Analysis of the Environmental and Economic Impacts of Industrial Restructuring and Identification of Key Sectors Based on an Industrial Correlation Perspective

Abstract

Systematically assessing the impact of industrial restructuring on carbon emissions and economic growth from the industrial correlation perspective holds great significance for realizing sustainable economic development. By extending the input–output analysis, this study developed a comprehensive assessment framework to evaluate the impacts of industrial restructuring on energy-related carbon emissions and economic growth within a multi-sectoral system from the industrial correlation perspective. An indicator system was established to identify key sectors for different industrial restructuring strategies. Taking Guangdong as a case, the results show that (1) the indirect impact of industrial restructuring in sectors such as equipment manufacturing and services on carbon emissions is more significant than that on economic growth, and the carbon intensity of its indirect impact is much larger than that of its direct impact; (2) industrial restructuring indirectly affects energy-related carbon emissions or economic growth mainly through a limited number of linked sectors, whereas the main linked pathways through which sector-specific industrial restructuring indirectly affects carbon emissions and economic growth are not consistent; (3) from the industrial correlation perspective, environmental benefits are higher for the service sector and lower for the construction sector; and (4) in industrial restructuring, the metal-processing sector is identified as a key sector for pursuing low-carbon transition, while the non-metallic mineral products sector is identified as a key sector for controlling production scale. The findings and framework can inform regional decisions on industrial restructuring and carbon reduction from the industrial correlation perspective.

1. Introduction

The rapid pace of industrialization and urbanization has undeniably improved living standards but has also led to substantial fossil energy consumption and CO₂ emissions. The resulting climate change has become a significant global challenge. As the world’s largest energy consumer, China accounts for 25% of global energy demand [1]. Meanwhile, as the largest carbon emitter, China’s emissions tripled from 2000 to 2021, contributing 35% of the global carbon emissions in 2023 [2]. The effective reduction in China’s fossil fuel emissions has become a worldwide concern [3,4]. The Chinese government has set ambitious targets for “carbon neutrality” and “carbon peaking” to address climate change [5]. However, like many other developing countries, China also seeks to improve its living standards through rapid economic development [6]. Therefore, balancing economic growth with carbon reduction has become an urgent issue [7,8].

Industrial restructuring has been identified as a crucial strategy for balancing the complex interplay among energy, environment, and economy, and is a key measure to combat climate change [4,9,10]. To ensure that China promptly fulfills its carbon mitigation commitments, the Chinese government is striving to achieve multiple sustainable objectives related to emissions reduction and economic growth through industrial restructuring [11,12]. Currently, the government has issued a series of capacity regulations and high-quality development policies to govern sectors that directly yield substantial carbon emissions, frequently including energy-intensive segments in the industrial system [4,13]. Simultaneously, policies have been successively issued to encourage the development of sectors characterized by low direct carbon intensity [14]. This approach largely reflects a focus on industrial restructuring guided by direct production considerations at the sectoral level [15,16]. However, it is essential to recognize that sectors within the industrial system are interdependent and interconnected. Sectors function not only as suppliers but also as demanders of other sectors [6,13]. These inter-sectoral relationships enable sectors to channel indirect environmental and economic impacts along the intricate industrial chain [16,17]. Thus, focusing solely on the direct environmental and economic effects of sectoral production may not fully capture the potential benefits of industrial restructuring [18]. It is crucial to understand the specific manifestations of various industries’ impacts on carbon emissions and economic growth during industrial restructuring from the industrial correlation perspective.

Existing studies have explored the environmental and economic consequences of industrial restructuring from various angles. On one hand, some studies have focused on using econometric theory to test the effectiveness of industrial restructuring in reducing carbon emissions and promoting economic growth [9,19,20]. For example, Dong et al. [3], Du et al. [18], and Zheng et al. [21] employed decomposition theory to demonstrate the positive role of industrial restructuring in reducing carbon emissions and energy consumption. Xia et al. [22] and Zhang et al. [23] employed the decoupling theory to confirm that industrial restructuring contributes to the decoupling of economic growth and carbon emissions at the national and sub-national levels. Hu et al. [4] used a nonlinear autoregressive lagged distribution model to confirm that China’s carbon emissions will decline when the industrial structure is more rationalized. Regarding economic growth, some studies have confirmed the long-term equilibrium or causal relationship between industrial structure upgrading and economic growth, suggesting that industrial restructuring plays a significant role in sustaining economic growth [7,24,25]. Conversely, Zhao et al. [19] argued that the impact of industrial structure rationalization on economic growth is greater than that of industrial structure upgrading, and that rationalization is a prerequisite for the effective promotion of economic growth through upgrading. They argue that an effective industrial restructuring policy for sustainable growth in China should focus on rationalizing the industrial structure and improving resource allocation efficiency, rather than solely emphasizing upgrading. On the other hand, some studies have focused on exploring the specific mechanisms by which industrial restructuring influences the environment and economy [20,26]. They concluded that industrial restructuring enhances energy and production efficiency, thus attenuating the associated emissions growth [27,28]. Industrial structural upgrading also exhibits an inverted N-shaped trend with carbon emissions alongside technological development [20]. A recent study also found that technological progress and industrial structure optimization exhibit a significant interaction effect on carbon emissions [29]. Several studies have further explored the industrial structure pathways based on optimization theory and scenario analysis to achieve the multiple energy–environment–economy sustainability goals by the target year [30,31,32,33,34]. Their findings confirm that rational industrial restructuring can unleash significant energy-saving and carbon-reducing potentials while promoting economic growth. China requires updating its current industrial structure to achieve its peak carbon goals.

Overall, the above studies provide strong evidence supporting the strategic role of industrial restructuring in achieving environmental and economic sustainable development goals [27,31]. Nevertheless, most of these studies are still limited to the three-industry structure, and more detailed multi-sector-level insights and characterizations of a complex and diverse industrial system are needed to guide practical decision making. This includes specifying restructuring priorities and complementary carbon reduction measures [7,23,27]. Furthermore, these studies have also not deeply investigated the mechanism and process by which industrial restructuring affects carbon emission and economic growth from the industrial correlation perspective.

