Comparative Analysis of Carbon Tax and Carbon Market Strategies for Facilitating Carbon Neutrality in China’s Coal-Fired Electricity Sector

Abstract

Carbon taxes and carbon markets both contribute to mitigating carbon emissions in China’s power industry. Nevertheless, the pricing mechanism within China’s national carbon market, confined solely to the power sector, faces challenges in accurately reflecting the diverse costs of emission-reduction efforts across various regions. Similarly, carbon taxes encounter difficulties in effectively harnessing the inherent emission-reduction capabilities of power enterprises. This study investigates which carbon-pricing mechanism—a carbon tax or the carbon market—can better promote carbon neutrality in China’s coal-based electricity industry. Using a stochastic electricity price model, we reveal the price shocks of both a carbon tax and the carbon market under different carbon-pricing goals. Taking the China Carbon Emission Trading Market as the research object, the results are as follows: First, both carbon tax and the carbon market could significantly trigger price volatility in the coal-based electricity industry, while the carbon market’s shock effect on the industry’s emissions is more significant than that of carbon tax. Second, through both carbon-pricing mechanisms, emissions could be reduced by as much as 20%—a key premise of achieving this goal is keeping the carbon price at the level of 100 yuan/ton. Third, the volatility range of the electricity price, which is policy based, does not manifest the incentivizing effect of economic instruments on emission reduction in the coal-based electricity industry. Policy allows for an upper limit of 15% in the floating electricity price. By clarifying the linkage between carbon-pricing tools and coal-based electricity costs, this study contributes to developing a carbon-pricing mechanism that could help China’s coal-based electricity industry achieve carbon neutrality in a timely manner.

1. Introduction

Given the increasingly serious effects of global warming, dealing with climate change has become more urgent than ever. Under the Paris Agreement, the world’s major carbon-emitting countries are now seeking ways to achieve carbon neutrality [1,2]. In 2020, China, for instance, committed to achieving “carbon peak” by 2030 and “carbon neutrality” by 2060. Achieving this could require China to adjust its primary energy structure from 85% fossil fuels to 85% renewable energy. As a major resource-consuming country, China relies heavily on its high-emission industrial system for economic growth. Coal-based electricity is currently China’s most stable and cost-efficient power source [3,4]. Globally, electricity generation accounts for 40% of emissions; in China, coal-based electricity generation accounts for 55% of its total power generation and coal combustion for 43% of its total emissions [5,6]. Although China has policies in place to promote renewable energy, difficulties associated with grid interconnection and investment gaps hinder their implementation. Installed coal-based power capacity still accounts for nearly half of the total installed capacity in China’s electricity industry (Figure 1). Thus, China’s transition from coal-based electricity faces challenges in terms of limited technological progress and structural optimization [7]. Therefore, in the context of China’s market-oriented reforms, controlling emissions from coal-based electricity will require an effective carbon-pricing design based on a suitable policy guarantee system.

Carbon-pricing mechanisms, which mainly include carbon trading and carbon taxes, define emission costs and are the most effective policy tools for achieving carbon neutrality. Through the decomposition of carbon-emission tasks, carbon pricing provides a clear price signal for the emission-reduction value at each stage and can guide firms and social capital to produce and invest accordingly [8]. However, whether it would be better for China to use an administratively oriented carbon tax or market-oriented carbon trading remains a topic of debate [9]. It has been suggested that both approaches could potentially eliminate coal-based electricity generation by increasing its associated costs [10]. While the unified carbon market enhances administrative efficiency, its implementation incurs significant costs, including compliance expenses for regulated firms (e.g., monitoring, reporting, and verification costs), transaction fees, and potential productivity losses from reallocating resources to emission reduction. These costs are particularly acute for smaller enterprises in less-developed regions, where carbon pricing may exacerbate existing financial constraints. However, the centralized design mitigates long-term systemic costs associated with coordinating fragmented regional markets, such as arbitrage risks and regulatory duplication. Existing studies predominantly emphasize the theoretical merits of carbon pricing but inadequately address its operational challenges in developing economies. These studies motivate our focus on the trade-offs between national uniformity and regional adaptability, thereby framing the policy’s cost–benefit dynamics as a core analytical thread. However, the carbon market is characterized by complex rules, management bottlenecks, and low process efficiency, which could delay emission reduction and bring higher costs. Carbon tax, meanwhile, involves minimal additional operational costs. Since the carbon tax sets a clear price for emissions, high investment in energy efficiency could be rewarded accordingly. Further, carbon taxes could be used to aid environmentally friendly projects and thus reduce emissions. However, a carbon tax will also increase tax burdens for firms [11].

Judging the effect of carbon pricing on reducing emissions from coal-based electricity requires analyzing the main factors influencing carbon prices. The key issue involves quantifying the transmission strength and specific paths of different carbon-pricing mechanisms in relation to the final electricity price. Such an approach can be used to address the following problems:

(1)

The linkage between coal and electricity prices needs to be clarified, and the effect of electricity price volatility on thermal coal prices should be quantified in the context of electricity market reform.

(2)

A transmission system model should be able to characterize the influence intensity and rules of different types of carbon pricing (i.e., carbon taxes and the carbon market) in relation to coal-based electricity prices.

Based on stochastic analysis, this study constructs a coal-based electricity price system that includes the thermal coal price, carbon market price, and carbon tax. The linkage price can describe the periodicity and volatility of coal and electricity prices. The system design parameters, including the carbon tax range and carbon market coverage, are adjusted to predict the coal-based electricity price risk under the adjustment of emission-reduction policies.

This study reveals the transmission path, effect intensity, and improvement direction of different carbon-pricing mechanisms with regard to achieving carbon neutrality in China’s electricity generation. Our theoretical and practical contributions are as follows:

(1)

Theoretically, our proposed multivariate stochastic analysis model—which comprises historical trends, carbon-pricing shocks, and market uncertainty—quantifies the effects of different carbon-pricing mechanisms on coal-based electricity prices. This offers a powerful tool for studying the transmission paths and effects of carbon constraints on the electricity industry. Existing models focus more on the carbon-pricing effects of specific periods on coal-based electricity. Our model, however, not only provides short-term market risk information for coal-based electricity plants but also quantifies the pricing effect of long-term emission-reduction measures. This study, therefore, reveals both the amount of carbon emission reduction and the corresponding revenues that could be generated by administrative orders (carbon tax) and market tools (carbon trading) in the coal-based electricity industry.

