Research on the Characteristics and Influencing Factors of Carbon Transfer Based on Value Chain in China

Research on the Characteristics and Influencing Factors of Carbon Transfer Based on Value Chain in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the Characteristics and Influencing Factors of Carbon Transfer Based on Value Chain in China Xiaocui Dong, Hongguang Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3887683/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Carbon emission reduction is an arduous task that the world needs to face and solve at present and in the coming period, and it is also an important goal in the process of China's economic and social development. It is also an important goal in the process of China's economic and social development. The study of carbon transfer based on the value chain is crucial to the realization of carbon emission reduction and the scientific and reasonable distribution of carbon emission reduction responsibilities. Based on the input-output method, this paper uses the value-based carbon emission accounting method to calculate and analyze the carbon emission transfer characteristics of 30 provinces, autonomous regions and cities in China (excluding Tibet, Hong Kong, Macao and Taiwan) in 2012 and 2017. As many indicators of different aspects as possible are selected, and the problem of covariance between indicators is reduced and eliminated through factor analysis, so as to analyze the key factors affecting carbon transfer. It was found that during 2012–2017, China's carbon emissions as a whole showed a transfer from regions with a higher level of economic development to regions with a lower level of economic development, and from the east to the west, with the net transfer out center of gravity shifting significantly to the north and west. The middle region (MR) has always been the region that transfers out the most carbon emissions, but the net transfer growth rate of some provinces in the MR has decreased more with other regions. During this period, the local resource availability, energy consumption level, and science and technology level had the greatest impact on the change of carbon transfer. Regions with abundant resources but lower levels of science and technology, and higher levels of energy consumption will increase their carbon transfer. The results of this paper are intended to improve the study of carbon transfer along the value chain, and provide a basis for the division of responsibility for carbon emission reduction and the formulation of policies in China. inter-provincial value chain carbon transfer influencing factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction In recent years, with the increase of greenhouse gas emissions, the greenhouse effect continues to intensify, causing a series of global ecological and environmental problems that threaten human survival and sustainable development, such as climate change and global warming. Controlling and reducing the greenhouse gas emissions is a major problem facing mankind at present. The issue of carbon emission reduction has been emphasized and concerned by countries around the world, so research on carbon emissions is being enriched. The main carbon emission accounting methods are the input-output method and the carbon emission coefficient method, and the research area includes all industrial sectors (Wang et al, 2023) as well as individual sectors such as industry (Liu et al, 2023), agriculture (Zhao and Du, 2023), tourism (Chandra et al, 2023 ), etc., and the scope of the research involves the global (Sun et al, 2023; Xu et al, 2024 ), regional (Saiful et al, 2023; Wei et al, 2023 ), national (Tsvetkov et al, 2024 ; Wang et al, 2024), provincial/state (Peng et al, 2023; Pouliasis et al, 2024 ), and city (Xing et al, 2024) levels at the macro scale, and is specific to a certain enterprise (Laurens and Thijs, 2024 ; Long and Zhang, 2024) or a certain piece of farmland (Dlamini et al, 2022 ; Shackelford et al, 2019 ) at the micro scale. In this paper, we will account for the carbon emissions of 42 industrial sectors in China based on China's inter-regional input-output tables. As research on carbon emissions continues to deepen, the focus is no longer only on carbon emissions directly generated by regional production and consumption products. The transfer of carbon emissions between regions based on the value chain is related to the overall regional emission reduction effect, economic growth and industrial structure upgrading (Li, 2018 ). The study on the transfer of carbon emissions based on the value chain is of guiding significance for the division of responsibility for emission reduction and the formulation and adjustment of regional carbon emission reduction policies. In the context of globalization, the carbon emissions embodied by international trade have first become a hot topic of research for experts and scholars. Muhammad et al. (2021) confirmed that economic complexity has heterogeneous effects on embodied carbon emissions from exports and imports, and that the effects of economic complexity on carbon emissions are asymmetric between high-income countries and low- and middle-income countries. High-income countries can achieve emission reductions not only by shifting production to low-income countries through trade, but also by shifting high-emissions-intensive production out and by maintaining low-emissions-intensive domestic production (Shukria et al, 2020). In less industrialized countries, the burden of emission reductions due to trade can be greater (Kozul et al, 2013), giving rise to significant carbon inequality at the global level. Among them, major carbon exporters are at a disadvantage in global trade and are victims of carbon inequality (Wang et al, 2022). A large number of scholars have researched on issues such as the embodied carbon emission transfer characteristics and responsibility allocation of inter-country trade (Banerjee, 2020 a; Banerjee, 2021 b; He and Jacquemin, 2022 ; Kim and Nikolas, 2021; Liu et al, 2020), and also proved the existence of carbon inequality. Guo ( 2021 ) and Huang (2021) et al. also show that as the carbon emissions embodied by regional trade are increasing, carbon inequality likewise appears between different regions in China. Economically backward regions such as northwest region have gradually become the main net transfer-out region of carbon emissions in the value chain, while economically developed regions such as southeast coast are the net transfer-in region (Guo and Rong, 2021 ; Liu et al, 2021). Regional carbon emission influencing factors include social development status, globalization policies (Justyna and Monika, 2024 ), geopolitical conflicts (Zhang et al, 2024), and energy consumption (Zhao et al, 2024), food consumption (Mustafa et al, 2023 ), Financial inclusion (Karamat et al, 2023 ), information and communication technologies (Lee et al, 2023), and so on. Despite the relatively large number of studies on the influencing factors of regional carbon emissions, there is a relative lack of research on the factors and mechanisms influencing the embodied carbon emissions of regional value chains. Deng (2023) and Xiao et al. (2019) only attempted to analyze the decomposition of influencing factors based on the LMDI decomposition method for the embodied carbon emissions of regional value chains. The number and type of indicators analyzed in the LMDI decomposition are limited, and they did not explore the influencing factors of carbon transfer in depth. Value chain-based carbon transfer is an important reason for carbon emissions to be generated in transfer-out regions and transferred in transfer-in and transfer-out regions (Liu et al, 2021). This has important implications for changes in the total amount and spatial pattern of carbon emissions in a country, as well as for coordinated regional development and industrial restructuring (Li, 2018 ). As the world's second-largest economy and the world's most populous country, China is actively responding to the issue of global climate change and taking on the task of energy conservation and emission reduction. It is of great practical significance to study the inter-provincial transfer of carbon emissions based on the value chain and analyze the reasons affecting the formation of the transfer characteristics, in order to promote the balanced and coordinated development of China's inter-region, reduce the division between the rich and the poor, and realize the carbon emission reduction in a more scientific and reasonable way. This paper calculates and analyzes the characteristics of carbon emission transfer based on the value chain among 30 provinces, autonomous regions and municipalities (except Tibet, Hong Kong, Macao and Taiwan) in China, and reveals the influencing factors of carbon transfer and its functioning mechanism through factor analysis, with a view to providing a basis for improving the study of carbon transfer in China and guiding the formulation and implementation of carbon emission reduction policies. In order to provide a basis for improving the study of carbon transfer in China and guiding the formulation and implementation of carbon reduction policies. 2 Methodology and Data 2.1 Methodology In this paper, through the value-based carbon emission accounting method, the inter-regional input-output tables of 42 sectors in 31 provinces, autonomous regions and municipalities in China are combined and calculated on the basis of the multiple regression model to analyze the mechanism of action and the degree of contribution of each influencing factor indicator. Regional division Referring to Liu Weidong et al.'s study, China is divided into eight regions, namely Beijing-Tianjin region (JJ), Northern Coastal region (NC), Eastern Coastal region (EC), Southern Coastal region (SC), Northeastern region (NE), Northwestern region (NW), Middle region (MR), and Southwestern region (SW). The specific calculations are as follows: ①Calculation of carbon emission coefficient $${f}^{n}={C}_{p}^{n}/{X}^{n}$$ 1 \({f}^{n}\) is the carbon emission coefficient of n provinces (autonomous regions and cities), in tons of carbon/10000 yuan; \({C}_{p}^{n}\) is the direct carbon emissions of n provinces, in tons of carbon; \({X}^{n}\) is the total output of n provinces, in 10000 yuan. ②Calculation of carbon emission based on the value $${C}_{v}^{n}=F\bullet {\rm K}\bullet {V}_{n}$$ 2 \({\text{C}}_{\text{v}}^{\text{n}}\) is the value-based carbon emissions of n provinces, in tons of carbon; F is the carbon emission coefficient matrix, ton carbon/10000 yuan; K is the inverse matrix \({\left(I-{H}^{T}\right)}^{-1}\) ; \({V}_{n}\) is the added value of n provinces, 10000 yuan. $${\rm K}={\left(I-{H}^{T}\right)}^{-1}$$ 3 Where I is the identity matrix and \({\text{H}}^{\text{T}}\) is the transposition of the distribution coefficient matrix H . $$H={X}_{in}/X$$ 4 \({\text{X}}_{\text{i}\text{n}}\) is the intermediate input matrix, 10,000 yuan; X is the total output matrix, 10000 yuan. that is, \({ℎ}^{nm}={X}_{in}^{nm}/{X}^{n}\) (5) \({ℎ}^{nm}\) is the distribution coefficient between n and m provinces; \({X}_{in}^{nm}\) is the intermediate investment of m province to n province, 10000 yuan; \({X}^{n}\) is the total output of n provinces, 10000 yuan. ③Calculation of net carbon transfer $$\text{N}{\text{C}}_{\text{v}}^{\text{n}\text{m}}={\text{C}}_{\text{v}}^{\text{n}\text{m}}-{\text{C}}_{\text{v}}^{\text{m}\text{n}}$$ 6 \(N{C}_{v}^{nm}\) is the value-based net carbon transfer, tons of carbon; \({C}_{v}^{nm}\) is the carbon emission transferred from m province to n province, tons of carbon; \({C}_{v}^{mn}\) is the carbon emission transferred from n province to m province, tons of carbon. If \(N{C}_{v}^{nm}>0\) , n provinces are net transfer-in regions; If \(N{C}_{v}^{nm}<0\) , n province is a net transfer-out region. ④Calculation of net carbon transfer growth rate $${S}_{{NC}_{v}}=\frac{\left|{NC}_{v,t}^{nm}\right|-\left|{NC}_{v,t-1}^{nm}\right|}{\left|{NC}_{v,t-1}^{nm}\right|}$$ 7 \({S}_{{NC}_{v}}\) is the growth rate of net carbon transfer; \({NC}_{v,t}^{nm}\) is the net carbon transfer between n and m provinces in t year, tons of carbon; \({NC}_{v,t-1}^{nm}\) is the net carbon transfer between provinces n and m in t-1 year, tons of carbon. 2.2 Data source (1) The 2012 China Inter-regional Input-Output Tables, the 2017 China Inter-regional Input-Output Tables, and the 2012 and 2017 national direct carbon emission data for 30 provinces, autonomous regions and cities are obtained from China Carbon Accounting Database (CEADs) ( https://www.ceads.net.cn/ ). (2) Data on influencing factors are obtained from the 2013 and 2018 Statistical Yearbooks of China and its Provinces, Regions and Municipalities. 3 Results 3.1 Characteristics of inter-provincial carbon transfer From 2012 to 2017, China's economically developed provinces (autonomous regions and municipalities) were the major carbon emissions transfer-in and out region of most provinces, and provinces with a weak base of economic development and lagging behind in science and technology were the smallest carbon transfer-out regions. Figure 1 shows the relationship between inter-provincial carbon transfer, the darker the color means, the larger the amount of inter-provincial carbon transfer. In 2012, Jiangsu was the largest carbon emissions transfer-in region for most provinces, followed by Guangdong. In terms of the direction of carbon emissions transfer out, Jiangsu was also the largest carbon emissions transfer-out region for most provinces. In 2017, the carbon emissions transferred in from various provinces were concentrated from Guangdong, Hebei, Inner Mongolia and Henan. The largest carbon emissions transferred in region for individual provinces was Jiangsu, Zhejiang, Inner Mongolia and Henan. In terms of the direction of carbon emissions transfer out, Jiangsu remains the largest carbon emissions transfer-out region of most provinces, followed by Guangdong. Jiangsu and Guangdong are the top two carbon emissions transfer-out regions for the vast majority of provinces. With growing economic strength and expanding industrial scale, Guangdong has closer trade links with other provinces, and more and more products are produced and processed through other provinces. From 2012 to 2017, the smallest carbon emissions transfer-out region of each province has always been Hainan as well as the western provinces of Qinghai and Ningxia, indicating that the development gap between the above regions and other provinces has always been large. 