Decomposition analysis of China's chemical sector energy-related CO 2 emissions: From an extended SDA approach perspective

Abstract

The excessive growth of carbon emissions (CO₂E) from industrial energy use not only exacerbates global warming and severely curbs the sustainable development of the economy and society. As a high energy-consuming sector second only to the fossil energy division, the power and heavy division, China's chemical industry should have received more attention for its CO₂E. However, there are limited literatures on energy CO₂E in China's chemical sector at present. Based on this fact, this current paper uses the energy utilization approach, the input–output analysis approach, and the extended structural decomposition method to evaluate the energy-related CO₂E of China's chemical sector from 2007 to 2017. (1) China's chemical sector energy-related CO₂E showed a trend of first growth and then a slow decline, demonstrating that the rapid growth of China's chemical sector energy-related CO₂E has been effectively controlled; However, it should be noted that the chemical industry is still dominated by high-CO₂E energy-related CO₂E at the current stage. (2) Input structure and energy intensity effects have a reduced influence on the growth of energy-related CO₂E in China's chemical sector. This is due to upgrading energy use technology and optimizing the generalized technology progress rate in the chemical sector. (3) Energy structure and final demand effects have encouraged the growth of the chemical sector's energy-related CO₂E. It shows that the industrial system's demand for chemical products is constantly expanding, and the chemical products still have the characteristics of high carbonization. Also, the chemical sector's supply-side energy utilization structure has not been significantly enhanced.

Keywords

Chemistry sector energy input–output analysis SDA

Introduction

The emission reduction of greenhouse gases, mainly carbon dioxide (hereafter CO₂E), is a common challenge facing the world.^1,2 Since the Paris Agreement put forward the goal of limiting the rise in the world temperature to well underneath 2°C, which is exceed initial-industrial stages, and need significant more struggle to minimize the temperature upsurge to 1.5°C exceeding pre-industrial stages, how to reduce CO₂E to effectively curb the trend of global warming has become the hot issue.^3,5 China is the largest CO₂E emitter country in the world.^6,7 From 2007 to 2012, China assumed more than 50% of the global increase in CO₂E, and in 2019, it accounted for 27.9% of global CO₂E. ^8,10 It can be seen that China's accomplishment in carbon reduction (zero carbon neutrality) is very important for the global temperature control target of 1.5°C.¹¹ As the world's biggest developing nation, China's government is also taking the initiative to undertake CO₂E reduction responsibilities. In 2020, Chinese President (Xi Jinping) addressed the very important speech/debate at the Universal Deliberation of the 75^th Conference of the UN General Assembly, stating that CO₂E should peak before 2030 and struggle to attain zero CO₂E neutrality goal before “2060.”^12,15

Based on the analysis of China's CO₂E from the perspective of industrial structure, we found that the chemical sector has become a high-energy-consuming sector second only to the fossil energy sector, the electric power sector, the transportation sector, the heavy industry and the construction sector.^16,19 Specifically, China's chemical sector produced 588 million tons of carbon emissions from energy consumption in 2019, accounting for about 6% of the total CO₂E. Meanwhile, we need to note that as an essential pillar sector of any economy,²⁰ the chemical sector has a sizeable economic aggregate, a lengthy industrial hub, and tens of thousands of goods linked to clothing, food, building, and transportation. Therefore, it is of extreme significance to preserving the constancy of the manufacturing and supply chains, encouraging the construction of ecological evolution, and guaranteeing people's lives. Therefore, the implementation of energy-related CO₂E control in the chemical sector is not only encouraging the realization of the “dual carbon” goal,^21,22 but is also of great significance in promoting low-carbon development and green transformation of the chemical industry.

However, to effectively and accurately control the excessive growth of energy carbon emissions from the chemical industry, the Chinese government must face two problems. First, the Chinese government must objectively understand the historical situation of China's chemical sector energy-related CO₂E. This essential work is conducive to depicting the actual situation of China's chemical sector energy-related CO₂E and providing objective data support for subsequent emission reductions. Second, the Chinese government should scrutinize the aspects that stimulate the growth of China's chemical industry (hereafter CN-CI) energy-linked CO₂E and find the factors that promote or inhibit the growth of the CN-CI energy-related CO₂E. This essential work provides practical and corresponding policy suggestions for the effective follow-up implementation of the chemical sector emission reduction.

Moreover, it should be noted that the chemical area energy-related CO₂E can be analyzed from two aspects. First, the supply point of view, the energy-related CO₂E of the chemical sector energy directly comes from the combustion of energy utilization on the supply point of view. Second, from the demand perspective, the demand and consumption of the chemical sector products also indirectly lead to the generation of energy-related CO₂E. Therefore, the chemical industry's supply and demand-sides energy-related CO2E can be effectively unified through the input–output analysis, which is helpful to a more inclusive investigation of the energy-linked CO₂E of the chemical zone.

This current research uses the energy consumption approach, the input–output technique, and the extended structural decomposition model (SDM) to scrutinize the energy-related CO₂E of China's chemical zones between 2007 and 2017. Likened with the previous published literature review, the novel novelty of this current research is emulated in the following three motivational points. Primarily, from the viewpoint of research techniques, this paper extends the existing standard SDM, which can scrutinize the contribution portion of individual industries in the demand effects from the industry level. Secondly, from the viewpoint of research content, this paper not only analyzes the structural features of CO₂E from the chemical sector but also investigates the factors affecting the change in CO₂E from the supply and demand sides. Thirdly, from the viewpoint of research, this current research uses the input–output method related theory to combine the demand and supply sides CO₂E of the chemical zones, which is beneficial to a more inclusive and systematic analysis of the chemical industry energy CO₂E.

This paper is helpful in objectively describing the historical energy-related CO₂E of CN-CI, then pointing out the achievements and shortcomings of CO₂E control in the previous chemical industry, and provides essential data support for subsequent chemical industry CO₂E reduction. At the same time, this research analyzed the factors affecting CO₂E from the supply and demand sides, which provided a theoretical analysis basis and practical guidance for CO₂E control from the perspective of input–output analysis. In general, this research not only further enriched the existing literature on CO₂E in the chemical sector in theory but also provided a guiding scheme for the chemical sector to effectively control the excessive growth of energy-related CO₂E from both the demand side and the supply side at the practical level. In addition, the follow-up arrangement of this paper is as follows. Section “Literature review” is the literature review. Section “Data presentation and model specification” explains the data presentation and model specification. Section “Results analysis and discussion” presents the results analysis and discussion, and the last section is conclusions and revelations.

