Expansion Direction Selection of the Coal-to-Olefin Industrial Chain in Inner Mongolia from the Value Chain Perspective

Abstract

Inner Mongolia is a key region for China’s clean energy production and a demonstration base for modern coal chemical industry production. However, modern coal chemical industry development in this region faces many problems, such as complex downstream product structures, low value-added products, and severe environmental pollution. With limited future coal allowance in the coal chemical industry, the scarce available coal will likely be allocated to fields with strong product competitiveness to enhance industrial chain value. Given this, the Inner Mongolia coal-to-olefin industrial chain is selected to explore its strategic expansion directions. An improved value chain accounting method is proposed to account for the value of each segment of the coal-to-olefin industrial chain in Inner Mongolia from 2011 to 2022. The improved value chain accounting method is then combined with system dynamics to construct a model for selecting the expansion directions of the coal-to-olefin industrial chain based on the value chain perspective. Finally, the differences in the value of each future expansion direction are analyzed through scenario simulation and comparison. The most important result is that the higher the extension of the coal-to-olefin industry chain in Inner Mongolia, the higher the value of the industry chain. Carbon punishment is the most important factor affecting the value of the industry chain, and enterprises and governments should increase investment in innovation and renewable energy. Accordingly, this study provides a decision-making method for selecting the optimal expansion direction of the modern coal chemical industrial chain in Inner Mongolia.

1. Introduction

Driven by China’s coal-rich, oil-poor, and gas-poor fossil resource endowment and the continuous strong consumption of downstream petrochemical products, the modern coal chemical industry has developed rapidly into an important supplement to the petrochemical industry [1,2]. In recent years, although the production scale of the coal chemical industry has been continuously expanding, overall, the production of olefins from petroleum is still in the main position. China’s dependence on foreign oil has reached over 70%, and domestic chemical products are greatly affected by fluctuations in international oil prices. At present, the international situation is turbulent, international oil prices are unstable, and the high dependence on foreign oil seriously threatens China’s energy security. China has abundant coal resources, and the development of coal-to-olefin plays an important role in reducing oil dependence.

The modern coal chemical industry often features complex categories, long industrial chains, and diverse downstream products (Figure 1). To facilitate its stable development, China has strictly controlled its production capacity and strengthened clean and efficient coal usage, aiming for high-end, diversified, and low-carbon development.

With limited future coal allocation to the coal chemical industry, the scarce available coal will be distributed to fields with higher product competitiveness to fulfill its complementary role and strengthen the entire chain. Although new coal-to-olefin projects are rarely approved, coal-to-olefin production is expected to remain a key focus due to the overcapacity and relatively limited product competitiveness in other bulk chemical industries. Hence, continuously promoting the high-quality development of the coal-to-olefin industry is of great significance to saving valuable petroleum resources, satisfying the growing demand for petrochemical consumption, and safeguarding China’s national energy security.

As of the end of 2022, China’s total coal(methanol)-to-olefin production capacity is about 17.72 million tons/year. However, the downstream end-products have seen less development, rendering this industrial chain relatively shorter. As a region with rich coal resources, Inner Mongolia has the largest annual coal-to-olefin production capacity in China. Nevertheless, selecting the optimal expansion direction of the coal-to-olefin industrial chain for greater benefits still lacks proper guidance.

Recent research on the coal chemical industrial chain mainly focuses on industrial chain upgrading and optimization. Cheng et al. have carried out extensive theoretical discussions on the concept and enhancement of industrial chain-supply chain modernization [3]. Yang et al. discussed the future trends, influencing factors, and implementation paths for industrial chain restructuring [4]. Scholars have also modified the well-established value-added model for agriculture based on techno-economics to develop a value-added model for coal chemical products of the coal chemical industrial chain [5]. This value chain model was subsequently applied to the research on the coal chemical industrial chain in Yulin City, Shaanxi Province, where the influence mechanism of industrial chain expansion in coal enterprises was studied, and a suitable industrial chain expansion path was proposed [6]. Meanwhile, this industrial chain cannot be expanded endlessly, and the degree of expansion is a factor worthy of comprehensive consideration, which is often analyzed with industrial chain optimization [7]. For example, the coal chemical industrial chain is most sensitive to the value of the entire coal industrial chain [8]. Hamed believes it is also important to develop moderately according to market dynamics, thus preventing the industry from losses [9].

For this reason, Song constructed a competitiveness evaluation model of the industrial chain under the coal origin transformation and transformation to the end market scenarios and systematically studied the competitiveness of the coal power and coal chemical industrial chain in Xinjiang [10]. Recent research on the modern coal chemical industry has focused on macro-development. Chen Yang summarized the modern coal chemical industry in Xinjiang [11], while Nie et al. reviewed the development status and strategies of the modern coal chemical industry in various Chinese provinces [12]. However, by reviewing the relevant research on the industrial chain, it can be seen that scholars’ research focuses on upgrading and optimizing the industrial chain, and there is relatively little research on the direction of industrial chain expansion. The practical research on industrial chain extension is lagging behind.

System dynamics, as an important system thinking and practical simulation tool, has strong applicability and flexibility, and is widely used in the research of the coal chemical industry chain. Cao et al. (2019) proposed a CO₂ emission estimation model from the perspective of direct costs and developed a system dynamics (SD) model to simulate CO₂ emission reduction scenarios throughout the entire lifecycle of a green coal supply chain. The system dynamics model includes production subsystems, transportation subsystems, storage subsystems, and consumption subsystems [13], but does not consider the value space of the coal supply chain and to some extent ignores the economic benefits of the industrial chain. Zhang Wenhui (2022) used the system dynamics method to study the multidimensional evaluation of modern coal-to-oil projects. The research field was limited to coal-to-oil projects, without conducting comprehensive comparisons and without considering the extension of the industrial chain [14].

Zhang Jiawei (2023) used the Logarithmic Mean Divisia Index method and System Dynamics model to explore the factors affecting CO₂ emissions from modern coal chemical industry in Inner Mongolia, but did not break down the industrial chain for careful study [15]. Yang Guoli (2023) used the Py SD model to study the development of the energy industry in the Ningdong region, but did not consider the environmental impact [16]. Peng Shengjiang et al. (2023) applied system dynamics theory and studied the wind hydrogen coal coupling system in Xinjiang based on relevant data from 2010 to 2020. Environmental factors were considered, but it was not combined with the extension of the industrial chain, which cannot guide the government and enterprises in choosing the direction of industrial chain expansion [17].

