The Impacts on Regional Development and “Resource Curse” by Energy Substitution Policy: Verification from China

Abstract

With the increasing “resource curse” phenomena relating to energy resources in China, research concerning energy substitution policies has become more meaningful. In this paper, a dynamic CGE model was built to evaluate the impacts of energy substitution policies on regional growth and the “resource curse”. The results show the following: (1) Compared to other regions, an energy substitution policy exerts a more considerable influence on economic growth in regions with high “resource curse”. (2) The changes in carbon emissions in regions with no or low “resource curse” are modest. The primary factor contributing to the rapid decline might be energy structure adjustments. As the intensity of substitution varies, the regional disparities in marginal emission reduction costs are expected to increase. (3) Energy substitution policies reduce the severity of high “resource curse” regions. As the intensity of energy substitution increases, the extent of “resource curse” regions decreases accordingly. Some suggestions are given as follows: (1) Further promote energy substitution and speed up the transformation of energy production and consumption modes in China. (2) Accelerate the energy transformation in “resource curse” regions to promote regional sustainable development. (3) Improve energy substitution policies in transportation, industry and other fields.

1. Introduction

Energy has always been considered one of the important sources of economic growth. However, the phenomenon of a “resource curse” has emerged in some countries or regions, meaning that abundant resources have not supported long-term economic growth and have instead become obstacles (Auty, 1993) [1]. It is generally believed that some key rules or mechanisms leads to a loss of sustainability in regional economic development, such as in the “Dutch disease”, rent-seeking effects, or others (Corden, 1982; Deng Ming, 2016; Yang Lili, 2014) [2,3,4]. According to “The China Energy Development Report”, China’s total energy consumption reached as many as 5.41 billion tons of standard coal in 2022. Meanwhile, the total consumption of coal was still increasing, and the dominant position of fossil fuels has not yet fundamentally changed (Lin Boqiang, 2015; Liu Zhibiao, 2020) [5,6]. The tasks of pollution control and carbon reduction remain arduous in China.

Due to the similar terminal consumption scenarios between renewable energy, coal, and oil and gas, there exists a practical foundation for energy substitution. According to the related report, China’s total energy consumption reached as many as 5.72 billion tons of standard coal in 2023, with coal consumption increasing by 5.6%. Under energy substitution conditions for coal, an additional 160 million tons of oil or 240 million m³ of natural gas are needed. Under the same conditions, it is necessary to increase renewable energy usage to replace coal by nearly 7 billion kilowatt hours (kWh). However, can energy substitution policies promote social and economic growth? Considering the significantly fluctuating power generation capacity of renewable energy, the potential for energy substitution to promote economic stability is a complex issue. Could a “resource curse” be alleviated through strategic energy substitutions? The issues relating to energy substitution policies still need to be further accurately evaluated. This is not only the key to sustainable development but an effective path to alleviate the regional “resource curse”.

As a powerful policy simulation and analysis tool, the computable general equilibrium model (CGE) was firstly proposed to elaborate on the benefits and income distribution effects of trade, taxation, and transfer policies and was widely used to assess the impact of different exogenous variables on the economy, resources, and environment (Xu Shichun, 2016) [7]. The CGE model uses equations to describe supply, demand, and market relationships, where commodities, production factors, prices, wages, and profits are all variables. Under a series of optimization constraints, the quantity and price that reach equilibrium in each market are obtained. When applying the model to economic, social, and environmental systems, the CGE model is flexible in terms of evaluating economic and environmental problems (Li Hongxin, 2008) [8]. With key parameters and mass data dynamic processing, the CGE model is more adaptable in terms of analyzing the input–output linkage or the correlation effect of socio-economic factors, which can effectively be applied to energy policy analysis in China.

Therefore, this paper first constructs a dynamic CGE model with a factor input and output module, a factor module, an income and expenditure module, an environment module, and an equilibrium module. To distinguish the special situations relating to the “resource curse” in China, the “resource curse” coefficient was used to measure the deviation between economic development and energy. Three conditions, including high, low, and no “resource curse”, are divided, and the average substitution rate and enhanced substitution rate were designed, respectively. Based on these, we used the dynamic CGE model to evaluate the effects of energy substitution policies on the economy, environment, and regional “resource curse”. Furthermore, some suggestions were provided for government decision-making concerning economic transition.

2. A Review of the Related Literature

Energy has consistently served as a crucial driving force for promoting social development, and economic growth has gone through energy revolutions and various kinds of energy substitution processes (Zhang Zhenhua, 2021) [9]. Safety, low cost, and cleanliness are essential requirements of an energy policy. Energy security ensures stability in meeting energy demand. The cost of energy drives changes in energy consumption. Clean energy measures promote changes from traditional energy to renewable energy usage. The core of the three lies in technological progress. Technological progress and energy endowment jointly promote energy substitution (Li Hong, 2017) [10].