Literature review

Since CO₂E from energy utilization in industrial systems are the critical source of CO₂E in China, controlling the excessive growth of CO₂E from energy utilization in industrial systems has become the primary goal of CO₂E control in China. However, it should be noted that compared with the existing published research literature on CO₂E of industrial systems, we can find that the existing research literature on CO₂E of industrial systems is mainly concentrated in the fossil-related energy zones,^23,24 the electric power and heating zones,^25,26 the transport zones,^26,29 the construction zones,^28,30,31 the heavy manufacturing zones,³² and other sectors.³³ Compared with the above industries/sectors, little literature analyzes the energy-related CO₂E of China's national chemical zones.^34,35 Existing research literature on CO₂E from the chemical industry mainly focuses on the following two aspects.

Calculation of CO₂E from the chemical sector

Li et al.³⁶ scrutinized China's characteristics and carbon flow from 2008 to 2012. The conclusions displayed that 75.12% of overall CO₂E flow is mainly linked to the terminal sub-division chemical industry. Moreover, Gong et al.³⁷ examined the circulation of China's export embodied carbon and its numerous industrial areas. The conclusions displayed that the industries with the maximum export CO₂E are metal products manufacturing, textile, machinery and equipment industrial and chemical sector so on. On the other hand, Lu et al.³⁸ used the model optimized by element swarm maximization algorithm to forecast the upcoming CO₂E of the heavy chemical zones over the period of 2017Y–2035Y. The outcomes presented that CO₂E from the heavy chemicals zones and their corresponding industries can peak under pre-determined mitigation circumstances. Furthermore, Zhang et al.³⁹ estimated the total CO₂E of China's national coal chemical zone. This study determined that the total CO₂E of China's coal-chemical sector in 2015 was 607 Mtons, calculating about/around 5.71% of China's whole CO₂E. Besides, Hou et al.⁴⁰ have investigated the 28 industries based on China's based input–output table and direct CO₂E over the period of 1992Y–2012Y. The outcomes demonstrate that the chemical industries zone is situated on the middle side of the industrial hub. Ma and Song⁴¹ used the industrial data to calculate and analyze CO₂E of chemical industry. The results showed that the carbon emissions of China's chemical industry showed a rapid growth trend from 2010Y to 2019Y. It is concluded that the task of reducing CO₂E of the chemical industry under the goal of carbon neutrality is very difficult. Liu et al.⁴² analyzed the carbon emission proportion of six high-consumption industries and proposed that the contribution of chemical raw materials and products to the CO₂E of energy-intensive industries is relatively large, which is an important factor leading to the increase of CO₂E brightness.

Influencing factors of CO₂E from the chemical sector

Based on provincial panel data, Lin and Long³⁴ explored the persuading factors of CO₂E variations in CN-CI by using the index decomposition analysis. The results showed that per capita output, energy structure, industrial economic scale, and energy intensity are the key aspects affecting energy structure from the chemical industries. Moreover, Zhao et al.⁴³ studied the CO₂E and emission minimization of ethylene construction in the CN-CI. The outcomes disclosed that policy administration significantly minimizes CO₂E by improving the typical technological level of CN-CI and shifting to a zero-carbon path. Recently, Huang et al.⁴⁴ empirical study evaluates the impacts of carbon detention and sequestration, carbon detention, application and sequestration, and besides, more technical factors on CO₂E reduction and economic growth of the Coal CN-CI. Wang et al.⁴⁵ has researched focus on China's economy input–output table from 2002Y to 2015Y and constructed 6 embodied CO₂E flow networks covering 30 sectors by using complex network theory. Ma and Song⁴¹ analyzed the path of CO₂E reduction in the chemical industry. They found that the emission structure effect is the main factor to slow down the growth of CO₂E in CN-CI, indicating that improving the emission structure of the chemical industry can effectively slow down the growth of CO₂E.

Research gap

After summarizing and analyzing the existing literature on CO₂E of the CN-CI, it can be determined that although some scholars have measured the CO₂E of the CN-CI, few scholars have analyzed the structural features of CO₂E of the CN-CI. Therefore, the research content is conducive to analyzing the characteristics of the chemical sector's energy-related CO₂E structure from the outlook of energy utilization structures (EUSs) and offers an objective basis for the follow-up chemical sector to control CO₂E from the supply-relate energy variations. Regarding the influencing factors of energy CO₂E in the chemical sector, some scholars have used exponential decomposition models and econometric analysis methods to scrutinize the influence of factors, for example, energy intensity and per capita income on the CO₂E of the CN-CI. However, few scholars use the input–output estimation technique and the structural decomposition method to scrutinize the effect of aspects for instant input structure and final demand effects (FDEs) on the CO₂E of the CN-CI. These methods are beneficial to unifying the supply and demand sides of CO₂E of the CN-CI and can more comprehensively analyze the CO₂E of the CN-CI.

This study intends to add to the existing literature as follows. Firstly, from the viewpoint of research techniques, this paper extends the existing standard SDM, which can scrutinize the contribution portion of the individual industry to the demand effects from the industry level. Secondly, from the viewpoint of research content, this paper not only analyzes the structural features of CO₂E from the chemical sector but also investigates the factors affecting the change in CO₂E from the supply and demand sides. Thirdly, from the viewpoint of research, this current research uses the input–output method related theory to combine the demand and supply sides CO₂E of the chemical zones, which is beneficial to a more inclusive and systematic analysis of the chemical industry energy CO₂E.

Data presentation and model specification

The data explanation

In this current research, the data are summarized into three main groups. The primary group denotes China's 42 industry input and output tables through 2007Y, 2010Y, 2012Y, 2015Y, and 2017Y. The second group states to China's 2008Y, 2011Y, 2013Y, 2016Y, and 2018Y energy use data for the CN-CI. The third group discusses the energy-linked CO₂E coefficients available in the IPCC database of Japan. data sources for these sources are summarized in Table 1 below.

Table 1.

Data explanation and sources.