(2)

Practically, we propose effective carbon-pricing methods for coal-based power marketization in two dimensions—energy transformation and energy security—balancing different power costs based on measuring the carbon revenue potential of different power sources. Existing studies mainly provide specific carbon-pricing schemes for the coal-based electricity industry at the regional level. However, the unreasonable linkage of natural gas prices, carbon prices, and electricity prices triggered the current EU energy crisis. Thus, we further calculate the reasonable fluctuation range of the carbon price and design an optimal combination that bridges carbon and electricity indicators, obtained by adjusting their volatility range. Our findings can help emerging economies understand carbon pricing’s effects on the basic energy price system in the pursuit of carbon neutrality, providing a reference for governments to formulate effective emission-reduction policies.

Next, Section 2 reviews the relationship between carbon pricing and electricity prices, noting the particularities of China’s electricity market. Section 3 presents our medium- and long-term price prediction model for coal-based electricity based on carbon-pricing mechanisms. In Section 4, we obtain both the price trends and emission-reduction effects of thermal coal in China, guided by different carbon-pricing policies based on historical data and our prediction model. Section 5 concludes and offers recommendations for policy.

2. Literature Review

2.1. Electricity Price Forecasting and the Factors That Influence It

Electricity pricing is more complex than that of general commodities. At present, electricity pricing systems can be divided into nonmarket pricing [12], market pricing (competitive pricing) [13], and contract pricing (negotiated pricing) [14,15]. Except for nonmarket pricing, electricity price determination is also influenced by social development, government involvement, environmental protection, power technology, and supply and demand [16,17,18].

In an unregulated electricity market, competition changes the structure of the market.

Table 1. Factors affecting electricity prices.

Indicator System	Factors
Market factors	Market historical load, system load rate, overcapacity/shortage, historical energy reserve, and generation capacity [19,20]
Nonstrategic uncertainty	Weather, temperature, crude oil price, natural gas price, fuel price, and energy reserve [21,22,23]
Random uncertainty	Circuit interruption emergency and circuit blocking [24,25]
Behavior index	Historical electricity price, flexible load, bidding strategy, and price shock [26,27,28]
Time effect	Settlement period (day, month, and season), holiday, and seasonal variation [29,30,31]

shows the main factors triggering volatility in electricity prices.

Fuel cost is one of the main aspects of electricity generation, and fuel price changes thus have significant effects on spot electricity prices [32]. At the same time, power demand is closely related to weather factors. Residential and tertiary-industry electricity consumption typically peak during summer and winter because of the increased use of refrigeration, air conditioning, and heating. Therefore, electricity prices are related to weather volatility, indicating obvious seasonal characteristics. Further, transmission capacity and energy reserve are the hardware conditions of power supply, and it is difficult to change them in a short amount of time. Therefore, these two indicators usually appear in the prediction of medium- and long-term power supply.

Given the various factors that influence electricity prices, several electricity price forecasting tools have been proposed, as shown in Figure 2.

(1)

Multiagent models construct the price processes of load and demand by simulating the operations of several heterogeneous and interrelated systems (e.g., generators and plants) [33,34]. These predictions are more suitable for markets with little price uncertainty (e.g., regulated power markets) than for competitive markets.

(2)

Fundamental models aim to capture the physical and economic characteristics of electricity production and trading [35]. Since the results of such models are sensitive to violations of their assumptions (e.g., regarding physical and economic relationships), the more detailed the model, the more complex the parameter adjustments with regard to risks in practical application.

(3)

Derived from the financial sector, reduced-form models do not aim to provide accurate price forecasts. Rather, they focus on replicating the characteristics of daily electricity prices, reflecting marginal distribution at a certain point, dynamic price changes, and relationships among commodities. Such models are mainly used for derivative prices and risk management.

(4)

Statistical models apply mathematical methods to historical price information or other price-related information to predict current prices (Nowotarski and Weron 2018 [36]). The prediction accuracy of these methods depends on data quality and the efficiency of the algorithm.

(5)

Computational intelligence models, or artificial intelligence models, use artificial intelligence to deal with complex problems that traditional methods cannot solve [37,38]. The advantage of such models is that they can flexibly deal with certain complex and nonlinear problems. However, the high complexity of such models results in inaccurate medium- and long-term predictions.

2.2. Price Transmission of Carbon Pricing

Carbon tax and the carbon market are economic means for promoting energy conservation and reducing emissions. Both price CO₂, thus providing a signal for the entire economic system to transition to high energy efficiency and low energy consumption [39]. A carbon market limits the total amount of emissions, wherein the market determines the price, while a carbon tax fixes the price of emissions. Regarding the specific effects on the coal-based electricity industry, these two carbon-pricing schemes have different transmission effects.

Carbon taxes have enormous effects on the coal-based electricity industry, squeezing its profit margins and shifting investors’ strategies from traditional energy to renewable energy [40]. First, a carbon tax policy increases the cost of coal-based electricity generation, reducing its market competitiveness [41]. Second, a carbon tax places considerable development pressure on the coal-based electricity industry by weakening national support for high-emission industries. Third, a carbon tax necessitates adjustments to the investment structure, which further promotes wind, hydropower, and renewable energy industries, and thus reduces the thermal power industry’s competitiveness [42].

The carbon market’s transmission effects on electricity prices also depend on the cost route. Transfer is an indirect transmission in which the price of carbon-emission quotas is converted into opportunity costs [43,44,45]. First, plants transfer quotas to electricity prices as “possible costs”, which generate extra profits and raise prices for consumers. Studies of market-oriented electricity industries (e.g., in Germany and the UK) have shown that the main cost of carbon-emission quotas is transferred to electricity prices, which are ultimately borne by consumers [46]. Second, in the industrial chain of power production, carbon trading tends to be conducted with upstream firms, and the profits of the entire coal power industrial chain decline when the carbon price is high. At the same time, as a result of the sharp increase in costs and sharp decrease in profits, the primary energy production industry is significantly affected [47].

2.3. Characteristics of China’s Electricity Market

The price distortion in China’s coal and electricity markets is a historical product of the transition period between planned economy and market economy [48]. Figure 3 shows the historical process of China’s coal and electricity reforms. Due to the reform and opening up policy, the previously planned electricity and planned coal can no longer provide the power required by the market. However, by the 1990s, only a dozen years of reform and opening up policy had not accumulated enough financial resources for China to invest in both coal and power generation markets. Therefore, in order to concentrate on power security, the Chinese government chose to open the coal market and introduce private capital, while continuing to control the power industry and carry out the “dual-track” policy of state investment [49,50,51]. With the further development of the thermal coal market and the long-term operation of the coal and electricity benchmark price, the relationship between coal and the power industry was gradually distorted, forming a situation where the benign development of the coal industry is inhibited, the self-generating capacity of the industry is insufficient, and the efficiency of resource utilization is low. In this process, the contradiction between “market coal” and “planned electricity” makes the government have to adopt the policy of “linkage of coal and electricity prices” to smooth the distorted relationship between coal and electricity prices [52,53,54]. Companies have to reduce the risk of unpredictable returns from excessive fuel cost fluctuations by long-term agreements on coal prices. In order to further promote the market-oriented reform, the Chinese government cancelled the coal and electricity price linkage mechanism for the coal-fired power generation that has not yet realized market-oriented transaction, and replaced the current benchmark on-grid price mechanism with a market-oriented mechanism of “base price + up and down”, under the background that the market-oriented transaction of coal-fired power generation has accounted for about 50% of the electricity, and the power generation cost is significantly lower than the benchmark price.

