Spatiotemporal Synergistic Effect and Categorized Management Policy of CO₂ and Air Pollutant Reduction and Economic Growth Under China’s Interregional Trade

Abstract

In this study, we utilized multi-regional input–output (MRIO) tables from 2012 to 2017 to determine the spatiotemporal characteristics of CO₂ emissions, air pollutant emissions, and value added associated with trade in different regions and industries, as well as the level of coupling coordination among them. Secondly, structural decomposition analysis (SDA) was used to identify the drivers of changes in the above indicators at the regional level. The results show that consumption-based emissions exceeded those based on production in developed regions such as Jing-Jin and the eastern coastal regions, while the opposite occurred in energy hubs such as the northern coastal and central regions; the results of the value added show higher levels in production areas than in consumption areas in the eastern coastal regions, while the opposite trend was observed in the northwestern regions. In different industries, energy production and heavy manufacturing contributed significantly to CO₂, PM_2.5, and SO₂ emissions, while the service industry contributed more to NO_x and VOC emissions and value added. The relationships among the changes in the three consumption-based indicators were divided into four categories: positive synergies, negative synergies, trade-offs A, and trade-offs B. Recommendations for targeted collaborative management strategies were delineated based on a regional resource-driven classification.

1. Introduction

The rapid progression of industrialization and urbanization not only promotes economic growth but also increases the release of greenhouse gases and air pollutants, posing a significant threat to the existence and advancement of humankind [1,2,3]. To mitigate these impacts, China has successively adopted many measures to effectively address the air pollution problem [4,5] and set the “dual carbon” goal [6]. However, significant challenges persist. There is a very close relationship between air pollution and carbon emissions. First, they have the same source characteristics, with their main source being fossil energy, which leads to the emission of large quantities of not only air pollutants, such as PM_2.5 and SO₂, but also greenhouse gases, such as CO₂, during the combustion process [7]. Second, the interaction between air pollution and carbon emissions can lead to poorer air quality and exacerbate climate change [8,9]. Third, many air pollutant-control policies reduce carbon emissions while reducing air pollution [10,11]. Given the above, coordinated governance for addressing climate change and air-quality issues simultaneously is possible [12,13]. In addition, interregional trade significantly contributes to achieving economic development and enhancing regional resource allocation efficiency [14,15]. However, trade liberalization can result in the transfer of CO₂ and air pollution emissions from production to consumption regions and even aggravate the environmental imbalance among regions [16,17], making emission reduction difficult [18,19]. Simultaneously achieving emission reduction and economic growth is a pressing challenge, especially in China. Thus, it is crucial to explore coordinated management strategies for the reduction in CO₂ and pollution and the promotion of economic growth derived from interregional trade for developing new quality productive forces and truly realizing green and high-quality development in China.

The “synergistic effect” was initially proposed by the IPCC and refers to the positive effects that policies or measures devised to achieve one goal may have on other goals [20], such as the implementation of climate change-mitigation policies also improving air quality [21,22]. At present, numerous researchers have analyzed synergistic effects related to the environment. From the perspective of the study regions, studies have encompassed synergistic effects related to the environment between China and other countries [23], as well as at the regional level, such as the Yangtze River Economic Belt [24] and the Yellow River Basin [25], and at the city level, including Beijing [26] and Shanghai [27]. Research indicates that implementing carbon emission-reduction measures can concurrently reduce the regional emissions of air pollutants. Many scholars have focused on synergistic emission reduction in specific industries, such as the power [28], steel [29], cement [30], and transportation industries [31]. The energy-saving technologies and emission control measures implemented have significantly contributed to synergistic emission reductions, so the synergistic effect is considered an effective method of emission control [32]. However, many relevant studies based on production emissions have not taken into account the implicit emissions derived from interregional trade and have not included the simultaneous analysis of emission reduction and economic growth. Hence, there is a pressing need to design strategies for both trade-derived CO₂ and air pollution abatement and economic growth. Additionally, the topic of emission-driving factors has attracted increasing attention, with commonly used methods being index decomposition analysis (IDA) and structural decomposition analysis (SDA) [33,34,35,36]. Compared with IDA, SDA considers the interconnection among production departments, enabling a more detailed analysis of interregional and industry-related issues; thus, it is gradually becoming a widely used method for analyzing trade-related CO₂ or pollution emission issues [37]. However, previous studies have mostly focused on the driving factors of a few outcomes, rarely exploring the synergistic effects of the same driving factor on multiple objectives, and have not included detailed industry-level analysis; this has impeded the scientific formulation of regional and industry collaborative emission control policies, which is not conducive to the country’s green development.