Data type	Data sources
The data of China's input–output table	China's input–output database tables for “42” sectors in 2007Y, 2010Y, 2012Y, 2015Y, and 2017Y available by National Bureau of Statistics of China
The data on energy consumption	China chemical industry end energy utilization data for 2008Y, 2011Y, 2013Y, 2016Y, and 2018Y are available by the National Bureau of Statistics of China.
Carbon emission factor	CO₂E coefficients of various energy sources available by Japan IPCC

It is clarified that China's input and output (I–O) database tables are upgraded after each fifth year and the enlarged tables are upgraded every two to three years. There is no reliability in the extant research intermission. For the time being, to enhance the simplicity and objectivity of the demonstration of the outcomes of this research, we have merged the 42 departments/sectors into nine sub-divisions based on related references.^24,46

Model construction

Although the existing common econometric models can scrutinize the influence and significance of numerous factors on CO₂E from the chemical industry, it is difficult to give the specific quantitative influence results of various factors on CO₂E from the chemical industry, while the decomposition model can. The existing decomposition models can be divided into two groups one is exponential decomposition^47,48 and the other is structural decomposition.⁴⁹ Comparing the two models, the SDM has more stringent requirements on data, while the exponential decomposition model has relatively lower requirements on data.^29,50 However, compared with the exponential decomposition model, the SDM can be divided into direct and indirect factors, especially the indirect factors on the demand side.⁵¹ Based on the above discussion, we select the SDM based on input–output analysis.

Input and output estimation

Input–output (I–O) estimation is primarily through the constructing I–O databases tables, the I–O tables of appropriate database to build I–O models,^52,54 to examine correlated economic actions. Hence, the I–O table is the primary of I–O estimation, which is the foundation of the I–O estimation model. In the I–O table, a matrix of I–O is built on the data composed by the economic division, and mathematical estimation models of I–O are built regarding the equilibrium association.^47,48,55

Z = [Z_{i j}], Z_{i j} = \frac{Q_{i j}}{Q_{j}}

(1)

(\begin{matrix} Q_{1} \\ Q_{2} \\ ⋮ \\ Q_{i} \end{matrix}) = (\begin{matrix} Z_{11} & Z_{12} & \dots & Z_{1 j} \\ Z_{21} & Z_{22} & \dots & Z_{2 j} \\ ⋮ & ⋮ & ⋱ & ⋮ \\ Z_{i 1} & Z_{i 2} & \dots & Z_{i j} \end{matrix}) (\begin{matrix} Q_{1} \\ Q_{2} \\ ⋮ \\ Q_{i} \end{matrix}) + (\begin{matrix} W_{1} \\ W_{2} \\ ⋮ \\ W_{i} \end{matrix})

(2)

Z Q + W = Q

(3)

Q = (I - Z)^{- 1} W

(4)

Whereas the economic implication of

Z_{i j}

is the straight utilization of j division manufacture component invention to i division products, Z is a pair matrix collected of direct utilization measures amongst the total economic industry,

Q = [Q_{j}]

is overall the output column vector,

W = [W_{j}]

is overall demand column vector, I is the unit matrix.

A decomposition model of CO₂E structure in China's chemical sector

In the I–O table only one sector such as chemical sector of the 42 industries, and the CN-CI is divided/separated into 5 sub-industries according the database of “China Energy Statistics Yearbook”—plastic products, chemicals manufacturing, rubber products, chemical raw materials, chemical fiber manufacturing and, pharmaceutical manufacturing and—there is a necessity for consolidation. Thus, the energy final utilization (hereafter EFUs) of the CN-CI in the I–O table is the EFUs of these five sectors. The utilization of electric relate-energy does not straightly release CO₂E, so the EFUs here essentially refers to the final utilization of fossil fuel-based energy.

A_{o} = A_{o}^{T} + A_{o}^{P} + A_{o}^{H} + A_{o}^{M} + A_{o}^{N}

(5)

V_{o} = A_{o} \times a_{o}

(6)

Whereas,

A_{o}^{T}

denote the quantity/measure of the o EFUs in the chemical manufacturing industry and chemicals sector raw materials,

A_{o}^{P}

shows the measure of the o EFUs in the pharmaceutical manufacturing sector,

A_{o}^{H}

is the amount of the o EFUs in the chemical fiber manufacturing sector,

A_{o}^{M}

is the amount of the o EFUs in the rubber products sector,

A_{o}^{N}

is the amount of the o EFUs in the plastic products sector,

V_{o}

is the total amount of the o EFUs of the chemical sector, and

a_{o}

is the converted to standard coal (energy conversion factor).

V_{o}

is the o energy foundation in the CN-CI to be transformed into the amount of standard coal.

Furthermore, the energy-related CO₂E aspects has used in this current research are collected the data from “the bulletin of the IPCC database of Japan,” supposing that is the matrix of CO₂E aspects of every energy operation by the CN-CI is $F = [f_{k}]$ , the energy structure matrix (hereafter ESMs) of the CN-CI is $D = [d_{i k}]$ , and the energy intensity matrix (hereafter EIMs) of the CN-CI is $I = [i_{i}]$ . The ESMs and ESMs of the CN-CI are denoted as follows:

\begin{aligned} D = & (\begin{matrix} \frac{V_{11}}{\sum_{o = 1}^{m} V_{1 o}} & \frac{V_{12}}{\sum_{o = 1}^{m} V_{1 o}} & \dots & \frac{V_{1 o}}{\sum_{o = 1}^{m} V_{1 o}} \\ \frac{V_{21}}{\sum_{o = 1}^{m} V_{2 o}} & \frac{C_{22}}{\sum_{o = 1}^{m} V_{2 o}} & \dots & \frac{C_{2 k}}{\sum_{o = 1}^{m} V_{2 o}} \\ ⋮ & ⋮ & ⋮ & ⋮ \\ \frac{V_{i 1}}{\sum_{o = 1}^{m} V_{i o}} & \frac{C_{i 2}}{\sum_{o = 1}^{m} V_{i o}} & \dots & \frac{C_{i o}}{\sum_{o = 1}^{m} V_{i o}} \end{matrix}), \\ I_{p \times p} = & (\begin{matrix} \frac{\sum_{o = 1}^{m} V_{1 o}}{W_{1}} & 0 & \dots & 0 \\ 0 & \frac{\sum_{o = 1}^{m} V_{2 o}}{W_{2}} & \dots & 0 \\ ⋮ & ⋮ & ⋱ & ⋮ \\ 0 & 0 & \dots & \frac{\sum_{o = 1}^{m} V_{p o}}{W_{p}} \end{matrix}) \end{aligned}

(7)

Where

V_{i o}

states to the use of category/type o energy utilization in sector i of the ESMs (transformed to standard coal), CO₂E

A_{i}

states in industry i denotes to the total of the numerous (assume there are m types) energy-related CO₂E utilized in that industry, as articulated in the model below.

