The Inter-Regional Embodied Carbon Flow Pattern in China Based on Carbon Peaking Stress

Abstract

Embodied carbon flows among regions have led to unfair carbon emission responsibility accounting based on production. However, the heterogeneity of carbon peaking stress between regions is significantly neglected for those embodied carbon flows. Incorporating the carbon peaking stress into the embodied carbon flows can more clearly show what causes the carbon peaking stress and which carbon flow paths are more critical. In this study, the decoupling index of carbon emissions and economy development was applied to characterize the carbon peaking stress in each region, and the environmental extended multi-regional input–output model was applied to re-evaluate the criticality of regional embodied carbon flows. The results showed that the carbon peaking stress in China improved from 2007 to 2012, but the rebound of carbon peaking stress in 2017 made most regions reverse the previous downward trend. The stress to reach carbon peaks varies considerably from region to region, and the stress in the northwest is much higher than that in developed eastern China. Considering the heterogeneity of carbon peaking stress, additional concerns should be given to the net embodied carbon output in the northwestern, northern, and central regions, which can help avoid the dilemma between outsourcing embodied carbon and reducing carbon emissions from production. The policy to reduce emissions should be implemented in all regions that benefit from the net embodied carbon output of the northern and northwestern regions, where the carbon peaking stress is higher. The focus should be on the actual improvement of the carbon peaking stress, not just on the transfer of stress. The increasing urgency of achieving carbon peaking targets and unequal stress for regional peaking emissions calls for differentiated regional mitigation measures to help the Chinese government scientifically and in an orderly manner promote the overall and local carbon peaking work.

1. Introduction

In order to actively respond to global climate change, China has put forward a double-carbon goal of achieving peak carbon by 2030 and carbon neutrality by 2060. This target-driven programmatic approach has been successfully implemented in China more than once [1,2,3]. Because of China’s vast size and regional diversity, ambitious emission reduction plans have been further broken down to the provincial level [4,5]. At present, all provinces and cities are actively introducing their own concrete implementation plans to reach peak carbon emissions. This means that China is not only seeking to achieve carbon peaking as a whole, but also at the regional level [6]. However, economic development is rapid and regional trade is expanding, making a region’s production-based carbon emissions closely linked to its trading partners [7,8,9]. It is obvious that inter-regional trade can significantly affect the achievement of each region’s carbon peaking target, which is assessed by production-based carbon emissions [10,11].

In 2020, a development strategy to smooth out the domestic circulation was further proposed by the Chinese government, which makes the carbon transfer under the economic circulation more deserving of attention. In China, the economically developed eastern region often imports goods from the less developed central and western regions, reducing its own carbon emissions and increasing those of the other side [12]. Based on our calculations for regional trade in 2017, 85.05% of carbon emissions resulting from Beijing’s commodity consumption behavior were outsourced from foreign provinces, and Xinjiang emitted an additional 41.6% of carbon emissions for the consumption behavior of foreign provinces. Unfortunately, in the face of the current strict pressure to reach peak carbon emissions, provinces may complain that their carbon emissions are caused by consumption in other provinces, thereby trying to avoid responsibility for these emissions [13].

However, the inequality of carbon peaking stress within embodied carbon flows has not received sufficient attention. The existing studies usually examine inter-regional embodied carbon flows only from a quantitative perspective [14,15,16,17,18]. That is, they assumed that the embodied carbon flows between different regions are neutral and should be treated equally, without considering regional differences in the carbon peaking situation. According to the Action Plan for Peaking Carbon Emission by 2030 issued by the Chinese government, each region should combine the local economic development, environmental endowment, and other actual conditions to carry out carbon peaking work in an orderly manner. In addition, the Chinese government also emphasizes that the regions with more severe carbon peaking situations should also strive to be peaking carbon emissions at the same time as the whole country. In fact, for the same scale of embodied carbon outflows, the stress on regions with severe peaking situations will be much greater than that on the regions with moderate peaking situations [19,20]. Therefore, in the context of all regions seeking their own carbon peak, different peaking situations will inevitably lead to different peaking stress, which requires a differentiated focus on the embodied carbon flows caused by domestic trade and a targeted mitigation strategy.

The novelty of this study is mainly reflected in the following three aspects: First, constructing a carbon peaking stress index on the basis of the decoupling index of economic development and carbon emissions, then comparing the spatial and temporal discrepancies of regional carbon peaking stress. Secondly, the embodied carbon flows between regions are re-evaluated from the perspective of the carbon peaking stress, and the most critical carbon transfer pathways for China as a whole and locally to achieve the carbon peaking goal are screened by combining the carbon peaking stress and embodied carbon flows. Finally, to reveal differentiated flow characteristics that may have been overlooked in previous studies, the embodied carbon flows examined from a scale perspective are compared with those from a carbon peaking stress perspective. This study uses a new approach, that is, combining the carbon peaking stress index, to comprehensively assess the embodied carbon flows among regions in China, and the findings are helpful for China to achieve overall and local carbon peaking targets in an appropriate and orderly manner.