The objectives of this study were to (i) determine the spatiotemporal characteristics of CO₂ emissions, air pollutant emissions, and value added associated with trade in different regions and industries, as well as the level of coupling coordination among them, from 2012 to 2017, in China, based on MRIO tables; (ii) determine the drivers of the changes in the above aspects based on SDA; and (iii) design synergistic control strategies to achieve CO₂ and pollution reduction and economic development according to the synergistic effects of drivers defined as positive synergies, negative synergies, trade-offs A, and trade-offs B, and to develop and strengthen new quality productive forces.

2. Materials and Methods

2.1. Inventories of CO₂ and Air Pollutant Emissions

Referring to the IPCC accounting contents and methods for CO₂ emission inventories in [38,39], the accounting formula is as follows:

$$E_{co2}=\sum _i{A}_i{\times LCV}_i{\times CC}_i\times {COR}_i\times {\displaystyle \frac{44}{12}}$$

(1)

where $E_{co2}$ is the CO₂ emissions, kg; i is the energy species; A is the energy consumption, kg; LCV is the average low calorific value, kJ/kg; CC is the CO₂ content per unit calorific value, tC/TJ; and COR is the rate of CO₂ oxidation (

Table 1. Energy sources and correlation coefficients of CO₂ emissions.

Energy Sources	LCV (kJ/kg)	CC (tC/TJ)	COR
Raw coal	20,908	26.37	0.94
Coke	28,435	29.5	0.93
Crude oil	41,816	20.1	0.98
Fuel oil	41,816	21.1	0.98
Gasoline	43,070	18.9	0.98
Kerosene	43,070	19.5	0.98
Diesel oil	42,652	20.2	0.98
Natural gas	38,931	15.3	0.99
Cleaned coal	26,344	25.4	1
Other washed coal	13,591	25.4	1
Briquette	15,473	33.6	0.90
Coke oven gas	179,813	13.6	0.99
Other coal gas	52,278	13.6	0.99
Other coking products	33,779	29.5	0.93
Other petroleum products	40,980	20.0	0.98

).

Referring to Liu et al.’s method of calculating air pollutant emissions based on emission factors [40], the accounting formula is as follows:

$$E_{PM2.5}=\sum _i{A}_i\times {EF}_i\times \left(1-{\eta }_i\right)$$

(2)

where $E_{PM2.5}$ is the PM_2.5 emissions, kg; i is the energy species; A is the energy consumption, t; EF is the emission factor [41,42], kg/t (

Table 2. Energy sources and emission factors of PM_2.5 emissions.

Energy Sources	Emission Factors
Raw coal	10.00 (g kg−1)
Coke	0.14 (g kg−1)
Crude oil	0.27 (g kg−1)
Fuel oil	1.34 (g kg−1)
Gasoline	0.27 (g kg−1)
Kerosene	0.90 (g kg−1)
Diesel oil	0.94 (g kg−1)
Natural gas	0.17 (g m−3)
Cleaned coal	1.65 (g kg−1)
Other washed coal	1.65 (g kg−1)
Briquette	0.625 (g kg−1)
Coke oven gas	0.14 (g m−3)
Other coal gas	0.14 (g m−3)
Other coking products	0.14 (g kg−1)
Other petroleum products	1.34 (g kg−1)

); and η is the pollutant removal efficiency, %. NOx, VOC, and SO₂ were derived from the Multi-Resolution Emission Inventory for China, and the emissions of various industries in each province were obtained according to the energy consumption [43].

2.2. Emissions and Value Added Derived from Interregional Trade

MRIO is a method of economic analysis to study the interdependence of different industries and the allocation of resources in an economic system [44]. In MRIO, the following equilibrium relation applies:

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

(3)

where X is the total output matrix, A is the direct consumption coefficient matrix, Y is the final demand matrix, I represents the unit, and (I − A)⁻¹ is the Leontief inverse matrix.

Linking the CO₂ emission intensity coefficient to MRIO allows for the construction of the environmentally extended MRIO (EE-MRIO). EE-MRIO can be employed to track environmental links among multiple regions [45]. Therefore, we utilized EE-MRIO to study and analyze the emissions of CO₂ derived from interregional trade. The CO₂ emission intensity coefficient is calculated as follows:

$$f_i^r={\displaystyle \frac{e_i^r}{x_i^r}}$$

(4)

where $f_i^r$ is the CO₂ emission intensity coefficient for sector i in region r, $e_i^r$ is CO₂ emissions for sector i in region r, and $x_i^r$ is the total output for sector i in region r.