In summary, existing literature has systematically studied the coal chemical industry chain from two aspects: industrial chain upgrading and industrial chain optimization, and proposed theories of value added and industrial chain extension. However, there are still some areas that need improvement: firstly, although many scholars have theoretically proposed to extend the coal chemical industry chain, they have not fully considered all the industrial chains of coal chemical industry, and cannot specifically focus on specific downstream industrial chains, which cannot solve the problems of whether extending the industrial chain can increase the value of the industrial chain and which expansion direction should be chosen in the process of extending the industrial chain. Secondly, the environmental issues caused by carbon emissions in the coal chemical industry have been criticized by experts and scholars. However, scholars rarely consider carbon emissions throughout the entire process when studying the extension of the industrial chain, and they do not address the issue of industrial chain extension when considering environmental issues. In short, existing literature rarely combines environmental factors with the extension of the industrial chain, and there is also a lack of refined investigation and analysis, as well as research on the extension of the coal chemical industry chain.

Therefore, the innovation of this article lies in that, unlike existing literature, the research object of this article is not the coal chemical industry (including traditional and modern coal chemical industries), but focuses on the coal-to-olefin branch in the modern coal chemical industry, which has the most economic benefits and capacity growth rate. Moreover, this article aims to address a series of issues related to the coal-to-olefin industry chain, such as whether extending the coal-to-olefin industry chain will necessarily enhance its value and what direction of extension should be chosen. Then, this article adopted an improved industry chain value accounting method and introduced environmental factors to account for the value of the coal-to-olefin industry chain under different extension scenarios. Subsequently, based on the SD model analysis framework from the perspective of the value chain, an SD model was constructed to select the value extension of the coal-to-olefin industry chain in Inner Mongolia, fully considering environmental factors and cost issues of the industry chain. This provides suggestions for how to choose the expansion direction of the coal-to-olefin industry chain in Inner Mongolia.

2. Coal-to-Olefin Industrial Chain Composition and Value Chain Accounting

2.1. Coal-to-Olefin Industrial Chain Composition

Within the coal-to-olefin industrial chain, coal is used as raw material to produce methanol through coal gasification syngas, which is then used to produce ethylene and propylene, finally producing polyolefin and other products [18]. The technical route is shown in Figure 2. The coal chemical industrial chain generally comprises the raw material supply chain, product processing chain, and product transportation and marketing chain. This study divides the coal-to-olefin industrial chain into six sub-industries according to its expansion directions and modes (raw coal mining, coal washing, coal-to-olefin industrial chain process products, and coal-to-olefin industrial chain product demand).

This study also divides the six industry chains into four expansions according to the expansion of each industry chain. From coal supply, the expansion of coal-to-methanol is 1, the expansion of coal-to-ethylene and propylene is 2, the expansion of coal-to-polyethylene and polypropylene is 3, and the expansion of coal-to-film is 4.

2.2. Coal-to-Olefin Value Chain and Accounting Method

The value chain concept was first proposed by Michael Porter. It covers the entire process from raw material procurement to the formation of final products or services, including value creation and transfer [19]. Focusing on the coal industry value chain, Ma proposed an industrial chain value decision model that uses profit as an indicator to measure the value of the coal chemical industrial chain, where the value was defined as the sum of spatial value and utilization value. Coal acquires added spatial value from the points of mining, transportation, and consumption and added utilization value as it is transformed into downstream products.

Under the goal of peaking carbon emissions and achieving carbon neutrality, the entire coal chemical industry is facing unprecedented pressure to reduce emissions. Compared to other coal chemical projects, the carbon emissions of coal-to-olefin products are as high as 11.1 tons of CO₂ [20], which puts them at a relatively high level in the entire industry. Therefore, the CO₂ emissions of coal chemical projects will inevitably be affected by local government carbon penalties, which will directly affect the value of the coal-to-olefin industry chain. When accounting for the value chain of the coal-to-olefin industry, we not only need to consider the two factors of spatial value and utilization value mentioned earlier, but also fully consider the factor of carbon emissions.

Therefore, this study proposes an improved industrial value chain accounting method, which divides the total profit of the coal-to-olefin industry value chain into the following six components: the profit of the end product, the profit of the intermediate surplus product, the profit of the by-product from the intermediate node product, the production cost (including fixed cost and variable cost), the transportation cost, and the carbon penalty cost.

The specific equations are as follows:

$$Z=Z_y-Z_c$$

(1)

$$Z_y=p_{i}x\times {\displaystyle \prod }_{j=1}^i{f}_j{t}_j+x{\displaystyle \sum _{i\in I}{p}_j}\left(1-t_j\right){\displaystyle \prod _{j<i}(1-f_{j-1}})+x{\displaystyle \sum }_{i\in I}{p}_{ik}{\displaystyle \prod }_{j<i}{t}_j\left(1-f_j\right)$$

(2)

$$Z_c={\displaystyle \sum _{i\in I,j\le I}{p}_c\times o_j}+c_{if}+c_{ig}+c_{il}$$

(3)

where $Z_y$ is the total income of developing the coal-to-olefin industrial chain, and $Z_c$ is the total cost of developing the coal-to-olefin industrial chain.

The improved value chain accounting method for the coal-to-olefin industry is expressed as follows:

$$Z_i=p_{i}x\times {\displaystyle \prod }_{j=1}^i{f}_j{t}_j+x{\displaystyle \sum _{i\in I}{p}_j}\left(1-t_j\right){\displaystyle \prod _{j<i}(1-f_{j-1}})+x{\displaystyle \sum }_{i\in I}{p}_{ik}{\displaystyle \prod }_{j<i}{t}_j\left(1-f_j\right)-{\displaystyle \sum }_{i\in I,j\le I}{p}_c\times o_j-c_{if}-c_{ig}-c_{il}$$

(4)

where ${\displaystyle {\displaystyle \sum }_{i\in I,j\le I}}{p}_c\times o_j$ is the carbon penalty for developing the coal-to-olefin industrial chain. The CO₂ emissions of the coal-to-olefin industry in Inner Mongolia are calculated [21]. Among them, the carbon emissions of the intermediate process product j (denoted as O_j) can be divided into energy-related CO₂ emissions and process CO₂ emissions. Energy-related CO₂ emissions equal energy consumption multiplied by the product energy-related CO₂ emission coefficient, and process CO₂ emissions equal actual product output multiplied by the product process CO₂ emission coefficient. The parameters are presented in

Table 1. Parameters of the proposed method.

Driving Factor	Meaning
Zi	The value of the industrial chain when the final product of the coal-to-olefin industrial chain is i.
fj	The conversion rate of intermediate product j in the production process.
pik	The average price of the by-product k generated by the production process when producing the final product i of the coal-to-olefin industrial chain.
pc	Unit price for carbon penalty.
Oj	Carbon emissions of intermediate process product j.
cif	The variable cost of the final product i in the coal-to-olefin industrial chain.
cig	The fixed cost of the final product i in the coal-to-olefin industrial chain.
cil	The transportation cost of final product i in the coal-to-olefin industrial chain
x	The raw coal required to produce the final product i of the coal-to-olefin industrial chain
i	The final product of the coal-to-olefin industrial chain
j	The intermediate process product of the industrial chain
k	The intermediate by-product of the industrial chain

.