Input–output analysis (IOA) is particularly effective at depicting the intricate economic and technological interconnections between sectors in an industrial system, offering a methodological framework for assessing the indirect impacts of industrial restructuring on the environment and economy through industrial linkages [35,36]. Based on the IOA, some scholars have investigated the direct and indirect effects of the development of sectors such as construction, services, fisheries, and marine industries on carbon emissions, confirming that the indirect impacts of sectors such as construction and services on energy consumption and carbon emissions through industrial linkages are much more significant than the direct effects [36,37,38,39,40]. However, these single-sector analyses are not particularly useful for guiding sector-differentiated restructuring strategies in a multi-sectoral industrial system. The developed indicator assessment systems can identify the key sectors for energy conservation and emission reduction within the industrial system [16,35,41]. However, these sectoral assessments based on industrial linkages have focused more on the unilateral impact of sectors on energy consumption or carbon emissions, with few studies comprehensively assessing the synergistic impacts on economic growth and carbon emissions [41]. Additionally, sectors play dual roles as downstream consumers and upstream suppliers in the industry chain, allowing industrial restructuring to simultaneously affect production in both upstream and downstream sectors [14,42]. This impact propagates along the chain, further influencing broader economic operations [14,16]. As the economy develops, evolving economic demand has driven automatic adjustments in industrial systems. It is imperative to consider this dynamic correlation between economic development and sectors when developing policy measures to effectively coordinate supply and demand and accelerate the process of industrial structure rationalization [41]. Existing studies have assessed the impact of supply and demand transformations in sectors such as the marine, oil, and financial sectors, confirming the need for integrated assessment in multi-sectoral systems to further refine policy [36,39,43]. However, relevant studies are still rare.

In summary, existing studies have provided valuable insights into industrial restructuring but also highlight several limitations. (1) Some studies based on econometric theory have demonstrated the effectiveness of industrial restructuring in reducing emissions and sustaining economic growth, but they have not analyzed the direct and indirect impacts of industrial restructuring in a multi-sectoral system from the industrial correlation perspective. (2) Some studies have investigated the linkage impacts of industrial restructuring through IOA, but these studies have focused on assessment from a single sector or a single perspective (energy consumption or emissions). A systematic assessment from multiple sectors or perspectives is still lacking to guide differentiated industrial restructuring and carbon reduction strategies. (3) Existing integrated assessments rarely consider the dynamic correlations between economic development and sectors. To some extent, this has hindered the development of effective industrial restructuring strategies.

To bridge this knowledge gap, this study extends IOA to establish a comprehensive assessment framework that systematically examines the specific performance of various industries during industrial restructuring in Guangdong, providing in-depth insights into regional-level industrial restructuring decision making from an industrial correlation perspective. Specifically, we systematically assessed the direct and indirect impacts of industrial restructuring on energy-related carbon emissions and economic growth in a multi-sectoral system and developed an indicator system to identify key sectors for differential restructuring strategies by comprehensively considering the dynamic correlation between economic development and sectoral development.

The novelties and contributions of this study are as follows: (1) We delve into the impacts of industrial restructuring on energy-related carbon emissions and economic growth in a multi-sectoral system, including both direct and indirect impacts, thus gaining insights into the complexity of industrial restructuring from the industrial correlation perspective. (2) We reveal the sources and disparities of the impacts of sector-specific industrial restructuring on energy-related carbon emissions and economic growth, which helps to formulate complementary carbon mitigation measures aligned with industrial restructuring for more effective harmonization of energy–environment–economy relations. (3) We comprehensively consider the dynamic correlation between economic and sectoral development, as well as the impact of sector-specific industrial restructuring on energy-related carbon emissions and economic growth, within the indicator system to identify priority sectors for different industrial restructuring strategies. Differentiating the responsibilities of various sectors will help achieve sustainability goals more efficiently. Moreover, the proposed comprehensive assessment framework has the potential for replication for analyzing similar issues in other regions.

The significance of choosing Guangdong, China, for the study, is clear in several respects. First, the realization of national-level energy conservation and emission reduction goals relies on regional effectiveness. As the region with the largest emissions, the second-largest energy consumption, and the largest GDP in China, Guangdong is highly relevant to achieving China’s overall sustainable development goals. Second, Guangdong serves as a testing ground for the implementation of China’s latest carbon emissions reduction policies. Policymakers are eager to explore effective sustainable development pathways in Guangdong for broader emulation [4,19]. Therefore, we chose Guangdong as the subject. The findings will offer lessons for other regions facing similar challenges.

The remainder of this paper is organized into five sections. Section 2 outlines the research methodology and data sources; Section 3 gives the results; Section 4 discusses our findings; Section 5 gives the conclusions; and Section 6 gives the policy implications.

2. Methodology and Data

By extending the IOA, we proposed an integrated assessment framework that encompasses four modules: data preparation, sectoral impact analysis, the coupling of economic and sectoral development, and comprehensive assessment. This framework systematically evaluates the impacts of industrial restructuring on carbon emissions and economic growth within a multi-sectoral system from an industrial linkage perspective and identifies key sectors for various restructuring strategies. Figure 1 presents the detailed functions and content of each module. The theoretical modeling process of the framework is described in detail below. The proposed framework is expected to offer a new perspective and methodology for other regions to follow in their research on related issues. For instance, industrial restructuring is also a priority for achieving carbon mitigation in western China, which has relatively poor cleaner production technologies.

2.1. Input–Output Framework

2.1.1. Linked Intermediate Inputs Required for Sectoral Production

Input–output analysis (IOA) can quantitatively analyze the relationships between inputs and outputs across different activities within an industrial system and effectively addresses issues related to industrial linkages. It has been applied to various areas, including energy use [33], carbon emissions [38], and other environmental issues. Based on the economic input–output framework, the intermediate inputs consumed by sector production not only include direct supply sector inputs but also indirect inputs from supply sectors to supply sectors [44,45]. The complete consumption coefficients are estimated as follows:

$$b_{ij}=a_{ij}+{\sum }_k{a}_{ik}{a}_{kj}+{\sum }_k{\sum }_m{a}_{im}{a}_{mk}{a}_{kj}+\dots \dots$$

(1)

where $a_{ij}$ represents the first round of direct input from sector i to sector j; however, sector i can provide multiple rounds of indirect input. For example, ${\sum }_k{a}_{ik}{a}_{kj}$ represents the second round of input from sector i via intermediate sector k, with sector j consuming products from sector k and sector k consuming additional products from sector i [46]. There will also be an intermediary sector m between sectors k and i, taking more rounds into account, the sum of which represents the total consumption of final production sector j to supply sector i.

2.1.2. Linked Production Changes Resulting from Industrial Restructuring

In industrial restructuring, production changes within a restructured sector lead to changes in demand for intermediate input goods. This shock propagates through the supply chain, further inducing adaptive changes in supply sector output [47]. Based on Equation (1), the total output change in the whole industrial system caused by sector-specific industrial restructuring can be given by Equation (2).

$$∆X_j=X_j{u}_j+{\sum }_i{b}_{ij}{X}_j{u}_j$$

(2)

Here, $∆X_j$ denotes the total change in the output of the whole industrial system caused by a change $X_j{u}_j$ in the restructured sector j’s production. On the right side of the equation, $X_j{u}_j$ represents the direct change occurring in the restructured sector j; and ${\sum }_i{b}_{ij}{X}_j{u}_j$ represents the sum of the linked output change of each linked sector to accommodate the production changes in the restructured sector, reflecting the indirect change due to the industrial correlation.