China’s current power dispatch mechanism does not comply with market demand, China’s carbon and electricity markets have not been effectively connected, and the relevant policies of the two markets have not been fully coordinated [55,56]. Therefore, the transmission effect of carbon trading in the electricity market is still being explored. On the one hand, compared with the power system with a high management system, the market-based electricity price formation mechanism will be more conducive to the stability of the carbon market quota price. On the other hand, compared with developed countries, the current electricity price regulation in China directly blocks the process of power enterprises transferring part of the carbon cost to the downstream power side, thus having three adverse effects on the operation of the carbon market [57]. First, the emission-reduction effect of the carbon market cannot be extended to the electricity consumption side, which not only does not encourage power users to save electricity, but also relatively reduces the electricity price and stimulates the electricity demand. Second, power enterprises face distorted excess carbon costs, which raises the risk of entering the carbon market. Third, on the basis of the direct impact of the first two aspects, the resource allocation of the carbon market is distorted, and the operation efficiency of the carbon market is reduced.

3. Coal-Fired Electricity Price System Coupled with Carbon Pricing

3.1. Transmission Mechanism of Carbon Pricing’s Effect on Coal-Fired Electricity Prices

As a type of pollution tax, a carbon tax can internalize the negative externality of production by transferring the environmental pollution cost to producers. Therefore, the transmission effect of the carbon tax on the price of coal-fired electricity is a direct increase in emission costs in the process of power production.

Different from carbon taxes, the carbon market is a trading mechanism following the principle of “cap and trade”. Under this framework, coal-fired electricity firms that hold a large quota demand complete the task by purchasing or reducing their own emissions. If the marginal cost of internal emission reduction is lower than the carbon price, firms could choose internal emission reduction. Conversely, firms have to purchase quotas in the carbon market.

When identifying the external transmission mechanism of the carbon market to the coal-fired electricity price, the degree of transmission from the carbon cost to the electricity price is usually expressed by the carbon cost pass-through rate (CPTR) [58,59]. This refers to the ratio of electricity price change caused by marginal carbon cost change, as in Equation (1):

$$CPTR={\displaystyle \frac{∆p}{∆cc}},$$

(1)

where $∆p$ and $∆cc$ are the changes in the electricity price and the carbon cost of electricity producers before and after the operation of the carbon market, respectively. Following [60], the formula for CPTR is:

$$CPTR={\displaystyle \frac{1}{\left(n+1\right)+Q\frac{p^{″}\left(Q\right)}{p^{\prime }\left(Q\right)}}},$$

(2)

where $Q$ is the total generating capacity of the electricity market, $p$ is the market price, which is the inverse demand function of electricity, and ${\displaystyle \frac{p^{″}\left(Q\right)}{p^{\prime }\left(Q\right)}}$ is the elasticity of the slope of the inverse demand function.

Since China’s coal-fired electricity market is a state monopoly market, the electricity grid is responsible for overall electricity trading, including the distribution of electricity production, grid price, and trading volume. The number of electricity producers is small, and electricity demand is inelastic. We assigned the specific form of ${\displaystyle \frac{p^{″}\left(Q\right)}{p^{\prime }\left(Q\right)}}$ as $Q{\displaystyle \frac{p^{″}\left(Q\right)}{p^{\prime }\left(Q\right)}}=-{\displaystyle \frac{1}{\alpha }}-1$, where $\alpha$ denotes the equal elasticity of electricity demand. In the short term, an increase or decrease in consumers’ electricity demand will not trigger electricity price volatility. Thus, $\alpha \to \infty$:

$$CPTR={\displaystyle \frac{1}{n-\frac{1}{\alpha }}}\to {\displaystyle \frac{1}{n}}.$$

(3)

As Equation (3) shows, under the current mechanism of China’s coal-fired electricity market, the cost of carbon trading could be transmitted to the cost of coal-fired electricity production according to the number of electricity firms.

With recent market-oriented reforms, the government’s strict control over coal and electricity has been broken, and the trading system and price volatility in the electricity market have transitioned toward supply-and-demand-oriented competition. Under the assumption that the carbon cost could be transferred to the electricity cost, we examined the market mechanism of coal prices coupled with carbon pricing, as illustrated in Figure 3.

As shown in Figure 4, we broke down the price trend of coal-based electricity into three parts. The first concerns the trends and seasonal changes that can be revealed by historical data, which are fitted by the sine-cosine function. The second part pertains to changes in policy behavior, such as carbon tax, carbon–electricity market linkage, and financial subsidies. This is mainly based on the judgment of policy information, and we describe prices based on policy connotations. Third, we used a stochastic model to directly denote the trend of uncertainty in random changes that cannot be explained by data.

3.2. Market-Oriented Price of Coal-Fired Electricity

Seasonal variations in coal-based electricity demand and consumption have high convergence and good consistency. The low and peak seasons of thermal coal consumption in the electricity industry basically correspond to the low and peak seasons of the overall coal consumption market. Low coal consumption corresponds to lower power supply, while high coal consumption corresponds to higher power supply. Figure 5 shows that winter is the peak season for purchase demand in the electricity industry, and the demand for electricity leads to an increase in thermal coal prices.

Based on the demand–dependence relationship between coal and electricity, we built a coal-based electricity price system based on the market-oriented pricing mechanism, where the price is determined by the ratio of supply and demand between the buyer and seller. Here, the price trend includes both stable and regular volatility and the abnormal volatility of uncertainty. Therefore, we used deterministic and stochastic models to fit the market price of coal-based electricity.

Suppose the spot price of coal-based electricity is $X_t$. The concrete expression includes two aspects: a time-dependent deterministic function $f\left(t\right)$ and a diffusion process with a single-state variable comprising the diffusion process $S_t$, as in Equation (4):

$$X_t=f\left(t\right)+S_t.$$

(4)

Assume $S_t$ satisfies the O-U-shaped stochastic diffusion process, which indicates that the long-term representation of the spot price tends toward the equilibrium point of supply and demand, as in Equation (5):

$$d{S}_t=-\kappa S_{t}dt+\sigma d{W}_t,$$

(5)

where $\kappa$ is a mean reversion rate, and $\kappa >0,S\left(0\right)=S_0$, and ${dW}_t$ is an increment of Brownian motion, which denotes the random section in price representation.

Theorem 1.