2.3. Olefin Value Chain Accounting

This study employs an enhanced value chain accounting method to assess the value of each expansion path of the coal-to-olefin industrial chain in Inner Mongolia, and the findings are summarized in

Table 2. 2011 to 2022 Inner Mongolia coal-to-olefin industrial chain values under different expansion degrees (×10¹⁰).

Industrial Chains	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Methanol	0.451	0.511	0.488	0.505	0.258	0.168	0.223	0.334	0.295	0.175	0.313	0.572
Ethylene	0.990	1.123	1.233	0.929	0.634	0.765	0.919	1.035	0.943	0.915	1.157	1.343
Propylene	0.603	0.697	0.721	0.794	0.526	0.483	0.605	0.796	0.706	0.543	0.882	1.156
Polyethylene	1.571	2.123	2.614	2.241	1.910	2.229	2.849	2.856	2.583	2.689	3.447	3.755
Polypropylene	1.242	1.611	1.971	2.082	1.844	2.006	2.624	3.099	2.692	2.460	3.577	3.885
Film	1.761	2.254	2.775	2.429	2.208	2.505	3.141	3.348	3.092	2.977	3.702	3.882

and Figure 3. From 2011 to 2022, the values of the coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, polypropylene, and film industrial chains in Inner Mongolia differed significantly. Higher expansion degrees corresponded with greater chain values, while the polyolefin and olefin values broadened over time.

In terms of variation trend, the values of the coal-to-methanol, coal-to-ethylene, and coal-to-propylene industrial chains showed relatively small fluctuation amplitudes from 2010 to 2022. The values of the coal-to-polyolefin and coal-to-film industrial chains in Inner Mongolia exhibited fluctuating and growing trends. Not much difference was observed between the values of the industrial chains of the two expansions, and their variations trends were basically the same (Figure 3). Among them, the values of the coal-to-methanol, coal-to-ethylene, and coal-to-propylene industrial chains showed average annual growth rates of 2.18%, 2.81%, and 7.00%, respectively. The value of the coal-to-polyethylene industrial chain increased by 139% from 2011 to 2022, with an average annual growth rate of 7.77%. The value of the coal-to-polypropylene industrial chain showed an average annual growth rate of 10.92%. The 2022 value of the coal-to-polypropylene industrial chain ranked top among all industrial chains with different expansion degrees, with the highest average annual growth rate.

3. Methods

3.1. System Dynamics Methods

System dynamics (SD) is a method of studying information feedback systems, which can solve social, economic and other aspects of the problem, usually with the help of computer research, the system can be simulated many times, so as to make a prediction of the real world, which is conducive to the understanding of the development of the law of things. Causality diagrams and system flow diagrams are two important structural descriptive tools when modeling SD. Causality diagrams show the relationships between different variables, while system flow diagrams can assign definite values to these relationships, so that quantitative relationships between variables can be shown. Implicit in causality is the theory of feedback, which allows SD to be constantly cyclical. When using SD for research, the SD model is simulated according to the needs of the study, the relevant data sets are analyzed, and the characteristics of the system can be summarized by changing the values of some variables or parameters, and observing the differences in the results of the system simulation in different situations.

Coal-to-olefin industry chain product production, value changes, environmental impacts and other factors are interrelated and affect each other, together constituting a complex system with a feedback loop. In order to accurately reflect the dynamic characteristics of the research object, this paper constructs a SD model for the expansion direction selection of the Inner Mongolia coal-to-olefin industry chain based on the improved industry chain value accounting method and the SD model analytical framework under the value chain perspective. Through the SD method, this paper carries out dynamic simulation of the model and studies the coal-to-olefin industry chain from quantitative and qualitative perspectives.

3.2. SD Model Analysis Framework from the Value Chain Perspective

Based on prior research and the enhanced value chain accounting method, this study establishes an integrated framework for the value chain analysis of the coal chemical industry (Figure 4) [22]. This framework centers on profit and encompasses raw material supply, production, inventory, and pricing. It adjusts for external factors in revenue and cost accounting. Uniquely, it integrates economic, social, and environmental factors with market considerations, thereby shifting from static to dynamic analysis. SD optimizes the results through dynamic assumptions and enhances the rigor through scenario simulations.

3.3. System Boundary

Figure 5 shows the boundary of the coal-to-olefin industrial chain. This study divides the entire process into six aspects: raw coal mining, coal washing, upstream and downstream coal-to-olefin product production, product transportation, market demand, and CO₂ capture and storage, and their value changes are calculated [23]. The following assumptions are made so that the established SD model can reasonably and accurately simulate the actual value of the coal-to-olefin industrial chain. This study only considers the situation where the demand exceeds the supply of products in the coal-to-olefin industrial chain, i.e., all products produced can be sold. The CO₂ emissions during product transportation are not considered, and 10% of the CO₂ generated from coal mining and washing is included during CO₂ accounting. Meanwhile, one-third of the raw coal from coal mining is used to produce downstream coal chemical products.

3.4. Causal Circuit Diagram and Main Feedback

The SD method can describe actual complex problems using the causality diagram with interconnected feedback loops. According to the above analysis framework and value chain accounting method of the Inner Mongolia coal-to-olefin industry and industrial chain selection, the causality diagram of the coal-to-olefin industrial chain is constructed (Figure 6). A causality loop in the upstream coal-to-methanol industrial chain model of the coal-to-olefin industry is selected as an example for demonstration.

B1 loop: total profit of coal-to-methanol → (+) investment in fixed assets of coal-to-methanol → (+) fixed assets of coal-to-methanol → (+) capacity increase in coal-to-methanol → (+) cumulative capacity of coal-to-methanol → (+) production of coal-to-methanol → (+) inventory of coal-to-methanol → (+) inventory cost → (−) profit of methanol production → (+) total profit of coal-to-methanol.

The rest of the coal-to-olefin sub-industrial chains is the expansion of the coal-to-methanol industrial chain, and the causal relationship is the same as the coal-to-methanol industrial chain.