3.2 Characteristics of inter-regional carbon transfer The MR was always the region that transferred the most carbon emissions out, while the JJ transferred the least. The region with the highest amount of carbon emissions transferred in shifted from the MR to the EC. The region with the lowest carbon emissions transferred in shifted from the SC to the JJ. As can be seen in Fig. 2, the MR had the highest total amount of carbon emissions transferred in and out in 2012. Carbon emissions transferred out of all regions to the MR were the largest, with the amount of carbon emissions transferred in of the MR reaching as high as 521.61 millions of tons. NE, NC, EC, SC, and MR all had the lowest carbon emissions transferred out to the SC. JJ, NW, SW transferred the least amount of carbon emissions to the JJ. The MR was also the largest source of carbon emissions for other regions, with a total of 537.132 million tons of carbon emissions transferred out. In 2017, the largest amount of carbon emissions transferred in was in the EC, and the largest carbon emissions transferred out was still in the MR. Except for NC, EC, and SC, other regions transferred out the most carbon emissions to the EC, and the EC carbon emissions transferred in was as high as 585.52 million tons. Except for NC and MR, carbon emissions transferred out of other regions to the JJ were the least, with only 176.59 million tons of carbon emissions transferred in of the JJ. Same as in 2012, the smallest carbon emissions transferred in of all 8 regions are from the JJ. Compared with 2012, carbon emissions transferred in of all other regions except for NE, NC, and NW were increased. In addition, carbon emissions transferred out of the JJ and the SC declined, while all other regions increased to varying degrees. It can be seen that with the continuous development of the economy and society, the carbon emissions generated by the economically developed regions themselves were decreasing, and the carbon emissions transferred through the value chain were increasing. This also shows that the economically developed regions were continuously transferring and relocating the high-carbon emission production chains to the less-developed regions. This had led to a continued increase in direct carbon emissions of less economically developed regions. 3.3 Characteristics of net carbon transfer China's carbon emissions as a whole show the characteristics of transferring from regions with a higher economic development level to lower regions, and from the east to the west. In 2012, the net transfer-out regions were mainly distributed in the regions with rich natural resources in Central and Western China, as well as economically developed Shanghai, Zhejiang, and Guangdong (Fig. 3 a). In 2017, the net transfer-out regions were mainly distributed in the NE, NW, SW, and some provinces in the MR with rich natural resources. As can be seen in Fig. 4 , Beijing was a complete net transfer-in region, that is, there were more transfers in than transfers out with all provinces. Tianjin, Jiangsu, Fujian, Jiangxi, Shandong, Henan, Hunan, Sichuan, and Shaanxi were also net transfer-in regions from 2012 to 2017. Fujian had the largest increase in net carbon transfer (Fig. 4 ). However, the net transfer amount of Tianjin, Jiangxi, Hunan, Sichuan, and Shaanxi decreased (Fig. 4 ). Ningxia was a complete net transfer out region, that is, there were more transfers out than transfers in with all the provinces. Shanxi, Inner Mongolia, Liaoning, Anhui, Guizhou, Yunnan, Gansu, and Xinjiang were also consistently net carbon transfer-out regions. Except for Shanxi and Anhui, the net transfer amount of other regions had increased, and the growth rate was high. Comparison of Fig. 3 shows that the net transfer direction of other provinces had changed from 2012 to 2017. Hebei, Jilin, Heilongjiang, Guangxi, Hainan, Chongqing, and Qinghai all changed from net transfer-in regions to net transfer-out regions, while Shanghai, Zhejiang, Hubei, and Guangdong changed from net transfer-out regions to net transfer-in regions. Although the net transfer direction of these regions had changed, the net transfer amount of most regions had increased to varying degrees. Carbon emissions from the JJ were mainly transferred out to the Northern China in close proximity, but the carbon emissions transferred out to the Southern China were also increasing (Fig. 1). In 2012, the net transfer amount between Beijing and Hebei was the largest (Fig. 5a). The net transfer amount between Tianjin and Jilin was the largest (Fig. 5a). By 2017, the net carbon transfer between Beijing and Henan was the largest (Fig. 5b). Tianjin had the largest net carbon transfer with Inner Mongolia (Fig. 5b). As an old industrial base in China, the NE is one of the net transfer-out regions of carbon emissions. As can be seen from Fig. 5, in 2012, Liaoning had the highest net transfers with Inner Mongolia, Guangdong and Shanxi, all of which Liaoning served as a net transfer-in region. By 2017, the largest net transfers were with Jiangsu, Guangdong and Shanghai, and Liaoning was a net transfer-out region. This also shows that the carbon emissions transferred in Liaoning through the value chain decreased and the carbon emissions transferred out increased. Net carbon transfers between Heilongjiang and Jilin were the highest in 2012–2017. Jilin was a net transfer-in region in 2012 and Heilongjiang was the net transfer-in region in 2017. Jilin's net carbon transfers increased with most provinces. In the future, Jilin will still continue to be a net transfer-out region to take over some of the production links from the provinces. Heilongjiang, on the other hand, had seen a decrease in net carbon transfer with most provinces. This indicates that the industrial structure of Heilongjiang was adjusting, and the reliance of the region on its heavy industry products was decreasing. In the NC of Hebei and Shandong, industries had been transformed and the direction of net carbon transfer had changed significantly. As can also be seen in Fig. 5, the largest net transfer was between Shandong and Guangdong in 2012, with Shandong being the net transfer-in region. In 2017, Guangdong then became a net transfer-in region for Shandong. This indicates that Guangdong needed Shandong to complete more energy-consuming industrial segments and emit more carbon emissions. While in 2017, the net transfer between Shandong and Inner Mongolia was the largest, and Shandong had been acting as a net transfer-in region. This illustrates the increased dependence of Shandong's industrial development on resource-based provinces such as Inner Mongolia. In 2012, Hebei had the largest net transfer of carbon emissions out to Beijing, followed by a net transfer in from Guangdong. In 2017, the largest net transfer was with Shanxi, followed by Shandong, Beijing and Guangdong, and all of Hebei became a net transfer-out region. The three provinces in the EC had larger net carbon transfers to the NE, SW and NW. The above regions can provide the EC with the production and processing of various agricultural products, fossil energy and other raw materials. It also can be seen in Fig. 5, Jiangsu had the largest net carbon transfer with Guangdong, Shanghai and Jiangxi in 2012. However, by 2017, net carbon transfers with all three of these regions had declined, and the net carbon transfers with Xinjiang, Inner Mongolia, and Liaoning were the largest. In 2012, Shanghai had the largest net carbon transfer with Jiangsu and Shandong, and Shanghai is all a net transfer-out region. In 2017, net carbon transfers with Inner Mongolia and Henan were the largest, and Shanghai is already a net transfer-in region. In 2012, the net carbon transfer between Zhejiang and Hebei, Guangdong and Shandong was the largest, and Zhejiang was the net transfer-out region of Hebei and Shandong. In 2017, the net carbon transfer between Zhejiang and Guangdong, Jiangsu and Inner Mongolia was the largest, and Zhejiang was also a net transfer-out region for Jiangsu and Guangdong. It indicates that the industrial structure and the direction of industrial transfer in Shanghai and Zhejiang had changed considerably during 2012–2017. Economic development was becoming more closely linked to other provinces and cities, and the dependence on other provinces was also increasing. The net carbon transfer of Guangdong, Fujian and Hainan in the SC was mainly from the Northern China. The net carbon transfers between 3 provinces in the SC and Jiangsu and Hebei, Shandong and Inner Mongolia in Northern China were significantly larger in 2012 (Fig. 5a). Of these, the largest net transfers in Fujian and Hainan were from Guangdong. By the time 2017 rolled around, the direction of the net transfer among the 3 regions had changed considerably. As can be seen in Fig. 5b, Fujian remained the largest net carbon transfer with Guangdong and Inner Mongolia. Hainan had larger net transfers mainly with Henan and the Yangtze River Delta region. Guangdong, on the other hand, had the largest net carbon transfer inwards from Xinjiang. Guangdong is a major textile producer and exporter, and has become the world's third-largest apparel export base. Xinjiang has a well-developed cotton cultivation and processing industry, and is an important production and supply location for the upstream and midstream segments of Guangdong's textile and apparel industry. The direction of net carbon transfers varies considerably across provinces in the MR. The net carbon transfer between Shanxi and Jiangsu was the largest in 2012 (Fig. 5a), but has since declined. The net carbon transfers between Shanxi and Guangdong and Hebei increased the most. Thus by 2017, the largest net carbon transfer was with the above two provinces (Fig. 5b). Shanxi is rich in mineral resources and is dominated by energy and metallurgical industries. This indicates that Guangdong and Hebei needed more energy and related products for their development during 2012–2017, while Jiangsu's demand decreased. The net carbon transfer characteristics of Hubei and Hunan provinces were more similar. The net carbon transfer with Shanxi and Inner Mongolia was larger in 2012, and with Beijing, Hebei and Inner Mongolia in 2017 (Fig. 5). Meanwhile, the net carbon transfer characteristics of Henan and Anhui were also more similar. The regions with larger net carbon transfers in both places were concentrated in Beijing, Inner Mongolia, Shanghai, Shandong, Guangdong and Xinjiang, and the direction of transfer was basically the same. Changes in the net carbon transfer volume in Jiangxi were generally characterized by a substantial decrease with the NC, SW, and part of the MR, and a substantial increase with other provinces. The continuing transfer of industry chain links from all regions of China to the NW had resulted in an increasing net carbon transfer between provinces in the NW and most of the country. In particular, the incremental net carbon transfer with China's economically developed Yangtze River Delta and Pearl River Delta regions were both particularly large. And the NW basically served as a net carbon transfer-out region (Fig. 5). It also can be seen in Fig. 5, the larger net carbon transfers of provinces in the NW were also basically with the JJ, NC, EC, and Guangdong, which have higher levels of economic development in China. As in the NW, the net carbon transfers between provinces in the SW and most provinces in China had increased. This is especially the case with the economically developed regions such as Beijing, the Yangtze River Delta and the Pearl River Delta, and regions with more abundant energy resources but a more homogenous industrial structure, such as the NE, NW, Inner Mongolia and Jiangxi. And except with the NW, the provinces in the NW basically were as a net transfer-out region (Fig. 5). Once again, the net carbon transfer in China was roughly characterized by a transfer from east to west and from southeast to northwest. 3.4 Analysis of influencing factors Carbon emissions are affected by many factors, including the natural environment, society and the economy. Because there are too many influencing factors to be considered, it is difficult for multiple regression to avoid the problem of indicator covariance, so factor analysis can be used to realize the downscaling and simplification of indicators, and better analyze the influencing factors of inter-provincial carbon emission transfer. Factor analysis summarizes independent influencing factors from many indicators, i.e., the common factors, which should reflect as much information as possible about the original variables. By calculating the correlation coefficient matrix, eigenroots and eigenvectors, and variance contribution ratio, the number of common factors and the number of original variables they represent can be judged. Multiple regression models are constructed through the common factors. In this paper, 9 indicators such as regional GDP per capita, percentage of secondary industry, total import and export of goods, number of patents for inventions granted, resident population, disposable income per capita, consumption levels of residents, energy consumption per unit GDP, primary energy production are selected as the original influencing factor indicators for factor analysis, which are shown in Table 1 . In KMO test, the probability is 0.000 is less than the level of significance and the original hypothesis is rejected, which is significantly different from the unit matrix.The KMO is 0.742, which indicates that it is suitable for factor analysis. The factor analysis process usually uses principal component analysis to select principal components with eigenvalues greater than 1 as common factors. In this paper, a total of three common factors were extracted (Fig. 6 ). From the data in Table 2 , it can be seen that the variance of the common factor of 7 of the 9 influencing factors is greater than 80%. The three common factors extracted together explained 82.505% of the information. Therefore the information loss of the original influence factor indicators is small, and the overall effect of the common factor extraction is ideal. Table 1 Impact factor indicators and meanings Indicator Meaning GDP per capita (I1) Economic development level Percentage of secondary industry (I2) Industrial Structure Total import and export of goods (I3) Trade Import/Export Demand Number of patents for inventions granted (I4) Technical level Resident population (I5) Population Concentration Disposable income per capita (I6) Living standard of the population Consumption levels of residents (I7) Resident consumption level Energy consumption per unit GDP (I8) Energy consumption Primary energy production (I9) Local resource availability In KMO test, the probability is 0.000 is less than the level of significance and the original hypothesis is