2. Literature Review

The first topic is inter-regional carbon transfer research. After the release of the Paris Agreement, the carbon transfer implicitly underneath commodity trade behavior has aroused extensive academic discussions due to the simultaneous producer and consumer roles [21,22]. The revelation of regional carbon inequalities, such as with consumption in developed countries and pollution in China [23,24,25], and consumption in developed eastern regions while pollution is caused in less developed western regions [26,27], has provided effective support for formulating differentiated emission reduction policies and balancing regional carbon peaking targets. For example, based on the inter-regional Ghosh input–output model, Xie et al. found that China’s supply-side carbon emissions show a west-to-east transfer pattern, and are mainly concentrated in the electricity and heat sectors, etc. [28]. Sun et al. found that in China’s internal carbon transfers, the eastern provinces play the role of bidirectional spillovers, the western provinces play the role of proxies, and the central provinces play the role of net beneficiaries [26]. Fang et al. found that Hungary, Romania, Slovenia, Iran, Ethiopia, and Iran are the main carbon exporting countries in the global embodied carbon transfer network, while the United States, Germany, the United Kingdom, Japan, Canada, and Spain are the main carbon importing countries [29]. Tong et al. identified the provinces (Hebei, Inner Mongolia, Guangxi, Fujian, Ningxia, and Anhui) and industries (energy, metal products, and mineral products) that have a higher potential for carbon emission reductions in the inter-provincial trade pattern [30].

The second topic is the study of inter-regional carbon inequality. Carbon emissions can often be seen as an undesired output of the economic production process, i.e., the environmental costs incurred by regions in order to develop their economies. Under the widespread phenomenon of carbon transfer, it has become a new concern to explore whether the environmental costs and economic benefits of regions match. For example, Zhang et al. revealed the deteriorating trend of regional carbon inequality in China by measuring economic value added and carbon emissions in trade behavior [31]. Mi et al. calculated the carbon footprint Gini coefficient to measure carbon inequality across regions and found that carbon inequality is declining with economic growth [32]. Xu et al. studied carbon inequality in China using a modified Thiel index, and the results showed that carbon inequality showed a fluctuating downward trend in China during 2003–2015 [33]. Wen et al. constructed a low-carbon sustainable development indicator system in six dimensions and found that while all Chinese provinces are moving toward carbon neutrality, the variability is growing [34]. Feng et al. analyzed the environmental conditions of each province and the unequal exchange of carbon between provinces by introducing pollution terms of trade into the MRIO model, and found that most of the developed eastern regions have gained both economic and environmental advantages through commodity trade compared to the northwest regions [35].

And then there is the study of regional carbon emission situations. The different development patterns and levels of China’s regions have led to significant differences in carbon emission dynamics, hindering the achievement of China’s overall “dual-carbon” goal. Therefore, it is important to clarify the carbon emission characteristics of each region in order to formulate differentiated emission-reduction policies and targeted concerted actions. For example, Li et al. found that there is a significant spatial clustering and radiation effect of carbon emission intensity among provinces in China, and that the key provinces need to take coordinated multi-regional actions to reduce emissions [36]. Liu et al. combined the STIRPAT model and the spatial Durbin model to find that the characteristics of carbon emissions vary across different regions in China, including the driving factors and their spatial interaction effects on carbon emissions, thus requiring differentiated mitigation strategies [37]. Further, Yu et al. measured the environmental pressure in the economic growth of 29 provinces in China from 2007 to 2016 through the decoupling index, and found that there are significant differences in the decoupling status among provinces, with Beijing and Shanghai being more optimistic in their decoupling status, while the western regions are facing greater pressure of rough development [38]. Liu et al. studied the relationship between electricity consumption and carbon emissions at the county level in China, and found that poor counties are more likely to decouple electricity consumption from carbon dioxide emissions, resulting in a better carbon peaking situation [39]. Hou et al. assessed the decoupling of carbon emissions from economic growth in China’s transport sector, and found that the Tapio–EKC coupling curves of the transport sector in the whole country and the eastern part of the country show an inverted N-shape, while the Tapio–EKC coupling curves of the transport sector in the central and western parts of the country show an inverted U-shape, and the decoupling situation is usually more volatile [40].