3.5. Stock and Flow Diagram and Main Equation Setting

Based on the above causality diagrams, the Vensim DSS 6.0 is used to map the stock and flow of the values of the coal-to-methanol and coal-to-film industrial chains (Figure 7 and Figure 8). The stock and flow diagrams of the values of industrial chains in the remaining expansion directions are the expansions of the coal-to-methanol and coal-to-film industrial chains. The expansion direction selection model for the Inner Mongolia coal-to-olefin industrial chain under the value chain perspective combines the industrial chain stock and flow diagrams in each expansion direction. The coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chain models contain 68, 83, 84, 101, 100, and 111 variables, respectively.

Due to space limitations, only some of the main model equations are listed. Taking the coal-to-methanol industrial chain as an example, other parameter settings are shown in Table 1.

(1)

u = u = DELAY FIXED (annual amount of raw coal mining, 1, 1)

(2)

x = IF THEN ELSE (Time = 2011, 78,900, 1)

(3)

Total profit of methanol production = (methanol production profit + washing by-product profit + methanol production by-product profit-CO₂ capture integration cost)/(1 + POWER (discount rate, n))

(4)

The total CO₂ emission of the coal-to-methanol production process = CO₂ emission of the mining process + CO₂ emission of the washing process + CO₂ emission of the methanol production process

(5)

Methanol conversion amount = 0.333 × (methanol allocation ratio × 0.365 × annual washing conversion amount × methanol conversion rate + actual utilization rate of coal-to-methanol capacity × cumulative capacity of coal-to-methanol + (0.290435 × methanol price − 163.882))

(6)

Coal-to-methanol production CO₂ emissions = (coal-to-methanol and energy-related CO₂ emissions + coal-to-methanol process CO₂ emissions) × technical progress impact factor

(7)

Coal-to-methanol and energy-related CO₂ emissions = coal-to-methanol energy consumption × coal-to-methanol and energy-related CO₂ emission coefficient

(8)

CO₂ emission of coal-to-methanol process = methanol conversion × CO₂ emission coefficient of coal-to-methanol process

(9)

Coal-to-methanol production by-product profit = methanol conversion × (1-methanol conversion)

(10)

Coal-to-methanol inventory = IF THEN ELSE (methanol conversion-annual market demand of coal-to-methanol ≥ 0, methanol conversion-annual market demand of coal-to-methanol, 0)

(11)

Coal-to-methanol fixed asset investment = IF THEN ELSE (total profit of coal-to-methanol ≤ 0, 0, 128.12 + 0.08 × total profit of coal-to-methanol)

(12)

Coal-to-methanol built fixed assets = DELAY FIXED (coal-to-methanol fixed assets investment, 2, 0)

(13)

Methanol production profit = annual market demand of coal-to-methanol × (methanol price-methanol production cost-transportation cost) − inventory cost

(14)

Methanol production cost = -STEP ((sales cost + capital cost + raw material cost + labor cost + miscellaneous expenses) × 0.1, 2023) -STEP ((sales cost + capital cost + raw material cost + labor cost + miscellaneous expenses) × 0.05, 2023)

(15)

Penalty unit price = STEP ((8 + RAMP (1, 2023, 2030)) × 7.314, 2023)

(16)

Coal chemical allocation ratio = 0.333 + RAMP (0.02, 2023, 2025) + RAMP (0.01, 2025, 2030).

3.6. Model Validation

The accuracy and effectiveness of the SD model were checked, including intuitive checking, operation checking, and historical data checking [24].

The model was visually checked during intuitive checking, revealing no technical errors in model construction. By observing whether the variable settings, feedback relationships, and function settings are in line with the actual value of the coal-to-olefin industry chain, the rationality of the model construction can be determined. When setting variables, the article always follows the principles of “what will it drive?” and “what is its driving force?” in conjunction with the real system to determine system variables; when determining the feedback relationship, strive to fit the causal relationship in the real coal-to-olefin industry chain; when setting the function, make the functional relationship of variables simulate the real value situation of the coal-to-olefin industry chain.

Second, no unrealistic simulation results (abnormal value fluctuation, value overflow, etc.) were found during the operation test, i.e., the model can simulate the value of the coal-to-olefin industrial chain to a large extent.

Finally, the SD model was subjected to historical data testing. The objectivity of the model determines the feasibility and effectiveness of the proposed system. Therefore, several key variables, such as the amount of coal used for methanol conversion and the amount of methanol used for ethylene conversion, were selected to test model validity by comparing historical data and simulation. The 2011 to 2022 period was selected as the simulation interval, and the step size was 1 year. The simulation results and historical data of key variables are listed in

Table 3. SD model historical data test.

Time	For Methanol Conversion (×104 t/Year)			For Ethylene Conversion (×106 t/Year)
	Actual Value	Simulated Value	Error (%)	Actual Value	Simulated Value	Error (%)
2011	686.67	686.66	0	283.12	281.8	0.46
2012	869.98	869.99	0	379.80	378.05	0.48
2013	1053.30	1053.00	0.03	454.92	452.81	0.46
2014	1236.61	1237.00	0.03	399.75	397.9	0.47
2015	1419.92	1420.00	0.01	404.69	402.82	0.46
2016	1603.23	1603.00	0.01	442.3	440.26	0.46
2017	1786.54	1787.00	0.03	575.87	573.19	0.46
2018	1963.32	1963.00	0.02	687.76	684.58	0.46
2019	2179.52	2180.00	0.02	670.1	666.98	0.46
2020	2280.48	2280.00	0.02	528.07	525.64	0.46
2021	2578.89	2579.00	0	552.09	549.46	0.46
2022	2719.32	2680.00	1.45	552.04	549.47	0.47

. The maximum absolute error rate between the simulated amount of coal used for methanol conversion and the historical value was 1.45% (2022). All absolute error rates between the simulated amounts of methanol used for ethylene conversion and the historical values were below 0.5%, with an average absolute error rate of 0.47%. The trends of the simulation results from the SD model matched those of the historical values, and the relative errors were reasonable. Therefore, the model can effectively describe the value changes in the coal-to-olefin industrial chain.

3.7. Data Source

The product outputs of the coal-to-olefin industrial chain nodes are mainly sourced from national statistical yearbooks, Inner Mongolia statistical yearbooks, and industry statistical reports. The production capacity data are the summary of completed project reports of various companies. Product price data are mainly from the Flush and Wind databases. The product capacity utilization rate is from the steel network and coal market network. The carbon emission factor, emission intensity, and energy consumption data are mainly from the National Energy Administration and published data [25,26,27,28]. The cost data are based on the improved data results from Han Hongmei’s publications [29]. The missing data for certain years are supplemented by research reports from various organizations, published papers, and other public information. For accounting purposes, Lurgi, MTO high-yield ethylene process, MTO high-yield propylene process, linear low-density polyethylene process, and kettle polypropylene are used in this study for coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, and coal-to-polypropylene projects, respectively.