rejected, which is significantly different from the unit matrix.The KMO is 0.742, which indicates that it is suitable for factor analysis. The factor analysis process usually uses principal component analysis to select principal components with eigenvalues greater than 1 as common factors. In this paper, a total of three common factors were extracted (Fig. 6). From the data in Table 2, it can be seen that the variance of the common factor of 7 of the 9 influencing factors is greater than 80%. The three common factors extracted together explained 82.505% of the information. Therefore the information loss of the original influence factor indicators is small, and the overall effect of the common factor extraction is ideal. Table 2 Common factor variance Initial Extracted I1 score 1.000 .901 I2 score 1.000 .658 I3 score 1.000 .756 I4 score 1.000 .851 I5 score 1.000 .863 I6 score 1.000 .960 I7 score 1.000 .968 I8 score 1.000 .649 I9 score 1.000 .821 Appropriate rotation of the loading matrix makes the common factor of the original influencing factors more obvious and more conducive to the interpretation of the actual problem. As can be seen from Table 3 , GDP per capita, disposable income per capita, and consumption levels of residents have the greatest contribution to the common factor 1 (F1), which is above 90%, so the F1 can be interpreted as the level of economic development. Resident population contributes the most to the common factor 2 (F2) with 92.1%, so the F2 can be interpreted as population agglomeration and social needs. Primary energy production contributes the most to the common factor 3 (F3) with 90.2%, so the F3 is interpreted as energy demand. Table 3 Rotated component matrix Component F1 F2 F3 I1 score .942 .100 − .058 I2 score − .525 .395 .475 I3 score .450 .741 − .060 I4 score .740 .530 − .149 I5 score − .107 .921 − .064 I6 score .960 .046 − .188 I7 score .970 .067 − .150 I8 score − .444 − .415 .528 I9 score − .042 − .076 .902 Extraction method: principal component analysis. Rotation method: kaiser normalized maximum variance method. a. The rotation has converged after 5 iterations. Finally, the standardized original matrix of influence factor indicators is multiplied by the component score coefficients (Table 4 ) to calculate the common factor scores. The expressions for each common factor are shown in Eqs. ( 8 )- ( 10 ) below: $$\text{F}1=0.286{\text{I}}^{{\prime }}1-0.107{\text{I}}^{{\prime }}2+0.062{\text{I}}^{{\prime }}3+0.158{\text{I}}^{{\prime }}4-0.139{\text{I}}^{{\prime }}5+0.269{\text{I}}^{{\prime }}6+0.278{\text{I}}^{{\prime }}7+0.011{\text{I}}^{{\prime }}8+0.191{\text{I}}^{{\prime }}9$$ 8 $$\text{F}2=-0.041{\text{I}}^{{\prime }}1+0.286{\text{I}}^{{\prime }}2+0.353{\text{I}}^{{\prime }}3+0.206{\text{I}}^{{\prime }}4+0.504{\text{I}}^{{\prime }}5-0.078{\text{I}}^{{\prime }}6-0.065{\text{I}}^{{\prime }}7-0.156{\text{I}}^{{\prime }}8+0.004{\text{I}}^{{\prime }}9$$ 9 $$\text{F}3=0.160{\text{I}}^{{\prime }}1+0.321{\text{I}}^{{\prime }}2+0.079{\text{I}}^{{\prime }}3+0.055{\text{I}}^{{\prime }}4-0.038{\text{I}}^{{\prime }}5+0.046{\text{I}}^{{\prime }}6+0.083{\text{I}}^{{\prime }}7+0.348{\text{I}}^{{\prime }}8+0.781{\text{I}}^{{\prime }}9$$ 10 where \({\text{I}}^{{\prime }}\) i denotes the standardized value of the raw impact factor indicator. Table 4 Matrix of component score coefficients Component F1 F2 F3 I1 score .286 − .041 .160 I2 score − .107 .286 .321 I3 score .062 .353 .079 I4 score .158 .206 .055 I5 score − .139 .504 − .038 I6 score .269 − .078 .046 I7 score .278 − .065 .083 I8 score .011 − .156 .348 I9 score .191 .004 .781 Extraction method: principal component analysis. Rotation method: kaiser normalized maximum variance method. Component Score. There is no linear relationship between the common factors (Table 5 ). Therefore, the regression coefficients of each influencing factor can be derived by regressing the three common factors on the net carbon transfer, so as to further analyze the relationship between each influencing factor and the changes in the net carbon transfer of inter-provincial. The F-value of the regression result is 9.952 and the p-value is 0.000 < 0.05 (Table 6 ), indicating that the model is constructed in a meaningful way and at least one of the independent variables will have an effect on the dependent variable. As can be seen from the p-values, all three public factors have a significant effect on the net carbon transfer. According to the regression results, the regression equation of the net carbon transfer with the three public factors can be obtained as: $$\text{Y}=0.539\text{F}1+0.273\text{F}2-0.359\text{F}3+0.27$$ $$=0.086{\text{I}}^{{\prime }}1-0.095{\text{I}}^{{\prime }}2+0.101{\text{I}}^{{\prime }}3+0.122{\text{I}}^{{\prime }}4+0.076{\text{I}}^{{\prime }}5 + 0.107{\text{I}}^{{\prime }}6+0.102{\text{I}}^{{\prime }}7-0.162{\text{I}}^{{\prime }}8-0.176{\text{I}}^{{\prime }}9$$ 11 Table 5 Component score covariance matrix Component 1 2 3 1 1.000 .000 .000 2 .000 1.000 .000 3 .000 .000 1.000 Extraction method: principal component analysis. Rotation method: kaiser normalized maximum variance method. Component Score. Table 6 Linear regression The net carbon transfer Coef. St.Err. t-value p-value [95% Conf Interval] Sig F1 .539 .115 4.69 0 .309 .769 *** F2 .273 .103 2.65 .011 .066 .48 ** F3 − .359 .104 -3.45 .001 − .569 − .15 *** 2012b 0 . . . . . 2017 − .541 .232 -2.33 .023 -1.005 − .077 ** Constant .27 .154 1.75 .085 − .039 .58 * Mean dependent var -0.000 SD dependent var 1.000 R-squared 0.420 Number of obs 60 F-test 9.952 Prob > F 0.000 Akaike crit. (AIC) 146.594 Bayesian crit. (BIC) 157.065 *** p < .01, ** p < .05, * p < .1 From Eq. ( 11 ), it can be seen that among the nine influencing factors, the percentage of secondary industry, energy consumption per unit GDP and primary energy production are negatively correlated with the value of net carbon transfer i.e., the larger the value is, the smaller the net carbon transfer is, i.e., carbon emissions transferred out is greater than transferred in. Other factors are positively correlated with the net carbon transfer, i.e., the larger the value is, the greater the net carbon transfer is, i.e., carbon emissions transferred in is greater than transferred out. The top three indicators of the correlation coefficient are primary energy production, energy consumption per unit GDP and number of patents for inventions granted, which have the greatest impact on the inter-provincial carbon transfer in China. Therefore, the local energy supply capacity, energy consumption level, and science and technology level of a certain region were most important for the difference between the carbon emissions transferred in and out of that region. It is the key to determine whether the region was a net transfer-in or net transfer-out region. China's resource-rich provinces, autonomous regions and municipalities had taken on more high-energy-consuming links in the industrial value chain, and it is most critical for such regions to reduce emissions by improving their independent innovation capabilities and production technology levels and reducing energy consumption levels. Provinces, autonomous regions and municipalities with high technological levels, whose resource holdings could not meet local development, transfer high-energy-consuming segments to other regions, so that their carbon emissions were transferred in more than they are transferred out. The coefficient for resident population is the smallest, suggesting that population size did not have a significant effect on changes in the net carbon transfer. In addition, disposable income per capita, consumption levels of residents and total import and export of goods all had a large positive effect on the change in net carbon transfer. It indicates that the greater the social demand in a certain region, the greater the net carbon transfer, i.e., the more carbon emissions were transferred. Therefore, the income of the population should be safeguarded and raised to stimulate consumption and expand domestic demand. Neither the percentage of secondary industry nor GDP per capita had a significant effect on the net carbon transfer. The industrial structure and economic development level of a region were not the key factors determining the difference between the carbon emissions transfer in and out of the region based on the value chain. 4 Conclusion and Discussion This paper applies the value-based carbon emission accounting method to measure and analyze the spatial and temporal characteristics of the carbon emissions transferred on the inter-provincial value chain in 2012 and 2017 based on the inter-regional input-output tables of 42 sectors in 31 provinces, autonomous regions and municipalities in China, and calculates and analyzes the influencing factors and the mechanism of the net carbon transfer through the factor analysis method, and finally obtains the following conclusions:. The economically developed Beijing-Tianjin and the southern coastal regions gained high economic value in the value chain but received little carbon emissions, while the economically underdeveloped central and western regions gained little economic value in the value chain but received a lot of carbon emissions. The largest carbon emissions transfer-in and out region of most provinces in 2012–2017 were provinces with high levels of economic development, and the carbon emissions transferred out to Hainan, the NW and other weak economic development regions were least. Except for JJ and the SC, the amount of carbon emissions transferred out of other regions had increased to different degrees. China's inter-provincial carbon transfer was generally characterized by the transfer from regions with higher levels of economic development to regions with lower levels of economic development, and from the east to the west. The center of gravity of the net carbon transfer had shifted significantly to the north and west, and the differentiation between the net transfer-in regions and the net transfer-out regions had become more obvious. Direct carbon emissions from economically developed regions themselves had decreased, but carbon emissions transferred through the value chain had increased. The net carbon transfer between most provinces had increased. The level of local resource ownership and energy consumption level determined the changes in the net carbon transfer in the value chain. During the period of 2012–2017, primary energy production, energy consumption per unit of GDP, and number of patents for inventions granted made the greatest contribution to the changes in the net carbon transfer between provinces. This can also reflect that China's high-energy-consuming industries generally resettled and shifted to resource-rich regions during the 2012–2017 period. And if such regions have insufficient scientific and technological innovation capacity, their relatively weak technological level will cause their energy consumption level to keep increasing, generating and transferring more carbon emissions. Therefore, China's resource-rich and insufficient scientific and technological innovation capacity of the region's emissions reduction burden is heavier, emissions reduction targets are relatively difficult to achieve. On the contrary, if the social demand of a region is large, local resources can not meet its own development of the region, it will reduce its own carbon emissions through the import of high-energy-consuming products to increase the carbon emissions of the importing place. The burden of emission reduction in such regions is significantly reduced. This has led to an unfair division of responsibility for carbon emission reduction. Due to the complexity of input-output table construction, China's inter-regional input-output tables at the national level are developed every five years, and the latest year is currently updated only to 2017, so the data are slightly behind. However, we mainly want to present the research methodology in the paper, and we can continue the analysis based on the methodology in the paper if new data are available subsequently. The analysis of influencing factors is only for this time period. How the main influencing factors affecting carbon transfer have changed since then needs to be further investigated. Declarations Ethical Approval Not applicable Availability of data and materials The datasets used in this study are available from the corresponding author on reasonable request. Competing interests All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding Not applicable Authors’ Contributions Hongguang Liu: Methodology, Validation, Writing - Review & Editing, Supervision. Xiaocui Dong: Conceptualization, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review & Editing, Visualization Acknowledgements Not applicable References Banerjee S. Carbon Emissions Embodied in India-United Kingdom Trade: A Case Study on North -South Debate. Foreign Trade Review. 2020; 55(2): 199-215. Banerjee, S. Addressing the carbon emissions embodied in India’s bilateral trade with two eminent Annex-II parties: with input–output and spatial decomposition analysis. Environ Dev Sustain. 2021;23:5430–5464. Chandra L V, Azharul M I , Md S N. Does tourism have an impact on carbon emissions in Asia? An application of fresh panel methodology. Environment, development and sustainability.2023;:11-19. Chengzhen Xu, Qingyuan Zhu, Xingchen Li, et al. Determinants of global carbon emission and aggregate carbon intensity: A multi-region input−output approach. Economic Analysis and Policy.2024;:81418-435. Chi Long, Hongmei Zhang. How digital finance affects carbon emission in manufacturing industry-A perspective based on digital transformation of enterprises. Finance Theory and Practice.2023;10:67-78. Chien-Chiang L, Zihao Y, Chi-Chuan L. A nonlinear analysis of the impacts of information and communication technologies on environmental quality: A global perspective. Energy Economics.2023;128. Deyun Xiao, Chunhui Wang, Huishuang He. Research on industrial carbon emission reduction in Henan Province under the background of global value chain. Journal of Beijing University of Posts and Telecommunications (Social Science Edition). 2019;21(04):7-16. Dlamini J. C., Cardenas L. M., Tesfamariam E. H., et al.Soil CO2 emissions in cropland with fodder maize (Zea mays L.) with and without riparian buffer strips of differing vegetation.Agroforestry Systems.2022;96(7):983-995. Feng G, Siqi M, Renjin S. The evolution characteristics and influence factors of carbon productivity in China's industrial sector: from the perspective of embodied carbon emissions. Environmental science and pollution research international. 