However, there is still some room for improvement in this literature. Firstly, existing studies tend to focus on the post-event inequality of embodied carbon flows under trading patterns, i.e., the unequal impacts of such flows, but there is still a lack of a pre-event perspective that examines the phenomenon of such transfers considering regional inequality, thus failing to clarify the priority and urgency of regional carbon transfers in the context of carbon peaking stress. Secondly, existing studies have often measured regional differentiated carbon emission dynamics through relatively static indicators such as the carbon emission scale, carbon emission intensity, decoupling index, etc., but have not further combined them with regional dynamic carbon transfer behaviors. Neglecting regional differentiated carbon peaking stress, the conclusion of inter-regional embodied carbon flows obtained only from the perspective of flow scale is difficult to be applied to the real needs of different regions to optimize their trade structure and carry out synergistic work. The scarcity revealed in economics makes the optimal allocation of resources an important process to reach goals [41]. For instance, some scholars have defined virtual water flows that take into account the extent of local water scarcity as scarce water, and research on scarce water has provided additional insights into addressing regional water scarcity [42,43,44]. To fill the gap of existing studies, this study used the decoupling index to characterize regional carbon peaking stress with reference to the scarcity water measurement idea, based on which the inter-regional embodied carbon flows were reassessed and compared with those assessed under the traditional scale perspective, in order to reveal the flow characteristics that may have been overlooked in previous studies.

3. Materials and Methods

3.1. Carbon Peaking Stress Index

Decoupling initially indicated the absence of a reactive relationship between two or more physical quantities. After being introduced into the field of environmental economics, the concept of decoupling has been widely used to measure the connection between regional economic development and carbon emissions, which helps to propose strategies to promote sustainable development [38,45,46]. As fossil energy dominates the energy mix in China, the increase in demand for fossil energy consumption due to economic growth is the main reason for the rapid growth in carbon emissions [47]. However, it is clear that reducing economic growth to achieve a pseudo-peak in carbon emissions is not the Chinese government’s pursuit [48]. The assumption of absolute decoupling of economic growth and carbon emissions is implicit in China’s attempt to successfully achieve carbon peaking while ensuring economic growth [49]. Specifically, when China’s carbon emissions achieve a peak value and then enter a downward path, its decoupling index of economic growth and carbon emissions must first be reduced to zero and continue to decline. Under the premise of ensuring economic growth, a larger decoupling index indicates a higher degree of correlation between carbon emissions and the economy. Therefore, this study applied the decoupling index to express the regionally differentiated stress to reach carbon peak.

The real GDP growth rate of each province in China was consistently positive during the study period, so the positivity of the decoupling index depends entirely on the direction of carbon emission change. The larger the decoupling index of a region, the higher the cost of carbon emissions it has to bear to maintain a certain level of economic growth, and thus the more pressure it is under to reach carbon peak [50].

$${\epsilon }_{(t-1)\to t}=\frac{\left(C_t-C_{t-1}\right)/C_{t-1}}{\left(GD{P}_t-GD{P}_{t-1}\right)/GD{P}_{t-1}}=\frac{\Delta C/C}{\Delta GDP/GDP}$$

(1)

In Equation (1), GDP represents gross domestic product and C represents carbon emissions. $∆C$ represents the variation of carbon emissions during the period of year t − 1 to year t. $∆GDP$ represents the variation of gross domestic product during the period of year t − 1 to year t. $\epsilon$ represents the decoupling index between economic growth and carbon emissions. In order to avoid anomalies in individual years, the average of ${\epsilon }_{(t-1)\to t}$ and ${\epsilon }_{t\to (t+1)}$ was used as the decoupling index in year t.

Then, drawing on the construction of the water stress index (WSI) [51] and the local water scarcity risk indicator (LSWR) [52], a logistic function was applied to transform the decoupling index into a continuous value between 0 and 1, and we finally obtained a carbon peaking stress index (CPS) for convenient comparison:

$$CPS=\frac{1}{1+e^{-3.5\epsilon }\left(\frac{1}{0.01}-1\right)}$$

(2)

The measured decoupling index between regional economic growth and carbon emissions had a minimum of −1.17 and a maximum of 2.46, while the corresponding values of the transformed CPS were 1.67 × 10⁻⁴ (carbon emissions in regions with a higher degree of decoupling also generate a small stress, rather than none at all) and 0.98. It is important to note in particular that the CPS is a relative indicator rather than an absolute one. CPS is only used when making relative comparisons. In other words, the ranking of carbon peaking stress between regions or years is of greater interest.

3.2. Environmentally Extended MRIO Model

For each sector, the amount of output of its products should be equal to the sum of the intermediate and final demand for the products of that sector; then, the following row-wise equilibrium relationship exists for the multi-regional input–output table:

$$x^r={\displaystyle \sum _{s=1}^{30}{a}^{rs}{x}^s+{\displaystyle \sum _{s=1}^{30}{y}^{rs}}}$$

(3)

In Equation (3), r, s denotes the region; x^r denotes the total output of region r; a^rs is the direct consumption coefficient, which represents the amount of inputs from region r needed for each unit of product produced in region s; x^s represents the total output of region s; y^rs denotes the final demand of region s for the products of region r.