Due to significant differences in natural endowments among different regions, the regional “resource curse” caused by energy is very common in China, such as Xinjiang, Inner Mongolia, Shanxi, etc. Since the 1990s, a “resource curse” has occurred frequently in western regions, attracting the attention of the academic community (Sachs, 1999; ANG, 1999) [11,12]. When the economy is heavily reliant on resource inputs, it may lead to the crowding out of other productive factors (Liang Zhenqiao, 2019; Funk Charle, 2020; Bonet-Morón Jaime, 2020) [13,14,15]. The excessive prosperity of the energy industry has attracted a large amount of investment and caused the cost of human resources to rise rapidly (Hua Wei, 2020; Wong Chi-Swian) [16,17]. Within the formation of a “resource curse”, rent-seeking, corruption, and imperfect laws and systems are considered to be important factors in “resource curse” formation (Ajide Kazeem Bello, 2022; Leonard Alycia et al., 2022; Sun Xiaohua et al., 2022) [18,19,20]. China has produced a series of research on verifying the “resource curse” and transmission mechanisms (Xu Kangning et al., 2006; Shao Shuai et al., 2011; Yu Xiangyu, 2019) [21,22,23]. Meanwhile, resource imports, spillover effects, technological innovations, and government behaviors are considered key factors involved in the “resource curse” (Xue Yawei et al., 2019; Zhao Wenyao, 2019; Huang Qingzi, 2021) [24,25,26].

As the “resource curse” phenomenon becomes more prevalent, rules and policies have become more important, attracting greater attention than before. Energy substitution policies are believed to be a useful way to achieve carbon peaking and neutrality goals. Improving the quality of rules and laws can effectively alleviate the “resource curse” (Shi Minjun, 2011; Xiong Ruoyu, 2020; Xi Yang, 2016) [27,28,29]. According to the history of energy substitution, there are several typical types of energy substitution. Energy cost is the first factor that should be considered during energy substitution. When coal prices rise in the international market, oil and gas become the optimal choice for short-term coal substitutions. Substituting coal with oil and gas can mainly be achieved through the introduction of electric vehicles replacing fuel vehicles and coal-derived chemicals replacing petrochemicals. Meanwhile, energy supply stability is key to energy substitution. Renewable energy fluctuates greatly with technological, environmental, and natural conditions; in contrast, electricity substitution can ensure a stable electricity supply via the transition from coal and renewable energy sources to more reliable forms of electricity generation.

With the heavy dependence on energy in China, the distortion of the energy consumption structure is an important reason for the increase in energy intensity (Shen Xiaobo et al., 2021) [30]. There is a significant correlation between energy consumption, GDP growth, and carbon emissions (Giovanni, 2014) [31]. As China’s energy structure is dominated by coal (Ma Limei, 2014) [32], increased energy consumption directly led to changes in the elasticity of environmental pollution in China (Cang Dingbang, 2020) [33], resulting in high carbon emissions (Yang Lili, 2016) [23]. Therefore, technological progress is becoming the main driving factor in terms of achieving the 2060 carbon emission reduction target (Zhou, 2021) [34]. Technological advancements and environmental governance can drive changes in the energy consumption structure, leading to significant reductions in environmental pollution. Meanwhile, carbon emission reductions potentially drive changes in the energy consumption structure, leading to significant reductions in coal usage and environmental pollution (Dai Yande, 2015) [35]. Total energy consumption and reduced energy consumption per unit of GDP complement each other (Wei Yiming, 2011) [36]. The low carbonization of China’s industrial system is primarily driven by changes in the energy consumption structure. Due to the inertia of energy consumption, the efficiency of energy use is significantly affected by the energy and industrial structure (Zhang Wei, 2016) [37]. Although countries around the world have different energy and industrial economic transformation paths, the mechanisms behind them are similar. The impacts of energy demand are more significant in high energy-consuming industries, such as steel, petrochemical and so on, and the changes in economic structure are the core factor (Fan Ying, 2021) [38]. Strengthening regulations in high energy-consuming industries will be key to achieving the goal of controlling total energy consumption (Zheng Xinye, 2019) [39]. At the same time, energy price fluctuations affect economic growth, but they can also effectively improve energy efficiency (Yang Mian, 2021) [40].

In general, research relating to the “resource curse” has formed a complex theoretical system, with a focus on the existence of the “Dutch disease”, the crowding-out effect, and the rent-seeking and transmission mechanisms of the “resource curse”. However, there has been relatively less attention on energy substitution policies in China. The substitution of energy in China is actively being promoted, with a significant increase in the proportion of clean energy consumption. However, traditional energy will still dominate for a long time. With technological advances and the promotion of policies, clean energy might gradually become the dominant form of energy in the future. The impact of energy substitution on the “resource curse” is mainly reflected in promoting efficient, intensive, and clean utilization of resources, thereby alleviating or eliminating the “resource curse”. Therefore, energy substitution has significance in terms of regional development and the “resource curse”.

3. The Dynamic CGE Model

3.1. Overview of the Dynamic CGE Model

The basic structure of the dynamic CGE model is as follows: (1) The entities in the model are composed of producers and consumers (residents, departments, and governments); (2) the behaviors of all entities obey the rational principle. The classic behavioral norms of the entities include the maximization of profits for producers, the maximization of utility for consumers, and the maximization of social welfare for governments; (3) variables (such as prices and wages) in the model reflect the economic consequences and impacts due to decisions; (4) module equations are used to describe the decision-making process and influencing factors of the entities; and (5) the equilibrium of the model refers to the conditions under which the equilibrium solution of the model exists.

According to the requirements of the CGE model, based on the trend and key factors of energy substitution in China, the dynamic CGE model was established on the following basic assumptions:

(1)

It is a dynamic model that includes socio-economic, energy, and environmental systems.

(2)

It consists of many modules, including factor input and production modules, price modules, income and expenditure modules, energy modules, and equilibrium modules.

(3)

In the model, there are three types of entities, such as the government, departments, and residents. Production sectors include agriculture, industry, construction, transportation, energy production and processing, and others.