2021;28(36):50611-50622. Fujia Li. Progress and outlook of research on implied carbon emission transfer in inter-regional trade. Progress in Geography.2018;37(10):1303-1313. He J, Jacquemin J. La balance du carbone incorpore au commerce bilateral entre la France et la Chine: Transferts d’emissions ou nouvelles creations? (Balance of Carbon Embodied in Bilateral Trade between France and China: Transfer of Emission or New Creation? With English summary.). Revue Economique. 2016;67(6):1179-1210. Heping Huang, Mengting Yi, Junwen Cao, et al. Spatial and temporal changes and impact effects of embodied carbon emissions from regional trade - taking the Yangtze River Economic Belt as an example. Economic Geography.2021;41(03): 49-57. Hongguang Liu, Zimeng Zhang, Jie Guo. Research on the embodied carbon emissions transfer in the Chinese interregional value chain. Management Review.2021;33(09):58-64. Hongguang Liu , Zhaoya Wang, Yanan Guo, et al. Carbon leakage or carbon reduction? Carbon emission effect of the production relocation of metal smelting industry in China. Environ Prog Sustainable Energy. 2023;e14332. Hotak, S., Islam, M., Kakinaka, M. et al. Carbon emissions and carbon trade balances: International evidence from panel ARDL analysis. Environ Sci Pollut Res.2020;27:24115–24128. Huogen Wang, Yuting Wang, Lixiang Xiao. Forecasting and analyzing carbon dioxide emissions from Chinese industries based on the IO-SDA method from 2020 to 2060. China Environmental Science. 2024;:1-15. Jieyu Wang, Yuli Shan, Can Cui, et al. Investigating the fast energy-related carbon emissions growth in African countries and its drivers.Applied Energy.2024;:357122494-. Jian Xue, Lulu Liu. Research on the pricing of embodied carbon rights based on the perspective of inter-provincial trade. Ecological Economy. 2022;38(11): 27-35. Jincai Zhao, Yongmeng Du. The impact and spatial effect of rural revitalization on agricultural carbon dioxide emissions: a case study of Henan Province. Frontiers in Environmental Science.2023,11. Jing Wang, Bo Meng, Zongxian Feng, et al. Research on Chinese inter-regional carbon emissions transfer from the perspective of value-added trade. Journal of Xi'an Jiaotong University (Social Science Edition). 2020;40(02): 85-94. Justyna B, Monika P. Convergence of CO 2 emissions in countries at different stages of development. Do globalization and environmental policies matter? Energy Policy. 2024;184. Jun Z, Kangyin D, Xiucheng D. How does energy poverty eradication affect global carbon neutrality? Renewable and Sustainable Energy Reviews.2024;191. Karamat K, Xuwen Y, Jie Z, et al. Financial inclusion, environmental degradation, and the moderating role of ICT: a global perspective. Environmental science and pollution research international.2023. Kejuan Sun, Jiabai Ye, Youfu Yue, et al. Tracing carbon emissions and intensity in relational global value chain activities.Journal of Cleaner Production.2023;429. Kozul-Wright, R., Fortunato, P. International Trade and Carbon Emissions. Eur J Dev Res. 2012;24:509–529. Laurens S, Thijs M. Corporate carbon emissions data for equity and bond portfolios. Managerial Finance.2024;50(1):118-139. Lingyu Deng. Analysis of the embodied carbon decoupling effect of the trade value chain in the Yangtze River Economic Belt under global value chains. Chongqing University of Commerce and Industry, China; 2023. Min Yan, Hui Sun. Research on the structural characteristics of the Chinese inter-provincial embodied carbon transfer network from the perspective of the economic internal cycle. Ecological Economy. 2022;38(02):13-21. Mustafa A, Lenny C S K, Lingxuan L, et al. Assessing pre-pandemic carbon footprint of diet transitions in UK nations and regions. International Journal of Production Research. 2023;61(18):6115-6130. Peixue Xing, Yanan Wang, Tao Ye, et al. Carbon emission efficiency of 284 cities in China based on machine learning approach: Driving factors and regional heterogeneity. Energy Economics.2024;:129107222-. Pouliasis K P, Papapostolou C N, Tamvakis N M, et al. Carbon Emissions in the U.S.: Factor Decomposition and Cross-State Inequality Dynamics. The Energy Journal. 2024;45(1):135-161. Qayyum, M., Yu, Y., Li, S. The impact of economic complexity on embodied carbon emission in trade: new empirical evidence from cross-country panel data. Environ Sci Pollut Res. 2021;28: 54015–54029. Qingyong Z, Xianqiang M, Jianhong L, et al. EU-Russia energy decoupling in combination with the updated NDCs impacts on global fossil energy trade and carbon emissions.Applied Energy.2024;:356122415-. Rui Peng, Xiaoxiong Tian, Dan Wu. Spatiotemporal characteristics and zonal analysis of carbon balance in county-level regions: case study of Jiangsu Province.Frontiers in Environmental Science.2023;11 Saiful M I, Habibur S R, Ur A R, et al. ICT's impact on CO2 emissions in GCC region: The relevance of energy use and financial development. Energy Strategy Reviews.2023;49. Shackelford E G, Kelsey R, Dicks V L. Effects of cover crops on multiple ecosystem services: Ten meta-analyses of data from arable farmland in California and the Mediterranean. Land Use Policy.2019;88(C):104204-104204. Silin Wei, Yinsheng Yang, Ying Xu. Regional development, agricultural industrial upgrading and carbon emissions: What is the role of fiscal expenditure? —Evidence from Northeast China.Economic Analysis and Policy.2023;:801858-1871. Tae-Jin Kim, Nikolas Tromp. Carbon emissions embodied in China-Brazil trade: Trends and driving factors. Journal of Cleaner Production.2021;293. Tsvetkov P, Andreichyk A, Kosarev O. The impact of economic development of primary and secondary industries on national CO 2 emissions: The case of Russian regions.Journal of Environmental Management.2024;:351119881-. Weidong Liu, Jie Chen, Zhipeng Tang, et al. Theory and practice of compiling inter-regional input-output table of 30 provinces, autonomous regions and cities in China in 2007. Beijing: China Statistics Press; 2012. Xilian Wang, Qing Jin. Embodied carbon emissions and responsibility sharing of China's trade in the context of carbon neutrality. Environmental Science & Technology. 2021; 44(11): 205-210. Yihan Wang, Siqin Xiong, Xiaoming Ma. Carbon inequality in global trade: Evidence from the mismatch between embodied carbon emissions and value added. Ecological Economics.2022;195. Yuna D, Jinjin C, Zhichao G, et al. Network Evolution and Influencing Factors of Global Trade Embodied Carbon Emission.Frontiers in Environmental Science.2022;10. Zhangqi Zhong, Xu Zhang, Lingyun He, et al. Inter regional carbon emission transfer, trade embodied carbon structure and cooperative emission reduction: an empirical analysis from 30 provinces in China. Journal of International Trade. 2018; (06): 94-104. Zhengquan Guo, Tong Rong. Analysis of the Spatio-temporal pattern of embodied carbon transfer in Chinese inter-regional trade. Journal of Shanxi University (Philosophy and Social Sciences Edition). 2021; 44(06): 97-108. Zhu Liu, Jing Meng, Zhu Deng, et al. Research on the transfer of embodied carbon emissions in Sino-US trade. Chinese Science: Earth Science. 2020; 50(11): 1633-1642. Additional Declarations No competing interests reported. Supplementary Files Listofabbreviations.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3887683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268937423,"identity":"4c620bd9-d15a-4f39-86d8-6013601ddbf7","order_by":0,"name":"Xiaocui Dong","email":"","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xiaocui","middleName":"","lastName":"Dong","suffix":""},{"id":268937424,"identity":"f1d5411e-3d9d-4983-b289-999cdcaf4c28","order_by":1,"name":"Hongguang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIiWNgGAWjYBACPmYGNgaGCgZmMI+HGC1sYC1nSNICQoxtUB5xWth5zB5+nXeHnX9GAuODt20M8uaEHcZjbiy77RmzxI0EZsO5bQyGOxsIazGTltx2mJnhRgKbNG8bQ4LBAaK0zDnMLH8jgf030VokPzYcZjYA2sJMpBa2MmmGY4eZDc88bJacc07CcAMhLfz8h7dJ/qg5nCx3PPnghzdlNvIEbQEBZmB0JDMwMDYA2RJEqAcCxh8MDHbEKR0Fo2AUjIIRCQDhmDUJuvog9QAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Hongguang","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-01-22 10:31:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3887683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3887683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50141300,"identity":"146ae653-8fdc-4efb-88a1-3565e8b8242a","added_by":"auto","created_at":"2024-01-25 06:52:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113099,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInter-provincial carbon transfer relationship, 2012-2017\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/eb7d6f11e63bdcf86d9f08bf.png"},{"id":50141304,"identity":"077ecf5d-89c5-48a4-8a11-59aa8e39a063","added_by":"auto","created_at":"2024-01-25 06:52:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":559940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCarbon emission transfer pathways among the 8 major regions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/9a6163e1d7e631fea4c0ee92.png"},{"id":50141301,"identity":"42616ff6-f14a-4299-a9d1-257a6eef1fde","added_by":"auto","created_at":"2024-01-25 06:52:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":378673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of net carbon transfer in China\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/bb5e8fe857f1329274ea4b4b.png"},{"id":50141305,"identity":"853ce2d6-8705-4898-800b-4a2db9c0419d","added_by":"auto","created_at":"2024-01-25 06:52:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in net carbon transfer of 30 provinces, autonomous regions and municipalities\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/990fe30c68d83676edf568eb.png"},{"id":50141672,"identity":"284df4a1-f1c4-4677-b2e9-c643d88ea84c","added_by":"auto","created_at":"2024-01-25 07:00:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInter-provincial net carbon transfer relationship, 2012-2017\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/2e6459a06d20ae6e4e1b402b.png"},{"id":50141303,"identity":"6ccdc18d-775c-4368-a565-f76266a72837","added_by":"auto","created_at":"2024-01-25 06:52:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69397,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGravel chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/36c1b084dad52fee94c5d59e.png"},{"id":50573737,"identity":"b1ac2dbd-abf0-40e4-8eaa-2185bbf5a370","added_by":"auto","created_at":"2024-02-02 16:52:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1483825,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/a73fd818-eb03-45be-83e7-a31782e1a780.pdf"},{"id":50141671,"identity":"785675bb-e4f6-47cd-9661-adfe84f45a69","added_by":"auto","created_at":"2024-01-25 07:00:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13345,"visible":true,"origin":"","legend":"","description":"","filename":"Listofabbreviations.docx","url":"https://assets-eu.researchsquare.com/files/rs-3887683/v1/fd2d28dde0819ea190b5f469.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Characteristics and Influencing Factors of Carbon Transfer Based on Value Chain in China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn recent years, with the increase of greenhouse gas emissions, the greenhouse effect continues to intensify, causing a series of global ecological and environmental problems that threaten human survival and sustainable development, such as climate change and global warming. Controlling and reducing the greenhouse gas emissions is a major problem facing mankind at present. The issue of carbon emission reduction has been emphasized and concerned by countries around the world, so research on carbon emissions is being enriched. The main carbon emission accounting methods are the input-output method and the carbon emission coefficient method, and the research area includes all industrial sectors (Wang et al, 2023) as well as individual sectors such as industry (Liu et al, 2023), agriculture (Zhao and Du, 2023), tourism (Chandra et al, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), etc., and the scope of the research involves the global (Sun et al, 2023; Xu et al, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), regional (Saiful et al, 2023; Wei et al, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), national (Tsvetkov et al, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al, 2024), provincial/state (Peng et al, 2023; Pouliasis et al, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and city (Xing et al, 2024) levels at the macro scale, and is specific to a certain enterprise (Laurens and Thijs, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Long and Zhang, 2024) or a certain piece of farmland (Dlamini et al, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shackelford et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) at the micro scale. In this paper, we will account for the carbon emissions of 42 industrial sectors in China based on China's inter-regional input-output tables.\u003c/p\u003e \u003cp\u003eAs research on carbon emissions continues to deepen, the focus is no longer only on carbon emissions directly generated by regional production and consumption products. The transfer of carbon emissions between regions based on the value chain is related to the overall regional emission reduction effect, economic growth and industrial structure upgrading (Li, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The study on the transfer of carbon emissions based on the value chain is of guiding significance for the division of responsibility for emission reduction and the formulation and adjustment of regional carbon emission reduction policies. In the context of globalization, the carbon emissions embodied by international trade have first become a hot topic of research for experts and scholars. Muhammad et al. (2021) confirmed that economic complexity has heterogeneous effects on embodied carbon emissions from exports and imports, and that the effects of economic complexity on carbon emissions are asymmetric between high-income countries and low- and middle-income countries. High-income countries can achieve emission reductions not only by shifting production to low-income countries through trade, but also by shifting high-emissions-intensive production out and by maintaining low-emissions-intensive domestic production (Shukria et al, 2020). In less industrialized countries, the burden of emission reductions due to trade can be greater (Kozul et al, 2013), giving rise to significant carbon inequality at the global level. Among them, major carbon exporters are at a disadvantage in global trade and are victims of carbon inequality (Wang et al, 2022). A large number of scholars have researched on issues such as the embodied carbon emission transfer characteristics and responsibility allocation of inter-country trade (Banerjee, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea; Banerjee, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003eb; He and Jacquemin, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kim and Nikolas, 2021; Liu et al, 2020), and also proved the existence of carbon inequality. Guo (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Huang (2021) et al. also show that as the carbon emissions embodied by regional trade are increasing, carbon inequality likewise appears between different regions in China. Economically backward regions such as northwest region have gradually become the main net transfer-out region of carbon emissions in the value chain, while economically developed regions such as southeast coast are the net transfer-in region (Guo and Rong, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al, 2021).