4. Scenario Design and Simulation Results Discussions

4.1. Scenario Design

The SD model has been verified to accurately represent and simulate the effects of the hypothesized strategies. It is utilized to explore the expansion degree selection of the Inner Mongolia coal-to-olefin industrial chain from the value chain perspective. To predict the impact of key variables on the value of the coal-to-olefin industrial chain in Inner Mongolia, the following five scenarios are designed, as shown in

Table 4. 2011 to 2022 Inner Mongolia coal-to-olefins industrial chain values under different expansion degrees (×10¹⁰).

Scenarios	Driving Factors	Assumptions
Business as usual (S1)	-	Maintain the development trend of the total profit of the coal-to-olefin industrial chain in Inner Mongolia from 2011 to 2022, and each variable works according to the existing setting.
Scenarios 2 (S2)	Carbon pricing	From 2023 to 2025 (transition period), the carbon price is levied at 8 to 10 USD/t, gradually increasing to 15 USD/ton from 2026 to 2030. Other parameters are consistent with S1.
Scenarios 3 (S3)	Coal chemical allocation ratio adjustment	The allocation ratio of the coal chemical industry will increase by 2% per year from 2023 to 2025 and decrease by 3% per year from 2026 to 2023. Other parameters are consistent with S2.
Scenarios 4 (S4)	Oil price fluctuation	Under the baseline scenario, the oil price increases by 10% to 25% per year. To adapt to the long-term 80 USD/barrel price, the increases will be 22.8%, 21.1%, 19.3%, and 17.65% per year, respectively, from 2027 to 2030. Other parameters are consistent with S1.
Scenarios 5 (S5)	Production cost change	The production cost will be reduced by 10% per year from 2023 to 2025 and by 15% per year from 2026 to 2030. Other parameters are consistent with S1.

.

Scenario 1 is the baseline, reflecting the current development trend of the coal-to-olefin industrial chain in the region without any variable changes. It simulates the future value of this industrial chain in Inner Mongolia, serving as a comparison benchmark for other scenarios.

Scenario 2 focuses on carbon pricing. Carbon pricing is recognized as the most effective GHG emission reduction policy. Despite China’s current lack of mandatory emission targets, carbon reduction will become inevitable under foreseeable environmental and international pressure [30]. To achieve the 30–60 goals and respond to the EU’s carbon tariff from 2026, China will likely penalize corporate carbon emissions. This scenario simulates the impact of carbon penalty price changes on the value of the coal-to-olefin industrial chain in Inner Mongolia.

Scenario 3 focuses on the adjustment of the coal chemical industry allocation ratio. China’s dependence on imported oil, gas, and chemicals threatens its national energy security. Accordingly, it plans to shift from petrochemicals to coal chemicals. However, coal chemicals come with significant CO₂ emissions, and Inner Mongolia will reduce coal consumption in this sector due to carbon taxing. This scenario simulates the effect of such adjustment on the value of the coal-to-olefin industrial chain in Inner Mongolia.

Scenario 4 focuses on oil price fluctuations. Despite the recent growth of the coal chemical industry, oil-to-olefin production remains dominant. China’s over 70% oil dependency renders domestic petrochemical production vulnerable to international oil price fluctuations. As key products of the coal chemical and petrochemical industries, olefins are also impacted by oil prices. Scenario 4 simulates the value changes in Inner Mongolia’s coal-to-olefin industrial chain due to these fluctuations.

Scenario 5 focuses on the production cost changes to discuss their impact on the value of the Inner Mongolia coal-to-olefin industrial chain in the case of production cost reduction.

4.2. Scenario Discussions

4.2.1. Coal-to-Olefin Industrial Chain Values in Each Expansion Direction

This study selects 2022 as the base period and 2030 as the cut-off prediction year (totaling 8 years) to ensure the accuracy and reliability of the SD model simulation prediction. Based on the SD model predictions, the values of the coal-to-olefin industrial chain with different expansion directions in Inner Mongolia under five different scenarios from 2023 to 2030 are as follows:

The carbon pricing scenario has led to an increase in costs for the coal chemical industry. Under this scenario, the output value of all six industrial chains has decreased, while the decline in the three industrial chains of coal-to-film, coal-to-polyethylene, and coal-to-polypropylene will increase in 2027. Under the baseline scenario, the 2030 values of the coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains are expected to reach 0.694, 2.984, 2.992, 7.315, 7.397 and 9.371 × 10¹⁰, respectively. In the case of different expansions, a higher industrial chain expansion indicates a higher industrial chain value. Other than the values of the coal-to-film, coal-to-polyolefin, and coal-to-ethylene industrial chains in 2027, the value of the industrial chain of each expansion is growing (Figure 9). The value of each industrial chain expansion grows over time, with film and polyolefin growing the fastest, olefin slower, and methanol remaining stable. With the same expansion, coal-to-ethylene’s value is lower than coal-to-polyethylene’s from 2025 to 2028, peaking at a 4.54 × 10¹⁰ gap, while coal-to-ethylene and coal-to-propylene’s values are similar. Coal-to-polyethylene’s value is slightly lower than polypropylene’s, with a maximum gap of 3.11 × 10¹⁰.

The implementation of a carbon pricing mechanism will directly lead to an increase in production costs in the coal-to-olefin industry. In order to maintain production, enterprises have to pay additional carbon emission fees, which will inevitably compress their profit margins and reduce the overall value of the industrial chain.

Under the carbon pricing scenario, by 2030 the values of the coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains are projected to reach 0.184, 2.428, 3.393, 4.089, 4.565, and 6.147 × 10¹⁰, respectively, in 2030. The industrial chain values are reduced by 0.509, 0.556, 0.462, 3.226, 2.832, and 3.224 × 10¹⁰, respectively, from the baseline scenario. In scenarios with varying expansion degrees, a higher expansion degree leads to a higher industrial chain value and annual growth rate (Figure 10).

In this scenario, the coal-to-film industrial chain value leads every year with a 10.26% average annual growth, while the coal-to-methanol industrial chain lags behind with negative growth. Other than the coal-to-methanol industrial chain, other expansion chains show fluctuating growth. The coal-to-methanol industrial chain peaks in 2024 and declines after that. With the same expansion, the coal-to-propylene and coal-to-ethylene industrial chains have similar values except from 2026 to 2028, with propylene averaging 2.18 × 10¹⁰ higher. The coal-to-polypropylene and coal-to-polyethylene industrial chains show similar trends and gaps, with polypropylene averaging 3.03 × 10¹⁰ higher. Enterprises should increase their investment in technological innovation and energy conservation and emission reduction to improve energy efficiency.