Electricity spot price $X_t$ is a mean reversion process $d{X}_t=\kappa \left({\alpha \left(t\right)-X}_t\right)dt+\sigma d{W}_t, \alpha \left(t\right)={\displaystyle \frac{1}{\kappa }}{\displaystyle \frac{df\left(t\right)}{dt}}+f\left(t\right)$, and $S_t$ is mainly influenced by actual power demand in the long term.

Proof. 

$$d{X}_t=d\left[f\left(t\right)+S_t\right]=df\left(t\right)+d{S}_t.$$

(6)

Substituting Equations (4) and (5) into Equation (6), we have:

$$df\left(t\right)-\kappa S_{t}dt+\sigma d{W}_t=df\left(t\right)+\kappa f\left(t\right)dt-\kappa Xdt+\sigma d{W}_t.$$

(7)

Transform the right side of Equation (4) into

$$\kappa \left[{\displaystyle \frac{1}{\kappa }}{\displaystyle \frac{df\left(t\right)}{dt}}+f\left(t\right)-X_t\right]dt+\sigma d{W}_t,$$

(8)

and let $\alpha \left(t\right)={\displaystyle \frac{1}{\kappa }}{\displaystyle \frac{df\left(t\right)}{dt}}+f\left(t\right)$. Theorem 1 is thus proven. □

According to Theorem 1, electricity spot price $X_t$ tends to approach equilibrium item $\alpha \left(t\right)$, indicating $X_t$ deviating against $\alpha \left(t\right)$; then, $X_t$ will be attracted to equilibrium level $\alpha \left(t\right)$. Additionally, the main component of $\alpha \left(t\right)$ is $f\left(t\right)$, which is related to actual power demand. This result shows that in the long term, the spot electricity price will be close to the actual demand of the quota value. At the same time, the recovery characteristics of price $X_t$ mainly depend on standard deviation $\sigma$ and recovery speed $\kappa$.

3.3. Expression of Thermal Coal Price

The thermal coal price has evident characteristics of seasonality and volatility. This study takes the price trend of thermal coal in China from 2014 to 2020 as historical data. Figure 6 shows that the higher annual price always appears at the end of each year, indicating that winter heating and festival electricity consumption are key factors influencing the consumption of thermal coal, causing prices to increase. At the same time, except for 2016, the price of thermal coal showed a trend from high to low, indicating that the overall volatility of the thermal coal price is limited, and its trend is greater than its periodicity.

Since the periodicity and seasonality of the coal-based electricity price can be characterized by the component of the thermal coal price, we did not use periodicity to further differentiate its trend term in the fitting model; rather, we reflected the linkage of price through coal-based electricity price volatility.

Let the spot price of thermal coal $Y_t$ be a mean reversion process, $d{Y}_t=\eta \left({\theta \left(t\right)-Y}_t\right)dt+{\sigma }_{2}d{W}_t$, where $\eta$ is the mean recovery rate and $\theta \left(t\right)$ is its mean term.

3.4. Expression of Coal-Based Electricity Price Based on Thermal Coal Price Volatility

Under the risk-neutral measure, a price trend model of coal-based electricity can be obtained by combining the equation between spot electricity price $X_t$ and thermal coal price $Y_t$. Since the price of raw materials accounts for more than 50% of the total cost in coal-based electricity generation, we believe the price trend of thermal coal not only influences the mean term of the coal-based electricity price but also partly influences the volatility of the coal-based electricity price, as is shown in Equation (9):

$$\left\{\begin{array}{c}d{X}_t=\kappa \left({\alpha \left(t\right)-X}_t-a{Y}_t\right)dt+{\sigma }_1\sqrt{Y_t}d{W}_t^1\\ d{Y}_t=\eta \left({\theta \left(t\right)-Y}_t\right)dt+{\sigma }_2\sqrt{Y_t}d{W}_t^2 \end{array}\right.,$$

(9)

where $a$ is the degree of influence of the thermal coal price on the average value of the coal-based electricity price. $W_t^1$ and $W_t^2$ are two standard Wiener processes with a correlation coefficient of $\rho$, and their relationship is ${dW}_t^1{dW}_t^2=\rho$.

The price trend between coal and electricity is a multivariable stochastic differential equation, which is difficult to solve directly. However, the characteristic function of any random variable completely defines its probability distribution, and here, we only needed to work out the characteristic equation of the linkage equation and then use inverse Fourier transform to obtain its distribution function.

The characteristic function of Equation (6) is defined as in Equation (10):

$$f\left(x,y,t;\varphi \right)=E\left[e^{i\varphi X\left(T\right)}\left|X_t=x,X_t=y,\alpha \left(t\right)=\alpha ,\theta \left(t\right)=\theta \right.\right],$$

(10)

where $T\ge \mathrm{t}$, $i=\sqrt{-1}$.

Theorem 2.

Assuming the coal-based electricity price $X_t$ follows model (9), the characteristic equation function of $X_t$ defined in Equation (10) is:

$$f\left(x,y,t;\varphi \right)=exp\left[B\left(\tau \right)+C\left(\tau \right)x+D\left(\tau \right)y+i\varphi x\right],$$

where $C\left(\tau \right)=i\varphi e^{-k\tau }-i\varphi$,

$B\left(\tau \right)=\eta \theta {\int }_t^{T}D\left(\tau \right)d\tau -\alpha i\varphi e^{-k\tau }$,

$D\left(v\right)={\displaystyle \frac{2v\kappa w^{\prime \left(v\right)}}{{\sigma }_2^{2}w\left(v\right)}}$,

$w\left(v\right)=e^{{\displaystyle \frac{-\kappa lnv-\eta lnv+{\sigma }_1{\sigma }_2\rho i\varphi v}{2\kappa }}}M\left({\displaystyle \frac{i\left(a\kappa {\sigma }_2-\eta \rho {\sigma }_1+\kappa \rho {\sigma }_1\right)}{2\kappa {\sigma }_1\sqrt{{\kappa }^2-{\rho }^2}}},-{\displaystyle \frac{\eta }{2\kappa }},-{\displaystyle \frac{{\sigma }_1{\sigma }_2\varphi v\sqrt{{\kappa }^2-{\rho }^2}}{\kappa }}\right)$.

(See Appendix A for proof.)

Quantifying the effect of different carbon-pricing modes on the coal-based electricity price, we further clarified the effect path of the carbon market and carbon tax on the coal-based electricity price and improved the deterministic time function $f\left(t\right)$ to capture the deterministic behavior of the electricity price. Function $f\left(t\right)$ contains prior knowledge that could be extracted from the historical price of thermal coal and electricity; accordingly, the expression of $f\left(t\right)$ should conform to the special supply-and-demand attributes of electricity. The instantaneous power supply, seasonal and random power demand, and reliable evaluation criteria determine that power transaction requires not only comprehensive coordination between the supply and demand sides but also a flexible clearing mechanism and reasonable carbon–electricity linkage rule in the context of marketization.