Equation (3) can be expressed as a matrix form as Equation (4).

$$X=AX+Y={\left(I-A\right)}^{-1}Y$$

(4)

In Equation (4), X is the output matrix; A is the direct consumption coefficient matrix; and Y is the final demand matrix, including meeting the local final demand and meeting the final demand outside the region. It is known that carbon emissions are equal to carbon intensity multiplied by total output, thus giving rise to the equation shown in Equation (5).

$$C=RX=R{\left(I-A\right)}^{-1}Y=RLY$$

(5)

In Equation (5), C represents the direct carbon emission matrix; R represents the carbon intensity; and L is the Leontief inverse matrix. As a result of the economic cycle, carbon emissions are transferred from the production sector to the demand sector in the form of embodied carbon [53]. The volume of embodied carbon flow from region r to region s can be accounted by Equation (6):

$$E{C}^{rs}={\displaystyle \sum _{t=1}^{30}{R}^r{L}^{rt}}{Y}^{ts}$$

(6)

To examine the influence of the regional carbon peaking stress on the embodied carbon flow pattern, the regional CPS is further assigned as a weight to the embodied carbon flows. The volume of carbon stress flow from region r to region s can be accounted by Equation (7):

$$S{C}^{rs}=CP{S}^r{\displaystyle \sum _{t=1}^{30}{R}^r{L}^{rt}}{Y}^{ts}$$

(7)

3.3. Data Sources and Processing

Chinese 2012 and 2017 MRIO tables are from Carbon Emission Accounts and Datasets (CEADs) [12], while Chinese 2007 MRIO table is from the study of Liu et al. [54]. Since the number of regions involved in the input–output tables for the three years was not consistent, to ensure vertical comparability among the input–output tables, we standardized them into 30 regional input–output tables, without Tibet, Hong Kong, Macao, and Taiwan. The GDP data of each region are from China Statistical Yearbook 2008, 2013, and 2018. In order to eliminate the influence of price factor, all GDP data were converted to the comparable price data in 2006.

Carbon emissions data for each region in China from 2006–2018 were also obtained from CEADs [55,56]. Since the input–output table reflects the balance relationship of production and consumption among sectors and products in the economic system, it does not include the residential living sector, then it should be that the total carbon emissions in this study does not include the carbon emissions directly generated by the residents’ living. Bias in emission data (activity data and emission factors are incomplete or have measurement errors) constitutes the main source of uncertainty in this study. Compared with other global databases, CEADs obtained emission inventories for China and its 30 provinces using a uniform accounting framework and a parameter list tailored to China’s reality, making the results fully comparable. In addition, CEADs applied Monte Carlo simulation to calculate the combined uncertainty of China’s emission data with reference to the IPCC Guidelines, and the results show that the uncertainty range is (−15.5%, 30.8%). Validation comparisons with other databases indicate that the scale of China’s carbon emissions revealed by CEADs is relatively low, but very close to China’s official emissions. The emission trends of different databases are generally more consistent, which effectively supports the robustness of the results of this study.

4. Results and Discussion

4.1. Analysis of Regional Carbon Peaking Stress

Figure 1a,b show that there are spatial differences in the carbon peaking stress across provinces in China. In terms of the ranking of the CPS, the regions with higher carbon peaking stress are mostly found in Western China. In 2007, the regions with a higher CPS were Chongqing (0.447), Ningxia (0.348), Qinghai (0.343), and Hainan (0.339). In 2012, the regions with a higher CPS were Xinjiang (0.851), Qinghai (0.497), and Jiangxi (0.324). And the regions with a higher CPS in 2017 were Ningxia (0.982), Inner Mongolia (0.923), and Fujian (0.371).

The change direction of the CPS shows that the carbon peaking stress in nine regions, including Beijing, Shanghai, and Jilin, continued to decline from 2007 to 2017, implying that economic development and carbon emissions in these regions moved towards decoupling, which laid the foundation for these regions to lead the way to peak carbon. However, the carbon peaking stress in other regions evolved upward to some extent during the whole study period. For example, Jiangsu, Guizhou, and other five regions show an increase in carbon peaking stress during 2007–2012 and a decrease during 2012–2017. This indicates that the decoupling between economic development and carbon emissions in these regions is relatively late and requires some extra effort to ensure that carbon peaks are as expected. The remaining 16 regions, such as Hebei, Shandong, Guangdong, and Zhejiang, show a decrease in the carbon peaking stress between 2007 and 2012, but an increase between 2012 and 2017. This shows that the economic growth and carbon emissions of the major industrial provinces like Hebei, Shandong, and Guangdong are still closely linked, and that these provinces’ economic development is coming at an increasing carbon cost after a brief decline, and the situation of carbon peak attainment is not optimistic.

In terms of the change to the extent of the CPS, Xinjiang experienced the largest increase in carbon peaking stress between 2007 and 2012 (+115.45%), while Chongqing (−133.71%), Inner Mongolia (−123.7%), and Ningxia (−84.98%) achieved a large decrease. The carbon peaking stress in Ningxia and Inner Mongolia reversed the previous downward trend between 2012 and 2017, and the levels of their carbon peaking stress in 2017 even significantly exceeded those in 2007. Xinjiang, on the other hand, reversed the upward trend of the previous period and achieved a large decrease in carbon peaking stress in 2017. In Qinghai and Jiangxi, the carbon peaking stress level also declined significantly.

