Analysis of China's carbon emission driving factors based on the perspective of eight major economic regions

Carbon emissions in China's urban agglomerations: spatio-temporal patterns, regional inequalities, and driving forces

Urban agglomerations are the centers of carbon emissions. However, research on sector-specific carbon emissions in different urban agglomerations is still limited. Drawing on the data of China's six urban agglomerations in 2005, 2010, and 2015, this study investigates the spatio-temporal patterns, regional inequalities, and driving forces of total, industrial, transportation, and residential carbon emissions. The study found that Beijing-Tianjin-Hebei was the total and sectoral emission center among the studied urban agglomerations. Additionally, regional carbon inequalities gradually decreased, implying a growing regional synergistic carbon pattern. The driving forces of carbon emissions, including population, GDP, energy intensity, secondary industry, tertiary industry, foreign investment, urbanization, and green coverage, varied across sectors and regions. Notably, foreign investment could lead to lower carbon emissions in less developed agglomerations like Beijing-Tianjin-Hebei, the Central Plains, and the middle reaches of the Yangtze River, whereas more developed agglomerations like the Yangtze River Delta and the Pearl River Delta benefited less from foreign investment. Besides, ChengYu has good ecological conditions and sustainable development modes, which linked urbanization and green space to reduced carbon emissions in the industrial sector. The findings can help formulate differentiated carbon policy and support sustainable development.

Pathways for regions to achieve carbon emission peak: New insights from the four economic growth poles in China

Regional synergy is critical to achieving High Quality Development (HQD) and reducing emissions in China. Economic growth poles (EGPS), namely Beijing-Tianjin-Hebei, the Yangtze River Delta, Guangdong-Hong Kong-Macao, and Cheng-Yu, are typical examples of regional synergy in China. It is critical to explore whether the pulling power of the EGPS to other regions can accelerate China's carbon peaking. First, this study applies the Miller-Round model to measure the spillover effects of the EGPS and selects the radiation-driven areas. Second, based on the environmental Kuznets curve hypothesis, a panel smoothing transformation model is applied to explore the relationship between regional HQD and carbon emissions. Finally, under different scenarios, the inter-regional spillover effect is used to explore the path to achieving the carbon emissions peak. The results show an inverted U-shaped relationship between carbon emissions and HQD. Additionally, with the spillover pull of the EGPS, the peak carbon emission time of all provinces is earlier by 1-6 years in different scenarios, and it can promote Ningxia, Qinghai, Gansu, Guizhou to achieve a carbon peak by 2030. However, the pulling effects of Shanxi, Shaanxi, Jilin, and Guangxi require further improvement. Therefore, the policy implications of increasing inter-regional production efficiency, improving innovation levels, and using renewable energy are proposed to improve the level of HQD, thus achieving a carbon peak. Moreover, improving the industrial linkage between the EGPS and other regions would also be effective. The industrial structure promotes the cultivation of the EGPS in Cheng-Yu and strengthens regional integration in the western region.

Analysis of carbon emission equity degrees based on regional heterogeneity in China

Carbon emission reduction is an environmental and development issue that needs to consider various factors, such as the economy and people's livelihood. Supporting the achievement of emission reduction targets has become an important planning goal for provincial governments; however, there are differences in provincial industrial structure and economic development, which cannot be ignored in goal setting. This study measures the equity degrees of carbon emissions based on economic output by using provincial panel data from 2000 to 2019 and evaluates the spatial distribution characteristics of the carbon emission inequity index (CII). Then, analysis of the influencing factors to CII is employed by spatial econometric methods. Furthermore, multi-index panel data factor analysis and cluster analysis are used to divide regions. The empirical results show that nearly half of the provinces have the problem of carbon emissions inequity with significant spatial correlation. For local development, economic growth and population expansion will significantly improve the equity degrees of carbon emissions. In contrast, the growth of urbanization level, the percentage of secondary industry, and increased energy intensity will significantly improve the equity degrees of carbon emissions in neighboring regions. Policymakers should consider the factors influencing CII and formulate emission reduction plans according to regional characteristics.