We used a Fourier function with a sine-cosine function to describe the seasonal price component of electricity demand, as is shown in Equation (11):

$$Cycle=a_0+a_{1}cos\left(w_{1}t\right)+b_{1}sin\left(w_{2}t\right),$$

(11)

where $a_0$, $a_1$, $w_1$, and $w_2$ are invariant parameters. The cosine function reflects the seasonal pattern and can also reflect the annual cycle by evolving into the annual variables.

Regarding the effect of the carbon market and carbon tax on the price of coal-based electricity, since it corresponds to the effect on the deterministic income part of the coal-based electricity price, we fit its price shock according to the characteristics of different carbon-pricing modes.

First, the carbon tax aims to reduce carbon dioxide emissions. Thus, the carbon tax’s effect on the coal-based electricity price is the deterministic effect on its emissions, which is expressed in Equation (12):

$$f_{Tax}\left(t\right)=Cycle+Q\times E\times Tax,$$

(12)

where $Q$ denotes the total amount of coal-based electricity production, $E$ denotes the intensity of carbon emissions, and $Tax$ denotes the carbon tax rate.

By contrast, the basic mechanism of the carbon market is “cap and trade”—namely, setting the total amount of emissions to allocate and trade. In the carbon market, a power plant obtains ownership of the remaining quota by the baseline method. If emissions from coal-based electricity production are above the baseline, it could trigger more power production, a larger quota, and higher income. On the contrary, if emissions from coal-based electricity are below the baseline, the corresponding carbon emission rights should be purchased in the carbon market for each unit of electricity produced to ensure production in the next stage. Necessary investment in emission-reduction technology can be achieved by way of multi-payment methods, as is expressed in Equation (13):

$$f_{Trade}\left(t\right)=Cycle+\left(Q\times E-A\right)\times P_T\times {\displaystyle \frac{1}{n}},$$

(13)

where $A$ is emissions from advanced coal-based electricity units, $P_T$ is the trading price of the quota in the carbon market, and $n$ is the number of electricity firms, which is assumed to be 1 for easy calculation.

4. Medium- and Long-Term Price Forecast of Coal-Based Electricity Under Carbon Pricing

4.1. Influence of Thermal Coal Price on Coal-Based Electricity Price

The data in this study were mainly based on annual national data. Regarding the thermal coal price, China’s coal resources are mainly distributed in the western region, while the developed areas along the southeastern coast have limited coal resources with higher prices. To bring the results more in line with the real situation in China, we take the provincial average thermal coal price as the research basis and fit its volatility cycle.

Based on the data-fitting results, Figure 7 shows the formula for the thermal coal price under the 95% confidence region as $Cycle=453.9+48.93cos\left(0.09735t\right)-87.74sin\left(0.09735t\right)$.

Generally, the main factor influencing the long-term trend in thermal coal prices consists of both the random dependence of thermal coal and electricity prices and the dependence of the actual price. In Figure 8, $\rho$ reflects the relationship between the thermal coal price and the random trend of the coal-based electricity price. The authors of [17] found that thermal coal and electricity prices had a weak long-term correlation, and this study assigned $\rho \in${0.1, 0.2, 0.3} to express this relationship. When the value of $\rho$ is small (0.1), the long-term electricity price is in an upward trend; when it is large (0.3), the long-term electricity price is in a downward trend. Currently, the price volatility of China’s thermal coal reflects normal volatility caused by market trading, and the volatility range is mainly influenced by the effect of supply-and-demand gambling. By contrast, the coal-based electricity price mainly depends on the force of policy. Despite setting a floating price range, the composition of the coal-based electricity price is more complex than that under a pure market mechanism. In this context, when the price randomness of thermal coal and coal-based electricity is greater (0.3), the randomness item of thermal coal could produce strong uncertainty in the price trend of coal-based electricity, leading to a bearish price forecast in the current moment. However, since the coefficient of randomness denotes the uncertainty superposition of thermal coal and coal-based electricity prices, its transmission effect is highly uncertain, and the credibility of high random correlation is low. Therefore, in the later simulation, we used a value of 0.1 to denote the random correlation between coal and electricity prices. The long-term trend of coal and electricity prices was bullish under this correlation.

In this study, $a$ refers to the influence of the actual value of the thermal coal price on the coal-based electricity price. Since the coefficient before $a$ is negative in Equation (6), the greater the value of $a$, the smaller the effect of the coal price on the electricity price. In Figure 7, the value change of $a$ does not change the price trend of coal-based electricity. When the value of $a$ is 0.5, the rising trend in the electricity price is most noticeable, indicating that when the dependence of the coal and electricity price is high, the electricity price remains bullish for a long time.

4.2. Influence of the Carbon Market and Carbon Tax on the Coal-Based Electricity Price Trend

The regional differences in China’s feed-in tariffs are significant (see Figure 9). Thermal coal prices in Shanxi, Inner Mongolia, and Xinjiang are relatively low; therefore, the costs in these regions are correspondingly low. At the same time, the thermal coal price cost in coal-deficient areas (e.g., southwest, south, and east China) is relatively high. Since coal is a planned commodity, the National Development and Reform Commission sets regional feed-in tariffs based on regional coal endowment and economic development. The benchmark feed-in tariffs in south, east, and central China are higher, while those in northwest and southwest China are lower.

Clearly, there are differences in the regional prices of coal-based electricity. Taking the national average price index of coal-based electricity, we obtained prediction results for China’s coal-based electricity price after the introduction of the carbon market and carbon tax (Figure 10).

Note that since China’s eight carbon pilot markets cover different industries and enterprises, the carbon price differences are large. Currently, Beijing’s carbon price is relatively high, ranging from 80 to 100 yuan/ton, while Chongqing’s fluctuates at 10 yuan/ton. We selected the two most extreme cases—10 yuan/ton carbon tax and carbon price and 100 yuan/ton carbon tax and carbon price—to compare coal-fired power prices affected by carbon pricing.

The carbon tax is characterized by comprehensive promotion but differentiated effects on coal-based electricity prices. This effect is not a simple sum but a differential transmission under different electricity price trends. Specifically, when the power cost is in the declining range, the carbon tax leads to an increase in power costs. When the power cost is in the rising range, the effect of the carbon tax on the power cost does not increase proportionally but lags slightly behind the rising period of the power cost. This can be explained in terms of the two aspects of technical and practical effects.