The above analysis initially shows that there are significant spatial differences in carbon peaking stress in China, and that stress varies over time across regions. The inequality of regional carbon peaking stress based on Gini coefficients is further revealed in Figure 1c. The findings demonstrate that China’s regional carbon peaking stress inequality increased dramatically from 2007 to 2012, and slightly improved from 2012 to 2017. That is, the distribution of carbon peaking stress is not equal among regions, and a small number of regions bear a larger share of the carbon peaking stress. According to our calculation, the share of carbon peaking stress in the three regions with the highest CPS was 28.72% in 2007, while it increased significantly to 64.09% in 2012 and decreased slightly in 2017. This indicates that the three regions with the highest carbon peaking stress in China are responsible for the majority of the carbon peaking stress.

Since there are differences in economic scale and emission reduction technologies between regions, the total carbon emissions and carbon intensity were combined to analyze carbon peaking stress in each region. The first column of Figure 2 shows scatter plots of the carbon peaking stress and the total carbon emissions for 2007, 2012, and 2017, and the second column shows scatter plots of the carbon peaking stress and the carbon emission intensity. Firstly, combining the carbon intensity and the carbon stress, Ningxia and Inner Mongolia show a double high characteristic of carbon peaking stress and carbon intensity in 2007 and 2017, which indicates that the carbon situation in these two regions is more severe and that carbon reduction technology is still not mature enough. The carbon intensity of Shanxi continued to decline during the study period, but the stress to reach the peak rebounded from 2012 to 2017. Thus, the three regions of Ningxia, Inner Mongolia, and Shanxi urgently need to develop emission reduction technologies to drive lower carbon costs of economic development. Xinjiang’s carbon intensity had a slight upward trend, but its carbon peaking stress plummeted from 2012 to 2017, so Xinjiang’s carbon intensity is inferred to be decreasing in the future.

Then, combining the total carbon emissions and the carbon peaking stress, no region had both large total carbon emissions and carbon peaking stress in 2007 and 2012. But, in 2017, Hebei, Shanxi, and Inner Mongolia finally showed the double high characteristic of total carbon emissions and carbon peaking stress. This indicates that the emission reduction situation in these three regions is deteriorating and the difficulty of emission reduction is increasing. Because of the large scale of carbon emissions in these three regions, it is important to seek peak carbon strategies in these three regions to accelerate the peak carbon process in China as a whole.

4.2. Analysis of Embodied Carbon Flows Based on Carbon Peaking Stress

The inequality in carbon peaking stress indicates that the urgency of regional carbon peaking varies, thus highlighting the need for regional embodied carbon flows to be given different levels of importance, especially in light of China’s present pursuit of full and simultaneous regional carbon peak achievement. The 15 most important paths of net embodied carbon flows and the 15 most important paths of net embodied carbon flows after considering the carbon peaking stress are revealed in Figure 3. During the period 2007–2017, Inner Mongolia, Hebei, and Shanxi were always important embodied carbon outflow regions, indicating that these three provinces provide a larger degree of carbon emission support for the consumption of other provinces. Jiangsu and Liaoning also become more important embodied carbon outflow regions after 2012. In general, Beijing, Tianjin, the eastern coastal provinces, and the southern coastal provinces are the main embodied carbon inflow regions. However, with the change of economic structure, there are differences in the proportion of inflows in the three years. In 2007, the above-mentioned provinces, as the end-points of inflows, were more evenly distributed, while the southern coastal region, especially Guangdong, absorbed a very significant quantity of embodied carbon inflows in 2012. And in 2017, the eastern coastal region, particularly Zhejiang and Jiangsu, was the focal point of embodied carbon inflows.

When the embodied carbon flows in the context of carbon peaking stress in each region were examined, the results were clearly different. For the embodied carbon flows in 2007, the carbon peaking stress further enhanced the concentration of Inner Mongolia and Hebei as embodied carbon outflow regions. Only one of the 15 pathways rescreened in 2007 was not sourced from Inner Mongolia or Hebei, but from Anhui. This indicates that the embodied carbon outflow from Inner Mongolia, Hebei, and Anhui in 2007 needs the most attention considering the regional carbon peaking stress. For the 2012 embodied carbon flows, the introduction of carbon peaking stress highlights the importance of Xinjiang and Anhui as embodied carbon outflow regions, which cannot be revealed when only the amount of embodied carbon flows is considered. The importance of embodied carbon outflows in Inner Mongolia and Hebei was instead reduced, due to the lower carbon peaking stress of both in 2012. For 2017, Inner Mongolia is given unprecedented importance as an embodied carbon outflow region, and the larger embodied carbon outflows and higher carbon peaking stress mean that each embodied carbon outflow pathway from Inner Mongolia to the outside should be given a very high degree of importance. In terms of the end-points of stressful carbon flows, they were still concentrated in Beijing, and the coastal regions in the east and the south in 2007 and 2012. And in 2017, the embodied carbon flow pathways that need attention were distributed almost nationwide, considering the unprecedented importance of Inner Mongolia as a pathway source.