(4)

Energy consumers choose the best consumption combination and quantity within their own budgetary constraints, and all consumption behaviors are price takers with the goal of maximizing their own utility.

(5)

Production and investment are endogenous variables, and production factors include labor, capital and energy. Intermediate inputs in the production process are also part of the production factors. The ultimate investment is the product, with a relatively unchanged production scale and a perfectly competitive product market.

(6)

All entities are rational economic agents, and their behavior patterns and standards are rational. Producers usually follow the principles of minimizing costs and maximizing profits, while consumers follow the principles of minimizing costs and maximizing consumption utility. The government aims to maximize social welfare as a neutral party.

(7)

Energy substitution is a lengthy process that will gradually affect regional development and ultimately impact the “resource curse”.

3.2. The Main Modules and Functional Equations

(1)

Factor input and output module.

In the factor input and output module, energy, capital and labor are defined as basic factors. The producer’s behavior obeys the Leontief/CES function.

The production function is as follows:

$$Q=\min {\displaystyle \sum _{i=1}^n[TV{A}_i(L,K,E)/{{\displaystyle \phi }}_1}+TI{P}_i/{{\displaystyle \phi }}_2]$$

(1)

Among them, $Q$ is the total output, $TVA$ is the total added value, $TIP$ is the intermediate input, and $E$ is energy.

The total added value function is as follows:

$$TVA=A({\alpha }_1^{1/\kappa }{L}^{\kappa -1/\kappa }+{\alpha }_2^{1/\kappa }{K}^{\kappa -1/\kappa }+{\alpha }_3^{1/\kappa }{E}^{\kappa -1/\kappa })$$

(2)

Among them, $TVA$ is the total added value, $\mathrm{E},K,L$ is energy, capital, and labor, $A$ is technology, and $\kappa$ is the elasticity of the substitution of factors.

The synthesis energy function is as follows:

$$E=A_E({\eta }_1^{1/\rho }{E}_1^{\rho -1/\rho }+{\eta }_2^{1/\rho }{E}_2^{\rho -1/\rho }+\cdots +{\eta }_n^{1/\rho }{E}_n^{\rho -1/\rho })$$

(3)

Among them, $E$ is the consumption of synthetic energy, $E_1,E_2,\cdots ,E_{\mathrm{n}}$ represent the consumption of coal, oil, natural gas, and renewable energy, $A_E$ is the comprehensive technical level of energy, ${\eta }_1,{\eta }_2,\cdots ,{\eta }_{\mathrm{n}}$ represent the technical coefficient of different energy, and $\rho$ is the elasticity of energy substitution.

The intermediate input function equation is as follows:

$$TIP=A_{ip}({\pi }_1^{1/\sigma }I{P}_1^{\sigma -1/\sigma }+{\pi }_2^{1/\sigma }I{P}_2^{\sigma -1/\sigma })$$

(4)

Among them, $TIP$ is the intermediate input amount, $I{P}_1,I{P}_2$ represent the regional energy input, and $\sigma$ is the energy substitution elasticity among different regions.

(2)

Price Module.

Energy prices encompass various components, including total intermediate input price and intra-regional and inter-regional energy sales price.

The total intermediate input price equation is as follows:

$$PINTA=({\displaystyle \sum PQ+{\displaystyle \sum P}}V)\mathrm{int}a$$

(5)

Among them, $PINTA$ is the total intermediate input price, $PQ$ is the composite product price, $PV$ is the factor price, and $\mathrm{int}a$ is the total intermediate input–output coefficient.

The energy price equation in the region is as follows:

$$Po=PP+T$$

(6)

Among them, $Po$ is the output price, $PP$ is the production price, and $T$ is the tax burden.

The energy price equation between regions is as follows:

$$PI=Pi(1+Ti)$$

(7)

Among them, $PI$ is the inter-regional transfer price of energy, $Pi$ is the intra-regional price of energy, and $Ti$ is the inter-regional transaction cost.

(3)

Income and Expenditure Module.

This module includes the residents, departments, and government. Residents obtain income expressed in the form of wages, with the utility function described by the Stone–Geary utility function. Departments generate returns through production in the form of profit. The government receives fiscal revenue through taxation.

Resident income and expenditure equation is as follows:

$$SH=w-c-TH+BH$$

(8)

Among them, $SH$ is the amount of household savings, $w$ is the household wage income, $c$ is household consumption, $TH$ is the tax burden, and $BH$ is the compensation income.

Departmental revenue and expenditure equation is as follows:

$$\mathit{SC}=Y-w-\mathit{rK}-e+\mathit{BH}$$

(9)

Among them, $\mathit{SC}$ is departmental savings, $Y$ is the department’s income, $w$ is the human expenditure, $e$ is the energy expenditure, $\mathit{rK}$ is the capital cost expenditure, and $BH$ is the compensation income.

Government revenue and expenditure equation is as follows:

$${\mathit{SG}=\mathit{QT}}_O{+\mathit{TRQ}}_R-\mathit{rK}-{\displaystyle {\sum }_{i=1}^n{I}_i{B}_i}$$

(10)

Among them, $SG$ is government savings, $Q{T}_O$ is the sum of taxes and fees, $TR$ is the tax rate, $Q_R$ is the amount of energy used, $\mathrm{B}$ is the amount of compensation, and $I$ is the object of compensation.

(4)

Environment Module.

