Measuring the Economic Effects and Benefits of Developing a Natural Gas Power Plant in Vietnam

Abstract

A stable electricity supply is a key factor for sustainable development in Vietnam, a rapidly growing developing country with increasing energy consumption. This article delves quantitatively into the economic effects and benefits arising from the construction of a 1.5 GW capacity natural gas-fired power plant (NGPP). Input–output analysis was applied to identifying the economic effects. Specifically, production-inducing effects and value-added creation effects were analyzed separately for the construction and operation of the NGPP. Based on the economic theory, the economic benefits were computed as the sum of the electricity price and consumer surplus resulting from electricity consumption. During the construction period of the NGPP, it is expected to induce USD 2315.60 million of production and USD 414.75 million of value-added for the Vietnamese economy. The production-inducing effects, value-added creation effects, and economic benefits ensuing from the operation of the NGPP in 2030 were estimated to be USD 833.36 million, USD 235.75 million, and USD 1164.33 million, respectively. The cost–benefit analysis revealed a benefit-to-cost ratio of 1.45, which is higher than 1, indicating the economic feasibility of the construction. Therefore, the construction of the NGPP can be implemented with social net benefits.

1. Introduction

Electricity generation or consumption generally has a positive impact on the economic growth of a country. Of course, some research has not detected this impact in specific countries (e.g., [1]). Furthermore, it has been pointed out that, beyond a certain income level, per capita electricity consumption can actually decrease [2]. However, there is no doubt that electricity is an important input factor that generates economic value in the manufacturing sector [3]. In particular, for developing countries, a stable electricity supply plays an essential role in national development. This is because, during the process of economic development, the demand for electricity increases not only in the industrial sector but also in the commercial and residential sectors. Moreover, electrification as a means to reduce greenhouse gas (GHG) emissions will accelerate further [4,5,6,7,8].

The situation in Vietnam, Asia, is no exception. The country’s electricity consumption is growing at an alarming rate. According to the International Energy Agency [9], electricity consumption has increased significantly, from 6.5 TWh in 1990 to 237.8 TWh in 2021, growth of approximately 36.6 times with a compound annual growth rate of 12.3%. Looking at a narrower time frame, electricity consumption has shown an average annual growth rate of 11.8% from 2000 (22.9 TWh) to 2021, which is similar to the average growth rate from 1990 to 2021. Conversely, the average annual economic growth rate from 2000 to 2021 was 6.6%. This means that the growth rate of electricity consumption was nearly twice the economic growth rate. The higher growth rate of electricity consumption compared to the economic growth rate is an interesting characteristic of Vietnam. As a result, Vietnam has been experiencing electricity shortages, leading to frequent short-term power outages [10,11,12,13].

However, it is not easy for Vietnam to secure sufficient infrastructure for power generation facilities within a short period of time. This is because of the significant costs involved in building power plants. Thus, due to limited financial resources, Vietnam is showing interest in attracting foreign investment in the power supply sector to ensure stability in the electricity supply. In fact, numerous large-scale power plant construction projects are currently being undertaken. These projects are being carried out in two main directions. The first direction focuses on the renewable energy (RE) sector, such as solar and wind power, through official development assistance programs.

The second direction involves foreign energy companies with capital building and operating large power plants in Vietnam in the form of foreign direct investment and recovering their investment costs through electricity bills. Vietnam declared its commitment to achieving carbon neutrality by 2050 in early 2021. Consequently, it is difficult to continue constructing large-scale coal-fired power plants as before. Instead, the construction of large-scale natural gas combined cycle power plants (NGCCPPs), which significantly reduce GHG emissions compared with coal-fired power plants, has emerged as an important task [14,15]. If the NGCCPPs are not built, realistically, coal-fired power plants should be built. Therefore, the NGCCPP is quite critical to reducing carbon dioxide (CO₂) emissions through coal-to-gas for developing countries like Vietnam (e.g., [16,17]).

According to the International Atomic Energy Agency, the amounts of carbon dioxide emission from coal-fired power generation and NG-fired power generation are 991 and 549 CO₂ g/kWh, respectively, with the latter accounting for 55.4% of the former. Thus, although NG is also a fossil fuel, converting coal-fired power generation to NG-fired power generation reduces CO₂ emissions by almost half. Reducing CO₂ emissions generates environmental benefits by delaying global climate change. In this respect, NG has the meaning of a bridge energy for ultimately moving to a carbon-free power source for sustainability.