To make inter-regional comparisons more convenient, the 30 provinces were combined into eight regions (Figure 4). From the overall situation of embodied carbon flows, the primary areas with net embodied carbon inflows are Beijing, Tianjin, the eastern coastal provinces, and the southern coastal provinces. The scale of net embodied carbon inflow in the eastern coastal provinces shows an ongoing upward trend, while BT and the southern coastal regions show a trend of increase and then decrease. The northern, northeastern, and northwestern regions are the main net embodied carbon outflow players. The northeastern and northwestern regions show a continuous increasing trend of net embodied carbon outflow, while the northern region shows an increase and then a decrease. As for the southwestern and central regions, their identities shifted significantly over the research period, changing from net embodied carbon outflow regions in 2007 and 2012 to net embodied carbon inflow regions in 2017. The 10 most important pathways of net embodied carbon flows are revealed in Figure 4a–c. Consistent with the above analysis, the embodied carbon export position of the northwestern and northeastern regions has strengthened, but the increase in the northwestern region was larger and flows to more regions. The embodied carbon export position of the central and northern regions has strengthened and then weakened, indicating a shift in the economic development patterns of both regions. 2007 and 2012 have seen net embodied carbon flows mainly to BT and the eastern and southern coastal regions, while the central and southwestern regions became important pathway endpoints in 2017.

During the period 2007–2017, the region with the highest carbon peaking stress was always the northwest. All regions experienced some improvement in carbon peaking stress in 2012 compared to 2007. The carbon peaking stress in the southeast, and the eastern coastal and southwest regions consistently decreased from 2007 to 2017. However, unfortunately the remaining five regions rebounded in 2017 after experiencing a decline from 2007 to 2012. The CPS of the northern, southern coastal, and northwestern regions even exceeded that of 2007 in 2017, which indicates that these three regions are facing more severe carbon peaking situation and need to establish a long-term low-carbon development mechanism. In Figure 4d–f, the 10 most important paths of net embodied carbon flow after considering the regional carbon peaking stress are revealed. According to the findings, the importance of regional embodied carbon flows for peaking carbon emission seems to be decreasing in that year due to the improvement of regional carbon peaking stress in 2012. And again, due to the lack of a long-term guarantee mechanism, the embodied carbon outflows of the northwestern and northern regions in 2007 and 2017 after considering the carbon peaking stress need to be given more attention.

4.3. Discussion

The existing studies have usually examined inter-regional carbon inequality from a post-event perspective, focusing on revealing the economic benefit–environmental cost mismatch brought about by the phenomena of carbon transfer and leakage. In this study, we re-examined China’s inter-provincial embodied carbon flow patterns from a pre-event perspective, which provided new insights into understanding the hierarchy of inter-regional embodied carbon flows under stringent emission reduction targets. We constructed a regional carbon peaking stress index to determine the degree of regional peak carbon urgency, and further incorporated it into a multi-regional input–output analysis to identify the more critical embodied carbon flow pathways. These efforts will help China to avoid a one-dimensional peak carbon action and provide lessons for other developing countries under pressure to reduce emissions.

Compared with the traditional scale perspective, this study obtained some new insights by assigning weights to inter-regional embodied carbon flows through regional carbon peaking stress.

First, this study applied the decoupling index to represent regional carbon peaking stress, and found that the region with the highest peak carbon pressure has always been the northwestern region. A study found that China’s carbon decoupling index from 2005 to 2019 generally showed a distribution pattern of high in the northwest and low in the southeast [57]. If this finding is transferred to this paper, the conclusion will be that carbon peaking stress is high in the northwest and low in the southeast. Liu constructed a carbon emission space allocation index system from two dimensions, namely, the economy and the environment, and examined the carbon peaking stress from the perspective of the balance of the carbon space, obtaining a similar result to that of this study, which is that the northwestern region has more pressure to reach peak carbon [58]. Although the method of quantifying the carbon peaking stress in this study was not consistent with the above studies, our basic consensus is that the carbon peaking stress is closely related to both the total carbon emissions and the degree of economic development. Sustainable development requires a balance between peak carbon goals and economic development [59], and carbon control will be less difficult if the economic fluctuations and social welfare losses caused by carbon emission reductions are smaller [60].