Regarding the technical effect, as the rising cycle of the thermal coal price lags one year behind the adjustment cycle of the electricity price per kWh, the government cannot regulate the feed-in tariffs as the market does. Based on the rules of the market, we added the cyclical trend of the thermal coal price to the process of fitting the price trend of coal-based electricity. Then, we estimated the parameters that can smooth the cyclical lag through the linkage of coal and electricity prices. The carbon tax mainly influences the price-trend term, and in the derivation process of the characteristic function, this influence process is an exponential process. Since the guiding of carbon tax $f\left(t\right)$ mainly influences $\alpha \left(t\right)$, and the coefficient before $\alpha \left(t\right)$ is negative, through inverse Fourier transform, this value is consistent with the direction of the electricity price in its falling stage; thus, the effect is stronger. At the same time, in the rising stage of the electricity price, the growth of carbon tax is different from the electricity price trend, and the effect is reduced accordingly.

In terms of the practical effect, the fitting result is also in line with the original intention of the carbon tax. As a way to achieve emission-reduction guidance through a tax, it is difficult to make carbon tax collection equal to a specific amount of emission reduction. This uses financial means to increase the burden on coal-based electricity firms and force them to reduce emissions. In the rising stage of the coal-based electricity price, the formation of the price-rising channel will gradually open up the price difference between coal-fired and clean-energy power and help enhance the competitiveness of the clean-energy industry. In this context, the market mechanism plays an important role. Without a punitive carbon tax, coal-based electricity plants will still seek clean development to reduce emissions. Therefore, carbon tax introduction is not an equal-proportion conduction to the price of coal-based electricity, and it cannot be simply added when predicting the carbon tax’s effect on the price of coal-based electricity.

In terms of the dependence of the carbon market and coal-based electricity prices, it is difficult to judge the actual effect of the carbon market on its price per kWh by the market level. First, the carbon market is a trading market that consists of supply-and-demand sides, and its risk is significantly higher than that of carbon tax. The lack of liquidity caused by “having a price with no deal” is the biggest challenge facing China’s carbon market. The coexistence of a false high carbon price and drastic volatility makes it difficult to identify the actual transmission path of the coal-based electricity price in the carbon market. The trend shown in Figure 9 can also clearly reflect this point. In the linkage of the carbon market and coal-based electricity prices, it is not assured that the electricity price trend is consistent, and it is at the upper end of the real price trend. Compared with the benchmark price of the carbon quota of 10 yuan/ton, the volatility in the electricity price caused by the carbon quota of 100 yuan/ton is more significant, and the peak value of volatility could reach 10% of the electricity price. Second, the higher the benchmark price, the more evident the effect of the carbon price downward transmitting the price trend of coal-based electricity. Taking 100 yuan/ton as the initial value, it is more likely that the price volatility range caused by the carbon price is lower than the price trend of coal-based electricity conducted by the carbon tax, and most of the carbon price appears below the carbon tax price. These two points show that the carbon tax is more advantageous than the carbon market based only on the price-transmission effect.

However, the ultimate goal of carbon pricing is to invest in low-carbon technology and guide coal-based electricity plants to fulfill their emission-reduction obligations. Although the actual price-transmission effect of carbon tax is better, the actual effect on emission reduction still needs to be further tested. At the same time, it should be noted that the low-carbon technological transformation of thermal power is also influenced by multiple factors, including renewable energy subsidy policies, environmental regulations, low-carbon transformation costs, low-carbon technological innovation, and social low-carbon awareness. Carbon taxes or carbon markets may amplify the emission-reduction effects brought by these overlapping factors. Thus, it is necessary to further examine the emission-reduction results of carbon pricing for thermal power under the superposition of multiple factors.

4.3. Emission-Reduction Efficiency of Coal-Based Electricity Industry Under the Effects of the Carbon Tax and Carbon Market

If coal-based electricity plants are willing to hedge the price pressure brought by the carbon tax, they need to invest in emission-reduction technology to reduce carbon emissions. In this study, we ignored the corresponding cost caused by investment in carbon capture and storage (CCS) technology and only took the cost of CCS operation as the calculation object of the emission-reduction cost.

The cost of each part of CCS is significantly different owing to different factors at specific locations. Currently, the capture cost (including compression) accounts for the largest share in most CCS systems.

Table 2. Cost components of CCS in China.

Basic Elements	Price
Capture cost	300 yuan/ton
Transportation	80–120 yuan/ton
Sequestration	5–10 yuan/ton
Oil displacement	10–45 yuan/ton

shows the main components and corresponding prices of CCS technology costs in China.

The values in Table 2 show that the cost composition of CCS technology is complex. When coal-based electricity plants do not need carbon capture and transportation, the cost of storage and oil displacement is the lowest, and the lowest CCS storage cost is 0.26 yuan/kWh. When coal-based electricity plants need to transport captured carbon dioxide and calculate other cost elements with the highest storage and oil displacement costs, the highest CCS storage cost is 0.399 yuan/kWh. With a carbon tax of 100 yuan/ton, the average coal-based electricity price will rise 8% compared with the original price of about 0.035 yuan/kWh. If all electricity prices increased by the carbon tax are used for CCS, at least 8.77% and at most 13.46% of carbon dioxide could be captured.

By contrast, the probability of a quota shortage in China’s carbon market is about 20% when the price of the carbon market soars to 100 yuan/ton [61]. Given that the probability of a 20% quota shortage is equal to the scale of a 20% quota limit, the emission-reduction effect of the carbon market is significantly higher than that of the carbon tax.

For the above analysis, we only tested the actual effect of a 100 yuan/ton carbon tax and carbon benchmark price on the emission reduction of coal-based electricity plants. The benefits of emission reduction (carbon tax and initial carbon price) obtained by government departments were not further included in the process of emission-reduction technology investment. Accordingly, we further considered the long-term price trend under carbon pricing by taking the government’s investment in low-carbon technology as an important factor.

We fit the price trend according to the average level of national feed-in tariffs, where ${\sigma }_1$ = 0.101, ${\sigma }_2$ = 0.025, $\eta$ = 0.1487, and $\kappa$ = −0.0928. The range of price volatility was derived from the government’s floating pricing policy of [−15%, 10%].

According to the price volatility range of [−15%, 10%], combined with the cyclical historical price volatility in thermal coal and coal-based electricity, Figure 11 forecasts the average price trend for coal-based electricity from 2020 to 2031. The price trend per kWh is relatively stable, showing a slow rising trend for a long time within the range of 6.8% average annual volatility. This forecast result is similar to that of previous studies [62]. With a decline in coal resources and an increase in environmental protection requirements, the prices of thermal coal and coal-based electricity will increase for a long time. Comparing the predicted price of coal-fired power with the real price, although China has operated a unified carbon market, excessive free quotas and low average prices have not been enough to increase coal-fired power price fluctuations in the blue area. To cause the power system price to reflect its environmental premium, China increased the national average coal-fired power price to 0.4335 yuan/kWh. However, long-term market testing is needed to determine whether the environmental premium of the administrative-dominated coal-fired power pricing system reflects carbon emissions.