A_{i o} = f_{k} \times V_{i o} = \frac{A_{i o}}{V_{i o}} \times V_{i o}, i = 1, 2, \dots, p; o = 1, 2, \dots, m

(8)

A_{i o} = \frac{A_{i o}}{V_{i o}} \times \frac{V_{i o}}{V_{i}} \times \frac{V_{i}}{Q_{i}} \times Q_{i}, i = 1, 2, \dots, p, j = 1, 2, \dots, m

(9)

A_{i o} = \frac{A_{i o}}{V_{i o}} \times \frac{V_{i o}}{V_{i}} \times \frac{V_{i}}{Q_{i}} \times (I - Z)^{- 1} W_{i}, i = 1, 2, \dots, p, j = 1, 2, \dots, m

(10)

A_{i} = \sum_{o = 1}^{m} A_{i o}

(11)

Whereas,

A_{i o}

denotes to CO₂E from the utilization of type o energy in industry i. The CO₂E of China's economy c sector (the chemical sector) can be specified as follows:

A_{c} = \sum_{o = 1}^{m} \frac{A_{c o}}{V_{c o}} \times \frac{V_{c o}}{V_{c}} \times \frac{V_{c}}{Q_{c}} \times (I - Z)^{- 1} W_{c} = F D I L W

(12)

Moreover, the difference in CO₂E amongst the years before and after China's economy c sector (the chemical sector) can be articulated as follows:

Δ A_{c} = F D_{t} I_{t} L_{t} W_{t} - F D_{t - 1} I_{t - 1} L_{t - 1} W_{t - 1}

(13)

\begin{aligned} Δ & A_{c} = \underset{Energy Structure Effects (ESEs)}{\underset{⏟}{\frac{1}{2} (F_{t} I_{t} L_{t} W_{t} - F_{t - 1} I_{t - 1} L_{t - 1} W_{t - 1}) Δ D}} + \underset{Energy Intensity Effects (EIEs)}{\underset{⏟}{\frac{1}{2} (F_{t} D_{t} L_{t} W_{t} - F_{t - 1} D_{t - 1} L_{t - 1} W_{t - 1}) Δ I}} \\ + \underset{Input Stucture Effects (ISEs)}{\underset{⏟}{\frac{1}{2} (F_{t} D_{t} I_{t} W_{t} - F_{t - 1} D_{t - 1} I_{t - 1} W_{t - 1}) Δ L}} + \underset{Final Demand Effect s (FDEs)}{\underset{⏟}{\frac{1}{2} (F_{t} D_{t} I_{t} L_{t} - F_{t - 1} D_{t - 1} I_{t - 1} L_{t - 1}) Δ W}} \end{aligned}

(14)

Δ A_{c} = Δ A_{D} + Δ A_{I} + Δ A_{L} + Δ A_{W}

(15)

Δ A_{D} = \frac{1}{2} (F I_{t} L_{t} W_{t} + F I_{t - 1} L_{t - 1} W_{t - 1}) Δ D

(16)

Δ A_{I} = \frac{1}{2} (F D_{t} L_{t} W_{t} + F D_{t - 1} L_{t - 1} W_{t - 1}) Δ I

(17)

Δ A_{L} = \frac{1}{2} (F D_{t} I_{t} W_{t} + F D_{t - 1} I_{t - 1} W_{t - 1}) Δ L

(18)

Δ A_{U} = \frac{1}{2} (F D_{t} I_{t} L_{t} + F D_{t - 1} I_{t - 1} L_{t - 1}) Δ W

(19)

Besides, the influence of the energy structure of CN-CI on CO₂E is

Δ A_{D}

, the effect of energy intensity of CN-CI on CO₂E is

Δ A_{I}

, the influence of input structure of CN-CI on energy-related CO₂E is

Δ A_{L}

, and the influence of overall demand of CN-CI on CO₂E is

Δ A_{U}

. Furthermore, we scrutinize the effect of every industry on the final demand effect. The formula is as follows:

FD P_{j} = \frac{Δ W_{j}}{\sum_{j = 1}^{m} Δ W_{j}} \times Δ A_{W}

(20)

Δ W_{j} = W_{t, j} - W_{t - 1, j}

(21)

whereas,

FD P_{j}

is the effect of every industry in the overall demand influence,

Δ W_{j}

is the demand variation of industry j,

W_{t - 1, j}

denote to demand effects of j at time

t - 1

, and

W_{t, j}

is the demand of sector j at time t.

Results analysis and discussion

CO₂E measurement conclusions for the chemical sector

The above models and data can be applied to evaluate the CO₂E of CN-CI from 2007Y to 2017Y as revealed in Figure 1 below.

Figure 1.

CO₂E relates to CN-CI from 2007Y to 2017Y.

Figure 1 displays that the energy-related CO₂E of the CN-CI in 2017Y, 2015Y, 2012Y, 2010Y, and 2007Y were 608.732, 759.983, 477.732, 426.890, and 379.225 Mtons, correspondingly. As an outcome, energy-related CO₂E from the CN-CI demonstrated an increasing movement from 2007Y to 2015Y, whereas energy-related CO₂E from the CN-CI showed a descending movement from 2015Y to 2017Y. The above discussion can also be much better clarified, and we can see that the energy utilization of the CN-CI in 2007Y–2015Y transformed into standard coal in order of 12,874.215, 14,327.160, 15,841.277, and 24,638.96 Mtons by categorizing the energy utilization of the CN-CI over the period of 2007Y to 2017Y. This also clarifies the rising movement in energy CO₂E from the CN-CI from 2007Y to 2015Y. Energy utilization in the CN-CI in 2017Y was 2028.6296 Mtons of standard coal, while it sustained to grow as associated to 2015Y. The portion of uncooked coal utilization in the energy consumption structure (hereafter ECSs) of the CN-CI declined significantly around 62% in 2015Y to 56% in 2017Y, although uncooked coal was a more carbon energy cause, conclusion in a slight reduction in energy CO₂E from the CN-CI in 2017Y linked to 2015Y.

