.08	496.25
	Relative error	0.0396	−0.0405	−0.0616

). The results show that the simulated values of the SD model are highly similar to the actual values, and the relative errors do not exceed 7% (most of them do not exceed 1%, and the maximum deviation is 6.16%). The deviation of the calculated results of renewable energy consumption is relatively high because of the significant volatility of the actual data of renewable energy consumption in recent years. It is verified that the model shows good performance in its simulation and prediction.

3.4. Scenario Designs

It is urgent to promote the coordinated development of the economic-socio-energy system. In the past, the development process has paid too much attention to economic growth. In contrast, unreasonable exploitation of natural resources and pollution of the environment has led to the deterioration of the ecological environment and the frequent occurrence of natural disasters, threatening human survival. Therefore, sustainable development has been given high priority. Various policies have been formulated to adjust the development pattern. Under the development state of relevant measures, the control parameters are optimized and adjusted (

Table 3. Main factors of scenario designs.

Scenario	GDP Growth Rate (GGR)	Renewable Energy Tax Factor (NETF)	Factors of CO2 Reduction Policies (FCRP)	Population Growth Rate (PGR)	Proportion of R&D (PRI0)	Proportion of Environmental Protection Investment (PEI0)
Rough development scenario (S1)	+0.02	−0.1	−0.001	+0.001	−0.002	−0.002
Intermediate development scenario (S2)	-	+0.1	+0.001	-	+0.001	+0.001
Transformation development scenario (S3)	−0.01	+0.12	+0.001	−0.001	+0.002	+0.002
Sustainable development scenario (S4)	−0.02	+0.15	+0.002	−0.001	+0.003	+0.003

Note: The time range of parameter adjustment is 2020–2050.

), and the development results of indicators under different policy scenarios are tested out to provide a reference basis for decision-making.

By selecting six parameters: GDP growth rate (GGR), renewable energy tax factor (NETF), factors of CO₂ reduction policies (FCRP), population growth rate (PGR), proportion of R&D (PRI₀) and proportion of environmental protection investment (PEI₀), four scenarios are set: rough development scenario (S1), intermediate development scenario (S2), transformation development scenario (S3) and sustainable development scenario (S4). S1 is a rough development model with model parameters projected according to historical trends, maintaining a high rate of population and economic development, reducing inputs related to scientific and technological R&D and environmental protection, and performing SD model simulations without interfering with other factors. S2 builds on S1 by strengthening energy taxation and supporting inputs related to scientific and technological R&D and environmental protection, with only GDP growth and population development left untouched. S3 takes multiple measures to support all factors positively and slows down economic development with a corresponding decrease in population. S4 steps into the ideal model of sustainable development, with a further slowdown in economic development while also maintaining a steady decline in population and a further strengthening of energy taxation, the impact of emission reduction policies, scientific and technological research and development, and investment in environmental protection.

4. Results

4.1. Analysis of Historical Trend and Baseline Scenario

The results of the simulation under the baseline scenario are shown in Figure 3. The four factors of GDP, energy consumption, CO₂ emissions and renewable energy almost all show an increasing trend over time, which is important as a reference for the change of the baseline for different scenario analyses

From an overall perspective, these four indicators are in a certain upward trend from 2000–2050, with GDP rising from about CNY 1000 billion in 2000 to more than CNY 30,000 billion in 2050. Energy intensity experiences a significant overall decline during 2000–2050, with a small fluctuation in the early period, and a brief upward trend between 2003–2004. Overall, it has decreased from 1.8 tce/CNY 10⁴ in 2000 to about 0.2 tce/CNY 10⁴. Energy consumption shows a sharp upward trend from 2000–2015 and a slow upward trend from 2016–2050. It has risen from less than 200 million tce in 2000 to about 450 million tce in 2015 and reached about 700 million tce after experiencing slow growth. The change curve of energy consumption structure shows an overall regional downward trend, with a small increase around 2005, but a sharp downward trend in 2000–2020, and a flat downward trend in 2021–2050, from around 450 in 2000 to around 50 in 2020, and almost down to 0 in 2050. The overall trend of CO₂ emissions is also divided into two phases. It is a sharp rise at first, then a slightly convex slow rise and fall. CO₂ emissions per capita also have the same trend. CO₂ emissions rise from 400 Mt CO₂ in 2000 to about 1050 Mt CO₂ in 2013. After a peak of 1200 Mt CO₂ in 2030, it resides at around 1150 Mt CO₂ in 2050. CO₂ emissions per capita also follows this trend from 5.5 t in 2000 to more than 12 t. The change trend of the output value of renewable energy and economic benefit of renewable energy in renewable energy is very consistent. It is mainly divided into two stages: a slow rise in 2000–2020 and a fast rise in 2021–2050. The output value of renewable energy rises from less than CNY 10 billion in 2000 to about CNY 500 billion, while the economic benefit of renewable energy rises from less than CNY 10 billion in 2000 to more than CNY 500 billion.

4.2. Scenario Analysis

4.2.1. Energy Consumption

The results of the scenario simulation of energy consumption are shown in Figure 4. In terms of conventional energy consumption, S1 is higher than the baseline scenario, S2, S3 and S4, and S2, S3 and S4 are significantly lower than the baseline scenario. The conventional energy consumption can reach around 60 million tce in 2050 under S1, and around 45 million tce in 2050 under the lowest S4. In contrast, the renewable energy consumption in S1 is lower than the baseline scenario, S2, S3 and S4, and S2, S3 and S4 are higher than the baseline scenario. The renewable energy consumption can reach around 47 million tce in 2050 under the development scenario of S4 and around 10 million tce in 2050 under the lowest development scenario of S1. However, in terms of conventional energy consumption, even the more stringent S3 and S4 scenarios would hardly lead to a significant decrease in conventional energy consumption compared to the S2 scenario. Conventional energy sources still play an important and irreplaceable role in China’s energy system.