Therefore, the accounting formula for CO₂ emissions derived from trade is as follows [46]:

$$E_p^r={\sum }_{s=1}^m{\widehat{f}}^r{\left(I-A\right)}^{-1}{\widehat{y}}^s$$

(5)

$$E_c^r={\sum }_{s=1}^m{\widehat{f}}^s{\left(I-A\right)}^{-1}{\widehat{y}}^r$$

(6)

where the symbol ^ represents the diagonal matrix; ${\widehat{f}}^r$ and ${\widehat{f}}^s$ represent the CO₂ emission intensity coefficients for regions r and s, respectively; ${\widehat{y}}^s$ and ${\widehat{y}}^r$ represent the final product consumption for regions s and r, respectively; $E_p^r$ represents the CO₂ emissions generated within region r to meet the final consumption demand locally and elsewhere, reflecting local production-based emissions; and $E_c^r$ represents the CO₂ emissions within all regions that result from satisfying the final consumption demand in region r, illustrating local consumption-based emissions. Differences in the results of the two accounting methods represent local variations according to different perspectives. Equivalent calculations can be performed for value added and air pollutant emissions, so they will not be repeated [47].

2.3. Coupling Coordination Degree Model

In this study, we utilized a coupling coordination degree model to analyze synergistic effects in various provinces, allowing for an accurate portrayal of past development. Because we considered four typical air pollutants as indicators of pollution reduction systems, it is necessary to define the weight of each pollutant according to the entropy weight method. The following is the calculation formula:

$$C={\displaystyle \frac{3\sqrt[3]{U_1{U}_2{U}_3}}{U_1+U_2{+U}_3}}$$

(7)

$$T={aU}_1+b{U}_2+c{U}_3$$

(8)

$$D=\sqrt{C\ast T}$$

(9)

where U₁ is the effect of the carbon dioxide-abatement system, which is characterized by CO₂ emissions; U₂ is the effect of the air pollutant-abatement system, which is characterized by weighted air-pollutant emissions; and U₃ is the effect of the economic growth system, which is characterized by value added. We used the range standardization method to eliminate the influence of dimensions, assuming that all three are of equal importance and setting a = b = c = 1/3. D represents the coupling coordination degree [48]. The division standard of D is shown in Supplementary Table S1.

2.4. Structural Decomposition Analysis (SDA)

We employed SDA to categorize change factors into three major classes, i.e., emission intensity, Leontief structure, and final demand, with the latter being further characterized by subfactors. According to the MRIO accounting model,

$$E=f{(I-A)}^{-1}F=\mathit{fBLMN}$$

(10)

where E represents the matrix of CO₂–air pollutant emissions–value added; f represents the diagonal matrix of CO₂–air pollutant emission intensity–value added coefficient; B represents the inverse Leontief; F is the final demand, which is the product of LMN; L represents the final demand level; N represents the final demand distribution; and M is the final demand coefficient, which is used to explain the departmental structure of final demand and show the position of each sector in each demand item [49,50].

We use subscript 1 to represent the calculation period and subscript 0 to represent the base period, thus obtaining the following:

$$∆E=E_1-E_0=f_1{B}_1{L}_1{M}_1{N}_1-f_0{B}_0{L}_0{M}_0{N}_0$$

(11)

According to the two-stage decomposition method, we obtain the following:

$$\begin{array}{ll}∆E=& {\displaystyle \frac{1}{2}}\left(∆f{B}_0{L}_0{M}_0{N}_0+∆f{B}_1{L}_1{M}_1{N}_1\right)+{\displaystyle \frac{1}{2}}\left(f_1∆B{L}_0{M}_0{N}_0+f_0∆B{L}_1{M}_1{N}_1\right)\\ & +{\displaystyle \frac{1}{2}}\left(f_1{B}_1∆L{M}_0{N}_0+f_0{B}_0∆L{M}_1{N}_1\right)+{\displaystyle \frac{1}{2}}\left(f_1{B}_1{L}_1∆M{N}_0+f_0{B}_0{L}_0∆M{N}_1\right)\\ & +{\displaystyle \frac{1}{2}}(f_1{B}_1{L}_1{M}_1∆N+f_0{B}_0{L}_0{M}_0∆N)\end{array}$$

(12)

which can be abbreviated as

$$∆E=E\left(∆f\right)+E\left(∆B\right)+E\left(∆L\right)+E\left(∆M\right)+E\left(∆N\right)$$

(13)

where $E\left(∆f\right)$ is the influence of the change in emission intensity or in the value added coefficient; $E\left(∆B\right)$ is the influence of the change in the Leontief structure effect; $E\left(∆L\right)$ is the influence of changes in the final demand level; $E\left(∆M\right)$ is the impact of the final demand coefficient; and $E\left(∆N\right)$ represents the impact of the final demand distribution.