Does China Overseas Economic and Trade cooperation Zone affect CO2 emissions in host countries? Evidence from a quasi-natural experimental of countries along the "Belt and Road"

China Overseas Economic and Trade Cooperation Zone (COCZs) which as a platform for China's foreign investment and trade has a potential impact on CO2 emissions, while strengthening bilateral investment and trade between China and the host countries. Since most of the COCZs are located in countries along the "Belt and Road," the purpose of this paper is to investigate the impact of COCZs on CO2 emissions of the countries along the "Belt and Road" and the mechanism of this impact. We constructed a panel data of 63 countries along the "Belt and Road" from 2000 to 2020, and conducted an empirical study using the difference-in-difference (DID) model. Our research result show that COCZs can significantly increase the CO2 emissions of the countries along the "Belt and Road." Then, we conduct a series of robustness tests and endogeneity test on the estimation results of the baseline model, and the results of the tests all support the conclusion reached by the baseline model. Our heterogeneity analysis reveals that the effect of COCZs on CO2 emissions is more significant in Asian countries with lower national income or industrialization and higher country risk. Finally, we analyzed industrial value added and energy depletion as possible impact mechanisms, the results of mechanism model shows that COCZs can increase the industrial value added and then significantly increase CO2 emissions, but energy depletion was not an efficient mechanism. Our paper provides a new insight into the impact of bilateral economic and trade cooperation zones on CO2 emissions in host countries.

Regional differences and driving factors of carbon emission intensity in China's electricity generation sector

As an industry with immense decarbonization potential, the low-carbon transformation of the power sector is crucial to China's carbon emission (CE) reduction commitment. Based on panel data of 30 provinces in China from 2000 to 2019, this research calculates and analyzes the provincial CE intensity in electricity generation (CEIE) and its spatial distribution characteristics. Additionally, the GTWR model based on the construction explains the regional heterogeneity and dynamic development trend of each driving factor's influence on CEIE from time and space. The main results are as follows: CEIE showed a gradual downward trend in time and a spatial distribution pattern of high in the northeast and low in the southwest. The contribution of driving factors to CEIE has regional differences, and the power structure contributes most to the CEIE of the power sector, which promotes regional CE. Concurrently, most provinces with similar economic development, technological level, geographic location, or resource endowment characteristics show similar spatial and temporal trends. These detections will furnish broader insights into implementing CE reduction policies for the regional power sector.

The influencing factors of carbon emissions in the railway transportation industry based on extended LMDI decomposition method: evidence from the BRIC countries

In the twenty-first century, global warming and other environmental issues have become the focus of international attention. The total generation of carbon emissions for the railway transportation industry in the BRIC countries (Brazil, Russia, Indian and China) accounted for 25.73% of the global carbon emissions in this industry during 2017. Therefore, it is necessary to identify the influencing factors of carbon emission in the railway transportation industry for the BRIC, in order to better control and reduce carbon emissions and to achieve the global goal of "net-zero emission." The logarithmic mean divisia index (LMDI) decomposition method was used to examine the factors that influenced carbon emissions from the railway transportation industry in the BRIC from 1997 to 2017. According to the findings, the total carbon emissions of the railway transportation industry in BRIC were 60.92 million tons in 2017, increased by 98.62% compared to 1997. The factor of economic output effect has contributed positively to the increase in carbon emissions in all identified countries. However, the effect of population size effect, energy structure, and transportation intensity effect for carbon emission demonstrated heterogeneity in BRIC. In addition, policy suggestions are put forward for the reduction of carbon emissions from the railway transportation industry in BRIC.

Digital economy, energy efficiency, and carbon emissions: Evidence from provincial panel data in China

Improving energy efficiency and lowering carbon emissions are of great importance to realize the "dual carbon" goal of carbon peak and carbon neutrality. Digital economy is a new engine of economic development, but whether or how it affects energy efficiency and carbon emissions are unclear. Utilizing panel data of China's 30 provinces from 2012 to 2019, this study empirically explores the relationships among digital economy, energy efficiency, and carbon emissions. Meanwhile, from the perspective of energy efficiency, applying mediation models and panel threshold model, it analyzes the direct, indirect, and nonlinear influencing mechanisms of digital economy on carbon emissions. The results reflect that the development of digital economy in China intensifies carbon emissions. Energy efficiency serves as a vital partial mediator between the two. The enhancement of energy efficiency can lower carbon emissions. However, the development of digital economy is not conducive to improving energy efficiency, thereby, indirectly increasing carbon emissions. The mediating effect of energy efficiency accounts for 30.58 % of the total effect of digital economy on carbon emissions. Meanwhile, taking energy efficiency into account, the impact of digital economy on carbon emissions has a significant double-threshold effect and presents an N-shaped trend. [0.824, 0.912] is the optimal range of energy efficiency, within which the growth of the digital economy can empower carbon emission abatement to some extent. In addition, the expansion of population size, the coal-based energy consumption structure, and the industrial structure significantly increase carbon emissions. The improvements in living standards and environmental regulations can help to decrease carbon emissions, but the emission abatement effects are not significant. Those conclusions reveal the importance of optimizing the level and quality of digital economy and adopting differentiated digital economy development policies based on energy efficiency to achieve carbon emission reduction.