Unlike coal-fired power plants that emit air pollutants such as nitrogen oxides, sulfur oxides, and benzene, NG-fired power plants emit only a small amount of nitrogen oxides. In addition, all NG-fired power plants to be built in the future will be equipped with selective catalytic reduction technology-equipped denitrification facilities. Thus, almost no nitrogen oxides will be emitted. It appears that, unlike coal-fired power plants, NG-fired power plants do not cause serious air pollution issues in most countries. NG-fired power generation can be quite an effective alternative to coal-fired generation.

In fact, since Vietnam has declared carbon neutrality by 2050, this project using NG, a fossil fuel, as a power source may not seem to be in line with that declaration. However, the Vietnamese government plans to completely eliminate coal-fired power generation by 2050, drastically reduce the total capacity of operating NG-fired power plants from 22.5 GW in 2030 to 7.9 GW in 2050, and significantly increase RE generation such as hydroelectric power, solar power, and wind power, while also increasing electricity imports. CO₂ emitted from the NG-fired power plants remaining in 2050 will be eliminated through carbon sinks such as forests, thereby ensuring compliance with that declaration. Currently, the introduction of a carbon tax or emissions trading system is not being considered.

The research questions in this paper are as follows: first, what is the size of the economic effects and economic benefits of the NGCCPP construction and operation project? Second, does the NGCCPP construction and operation project secure economic feasibility or not? Only when economic benefits are quantitatively estimated can the establishment of the economic feasibility of the project be confirmed. Moreover, only when economic feasibility is established is it socially desirable to carry out the project. Quantitative information on the economic effects arising from the execution of the project is required by government authorities.

The purpose of this research is, therefore, to quantitatively analyze the economic effects generated by the construction and operation of a large-scale NGCCPP in Vietnam. This analysis is conducted in two main directions. Firstly, two economic effects of production-inducing and value-added creation effects related to the construction and operation of the NGCCPP in Vietnam are explored from an input–output (IO) perspective. To this end, IO analysis (IOA) is applied to the latest IO table (IOT) for the year 2018 published by the Vietnamese Statistical Office. In particular, since construction and operation occur in different time periods, they are analyzed separately. Secondly, the economic benefits obtained by the Vietnamese people who consume the electricity supplied by the NGCCPP are estimated using the theory of consumer behavior in microeconomics. A feasibility study of the construction of the NGCCPP using information on the benefits is urgently needed (e.g., [18]).

There is a limited number of previous studies specifically related to the power sector in Vietnam. Nguyen [19], Phu [20], Trung and Tuyet [21], and Do and Le [22] estimated the electricity demand function in Vietnam and derived the price elasticity or income elasticity of demand. Their findings will be used later to estimate the economic benefits of electricity consumption. Hien [23] compared the electricity intensity of Vietnam with that of other countries and pointed out that the former was excessively higher than the latter. Son and Yoon [24] analyzed the determinants of household electricity consumption in Vietnam. Roy et al. [25] evaluated the GHG emissions that could be reduced if fossil fuel-fired power generation was replaced with renewable energy in Vietnam. Duy et al. [13] analyzed the impact of grid expansion on household electricity use in Vietnam and confirmed that improved electricity access did not lead to an increase in its use in low-income households. Man and Thuy [26] investigated reasonable electricity rates in Vietnam using the stochastic payment card method.

Thus, only a few studies estimating electricity demand functions were found in the literature. The authors were unable to obtain systematic analyses of the economic effects of power plant construction and operation or the economic benefits of electricity consumption from the perspective of Vietnamese consumers. This study can add a contribution to the literature as it is the first one to delve into the economic effects and benefits ensuing from NGCCPP construction and operation. Moreover, the implications of this study are even more useful because it is the first study targeting Vietnam, a developing country with rapidly increasing electricity demand due to rapid economic growth. Consequently, this research can provide a systematic framework within which to empirically analyze and demonstrate the economic effects of new power plant construction and operation from the perspective of a national economy and the economic benefits that arise from electricity consumption. This is an interesting part of this research. The authors believe that this study can make a meaningful contribution to the literature as a pioneering attempt.