2.5. Data Sources

The data used in this study were (i) MRIO tables and fossil energy-consumption data of different provinces and industries in the Chinese mainland. These tables were obtained from China Emission Accounts and Datasets (CEADs), from which we selected data from 2012, 2015, and 2017 as the research basis [51]. The study area included 30 Chinese provinces, autonomous regions, and municipalities (excluding Tibet, Hong Kong, Macao, and Taiwan), and the types of energy sources considered included raw coal, coke, crude oil, fuel oil, gasoline, kerosene, diesel oil, natural gas, cleaned coal, other washed coal, briquette, coke oven gas, other coal gas, other coking products, and other petroleum products, totaling 15 types. (ii) CO₂ and air pollutant emission data included CO₂ emissions calculated with the IPCC method and PM_2.5 emissions calculated with the emission factor method based on energy consumption data. NOx, VOC, and SO₂ emissions were derived from the Multi-Resolution Emission Inventory for China (MEIC), and the pollutant emission data of different industries in each province were estimated, combined with energy consumption data [52]. (iii) Lastly, we used the price index to compare the data in the input–output tables, taking 2015 as the base period. The price indices for agricultural production, factory prices of industrial producers, and investment in fixed assets for construction and installation projects were all obtained from the China Statistical Yearbook published by the National Bureau of Statistics. The index of value added of the tertiary industry was obtained from the China Statistical Yearbook of the Tertiary Industry. The price indices of various industries are shown in Supplementary Table S2. For the convenience of description, 29 departments were further merged into six major industries, in accordance with Shan et al. [53].

3. Results and Discussion

3.1. Spatiotemporal Characteristics of CO₂ and Air Pollutant Emissions and Value Added Derived from Interregional Trade

In this study, we divided 30 provinces in China into eight regions (Supplementary Figure S1 and Table S3). The spatiotemporal characteristics of emissions and value added derived from interregional trade from 2012 to 2017 are shown in Figure 1. Overall, whether based on the production or consumption accounting method, CO₂ emissions, VOC emissions, and value added showed an upward trend from 2012 to 2017. In 2017, consumption-based CO₂ emissions reached 7944 Mt, VOC emissions reached 25,361 kt, and value added reached CNY 64,162 billion; this is attributed to China’s industrialization process, which has led to increased CO₂ and VOC emissions alongside economic growth. However, with the implementation of clean-energy measures, PM_2.5, NO_x, and SO₂ emissions were reduced, with the latter decreasing by 47% compared with 2012 [54,55]. From 2012 to 2017, regions such as Jing-Jin (JJ), the eastern coast (EC), the southern coast (SC), and the southwest (SW) demonstrated higher levels of emissions based on consumption than those based on production. Conversely, other regions exhibited higher production-based emissions than consumption-based emissions. This indicates that, in developed areas such as Beijing (BJ) and Tianjin (TJ), the produced local CO₂ emissions were lower than those arising from the consumption of final products. The reason is that, on account of interregional trade, developed regions can restrict CO₂ emissions to production areas by importing products with high energy consumption and large CO₂ emissions from other regions, thus reducing local direct CO₂ emissions. In contrast, in regions such as the northern coast (NC), which are primarily energy hubs or industrially developed provinces, the locally generated CO₂ emissions were higher than the emissions from final product consumption, calling for more significant CO₂ reduction. Although air pollutants differed from CO₂ in terms of quantities, their spatial features were similar to those of CO₂ emissions, indicating the feasibility of implementing synergistic CO₂ and air pollution reduction.

In the eastern coastal regions, higher levels of value added were found in production areas than in consumption areas, while the opposite was true for the northwestern (NW) regions, suggesting that the eastern coastal regions reap higher benefits from interregional trade due to their higher economic and technological levels, yielding more high-value-added products and profits. On the other hand, the northwestern regions not only are faced with the CO₂ and air pollutant-inflow challenge but also experience outflows of value added, resulting in regional imbalance [56]. The traditional CO₂ and pollution reduction measures based on the production perspective may not only cause the transfer of pollution emissions but also aggravate the imbalance among regions. Therefore, the following will explore collaborative measures for CO₂ and air pollution reduction and economic growth derived from trade from the perspective of consumption.

3.2. Distribution Characteristics of Consumption-Based CO₂ and Air Pollutant Emissions and Value Added in Various Industries