Spatiotemporal association of carbon dioxide emissions in China's urban agglomerations

The reduction of carbon dioxide (CO₂) emissions and sustainable development in low-carbon ways are of great significance to urban agglomerations. However, few studies are exploring the relationship between CO₂ emissions and socioeconomic development at city levels from the perspective of clusters of regions. Based on the open data of inventory for anthropogenic CO₂ emissions, nighttime light data, and population dataset as a proxy for the socioeconomic development levels of urban agglomerations, we used Mann-Kendall trend test, Tapio decoupling analysis, and spatial autocorrelation analysis to explore the spatiotemporal association of CO₂ emissions and the impact of socioeconomic development on emissions in the nineteen urban agglomerations in China. Findings showed that the growth of CO₂ emissions in China was primarily concentrated in urban agglomerations. The CO₂ emissions in eastern coastal and northern urban agglomerations were much higher than those in other areas, while the emissions in western urban agglomerations were the lowest. The periodic characteristics of CO₂ emissions were consistent with China's five-year development plan. Urban agglomerations in the early stage from 2000 to 2002 or with developed and stable industrial structures tended to achieve decoupling. High-high (HH) clusters of socioeconomic development with CO₂ emissions were mainly distributed in urban agglomerations of the Beijing-Tianjin-Hebei region (BTH), the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), Yangtze River Delta (YRD), Huhhot-Baotou-Ordos-Yulin (HBOY), Shandong Peninsula (SP), and Central Shanxi (CS). Most of the clusters except those in HBOY shrunk from 2000 to 2010 and remained relatively stable from 2010 to 2019. These urban agglomerations should promote synergistic emission reduction. High-low (HL) clusters mostly appeared in central cities with a high socioeconomic level and surrounding cities with low CO₂ emissions s, i.e., in urban agglomerations of Chengdu-Chongqing region (CC), the Beibu Gulf (BG), and Lanzhou-Xining (LX). These urban agglomerations with prominent polarization phenomena should adhere to regional overall coordination and thus minimize total regional costs of CO₂ emission reduction. The results could provide references for the synergistic reduction of CO₂ emissions and the coordinated development in urban agglomerations.

Embodied carbon emissions transfers via inter-regional trade: evidence from value-added extended decomposition model in China

The allocation of carbon emission reduction responsibility is severe issue in China for these years. In case to find a fairer and more effective way to divided the responsibility to each region of China, this paper examines embodied carbon emissions (ECEs) transfers in China's inter-regional trade by applying value-added extended decomposition model. This study allows policymakers to trace CO₂ emissions at regional levels and provides three key findings. Firstly, using novel data on the physical consumption of energy by region, we observe a strong and robust negative association between the regional direct CO₂ emission coefficient and the regional economic development level. Secondly, employing the latest inter-regional input-output table of China to calculate ECEs and uncover transfer characteristics via inter-regional trade, results show that central region, eastern region and northern region are the three highest ECEs regions. Thirdly, ECEs in value-added trade are generally transferred from inland China to coastal areas of China. Northeast region, north coastal region, central region and northwest region are net ECEs outflow regions, the rest regions are net ECEs inflow regions.

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This paper calculates embodied carbon emissions via inter-regional trade in China.

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An exogenous treatment of China's regional input-output table is carries out to make the intermediate input-output relationship between any two regions can be expressed by the square matrix of intermediate consumption coefficient.

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A value-added extended decomposition model is applied to avoid carbon leakage and double calculation in traditional methods.

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Carbon emission network analysis have been conducted not only to uncover embodied carbon emissions transfer characteristics at regional level, but also to identify the major carbon emitters and their complex relations.