2.2. Impacts of Industrial Restructuring on Carbon Emissions and Economic Growth

The linked sector can generate carbon emissions and economic benefits during the production process. By treating them as exogenous transactions, we can calculate both the direct and indirect impacts of sector-specific industrial restructuring on carbon emissions and economic growth.

2.2.1. Direct and Indirect Impacts on Economic Growth

Equation (3) can be used to assess the impact of sector-specific industrial restructuring on economic growth.

$$∆Y_j={\epsilon }_j{X}_j{u}_j+{\sum }_i{\epsilon }_i{b}_{ij}{X}_j{u}_j$$

(3)

where ${\epsilon }_i$ is the economic value-added rate of sector i; $∆Y_j$ is the corresponding total change in economic growth of the entire industrial system resulting from production change $X_j{u}_j$ in restructured sector j, encompassing both the direct and indirect impacts. ${\epsilon }_j{X}_j{u}_j$ is the direct change in the restructured sector j, reflecting the direct impact through the restructured sector; and ${\sum }_i{\epsilon }_i{b}_{ij}{X}_j{u}_j$ is the sum of indirect changes in the economic growth of each linked sector, reflecting the indirect impact due to the industrial correlation.

2.2.2. Direct and Indirect Impacts on Carbon Emissions

Equation (4) can be used to assess the impact of sector-specific industrial restructuring on carbon emissions.

$$∆C_j=c_j{X}_j{u}_j+{\sum }_i{c}_i{b}_{ij}{X}_j{u}_j$$

(4)

Here, $c_i$ is the carbon emissions coefficient of sector i; $∆C_j$ is the corresponding total change in carbon emissions for the entire industrial system, reflecting the total impact on carbon emissions; $c_j{X}_j{u}_j$ is the direct change in the restructured sector j, reflecting the direct impact through the restructured sector; and ${\sum }_i{c}_i{b}_{ij}{X}_j{u}_j$ is the sum of indirect changes in the carbon emissions of each linked sector, reflecting the indirect impact due to the industrial correlation.

2.3. Evaluating Sectoral Performance from an Industrial Correlation Perspective

2.3.1. Total Carbon Intensity

Based on total changes in economic growth and carbon emissions of the entire industrial system, total carbon intensity (TCI) can be expressed as follows:

$${TCI}_j={\displaystyle \frac{∆C_j}{∆Y_j}}={\displaystyle \frac{c_j{X}_j{u}_j+{\sum }_i{c}_i{b}_{ij}{X}_j{u}_j}{{\epsilon }_i{X}_j{u}_j+{\sum }_i{\epsilon }_i{b}_{ij}{X}_j{u}_j}}$$

(5)

${TCI}_j$ measures the total carbon intensity of the restructured sector j, reflecting the restructured sector’s environmental and economic efficiency from an industrial correlation perspective. In a production-limitation plan of industrial restructuring, we expect the restructured sector to have a high TCI to trade the same economic loss for a greater return in terms of emissions reduction. In contrast, we expect the restructured sector to have a low TCI to trade the same environmental cost for a higher economic return in a production stimulation plan of industrial restructuring. Overall, TCI helps identify the key potential sectors for balancing economic growth and carbon mitigation from an industrial correlation perspective, guiding the development of targeted sectoral programs.

2.3.2. Economic Output Supply Multiplier

Sectors that act as downstream consumers also act as upstream suppliers of other sectors within the supply chain. Restructuring in sectors can affect the normal functioning of the entire industrial system through the industrial correlation. Policies causing underdevelopment in a sector may disrupt the supply of products essential for economic growth [14]. For instance, restructuring of energy-intensive industries that drastically reduces steel and cement production capacity could hinder the machinery and equipment manufacturing sectors due to insufficient steel and construction sectors due to a lack of cement. These downstream industries then are forced to lower production scale, and the ripple effect will spread throughout the industrial system via the supply chain, affecting more sectors in normal production. To formulate effective industrial restructuring strategies, it is crucial to consider the coupling of economic development with sectoral development. We introduce the economic output supply multiplier (ESM) to characterize this coupling:

$${ESM}_i={\displaystyle \frac{F_i}{F}}+{\sum }_j{\displaystyle \frac{F_j}{F}}{b}_{ij}$$

(6)

where ${ESM}_i$ is the economic output supply multiplier of the restructured sector i, indicating the sector i’s product supply needed per unit of economic demand. The $F_j/F$ is the share of sector j in the overall economic demand, determined by the current economic development pattern. Economic demand includes consumption, investment, and exports. A high ESM implies a strong coupling between economic development and sector development, while changes in the ESM reflect shifts in this coupling strength. In formulating sectoral intervention policies, policymakers should avoid the problem of the restructured sector’s inability to meet the supply of products for normal economic development resulting from the implementation of production reduction policies for the sector with a high ESM or with a sustained growth in the ESM. Overall, the ESM provides more information for developing industrial restructuring strategies.

2.3.3. Forecasting Economic Output Supply Multipliers

As noted previously, changes in the ESM provide important information for relevant policy formulation. We, thus, forecast the future ESM changes for each sector based on recent Ref. [38]. The $b_{ij}$ in Equation (1) captures the sector j’s consumption intensity for the input from supply sector i, representing the associated technical efficiency. The ESM for the restructured sector i in year t can be estimated by the change in economic demand and technical efficiency relative to the base year:

$${ESM}_i^t={\displaystyle \frac{\left(1+{\beta }_i\right)F_i^{t-1}}{\sum _i\left(1+{\beta }_i\right)F_i^{t-1}}}+{\sum }_j{\displaystyle \frac{\left(1+{\beta }_j\right)F_j^{t-1}}{\sum _i\left(1+{\beta }_i\right)F_i^{t-1}}}\left(1+{\alpha }_i\right)b_{ij}^{t-1}$$

(7)

where ${\beta }_j$ is the annual growth rate of economic demand in sector j, and ${\alpha }_i$ is the annual rate of change in the sector j’s consumption intensity to sector i.