Second, we observed that the unevenness of pressure to achieve peak carbon between regions in China increased significantly between 2007 and 2012, while this trend eased between 2012 and 2017. Overall, the unevenness of pressure to achieve peak carbon in China’s regions showed an upward trend between 2007 and 2017, suggesting that different regions are increasingly diverging in terms of the point at which they reach peak carbon emissions. Some scholars have pointed that the more economically developed provinces have taken the lead in achieving peak carbon emissions; however, the failure of the less economically developed provinces to peak on schedule may have an impact on the timing and targets for national peak carbon emissions [61]. An in-depth understanding of the spatial heterogeneity of the pressure to reach peak carbon emissions is central to the development of targeted emission reduction strategies. As regions with higher levels of economic development in China, the eastern and central regions are more technologically advanced and innovative, and are home to most of the country’s technology-intensive enterprises; technological progress has played a key role in decoupling carbon emissions from economic growth in these regions [57]. Comparatively speaking, in regions with lower levels of economic development, environmental investment [62] and factor substitution [57] may be important drivers for emissions reductions. In addition, regional differences in energy structure [63] and efficiency of energy use [64] have a profound impact on the imbalance of pressure for carbon peaking. China’s less developed regions are mostly resource-based cities, which tend to be dominated by heavy industry and have a coal-based energy structure, resulting in a higher share of the total national carbon emissions and a significant inertia of high-carbon development [65]. Therefore, curbing this high-carbon development inertia and promoting industrial structure upgrading [66], complemented by green technology innovation [67], is a necessary path for these regions to achieve emissions reduction.

Finally, regardless of whether or not the pressure of carbon peaking is considered, the main areas of carbon flow are concentrated in BT, and the eastern coastal and southern coastal areas. Under the weighted influence of carbon peaking stress, the key net embodied carbon outflow provinces have changed somewhat, with Anhui Province being one of them. The environmental impacts of embodied carbon outflows in Anhui have exceeded expectations, with the main carbon reduction stress stemming from the high share of carbon emissions from energy-intensive industries (e.g., the cement and steel industries, which account for 38 per cent of the province’s carbon emissions from fossil energy consumption, which is 13 percentage points above the national average [68]) and the lack of a total amount of low-carbon alternative energy sources. According to several projections [69,70,71], Anhui Province will not be among the first team to reach the peak. Inner Mongolia has consistently been a key net embodied carbon outflow province between 2007 and 2017, which is closely related to its positioning as an energy production base and its industrial structure. Inner Mongolia’s rich coal resources generate a large amount of carbon dioxide during mining, processing, and use, coupled with the fact that most of its pillar industries are in the upper reaches of the industrial chain, with domestic trade in resource-based products in exchange for downstream technology-intensive products [72], which has led to the region’s current deep reliance on high-carbon trade, making it difficult to achieve carbon peaking quickly.

In addition, due to the lag in the release of statistical data and input–output tables, it was difficult to analyze China’s embodied carbon flows in real time in this study. Given the relative stability of inter-regional input–output correlations, we additionally calculated China’s carbon peaking stress index in 2021 for comparative discussion. The results showed that the carbon peaking stress in Northwest China shows a relatively better trend between 2017 and 2021, and the coupling between carbon emissions and economic development has decreased in Inner Mongolia, Gansu, and Ningxia; however, the carbon peaking stress index of the east coast and the south coast shows a relatively worsening trend, which suggests that the carbon cost of economic development in these regions has rebounded. This anomaly can be attributed to some extent to the volatility of the decoupling situation, which has been widely mentioned in previous studies. However, more attention should be paid to the fact that it implies that the scale of carbon emissions in less developed regions has remained slow to increase even after the early abrupt increase, i.e., these regions need stronger exogenous incentives to reverse the solidifying trend of increasing carbon emissions. In addition, this phenomenon also hints at the dilemma of developed regions having to revert to carbon-intensive industries to drive their economies to maintain high growth rates after earlier industrial shifts. The opposing trends of carbon peaking stress in less developed regions and developed regions provide some evidence that inter-provincial trade seems to achieve only a transfer of stress, with a limited contribution to easing China’s overall carbon peaking stress. Thus, stronger industrial transformation programs and more sustainable economic growth patterns are still needed to achieve China’s overall peak carbon goal as soon as possible.

5. Conclusions and Policy Recommendations

5.1. Conclusions

The action plan for carbon dioxide peaking before 2030 requires all regions to carry out emission reduction work in combination with local economic development reality and resource and environmental endowment, and requires regions with a severe carbon peak situation to achieve their carbon peak simultaneously with the whole country. However, assessing production-based carbon emissions is unfair to achieve the carbon peak goals of western China. This is because the less developed western provinces tend to produce carbon-intensive products to satisfy the more developed eastern regions, thus leading to massive additional outsourcing of carbon emissions. More importantly, the differential carbon peaking stress caused by the actual regional economic development and environmental endowment is basically missing in the assessment of embodied carbon flows, which may result in the trivialization of embodied carbon outflows from regions with severe carbon peaking conditions.