Decomposition of China's regional carbon emission paths: an analysis of environmental input and output considering regional development differences

At present, the imbalance in regional development and carbon emissions are the two major challenges that China faces in terms of achieving high-quality development. This paper takes regional development differences as the starting point. First, we adopt the improved CRITIC method to measure the comprehensive development level of 30 regions in China and use K-means clustering to divide the 30 regions into five development levels. Second, the structural path analysis for environmental input-output analysis (EIOA-SPA) model is used to quantify the transfer of carbon emissions between sectors in various regions. Finally, a comprehensive analysis is performed based on the development characteristics of each region and the decomposition results of the carbon emission paths. Then, more precise carbon emission reduction strategies are proposed for the development of different regions in China. The results show that first, the development gap between regions in China has improved, and the development of the central and western regions has achieved remarkable results. However, differences between the north and the south and the gap between coastal and inland regions still exist. Second, the direct carbon emissions of regions with different levels of development are mainly derived from high energy-consuming sectors, especially the production and supply of electricity and heat sector. Third, there are certain differences in the indirect carbon emission pathways of regions with different development levels. The transportation, storage, and postal sector in high developed regions have obvious driving effects on carbon emissions. The building sector plays a prominent role in driving carbon emissions in high developed regions and medium-high developed regions. The building sector, nonmetallic mineral products sector, metal smelting sector, and rolled processed product sector in medium developed regions and medium-low developed regions have relatively high carbon emission-stimulating effects. Therefore, it is necessary to adopt differentiated emission reduction strategies for regions with different development levels in China to achieve adequate carbon emission reductions. This effort would further promote the construction of China's ecological civilization.

Forecasting carbon emissions using MGM(1,m|λ,γ) model with the similar meteorological condition

Carbon emission is a common concern of the international community and effectively predicting its future trend is necessary for emission reduction planning. Considering that the change trend of carbon emissions is unstable, more attention should be paid to the correction effect of new information on the development trend. Therefore, based on the traditional MGM(1,m) model, this paper introduces the new information priority operator λ and nonlinear parameter γ to strengthen the role of new information, further constructs three comparison models of MGM(1,m|λ), MGM(1,m|γ) and MGM(1,m|λ,γ).Then we apply the new model to the carbon emission prediction of different regions (cities, countries and continents) and different trends (fluctuating, rising and declining). The results illustrate that the new model has higher prediction accuracy, and adding dynamic parameters is a scientific and practical method to improve the forecasting ability of the grey forecasting model. Finally, we analyze the current situation and future development trend of carbon emissions, and put forward reasonable suggestions.

Key mitigation regions and strategies for CO2 emission reduction in China based on STIRPAT and ARIMA models

China is facing increasing pressure to reduce CO₂ emissions from energy consumption. Given this issue, understanding the characteristics, influencing factors, and trends can provide adequate information for decision-makers to solve the CO₂ emission problem. This study analyzes the characteristics of CO₂ emissions from energy consumption in 30 regions of China from 2005 to 2018 and applies the STIRPAT model to identify the impact of the influencing factors. Combined with the CO₂ emission trend in 2030 as predicted by the ARIMA model, the key mitigation regions and strategies reduction have been determined. Results indicate that CO₂ emissions have been increasing from 2005 to 2018 in China, thus showing the characteristic of the east being larger than the west spatially. Under the baseline scenario, these emissions will continue to rise in 2030. Carbon emissions intensity is declining, and the gap between provinces with the highest and lowest per capita CO₂ emissions is widening. Although per capita GDP is significantly positively correlated with provinces, population is the key factor influencing more provinces, followed by the proportion of the secondary industry and urbanization rate. To achieve low-carbon sustainable development, Shandong, Shanxi, Inner Mongolia, Guangdong, Shaanxi, Xinjiang, and Ningxia are considered the key regions of concern for emission reduction. The heterogeneity of CO₂ emission characteristics and influencing factors among regions provides a direction for the development of targeted and differentiated regional emission reduction strategies.

Economic Growth Targets and Carbon Emissions: Evidence from China

Carbon emissions have become a new threat to sustainable development in China, and local government actions can play an important role in energy conservation and emission reduction. This paper explores the theoretical mechanisms and transmission paths of economic growth targets affecting carbon emissions from the perspective of economic growth targets and conducts an empirical analysis based on 30 provincial panel data in China from 2003 to 2019. The results show that: economic growth targets are positively correlated with carbon emissions under a series of endogeneity and robustness; there are regional heterogeneity, target heterogeneity and structural heterogeneity in the impact of economic growth targets on carbon emissions; after economic growth targets are set, government actions can influence carbon emissions by affecting resource mismatch and industrial restructuring; It is further found that there is a “U” shaped relationship between economic pressure and carbon emissions. Based on the above findings, this paper further proposes that a high-quality performance assessment mechanism should be developed to bring into play the active role of local governments in achieving carbon reduction goals, and thus contribute to high-quality economic development.