\u003c/p\u003e \u003cp\u003eRegional carbon emission influencing factors include social development status, globalization policies (Justyna and Monika, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), geopolitical conflicts (Zhang et al, 2024), and energy consumption (Zhao et al, 2024), food consumption (Mustafa et al, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Financial inclusion (Karamat et al, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), information and communication technologies (Lee et al, 2023), and so on. Despite the relatively large number of studies on the influencing factors of regional carbon emissions, there is a relative lack of research on the factors and mechanisms influencing the embodied carbon emissions of regional value chains. Deng (2023) and Xiao et al. (2019) only attempted to analyze the decomposition of influencing factors based on the LMDI decomposition method for the embodied carbon emissions of regional value chains. The number and type of indicators analyzed in the LMDI decomposition are limited, and they did not explore the influencing factors of carbon transfer in depth.\u003c/p\u003e \u003cp\u003eValue chain-based carbon transfer is an important reason for carbon emissions to be generated in transfer-out regions and transferred in transfer-in and transfer-out regions (Liu et al, 2021). This has important implications for changes in the total amount and spatial pattern of carbon emissions in a country, as well as for coordinated regional development and industrial restructuring (Li, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As the world's second-largest economy and the world's most populous country, China is actively responding to the issue of global climate change and taking on the task of energy conservation and emission reduction. It is of great practical significance to study the inter-provincial transfer of carbon emissions based on the value chain and analyze the reasons affecting the formation of the transfer characteristics, in order to promote the balanced and coordinated development of China's inter-region, reduce the division between the rich and the poor, and realize the carbon emission reduction in a more scientific and reasonable way. This paper calculates and analyzes the characteristics of carbon emission transfer based on the value chain among 30 provinces, autonomous regions and municipalities (except Tibet, Hong Kong, Macao and Taiwan) in China, and reveals the influencing factors of carbon transfer and its functioning mechanism through factor analysis, with a view to providing a basis for improving the study of carbon transfer in China and guiding the formulation and implementation of carbon emission reduction policies. In order to provide a basis for improving the study of carbon transfer in China and guiding the formulation and implementation of carbon reduction policies.\u003c/p\u003e"},{"header":"2 Methodology and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Methodology\u003c/h2\u003e \u003cp\u003eIn this paper, through the value-based carbon emission accounting method, the inter-regional input-output tables of 42 sectors in 31 provinces, autonomous regions and municipalities in China are combined and calculated on the basis of the multiple regression model to analyze the mechanism of action and the degree of contribution of each influencing factor indicator. Regional division Referring to Liu Weidong et al.'s study, China is divided into eight regions, namely Beijing-Tianjin region (JJ), Northern Coastal region (NC), Eastern Coastal region (EC), Southern Coastal region (SC), Northeastern region (NE), Northwestern region (NW), Middle region (MR), and Southwestern region (SW). The specific calculations are as follows:\u003c/p\u003e \u003cp\u003e①Calculation of carbon emission coefficient\u003c/p\u003e \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${f}^{n}={C}_{p}^{n}/{X}^{n}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({f}^{n}\\)\u003c/span\u003e \u003c/span\u003eis the carbon emission coefficient of \u003cem\u003en\u003c/em\u003e provinces (autonomous regions and cities), in tons of carbon/10000 yuan; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{p}^{n}\\)\u003c/span\u003e\u003c/span\u003e is the direct carbon emissions of \u003cem\u003en\u003c/em\u003e provinces, in tons of carbon; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}^{n}\\)\u003c/span\u003e\u003c/span\u003e is the total output of \u003cem\u003en\u003c/em\u003e provinces, in 10000 yuan.\u003c/p\u003e \u003cp\u003e②Calculation of carbon emission based on the value\u003c/p\u003e \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${C}_{v}^{n}=F\\bullet {\\rm K}\\bullet {V}_{n}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{C}}_{\\text{v}}^{\\text{n}}\\)\u003c/span\u003e \u003c/span\u003e is the value-based carbon emissions of \u003cem\u003en\u003c/em\u003e provinces, in tons of carbon; \u003cem\u003eF\u003c/em\u003e is the carbon emission coefficient matrix, ton carbon/10000 yuan; \u003cem\u003eK\u003c/em\u003e is the inverse matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(I-{H}^{T}\\right)}^{-1}\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({V}_{n}\\)\u003c/span\u003e\u003c/span\u003e is the added value of \u003cem\u003en\u003c/em\u003e provinces, 10000 yuan.\u003c/p\u003e \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${\\rm K}={\\left(I-{H}^{T}\\right)}^{-1}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere I is the identity matrix and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{H}}^{\\text{T}}\\)\u003c/span\u003e\u003c/span\u003e is the transposition of the distribution coefficient matrix \u003cem\u003eH\u003c/em\u003e.\u003c/p\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$H={X}_{in}/X$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\text{X}}_{\\text{i}\\text{n}}\\)\u003c/span\u003e \u003c/span\u003e is the intermediate input matrix, 10,000 yuan; \u003cem\u003eX\u003c/em\u003e is the total output matrix, 10000 yuan.\u003c/p\u003e \u003c/p\u003e \u003cp\u003ethat is, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ℎ}^{nm}={X}_{in}^{nm}/{X}^{n}\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({ℎ}^{nm}\\)\u003c/span\u003e \u003c/span\u003e is the distribution coefficient between \u003cem\u003en\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e provinces; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{in}^{nm}\\)\u003c/span\u003e\u003c/span\u003e is the intermediate investment of m province to \u003cem\u003en\u003c/em\u003e province, 10000 yuan; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}^{n}\\)\u003c/span\u003e\u003c/span\u003e is the total output of \u003cem\u003en\u003c/em\u003e provinces, 10000 yuan.\u003c/p\u003e \u003cp\u003e③Calculation of net carbon transfer\u003c/p\u003e \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\text{N}{\\text{C}}_{\\text{v}}^{\\text{n}\\text{m}}={\\text{C}}_{\\text{v}}^{\\text{n}\\text{m}}-{\\text{C}}_{\\text{v}}^{\\text{m}\\text{n}}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(N{C}_{v}^{nm}\\)\u003c/span\u003e \u003c/span\u003e is the value-based net carbon transfer, tons of carbon; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{v}^{nm}\\)\u003c/span\u003e\u003c/span\u003e is the carbon emission transferred from \u003cem\u003em\u003c/em\u003e province to \u003cem\u003en\u003c/em\u003e province, tons of carbon; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{v}^{mn}\\)\u003c/span\u003e\u003c/span\u003e is the carbon emission transferred from \u003cem\u003en\u003c/em\u003e province to \u003cem\u003em\u003c/em\u003e province, tons of carbon. If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N{C}_{v}^{nm}\u0026gt;0\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003en\u003c/em\u003e provinces are net transfer-in regions; If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(N{C}_{v}^{nm}\u0026lt;0\\)\u003c/span\u003e\u003c/span\u003e, \u003cem\u003en\u003c/em\u003e province is a net transfer-out region.\u003c/p\u003e \u003cp\u003e④Calculation of net carbon transfer growth rate\u003c/p\u003e \u003cdiv id=\"Equ6\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${S}_{{NC}_{v}}=\\frac{\\left|{NC}_{v,t}^{nm}\\right|-\\left|{NC}_{v,t-1}^{nm}\\right|}{\\left|{NC}_{v,t-1}^{nm}\\right|}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({S}_{{NC}_{v}}\\)\u003c/span\u003e \u003c/span\u003e is the growth rate of net carbon transfer; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({NC}_{v,t}^{nm}\\)\u003c/span\u003e\u003c/span\u003e is the net carbon transfer between \u003cem\u003en\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e provinces in \u003cem\u003et\u003c/em\u003e year, tons of carbon; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({NC}_{v,t-1}^{nm}\\)\u003c/span\u003e\u003c/span\u003e is the net carbon transfer between provinces \u003cem\u003en\u003c/em\u003e and \u003cem\u003em\u003c/em\u003e in \u003cem\u003et-1\u003c/em\u003e year, tons of carbon.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data source\u003c/h2\u003e \u003cp\u003e(1) The 2012 China Inter-regional Input-Output Tables, the 2017 China Inter-regional Input-Output Tables, and the 2012 and 2017 national direct carbon emission data for 30 provinces, autonomous regions and cities are obtained from China Carbon Accounting Database (CEADs) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ceads.net.cn/\u003c/span\u003e\u003cspan address=\"https://www.ceads.net.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). (2) Data on influencing factors are obtained from the 2013 and 2018 Statistical Yearbooks of China and its Provinces, Regions and Municipalities.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of inter-provincial carbon transfer\u003c/h2\u003e \u003cp\u003eFrom 2012 to 2017, China's economically developed provinces (autonomous regions and municipalities) were the major carbon emissions transfer-in and out region of most provinces, and provinces with a weak base of economic development and lagging behind in science and technology were the smallest carbon transfer-out regions. Figure\u0026nbsp;1 shows the relationship between inter-provincial carbon transfer, the darker the color means, the larger the amount of inter-provincial carbon transfer. In 2012, Jiangsu was the largest carbon emissions transfer-in region for most provinces,\u003c/p\u003e \u003cp\u003efollowed by Guangdong. In terms of the direction of carbon emissions transfer out, Jiangsu was also the largest carbon emissions transfer-out region for most provinces. In 2017, the carbon emissions transferred in from various provinces were concentrated from Guangdong, Hebei, Inner Mongolia and Henan. The largest carbon emissions transferred in region for individual provinces was Jiangsu, Zhejiang, Inner Mongolia and Henan. In terms of the direction of carbon emissions transfer out, Jiangsu remains the largest carbon emissions transfer-out region of most provinces, followed by Guangdong. Jiangsu and Guangdong are the top two carbon emissions transfer-out regions for the vast majority of provinces. With growing economic strength and expanding industrial scale, Guangdong has closer trade links with other provinces, and more and more products are produced and processed through other provinces. From 2012 to 2017, the smallest carbon emissions transfer-out region of each province has always been Hainan as well as the western provinces of Qinghai and Ningxia, indicating that the development gap between the above regions and other provinces has always been large.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Characteristics of inter-regional carbon transfer\u003c/h2\u003e \u003cp\u003eThe MR was always the region that transferred the most carbon emissions out, while the JJ transferred the least. The region with the highest amount of carbon emissions transferred in shifted from the MR to the EC. The region with the lowest carbon emissions transferred in shifted from the SC to the JJ. As can be seen in Fig.