The rest of this paper is organized into four sections. The second section provides an overview of the NGCCPP under consideration. The methodology is presented in the third section. The fourth section is composed of a description of the data, the presentation of the results, and a discussion. The conclusion is given in the fifth section.

2. Overview of the NGCCPP Under Consideration

Before introducing the methodology in earnest, an overview of the NGCCPP construction project considered in this study is provided. As mentioned in Section 1, the electricity demand in Vietnam is increasing at a rapid pace due to economic growth. Figure 1 depicts the electricity consumption performance from 1990 to 2021. The numbers in this figure were obtained from the International Energy Agency [9]. The project under consideration in this paper is the construction of a large-scale NGCCPP in the Quang Tri region of central Vietnam. The project is scheduled to be carried out in two stages. The first stage of the project is to build a 1.5 GW capacity NGCCPP that uses 1.5 million tons of liquefied NG (LNG) annually. The second stage of the project is to expand the capacity of the power plant to 4.5 GW. Both the first and the second stages are being promoted as part of Vietnam’s Revised Power Master Plan VII and Vietnam’s Power Master Plan VIII for 2021–2030 with a vision to 2045, respectively [27,28]. The first stage of the project, for which specific construction and investment plans are relatively well established, is selected for this study. The first stage of the project requires an investment of approximately USD 2.3 billion and is being promoted by a consortium consisting of Vietnam’s T&T Group and three South Korean companies.

The project involves two public companies and one private company from South Korea investing 60% of the total project cost, while the remaining 40% will be invested by a local Vietnamese company (T&T Group). Given the substantial scale of the total project cost, there exists considerable financial uncertainty surrounding it. For instance, exchange rates may fluctuate, funding issues may arise, and changes in the Vietnamese government’s electricity pricing policy could deteriorate financial viability, making continuous investment difficult. Particularly, LNG, which is used as a fuel for power generation, tends to have high price volatility; if LNG prices rise significantly, the project itself could be jeopardized mid-way. However, as of now, the feasibility study for the project has received final approval from the Vietnamese government, and the project is progressing smoothly.

The first stage of the project, an NGCCPP, is expected to be constructed over four years from 2025 to 2028 and then commercially operated from 2029 for a period of 25 years. Typically, NGCCPPs are guaranteed to be operated for 30 years by the manufacturer. However, due to the high utilization rate and increased fatigue caused by the increasing electricity demand in Vietnam, it is assumed that the plant will operate for only 25 years. The electricity produced will be supplied to consumers through Vietnam Electricity (EVN). The annual average electricity production will be approximately 8.6 TWh and will vary slightly each year depending on changes in the in-house power consumption rate and power generation reduction rate.

Unfortunately, it was difficult to obtain or find scientific information on future electricity prices and sales volumes related to the project. Thus, the authors inquired about the business entities and collected information about them. In determining the size of the NGCCPP, the business entities estimated the electricity price and sales volume they want to receive for financing it by taking into account the price of LNG used as fuel and various financial costs. This study accepts and utilizes this information as it is. The anticipated annual electricity sales quantity and price during the operation of the power plant are presented in

Table 1. Expected price and amount of electricity supplied from the proposed natural gas combined cycle power plant.

Years	Price (Unit: USc per kWh)	Electricity Supplied (Unit: GWh)
2029	9.54	8753
2030	9.73	8719
2031	9.88	8708
2032	10.04	8698
2033	10.18	8711
2034	10.37	8677
2035	10.54	8667
2036	10.71	8656
2037	10.87	8669
2038	11.07	8635
2039	11.25	8625
2040	11.44	8615
2041	11.61	8629
2042	11.83	8594
2043	12.04	8584
2044	12.24	8573
2045	12.43	8587
2046	12.67	8553
2047	12.89	8543
2048	13.12	8532
2049	13.32	8546
2050	13.59	8512
2051	13.83	8502
2052	14.08	8492
2053	14.25	8570

Note: These values were estimated by both the business entities.

.