The distribution characteristics are shown in Figure 2. In terms of total amounts, Jiangsu (JS), Zhejiang (ZJ), and Guangdong (GD) were the primary generators of high emissions of CO₂; and Hebei (HE), Shandong (SD), and Henan (HA), in the central (C) regions, also had high emissions. Regarding air pollutants, Jiangsu, Zhejiang, and Guangdong continued to lead in emissions; and Hubei (HB), Hunan (HN), and Henan contributed significantly to emissions in the central region. In terms of value added, economically and technologically advanced provinces had higher levels. From the industrial point of view, energy production and heavy manufacturing contributed primarily to CO₂ and PM_2.5 emissions, and the contribution rate of energy production to CO₂ emissions was mostly over 50%, with the highest rate reaching 68%. The contribution rate of energy production to PM_2.5 emissions was mostly between 40% and 55%, while heavy manufacturing contributed between 25% and 35%. This is because energy production and heavy manufacturing consume large amounts of fossil fuels, resulting in CO₂ and particulate emissions, making them the primary contributors. Energy production and services contributed the most to NOx emissions, and the sum of the contribution rates of these two industries reached 70–80%. In addition to energy production, the transportation sector in the service industry was also a primary source of NO_x emissions. Heavy manufacturing and services contributed the most to VOC emissions. In 2012, the contribution rate of the service industry to VOC emissions was mostly about 50%, decreasing to around 40% by 2017. This was due to the strict control measures implemented in the transportation sector, which slowed down the growth trend of VOC emissions from the service industry [57]. Energy production, heavy manufacturing, and services contributed the most to SO₂ emissions, but the contribution of energy production to SO₂ emissions declined in 2017 due to the widespread adoption of flue gas desulfurization technology, greatly reducing overall SO₂ emissions. The contribution of the service industry to SO₂ emissions was relatively increased, largely more than 50%. The service industry contributed the most to value added, followed by heavy manufacturing, construction, and agriculture, all contributing around 10%, while energy production and light manufacturing contributed around 5%. It is evident that different industries make significantly different contributions to CO₂ emissions, air pollutant emissions, and value added. Therefore, designing synergistic control strategies for CO₂ and air pollution abatement and economic development in key industries could achieve significant results.

3.3. Spatiotemporal Characteristics of Synergistic Effect of CO₂ and Air Pollution Reduction and Economic Growth

To research the coordinated effects of the CO₂ and air pollution abatement and economic growth system in various economic regions, the distribution characteristics of its coupling coordination degree in eight economic Chinese regions from 2012 to 2017 were determined, as shown in

Table 3. Coupling coordination degree of CO₂ and air pollution reduction and economic growth system in eight major economic regions from 2012 to 2017.

Year	JJ	NC	NE	EC	C	SC	SW	NW
2012	0.700	0.455	0.654	0.686	0.649	0.532	0.657	0.558
2015	0.720	0.469	0.674	0.686	0.672	0.635	0.692	0.603
2017	0.709	0.572	0.667	0.669	0.673	0.563	0.709	0.621

. From 2012 to 2015, the coupling coordination level of each region increased to varying degrees. From 2015 to 2017, the synergistic level of the northern coastal, central, and northwestern regions continued to rise, while the level declined slightly in the other regions. This was because the clean-energy policies implemented in China from 2013 to 2017 effectively reduced pollutant emissions, thereby promoting the increase in coupling coordination levels to some extent [58]. However, the emissions in developed regions, such as Jing-Jin, increased, so the level declined from 2015 to 2017. Jing-Jin presented a much higher level than that of other regions and remained an intermediate coordinated development zone throughout the study period. This region, characterized by its advanced economic development and prioritization of high-tech industries, boasts a well-organized industrial structure. This has resulted in a higher coordination level throughout the region. In addition, the northeastern (NE) region was also classified as a primary coordinated development zone. The northwestern region transitioned from being a transitional harmonic zone to being a coordinated development zone, indicating that its economic level has been continuously increasing in recent years, with environmental governance achieving significant results, further increasing the coordination level. Finally, the northern coastal region remained a transitional harmonic zone, indicating that it needs to further improve its coordination level.

To further analyze the synergistic effect in each province, the spatiotemporal evolution characteristics of the synergistic level of the emission reduction and economic growth system in 30 Chinese provinces from 2012 to 2017 were determined, as shown in Figure 3. It was observed that the synergistic level of CO₂ and air pollution reduction and economic growth exhibited significant spatial clustering characteristics and regional heterogeneity. Overall, most provinces were coordinated development zones, and over time, the number of provinces classified as imbalanced recession zones was further reduced, as the level of most provinces was gradually improved. By 2017, the number of provinces classified as intermediate coordinated development zones had increased. This indicates that, during the study period, most provinces were able to implement national policies on energy conservation. Specifically, in 2012, Shandong, Qinghai (QH), and Guangdong were mildly imbalanced recession zones; Gansu (GS), Ningxia (NX), Hubei, and Hainan (HI) were transitional harmonic zones; and other provinces were coordinated development zones. In 2015, Qinghai, Ningxia, Hubei, and Guangdong achieved a better level of coupling coordination; and Sichuan (SI), Guangxi (GX), Anhui (AH), and Shanghai further transitioned into intermediate coordinated development zones. In 2017, the coupling coordination level of Hubei, Jiangxi (JX), Chongqing (CQ), Yunnan (YN), and other provinces was also improved; specifically, Fujian became a good coordinated development zone. However, the coupling coordination level of Hebei, Shandong, Henan, and Guangdong decreased slightly; this is because these provinces are characterized by large energy consumption due to their economic level, population, and resource endowment, which leads to high emissions, thus affecting the improvement in the coupling coordination level of the emission reduction and economic growth system. Therefore, concentrating on these regions when promoting the coordinated management of CO₂ and pollution reduction and economic growth could achieve significant results with comparatively less effort.