Based on the price-trend prediction for coal-based electricity, the 100 yuan/ton carbon tax and 100 yuan/ton carbon benchmark price were added to obtain the price-trend prediction for coal-based electricity based on the effect of the carbon tax and the carbon market. The blue curve and purple range in Figure 10 show the corresponding prediction results, which have three characteristics. First, whether carbon tax or carbon market, the effect on the price of coal-based electricity is not in equal proportion. Overall, the rising effect of the carbon tax and carbon market on the rising range of coal-based electricity costs is significantly higher than that on the falling range of coal-based electricity costs. The reason for this pertains to the fitting result of the actual trading year. The average transmission direction of the carbon tax and carbon market regarding electricity price determines the transmission effect. Second, the carbon tax is more significant than the carbon market with regard to increasing the price of the coal-based electricity market. In Figure 10, most of the increase in power cost caused by the carbon market is below the curve of the electricity price increase caused by the carbon tax. This can be explained in terms of the law of market price formation. A high carbon price of 100 yuan/ton means that the probability of market quota shortage is 20%. In the absence of follow-up policy stimulus, the market will maintain the trading volume of the 20% quota. However, like the general financial market, the high carbon price reflects investors’ judgments about the future of the industry. Without the expectation of annual quota tightening, it is difficult for the market to have an upward price trend. Therefore, compared with the carbon tax, the transmission degree of the carbon price to the cost of coal-based electricity is lower. Third, in the upward range of the coal-based electricity price, the introduction of the carbon tax and carbon market could easily cause the electricity price to exceed the upper limit of the 10% upward range set by the policy. This result shows that, in the range of 3–5 years, a substantial increase in the carbon tax and carbon market price level could make the price of coal-based electricity bear excessive upward pressure, causing the upper-limit threshold of the per kWh power cost volatility range to restrain the excessively fast-rising electricity price. However, in the range of 5–8 years, the price limit disappears owing to the cyclical decline of the thermal coal cost, and this part of the cost cannot be transmitted to the active emission-reduction investment of coal-based electricity plants. Accordingly, the upper limit of the threshold will force coal-based electricity plants to digest this part of the carbon-emission cost, resulting in a decline in the profit margin.

We further calculated the final value of emission reduction that can be achieved by the carbon price and carbon tax by investing in existing emission-reduction technologies. In the carbon market, the benefit generated by the transaction belongs to the covered firms, and the total emission cap directly determines the emission reduction that could be achieved in the market. Therefore, 20% of total emissions could be reduced based on a carbon price of 100 yuan/ton. Compared with the carbon market, the beneficiary of the carbon tax is the government. If all carbon taxes are used as financial subsidies for CCS technologies, the cost of CCS could be reduced to 215 yuan/ton, which is equal to a 0.18 yuan/kWh reduction in CCS costs for coal-based electricity emission. On this basis, the maximum emission reduction could be achieved by the superposition of the CCS subsidy, and the carbon tax is 19.3% of total emissions. On the surface, the amount of emission reduction that the carbon tax can achieve is similar to that of the carbon market. In practice, however, it is difficult to ensure that coal-based electricity plants can bury carbon emissions on the spot. Moreover, the extensive coverage of CCS and comprehensive CCS subsidies are a great test for the carbon tax to achieve the goal of emission reduction. Further, the carbon tax has a significant effect on the price of coal-based electricity plants, and advanced low-emission plants cannot obtain any real benefits from this policy.

5. Conclusions and Policy Recommendations

5.1. Conclusions

Our findings indicated that, compared with the direct transmission of the carbon tax to the final price of coal-fired electricity, the carbon market transmits the emission cost to the production cost of coal-fired electricity, which is more helpful for enhancing the emission-reduction enthusiasm of coal-fired electricity firms. Thus, under the current linkage system of coal-based electricity cost, the carbon market is conducive to lowering emission-reduction costs and achieving carbon neutrality in China’s coal-based electricity industry. Additionally, we derived the following conclusions.

First, whether adopting a carbon tax or the carbon market, the maximum emission reduction that could be achieved in the coal-based electricity industry was 20% of total emissions based on current power production and emission-reduction technologies. Second, using market approaches to achieve the goal of a 20% emission reduction in the coal-based electricity industry, the carbon tax or the initial benchmark price of the carbon market should be maintained at a high level of 100 yuan/ton. Third, the current price volatility range of [−15%, 10%] will limit the final market effect of emission reduction. The upper limit of the electricity price volatility range should be raised to 15%.

5.2. Recommendations for Carbon-Pricing Policies in Emerging Market Countries

In their efforts to achieve carbon neutrality, China and other emerging economies face huge challenges related to power conversion and electricity market reform. To address these two challenges, carbon-pricing mechanisms need both strong policy support and a flexible policy adjustment space to maximize the greening of energy consumption within an affordable economic range.

China should take carbon market development as the main measure for reducing the emissions of coal-based electricity plants. Our results indicated that in countries with electricity marketization and an optimized carbon market system, the market mechanism is more helpful for guiding emission-reduction competition among coal-based electricity firms, thus striking a balance between economic development and emission reduction. China’s carbon market is currently undergoing development and improvement. Although its overall development trend is good, the development level is not balanced.

Meanwhile, emerging economies need to proactively formulate flexible and elastic price systems for their electricity markets to effectively connect carbon-pricing mechanisms with the electricity market. China’s electricity industry currently has the highest level of emissions worldwide. However, this market is still in the process of market-oriented reform, the dispatching mechanism of the power grid is still not flexible enough, and the formation of electricity prices is still under government control. Under such conditions, the carbon market price signal cannot effectively guide a high-efficiency, low-carbon power plant to give priority to the grid. Our results indicated that, to effectively incorporate market-oriented emission-reduction measures into the power industry’s price system, the current upper limit of 10% price volatility should be adjusted to 15%. Accordingly, China needs to speed up the market-oriented reform of the power industry, straighten out the electricity price formation mechanism, effectively link electricity and carbon prices, and fully reflect the real cost of carbon emissions so the carbon market mechanism could play a real role in reducing the cost of emission reduction in the electricity industry. For other emerging market countries, the electricity price system has included both the state-owned stable prices and the market-led flexible prices [36,63]. However, the electricity price system of these countries is mainly composed of medium- and long-term electricity contracts, which makes it difficult to dynamically reflect the cost of carbon emissions and is not conducive to the integration of the carbon and electricity markets. Thus, when choosing the carbon market as the emission-reduction measure in the electricity market, it is necessary to further promote the compatibility between electricity pricing and carbon pricing.