Of course, there are certain model limitations in the assumptions about carbon pricing and carbon penalties in the article. However, as the pressure to reduce carbon emissions increases, the authenticity of policies will also become greater. The carbon penalty mechanism will promote the transformation of the coal-to-olefin industry towards a more environmentally friendly and low-carbon direction. For the government, it should increase research and development investment, intensify research and development efforts, and promote the development of low-carbon technologies. To enhance the value of the coal-to-olefin industry chain, Inner Mongolia should increase scientific research investment in the coal-to-olefin industry and develop corresponding low-carbon emission reduction technologies to reduce CO₂ emissions from the industry.

This adjustment will intensify the competitive pressure in the coal-to-methanol industry chain; For other coal-to-olefin industry chains, this adjustment may bring some degree of relief, as it can encourage the industry to pay more attention to optimizing and upgrading the industry chain, as well as improving resource utilization efficiency.

Under the scenario with adjustments to the allocation ratio of the coal chemical industry, the values of the coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains are expected to reach 0.067, 3.551, 3.393, 5.126, 5.779, and 7.688 × 10¹⁰, respectively, in 2030. The industrial chain values in 2030 are expected to increase by −0.117, 1.123, 0.863, 1.037, 1.214, and 1.541 × 10¹⁰ compared with the carbon pricing scenario, respectively, which are still lower than the values in the baseline scenario. Specifically, the values of the industrial chains with different expansion degrees are still higher under higher industrial chain expansion, and the values of the industrial chains with all expansion degrees but coal-to-methanol show fluctuating growth trends (Figure 11). The value of the coal-to-methanol industrial chain peaked in 2024 and decreased afterward. However, the average annual growth rate of the industrial chain value no longer follows the patterns in the carbon pricing and baseline scenarios, where a higher industrial chain expansion degree leads to a higher average annual growth rate of the industrial chain value.

In this scenario, the value of the coal-to-film industrial chain still leads every year, and the average annual growth rate of the industrial chain value is up to 10.26%. The value of the coal-to-methanol industrial chain is the lowest every year, and the average annual growth rate of the industrial chain value is negative. The value of the coal-to-ethene industrial chain ranks third, with the average annual growth rate exceeding that of propylene and polyethylene. With the same expansion, the value of the coal-to-ethylene industrial chain is lower than that of the coal-to-propylene industrial chain from 2023 to 2026 but higher than that of the propylene industrial chain after that. The trends of the values of the coal-to-polypropylene and coal-to-polyethylene industrial chains are basically the same as those of the carbon pricing scenario. Under the implementation of carbon punishment mechanism, enterprises should prioritize choosing high extension industrial chains for production.

Under the oil price fluctuation scenario, due to the fact that petroleum to olefins remains the main production method, fluctuations in international oil prices can cause changes in the cost and value of olefin products, the values of the coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains are expected to reach 0.851, 2.978, 3.208, 7.498, 7.557, and 7.779 × 10¹⁰, respectively, in 2030. Specifically, a higher expansion degree still leads to a higher industrial chain value under different expansion degrees. Moreover, the value of the coal-to-polyolefin industrial chain differs greatly from that of the coal-to-olefin industrial chain.

The values of all industrial chains except the coal-to-methanol industrial chain show fluctuating growth trends, and the value of the coal-to-methanol industrial chain grows slowly until 2026 and declines slowly afterward (Figure 12). With the same expansion, the value of the coal-to-propylene industrial chain is higher than that of the coal-to-ethylene industrial chain, with an annual average of 3.459 × 10¹⁰. The value of the coal-to-polypropylene industrial chain is higher than that of the coal-to-polyethylene industrial chain. The annual difference between the values of the coal-to-polypropylene and coal-to-polyethylene industrial chains from 2023 to 2027 is small, and their trends are basically the same. The value of the coal-to-polyethylene industrial chain from 2027 to 2030 is basically the same as that of the coal-to-polyethylene and coal-to-polypropylene industrial chains. For low elongation industrial chains, due to their relatively low added value, they are more sensitive to changes in oil prices. Once oil prices fall, the value of the industrial chain may be greatly affected.

Under the cost change scenario, the reduction in production costs has led to a significant increase in the value of various industrial chains, the values of the coal-to-methanol, coal-to-ethylene, coal-to-propylene, coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains are expected to reach 1.099, 3.156, 3.215, 7.782, 7.667 and 9.556 × 10¹⁰, respectively, by 2030. Each year, the industrial chain value is higher than the baseline scenario.

Specifically, the values of the industrial chains with different expansion degrees are still higher under higher industrial chain expansions, and the values of all industrial chains except the coal-to-methanol industrial chain show fluctuating growth trends (Figure 13). The value of the coal-to-methanol industrial chain tends to be stable, with an average annual growth rate of only 3.81%. With the same expansion, there is not much difference between the values of the coal-to-propylene and coal-to-ethylene industrial chains, and the variation trends resemble that of the baseline scenario. However, other than the 2026 to 2028 period, the value of the coal-to-propylene industrial chain is higher than that of coal-to-ethylene, with an average annual increase of 2.17 × 10¹⁰. From 2023 to 2027, the value of the coal-to-polypropylene industrial chain is higher than that of coal-to-polyethylene, and that of coal-to-polyethylene is higher than that of coal-to-polypropylene after that. Nevertheless, the coal-to-polypropylene and coal-to-polyethylene industrial chain value variation trends and magnitudes are basically the same, and the average annual difference in industrial chain value is only 168 million.

Overall, the extension of the industrial chain will bring about an increase in value. This is also in line with the theory of industrial economy. Extending the extension of the industrial chain can help improve the quality level of products, make them more in line with market demand, enhance product added value, and thus increase the value of the industrial chain. The extension of the industrial chain also stabilizes the supply chain, effectively controls industrial costs, and helps to enhance the value of the industrial chain.

4.2.2. Effects of External Factors on the Values of Coal-to-Olefin Industrial Chains with Different Expansion Directions

Based on the SD model predictions, the effect magnitudes of external factors on the value of the Inner Mongolia coal-to-olefin industrial chains with different expansion directions from 2023 to 2030 are as follows.

Figure 14 presents the value of the coal-to-methanol industrial chain under the influence of various factors. The value of the coal-to-methanol industrial chain varies greatly under different influencing factors. The scenarios with decreasing production costs and fluctuating oil prices positively impact the value of the coal-to-methanol industrial chain. The scenarios with carbon pricing and adjustments to the allocation ratio of the coal chemical industry negatively impact the value of the coal-to-methanol industrial chain.