A Heterogeneity Study of Carbon Emissions Driving Factors in Beijing-Tianjin-Hebei Region, China, Based on PGTWR Model

The Beijing–Tianjin–Hebei region is an important economic growth pole in China and achieving carbon emission reduction in the region is of great practical significance. Studying the heterogeneity of the influencing factors of carbon emission in this region contributes to formulating targeted regional carbon emission reduction policies. Therefore, this paper adopted thirteen cities as individuals of cross-section and conducted spatial and temporal heterogeneity analysis of the influencing factors of converted carbon emissions in the region with panel data from 2013 to 2018 based on the PGTWR model. From a space-time perspective, the regression coefficient of each influencing factor in this region has obvious heterogeneity, which is mainly reflected in the time dimension. In the study period, the impact of industrial structure, the level of urbanization, energy intensity, and the level of economic growth on carbon emission showed a decline curve, while the impact of the level of opening up and the size of population was on the rise, indicating that more attention should be paid to the latter two factors for the time to come. In terms of space, the differences in the influence of industrial structure and energy intensity on carbon emission vary significantly.

Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality

Cities are areas featuring a concentrated population and economy and are major sources of carbon emissions (CEs). The spatial differences and influential factors of urban carbon emissions (UCEs) need to be examined to reduce CEs and achieve the target of carbon neutrality. This paper selected 264 cities at the prefecture level in China from 2008 to 2018 as research objects. Their UCEs were calculated by the CE coefficient, and the spatial differences in them were analyzed using exploratory spatial data analysis (ESDA). The influential factors of UCEs were studied with Geodetector. The results are as follows: (1) The UCEs were increasing gradually. Cities with the highest CEs over the study period were located in the urban agglomerations of Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu–Chongqing. (2) The UCEs exhibited certain global and local spatial autocorrelations. (3) The industrial structure was the dominant factor influencing UCEs.

Environmental regulation and manufacturing carbon emissions in China: a new perspective on local government competition

Environmental regulation is a crucial way to achieve manufacturing green transformation. However, few studies have explored the spatial spillover effects and regional boundaries of environmental regulation on manufacturing carbon emissions from the perspective of local government competition. Based on the manufacturing panel data of 30 provinces in China from 2007 to 2019, this paper uses the spatial Durbin model to examine the impact mechanisms, spatial spillover effects, regional boundaries and industry heterogeneity of environmental regulation, and local government competition on manufacturing carbon emissions. The results show that (1) environmental regulation suppresses local manufacturing carbon emissions, local government competition increases local manufacturing carbon emissions, but the interaction indicates that local governments tend to top-to-top competition under the constraints of environmental regulation. (2) The spatial spillover effect of environmental regulation has regional boundaries. The regional boundary with a positive spillover effect is 600 km, and the regional boundary with a negative spillover effect is 1600 km. (3) Environmental regulation and local government competition have spatial heterogeneity in the carbon reduction effects of seven-type manufacturing industries. These findings suggest concrete evidence for developing policies for further encouraging green development in manufacturing.

Research on carbon emission differences decomposition and spatial heterogeneity pattern of China's eight economic regions

To explore the sources of regional carbon emission differences and the evolution characteristics of spatial heterogeneity pattern, this paper first calculates the corresponding carbon emissions according to the relevant statistical data of eight economic regions in China from 2005 to 2019. It analyzes the overall differences and temporal and spatial evolution characteristics of regional carbon emissions combined with the visualization method of GIS. Then, the total carbon emission difference is decomposed by the Theil index to find out the primary sources affecting the regional carbon emission difference. Finally, the driving factors affecting the spatial heterogeneity pattern of regional carbon emissions are studied with the help of the Geodetector method. The results show that (1) significant differences in carbon emissions among China's eight economic regions. The contribution rate of inter-regional and intra-regional differences of carbon emissions in different regions to the overall carbon emission difference is diverse. (2) Regional carbon emissions are affected by single driving factors and the interaction of two driving factors. The interaction has an increasing impact on the determinant of regional carbon emission spatial differentiation. (3) The factor detection results and interaction detection results, respectively, show that the level of energy consumption, industrialization, and technological development has always been the main driving factors affecting the spatial heterogeneity pattern of regional carbon emissions, and the critical interaction factors have multiple spatial superposition interaction effects. Therefore, regional carbon emission reduction should consider the national strategic objectives and own regional characteristics and implement differentiated emission reduction schemes.