2.4. Data Source

The following data are used for the study: industrial economic data, energy consumption data by industry, carbon emissions factors, price index, input–output table, and economic demand data. Industrial economic data for 2010–2021 are from Guangdong Provincial Bureau of Statistics (GDBS). The time series energy-consumption data by sector for 2010–2021 were collected from the Statistical Yearbook published by the Guangdong Provincial Bureau of Statistics and the China Emission Accounts and Datasets [48,49]. Only energy-related carbon emissions are considered in this paper. We used electricity consumption multiplied by the electricity emission factor to include carbon emissions from electricity consumption in the sectoral emissions. As for carbon emissions from the electricity production sector, we only calculated emissions from non-feedstock energy consumption in the production process, while emissions from primary energy inputs for electricity generation would be excluded. Carbon emissions factors for electricity and other energy types were obtained from data published by the National Center for Strategic Research and International Cooperation to Address Climate Change [50,51]. Input–output tables hold enormous data volumes to describe the complex economic and technological linkages between industries, but this also makes their compilation difficult and time-lagged [3,52]. Guangdong publishes the input–output tables every five years, and the latest data are based on 2017. Therefore, the input–output data of this paper are limited to 2007–2017. The time series input–output tables were obtained from the Guangdong Provincial Bureau of Statistics [53]. We used the double deflation method to convert all input–output tables to constant prices in 2017 [3,54]. Price indices were collected from the National Bureau of Statistics [55]. Economic demand data can be extracted from the re-compiled constant price input–output tables. There are 42 sectors in the original input–output table. Based on the industrial characteristics of Guangdong, we organized 12 sectoral classifications for the study.

Table 1. Sector classifications and abbreviations.

	Abbreviation	Examined Sectors
1	AGR	Agriculture
2	EXT	Extractive industry
3	LIG	Light industry
4	PPC	Petroleum processing and coking
5	CHE	Chemical products
6	MIN	Non-metallic mineral products
7	MEP	Metal processing
8	EQU	Equipment manufacturing
9	EGH	Electricity, gas, and heat production
10	CON	Construction
11	TRA	Transportation
12	SER	Services

presents the sector classifications and abbreviations.

3. Results

3.1. Industrial Growth and Carbon Emission Characteristics of Guangdong

Figure 2 summarizes the historical industrial growth and carbon emissions in Guangdong during 2010–2021. Overall, Guangdong’s total economic volume increased by 7% annually from CNY 3.6 trillion in 2010 to CNY 7.8 trillion in 2021. Notably, the tertiary sector experienced the most significant growth, with an average annual increase of 9%. Its economic contribution rose from 45% in 2010 to 56% in 2021, replacing the secondary sector as the largest driver of economic growth. Overall, Guangdong’s industrial pattern has achieved a shift from industrialization to service orientation, with the service sector becoming the dominant industry.

In terms of emissions, Guangdong’s total industrial carbon emissions reached 480 Mt CO₂ in 2021. The secondary sector remained the largest emitter, accounting for nearly 70% of total emissions. Over the period from 2010 to 2021, total carbon emissions increased by 62 Mt CO₂, with the tertiary sector emerging as the primary driver of this growth, contributing more than 70%. Figure 2c further shows the carbon intensity in Guangdong during 2010–2021. In 2021, the overall industrial carbon intensity stood at 0.61 t CO₂/CNY 104, with the secondary sector exhibiting the highest carbon intensity compared to other industries. Specifically, the carbon intensity for the primary, secondary, and tertiary sectors was 0.38, 1.01, and 0.33 t CO₂/CNY 104, respectively. With the practice of scientific development and series of energy-saving and emission-reduction targets, the overall industrial carbon intensity has decreased significantly by 53% during 2010–2021. Furthermore, the carbon intensity of the secondary sector has also declined significantly by 59%.

Figure 3 further gives the composition of the secondary sector in Guangdong. The equipment manufacturing, light industry, construction, and chemical industries are the pillars of Guangdong’s industrial growth, contributing 51%, 14%, 10%, and 9% of the total growth in 2021, respectively. With the advancement of Guangdong’s industrial restructuring strategy, the equipment manufacturing industry, characterized as technology-intensive, has played an increasingly crucial role in industrial growth, with its contribution rising from 45% in 2010 to 51% in 2021. In contrast, traditional labor-intensive industries, such as light industry and iron and steel production, have experienced a decline in their economic status.

3.2. Impact of Industrial Restructuring on Carbon Emissions and Economic Growth

3.2.1. Direct and Indirect Impacts on Carbon Emissions and Economic Growth

Figure 4 depicts the impacts of industrial restructuring on carbon emissions and economic growth of the entire industrial system when the production size of a given sector changes by 1% resulting from industrial restructuring. In terms of carbon emission, equipment manufacturing (EQU), light industry (LIG), and services (SER) show a greater impact, causing total carbon emissions changes of 7.9, 2.7, and 2.6 Mt, respectively (Figure 4a). Equipment manufacturing (EQU), services (SER), and light industry (LIG) show a greater impact on economic growth, causing total GDP changes of CNY 82, 76, and 32 billion, respectively (Figure 4c). A clear difference exists between the carbon and economic sensitivities of the sector in industrial restructuring. In the case of SER and LIG, even though LIG brings about a larger change in emission compared to SER, it brings about a significantly smaller change in economic growth.

Figure 4 further categorizes the impact of industrial restructuring into direct impact through the restructuring sector itself and indirect impact through the industrial correlation. The results show that industrial restructuring contributes to over 70% of the indirect carbon emissions for five sectors, including equipment manufacturing (EQU), construction (CON), and light industry (LIG). As for economic growth, industrial restructuring contributes to over 70% of the indirect economic growth for seven sectors, including construction (CON), metal processing (MEP), and equipment manufacturing (EQU). The indirect effect is far more important than the direct effect regarding the impacts of these sectors on carbon emissions or economic growth.

A clear asymmetry exists in the structure of the environmental and economic impacts of industrial restructuring. For example, in the services sector (SER), the direct and indirect effects of industrial restructuring constitute 25% and 75% of the impact on carbon emissions and 59% and 41% of the impact on economic growth, respectively. The indirect effect plays a more critical role in the impact of carbon emissions. This similar phenomenon can be observed in the industrial restructuring of the equipment manufacturing (EQU) and construction (CON) sectors. To further analyze this asymmetry, Figure 5 compares the carbon intensity, equaling carbon emissions per unit of economic growth, of direct and indirect impacts by sector. Consistently, it can be found that the carbon intensity of indirect impact is 2.9, 3.0, and 4.3 times higher than the carbon intensity of direct impact in the EQU, CON, and SER sectors, respectively. Conversely, the carbon intensity of direct impact is much lower than that of indirect impact in the MEP and MIN sectors.

