Simulation of China’s Carbon Emission based on Influencing Factors

Study on factors influencing carbon dioxide emissions and carbon peak heterogenous pathways in Chinese provinces

Implementing a Carbon Peak Action Plan at the regional level requires comprehensive consideration of the developmental heterogeneity among different provinces, which is an effective pathway for China to realize the goal of carbon peak by 2030. However, there is currently no clear provincial roadmap for carbon peak, and existing studies on carbon peak pathways inadequately address provincial heterogeneity. Therefore, this paper employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to decompose assess 8 factors influencing carbon emissions of 30 provinces. According to scenario analysis, the paper explores the differentiated pathways for provincial carbon peaks based on policy expectation indicators (including population, economy, and urbanization rate) and comprises policy control indicators (including the energy structure, energy efficiency, industrial structure, transportation structure, and innovation input). The results indicate that population, per capita GDP, urbanization rate, and innovation input are the primary factors for influencing (negatively) the growth of carbon emissions. In contrast, the optimization and upgrading of the industrial structure, energy intensity, energy structure, and transportation structure have mitigating effects on carbon emissions, especially for the first two factors. The forecasting results reveal that robust regulations of the energy and industry can effectively accelerate carbon peak at a reduced magnitude. If developed at BAU, China cannot achieve carbon peak by 2030, continuing an upward trend. However, by maximizing the adjustment strength of energy and industrial transformation within the scope of provincial capabilities, China could achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. In this scenario, 24 provinces could achieve carbon peak before 2030. Overall, this study suggests the feasibility of differentiated pathway to achieve carbon peaks in China, exploring the carbon peak potential and paths of 30 provinces, and identifying provinces where carbon peak is more challenging. It also provides a reference for the design of carbon peak roadmaps at both provincial and national levels and offers targeted recommendations for the implementation of differentiated policy strategies for the government.

How does regional economic integration affect carbon emission efficiency? Evidence from the Yangtze River Delta, China

Rapid urbanization and industrialization promote economic growth as well as bring carbon emissions, which seriously threaten the eco-environment and socioeconomic sustainable development. Facing increasing resource constraints, improving carbon emissions efficiency (CEE) is conducive to promote coordinated development of economy and environmental protection. In recent years, regional economic integration (REI) has rapidly developed. It can not only promote factors flow between regions but also achieve industrial and economic agglomeration. However, few studies have been reported in the literature about the relationship between the REI and CEE. In this study, we first illustrate how the REI influences CEE in theory, then take the Yangtze River Delta (YRD) as a case study to conduct empirical research. The results show that (1) the overall CEE value in the YRD has exhibited an upward trend from 2000 to 2020, and its spatial distribution has revealed a significant auto-correlation pattern. (2) On the whole, the REI act a noteworthy positive impact on CEE. When considering types of cities, it is found to have significant positive impacts for the CEE in economically developed cities, while it exhibits a negative impact in the less-developed ones. (3) Upgrading industrial structure and increasing per capita GDP can promote the CEE, but hinder its growth in surrounding areas. Our findings suggest that the government should formulate a unified overall plan to facilitate REI development and establish a modern industrial system of clean and low-carbon to promote regional sustainable development.

Spatial-temporal evolution of carbon emissions and spatial-temporal heterogeneity of influencing factors in the Bohai Rim Region, China

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.

Spatial-temporal pattern and spatial convergence of carbon emission intensity of rural energy consumption in China

Based on the panel data of 30 provinces (municipalities and autonomous regions) in China from 2005 to 2019, this paper uses Gini coefficient decomposition and kernel density estimation to investigate the regional differences and dynamic evolution trend of rural energy carbon emission intensity in China. Then, the convergence model is used to analyze the convergence characteristics and influencing factors of carbon emission intensity. The study found the following: (1) During the observation period, the carbon emissions of coal energy and oil energy were much higher than those of gas energy. The carbon emissions of rural energy consumption experienced three stages of development, and the carbon emission intensity showed a downward trend as a whole. The spatial distribution pattern of total carbon emissions present an "adder" distribution, and the spatial agglomeration phenomenon gradually strengthens with the passage of time. (2) The Gini coefficient of China's rural energy consumption carbon emission intensity shows a trend of "Inverted N-shaped." The Gini coefficient of carbon emission intensity in the eastern and northeastern regions shows an increasing trend, while the Gini coefficient of carbon emission intensity in the western and central regions shows a downward trend. The super variable density is the main source of carbon emission intensity difference. The peak value of the main peak of the nuclear density curve of the carbon emission intensity increased significantly, the bimodal form evolved into a single peak form, and the density center moved to the left. (3) The carbon emission intensity of rural energy consumption in the whole, central, and western regions of China has the characteristic of σ convergence, while the carbon emission intensity in the eastern and northeastern regions does not have the characteristic of σ convergence. There is a significant spatial positive correlation in the carbon emission intensity, there is also a significant β convergence characteristic, the speed of conditional β convergence is significantly higher than that of absolute β convergence, and the spatial interaction will further improve the convergence speed. Industrial structure, industrial agglomeration, and energy efficiency will increase the convergence speed. In terms of sub-regions, the conditional convergence rate of carbon emission intensity in the four regions shows a decreasing trend in the northeast, central, eastern, and western regions.