The success of a project for the construction of power plants, like this one, depends on financing and government approvals. Since Vietnam lacks sufficient funds, the project will secure financing from a consortium of three South Korean companies, while the local company T&T Group will contribute land and other assets. The first of the three South Korean companies is a public power generation company with extensive experience in the construction and operation of NGCCPPs. The second is a public company that is the largest LNG buyer globally, importing about 78% of the NG consumed in South Korea and operating the pipeline network. The third is a private company that is constructing and operating an NGCCPP while importing LNG. These three companies will secure funding from state-run banks in South Korea. T&T Group supplies electricity to consumers and collects fees. The revenues generated from electricity fees will be distributed among the four companies, and there are no additional incentives provided by the Vietnamese government. Therefore, this project takes the form of a typical public–private partnership.

The composition of Vietnam’s power generation needs to be explained in more detail. As of the end of 2019, Vietnam’s total installed power capacity is approximately 56 GW. The capacity by source is 20 GW from hydropower, 20 GW from coal, 7.5 GW from NG, 1.5 GW from oil, and 5.2 GW from RE, indicating a high dependence on hydropower and coal. While electricity demand continues to rise, GHG emissions from the power sector need to be reduced. Therefore, the Vietnamese government is implementing an energy transition policy to reduce coal-fired power generation and increase RE and NG generation. In particular, the country has significant potential for solar and wind power in terms of sunlight and wind resources.

Additionally, a substantial increase in NG-fired generation is anticipated. According to governmental plans, NG-fired generation capacity is expected to reach 22.5 GW by 2030. This is due to two main reasons. First, with the increase in variable RE sources such as solar and wind, there is a need to utilize NG as a stable baseload power source that can supply electricity reliably while emitting fewer GHGs. Expanding battery storage systems is too costly, making it a difficult option for developing countries like Vietnam. Second, Vietnam faces a significant shortage of power grids that require substantial investments, making it challenging to use the electricity generated from RE sources at the point of demand. Therefore, the construction of NG-fired power plants near the point of demand is essential.

3. Methodology for Delving into the Economic Effects and Benefits

The methodology adopted in this study to explore economic effects and benefits in earnest will now be explained in this section in detail. First, the economic effects of inducing production and value-added will be described, and then the economic benefits will be dealt with.

3.1. Measurement of the Economic Effects on Inducing Production and Value-Added

3.1.1. Basic Method: IOA

As previously mentioned, IOA is applied in this study to determine how much investment in constructing and operating a large-scale NGCCPP in Vietnam generates production and value added within the national economy. IOA, the basic theory developed by Leontief [29], is quite useful not only when dealing with macro issues, such as employment, prices, and output, but also when analyzing the impact of investment in a specific sector on each sector and the economy as a whole. In this way, IOA is not only useful in analyzing various pervasive effects within a national economy but can also demonstrate its value in dealing with pervasive effects caused by economic activities in a specific sector. This is because the specific sector is part of a national economy, and the chain of economic effects can be quantitatively identified through the empirical analysis of general equilibrium theory, which is the core of IOA.

There are a number of studies in which IOA has been applied to electricity-related issues in the literature (e.g., [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47]). When analyzing economic effects, this study divides investment in the NGCCPP into construction and operation phases. This is because the construction and operation phases do not overlap temporally and are clearly distinguishable and because the detailed sectors in which investment is made differ between the two phases.

IOA uses an IOT, which represents the flow of all sectors in an economy in terms of inputs and outputs in a single table. IOTs are classified into competitive tables, which include imports from foreign countries, and non-competitive tables, which exclude imports. The object of interest in this study is not the impact on Vietnam’s foreign economy, but the impact on its domestic economy. If a competitive table is used, the impact on imports would be included, which could overestimate the impact on the domestic economy. Therefore, this study uses the non-competitive IOT, and all the subsequent explanations are based on it. Since IOTs are being issued not only in developed countries but also in developing countries, IOA using an IOT can be applied to developing countries as well. Specifically, the IOT for domestic transactions in Vietnam in 2018 is used.