The scenario with production cost changes follows the same trend as the baseline scenario, with the value of the coal-to-methanol industrial chain increasing from 2022 to 2023, decreasing in 2024, and rising steadily afterward. The declined production costs significantly increase the value of the coal-to-methanol industrial chain. Under the oil price fluctuation scenario, the value of the coal-to-methanol industrial chain rises steadily from 2022 to 2026 and starts to decline after 2026. The industrial chain value is higher than that in the baseline scenario but lower than that in the cost change scenario. The scenarios with carbon pricing and adjustments to the allocation ratio of the coal chemical industry exhibit similar trends in the industrial chain value, both increasing between 2022 and 2023, decreasing between 2023 and 2030, and are below that in the baseline scenario. However, the scenario with adjustments to the allocation ratio of the coal chemical industry based on carbon pricing exhibits more rapid industrial chain value decreases.

Figure 15 presents the value of the coal-to-ethene industrial chain as affected by various factors. Different scenarios have different influence magnitudes on the value of the coal-to-ethylene industrial chain. However, the trends are similar. The values of the coal-to-ethylene industrial chain under the baseline scenario, the cost change scenario, the oil price fluctuation scenario, and the carbon pricing scenario show an inverted U-shape from 2023 to 2026 and an upward trend from 2026 to 2030. From 2026 to 2030, the industrial chain value under each scenario shows an upward trend. In the scenario with adjustments to the allocation ratio of the coal chemical industry, the value of the coal-to-methanol industrial chain maintains an upward trend, with an average annual growth rate of up to 18.98%. However, adjusting the coal chemical industry allocation ratio scenario has a certain negative impact on the value of the coal-to-ethylene industrial chain until 2027. The carbon pricing scenario has a strong negative impact on the value of the coal-to-ethylene industrial chain.

Figure 16 presents the value of the coal-to-propylene industrial chain as affected by various factors. The variation trend of the value of the coal-to-propylene industrial chain under different scenarios is basically the same, the exception being the industrial chain value decreases in 2027 in a few scenarios. The carbon pricing scenario also has a strong negative impact on the value of the coal-to-propylene industrial chain. However, the scenario adjusting the allocation ratio of the coal chemical industry based on carbon pricing can alleviate the negative impact of carbon pricing and rapidly increase the value of the coal-to-propylene industrial chain. The effects of the oil price change and production cost reduction scenarios on the value of the coal-to-propylene industrial chain are basically the same.

Figure 17, Figure 18 and Figure 19 present the values of the coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains as affected by various factors, respectively. These industrial chain values show the same variation trend under each scenario, being a fluctuating growth state as in the baseline scenario. However, the effects of the scenarios differ significantly. Scenarios with carbon pricing and adjustments to the allocation ratio of the coal chemical industry have a strong negative impact on the value of the coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains. The adjusting coal chemical industry allocation ratio scenario mitigates the negative effects of the carbon pricing scenario to a certain extent. The oil price fluctuation and cost change scenarios have a weak positive effect on the value of these three industrial chains, and the positive effect of the oil price fluctuation scenario on the value of the coal-to-polyethylene industrial chain is smaller than that of the cost change scenario.

5. Uncertainty Analysis

5.1. Monte Carlo Simulation Settings

Monte Carlo simulation leverages a large number of stochastic computer simulations to effectively solve complex problems with dynamics and uncertainty. Using the baseline scenario data as an example, the influence of the variations in carbon penalty unit price, coal chemical industry allocation ratio, oil price, and cost on the value of the coal-to-olefin industrial chain in each expansion direction is analyzed.

Among them, the variation range of carbon penalty unit price is 0 to 30 USD/t, the variation range of coal chemical industry allocation ratio is 0.1 to 0.5, the variation range of oil price is 40 to 110 USD/t, the variation range of the cost of coal-to-methanol production is 500 to 4000 USD/t, the variation range of the cost of coal-to-ethanol production is 4000 to 12,000 USD/t, the variation range of the cost of coal-to-propylene production is 4000 to 12,000 USD/t, and the variation range of the cost of coal-to-olefin production is 500 to 4000 USD/t. The cost change interval of coal-to-methanol production is 4000 to 12,000 CNY/t, the cost change interval of coal-to-propylene production is 4000 to 13,000 CNY/t, the cost change interval of coal-to-polyethylene production is 7000 to 13,000 CNY/t, the cost change interval of coal-to-polypropylene production is 5000 to 14,000 CNY/t, and the cost change interval of coal-to-film production is 4000 to 13,000 CNY/t. All the observed variables in the sensitivity analysis fluctuate within the confidence interval of 0.5 to 1, and the simulation is repeated 500 times.

This study analyzed the uncertainty of all six coal-to-olefin industry sub-chains. Due to the limited length of the article, only the uncertainty analysis and results of the coal-to-ethylene and coal-to-polyethylene industrial chains are presented.

Authors should discuss the results and how they can be interpreted from the perspective of previous studies and of the working hypotheses. The findings and their implications should be discussed in the broadest context possible. Future research directions may also be highlighted.

5.2. Monte Carlo Simulation Results Analysis

The Monte Carlo simulation analysis results of the value of the coal-to-ethylene industrial chain in Inner Mongolia are shown in Figure 20. With adjustments to the allocation ratio of the coal chemical industry, the value of the coal-to-ethylene industrial chain varies between 10 billion and 15 billion from 2011 to 2023 and between 10 billion and 44 billion from 2023 to 2030. That is, adjusting the allocation ratio of the coal chemical industry based on carbon pricing has a significant impact on the value of the coal-to-ethylene industrial chain, and this impact is gradually strengthened over time.

Meanwhile, adjusting the allocation ratio of the coal chemical industry based on carbon pricing has a very small impact on the value of the coal-to-ethylene industrial chain. As the carbon penalty unit price is altered, the value of the coal-to-ethylene industrial chain varies from about 5 billion to 30 billion, a more significant impact on the industrial chain value. Similarly, the cost change also significantly impacts the industrial chain value, which varies from about 8 billion to 30 billion, a smaller impact than adjusting the allocation ratio of the coal chemical industry and carbon pricing. While the effect of oil price fluctuations on the value of the coal-to-ethylene industrial chain is first weakened and then strengthened in the eight years, the effect is always the smallest among the four observed variables.

Figure 21 indicates that carbon pricing and adjusting the allocation ratio of the coal chemical industry significantly impact the industrial chain value, with the value of the coal-to-polyethylene industrial chain varying between about 8 billion to 70 billion and 7.5 billion to 58 billion, respectively. Figure 21a,b show that carbon pricing has a drastic negative impact on the coal-to-olefin industrial chain and that the negative impact of carbon pricing can be mitigated to a certain extent by adjusting the coal-to-chemical allocation ratio. Cost changes have a relatively weak negative impact on the value of the coal-to-polyethylene industrial chain, i.e., lowering the cost of coal-to-olefin production will not bring much profit to the coal-to-polyethylene production enterprises. However, the extent of the impact of cost changes on the value of the coal-to-olefin industry chain is gradually increasing over time. Finally, the value of the coal-to-olefin industrial chain is the least sensitive to oil price fluctuations.