Multi-scenario simulation of carbon budget balance in arid and semi-arid regions

The carbon budget has emerged as a central focus in global carbon cycle research. The limited understanding of carbon budget balance dynamics has led to an increasing imbalance between ecological and socio-economic benefits. Building upon a comprehensive analysis of carbon storage and emission in Lanzhou from 2000 to 2020, this study develops a novel deep learning model (CNN-LSTM) to simulate carbon budget under various scenarios from 2030 to 2050. Additionally, scientifically grounded recommendations for carbon compensation are provided. The results demonstrate several key findings: (1) The deep learning model exhibits outstanding performance, with an average overall accuracy exceeding 0.93. The coupled model outperforms individual models, underscoring the significance and necessity of incorporating both temporal and spatial features in land use simulation. (2) Under the ecological protection redline scenario from 2030 to 2050, a noteworthy augmentation in carbon storage and a proficient constraint on carbon emissions are observed. This substantiates the effectiveness of ecological protection interventions. (3) Carbon compensation payment areas are predominantly concentrated in built-up land, with the extent of these areas expanding over time. (4) The disparities in carbon balance effects of forest were more conspicuous than that of built-up land across diverse temporal and scenarios.

Enhanced sequestration of CO2 from simulated electrolytic aluminum flue gas by modified red mud

The aluminum industry is facing severe economic and environmental problems due to increasing carbon emissions and growing stockpiles of red mud (RM). RM is a strongly alkaline, high-emission solid waste from the alumina industry with potential for CO2 sequestration. However, the effectiveness of RM carbon sequestration is poor, and the mechanism behind it is not well understood. In this study, the effect of microwave and tube furnace activation of RM on CO2 sequestration in alumina was first investigated at different temperatures. The result showed that the CO2 sequestration capacity of unmodified RM (URM) was only 14.35 mg/g at ambient temperature and pressure, and the CO2 sequestration capacity could be increased to 52.89 mg/g after high-temperature activation and modification. Besides, high-temperature activation and modification will effectively improve the carbon sequestration capacity of RM. The carbonized RM was characterized by FT-IR, SEM, XRD, laser particle size, TG-DSC, and pH measurements. In addition, the mechanism of RM capturing CO2 was also proposed, which shows that CO2 was finally sequestered in the RM as CaCO3. The change in particle size distribution and the mineral phase in the RM indicated that high-temperature activation modification positively affects the application of RM to the sequestration of CO2. This study can provide a promising technology for the low-carbon and green development of the aluminum industry, as well as achieving the waste treatment and utilization objective.

The impact of industrial land mismatch on carbon emissions in resource-based cities under environmental regulatory constraints-evidence from China

Achieving carbon neutrality has become a global common goal. For China, to reach peak carbon emissions and long-term carbon neutrality, the transformation and development of resource-based cities are essential. This study uses data from 114 prefecture-level resource-based cities from 2008 to 2019 as a sample and empirically tests the impact of industrial land mismatch on carbon emissions using the fixed effects model. In addition, we analyze the heterogeneous influence of environmental regulation as a moderating effect on resource-based cities at different development stages. The study reveals that (1) there is a significant positive correlation between the imbalance in industrial land supply in resource-based cities and carbon emissions. The more severe the imbalance, the higher the carbon emissions. The improper supply mode of industrial land is also positively correlated with carbon emissions, although the impact is not significant. (2) Environmental regulation can significantly curb the carbon emission issues caused by the mismatch and imbalance in the scale of industrial land supply and the improper supply mode of industrial land. (3) Compared to strong resource-based cities, weak resource-security cities have a smaller impact on carbon emissions due to an imbalance in the supply of industrial land. This is mainly because resources in weak resource-security cities are becoming exhausted, making "ecology first, green and low carbon" the main tune for economic and social development. Both types of cities show a positive correlation between the improper supply of industrial land and carbon emissions, although neither is significant. (4) The intensity of the regulatory effect of environmental regulations on resource-based cities is influenced by resource abundance. The suppression of carbon emissions by environmental regulations is more apparent in strong resource-security cities than in weak resource-security cities.

Analysis of carbon emission drivers and multi-scenario projection of carbon peaks in the Yellow River Basin

The Yellow River Basin is a key ecological barrier and commercial zone in China, as well as an essential source of energy, chemicals, raw materials, and fundamental industrial foundation, the achievement of its carbon peaking is of great significance for China's high-quality development. Based on this, we decomposed the influencing factors of carbon dioxide emissions in the Yellow River Basin using the LMDI method and predicted the carbon peaking in the Yellow River Basin under different scenarios using the STIRPAT model. The results show that (1) the energy intensity effect, economic activity effect and population effect play a positive role in promoting carbon emissions during 2005-2020. The largest effect on carbon emissions is the population size effect, with a contribution rate of 65.6%. (2) The STIRPAT model predicts that the peak of scenarios "M-L", "M-M" and "M-H" will occur in 2030 at the earliest. The "M-H" scenario is the best model for controlling carbon emissions while economic and social development in the Yellow River Basin. The results of this paper can provide a theoretical basis for the development of a reasonable carbon peak attainment path in the Yellow River Basin and help policy makers to develop a corresponding high-quality development path.