This paper obtains CO₂ emissions by energy consumption, and the formula used for CO₂ emissions is as follows:

$$Q_{{\mathit{CO}}_2}{=(\mathrm{R}}_p\times {A-B}_c)\times R_e\times 3.67$$

(11)

where $Q_{{\mathrm{CO}}_2}$ is the quantity of carbon emissions, $R_p$ is consumption, $A$ is the unit of resource carbon content, $B_c$ is carbon sequestration, and $R_e$ is the rate of oxidation.

Wastewater is mainly related to the energy consumed in terms of industrial activities and residents, with approximately 30% of wastewater coming from industrial sources and 70% from residential sources.

Industrial solid waste discharged is related to resource consumption and emissions generated from industrial solid waste are influenced by the level of activity in the industrial sector.

(5)

Equilibrium module.

The equilibrium module includes three kinds of macro-equilibria: the exchange equilibrium, input and output equilibrium, and input and output equilibrium, respectively. Additionally, three kinds of micro-equilibria are defined: resource and environment market clear, capital market clear, and product market clear, respectively.

The formula used for resource and environment market clear is as follows:

$$YR(t)=YE(t)+YI(t)$$

(12)

where $YR(t)$ is the benefits of resource and environment accounting, $YE(t)$ is the inputs for resource and environment recovery, and $YI(t)$ is the output after resource and environment recovery.

The formula of capital market clear is as follows:

$$K(t)={\displaystyle \sum D(t)+k(t)}$$

(13)

where $K(t)$ is capital stock, $D(t)$ is capital discount, and $k(t)$ is the amount of initial capital.

The formula of products market clear is as follows:

$$Q(t)={\displaystyle \sum X(t)+}{\displaystyle \sum Y(t)+}{\displaystyle \sum Z(t)}$$

(14)

where $Q(t)$ is the total demand, ${\displaystyle \sum X(t)}$ is the government demand, ${\displaystyle \sum Y(t)}$ is household demand, and ${\displaystyle \sum Z\left(t\right)}$ is department demand.

3.3. Basic Data of the Dynamic CGE Model

The social accounting matrix (SAM) captures the quantitative relationships and interactions between various economic entities, providing an intuitive representation of changes among the government, departments, residents, and other entities.

The SAM table includes two parts: physical and value accounting. Physical accounting encompasses economic development, energy consumption, and compensation and restoration, reflecting the balance between economic and environmental systems. Value accounting includes four types: products and activities, factors of production, income and expenditure, and investment. The primary data used in the SAM table are sourced from the “China Statistical Yearbook”, “China’s Input-Output Table”, and the “China Tax Yearbook”, as shown in

Table 1. Main data sources in SAM.

Account	Content	Meaning	Data Source
Product	Activity	Total output	The total output of “China’s input-output table”
Activity	Resident	Resident consumption	“China’s input-output table” Total residents’ consumption
	Government	Government consumption	“China’s input-output table”, government gazette
	Production activities	Total output	“China’s input-output table”, government gazette
	Environment recovery	Environment compensation output	Government gazette
Elements	Labor	Labor input	“China input-output table” Total remuneration of laborers
	Capital	Gross capital formation	“China’s input-output table” Gross capital formation
	Energy	Energy input	Total energy input in China input-output table
Resident	Elements	Labor compensation	“China input-output table” Total remuneration of laborers
	Compensate	For resident grants	Government gazette, incremental revenue multiplied by rebate percentage
Department	Elements	Total profit	Depreciation plus profit of fixed assets “China input-output table”
Government	Tax	Tax income	China Tax Yearbook, government gazette
Savings	Resident	Household savings	China Statistical Yearbook, government gazette
	Department	Sector savings	China Statistical Yearbook, government gazette
	Government	Government Savings	China Statistical Yearbook, government gazette

:

To address the differences arising from various data sources and of different calibers in the SAM table, cross-entropy and Shannon entropy were adopted to evaluate the relevance and consistency of the data.

4. Simulated Results

4.1. Policy Scenarios Setting

(1)

The “resource curse” regional division of China.

This paper uses the “resource curse” coefficient to assess the deviation between economic growth and energy. A higher “resource curse” coefficient indicates a more severe manifestation of the “resource curse”. A coefficient value greater than 1 indicates that the region is experiencing the adverse effects of the “resource curse”.

The formula is as follows:

$${\mathrm{CI}=(\mathrm{E}}_{\mathrm{i}}/{\displaystyle \sum {\mathrm{E}}_{\mathrm{i}}}{)/(\mathrm{GDP}}_{\mathrm{i}}/{\displaystyle \sum {\mathrm{GDP}}_{\mathrm{i}}})$$

(15)

Among them, $\mathrm{CI}$ is the “resource curse” index, $\mathrm{E}$ is energy, $\mathrm{GDP}$ is the gross domestic product, and $\mathrm{i}$ is the number of regions.

The regional coefficient of “resource curse” can be determined, as shown in

Table 2. “Resource curse” coefficients in each province.

Provinces	Shanghai	Zhejiang	Jiangsu	Hainan	Beijing	Guangdong	Hubei	Fujian	Guangxi	Jiangxi
RCI	0.03	0.07	0.13	0.14	0.17	0.22	0.25	0.65	1.36	1.14
Provinces	Liaoning	Henan	Anhui	Sichuan	Chongqing	Yunnan	Gansu	Heilongjiang	Shaanxi	Qinghai
RCI	2.23	1.19	1.17	1.44	1.12	1.57	1.89	2.33	4.16	2.85
Provinces	Tianjin	Hebei	Hunan	Shandong	Jilin	Xinjiang	Ningxia	Guizhou	Inner Mongolia	Shanxi
RCI	0.61	0.71	0.78	0.78	3.17	5.67	3.24	4.12	5.29	6.58

:

To specify the “resource curse” situations in China, three conditions were used: high, low, and no “resource curse”. These conditions are shown in

Table 3. Regional division of China’s “resource curse”.