3.4. Driving Factors of Changes in CO₂ and Air Pollutant Emissions and Value Added

To formulate collaborative management strategies for CO₂ and air pollution reduction and economic growth, the driving factors of their changes need to be accurately identified. The results of decomposing the driving factors with SDA are shown in Figure 4. It can be seen that, for CO₂, the emission intensity and Leontief structure effects were the primary drivers of emission reduction, where the latter was responsible for a reduction of 423 Mt in the northern coastal region; this indicates that cleaner-energy production technology in the northern coastal region made great progress in 2012–2017, effectively reducing CO₂ emissions. On the other hand, the main driver of the surge in the latter was the increase in the final demand level, which increased CO₂ emissions in the central region by 522 Mt. This suggests that the increase in final demand significantly contributed to higher CO₂ emissions throughout the study period. For PM_2.5, NO_x, and SO₂, the effects of emission intensity and the Leontief structure on reducing pollutant emissions outweighed the effect of increased emissions due to the final demand level. Consequently, the air pollutant emissions in the eight major regions decreased in 2017 compared with 2012, indicating further optimization of the national industrial structure and significant improvements in input–output technologies during this period. Regarding VOC, although the effects of emission intensity and the Leontief structure were drivers of a reduction in emissions, they did not counterbalance the increase in the latter. For value added, the vast majority of districts showed an upward trend in total volume throughout the study period. The value-added coefficient and final demand level were the main driving forces of economic growth.

A further analysis of each province is shown in Figure 5. For CO₂, emission intensity and Leontief structure were the primary factors for CO₂ emission reduction in each province, especially prominent in provinces such as Hebei, Henan, and Hubei, attributable to the promotion of industrial transformation in these provinces. For example, in 2017, Shandong’s total coal consumption decreased by 20 Mt compared with 2012, and Hebei eliminated backward coal-fired units and cut steel production capacity by 60 Mt. For air pollutants, unlike CO₂, the emission-intensity effect was the factor for the decline of emissions in various provinces, which is due to the stricter control measures implemented by the government in the energy structure. For example, Henan vigorously pushed forward the construction of “replacing coal with electricity” and “replacing coal with gas”, and Guangdong implemented the elimination of small boilers and limitation of the use of low-sulfur coal, which effectively promoted the emission reduction. Additionally, the study found that the SO₂ reduction in each province was significantly higher than that of other air pollutants. For value added, different provinces showed different growth trends, and the final demand level promoted the growth of value added in each province, particularly prominent in developed regions such as Jiangsu and Guangdong.

The results of a further analysis of each province are shown in Figure 5. For CO₂, emission intensity and the Leontief structure were the primary factors of emission reduction in each province, and this was especially prominent in provinces such as Hebei, Henan, and Hubei, and attributable to the promotion of industrial transformation in these provinces. For example, in 2017, Shandong’s total coal consumption decreased by 20 Mt compared with 2012, and Hebei eliminated underdeveloped coal-fired units and cut steel production capacity by 60 Mt. For air pollutants, unlike CO₂, the emission-intensity effect was the driver of the decline in emissions in various provinces, which was due to the stricter control measures for the energy structure implemented by the government. For instance, in Henan, “replacing coal with electricity” and “replacing coal with gas” strategies were highly promoted, and in Guangdong, eliminating small boilers and limiting the use of low-sulfur coal were implemented, which effectively fostered emission reduction. Additionally, we found that the SO₂ reductions in each province were significantly higher than those in other air pollutants. For value added, although different provinces showed different growth trends, generally, the final demand level drove the increase in value added, which was particularly prominent in developed regions such as Jiangsu and Guangdong.

3.5. Coordinated Management Strategies Based on Driving Factors

To research the synergy of drivers of CO₂ and pollution reduction and economic growth, we categorized the relationships of the changes in consumption-based CO₂ emissions, air pollutant emissions, and value added into four types. Category I included negative synergies, where driving factors increase carbon and air pollutant emissions while inhibiting economic growth and should thus be adjusted and restricted to promote positive synergies and mitigate negative impacts. Category II included trade-offs B, where factors hinder economic growth and result in a decrease in CO₂ or air pollutant emissions and should thus be optimized to promote economic growth while maintaining the reduction in emissions. Category III included trade-offs A, where factors foster economic growth but cause an increase in CO₂ or air pollutant emissions and thus require adjustments to decrease emissions. Category IV included positive synergies, where driving factors result in a decrease in CO₂ and air pollutant emissions while promoting economic growth and should thus be further strengthened. The synergies among emission and value-added changes driven by different factors from 2012 to 2017 are shown in Figure 6.