3.2.2. Decomposition of Indirect Impacts on Carbon Emissions and Economic Growth

Figure 6 further decomposes the indirect impacts based on specific industrial linkage forms. It can be found that industrial restructuring in various sectors indirectly affects the environment or economy mainly through a limited number of linked sectors. In terms of carbon emissions, the indirect impact of the equipment manufacturing (EQU) sector is mainly in the metal processing (MEP) sector, which contributes 48% of the change; the indirect impact of the construction (CON) sector is mainly in the non-metallic mineral products (MIN) sector, which contributes 48% of the change; and the indirect impact of the transportation (TRA) sector is mainly in the petroleum processing and coking (PPC) and electricity, gas, and heat production (EGH) sectors, which together contributes 43% of the change. These linkages, thus, constitute the main pathways through which industrial restructuring affects carbon emissions. However, the indirect economic growth seems to occur mainly in the services (SER) and primary extractive industries (EXT) sectors. For example, SER and EXT contribute 33% and 14% of the indirect economic growth of CON’s industrial restructuring, respectively. In addition, industrial restructuring can also have a key environmental–economic impact through industrial linkages within the restructured sector.

Moreover, significant differences exist among the primary sources of environmental and economic indirect impacts of restructuring in specific sectors. These linkage pathways related to energy-intensive sectors emerge as the primary sources of indirect changes in carbon emissions, but not of indirect changes in economic growth. For most sectors, such as services (SER), construction (CON), and equipment manufacturing (EQU), the indirect economic growth primarily occurs in services (SER) or extractive industries (EXT), while the indirect carbon emissions primarily occur in specific upstream energy-intensive linkages sectors, such as transportation (TRA), non-metallic mineral products (MIN), and metal processing (MEP).

3.3. Assessing Sectoral Characteristics from an Industrial Correlation Perspective

Figure 7 provides a comprehensive assessment of the environmental and economic impact characteristics of each sector in industrial restructuring from an industrial correlation perspective. In this index assessment system, the TCI provides information on the environmental–economic efficiency characteristics of sectors, including both direct and indirect impacts, while the ESM provides information on the extent to which economic development relies on sectoral development. Based on 2017 data as input, the results show that the TCI for each sector ranges from 0.33 tCO₂/CNY 10⁴ to 2.2 tCO₂/CNY 10⁴. In contrast, sectors such as non-metallic mineral products (MIN), metal processing (MEP), and petroleum processing and coking (PPC) exhibit higher TCIs, indicating that industrial restructuring in these sectors has a greater impact on emissions at the same level of economic impact, thus demonstrating lower environmental benefits in the assessment. Meanwhile, sectors such as services (SER), agriculture (AGH), and equipment manufacturing (EQU) have lower TCIs, which means that industrial restructuring in these sectors shows higher environmental benefits in the assessment. For ESM, values for each sector range from 0.27 to 0.85. In contrast, the equipment manufacturing (EQU), services (SER), and light industry (LIG) sectors have larger ESM values, implying that economic development is more coupled to the development of these sectors.

In industrial restructuring, focusing on key sectors is conducive to accelerating the low-carbon process, which requires a rational differentiation of industrial groups. We extracted four types of sectors. Category A sectors are characterized by a high TCI and ESM, including the metal processing (MEP) and electricity, gas, and heat production (EGH) sectors. From the industrial correlation perspective, these sectors have lower environmental benefits but a high coupling with economic development. Category B sectors are characterized by a high TCI and low ESM, including transportation (TRA), construction (CON), petroleum processing and coking (PPC), and non-metallic mineral products (MIN). From the industrial correlation perspective, these sectors have lower environmental benefits and are also weakly coupled to economic development. Category C sectors are characterized by a low TCI and high ESM, including services (SER), equipment manufacturing (EQU), light industry (LIG), and chemical products (CHE). From the industrial correlation perspective, these sectors have higher environmental benefits and are highly coupled with economic development. Category D sectors are the remaining low TCI and low ESM sectors, including agriculture (AGH) and extractive industry (EXT). The different characteristics of the various industry classifications will imply distinctly different industrial restructuring policies.

It should be obvious that the robustness of this characteristic over time cannot be confirmed based on only one year of the dataset. Revealing the evolutionary patterns of sectoral characteristics can also provide valuable information for industrial restructuring. Consequently, we collected data from several time points for measurement, including 2007, 2012, 2017, and projections for 2030, and the results are shown in Figure 7 (See

Table A4. Economic demand for Guangdong for the base year and its projected growth rates.

Sector	Economic Demand in 2017 (Billion CNY)	Economic Demand in 2030 (Billion CNY)	2017–2030 (%)
AGR	444.79	668.73	3.2%
EXT	60.78	42.49	−2.7%
LIG	2663.89	4435.57	4.0%
PPC	154.10	84.69	−4.5%
CHE	848.03	1412.03	4.0%
MIN	258.72	531.87	5.7%
MEP	279.66	543.46	5.2%
EQU	6631.59	16,979.61	7.5%
EGH	261.00	800.18	9.0%
CON	2308.89	4746.55	5.7%
TRA	399.75	963.34	7.0%
SER	4658.22	12,668.61	8.0%

Notes: Projections of the growth rate of economic demand are guided primarily by historical economic data, policy planning, and other relevant studies. According to the 14th Five-Year Development Plan of Guangdong, the average annual growth rate of the province’s GDP will be maintained at around 5% during the 14th Five-Year Plan period [58]. We use this as a benchmark for Guangdong’s average annual growth rate of total economic demand. Based on Guangdong’s historical population data, we used the Leslie matrix difference model to predict Guangdong’s future population data. We further project the average annual growth rate of demand in sectors such as AGR and SER based on future economic development plans and demographic data [58,59,61]. Rapid contraction of exports is the main reason for the rapid decline in economic demand for PPC during 2012–2017, which will continue under pressure to reduce emissions from carbon tariffs and current carbon mitigation policies. The demand growth rate of PPC is further forecasted [58]. The demand growth rate projections for LIG and CHE are referenced to the Light Industry Growth Stabilization Work Program issued by government departments [62]. The urbanization process previously generated a construction boom, leading to a rapid growth in demand for CON and MIN. It is clear that this growth rate will slow down due to factors such as population and development patterns [38,63]. Future growth cannot be inferred simply by relying on past changes; thus, we use the predictions of previous researchers as the projection rate [38]. As the major raw material production sector for machinery and equipment manufacturing, the demand for MEP will continue to exist in the context of China’s manufacturing-based economy. Thus, we refer to Guangdong’s future GDP growth rate forecast to further predict the demand growth for MEP. Guangdong’s 14th Five-Year Development Plan has proposed that the value added by high-tech manufacturing industry will account for 33% of the value added by above-scale industry by 2025, and the operating income of the strategic emerging industry clusters will increase by over 10% annually [58,64]. Based on this, we predict the economic demand growth rate of EQU. The TRA’s economic demand growth rate projections refer to the national and Guangdong planning outlines for a comprehensive three-dimensional transportation network [65,66]. Using 2017 as the base year, ${\alpha }_{\mathrm{i}}$ is assumed to be 1, indicating a fixed correlation among all sectors derived from the IOA [38].