3.1.2. Demand-Side Model for Measuring the Economic Effects

To explore the impact on production and value-added, a demand-side model can be applied. Let us assume that there is a total of $n$ sectors in the economy. If we denote the total output of sector $i$ as $X_i$, it consists of the sum of intermediate demands supplied from each sector in the economy, $z_{ij}$, and the final demand for sector $i$, $Y_i$. In other words, the following equation is derived:

$$\sum _{j=1}^n{z}_{ij}+Y_i=X_i$$

(1)

To apply IOA to the IOT, it is essential to deal with matrices since the IOT is composed of multiple matrices. For this purpose, matrix notation is required. Accordingly, the coefficient of inputs, $a_{ij}$, is defined as $z_{ij}/X_j$. Since $a_{ij}$ represents the technological characteristics in producing $X_i$, the coefficient of inputs is also known as the technical coefficient. Let us denote $X$, Y, and $A$ as matrices composed of $X_i$, $Y_i$, and $a_{ij}$, respectively. With some manipulations, Equation (1) can be transformed into the following matrix notation:

$$X={(I-A)}^{-1}Y$$

(2)

where $I$ represents the $n$-dimensional identity matrix. The objective of this study is not to determine the impact of changes in the final demand but to assess the impact of investments in the construction and operation sector of the NGCCPP. Therefore, it is necessary to separate the NGCCPP sector included in the endogenous sectors and transform it into an exogenous sector, a process known as exogenous specification. Let us denote the NGCCPP sector as $NG$, so that the transformation of Equation (2) into a model in which $NG$ is exogenously specified can be expressed as follows:

$$X^e={\left(I^e-A^e\right)}^{-1}(Y^e+A^{NG}{X}_{NG})$$

(3)

where $X^e$ and $Y^e$ are $(n-1)\times 1$ matrices obtained by removing sector $NG$ from $X$ and $Y$, respectively, $I^e$ is an $(n-1)$-dimensional identity matrix, $A^e$ is the matrix obtained by deleting all rows and columns related to sector $NG$ from $A$, $A^{NG}$ is the matrix obtained by removing the elements corresponding to sector $NG$ from the columns related to sector $NG$ in $A$, and $X^{NG}$ is the total output of sector $NG$.

Our interest lies not in the fluctuations of the final demand but in the fluctuations of the total output of sector $NG$. Therefore, after transforming Equation (3) into a model of fluctuations, we can assume that $∆Y^e=0$. Then, we can derive the following equation:

$$∆X^e={\left(I^e-A^e\right)}^{-1}{A}^{NG}{∆X}_{NG}$$

(4)

The $(n-1)\times 1$ matrix, ${\left(I^e-A^e\right)}^{-1}{A}^{NG}$, on the right-hand side represents how much investment in sector $NG$ in terms of monetary units stimulates production in other sectors. In this study, Equation (4) is used to analyze the production-inducing effects of investment in the construction and operation phases of the NGCCPP on other sectors and the overall economy.

To calculate the value-added creation effects instead of the production-inducing effects, Equation (4) needs to be manipulated from the perspective of value-added. For this purpose, the value-added coefficient matrix of other sectors excluding sector $NG$, $A_V^e$, is introduced. These elements are defined as the proportion of value-added to the total input in each sector. In other words, the element $a_V^j$ is derived by dividing the value-added of sector $j$ by its total input. The increase or decrease in value-added in each sector caused by a unit of investment in sector $NG$, $∆V^e$, can be derived as follows:

$$∆V^e={\widehat{A_V^e}\left(I^e-A^e\right)}^{-1}{A}^{NG}{∆X}_{NG}$$

(5)

where $\widehat{A_V^e}$ represents the diagonal matrix of $A_V^e$. Ultimately, the $(n-1)\times 1$ matrix, ${\widehat{A_V^e}\left(I^e-A^e\right)}^{-1}{A}^{NG}$, on the right-hand side indicates how much investment in sector $NG$ in terms of monetary units stimulates the value-added in other sectors.

In this study, Equation (5) is used to analyze the value-added creation effects of investment in the construction and operation phases of the NGCCPP on other sectors and the overall economy. The variables included in the model described so far are summarized in

Table 2. Description of the variables used in the demand-side model.