In the production activities of the CN-CI industry, energy is involved in economic production as an intermediate factor input. Among them, energy consumption at the end of the chemical industry production process will produce the unintended output of energy-related CO₂E. By searching the available relevant literature, it can be found by Gong et al.³⁷ and Hou et al.⁴⁰ did not quantify the CO₂E of the CN-CI, although they analyzed the CO₂E of the CN-CI. Such as, Comparing and discussing the research results in this part with the previous literature, it can be searched by Gong et al.³⁷ and Hou et al.⁴⁰ did not quantify the CO₂E of the CN-CI, although they analyzed the CO₂E of the CN-CI. Based on the first-hand database of “23” coal-related chemical enterprises, Zhang et al.³⁹ concluded that the total CO₂E of China's coal chemical sector in 2015Y was 607 Mtons. By comparing this result with that of this study, it can be found that the calculation result in this paper is slightly higher. This is because the calculation result of Zhang et al.³⁹ is based on 23 coal chemical enterprises and does not include all chemical enterprises. Therefore, the calculation result in this paper is slightly higher. In addition, the research interval in this paper is also significantly unlike from that of Zhang et al.³⁹ The research interval in this paper is 2007Y–2017Y. This part of the research results can not only provide data support for the subsequent investigation of the influencing factors of CO₂E change in the CN-CI in this paper, but also relatively objectively and directly pointed out that within the sample range, although the trend of CO₂E growth of CN-CI has been efficiently minimized, but there is a huge gap to future emission reduction.

Investigation of CO₂E structure in the chemical sector

According to the upper mention models and data, the CO₂E structure of CN-CI from 2007Y to 2017Y can be measured as revealed in Figures 3 and 7 below.

By investigating in Figures 2 to 6, the ECSs, there, we can define the following two outcomes. On the one side, above 50 percent of the energy-related CO₂E of CN-CI was yet produced by the burning of coke and uncooked coal. But, the CO₂E from uncooked coal use described for proximately 50.1%, 61.4%, 61.4%, 68.4% and around 64% of the overall energy-linked CO₂E in 2007Y, 2010Y, 2012Y, 2015Y and 2017Y correspondingly; The CO₂E from coke use described for 18.3%, 12.5%, 16.6%, 14.3% and 18.5% of the overall energy-linked CO₂E in 2007Y, 2010Y, 2012Y, 2015Y, and 2017Y individually. By categorization the energy use data of the chemistry sector over the period of 2007Y–2017Y, it can be determined that the use of diesel, oil, and uncooked coal are estimated for above 10% and 40% of the overall energy consumption in the total study series. For the time being, coke and raw coal as high-CO₂E related energy and natural gas are less-CO₂E related energy. In the above paragraphs, there are two reasons/points mutually clarified. On the other side, on the opposing, the usage of natural gas is considered as clean energy (pollution-free), less than around 5% of the overall energy-linked CO₂E. By categorizing the energy use database in the range from 2007Y to 2017Y, it is clarified that the usage of LNG has been described for less great than nine percent of the utilization of fossil-based energy and primary related energy in the total investigation duration. For the present, the usage of natural gas as a kind of less-CO₂E energy, its CO₂E measure of less burning. The above paragraphs have explained two points mutually instance, natural gas use accounts for a comparatively minor quantity of overall energy-linked CO₂E.

Figure 2.

CO₂E structure of the CN-CI in 2007Y.

Figure 3.

CO₂E structure of the CN-CI in 2010Y.

Figure 4.

CO₂E structure of the CN-CI in 2012Y.

Figure 5.

CO₂E structure of the CN-CI in 2015Y.

Figure 6.

CO₂E structure for the CN-CI in 2017Y.

By investigating in Figures 2 to 6, we can explain that the portion of CO₂E of high CO₂E-related energy instance, LNG, coke, and uncooked coal, showed as a rising trend/movement from 2007Y to 2017Y. In particularly, uncooked coal enlarged from proximately 50.1% in 2007Y to 64% in 2017Y but Coke enhanced from 18.9% in 2007Y to 18.5% in 2017Y; LNG enlarged from around 0% in 2007Y to around 4.1% in 2017Y. In the above paragraph examination, it can be determined that the ECSs of the CN-CI on the supply-side have not been enhanced from 2007Y to 2017Y. And it is determined that maximum-carbon CO₂E for instant coke and uncooked coal are still the essential factors of energy-linked CO₂E in the CN-CI sector. Yet, there is a large gap for the further move improvement of the energy use structure of CN-CI.

Comparing the research results in this part with previous literatures, it can be found that although some researchers have measured CO₂E from the chemical sector^37,39,40 and Lu et al.³⁸ predicted the future CO₂E of the heavy and chemical sector, but few scholars analyzed the structural characteristics of CO₂E of CN-CI. Therefore, this research is of great significance in accurately controlling the CO₂E growth of the chemical sector from energy types. Based on this, we not only measure the CO₂E of the chemical sector, but also further refine and analyze the structural characteristics of the chemical sector CO₂E in the sample period. Through the analysis of the CO₂E structure of the CN-CI, it can be determined that the ECSs of the chemical sector from the supply point of view it has not been significantly improved/upgraded over the period of 2007Y to 2017Y, and 2015Y to 2017Y is the only period in which the access of the chemical sector from the supply side has been improved within the sample assortment. In the current situation, maximum CO₂E energy such as coke, diesel and uncooked coal is still the key factor of the CO₂E of CN-CI, so there is still a large gap for enhancing the energy use structure of CN-CI in the future.

Investigation of emissions reduction aspects in the chemical industry

Based on the above SDM and I–O model, there are four-factor influences on the growth of energy-linked CO₂E in CN-CI can be attained, and the conclusions are stated in Table 2 as follows.

Table 2.

Stimulus of four aspects on the growth of energy CO₂E from the CN-CI (Unit: Tons).

Years	ESEs	EIEs	ISEs	FDEs
2007Y–2010Y	310.443	−6052.934	1754.291	5573.887
2010Y–2012Y	305.954	−3318.409	−1385.190	7301.059
2012Y–2015Y	862.801	3356.277	3164.376	6379.731
2015Y–2017Y	−875.148	2834.129	−7167.919	−18,312.688
2007Y–2017Y	461.185	−3992.592	−3790.759	1856.892

According to Table 2, the time evolution demonstrates the following four important opinions.