and Figure A1 and Figure A2 for more prediction information). Realizing the “Peak Carbon” goal is currently the main task of low-carbon development in various regions of China, with profound impacts on the development patterns of regional industries and economies [34,52]. The current industrial restructuring is also oriented to this goal. Therefore, we have focused here on projecting the ESM in 2030. It can be found that the TCIs of various sectors have experienced varying degrees of decrease over time, with EQU experiencing the most significant decrease of 40%. This rate far exceeds the national rate of carbon intensity decline over the same period, which is largely attributed to Guangdong’s relentless efforts to improve energy efficiency and the utilization of clean energy such as wind power and photovoltaics [3]. However, the ESM across sectors shows a significantly differentiated evolutionary trajectory as the economic transformation. For example, the ESMs of SER (26%), TRA (45%), and MEP (33%) sectors have experienced sustained growth during 2007–2017, while the ESMs of PPC and EXT have experienced sustained declines of 61% and 53%, respectively, during 2007–2017 (Figure 5). The projections for 2030 further indicate that the coupling of economic development with CON, MIN, and PPC will weaken significantly in the coming decade, with their ESMs experiencing a significant decline. Overall, for different years of data inputs, EQU, SER, and LIG consistently demonstrate a high TCI and low ESM, MEP consistently demonstrates a high TCI and ESM, and TRA, PPC, and MIN consistently demonstrate high TCI and low ESM characteristics.

4. Discussions

4.1. Environmental–Economic Impacts of Sectoral Restructuring

In formulating restructuring policies, it is crucial to consider the indirect impacts arising from industrial correlation. Our findings indicate that the indirect effect is far more important than the direct effect of industrial restructuring on carbon emissions in sectors such as equipment manufacturing (EQU) and construction (CON). This may be because these sectors have stronger industrial linkages with other sectors, and their industrial chain activities consume large amounts of embodied energy [33,41]. Previous scholars have made similar findings when assessing the carbon mitigation potential of sectors such as services and construction [37,38,56]. These findings underscore that neglecting the impact of industrial linkages will somewhat underestimate the carbon pressure associated with industrial restructuring. In particular, many regions are currently striving to enhance the economic status of the service sector to realize industrial upgrading, and it is necessary for policymakers to take additional carbon reduction measures to control indirect carbon emissions, among which, industry chain management seems to be an effective choice [17].

However, we found that industrial restructuring also results in significant indirect economic growth. In the case of EQU and CON, industrial restructuring functions as a stimulus to economic growth mainly through industrial correlations. Strengthening industrial correlation in practice also helps to unlock the potential of industrial restructuring in promoting economic growth. Moreover, our results further reveal a clear asymmetry in the composition of the environmental and economic impacts of industrial restructuring. This asymmetry can be attributed to the remarkable disparities in chain organization patterns across industries, with some sectors preferring to use energy-intensive industries as intermediate inputs [57]. This further requires policymakers to comprehensively assess the environmental and economic impacts of each sector during industrial restructuring. Our assessment suggests that focusing solely on direct production tends to overestimate the environmental and economic benefits of sectors such as equipment manufacturing (EQU) and construction (CON) and underestimate the environmental–economic benefits of sectors such as metal processing (MEP) and nonmetallic mineral products (MIN). In particular, the former are customarily considered as cleaner production sectors and the latter as carbon-intensive sectors in direct production assessments.

In the correlation decomposition of indirect effects, our results further suggest that industrial restructuring affects carbon emissions and economic growth through only a few key linked pathways. This is mainly because those downstream sectors have greater correlation coefficients with specific energy-intensive and high-value-added sectors upstream. For example, CON has a higher consumption coefficient for MIN and EQU for MEP (

Table A1. Consumption coefficient matrix in 2017 (column indicates the supply sector).

Sector	AGR	EXT	LIG	PPC	CHE	MIN	MEP	EQU	EGH	CON	TRA	SER	Average
AGR	0.15	0.01	0.20	0.01	0.07	0.03	0.02	0.02	0.01	0.03	0.04	0.02	0.05
EXT	0.03	0.38	0.07	0.49	0.10	0.32	0.25	0.10	0.30	0.22	0.08	0.02	0.20
LIG	0.31	0.04	0.68	0.04	0.16	0.16	0.09	0.11	0.04	0.13	0.08	0.09	0.16
PPC	0.02	0.05	0.03	0.33	0.10	0.06	0.04	0.03	0.05	0.06	0.15	0.02	0.08
CHE	0.17	0.10	0.33	0.08	0.98	0.34	0.12	0.22	0.09	0.22	0.07	0.09	0.23
MIN	0.01	0.10	0.03	0.04	0.04	0.34	0.03	0.07	0.03	0.47	0.01	0.01	0.10
MEP	0.06	0.06	0.19	0.04	0.17	0.18	1.51	0.62	0.06	0.13	0.04	0.05	0.26
EQU	0.09	0.10	0.26	0.07	0.22	0.23	0.28	1.09	0.14	0.29	0.11	0.12	0.25
EGH	0.06	0.15	0.14	0.12	0.16	0.24	0.19	0.13	1.21	0.21	0.15	0.07	0.24
CON	0.00	0.01	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.02	0.01	0.01	0.01
TRA	0.04	0.05	0.08	0.08	0.10	0.08	0.07	0.08	0.05	0.14	0.28	0.08	0.09
SER	0.15	0.22	0.35	0.32	0.40	0.36	0.29	0.34	0.25	0.45	0.48	0.43	0.34
SUM	1.10	1.26	2.38	1.62	2.50	2.34	2.89	2.81	2.23	2.38	1.51	1.00	2.00
Average	0.09	0.10	0.20	0.13	0.21	0.20	0.24	0.23	0.19	0.20	0.13	0.08	0.17

). These results provide more detailed knowledge on the management of indirect carbon emissions and economic growth from industrial restructuring so that stakeholders can focus on managing these critical linkages with measures such as energy efficiency improvements and intermediate input management.