4.2.2. Renewable Energy Development

The simulation results of renewable energy development are shown in Figure 5. Both the output value and proportion of renewable energy sources keep growing faster after 2020, and the growth rate accelerates further after 2030. In terms of output value, the increase in the baseline scenario and S1 is significantly smaller than in the other three scenarios. By 2050, the output value of renewable energy in the baseline scenario and S1 are both around CNY 400 billion, while the highest output value in S4 can reach around CNY 1800 billion. In terms of proportion, the comparison of different scenarios and baseline scenario shows a more even discrete distribution, with S4 being higher than S3, S2, baseline scenario, and S1, respectively. By 2050, it can reach about 50% under the development scenario of S4, while it can only reach about 13% under the rough development of S1. Therefore, under the high-quality socio-economic development and policy encouragement, China’s renewable energy industry will achieve a remarkable development.

4.2.3. CO₂ Emissions

The simulation results of carbon emissions are divided into four perspectives (Figure 6): CO₂ emissions, total CO₂ emissions, CO₂ emission intensity and CO₂ emissions per capita. The triangle in Figure 6a indicates the peak year in the scenario projection. The growth rate of CO₂ emissions in BTH starts to slow down after 2015, and the peak target is expected to be achieved around 2030 under S2, S3 and S4. In terms of total CO₂ emissions, under the rough development scenario of S1, the total CO₂ emissions will keep growing faster and the cumulative emissions will exceed 680,000 Mt CO₂ by 2050. If certain policy and economic measures are adopted, 100,056 Mt CO₂ cumulative emissions are expected to be reduced under the development scenario of S2 by 2050 compared with the baseline scenario. In terms of CO₂ emission intensity, the impact of emission reduction measures on CO₂ emission intensity shows some marginal effects over time. However, in general, the differences between scenarios are not significant. By 2050, the lowest CO₂ emission intensity is in the development scenario of S2, which may be related to the fact that S3 and S4 reduce the economic development rate to a certain extent. In terms of per capita carbon emissions, S1 has lower per capita carbon emissions than the other scenarios due to the large population growth in S1 that shares the carbon emissions. In addition, the difference of per capita carbon emissions in S2 and S3 is relatively small.

5. Discussion

Based on the socio-economic-energy-carbon emission coupling SD model, this study simulates and compares the future scenario of carbon emission and renewable energy in BTH, and finds that a deep energy transition with renewable energy as the main source is the inevitable choice to achieve the carbon peak target by 2030. The rate of economic development, population growth, tax policies, environmental protection policies and technology investment can all play a role in the process of energy conservation and emission reduction, ultimately affecting carbon emissions and the renewable energy industry’s development. The SDA analysis of carbon emissions from residential consumption in BTH [36] also demonstrates that economic growth, technological progress and population size are the main factors affecting carbon emissions in the region. Based on the results derived from this study regarding scenario prediction, suggestions for policy optimization related to future energy development and emission reduction in BTH are proposed:

(1)

Adjust the energy consumption structure while developing and utilizing renewable energy sources. By comparing the energy structure trends in the baseline scenario with those in the S2, S3 and S4 scenarios, without economic support or policy incentives for the development of the renewable energy industry, it was found that the energy transition will be slow and the 2030 carbon peak target will not be achieved.

(2)

Promote the overall green transformation and explore the potential of renewable energy development. According to the scenario simulation results, under the future transition to green development scenarios (S2, S3, S4), the BTH has great potential for renewable energy development. Comprehensive measures should be taken to promote the development transition in the BTH.

(3)

Increase investment in environmental pollution treatment and innovate environmental pollution treatment modes. The study shows that taxation, technology, energy structure and finance all contribute to the energy transition and green development. Therefore, policies can be developed in the following areas: introduce pollution taxes and other related green taxes at appropriate levels and adjust the scope of energy tax levies and tax rates for energy products; moderately reduce the share of industry and vigorously develop the tertiary industry, while optimizing the structure of the energy industry and increasing the share of renewable energy; modestly increase investment in environmental protection and energy conservation, and increase green financial subsidies; establish a stable growth mechanism for R&D investment, strengthen the construction of R&D talents, and optimize energy conservation and emission reduction policies to promote energy consumption and pollution emission stock reduction and incremental control, so as to continue to comprehensively promote energy conservation and emission reduction.

6. Conclusions

In the context of the need to transform the energy structure and achieve the carbon peak and neutrality goals in BTH, this study establishes a socio-economic-energy-carbon emission coupling SD model. It designs different future scenarios, predicts renewable energy development and carbon emissions in BTH, and examines the impact of different policies and carbon reduction target combinations on the future development of the region. The results show that:

(1)

Under the baseline scenario of maintaining the status quo, the BTH will have difficulty achieving the peak target by 2030 and has the highest per capita carbon emissions of all the scenarios. Under this scenario, renewable energy consumption will grow rapidly after 2025, but will still account for less than 30% of the overall energy consumption in 2050.

(2)

The intermediate development scenario (S2) is probably the best choice for BTH to balance carbon reduction, renewable energy development and socio-economic growth in the future. The S2 scenario achieves the 2030 carbon peak target while maintaining the status quo of social and economic development, and has the lowest carbon intensity of all scenarios.

(3)

The development of the renewable energy industry effectively reduces the cumulative carbon emissions in BTH. From 2020, the renewable energy industry in BTH will see explosive growth, and the growth rate will still accelerate in the future.