The emission-intensity effect and value-added coefficient resulted in positive synergies in 15 provinces, such as Shanghai and Jiangsu, indicating that they contributed to the coordinated management of CO₂ and air pollution abatement and economic development in these provinces. However, for seven provinces, including Hebei and Liaoning, these factors resulted in trade-offs A, indicating that while they fostered economic growth, they also led to increased CO₂ or air pollutant emissions. Finally, these factors resulted in trade-offs B in eight provinces, including Beijing and Tianjin, indicating that they inhibited economic growth but, at the same time, led to CO₂ or air pollutant-emission reduction. Reducing emission intensity is an effective way to restrain CO₂ and air pollutant emissions, and improving the value-added coefficient is an effective way to promote economic growth. China is a major coal consumer, with a low energy efficiency rate, leading to a high emission intensity. Therefore, an effective coordinated management strategy could enhance clean-energy utilization, boost energy efficiency, and advocate for resource-intensive practices actively to increase the value-added coefficient.

The Leontief structure effect in Shaanxi was found to foster the development of coordinated management. However, this factor resulted in trade-offs A in eight provinces, including Beijing and Shanghai, and trade-offs B in other provinces, such as Tianjin and Hebei. The change in the Leontief structure effect reflects various economic and technological changes, including a reduction in the use of raw materials on account of the development of coordinated management. Although reducing emission intensity and improving the value-added coefficient are effective ways to achieve coordinated management, developing cleaner-energy production technology and fostering green technology innovation are crucial methods to reduce emission intensity and improve resource utilization. The change in the Leontief structure effect contributed to economic growth in developed regions such as Beijing; therefore, the government should further strengthen coordinated emission-reduction policies. This factor also contributed to the development of coordinated management in large industrial provinces, such as Hebei, due to the higher potential in these provinces for emission reduction, which can be effectively realized with green technology innovation. Therefore, local governments should encourage the use of resources to allow this factor to result in positive synergies.

The final demand level resulted in trade-offs A in all provinces, indicating that the increase in final demand was a significant driver of emission increase and economic growth. However, rather than suppressing this factor, policymakers should aim toward reducing emission intensity and promoting production technology advancements so as to offset the increase in emissions and optimize the domestic demand structure, thus implementing coordinated management.

The final demand coefficient resulted in negative synergies in Liaoning, Anhui, and Shanxi; trade-offs A in 18 provinces, including Beijing; and trade-offs B in other provinces, such as Shandong. This factor represents the industrial structure of final demand, indicating the position of each industry within various demand items. According to our results, in provinces such as Liaoning, the development of energy-intensive industries should be restricted and that of high-output and less-polluting sectors promoted to optimize the industrial structure, thus allowing the effects of the final demand coefficient to transition from negative to positive synergies.

The final demand distribution resulted in trade-offs A in 15 provinces, such as Jiangsu and Zhejiang, and trade-offs B in other provinces, such as Beijing. This factor represents the relative share of various types of final needs in relation to aggregate need. Because the emission intensity required by different consumption types in different regions varies, the emissions of CO₂ and air pollutants in different regions are of different types. Jiangsu and other provinces have a great demand for high emission intensity, so the demand structure should be optimized, and the demand for low emission intensity, such as cleaner-energy production technologies and products, should be improved, thus allowing the final demand distribution to lead to positive synergies.

In summary, reducing the emission-intensity effect and improving the value-added coefficient could effectively promote coordinated management in all provinces, and developing cleaner-energy production technologies could make a positive contribution to some extent. With the increase in the final demand level, there needs to be a reduction in emission intensity and production technology development to offset emissions. Additionally, restructuring the industrial and demand structure by developing high-value-added, low-pollution industries and products to achieve positive synergies is essential.

4. Conclusions and Policy Recommendations

4.1. Conclusions

(i) In developed regions such as Jing-Jin and the eastern coastal regions, consumption-based CO₂ and air pollutant emissions exceeded those based on production. Conversely, in energy hubs such as the northern coastal and central regions, the opposite was observed. The northwestern region displayed differing characteristics, according to the distribution characteristics from the perspective of consumption, Guangdong, Jiangsu, Zhejiang, Hebei, Shandong, and Henan had higher levels than others. Among different industries, energy production and heavy manufacturing contributed significantly to CO₂, PM_2.5, and SO₂ emissions, while the service industry contributed more to NOx and VOC emissions and value added.