In addition, we also find a clear difference in the primary composition of indirect environmental and economic impacts resulting from sector-specific industrial restructuring. The results show that some linkage pathways associated with energy-intensive sectors emerge as the primary sources of indirect changes in carbon emissions, but not of changes in economic growth. This is mainly due to the significant disparities in the emission costs and economic profitability of these key linked sectors [57]. In industrial restructuring, it would be more effective to reduce carbon emissions while promoting economic growth by reducing the inputs associated with energy-intensive sectors and increasing the inputs associated with services. This considerably unleashes the potential of industrial restructuring in achieving sustainable development. Feasible measures include cleaner sourcing and product substitution of energy-intensive intermediate products, as well as the promotion of knowledge-based and production-based services in manufacturing [16,33].

4.2. Key Sectors and Measures for Reconciling the Challenges of Economic Growth and Carbon Mitigation

By extracting key sectors through the indicator system, differentiated industrial policies can be formulated for different key sectors, further indicating the direction of industrial restructuring [16,41]. Specifically, the following key sectoral categorizations could be included.

(1) Key sectors seeking a low-carbon transformation. Such sectors should include electricity, gas, and heat production (EGH), metal processing (MEP), and transportation (TRA). In Industrial restructuring, these sectors have a greater impact on emissions at the same level of economic impact, thus demonstrating poor environmental–economic performance. Meanwhile, these sectors are also strongly correlated with current economic development. They are the main linked sectors for sectors such as EQU and SER, such that Guangdong’s economy, which is dominated by manufacturing and services, is dependent on the expansion of these sectors. Previous studies have also emphasized the critical role of these sectors in achieving energy conservation and emission reduction, arguing that their production scale should be controlled [8,14,33,34]. However, our assessment further emphasizes their critical role in economic development. Consistently, Dong et al. [25] also found that current economic growth is highly coupled with transportation and manufacturing. For these sectors, a low-carbon transition through technological upgrading and process innovation may be a more desirable choice. These sectors can promote the use of more clean energy. For example, the hydrogen-reduced iron process is vigorously developed in MEP, the installed share of clean energy rapidly increases in EGH, and more electric vehicles and even hydrogen-fueled vehicles are used in TRA. Energy policies should be complemented by industrial restructuring policies. Such strategies would strongly contribute to the rationalization of the current industrial structure and improvement of the economic efficiency, thus promoting economic sustainability [4,19].

(2) Key sectors for production scale control. Such sectors should include construction (CON), non-metallic mineral products (MIN), and petroleum processing and coking (PPC). In industrial restructuring, these sectors are weakly correlated with economic development while exhibiting poor environmental–economic performance. Guangdong’s economic transition from an investment-oriented to a consumption-oriented economy and the advocated sustainable concepts of “high-quality development” and “new urbanization” would further reduce the demand for such industries [58,59]. Consistently, previous research has also argued that controlling the production scale of industries such as MIN and PPC can help to achieve the sustainable development goals of carbon emission reduction and economic growth [8,26,52]. We include CON on this basis. In industrial restructuring, such a strategy will also help to reduce carbon intensity and, thus, further fulfill the current emission reduction commitments [10,14].

(3) Key sectors for sustainable economic growth. Such sectors should include services (SER), equipment manufacturing (EQU), and light industry (LIG). The results show that these sectors have a high TCI together with a high or growing ESM. In industrial restructuring, these sectors are strongly correlated with current economic development while having excellent environmental–economic performance. They are the predominant sectors of economic demand, contributing over 70%. Meanwhile, the industrial linkages associated with these sectors are continuing to strengthen with the development of manufacturing and service-based economies, and the associated consumption coefficients are continuing to grow (see Table A1,

Table A2. Consumption coefficient matrix in 2012 (column indicates the supply sector).

Sector	AGR	EXT	LIG	PPC	CHE	MIN	MEP	EQU	EGH	CON	TRA	SER	Average
AGR	0.15	0.01	0.14	0.01	0.06	0.02	0.01	0.02	0.01	0.03	0.01	0.03	0.04
EXT	0.04	0.24	0.09	0.76	0.12	0.23	0.25	0.10	0.38	0.14	0.14	0.05	0.21
LIG	0.33	0.04	0.63	0.05	0.14	0.11	0.06	0.08	0.04	0.11	0.07	0.11	0.15
PPC	0.03	0.07	0.05	0.36	0.11	0.09	0.08	0.05	0.05	0.07	0.19	0.05	0.10
CHE	0.15	0.19	0.29	0.28	0.97	0.28	0.20	0.22	0.12	0.27	0.10	0.09	0.26
MIN	0.01	0.01	0.02	0.01	0.01	0.34	0.03	0.04	0.01	0.27	0.01	0.01	0.06
MEP	0.06	0.05	0.25	0.05	0.12	0.10	1.09	0.50	0.04	0.32	0.10	0.05	0.23
EQU	0.12	0.20	0.34	0.18	0.40	0.23	0.61	1.48	0.15	0.58	0.46	0.19	0.41
EGH	0.06	0.13	0.13	0.13	0.16	0.28	0.25	0.15	0.80	0.16	0.14	0.07	0.21
CON	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.06	0.00	0.00	0.01
TRA	0.04	0.12	0.09	0.11	0.10	0.11	0.10	0.09	0.09	0.10	0.22	0.06	0.10
SER	0.12	0.23	0.33	0.26	0.37	0.34	0.27	0.29	0.27	0.36	0.34	0.35	0.29
SUM	1.11	1.29	2.37	2.19	2.57	2.13	2.96	3.03	1.98	2.45	1.78	1.05	2.08
Average	0.09	0.11	0.20	0.18	0.21	0.18	0.25	0.25	0.17	0.20	0.15	0.09	0.17

and

Table A3. Consumption coefficient matrix in 2007 (column indicates the supply sector).

Sector	AGR	EXT	LIG	PPC	CHE	MIN	MEP	EQU	EGH	CON	TRA	SER	Average
AGR	0.17	0.01	0.26	0.01	0.07	0.02	0.02	0.04	0.01	0.04	0.02	0.04	0.06
EXT	0.04	0.33	0.12	1.00	0.21	0.34	0.69	0.24	0.43	0.22	0.23	0.05	0.32
LIG	0.20	0.03	0.64	0.03	0.15	0.09	0.06	0.15	0.03	0.09	0.07	0.10	0.14
PPC	0.04	0.19	0.07	0.36	0.16	0.15	0.20	0.11	0.20	0.10	0.28	0.03	0.16
CHE	0.10	0.05	0.24	0.06	0.97	0.15	0.12	0.37	0.06	0.16	0.09	0.11	0.21
MIN	0.01	0.04	0.02	0.03	0.02	0.42	0.04	0.05	0.02	0.24	0.02	0.01	0.08
MEP	0.02	0.03	0.06	0.03	0.06	0.05	1.00	0.38	0.03	0.16	0.06	0.03	0.16
EQU	0.10	0.16	0.30	0.18	0.31	0.25	0.58	1.54	0.21	0.47	0.39	0.18	0.39
EGH	0.05	0.11	0.13	0.13	0.20	0.30	0.22	0.18	0.75	0.32	0.07	0.05	0.21
CON	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.01	0.00
TRA	0.03	0.05	0.05	0.05	0.07	0.06	0.06	0.08	0.03	0.07	0.11	0.03	0.06
SER	0.10	0.14	0.27	0.17	0.34	0.28	0.26	0.35	0.22	0.29	0.34	0.29	0.25
SUM	0.85	1.14	2.18	2.06	2.56	2.13	3.25	3.49	2.00	2.15	1.68	0.93	2.04
Average	0.07	0.10	0.18	0.17	0.21	0.18	0.27	0.29	0.17	0.18	0.14	0.08	0.17