First, in 2007Y–2010Y, the FDEs, input structure effects (ISEs), and energy structure effects (ESEs) were the important factors of the growth-related energy CO₂E in the CN-CI. Particularly, the “huge carbonization” of the overall product imitates the maximum energy-related input features of CN-CI. The ISEs follow the general technical development; the outcomes demonstration that the outline of “extensive” economic development of CN-CI in 2007Y–2010Y have not been upgraded, and the ECSs to the supply side of CN-CI still has not been upgraded. EIEs have a diminishing conclusion on the growth-related energy CO₂E in CN-CI. Particularly, the EIEs deliberate the energy usage (each unit) of output and the outcomes demonstrate that the energy-related technology of CN-CI enhanced significantly through of 2007Y–2010Y, which means that the EIEs play a significant role in diminishing the CO₂E.

Second, the FDEs and ESEs were also important factors for the growth of energy CO₂E in the CN-CI in 2010Y–2012Y. This outcome also displays that in this current research range, the CN-CI as a final demand product (FDPs) still has the features of “high carbonization” (burning, heating), although CN-CI to the supply-side EUSs still has not been upgraded. The EIEs and ISEs have a diminishing consequence on the growth of energy-related CO₂E in CN-CI. This outcome also demonstrates that the energy-correlated technology of CN-CI has been upgraded significantly in this current investigation duration, and the generalized technological development of the feedback of ISEs has also been enhanced.

For the third point of view, in 2012Y–2015Y, all four factors (i.e., FDEs, ISEs, ESEs, and EIEs) were the motive force for the growth-related energy CO₂E from the CN-CI. This also displays that in this study period: the CN-CI as the FDPs still has maximum carbonization features; the chemical sector to the supply-side EUSs has not been upgraded and the CN-CI energy-related technology still has not been significantly enhanced the CO₂E; the overall technical development of the response ISEs has not been optimized.

For the Fourth point of view, in 2015Y–2017Y, the EIEs will collaborate to the growth of energy CO₂E in CN-CI. It also demonstrations that the energy-related technology of CN-CI has not been efficiently enhanced in this study range. The FDEs, EIEs, and ISEs have a diminishing influence on the growth of energy CO₂E in CN-CI. These results also demonstrate that in this study range, the EUSs of CN-CI has been upgraded; the overall technical evolution of the response to the ISEs has been enhanced; the overall product “high carbonization” replicates that CN-CI has the features of maximum energy input has also been efficiently improved.

Overall, the ISEs and EIEs have the effect of minimizing the CO₂E in CN-CI over the period of 2007Y to 2017Y, while the FDEs and ESEs will have the influence of helping the growth of China's chemical industries energy-related CO₂E. By comparing the above mention research outcomes with existing published literature, it can be concluded that the ISEs and EIEs have minimized the environmental degradation on the growth of CN-CI energy-linked CO₂E, while the FSEs and ESEs have a promotion effect on the growth of CN-CI energy-related CO₂E. It indicates on that the supply-side, the ECSs of CN-CI hs not been significantly enhanced, and the chemical sector products still have the characteristics of huge carbonization. Next step, we will additionally refine/analyze the ESEs and EIEs of the CN-CI, because the ISEs and FDEs of the generalized technological improvement are described by the I–O database table, so this current research cannot be decorative on the analysis and discussion.

Based on the analysis of existing literature, it can be found that some scholars have discussed and studied the influencing factors of CO₂E from China's chemical sector. Such as, Lin and Long's³⁴ research outcomes demonstrate that industrial economic scale and per capita output, EIEs and ESEs are the key features affecting the change in CO₂E from the chemical industry. But, the quantitative influence of EIEs and ESEs on the growth of CO₂E from the chemical industry is not given because the literature adopts an econometric model. Our study gives quantitative results of the impact. Zhao et al.⁴³ showed that policy management/regulation has played a crucial role in minimizing CO₂E by improving the typical technological level of CN-CI and shifting to a zero carbon naturality path. Our conclusion is consistent with that the ISEs reflecting generalized technological progress have a preventive consequence on the growth of CO₂E from CN-CI. At the same time, we further quantify the quantitative reduction of technological progress on the growth of CO₂E in the chemical industry. In addition, through the review of existing literatures on CO₂E from the chemical industry, few scholars have analyzed the impact of FDEs on the growth of CO₂E from the chemical industry, which is also a relatively new point in the research content of this paper.

It should be noted that, compared with index analysis and econometric analysis, the biggest advantage of structural decomposition analysis is that it can analyze the influence of demand-side indirect factors on CO₂E of CN-CI.²⁸ Meanwhile, in this paper, we extend the existing standard SDM, and the extended SDM can not only analyze the impact of the final demand effect on the CO₂E from CN-CI, but also further refine the analysis of the contribution of each demand-side industry to the growth of CO₂E from CN-CI, which provides basic theoretical analysis and practical guidance way for the CN-CI to precisely and effectively control the growth of CO₂E from the demand-side industry level. In general, from the point of view of emission reduction mode, the final demand effect, and input structure effect and energy utilization intensity effect are important means to realize long-term emission reduction in the CN-CI, and the optimization of energy utilization structure can attain emission minimization in a short period.

Investigation of energy structure effect

For the above mention models and data, the EUSs on the supply side of CN-CI for 2007Y–2017Y can be attained as revealed in Table 3 below.

Table 3.

Energy utilization structure of CN-CI.

Kinds of energy	2007Y	2010Y	2012Y	2015Y	2017Y
(Raw coal)	43.7888%	54.2648%	54.9454%	62.6660%	56.9702%
(Cleaned coal)	2.5648%	2.4475%	2.6138%	4.3047%	0.0000%
(Other washed relate coal)	0.5240%	0.5321%	0.4073%	0.2234%	0.5112%
(Coke)	17.1935%	11.8974%	16.0122%	14.1175%	17.6881%
(Coke oven relate gas)	0.0456%	0.0887%	0.1348%	0.1321%	0.1624%
(Blast furnace relate gas)	0.0000%	0.0589%	0.0628%	0.0537%	0.0445%
(Converter relate gas)	0.0000%	0.0008%	0.0016%	0.0020%	0.0009%
(Other gas)	0.0841%	0.1234%	0.0450%	0.0582%	0.1050%
(Other coking)	2.4661%	2.1114%	3.1570%	2.9748%	3.4688%
(Crude oil product)	2.4432%	2.1884%	0.2689%	1.5622%	0.0154%
(Gasoline product)	1.0301%	0.9852%	0.7422%	0.4071%	0.3975%
(Kerosene product)	0.0661%	0.0589%	0.0369%	0.0235%	0.0183%
(Diesel oil product)	2.0990%	2.5834%	1.0582%	0.5835%	0.5982%
(Fuel oil product)	4.9929%	1.7861%	1.0410%	0.9273%	0.4341%
(LPG product)	0.9515%	1.3377%	1.4091%	1.7067%	4.7118%
(Refinery gas product)	0.8666%	0.6957%	1.0319%	0.4959%	0.5245%
(Other petroleum products)	18.7820%	13.7389%	10.9418%	4.8838%	6.0811%
(Natural gas, etc.)	2.1016%	1.2248%	1.5444%	0.9752%	1.1312%
(LNG)	0.0000%	3.8758%	4.5456%	3.9025%	7.1369%