6. Conclusions

6.1. Findings and Practical Implications

This study focuses on the Inner Mongolia coal-to-olefin industrial chain, assessing its profitability across various expansions using an improved value chain accounting approach. Through SD modeling, the industrial chain values in different expansion directions are explored to reveal value differences and whether an expansion enhances the value. By comparing the baseline, carbon pricing, allocation ratio adjustment, oil price fluctuation, and cost change scenarios, the optimal expansion directions are identified. This study offers a decision-making framework for coal-to-olefin production enterprises and policy recommendations for Inner Mongolia’s coal-to-olefin industry. The key findings are outlined as follows.

• This study proposes an improved industrial chain value accounting method, which summarizes the total profit of the coal-to-olefin industrial chain into the following six aspects: the profit of the end products, the profit of intermediate products, the profit of the by-products from intermediate node products, the cost of production (including fixed and variable costs), the cost of transportation, and the cost of carbon penalties. This accounting method is embedded into the SD model for selecting expansion directions of the coal-to-olefin industrial chain. By analyzing the historical data from 2011 to 2023 and calibrating the SD model, the proposed decision-making method proves accurate and effective for selecting the industrial chain expansion direction.
• The industrial chain value of each expansion direction is positively correlated with its expansion degree, i.e., expanding the industrial chain helps to improve its value. From 2011 to 2022, the values of the coal-to-methanol, coal-to-ethene, coal-to-propylene, coal-to-polyethylene, coal-to-polypropylene, and coal-to-film industrial chains differ markedly, and the industrial chain values in 2023 reach 57.2, 134.3, 115.6, 375.5, 388.5, and 38.82 billion, respectively. From 2023 to 2030, the value of the coal-to-methanol industrial chain shows great differences under different scenarios, and the value variation trends of the other industrial chains are basically the same in different scenarios.

The sensitivity analysis shows that imposing carbon penalties (carbon pricing) on the coal-to-olefin industry significantly decreases the industrial chain value. However, adjusting the allocation ratio of the coal chemical industry has a certain mitigating effect on other coal-to-olefin industrial chains while exacerbating the effect on the coal-to-methanol industry chain. Cost and oil price changes have a more significant impact on the low-expansion coal-to-olefin industrial chains, while the high-extension industrial chains show relatively low sensitivities.

The carbon penalty factor is indeed an important factor that cannot be ignored when choosing the expansion direction of the coal-to-olefin industry chain. For the coal-to-olefin industry, carbon emissions during the production process will be subject to stricter restrictions and penalties. This will inevitably compress the profit margin of enterprises and reduce the overall value of the industrial chain. Secondly, the carbon punishment mechanism will encourage the coal-to-olefin industry to transform towards a more environmentally friendly and low-carbon direction. Enterprises may increase their investment in technological innovation and energy conservation to improve energy efficiency and reduce carbon emissions. This transformation may increase the investment of enterprises in the short term, but in the long run, it will help enhance the competitiveness of the industry and achieve sustainable development.

Inner Mongolia should increase its research investment in the coal-to-olefin industry and develop corresponding low-carbon emission reduction technologies to reduce CO₂ emissions from this industry. The country can also take the lead in organizing major provinces in the coal-to-olefin industry to learn and collaborate on research and development together, promoting the development of coal-to-olefin industry technology. In addition, it is necessary to explore the coupling of coal-to-olefin industry with other clean energy technologies to reduce its CO₂ emissions. At present, Inner Mongolia has abundant new energy resources, so it is necessary to fully utilize renewable energy sources such as wind and solar energy to develop green electricity, accelerate the coupling development of coal-to-olefin with new energy sources such as green electricity, green hydrogen, energy storage, and heat storage, reduce direct carbon emissions and process carbon emissions, and improve carbon resource utilization.

3.

The values of Inner Mongolia’s coal-to-olefin industrial chains under the baseline, carbon pricing, oil price fluctuation, and production cost change scenarios rank in descending order as coal-to-film > coal-to-polypropylene > coal-to-polyethylene > coal-to-ethylene > coal-to-propylene > coal-to-methanol. The values of Inner Mongolia’s coal-to-olefin industrial chains under the scenario of adjusting the allocation ratio of the coal chemical industry rank as coal-to-film > coal-to-polypropylene > coal-to-polyethylene > coal-to-propylene > coal-to-ethylene > coal-to-methanol. The risk resistance of Inner Mongolia coal-to-olefin industrial chains rank in descending order as coal-to-propylene > coal-to-ethylene > coal-to-ethylene > coal-to-polyethylene, coal-to-polypropylene, and coal-to-film > coal-to-methanol. Thus, enterprises pursuing high value for production can establish integrated production mode and choose the coal-to-film industry chain; enterprises seeking stable income can choose coal-to-propylene and coal-to-ethylene productions with stronger risk-resistance.

6.2. Theoretical Implications and Research Strengths

Although scholars have proposed to extend the coal chemical industrial chain, its specific downstream industrial chain has received less research attention. Existing research has not provided a clear answer to whether expanding the industry chain definitely improves its value and how to choose the optimal industrial chain. This paper focuses on the coal-to-olefin branch with the most economic benefits and the fastest production capacity growth in the modern coal chemical industry. The impact of expanding the industrial chain in different directions on its value is derived, and a method to choose the direction of industrial chain expansion in different situations is proposed, which is of theoretical significance.

Existing literature rarely delved into the carbon emission problem while expanding the industrial chain. This study innovatively adopts the improved industrial chain value accounting method and integrates the carbon emission factor into the industrial chain expansion research. Subsequently, an SD model is constructed based on the SD model analysis framework from the value chain perspective to select the expansion direction of the coal-to-olefin industrial chain in Inner Mongolia. An uncertainty analysis is conducted using the Monte Carlo method, and the validified model provides decision-making support for enterprises to select the expansion direction of the coal-to-olefin industrial chain under different scenarios.

6.3. Limitations and Prospects

Despite the systematic and comprehensive analyses in this study, the following limitations warrant follow-up research.

• During SD modeling, some factors that may affect the value of each coal-to-olefin industrial chain were ignored or simplified due to incomplete or absent statistical data, which may impact the simulation results.
• During scenario simulation, some variables and parameters were set as constants. In real life, however, these parameters change with the system. Although these model parameters were set based on the results of existing research and real-life policy changes, large inaccuracies remained, requiring further optimization.

This section is not mandatory but can be added to the manuscript if the discussion is unusually long or complex.