Regional Division	“Resource Curse” Coefficient Range	Regional Distribution	Regional Characteristics
No “resource curse”	0 ≤ Rci < 1	Shanghai, Zhejiang, Jiangsu, Hainan, Beijing, Guangdong, Hubei, Fujian, Shandong, Tianjin, Hubei	Economic growth is much higher than resource endowment, and there is no “Resource curse” phenomenon
Low “resource curse”	1 ≤ Rci < 3	Liaoning, Henna, Anhui, Sichuan, Chongqing, Yunnan, Gansu, Heilongjiang, Qinghai	Economic growth should lag slightly behind resource endowment conditions, and the “Resource curse” phenomenon appears in some regions
High “resource curse”	3 ≤ Rci	Jilin, Xinxiang, Ningxia, Huizhou, Inner Mongolia, Shanxi, Shaanxi	Economic growth and resource endowment conditions are extremely mismatched, and resource endowment seriously hinders economic growth

:

(2)

Energy substitution policy scenarios.

Energy substitution policies are divided into three categories, including oil and gas substitution, non-fossil energy substitution, and comprehensive energy substitution, as shown in

Table 4. Design of energy substitution policy scenarios.

Policy Scenarios	Key Indicators	Average Substitution Rate	Enhanced Substitution Rate
Oil and gas energy substitution	The ratio of the sum of oil and natural gas consumption to coal consumption	0.4089	0.4498
Non-fossil energy substitution	The ratio of non-fossil energy consumption to fossil energy consumption	0.1531	0.1684
Comprehensive energy substitution	Geometric mean of oil and gas replacing coal and non-fossil energy replacing fossil energy	0.6374	0.7011

.

The oil and gas energy substitution (CE) is described as the ratio of the oil and natural gas to coal consumption. Non-fossil energy substitution (DE) is represented by the ratio of non-fossil energy consumption to fossil energy consumption. Comprehensive energy substitution (FE) is obtained by calculating the geometric mean of oil and gas substitution and non-fossil energy replacing fossil energy.

The base time of the model is 2021, and the simulation period is set from 2025 to 2035. GAMS was adopted as the simulation tool.

4.2. The Simulated Results

4.2.1. Historical Fitting Analysis

To verify the reliability of the simulation results, the actual situation from 2012 to 2020 was recalibrated for GDP using the average energy substitution rate as an exogenous variable, and historical fitting analysis was conducted based on this. The error values of the historical fitting and statistical yearbook are shown in

Table 5. The error values of the historical fitting and statistical yearbook.

	Investment	Consumption	GDP	Energy Consumption
2012	0.10	0.08	0.05	0.12
2013	−0.07	−0.06	−0.06	−0.07
2014	−0.04	−0.07	−0.02	−0.09
2015	0.01	0.06	0.03	0.05
2016	−0.08	−0.14	−0.15	−0.12
2017	−0.11	−0.17	−0.13	−0.15
2018	−0.05	−0.03	−0.06	−0.08
2019	−0.06	−0.08	−0.05	−0.07
2020	−0.07	−0.04	−0.05	−0.09

and

Table 6. The error values of other historical fittings and statistical yearbooks.

	Energy Price	Income and Expenditure			Pollutant Remission
	Coal Price Index	Resident	Department	Government	CO2	SO2	Waste Water	Solid Waste
2012	0.03	0.06	0.03	0.09	−0.05	−0.03	−0.10	−0.08
2013	0.05	0.04	0.05	0.07	−0.04	−0.05	−0.07	−0.07
2014	0.05	0.05	0.06	0.08	−0.05	−0.04	−0.08	−0.05
2015	0.04	0.07	0.05	0.07	−0.06	−0.06	−0.05	−0.06
2016	0.05	0.08	0.07	0.05	0.02	−0.04	0.03	−0.03
2017	0.07	0.03	0.05	0.04	0.04	−0.03	0.05	−0.05
2018	0.06	0.05	0.03	0.05	0.05	0.02	−0.03	−0.02
2019	0.04	0.06	0.05	0.06	−0.03	0.04	0.05	0.03
2020	0.05	0.04	0.04	0.03	0.05	0.05	0.03	−0.02

.

It can be seen that most of the errors between historical fitting and the data in the statistical yearbook are within 10%, and the historical fitting effect of the main indicators is trustful; therefore, model credibility is feasible.

After the historical fitting analysis of the dynamic CGE model, different energy simulation scenarios, including average energy substitution rate and enhanced energy substitution rate, are conducted. The impacts of energy substitution are mainly reflected in the changes in regional economic growth, energy, environment, and the resource curse coefficient.

4.2.2. The Simulated Results and Analysis

(1)

The impacts on economic growth by energy substitution policy.