According to Table 3 from the point of view of energy supply side of CN-CI, find out the utilization of high CO₂E-related energy such as coke and uncooked coal displays a rising movement. This also clarifies that the EUSs of Table 3 does not have a plummeting influence on the development of energy-related CO₂E in CN-CI. While the share of utilization of less-CO₂E energy basics such as liquid natural gas use is on the upsurge, the share of utilization of maximum carbon energy foundations for instance coke and uncooked coal has enlarged slightly. It is vital to point out that the ESEs of 2015Y–2017Y in the 2007Y–2017Y research duration have had a less emission effect. According to Table 3, the portion of uncooked coal utilization minimized significantly from 62.7% to 56.97% in 2010Y–2012Y, while the portion of LNGs utilization enhanced significantly, from 3.90% to 7.13%. Compared with Lin and Long,³⁴ which pointed out that ESEs influenced the growth of CO₂E from the CN-CI, but lacked to further analyzed the changing characteristics of numerous energy varieties in the total ECSs. Therefore, the results in this part of our research elaborate on the effect of energy varieties on the ESEs, which is conducive to the CN-CI to optimize the ECSs more accurately and effectively from the supply side energy variations.

Investigation of energy intensity effect

For the above models and data, the energy intensity (EIEs) coefficient of CN-CI for 2007Y–2017Y is exposed in Figure 7 (per unit (10,000 yuan's/standard coal)).

Figure 7.

Energy intensity coefficient of CN-CI from 2007Y to 2017Y.

Figure 7 displays that the EIEs measure of CN-CI has been viewing a descending trend in the current research period excluding 2012Y–2017Y. This also clarifies that, in 2012Y–2017Y, the EIEs has been a plummeting CO₂E of CN-CI. It can also be described that the EIEs coefficient diminished the most in the 2007Y–2010Y periods and moreover, the EIEs coefficient has more declined in the period of 2010Y–2012Y. In 2012Y–2015Y outcomes also in the strongest minimization effect of the EIEs while less reduction influence of the EIEs in 2007Y–2010Y. Joint with the above paragraph conclusions, it can be determined that the diminution of EIEs measure is the cause why the EIEs play an important role in minimizing CO₂E in CN-CI. Some scholars presented out that EISs had a preventive effect on the growth-related CO₂E from CN-CI.³⁴ However, no literature gave the parameters of energy use efficiency or energy intensity of CN-CI in each year. The results in this part explained the inhibitory effect of EIEs on CO₂E growth of CN-CI, and also provided objective data reference for subsequent optimization of energy intensity of CN-CI.

Analysis of final demand effect

The contribution portion of individually industry in the final demand effect (FDEs) from 2007Y to 2017Y can be obtained by using the upgraded SDM, as shown in Figure 8 below.

Figure 8.

Contribution share of individually industry in final demand effect.

Figure 8 shows that the contribution shares of individually industry in the FDEs are significantly different in the sample range. Specifically, agriculture, chemical sector, heavy industry, and so on are the key contributors driving the FDEs to play a promoting role, while the service industry is the only part that has a negative contribution to the FDEs. For this point, we compared the input–output table of China in 2007Y and 2017Y, and find that the service industry's input–output demand for chemical sector products in 2017Y shows an obvious trend of decline compared with 2007Y, which is consistent with the conclusion of this paper. Hence, we concluded that the focus of controlling CO₂E growth on the demand side of chemical industry products still needs to focus on chemical industry, heavy industry and agriculture industry.

Conclusions and revelations

The CO₂E from industrial energy use not only exacerbates global warming and severely curbs the sustainable development of the economy and society. As a high energy-consuming sector second only to the fossil energy division, the power and heavy division, China's chemical industry should have received more attention for its CO₂E. However, there is limited literature on energy CO₂E in China's chemical sector. Based on this fact, this current paper uses the energy utilization approach, the input–output analysis approach, and the extended structural decomposition method to evaluate the energy-related CO₂E of China's chemical sector from 2007 to 2017.

The outcomes demonstrate that in the study sample data duration: (1) CN-CI energy-related CO₂E showed a trend of first growth and then a slow decline, demonstrating that the rapid growth of CN-CI energy-related CO₂E has been effectively controlled; However, it should be noted that the chemical industry is still dominated by high-CO₂E energy-related CO₂E at the current stage. The EIEs and ISEs have a reduced influence on the growth of energy-related CO₂E in CN-CI. This is due to upgrading energy use technology and optimizing the generalized technology progress rate in the CN-CI. (3) The ESEs and FDEs have encouraged the growth of the chemical sector's energy-related CO₂E. It shows that the industrial system's demand for chemical products is constantly expanding, and the chemical products still have the characteristics of high carbonization. Also, the chemical sector's supply-side energy utilization structure has not been significantly enhanced.