5.3. Recommendations for Carbon Market Development

Electricity market access is an important force for the sustainable development of the carbon market. A healthy carbon market should maintain a moderately high carbon price with a sound market mechanism. Prior studies have shown that maintaining the carbon price at a high level could achieve the emission reduction [64,65]. The research results of this paper further support this conclusion that a low carbon price signal could not be effectively transmitted to the firm emission-reduction level, and a carbon price of nearly 100 yuan/ton is needed to put more pressure on coal-based electricity plants. However, the supply and demand in the carbon market should be in a state of tight balance. Ensuring the high carbon price, it also avoids the systematic risk of energy caused by the high carbon price. For emerging market countries, the formation time of the carbon market and its related products is short, the data resource is relatively scarce, factors triggering the price-operation mechanism of carbon-market-related products are complex, and regional pilot markets are in a fragmented state, resulting in different pricing mechanisms and market activities among different market pilots. Therefore, it is necessary to strengthen new policy tools to overcome market failure and the externalities of technological development by continuously reducing the number of top-level quotas and strengthening the emission monitoring, reporting, and verification (MRV) principle, quota allocation principle, quota trading rules, and the binding effects of quota compliance and the punishment mechanism. For mature markets, maintaining a relatively stable carbon price will help alleviate inflation caused by energy security. The current rise in the EU natural gas price makes coal-fired power, which originally bears the high cost of carbon emissions, more economical. In the short term, the increase in coal-fired power production will inevitably lead to insufficient carbon quotas, trigger the rise in carbon prices, and push up the final price of coal-fired power and other power resources. The low flexibility of supply-and-demand allocation in the carbon market inhibits the role of coal-fired power in the safe dispatching and risk control of the energy system. Accordingly, the government needs to actively introduce the dynamic adjustment measures of carbon quota supply to help the carbon market truly realize marketization.

Based on the conclusions drawn in this section, relevant policy recommendations to promote coal power emission reductions are further proposed.

First, we took the development of the carbon market as the main measure to improve the efficiency of active emission reduction of coal power enterprises. The research results in this section showed that under the background of the same emission reduction, the cost pressure brought by the carbon market was significantly lower than the cost pressure brought by the same carbon tax. Therefore, in countries with electricity marketization and a carbon trading system, the market mechanism is more helpful to guide the competition of coal power enterprises to reduce emissions and achieve the result of balancing economic development and energy saving and emission reduction. Specifically, China’s carbon trading market is currently under development and improvement. China’s unified carbon market has been officially launched, and the national carbon market will officially start trading in the near future. Although the overall development trend of China’s carbon trading market is good, the development level is uneven, and the development industry is single. In this context, we should focus on improving the carbon market trading mechanism, coordinating the ratio of carbon trading volume and turnover, and promoting emission reduction to the greatest extent through effective control of the trading volume. At the same time, as a market means for the purpose of emission reduction, the Chinese government should also try to control the speculative demand in the carbon market and formulate an effective system, including pricing, verification, and emission reduction, so as to stabilize the carbon price in a reasonable fluctuation range to control market risks and ensure market liquidity.

Second, on the basis of promoting market means to help in coal power emission reduction for the power industry to achieve carbon neutrality goals, the government should actively expand policy, administrative and legal means, in addition to market means, and increase policy support for emission-reduction technology and clean energy development. The research results in this section showed that under the principle of not overly affecting economic development, the market approach could only achieve a 20% carbon emission reduction. Compared with the post-industrial countries, China’s development stage is more dependent on energy and has a larger base of carbon emissions. The goal of carbon neutrality makes the path of energy saving and emission reduction even steeper and more difficult to achieve. Therefore, in order to ensure that the power industry achieves the goal of carbon neutrality, the Chinese government should continue to intensify the promotion of electric energy substitution by administrative and other compulsory means, promote the consumption of clean energy, improve the level of social electrification and energy efficiency, jointly promote clean and low-carbon development, jointly promote the breakthrough of electric energy substitution technology, and jointly build a good low-carbon industrial ecology.

Third, we should promote the healthy development of the carbon market and maintain an appropriately high carbon price with a sound market mechanism. Existing studies have shown that maintaining a high carbon price can ensure the emission-reduction effect of the carbon market, and the research results in this section further supported this conclusion. That is, the low carbon price signal cannot be effectively transmitted to the level of enterprise emission reduction, and the carbon price of nearly 100 yuan/ton is needed to put more pressure on coal power enterprises in the cost of kWh electricity. However, as an emerging thing, the formation time of the carbon market and related products is short, the data resources are relatively scarce, the influencing factors of price operation mechanism of carbon-market-related products are complex, the regional pilot market is in a fragmented state, and different markets present different pricing mechanisms and price operation mechanisms. These reasons lead to large differences between carbon price markets and sharp price fluctuations. The attribute of a market-based emission-reduction tool requires three supporting conditions: economic market conditions, a legal and regulatory environment, and coordination between policies, to maintain the effective operation of the carbon market price. However, the decline in the overall carbon price level reduces the compliance costs of high-energy consumption and high-emission enterprises, which is not conducive to the realization of the long-term industrial restructuring goal. Therefore, it is necessary to further strengthen new policy tools to overcome the market failure and the external contradictions of technological development by continuously reducing the number of top-level quotas, strengthening the MRV principle of emissions, the quota allocation principle, the quota trading rules, the quota performance mechanism, and the punishment mechanism.

The carbon tax collected by the Chinese government is mainly used for environmental protection and governance, supporting the research and development and promotion of low-carbon technologies, promoting the transformation of energy structure, coping with climate change, and replenishing public finance. In order to focus on the emission-reduction utility of carbon tax, this paper assumed that all carbon tax is used for CCS investment; that is, carbon tax is used for low-carbon technology promotion. This paper did not consider the low-carbon technological transformation of thermal power, including CCS.

In future research, first of all, we will add a subsection explaining the mechanism for coordinating carbon pricing with electricity market reforms, including the role of cross-departmental task forces (e.g., Ministry of Ecology and Environment) to harmonize emission caps with grid dispatch rules. We will expand the discussion on blockchain-based platforms for real-time data sharing between carbon registries and power grids. Examples include pilot projects in Guangdong Province using smart meters to track emissions and electricity consumption simultaneously. Secondly, we will introduce a mathematical model to formalize the relationship between carbon price floors/ceilings and electricity demand elasticity. Case studies from the EU ETS and Shenzhen pilot program will be cited to validate the model. Third, we will expand on public awareness campaigns, including partnerships with tech platforms (e.g., Alibaba’s “Ant Forest”), to gamify carbon reduction. We will add survey data from Shanghai households to quantify behavioral shifts. Finally, we will add a policy analysis of China’s “dual-carbon” funds, emphasizing their allocation to grid-scale energy storage and hydrogen infrastructure. Case examples will include the Ningxia renewable energy hub. We will also discuss advancements in AI-driven monitoring systems (e.g., satellite-based CO₂ tracking) and their integration with China’s national carbon accounting platform.