Generally speaking, energy substitution policies have modest impacts on China’s economy, with potential positive effects becoming more evident in the long term. Energy substitution policies contribute to a more sustainable energy structure and can enhance economic resilience and growth over time. For example, the GDP growth rate, total output, and the total investment increase from 2025 to 2035 under an average substitution rate. As energy substitution requires a longer period to fully implement, the GDP growth rate and total output are less affected by a comprehensive energy substitution policy, which decreases by −0.02% and −0.05% in 2025. However, these effects are reversed by 2035, with increases of 0.03% in GDP growth rate and 0.04%, respectively. The total investment increases by 0.33% and 0.39% from 2025 to 2035. The price of renewable energy power generation in China is nearly equivalent to fossil energy and will decrease in the future, reducing the impact on economic growth, as shown in Figure 1 and Figure 2.

When adopting the enhanced energy rate substitution rate, the changes in key economic indexes are obvious (see Figure 2). The reason for these changes might be the substantial increase in fixed asset investments concerning energy substitution. Meanwhile, total energy consumption has not changed greatly, but energy substitution has been incentivized in the renewable energy equipment manufacturing industry, meeting the growth seen in China’s electricity demand, such as in the case of electric vehicles. Because 50% of China’s oil is used as transportation fuel, electric vehicles will reduce oil consumption. Energy substitution is a way to reduce energy imports and increase domestic energy production. Replacing traditional energy imports with renewable energy power generation is also beneficial for increasing China’s economic growth.

However, there are still significant differences in regional economic growth. Energy substitution policies have more pronounced impacts in high “resource curse” regions compared to regions with no or low “resource curse”.

In high “resource curse” regions, the GDP growth rate drops by −0.21% and −0.09% from 2025 to 2035. The total output and total investment reduce to a certain extent. From 2025 to 2035, total output declines by −0.13% and −0.16%, and total investment increases by 0.52% and 0.49%. The reason might be that the high “resource curse” regions mostly belong to the central and western provinces that have superior energy endowments and relatively less energy demand. In detail, the effect of non-fossil substitution policies on the regional economy is stronger than oil and gas energy substitution policies. Under a non-fossil substitution policy, the GDP growth rate and total consumption are less affected. The impacts of an energy substitution policy on the regional economy are shown in

Table 7. The impacts of an energy substitution policy on economic growth in %.

	Time	Resource Curse	Oil and Gas Energy Substitution			Non-Fossil Energy Substitution			Comprehensive Energy Substitution
			GDP Growth	Total Investment	Total Output	GDP Growth	Total Investment	Total Output	GDP Growth	Total Investment	Total Output
Average substitution rate	2025	No	−0.05	0.15	−0.03	−0.07	0.26	−0.05	−0.06	0.21	−0.04
		Low	−0.09	0.23	−0.06	−0.13	0.31	−0.08	−0.08	0.29	−0.12
		High	−0.18	0.46	−0.12	−0.27	0.59	−0.15	−0.21	0.52	−0.13
	2030	No	−0.03	0.16	−0.02	−0.05	0.29	−0.03	−0.04	0.20	−0.03
		Low	−0.05	0.28	−0.05	−0.11	0.36	−0.06	−0.06	0.24	−0.05
		High	−0.11	0.45	−0.17	−0.25	0.55	−0.12	−0.17	0.47	−0.14
	2035	No	0.02	0.18	−0.03	0.03	0.35	−0.04	0.04	0.19	0.03
		Low	0.03	0.29	−0.05	0.05	0.52	−0.07	0.03	0.37	0.05
		High	−0.06	0.49	−0.19	−0.17	0.61	−0.14	−0.09	0.49	0.06
Enhanced substitution rate	2025	No	−0.06	0.19	−0.04	−0.05	0.23	−0.05	−0.07	0.21	−0.04
		Low	−0.20	0.27	−0.07	−0.19	0.31	−0.09	−0.20	0.09	−0.08
		High	−0.22	0.55	−0.15	−0.25	0.63	−0.18	−0.24	0.60	−0.19
	2030	No	−0.04	0.23	−0.03	−0.05	0.26	−0.04	−0.05	0.24	−0.03
		Low	−0.07	0.29	−0.06	−0.08	0.34	−0.08	−0.09	0.32	−0.08
		High	−0.15	0.59	−0.19	−0.18	0.68	−0.17	−0.17	0.65	−0.18
	2035	No	0.01	0.25	−0.04	0.02	0.31	−0.03	0.02	0.30	−0.03
		Low	0.02	0.32	−0.08	0.04	0.39	−0.07	0.03	0.35	−0.08
		High	−0.05	0.65	−0.21	−0.09	0.71	−0.20	−0.07	0.68	−0.20

:

Although non-fossil energy policies are helping to promote the economy’s transition to low-carbon development, the direct impacts on GDP growth rate and total consumption in the short term are relatively small, which is more reflected in long-term economic restructuring and sustainable development.

Furthermore, fossil energy policies are designed to reduce dependence on fossil fuels and promote energy transition; however, in the short term, the direct impacts of these policies on GDP growth rates may not be significant. Although these policies encourage the use of non-fossil energy, traditional energy still dominates in terms of actual economic activities. Therefore, the direct driving effect of non-fossil energy substitution on economic growth is limited. Moreover, non-fossil energy policies may affect consumer behavior, especially in terms of energy and related products. However, the impacts of a non-fossil energy policy are limited because consumers’ habits and preferences do not undergo fundamental changes in the short term. In addition, the prices of non-fossil energy products are higher than others, which may limit their popularity. These factors lessen the impacts of non-fossil substitution policies on the GDP growth rate and total consumption. Therefore, understanding these main influencing factors is meaningful in terms of achieving sustainable development.

(2)

Impacts on environmental pollutants.