\u0026nbsp;2, the MR had the highest total amount of carbon emissions transferred in and out in 2012. Carbon emissions transferred out of all regions to the MR were the largest, with the amount of carbon emissions transferred in of the MR reaching as high as 521.61 millions of tons. NE, NC, EC, SC, and MR all had the lowest carbon emissions transferred out to the SC. JJ, NW, SW transferred the least amount of carbon emissions to the JJ. The MR was also the largest source of carbon emissions for other regions, with a total of 537.132\u0026nbsp;million tons of carbon emissions transferred out. In 2017, the largest amount of carbon emissions transferred in was in the EC, and the largest carbon emissions transferred out was still in the MR. Except for NC, EC, and SC, other regions transferred out the most carbon emissions to the EC, and the EC carbon emissions transferred in was as high as 585.52\u0026nbsp;million tons. Except for NC and MR, carbon emissions transferred out of other regions to the JJ were the least, with only 176.59\u0026nbsp;million tons of carbon emissions transferred in of the JJ. Same as in 2012, the smallest carbon emissions transferred in of all 8 regions are from the JJ. Compared with 2012, carbon emissions transferred in of all other regions except for NE, NC, and NW were increased. In addition, carbon emissions transferred out of the JJ and the SC declined, while all other regions increased to varying degrees. It can be seen that with the continuous development of the economy and society, the carbon emissions generated by the economically developed regions themselves were decreasing, and the carbon emissions transferred through the value chain were increasing. This also shows that the economically developed regions were continuously transferring and relocating the high-carbon emission production chains to the less-developed regions. This had led to a continued increase in direct carbon emissions of less economically developed regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Characteristics of net carbon transfer\u003c/h2\u003e \u003cp\u003eChina's carbon emissions as a whole show the characteristics of transferring from regions with a higher economic development level to lower regions, and from the east to the west. In 2012, the net transfer-out regions were mainly distributed in the regions with rich natural resources in Central and Western China, as well as economically developed Shanghai, Zhejiang, and Guangdong (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In 2017, the net transfer-out regions were mainly distributed in the NE, NW, SW, and some provinces in the MR with rich natural resources. As can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Beijing was a complete net transfer-in region, that is, there were more transfers in than transfers out with all provinces. Tianjin, Jiangsu, Fujian, Jiangxi, Shandong, Henan, Hunan, Sichuan, and Shaanxi were also net transfer-in regions from 2012 to 2017. Fujian had the largest increase in net carbon transfer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, the net transfer amount of Tianjin, Jiangxi, Hunan, Sichuan, and Shaanxi decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNingxia was a complete net transfer out region, that is, there were more transfers out than transfers in with all the provinces. Shanxi, Inner Mongolia, Liaoning, Anhui, Guizhou, Yunnan, Gansu, and Xinjiang were also consistently net carbon transfer-out regions. Except for Shanxi and Anhui, the net transfer amount of other regions had increased, and the growth rate was high. Comparison of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the net transfer direction of other provinces had changed from 2012 to 2017. Hebei, Jilin, Heilongjiang, Guangxi, Hainan, Chongqing, and Qinghai all changed from net transfer-in regions to net transfer-out regions, while Shanghai, Zhejiang, Hubei, and Guangdong changed from net transfer-out regions to net transfer-in regions. Although the net transfer direction of these regions had changed, the net transfer amount of most regions had increased to varying degrees.\u003c/p\u003e \u003cp\u003eCarbon emissions from the JJ were mainly transferred out to the Northern China in close proximity, but the carbon emissions transferred out to the Southern China were also increasing (Fig.\u0026nbsp;1). In 2012, the net transfer amount between Beijing and Hebei was the largest (Fig.\u0026nbsp;5a). The net transfer amount between Tianjin and Jilin was the largest (Fig.\u0026nbsp;5a). By 2017, the net carbon transfer between Beijing and Henan was the largest (Fig.\u0026nbsp;5b). Tianjin had the largest net carbon transfer with Inner Mongolia (Fig.\u0026nbsp;5b).\u003c/p\u003e \u003cp\u003eAs an old industrial base in China, the NE is one of the net transfer-out regions of carbon emissions. As can be seen from Fig.\u0026nbsp;5, in 2012, Liaoning had the highest net transfers with Inner Mongolia, Guangdong and Shanxi, all of which Liaoning served as a net transfer-in region. By 2017, the largest net transfers were with Jiangsu, Guangdong and Shanghai, and Liaoning was a net transfer-out region. This also shows that the carbon emissions transferred in Liaoning through the value chain decreased and the carbon emissions transferred out increased. Net carbon transfers between Heilongjiang and Jilin were the highest in 2012\u0026ndash;2017. Jilin was a net transfer-in region in 2012 and Heilongjiang was the net transfer-in region in 2017. Jilin's net carbon transfers increased with most provinces. In the future, Jilin will still continue to be a net transfer-out region to take over some of the production links from the provinces. Heilongjiang, on the other hand, had seen a decrease in net carbon transfer with most provinces. This indicates that the industrial structure of Heilongjiang was adjusting, and the reliance of the region on its heavy industry products was decreasing.\u003c/p\u003e \u003cp\u003eIn the NC of Hebei and Shandong, industries had been transformed and the direction of net carbon transfer had changed significantly. As can also be seen in Fig.\u0026nbsp;5, the largest net transfer was between Shandong and Guangdong in 2012, with Shandong being the net transfer-in region. In 2017, Guangdong then became a net transfer-in region for Shandong. This indicates that Guangdong needed Shandong to complete more energy-consuming industrial segments and emit more carbon emissions. While in 2017, the net transfer between Shandong and Inner Mongolia was the largest, and Shandong had been acting as a net transfer-in region. This illustrates the increased dependence of Shandong's industrial development on resource-based provinces such as Inner Mongolia. In 2012, Hebei had the largest net transfer of carbon emissions out to Beijing, followed by a net transfer in from Guangdong. In 2017, the largest net transfer was with Shanxi, followed by Shandong, Beijing and Guangdong, and all of Hebei became a net transfer-out region.\u003c/p\u003e \u003cp\u003eThe three provinces in the EC had larger net carbon transfers to the NE, SW and NW. The above regions can provide the EC with the production and processing of various agricultural products, fossil energy and other raw materials. It also can be seen in Fig.\u0026nbsp;5, Jiangsu had the largest net carbon transfer with Guangdong, Shanghai and Jiangxi in 2012. However, by 2017, net carbon transfers with all three of these regions had declined, and the net carbon transfers with Xinjiang, Inner Mongolia, and Liaoning were the largest. In 2012, Shanghai had the largest net carbon transfer with Jiangsu and Shandong, and Shanghai is all a net transfer-out region. In 2017, net carbon transfers with Inner Mongolia and Henan were the largest, and Shanghai is already a net transfer-in region. In 2012, the net carbon transfer between Zhejiang and Hebei, Guangdong and Shandong was the largest, and Zhejiang was the net transfer-out region of Hebei and Shandong. In 2017, the net carbon transfer between Zhejiang and Guangdong, Jiangsu and Inner Mongolia was the largest, and Zhejiang was also a net transfer-out region for Jiangsu and Guangdong. It indicates that the industrial structure and the direction of industrial transfer in Shanghai and Zhejiang had changed considerably during 2012\u0026ndash;2017. Economic development was becoming more closely linked to other provinces and cities, and the dependence on other provinces was also increasing.\u003c/p\u003e \u003cp\u003eThe net carbon transfer of Guangdong, Fujian and Hainan in the SC was mainly from the Northern China. The net carbon transfers between 3 provinces in the SC and Jiangsu and Hebei, Shandong and Inner Mongolia in Northern China were significantly larger in 2012 (Fig.\u0026nbsp;5a). Of these, the largest net transfers in Fujian and Hainan were from Guangdong. By the time 2017 rolled around, the direction of the net transfer among the 3 regions had changed considerably. As can be seen in Fig.\u0026nbsp;5b, Fujian remained the largest net carbon transfer with Guangdong and Inner Mongolia. Hainan had larger net transfers mainly with Henan and the Yangtze River Delta region. Guangdong, on the other hand, had the largest net carbon transfer inwards from Xinjiang. Guangdong is a major textile producer and exporter, and has become the world's third-largest apparel export base. Xinjiang has a well-developed cotton cultivation and processing industry, and is an important production and supply location for the upstream and midstream segments of Guangdong's textile and apparel industry.\u003c/p\u003e \u003cp\u003eThe direction of net carbon transfers varies considerably across provinces in the MR. The net carbon transfer between Shanxi and Jiangsu was the largest in 2012 (Fig.\u0026nbsp;5a), but has since declined. The net carbon transfers between Shanxi and Guangdong and Hebei increased the most. Thus by 2017, the largest net carbon transfer was with the above two provinces (Fig.\u0026nbsp;5b). Shanxi is rich in mineral resources and is dominated by energy and metallurgical industries. This indicates that Guangdong and Hebei needed more energy and related products for their development during 2012\u0026ndash;2017, while Jiangsu's demand decreased. The net carbon transfer characteristics of Hubei and Hunan provinces were more similar. The net carbon transfer with Shanxi and Inner Mongolia was larger in 2012, and with Beijing, Hebei and Inner Mongolia in 2017 (Fig.\u0026nbsp;5). Meanwhile, the net carbon transfer characteristics of Henan and Anhui were also more similar. The regions with larger net carbon transfers in both places were concentrated in Beijing, Inner Mongolia, Shanghai, Shandong, Guangdong and Xinjiang, and the direction of transfer was basically the same. Changes in the net carbon transfer volume in Jiangxi were generally characterized by a substantial decrease with the NC, SW, and part of the MR, and a substantial increase with other provinces.\u003c/p\u003e \u003cp\u003eThe continuing transfer of industry chain links from all regions of China to the NW had resulted in an increasing net carbon transfer between provinces in the NW and most of the country. In particular, the incremental net carbon transfer with China's economically developed Yangtze River Delta and Pearl River Delta regions were both particularly large. And the NW basically served as a net carbon transfer-out region (Fig.\u0026nbsp;5). It also can be seen in Fig.\u0026nbsp;5, the larger net carbon transfers of provinces in the NW were also basically with the JJ, NC, EC, and Guangdong, which have higher levels of economic development in China.\u003c/p\u003e \u003cp\u003eAs in the NW, the net carbon transfers between provinces in the SW and most provinces in China had increased. This is especially the case with the economically developed regions such as Beijing, the Yangtze River Delta and the Pearl River Delta, and regions with more abundant energy resources but a more homogenous industrial structure, such as the NE, NW, Inner Mongolia and Jiangxi. And except with the NW, the provinces in the NW basically were as a net transfer-out region (Fig.\u0026nbsp;5). Once again, the net carbon transfer in China was roughly characterized by a transfer from east to west and from southeast to northwest.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of influencing factors\u003c/h2\u003e \u003cp\u003eCarbon emissions are affected by many factors, including the natural environment, society and the economy. Because there are too many influencing factors to be considered, it is difficult for multiple regression to avoid the problem of indicator covariance, so factor analysis can be used to realize the downscaling and simplification of indicators, and better analyze the influencing factors of inter-provincial carbon emission transfer. Factor analysis summarizes independent influencing factors from many indicators, i.e., the common factors, which should reflect as much information as possible about the original variables. By calculating the correlation coefficient matrix, eigenroots and eigenvectors, and variance contribution ratio, the number of common factors and the number of original variables they represent can be judged. Multiple regression models are constructed through the common factors. In this paper, 9 indicators such as regional GDP per capita, percentage of secondary industry, total import and export of goods, number of patents for inventions granted, resident population, disposable income per capita, consumption levels of residents, energy consumption per unit GDP, primary energy production are selected as the original influencing factor indicators for factor analysis, which are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn KMO test, the probability is 0.000 is less than the level of significance and the original hypothesis is rejected, which is significantly different from the unit matrix.The KMO is 0.742, which indicates that it is suitable for factor analysis. The factor analysis process usually uses principal component analysis to select principal components with eigenvalues greater than 1 as common factors. In this paper, a total of three common factors were extracted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e6\u003c/span\u003e). From the data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be seen that the variance of the common factor of 7 of the 9 influencing factors is greater than 80%. The three common factors extracted together explained 82.505% of the information. Therefore the information loss of the original influence factor indicators is small, and the overall effect of the common factor extraction is ideal.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact factor indicators and meanings\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeaning\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP per capita (I1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic development level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of secondary industry (I2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial Structure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal import and export of goods (I3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrade Import/Export Demand\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patents for inventions granted (I4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResident population (I5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation Concentration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisposable income per capita (I6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiving standard of the population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumption levels of residents (I7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResident consumption level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy consumption per unit GDP (I8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy consumption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary energy production (I9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal resource availability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eIn KMO test, the probability is 0.000 is less than the level of significance and the original hypothesis is rejected, which is significantly different from the unit matrix.The KMO is 0.742, which indicates that it is suitable for factor analysis. The factor analysis process usually uses principal component analysis to select principal components with eigenvalues greater than 1 as common factors. In this paper, a total of three common factors were extracted (Fig. 6). From the data in Table 2, it can be seen that the variance of the common factor of 7 of the 9 influencing factors is greater than 80%. The three common factors extracted together explained 82.505% of the information. Therefore the information loss of the original influence factor indicators is small, and the overall effect of the common factor extraction is ideal.