Study of the spatio-temporal variation of environmental sustainability at national and provincial levels in China

Environmental problems create a significant barrier for China in achieving its Sustainable Development Goals (SDGs). Assessing environmental sustainability is critical for China to meet the SDGs. Few studies, however, have looked into environmental sustainability in China. This research created a systematic and comprehensive environmental sustainability framework in line with the SDGs (SDG 6, SDG 11, SDG 12, SDG 13, SDG 14, SDG 15). From 2010 to 2018, we used a Constant Elasticity of Substitution (CES) model to assess China's spatio-temporal variation in environmental sustainability at the national and provincial levels. We also evaluated the results with changes to the substitution elasticity value, validating the feasibility of the proposed calculation method. Our results show that the scores of SDG 6, SDG 11, SDG 12, SDG 13, and SDG 15 experienced an increasing trend, while SDG 14 experienced a decline. China's Environmental Sustainability Index (ESI) scores indicate that China's overall environmental sustainability has been improved over time. At the provincial level, the ESI scores of all provinces increased at different levels from 2010 to 2018. The results of this paper may facilitate improvements in environmentally-related SDGs in China's provinces, and help realize China's sustainable development.

Industrialization, urbanization, and carbon emission efficiency of Yangtze River Economic Belt-empirical analysis based on stochastic frontier model

Carbon emission efficiency directly determines the level of green economic development. Based on the panel data of China's Yangtze River Economic Belt (YEB) from 2008 to 2017, this paper uses the stochastic frontier analysis (SFA) model to analyze the overall carbon emission efficiency level, influencing factors, and changing trends, with a view to discussing the relationship between economic development and carbon emission efficiency. The results suggest, first, the overall carbon emission efficiency of the YEB is on an upward trend, but there is still much room for improvement. Second, the impact of industrialization and urbanization on carbon emission efficiency follows a U-shaped. As industrialization and urbanization progress, the impact on carbon emission efficiency shows a downward and then upward trend. Third, due to the rebound effect, technological progress has a slight negative impact on carbon emission efficiency. Energy consumption structure, government intervention, and foreign trade are all negative incentive factors. Therefore, efforts to improve carbon emission efficiency in the YEB should focus on transforming the economic growth model, adjusting the industrial structure, improving the energy consumption structure, and innovating green technology. The research results can provide a reference for the government policymakers to develop a green economy.

Spatio-Temporal Patterns of CO2 Emissions and Influencing Factors in China Using ESDA and PLS-SEM

Controlling carbon dioxide (CO2) emissions is the foundation of China’s goals to reach its carbon peak by 2030 and carbon neutrality by 2060. This study aimed to explore the spatial and temporal patterns and driving factors of CO2 emissions in China. First, we constructed a conceptual model of the factors influencing CO2 emissions, including economic growth, industrial structure, energy consumption, urban development, foreign trade, and government management. Second, we selected 30 provinces in China from 2006 to 2019 as research objects and adopted exploratory spatial data analysis (ESDA) methods to analyse the spatio-temporal patterns and agglomeration characteristics of CO2 emissions. Third, on the basis of 420 data samples from China, we used partial least squares structural equation modelling (PLS-SEM) to verify the validity of the conceptual model, analyse the reliability and validity of the measurement model, calculate the path coefficient, test the hypothesis, and estimate the predictive power of the structural model. Fourth, multigroup analysis (MGA) was used to compare differences in the influencing factors for CO2 emissions during different periods and in various regions of China. The results and conclusions are as follows: (1) CO2 emissions in China increased year by year from 2006 to 2019 but gradually decreased in the eastern, central, and western regions. The eastern coastal provinces show spatial agglomeration and CO2 emission hotspots. (2) Confirmatory analysis showed that the measurement model had high reliability and validity; four latent variables (industrial structure, energy consumption, economic growth, and government management) passed the hypothesis test in the structural model and are the determinants of CO2 emissions in China. Meanwhile, economic growth is a mediating variable of industrial structure, energy consumption, foreign trade, and government administration on CO2 emissions. (3) The calculated results of the R2 and Q2 values were 76.3% and 75.4%, respectively, indicating that the structural equation model had substantial explanatory and high predictive power. (4) Taking two development stages and three main regions as control groups, we found significant differences between the paths affecting CO2 emissions, which is consistent with China’s actual development and regional economic pattern. This study provides policy suggestions for CO2 emission reduction and sustainable development in China.