The results presented in

Table 8. Impacts of energy substitution policies on environmental pollutants (2035)%.

		Environmental Pollutants	Oil and Gas Substitution			Non-Fossil Energy Substitution			Comprehensive Energy Substitution
			2025	2030	2035	2025	2030	2035	2025	2030	2035
Average substitution rate	No resource curse area	Waste water	−3.06	−3.42	−3.81	−5.73	−6.81	−7.76	−5.12	−5.88	−6.46
		So2 emissions	−4.48	−4.93	−5.77	−6.41	−7.35	−8.49	−5.79	−6.53	−7.44
		Co2 emissions	−5.06	−5.84	−7.68	−7.42	−8.84	−9.62	−6.26	−7.68	−8.75
		Industrial solid waste	−2.86	−3.16	−3.92	−3.93	−4.78	−5.83	−3.55	−4.27	−5.42
	Low resource curse area	Waste water	−3.54	−3.94	−4.83	−6.17	−7.64	−8.67	−5.47	−6.21	−7.87
		So2 emissions	−5.25	−5.89	−6.74	−6.95	−7.99	−9.14	−6.15	−7.04	−8.21
		Co2 emissions	−6.02	−6.71	−7.83	−7.23	−8.76	−9.83	−6.53	−7.69	−8.89
		Industrial solid waste	−3.38	−3.85	−4.73	−4.31	−5.15	−6.24	−3.74	−4.87	−5.68
	High resource curse area	Waste water	−4.77	−5.86	−7.13	−6.57	−7.64	−8.67	−6.12	−6.84	−7.89
		So2 emissions	−7.92	−8.53	−9.98	−6.95	−7.99	−9.14	−7.25	−8.06	−9.32
		Co2 emissions	−7.85	−8.57	−9.73	−7.23	−8.76	−9.83	−7.51	−8.86	−9.78
		Industrial solid waste	−4.17	−4.86	−6.15	−4.31	−5.15	−6.24	−4.22	−5.09	−6.18
Enhanced substitution rate	No resource curse area	Waste water	−4.12	−4.53	−4.93	−6.73	−7.88	−8.83	−5.93	−7.03	−8.11
		So2 emissions	−6.54	−7.28	−7.96	−7.53	−8.74	−9.59	−7.12	−8.24	−9.15
		Co2 emissions	−6.12	−6.95	−7.84	−8.52	−9.74	−10.64	−7.95	−9.12	−10.08
		Industrial solid waste	−3.19	−3.85	−4.61	−4.63	−5.74	−6.63	−4.22	−5.14	−6.02
	Low resource curse area	Waste water	−5.24	−6.02	−6.73	−6.78	−7.69	−8.74	−6.12	−7.09	−7.97
		So2 emissions	−7.81	−8.47	−9.36	−8.64	−9.36	−9.89	−8.23	−8.96	−9.16
		Co2 emissions	−7.16	−7.93	−8.67	−8.33	−9.12	−10.13	−8.05	−8.92	−9.41
		Industrial solid waste	−4.08	−4.86	−5.72	−4.78	−5.52	−6.31	−4.46	−5.11	−6.07
	High resource curse area	Waste water	−6.17	−6.94	−8.12	−7.75	−8.75	−9.62	−7.03	−7.96	−8.79
		So2 emissions	−8.84	−9.79	−11.05	−9.57	−10.34	−11.75	−9.12	−9.64	−10.35
		Co2 emissions	−8.45	−9.26	−10.74	−9.72	−10.65	−11.84	−9.41	−10.05	−10.77
		Industrial solid waste	−4.62	−5.36	−6.67	−5.68	−6.32	−7.41	−5.13	−5.94	−7.20

show that energy substitution policies will lead to a gradual reduction in environmental pollution from 2025 to 2035, with an average annual decline rate of 4.1%. In terms of regional impact, the changes in carbon emissions in regions with no and low “resource curse” are relatively minor, which is attributed to the optimization of the energy structure and the implementation of the oil and gas energy substitution policy. In the high “resource curse” region, environmental pollutants decrease rapidly, indicating that the emission-reducing effect of the energy substitution policy is obvious. Because the high “resource curse” regions are mainly concentrated in the western region, rich in energy resources, these areas possess a significant advantage in terms of reducing energy consumption intensity and adjusting their energy structure.

More importantly, energy substitution policies have a significant inhibitory effect on environmental pollutants. Wastewater, SO₂, CO₂, and industrial solid waste reduces by more than 5% from 2025 to 2035, as shown in Table 8.

This paper further investigated the effects of different energy substitution policies, proving that a higher proportion of green energy in an energy substitution policy can significantly improve emission-reducing effects. The results presented in Figure 3 and Figure 4 also show that China’s total emission reduction cost changes with energy substitution policy; however, the regional differences in marginal emission reduction costs increase. In 2035, the total emission reduction costs reduced in relation to the composite energy substitution policy are estimated to be RMB 7192 billion, accounting for 4.2% of the GDP. Among them, the non-fossil energy substitution policy has a remarkable effect on emission reduction costs. The oil and gas energy substitution saves RMB 27 billion and RMB 869 billion in high and low “resource curse” regions. From a regional perspective, the impact of the energy substitution policy on reducing emission reduction costs in regions with a high “resource curse” is higher than others. For example, under the energy substitution policy, the total emission reduction cost of high resource curse regions will be reduced by 0.12% and 4.2% from 2025 to 2035.