\u0026nbsp;\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCommon factor variance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtracted\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI1\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI2\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.658\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI3\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI4\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI5\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI6\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI7\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI8\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.649\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI9\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAppropriate rotation of the loading matrix makes the common factor of the original influencing factors more obvious and more conducive to the interpretation of the actual problem. As can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, GDP per capita, disposable income per capita, and consumption levels of residents have the greatest contribution to the common factor 1 (F1), which is above 90%, so the F1 can be interpreted as the level of economic development. Resident population contributes the most to the common factor 2 (F2) with 92.1%, so the F2 can be interpreted as population agglomeration and social needs. Primary energy production contributes the most to the common factor 3 (F3) with 90.2%, so the F3 is interpreted as energy demand.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRotated component matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI1\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI2\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI3\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI4\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI5\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI6\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI7\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.150\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI8\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI9\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eExtraction method: principal component analysis.\u003c/p\u003e \u003cp\u003eRotation method: kaiser normalized maximum variance method.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ea. The rotation has converged after 5 iterations.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eFinally, the standardized original matrix of influence factor indicators is multiplied by the component score coefficients (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) to calculate the common factor scores. The expressions for each common factor are shown in Eqs.\u0026nbsp;(\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e8\u003c/span\u003e)- (\u003cspan refid=\"Equ9\" class=\"InternalRef\"\u003e10\u003c/span\u003e) below:\u003c/p\u003e \u003cdiv id=\"Equ7\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\text{F}1=0.286{\\text{I}}^{{\\prime }}1-0.107{\\text{I}}^{{\\prime }}2+0.062{\\text{I}}^{{\\prime }}3+0.158{\\text{I}}^{{\\prime }}4-0.139{\\text{I}}^{{\\prime }}5+0.269{\\text{I}}^{{\\prime }}6+0.278{\\text{I}}^{{\\prime }}7+0.011{\\text{I}}^{{\\prime }}8+0.191{\\text{I}}^{{\\prime }}9$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ8\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$$\\text{F}2=-0.041{\\text{I}}^{{\\prime }}1+0.286{\\text{I}}^{{\\prime }}2+0.353{\\text{I}}^{{\\prime }}3+0.206{\\text{I}}^{{\\prime }}4+0.504{\\text{I}}^{{\\prime }}5-0.078{\\text{I}}^{{\\prime }}6-0.065{\\text{I}}^{{\\prime }}7-0.156{\\text{I}}^{{\\prime }}8+0.004{\\text{I}}^{{\\prime }}9$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e \u003cdiv id=\"Equ9\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$$\\text{F}3=0.160{\\text{I}}^{{\\prime }}1+0.321{\\text{I}}^{{\\prime }}2+0.079{\\text{I}}^{{\\prime }}3+0.055{\\text{I}}^{{\\prime }}4-0.038{\\text{I}}^{{\\prime }}5+0.046{\\text{I}}^{{\\prime }}6+0.083{\\text{I}}^{{\\prime }}7+0.348{\\text{I}}^{{\\prime }}8+0.781{\\text{I}}^{{\\prime }}9$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{I}}^{{\\prime }}\\)\u003c/span\u003e\u003c/span\u003ei denotes the standardized value of the raw impact factor indicator.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMatrix of component score coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI1\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI2\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI3\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI4\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI5\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI6\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI7\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI8\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI9\u003c/b\u003e score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eExtraction method: principal component analysis.\u003c/p\u003e \u003cp\u003eRotation method: kaiser normalized maximum variance method.\u003c/p\u003e \u003cp\u003eComponent Score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eThere is no linear relationship between the common factors (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Therefore, the regression coefficients of each influencing factor can be derived by regressing the three common factors on the net carbon transfer, so as to further analyze the relationship between each influencing factor and the changes in the net carbon transfer of inter-provincial. The F-value of the regression result is 9.952 and the p-value is 0.000\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), indicating that the model is constructed in a meaningful way and at least one of the independent variables will have an effect on the dependent variable. As can be seen from the p-values, all three public factors have a significant effect on the net carbon transfer. According to the regression results, the regression equation of the net carbon transfer with the three public factors can be obtained as:\u003c/p\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{Y}=0.539\\text{F}1+0.273\\text{F}2-0.359\\text{F}3+0.27$$\u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Equ10\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$=0.086{\\text{I}}^{{\\prime }}1-0.095{\\text{I}}^{{\\prime }}2+0.101{\\text{I}}^{{\\prime }}3+0.122{\\text{I}}^{{\\prime }}4+0.076{\\text{I}}^{{\\prime }}5 + 0.107{\\text{I}}^{{\\prime }}6+0.102{\\text{I}}^{{\\prime }}7-0.162{\\text{I}}^{{\\prime }}8-0.176{\\text{I}}^{{\\prime }}9$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComponent score covariance matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eExtraction method: principal component analysis.\u003c/p\u003e \u003cp\u003eRotation method: kaiser normalized maximum variance method.\u003c/p\u003e \u003cp\u003eComponent Score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe net carbon transfer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSt.Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003e[95% Conf Interval]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSig\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e-0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eSD dependent var\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eNumber of obs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eF-test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e9.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e146.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eBayesian crit. (BIC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e157.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;.1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom Eq.\u0026nbsp;(\u003cspan refid=\"Equ10\" class=\"InternalRef\"\u003e11\u003c/span\u003e), it can be seen that among the nine influencing factors, the percentage of secondary industry, energy consumption per unit GDP and primary energy production are negatively correlated with the value of net carbon transfer i.e., the larger the value is, the smaller the net carbon transfer is, i.e., carbon emissions transferred out is greater than transferred in. Other factors are positively correlated with the net carbon transfer, i.e., the larger the value is, the greater the net carbon transfer is, i.e., carbon emissions transferred in is greater than transferred out.\u003c/p\u003e \u003cp\u003eThe top three indicators of the correlation coefficient are primary energy production, energy consumption per unit GDP and number of patents for inventions granted, which have the greatest impact on the inter-provincial carbon transfer in China. Therefore, the local energy supply capacity, energy consumption level, and science and technology level of a certain region were most important for the difference between the carbon emissions transferred in and out of that region. It is the key to determine whether the region was a net transfer-in or net transfer-out region. China's resource-rich provinces, autonomous regions and municipalities had taken on more high-energy-consuming links in the industrial value chain, and it is most critical for such regions to reduce emissions by improving their independent innovation capabilities and production technology levels and reducing energy consumption levels. Provinces, autonomous regions and municipalities with high technological levels, whose resource holdings could not meet local development, transfer high-energy-consuming segments to other regions, so that their carbon emissions were transferred in more than they are transferred out. The coefficient for resident population is the smallest, suggesting that population size did not have a significant effect on changes in the net carbon transfer.\u003c/p\u003e \u003cp\u003eIn addition, disposable income per capita, consumption levels of residents and total import and export of goods all had a large positive effect on the change in net carbon transfer. It indicates that the greater the social demand in a certain region, the greater the net carbon transfer, i.e., the more carbon emissions were transferred. Therefore, the income of the population should be safeguarded and raised to stimulate consumption and expand domestic demand. Neither the percentage of secondary industry nor GDP per capita had a significant effect on the net carbon transfer. The industrial structure and economic development level of a region were not the key factors determining the difference between the carbon emissions transfer in and out of the region based on the value chain.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion and Discussion","content":"\u003cp\u003eThis paper applies the value-based carbon emission accounting method to measure and analyze the spatial and temporal characteristics of the carbon emissions transferred on the inter-provincial value chain in 2012 and 2017 based on the inter-regional input-output tables of 42 sectors in 31 provinces, autonomous regions and municipalities in China, and calculates and analyzes the influencing factors and the mechanism of the net carbon transfer through the factor analysis method, and finally obtains the following conclusions:.\u003c/p\u003e \u003cp\u003eThe economically developed Beijing-Tianjin and the southern coastal regions gained high economic value in the value chain but received little carbon emissions, while the economically underdeveloped central and western regions gained little economic value in the value chain but received a lot of carbon emissions. The largest carbon emissions transfer-in and out region of most provinces in 2012\u0026ndash;2017 were provinces with high levels of economic development, and the carbon emissions transferred out to Hainan, the NW and other weak economic development regions were least. Except for JJ and the SC, the amount of carbon emissions transferred out of other regions had increased to different degrees. China's inter-provincial carbon transfer was generally characterized by the transfer from regions with higher levels of economic development to regions with lower levels of economic development, and from the east to the west. The center of gravity of the net carbon transfer had shifted significantly to the north and west, and the differentiation between the net transfer-in regions and the net transfer-out regions had become more obvious. Direct carbon emissions from economically developed regions themselves had decreased, but carbon emissions transferred through the value chain had increased. The net carbon transfer between most provinces had increased.\u003c/p\u003e \u003cp\u003eThe level of local resource ownership and energy consumption level determined the changes in the net carbon transfer in the value chain. During the period of 2012\u0026ndash;2017, primary energy production, energy consumption per unit of GDP, and number of patents for inventions granted made the greatest contribution to the changes in the net carbon transfer between provinces. This can also reflect that China's high-energy-consuming industries generally resettled and shifted to resource-rich regions during the 2012\u0026ndash;2017 period. And if such regions have insufficient scientific and technological innovation capacity, their relatively weak technological level will cause their energy consumption level to keep increasing, generating and transferring more carbon emissions. Therefore, China's resource-rich and insufficient scientific and technological innovation capacity of the region's emissions reduction burden is heavier, emissions reduction targets are relatively difficult to achieve. On the contrary, if the social demand of a region is large, local resources can not meet its own development of the region, it will reduce its own carbon emissions through the import of high-energy-consuming products to increase the carbon emissions of the importing place. The burden of emission reduction in such regions is significantly reduced. This has led to an unfair division of responsibility for carbon emission reduction.