The following policy suggestions can be attained in this paper: (1) Accelerate energy structure adjustment and optimize the ECS of the chemical industry. Specifically, China should rationalize the ECS from the macro level, continuously reduce the proportion of high-carbon energy sources such as raw coal and coke, and increase the proportion of low-carbon energy sources such as natural gas; at the same time, actively and vigorously develop new energy sources such as wind power, hydropower, and solar energy. (2) Attach importance to the innovation of energy science and technology, and steadily promote the technological upgrading of the chemical industry. Specifically, fiscal, tax, monetary, and other policies can be used to continuously increase the innovation of energy utilization technology, effectively reduce the energy intensity of the chemical industry; At the same time, it is necessary to actively promote the technological renewal and upgrading of the chemical industry, introduce domestic and foreign advanced chemical industry technology and management methods, and effectively improve the production efficiency of the chemical industry. (3) Rationally plan and regulate the chemical industry's scale to avoid wasting resources caused by excessive expansion. Specifically, effectively allocating chemical products in a market-oriented way can make it easier for enterprises with “low consumption of resources” to obtain chemical products, indirectly forcing industries with “high consumption of resources” to upgrade and innovate. Therefore, China should focus on adjusting and optimizing its industrial structure, actively promote the development of low-carbon industries, and speed up the transformation and upgrading of traditional industries. Specifically, China should reasonably control the excessive growth of demand for chemical products from “resource-intensive” sectors, such as the chemical sector, construction sector, and heavy sector. Also, China should actively develop low-carbon sectors, such as the service and light sector to effectively control the excessive growth of CO₂E from the CN-CI.

There are still some limitations in this paper that need to be solved in the future. First of all, this research is based on the database of China's economy related input–output table under the non-open economy, and imports and exports are not included in the category of factors affecting the CO₂E of the chemical zones. In the future, we can establish an analysis framework for influencing factors of CO₂E from the chemical industry in the open economy and further analyze the effects of import and export on carbon emissions changes in the chemical industry. Secondly, the paper analyzes the impact of input structure effect reflecting generalized technological development on CO₂E from the chemical industry but fails to further analyze its mechanism or transmission mechanism. In the future, econometrics can further analyze the mechanism and effect of scientific and technological innovation on chemical industry emission reduction.

Footnotes

Acknowledgments

We thank the reviewers and editors for suggestions.

Author Contributions

All authors participated to the analysis and developed the manuscript.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Social Science Fund of China, the Project supported by the Education Department of Hainan Province, the National Natural Science Foundation of China, the Hainan Provincial Philosophy and Social Science 2021 Planning Project, the Hainan Provincial Natural Science Foundation of China (grant number 22CJY060, Hnky2022-11, 71963009, HNSK(JD)21–16, 722RC635).

ORCID iDs

Yang Yu

Atif Jahanger

Daniel Balsalobre-Lorente

Magdalena Radulescu

Tangyang Jiang

Yang Yu is an associate professor at the School of Economics, Hainan University, China. Her current research interests lie in the areas of environmental economics, sustainable development, climate change economics and energy economics. She is currently editor and reviewer in various Web of Science indexed journals. Her work has been published in the Journal of Cleaner Production, Resources policy, Sustainable Production and Consumption, and Economic Analysis and Policy, among other high-ranking journals.

Jie Hou is an undergraduate student in the School of Internet at Anhui University, China. His current research focuses on resources and environmental economics.

Atif jahanger worked as an assistant professor in the School of Economics at Hainan University, China. His fields of interest are sustainable development, energy economics, and environmental economics in which the relationship among macro-variables of the economy, energy consumption, and environmental indicators are investigated. He serves as guest editor for Sustainability (MDPI), Frontiers in Environmental Sciences, Frontiers in Energy Research, and Environmental Sciences and Pollution Research journals. Furthermore, he serves as academic editor in PLOS ONE journal and Associate Editor in Cogent Economies and Finance; SN Business & Economics, and Environmental Quality Management journals. He has published numerous papers in International Referred Journals including Renewable Energy, Energy, Resources Policy, Sustainable Development, Sustainable Production and Consumption, Technology in society, Energy Economics and Gondwana research journals, etc. He is serving as a referee of numerous well-reputed journals worldwide.

Xiang Cao is an associate professor at the School of Economics, Hainan University, China. His current research interests lie in the areas of environmental economics, sustainable development, International Trade and environment. Currently, he is working on Sustainable Development of Chinese Economy.

Daniel Balsalobre-Lorente worked as a professor at the Department of Applied Economics I, University of Castilla-La Mancha, Spain. Also, he worked at Department of Management, Faculty of Economics and Management, Czech University of Life Sciences Prague, Czech Republic. His expertise is in public finances, energy economics, economic growth, environment, tourism and innovation processes. He is currently editor and reviewer in various Web of Science indexed journals. He has more than 100 publications in ScienceDirect, Wiley and Springer etc.

Magdalena Radulescu holds PhD degree and is currently affiliated with University of Pitesti. She is Full Professor at the Department of Finance, Accounting and Economics, Faculty of Economic Studies, University of Pitesti, Romania. She is PhD Supervisor (Economics) at the University Lucian Blaga of Sibiu, Romania. Her research interests are (i) Macroeconomics: un-employment, economic growth, FDI, economic convergence in EU (ii) Environmental change, energy economics and policy (iii) Time series & panel data: Linear-nonlinear modelling, cointegration, estimation. She serves as Topic Editor for Energies, and Guest Editor for Journal of Risk and Financial Management, Frontiers in Environmental Sciences, Frontiers in Energy Research, ESPR and Utilities Policy Journals. She has completed many reviewer assignments for Energies, ESPR, Utilities Policy, Energy and Environment, Sustainability, Journal of Hospitality and Tourism Technology, International Journal of Managerial Finance, Economic Research, Eastern European Economics.

Tangyang Jiang is an assistant professor at the School of Internet at Anhui University, China. His current research interests lie in the areas of environmental change, energy economics and policy.

Nomenclature

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Decomposition analysis of China's chemical sector energy-related CO 2 emissions: From an extended SDA approach perspective

Abstract

Keywords

Introduction

Literature review

Calculation of CO2E from the chemical sector

Influencing factors of CO2E from the chemical sector

Research gap

Data presentation and model specification

The data explanation

Model construction

Input and output estimation

A decomposition model of CO2E structure in China's chemical sector

Results analysis and discussion

CO2E measurement conclusions for the chemical sector

Investigation of CO2E structure in the chemical sector

Investigation of emissions reduction aspects in the chemical industry

Investigation of energy structure effect

Investigation of energy intensity effect

Analysis of final demand effect

Conclusions and revelations

Footnotes

Acknowledgments

Author Contributions

Declaration of conflicting interests

Funding

ORCID iDs

Nomenclature

References

Calculation of CO₂E from the chemical sector

Influencing factors of CO₂E from the chemical sector

A decomposition model of CO₂E structure in China's chemical sector

CO₂E measurement conclusions for the chemical sector

Investigation of CO₂E structure in the chemical sector