(3)

The changes in regional “resource curse”.

The results presented in

Table 9. The coefficient of “resource curse” by the composite energy substitution policy with an average substitution rate in 2035.

Provinces	Shanghai	Zhejiang	Jiangsu	Hainan	Beijing	Guangdong	Hubei	Fujian	Guangxi	Jiangxi
Rci	0.02	0.06	0.11	0.12	0.13	0.21	0.22	0.62	1.22	0.96
Provinces	Liaoning	Henan	Anhui	Sichuan	Chongqing	Yunnan	Gansu	Heilongjiang	Shaanxi	Qinghai
Rci	1.77	0.95	0.94	0.95	1.05	1.48	1.83	2.18	3.08	2.65
Provinces	Tianjin	Hebei	Hunan	Shandong	Jilin	Xinjiang	Ningxia	Guizhou	Inner Mongolia	Shanxi
Rci	0.60	0.68	0.75	0.72	2.76	4.68	2.61	3.75	4.15	5.12

and

Table 10. The coefficient of “resource curse” by the composite energy substitution policy with an enhanced substitution rate in 2035.

Provinces	Shanghai	Zhejiang	Jiangsu	Hainan	Beijing	Guangdong	Hubei	Fujian	Guangxi	Jiangxi
Rci	0.02	0.05	0.10	0.12	0.11	0.18	0.21	0.59	1.20	0.96
Provinces	Liaoning	Henan	Anhui	Sichuan	Chongqing	Yunnan	Gansu	Heilongjiang	Shaanxi	Qinghai
Rci	1.74	0.94	0.93	0.93	0.98	1.45	1.78	2.14	2.97	2.62
Provinces	Tianjin	Hebei	Hunan	Shandong	Jilin	Xinjiang	Ningxia	Guizhou	Inner Mongolia	Shanxi
Rci	0.58	0.64	0.72	0.71	2.72	4.63	2.55	3.71	4.12	4.96

show that the regions with high a “resource curse” have diminished significantly under an energy substitution policy with an average substitution rate. The coefficient of the “resource curse” in Henan, Anhui, Sichuan, and Jiangxi changes to less than 1, meaning that the “resource curse” has disappeared. Moreover, the coefficient of the “resource curse” changes to less than 3 in Jilin and Ningxia, indicating the lessening of the “resource curse”. The changes in the regional “resource curse” coefficients (2035) are shown in Table 9 and Table 10.

The energy substitution policy effectively alleviates the phenomenon of regional “resource curse” with both an average substitution rate and an enhanced substitution rate, respectively.

5. Conclusions and Policy Implications

In this paper, a dynamic CGE model was built to simulate the impacts of an energy substitution policy with average and enhanced substitution rates, respectively, on China’s economic growth, environmental pollution and regional “resource curse”. The results show that (1) the energy substitution policy slightly impacted the economic growth in China. In the short term, GDP and total output decrease; however, they will steadily increase in the future. There is no notable change in GDP and the total output in regions with no “resource curse”, while a significant decline is noted in regions with a high “resource curse”. Meanwhile, the economic impacts relating to the non-fossil substitution policy is stronger than the oil and gas substitution policy. (2) The carbon emission changes in regions with no and low “resource curse” are smaller, while the reduction effect in regions with a high “resource curse” is remarkable. The main reason might be the optimization of the energy structure, especially the significant reduction caused by the non-fossil energy substitution policy. Meanwhile, the total reduction cost changes with the energy substitution policy; as the intensity of substitution varies, the regional differences in marginal emission reduction costs increase. (3) The energy substitution policy reduces the number of areas with a high “resource curse”, and the effect of a non-fossil energy substitution policy is higher than that of the oil and gas substitution. Energy substitution policies effectively alleviate the “resource curse” phenomenon.

In summary, the key to avoiding the resource curse lies in promoting energy transformation and diversified development. It is necessary for high “resource curse” regions to speed up technological innovation and the implementation of green energy through an energy substitution policy. For example, Shanxi and Inner Mongolia are both high “resource curse” regions, which have realized the importance of energy substitution. The development model seen in Shanxi’s excessive reliance on resource-based industries, such as coal, has led to the formation of a single industrial structure, a lack of support from other industries, and a continuous decline in growth rate. In order to solve this dilemma, Shanxi focused on promoting industrial structure upgrades, changing the industrial development pattern of “coal dominates”, and building a diversified and modern industrial system through energy substitution policies, reducing the region’s excessive dependence on coal and enhancing economic resilience and sustainability. Inner Mongolia is promoting the diversification of the energy structure by accelerating the development of clean energy, such as wind power and photovoltaic power. Both are exploring their own transformation paths to get rid of their regional “resource curse”. Based on these cases, “resource curse” regions can promote the optimization of the economy and energy consumption structure and accelerate the transformation and development of “resource curse” areas, thus alleviating the resource curse phenomenon.

This paper provides some suggestions: (1) Further promote energy substitution policies in China and speed up the transformation of energy production and consumption modes, emphasizing the process of non-fossil energy substitution to build a clean, low-carbon, safe, and efficient energy system. (2) Moreover, “resource curse” regions should accelerate their transformation from “single industry dominance” to “multi industry co-prosperity” by promoting the substitution of traditional energy with renewable energy, developing emerging energy industries and traditional energy together. (3) Improve energy substitution policies in some important fields, such as transportation, industry and so on, as well as promote low-carbon tools and strengthen the construction of energy substitution infrastructure.