). Consistently, previous studies have also suggested that promoting the development of the service and equipment manufacturing industries will help realize sustainable economic growth and alleviate the employment pressure in the low-carbon transition [8,34,41,60]. In industrial restructuring, these sectors have a small impact on emissions at the same level of economic impact. Their development creates wealth and preserves the environment. In addition, a manufacturing boom in technology-intensive clean energy equipment, such as photovoltaics and nuclear power, will also support the clean energy transition in Guangdong. Overall, the implementation of such industrial policy will contribute to industrial structure upgrading and improve the overall operational efficiency of the economy, further forming a new engine of economic growth under the development theme of “Peak Carbon” [10,52].

4.3. Limitations and Future Research

The study provides some new insights but also exposes some limitations. First, IOA has unique advantages in studying industrial linkages but also faces the shortcoming of data lag. As the economic transformation continues, it is worthwhile to reevaluate the sectoral impacts by adopting the latest input–output tables in the future. Second, this study has deepened the linkage analysis on the basis of previous studies but has not investigated the specific industrial chain paths and transmission mechanisms of the linkage impacts. In the future, in-depth research can be conducted by combining models such as structural path analysis or subsystem analysis, so as to formulate more refined industrial restructuring policies [3,57]. In addition, it is worthwhile to further quantitatively assess the synergistic effects of clean energy utilization and industrial restructuring on future economic growth and carbon emissions based on models such as scenario analysis. Finally, this study has not considered the impact of regional cooperation. Manufacturing outsourcing reshapes the local industrial system, causing a redistribution of carbon emissions and economic growth. Effective industrial restructuring paths across regional cooperation efforts deserve further research in the future.

5. Conclusions

Employing an extended input–output framework, this study systematically assesses the direct and indirect impacts of industrial restructuring on energy-related carbon emissions and economic growth from the industrial correlation perspective in a multi-sectoral system in Guangdong and further develops an indicator assessment system to identify key sectors for targeted industrial restructuring strategies. The study helps to formulate industrial restructuring and carbon mitigation measures from the industrial linkage perspective, thereby realizing environmental–economic sustainable development goals more effectively. The study’s findings are as follows:

(1) Industrial restructuring in the equipment manufacturing (EQU), construction (CON), and light industry (LIG) sectors has a much more important indirect impact than direct impact on energy-related carbon emissions through industrial linkages. Industrial restructuring in sectors such as construction (CON) and metal processing (MEP) has a much more important indirect impact than direct impact on economic growth through industrial linkages.

(2) A clear asymmetry exists between the impact of industrial restructuring on carbon emissions and that on economic growth. The indirect impact of industrial restructuring in sectors such as equipment manufacturing (EQU) and services (SER) on carbon emissions is more significant than that on economic growth, with the carbon intensity of its indirect impact being much greater than that of its direct impact; whereas the indirect impact of industrial restructuring in sectors such as metal processing (MEP) and non-metallic mineral products (MIN) on economic growth is more significant than the impact on carbon emissions, with the carbon intensity of its indirect impact being much smaller than that of its direct impact.

(3) Industrial restructuring indirectly affects carbon emissions or economic growth mainly through a limited number of linked sectors, while the main linked pathways through which sector-specific industrial restructuring indirectly affects carbon emissions and economic growth are not consistent. Some linkage pathways related to energy-intensive sectors emerge as the primary sources of indirect changes in carbon emissions, but not of indirect changes in economic growth.

(4) From an industrial correlation perspective, services (SER), equipment manufacturing (EQU), and light industry (LIG) possess higher environmental benefits, while construction (CON), non-metallic mineral products (MIN), and metal processing (MEP) possess lower environmental benefits. In restructuring, electricity, gas, and heat production (EGH), metal processing (MEP), and transportation (TRA) are key sectors for pursuing a low-carbon transition, whereas construction (CON) and petroleum processing and coking (PPC) are key sectors for production scale control.

6. Policy Implications

Based on the findings, we propose targeted policy recommendations.

First, effective measures should be implemented to manage the indirect impacts of industrial restructuring on carbon emissions. Industrial restructuring and the mitigation of carbon emissions from energy-intensive industries should proceed concurrently, given the critical role performed by the energy-intensive linked industries in the indirect impacts. It is worth advocating energy-saving renovation, fuel switching, and outdated capacity elimination in energy-intensive industries such as iron and steel, cement, and electric power.

Second, some linkage pathways associated with energy-intensive sectors emerge as the primary sources of indirect changes in carbon emissions, but not of changes in economic growth. Meanwhile, the linkage pathways associated with the services sector show the opposite. Efforts should be made to reduce the consumption of intermediate products associated with energy-intensive sectors and to stimulate intermediate inputs associated with services during industrial restructuring. The measures encouraged include promoting the cleaner sourcing of upstream energy-intensive products by downstream manufacturing through tax policies; enhancing resource efficiency in downstream manufacturing; promoting the substitution and remanufacturing of production raw materials in construction and manufacturing; and strengthening the linkages between manufacturing and production-based services, such as financial and knowledge services, thereby reinforcing the role of upgrading industrial services in economic growth. Governments should also create an enabling environment through tax and incentive policies.

Finally, differentiated restructuring measures should be implemented across sectors to achieve sustainability goals more efficiently. For sectors such as services (SER) and equipment manufacturing (EQU), support should be provided for the development of technology-intensive and competitive segments, guiding their orderly expansion to achieve high-quality development. For sectors such as metal processing (MEP) and electricity and heat production (EGH), a low-carbon transition should be realized through improved production processes and energy-efficient technologies. Finally, for sectors such as non-metallic mineral products (MIN) and petroleum refining (PPC), it is important to strengthen the elimination of excess capacity while strictly controlling new additions, so as to achieve a low-carbon transformation of the economy.