\u003c/p\u003e \u003cp\u003eDue to the complexity of input-output table construction, China's inter-regional input-output tables at the national level are developed every five years, and the latest year is currently updated only to 2017, so the data are slightly behind. However, we mainly want to present the research methodology in the paper, and we can continue the analysis based on the methodology in the paper if new data are available subsequently. The analysis of influencing factors is only for this time period. How the main influencing factors affecting carbon transfer have changed since then needs to be further investigated.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval \u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u0026nbsp;\u003c/strong\u003eThe datasets used in this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u0026nbsp;\u003c/strong\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions \u0026nbsp;\u003c/strong\u003eHongguang Liu: Methodology, Validation, Writing - Review \u0026amp; Editing, Supervision. Xiaocui Dong: Conceptualization, Validation, Formal analysis, Investigation, Resources, Data Curation, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp; Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBanerjee S. Carbon Emissions Embodied in India-United Kingdom Trade: A Case Study on North -South Debate. Foreign Trade Review. 2020; 55(2): 199-215. \u003c/li\u003e\n\u003cli\u003eBanerjee, S. Addressing the carbon emissions embodied in India\u0026rsquo;s bilateral trade with two eminent Annex-II parties: with input\u0026ndash;output and spatial decomposition analysis. Environ Dev Sustain. 2021;23:5430\u0026ndash;5464. \u003c/li\u003e\n\u003cli\u003eChandra L V, Azharul M I , Md S N. Does tourism have an impact on carbon emissions in Asia? An application of fresh panel methodology. Environment, development and sustainability.2023;:11-19.\u003c/li\u003e\n\u003cli\u003eChengzhen Xu, Qingyuan Zhu, Xingchen Li, et al. Determinants of global carbon emission and aggregate carbon intensity: A multi-region input\u0026minus;output approach. Economic Analysis and Policy.2024;:81418-435.\u003c/li\u003e\n\u003cli\u003eChi Long, Hongmei Zhang. How digital finance affects carbon emission in manufacturing industry-A perspective based on digital transformation of enterprises. Finance Theory and Practice.2023;10:67-78.\u003c/li\u003e\n\u003cli\u003eChien-Chiang L, Zihao Y, Chi-Chuan L. A nonlinear analysis of the impacts of information and communication technologies on environmental quality: A global perspective. Energy Economics.2023;128.\u003c/li\u003e\n\u003cli\u003eDeyun Xiao, Chunhui Wang, Huishuang He. Research on industrial carbon emission reduction in Henan Province under the background of global value chain. Journal of Beijing University of Posts and Telecommunications (Social Science Edition). 2019;21(04):7-16.\u003c/li\u003e\n\u003cli\u003eDlamini J. C., Cardenas L. M., Tesfamariam E. H., et al.Soil CO2 emissions in cropland with fodder maize (Zea mays L.) with and without riparian buffer strips of differing vegetation.Agroforestry Systems.2022;96(7):983-995. \u003c/li\u003e\n\u003cli\u003eFeng G, Siqi M, Renjin S. The evolution characteristics and influence factors of carbon productivity in China\u0026apos;s industrial sector: from the perspective of embodied carbon emissions. Environmental science and pollution research international. 2021;28(36):50611-50622.\u003c/li\u003e\n\u003cli\u003eFujia Li. Progress and outlook of research on implied carbon emission transfer in inter-regional trade. Progress in Geography.2018;37(10):1303-1313.\u003c/li\u003e\n\u003cli\u003eHe J, Jacquemin J. La balance du carbone incorpore au commerce bilateral entre la France et la Chine: Transferts d\u0026rsquo;emissions ou nouvelles creations? (Balance of Carbon Embodied in Bilateral Trade between France and China: Transfer of Emission or New Creation? With English summary.). Revue Economique. 2016;67(6):1179-1210. \u003c/li\u003e\n\u003cli\u003eHeping Huang, Mengting Yi, Junwen Cao, et al. Spatial and temporal changes and impact effects of embodied carbon emissions from regional trade - taking the Yangtze River Economic Belt as an example. Economic Geography.2021;41(03): 49-57. \u003c/li\u003e\n\u003cli\u003eHongguang Liu, Zimeng Zhang, Jie Guo. Research on the embodied carbon emissions transfer in the Chinese interregional value chain. Management Review.2021;33(09):58-64.\u003c/li\u003e\n\u003cli\u003eHongguang Liu , Zhaoya Wang, Yanan Guo, et al. Carbon leakage or carbon reduction? Carbon emission effect of the production relocation of metal smelting industry in China. Environ Prog Sustainable Energy. 2023;e14332.\u003c/li\u003e\n\u003cli\u003eHotak, S., Islam, M., Kakinaka, M. et al. Carbon emissions and carbon trade balances: International evidence from panel ARDL analysis. Environ Sci Pollut Res.2020;27:24115\u0026ndash;24128. \u003c/li\u003e\n\u003cli\u003eHuogen Wang, Yuting Wang, Lixiang Xiao. Forecasting and analyzing carbon dioxide emissions from Chinese industries based on the IO-SDA method from 2020 to 2060. China Environmental Science. 2024;:1-15.\u003c/li\u003e\n\u003cli\u003eJieyu Wang, Yuli Shan, Can Cui, et al. Investigating the fast energy-related carbon emissions growth in African countries and its drivers.Applied Energy.2024;:357122494-.\u003c/li\u003e\n\u003cli\u003eJian Xue, Lulu Liu. Research on the pricing of embodied carbon rights based on the perspective of inter-provincial trade. Ecological Economy. 2022;38(11): 27-35.\u003c/li\u003e\n\u003cli\u003eJincai Zhao, Yongmeng Du. The impact and spatial effect of rural revitalization on agricultural carbon dioxide emissions: a case study of Henan Province. Frontiers in Environmental Science.2023,11.\u003c/li\u003e\n\u003cli\u003eJing Wang, Bo Meng, Zongxian Feng, et al. Research on Chinese inter-regional carbon emissions transfer from the perspective of value-added trade. Journal of Xi\u0026apos;an Jiaotong University (Social Science Edition). 2020;40(02): 85-94.\u003c/li\u003e\n\u003cli\u003eJustyna B, Monika P. Convergence of CO\u003csub\u003e2\u003c/sub\u003e emissions in countries at different stages of development. Do globalization and environmental policies matter? Energy Policy. 2024;184.\u003c/li\u003e\n\u003cli\u003eJun Z, Kangyin D, Xiucheng D. How does energy poverty eradication affect global carbon neutrality? Renewable and Sustainable Energy Reviews.2024;191.\u003c/li\u003e\n\u003cli\u003eKaramat K, Xuwen Y, Jie Z, et al. Financial inclusion, environmental degradation, and the moderating role of ICT: a global perspective. Environmental science and pollution research international.2023.\u003c/li\u003e\n\u003cli\u003eKejuan Sun, Jiabai Ye, Youfu Yue, et al. Tracing carbon emissions and intensity in relational global value chain activities.Journal of Cleaner Production.2023;429.\u003c/li\u003e\n\u003cli\u003eKozul-Wright, R., Fortunato, P. International Trade and Carbon Emissions. Eur J Dev Res. 2012;24:509\u0026ndash;529. \u003c/li\u003e\n\u003cli\u003eLaurens S, Thijs M. Corporate carbon emissions data for equity and bond portfolios. Managerial Finance.2024;50(1):118-139. \u003c/li\u003e\n\u003cli\u003eLingyu Deng. Analysis of the embodied carbon decoupling effect of the trade value chain in the Yangtze River Economic Belt under global value chains. Chongqing University of Commerce and Industry, China; 2023.\u003c/li\u003e\n\u003cli\u003eMin Yan, Hui Sun. Research on the structural characteristics of the Chinese inter-provincial embodied carbon transfer network from the perspective of the economic internal cycle. Ecological Economy. 2022;38(02):13-21.\u003c/li\u003e\n\u003cli\u003eMustafa A, Lenny C S K, Lingxuan L, et al. Assessing pre-pandemic carbon footprint of diet transitions in UK nations and regions. International Journal of Production Research. 2023;61(18):6115-6130.\u003c/li\u003e\n\u003cli\u003ePeixue Xing, Yanan Wang, Tao Ye, et al. Carbon emission efficiency of 284 cities in China based on machine learning approach: Driving factors and regional heterogeneity. Energy Economics.2024;:129107222-.\u003c/li\u003e\n\u003cli\u003ePouliasis K P, Papapostolou C N, Tamvakis N M, et al. Carbon Emissions in the U.S.: Factor Decomposition and Cross-State Inequality Dynamics. The Energy Journal. 2024;45(1):135-161.\u003c/li\u003e\n\u003cli\u003eQayyum, M., Yu, Y., Li, S. The impact of economic complexity on embodied carbon emission in trade: new empirical evidence from cross-country panel data. Environ Sci Pollut Res. 2021;28: 54015\u0026ndash;54029. \u003c/li\u003e\n\u003cli\u003eQingyong Z, Xianqiang M, Jianhong L, et al. EU-Russia energy decoupling in combination with the updated NDCs impacts on global fossil energy trade and carbon emissions.Applied Energy.2024;:356122415-.\u003c/li\u003e\n\u003cli\u003eRui Peng, Xiaoxiong Tian, Dan Wu. Spatiotemporal characteristics and zonal analysis of carbon balance in county-level regions: case study of Jiangsu Province.Frontiers in Environmental Science.2023;11\u003c/li\u003e\n\u003cli\u003eSaiful M I, Habibur S R, Ur A R, et al. ICT\u0026apos;s impact on CO2 emissions in GCC region: The relevance of energy use and financial development. Energy Strategy Reviews.2023;49.\u003c/li\u003e\n\u003cli\u003eShackelford E G, Kelsey R, Dicks V L. Effects of cover crops on multiple ecosystem services: Ten meta-analyses of data from arable farmland in California and the Mediterranean. Land Use Policy.2019;88(C):104204-104204.\u003c/li\u003e\n\u003cli\u003eSilin Wei, Yinsheng Yang, Ying Xu. Regional development, agricultural industrial upgrading and carbon emissions: What is the role of fiscal expenditure? \u0026mdash;Evidence from Northeast China.Economic Analysis and Policy.2023;:801858-1871.\u003c/li\u003e\n\u003cli\u003eTae-Jin Kim, Nikolas Tromp. Carbon emissions embodied in China-Brazil trade: Trends and driving factors. Journal of Cleaner Production.2021;293. \u003c/li\u003e\n\u003cli\u003eTsvetkov P, Andreichyk A, Kosarev O. The impact of economic development of primary and secondary industries on national CO\u003csub\u003e2\u003c/sub\u003e emissions: The case of Russian regions.Journal of Environmental Management.2024;:351119881-.\u003c/li\u003e\n\u003cli\u003eWeidong Liu, Jie Chen, Zhipeng Tang, et al. Theory and practice of compiling inter-regional input-output table of 30 provinces, autonomous regions and cities in China in 2007. Beijing: China Statistics Press; 2012.\u003c/li\u003e\n\u003cli\u003eXilian Wang, Qing Jin. Embodied carbon emissions and responsibility sharing of China\u0026apos;s trade in the context of carbon neutrality. Environmental Science \u0026amp; Technology. 2021; 44(11): 205-210.\u003c/li\u003e\n\u003cli\u003eYihan Wang, Siqin Xiong, Xiaoming Ma. Carbon inequality in global trade: Evidence from the mismatch between embodied carbon emissions and value added. Ecological Economics.2022;195. \u003c/li\u003e\n\u003cli\u003eYuna D, Jinjin C, Zhichao G, et al. Network Evolution and Influencing Factors of Global Trade Embodied Carbon Emission.Frontiers in Environmental Science.2022;10.\u003c/li\u003e\n\u003cli\u003eZhangqi Zhong, Xu Zhang, Lingyun He, et al. Inter regional carbon emission transfer, trade embodied carbon structure and cooperative emission reduction: an empirical analysis from 30 provinces in China. Journal of International Trade. 2018; (06): 94-104.\u003c/li\u003e\n\u003cli\u003eZhengquan Guo, Tong Rong. Analysis of the Spatio-temporal pattern of embodied carbon transfer in Chinese inter-regional trade. Journal of Shanxi University (Philosophy and Social Sciences Edition). 2021; 44(06): 97-108. \u003c/li\u003e\n\u003cli\u003eZhu Liu, Jing Meng, Zhu Deng, et al. Research on the transfer of embodied carbon emissions in Sino-US trade. Chinese Science: Earth Science. 2020; 50(11): 1633-1642.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"inter-provincial, value chain, carbon transfer, influencing factors","lastPublishedDoi":"10.21203/rs.3.rs-3887683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3887683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCarbon emission reduction is an arduous task that the world needs to face and solve at present and in the coming period, and it is also an important goal in the process of China's economic and social development. It is also an important goal in the process of China's economic and social development. The study of carbon transfer based on the value chain is crucial to the realization of carbon emission reduction and the scientific and reasonable distribution of carbon emission reduction responsibilities. Based on the input-output method, this paper uses the value-based carbon emission accounting method to calculate and analyze the carbon emission transfer characteristics of 30 provinces, autonomous regions and cities in China (excluding Tibet, Hong Kong, Macao and Taiwan) in 2012 and 2017. As many indicators of different aspects as possible are selected, and the problem of covariance between indicators is reduced and eliminated through factor analysis, so as to analyze the key factors affecting carbon transfer. It was found that during 2012\u0026ndash;2017, China's carbon emissions as a whole showed a transfer from regions with a higher level of economic development to regions with a lower level of economic development, and from the east to the west, with the net transfer out center of gravity shifting significantly to the north and west. The middle region (MR) has always been the region that transfers out the most carbon emissions, but the net transfer growth rate of some provinces in the MR has decreased more with other regions. During this period, the local resource availability, energy consumption level, and science and technology level had the greatest impact on the change of carbon transfer. Regions with abundant resources but lower levels of science and technology, and higher levels of energy consumption will increase their carbon transfer. The results of this paper are intended to improve the study of carbon transfer along the value chain, and provide a basis for the division of responsibility for carbon emission reduction and the formulation of policies in China.\u003c/p\u003e","manuscriptTitle":"Research on the Characteristics and Influencing Factors of Carbon Transfer Based on Value Chain in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 06:52:21","doi":"10.21203/rs.3.rs-3887683/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb3bf68a-67d9-4de6-a0dd-1a612dcb3621","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-15T06:20:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 06:52:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3887683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3887683","identity":"rs-3887683","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}