The results showed that there is inequality in the carbon peaking stress of each region. Northwest China (Inner Mongolia, Ningxia) has a much greater carbon peaking stress than other places, and between 2007 and 2017 this discrepancy grew even more (the Gini coefficient changed from 0.46 to 0.74). In addition, the carbon peaking stress in most regions experienced a process of decline and rebound. In some regions, the carbon peaking stress in 2017 was even far greater than the level in 2007, which indicates that the carbon emissions in these regions are not ready for a stable peak. Furthermore, the areas of net embodied carbon output that need attention will be more concentrated when considering the inequality of carbon peaking stress. In particular, the northwestern regions (Inner Mongolia and Xinjiang) which are under greater stress to reach peak emissions and have a large scale of embodied carbon net output suffer more severe unequal treatment than expected in the dilemma of being required to reach the 2030 carbon peak target and contracting out carbon emissions for other provinces. The key net import regions of embodied carbon after considering regional carbon peaking stress are not limited to the more developed Beijing, Tianjin, eastern coastal, and southern coastal regions (although they are still important), as the flow of embodied carbon from the northwestern and northern provinces to the central and southwestern regions cannot be neglected either.

5.2. Policy Recommendations

Oversimplified policy choices, such as an explicit emphasis on stricter environmental controls, avoidance of heavy industry, and adoption of more clean energy sources, add little to the discussion of China’s atmospheric and climate governance [73]. Considering policy effectiveness, the government should adopt differentiated regulatory measures to promote a low-carbon transition according to time and place [74]. Some scholars have found that the effectiveness of different types of environmental regulations is not consistent for Chinese cities in different regions, with different population sizes and different carbon emission scales [75], which reveals that we need to formulate targeted emission reduction programs according to the level of carbon emissions and carbon peaking stress in different regions.

In order to successfully peak China’s overall and local carbon emissions, relevant study has given rich policy recommendations from the production side [76,77] and the consumption side [78,79,80]. The western region needs to actively pursue the development of clean energy and low-carbon technology, and actively encourage a low-carbon transition of industrial structure. The eastern region needs to lead the promotion of green consumption, and actively transfer its own advanced technology while outsourcing carbon emissions. From a national perspective, firstly, a broad carbon trading market deserves to be given significant consideration, and secondly, a carbon responsibility system that takes into account both production and consumption needs to be established. These policy implications are also applicable to this study. However, after considering the difference in carbon peaking stress (the calculation results of each province in 2017 shall prevail), some additional policy recommendations of emission reduction have been given: (1) For regions that show a net outflow of embodied carbon and are under greater carbon peaking stress (such as Inner Mongolia, Ningxia, Shanxi), on the one hand, strong emission reduction policies are needed to improve energy efficiency and encourage greener production technologies to reduce carbon emissions attached to economic production, and on the other hand, trade structures need to be optimized to selectively undertake low-carbon industries and export low-carbon products; (2) for regions that show a net outflow of embodied carbon and are under less carbon peaking stress (such as Tianjin, Jilin, Heilongjiang), they are encouraged to make use of their technological advantages to expand their foreign trade scale if they have spare capacity; (3) for regions that show a net inflow of embodied carbon and are under greater carbon peaking stress (such as Fujian, Yunnan), the focus of their carbon peaking efforts should be on supporting environmental protection and clean energy development, optimizing the energy and industrial structures, and accelerating the construction of local green economies and low-carbon ecosystems; and (4) for regions that show a net embodied carbon input flow and are under less carbon peaking stress (like Beijing, Zhejiang and Guangdong), given their twofold benefits in carbon transfer and low-carbon transformation, they need to explore cutting-edge emission reduction methods, boost funding for scientific research to serve as a catalyst for breakthroughs in technology, establish cutting-edge carbon emission reduction models, and disseminate their findings.

The cost of reducing emissions varies significantly from region to region. Regions that have a low degree of decoupling of economic development from carbon emissions may face challenges in realizing the cost-effectiveness of emission reductions, which may lead to increased carbon risk [81]. In regions with a leading edge in terms of production technology and scale (Zhejiang, Guangdong, etc.), the risk of embodied carbon outflow is relatively high, so it is more advisable for them to undertake more industrial production tasks based on the integration of a higher degree of industrial production processes. These regions should be encouraged or required to increase investment in research and development to play a key role in technological breakthroughs and actively promote advanced emission reduction technologies and experience. In addition, given the impracticality of transferring industries out of less developed regions, the export of carbon-intensive products from less developed regions to developed regions is largely irreversible. Therefore, establishing a national carbon trading market [82] and fully considering the pressure of carbon peaking in the carbon price formation mechanism [83] may be an effective way to balance carbon risk.

5.3. Shortcomings and Prospects

There are a few areas in which this research requires to be strengthened: Firstly, although the basic data provided by CEADs for this study were confirmed to have low uncertainty, further improvement of data accuracy and year continuity in the future will make the research conclusions more accurate. Secondly, this study focused on the carbon peaking stress and embodied carbon outflows on the production side, and future studies could try to explore the pattern of embodied carbon inflows under the combination of regionally differentiated carbon peaking stress and carbon emissions on the consumption side. Thirdly, this research measured the regional carbon peaking stress using the transformed decoupling index. This method is feasible when there is no direct stress index. But in the future, especially when considering the carbon neutrality goal, the inclusion of regional carbon sink-related indicators will make our measurement of regional carbon emission stress more scientific.