(ii) The synergistic level of CO₂ and air pollution reduction and economic growth in the Jing-Jin region was greater than elsewhere, while the northern coastal region remained a transitional harmonic zone. Over time, the number of provinces classified as imbalance recession zones was further reduced, with the coupling coordination level gradually improving in most provinces. However, the coupling coordination level of Guangdong, Hebei, Shandong, and Henan was slightly reduced. The SDA indicated that the primary drivers of emission reduction were emission intensity and the Leontief structure. Conversely, the increase in value added was primarily driven by the level of final demand.

(iii) Fostering the development of industries with high value added and low pollution is essential to counterbalancing the potential negative effects of increasing the domestic demand and to achieving positive synergies. The relationships among consumption-based CO₂ emissions, air pollutant emissions, and value-added changes were categorized into positive synergies, negative synergies, trade-offs A, and trade-offs B. Reducing the emission-intensity effect and increasing the value-added coefficient could effectively promote positive synergies in the studied provinces, as could developing cleaner-energy-production technology to some extent. It is essential to foster an industrial structure characterized by high value and minimal pollution to achieve positive synergies.

4.2. Policy Recommendations

(i) The emission-monitoring system should be improved based on the dual perspectives of production and consumption and a regional mechanism for sharing responsibility for emission reduction and compensation established. First, for production-based emissions, each region should define the types of emission sources; comprehensively link emission source statistics, emission permits, activity levels, emission coefficients, statistics, and other data; and establish a comprehensive list of local carbon and air pollutants. Secondly, for consumption-based emissions, consumer-goods trade data should be used to track and count emissions from the interregional trade of goods and the service industry and quantify consumption-induced emission flows to achieve accurate monitoring. Finally, a mechanism for sharing responsibility and compensation for emission reduction based on the combination of the production and consumption perspectives should be constructed. For example, in this study, consumption-based emissions in developed regions such as Jing-Jin were higher than those from the production perspective, suggesting that Jing-Jin has transferred polluting emissions to other regions; therefore, developed regions such as Jing-Jin could provide compensation in the form of technology and capital to the central and northwestern regions to support their pollution control through environmental taxes, emission trading systems, and the establishment of environmental protection funds.

(ii) Policymakers should adhere to the precise governance concept of “one key fits one lock” and formulate differentiated coordinated management strategies for regions and industries jointly. For the eastern coastal region, consumption-based emissions depend on the supply of industrially developed regions, so priority should be given to the mitigation approaches that incorporate cleaner-energy production strategies into the whole supply chain, especially energy production and heavy industry. For example, large enterprises should be encouraged to establish green supply chains with small- and medium-sized enterprises and implement green procurement and clean-energy technology sharing mechanisms. The development of energy-efficient green technologies (e.g., efficient combustion and green catalysts) should be promoted, and upstream suppliers should be required to provide environmentally compliant products and services, thus fostering green transformation along the entire chain. For energy and heavy-industry hubs such as the northern coastal and central regions, the government could introduce specific policies, such as tax incentives for clean-energy projects, to promote the gradual phasing out of coal and other highly polluting energy sources; it could formulate a plan to enforce low-emission technological standards and gradually shut down outdated production facilities; finally, it could implement a differentiated environmental protection tax or sewage charge for enterprises that are heavily polluting the environment. Although the direct emissions of the service industry are low, the latter has a greater impact on resource allocation and economic structure. Overall emissions can be reduced by incentivizing green investment in the service sector and the adoption of cleaner-energy technologies (e.g., green buildings, environmentally friendly transportation, and energy-efficient office equipment).

(iii) New, quality productive forces should be developed, and green and high-quality development should be promoted in China. As the reduced emission-intensity effect can effectively promote positive synergies among provinces, the government should develop new strategies, such as enhancing support for R&D and the application of clean energy (e.g., wind, solar, hydrogen, and natural gas) and promoting the green power trading market, to promote clean-energy transformation and reduce carbon emission intensity. Moreover, cleaner-energy production technologies can, to a certain extent, facilitate the realization of positive synergies among provinces. Therefore, the government should develop new strategies, such as increasing R&D support for green technology development (e.g., cleaner production technologies, green chemical technologies, and low-carbon building technologies), setting up a green technology innovation fund, and prioritizing the funding of green technologies that have the potential for wide application. The government should also develop new industries. For example, it should formulate clear policies on industrial restructuring, phase out enterprises in high-pollution and high-energy-consumption industries, eliminate underdeveloped production facilities, and promote the development of high-value-added and low-pollution industries. In addition, the government should promote green consumption by optimizing the demand structure, especially in the areas of daily life, transportation, and construction, to increase the demand for low-emission and clean-energy technology products and to define new formats. Fostering the development of new quality productive forces based on new policies, technologies, industries, and formats is a highly sustainable and dynamic growth model that can promote green and high-quality development in China.