Energy-water nexus in low-carbon electric power systems: A simulation-based inexact optimization model

Energy and water resources are closely linked in electric power systems, and the application of low-carbon technologies further affects electricity generation and water consumption in those systems. The holistic optimization of electric power systems, including generation and decarbonization processes, is necessary. Few studies have considered the uncertainty associated with the application of low-carbon technologies in electric power systems optimization from an energy-water nexus perspective. To fill such a gap, this study developed a simulation-based low-carbon energy structure optimization model to address the uncertainty in power systems with low-carbon technologies and generate electricity generation plans. Specifically, LMDI, STIRPAT and grey model were integrated to simulate the carbon emissions from the electric power systems under different socio-economic development levels. Furthermore, a copula-based chance-constrained interval mixed-integer programming model was proposed to quantify the energy-water nexus as the joint violation risk and generate risk-based low-carbon generation schemes. The model was applied to support the management of electric power systems in the Pearl River Delta of China. Results indicate that, the optimized plans could mitigate CO₂ emission by up to 37.93% over 15 years. Under all scenarios, more low-carbon power conversion facilities would be established. The application of carbon capture and storage would increase energy and water consumption by up to [0.24, 7.35] × 10⁶ tce and [0.16, 1.12] × 10⁸ m³, respectively. The optimization of the energy structure based on energy-water joint violation risk could reduce the water utilization rate and the carbon emission rate by up to 0.38 m³/10⁴ kWh and 0.04 ton-CO₂/10⁴ kWh, respectively.

Digitalization, Electricity Consumption and Carbon Emissions-Evidence from Manufacturing Industries in China

The development of China's manufacturing industry is constrained by factors such as energy and resources, and low-carbon development is arduous. Digitalization is an important method to transform and upgrade traditional industries. Based on the panel data of 13 manufacturing industries in China from 2007 to 2019, a regression model and a threshold model were used to empirically test the impact of digitalization and electricity consumption on carbon emissions. The research results were as follows: (1) The digitalization level of China's manufacturing industry was steadily increasing; (2) The proportion of electricity consumption in China's manufacturing industries in the total electricity consumption hardly changed from 2007 to 2019, basically maintaining at about 6.8%. The total power consumption increased by about 2.1 times. (3) From 2007 to 2019, the total carbon emissions of China's manufacturing industry increased, but the carbon emissions of some manufacturing industries decreased. (4) There was an inverted U-shaped relationship between digitalization and carbon emissions, the higher the level of digitalization input, the greater the carbon emissions of the manufacturing industry. However, when digitalization develops to a certain extent, it will also suppress carbon emissions to a certain extent. (5) There was a significant positive correlation between electricity consumption and carbon emissions in the manufacturing industry. (6) There were double energy thresholds for the impact of labor-intensive and technology-intensive manufacturing digitalization on carbon emissions, but only a single economic threshold and scale threshold. There was a single scale threshold for capital-intensive manufacturing, and the value was -0.5352. This research provides possible countermeasures and policy recommendations for digitalization to empower the low-carbon development of China's manufacturing industry.

Research on the Carbon Credit Exchange Strategy for Scrap Vehicles Based on Evolutionary Game Theory

In this article, we construct a game model that uses government regulators and scrap vehicle owners as the main parties to investigate the carbon credit exchange strategy of scrap vehicles using evolutionary game theory. The results were validated using Matlab simulation analysis to reveal the dynamic evolution process of the strategy of both sides of the game. A sensitivity analysis of the key parameters was conducted to explore the influence of each parameter on the evolution process and the stabilization trends. The study shows that (1) The time for the game system to reach a steady state is inversely related to the size of the initial willingness of the parties to cooperate. (2) In the mixed steady-state scenario, when the overall return differential between the positive and negative regulatory verification by government departments is positive, the steady state is participation and positive scrapping. (3) When the probability of the government verifying and being successful in verifying the punishment of the owner's negative scrapping behavior increases, both parties of the game will eventually choose the strategy of participation and positive scrapping. When the cost of the government participation strategy and the cost of the government verification strategy increase, both sides of the game will eventually choose the strategy combination of no participation and positive scrapping. (4) When the owner's reward for cooperating with the strategy, the owner's cost of scrapping the vehicle, and the benefits of the owner's negative cooperation strategy change, they will not change the strategy stability results but will affect the time it takes for the game system to reach a stable state. This study has theoretical implications for government policies in the scrapping industry and how to guide vehicle owners to actively scrap their vehicles.