Variables Used	Definitions
$z_{ij}$	Intermediate demand of sector $j$ supplied from sector $i$
$Y_i$	Final demand for sector $i$
$X_i$	Total output of sector $i$
$a_{ij}$	Coefficient of input defined as $z_{ij}/X_j$
$X$	Matrix composed of $X_i$
$I$	Identity matrix
$Y$	Matrix composed of $Y_i$
$A$	Matrix composed of $a_{ij}$
$X^e$	Matrix obtained by removing natural gas combined cycle power plant (NGCCPP) sector from $X$
$I^e$	Identity matrix obtained by removing NGCCPP sector from $I$
$A^e$	Matrix obtained by deleting all rows and columns related to NGCCPP sector from $A$
$Y^e$	Matrix obtained by removing NGCCPP sector $Y$
$A^{NG}$	Matrix obtained by removing the elements corresponding to NGCCPP sector from the columns related to NGCCPP sector in $A$
$X_{NG}$	Total output of sector NGCCPP
$a_V^j$	Proportion of value-added to the total input in sector $j$
${∆V}^e$	Value-added in each sector caused by a unit investment in NGCCPP sector
$A_V^e$	Value-added coefficient matrix of other sectors excluding NGCCPP sector
$\widehat{A_V^e}$	Diagonal matrix of $A_V^e$

.

3.1.3. Underlying Assumptions of the IOA

It is necessary to mention three representative assumptions underlying the application of IOA. First, it is assumed that the production function within a country’s economy follows the Leontief production system. In reality, the Leontief production function is not quite flexible. Nevertheless, this assumption is unavoidable when applying IOA. Second, it is assumed that one sector produces only one type of product. It would be more reasonable to consider that various types of products are produced within one sector. However, since IOA is fundamentally based on the monetary unit IOT, even somewhat heterogeneous products can be treated as one product under the same standard of the monetary unit. This is also an unavoidable assumption in IOA.

Third, the fixedness of input coefficients, $a_{ij}$, is assumed. As will be discussed later, the IOT of Vietnam used in this study is for the year 2018. Changes in production technology and structural economic shifts over the period 2029–2053 cannot be immediately reflected. If input coefficients remain stable over time, using past IOTs does not pose significant issues. However, if they do not, problems arise due to a lack of timeliness in the results. One clear fact is that in IOA, the input coefficient matrix $A$ is not used, but rather the input inverse matrix, ${(I-A)}^{-1}$, is utilized. As pointed out by Miller and Blair [48], while $A$ may not be stable over time, ${(I-A)}^{-1}$ is known to be stable over time. Therefore, the authors believe that using the somewhat outdated 2018 IOT does not pose any significant issues in this study.

3.2. Measurement of the Economic Benefits

3.2.1. Basic Consumer Behavior Theory for Measuring the Economic Benefits

Dupit, a French engineer and economist, first established the concept of economic benefits in 1844 [49]. He stated that the area under the demand function can be interpreted as economic benefits. Thus, economic benefits are originally an economic concept, and its theoretical background can be said to be microeconomics. According to microeconomic theory, the demand function for a particular good is derived as a solution to the problem of maximizing the consumer’s utility for that good under an income constraint [50]. That is, the demand function for a specific good, $Q^{*}(·)$, is derived as follows:

$$Q^{*}(·)=\underset{Q}{\mathrm{argmax}}\left\{U(Q,G)|P_{Q}Q+P_{G}G\le M\right\}$$

(6)

where $U\left(·\right)$ is utility function, $G$ is the consumption of a composite good, which means all goods except $Q$, $P_Q$ is the price for the good under consideration, $P_G$ is the price for the composite good, and $M$ is consumer’s income.

In fact, another way to express economic benefits is the willingness-to-pay (WTP). That is, the economic benefits that arise from the consumption of a specific good are evaluated by the amount that people are willing to pay to consume that good. The demand function is known as the marginal WTP function or the marginal benefit function. The height of the demand function represents the WTP or benefit generated by consuming an additional unit of the good at that level of demand. Therefore, the area under the demand curve represents the WTP or economic benefits generated by consuming a certain level of the good. The WTP or economic benefits can be decomposed into two components, the first component being the consumer’s actual expenditure and the second component being the consumer surplus. This relationship is illustrated in Figure 2. The method of deriving the economic benefit as the area under the demand curve is sometimes called the demand function approach.

3.2.2. Approximation of the Consumer Surplus and the Economic Benefits

The actual expenditure of consumers is easily derived by multiplying the amount of consumption and the price in the market. However, to find the consumer surplus, the demand function must be integrated, and if it is difficult to find a price at which the demand becomes zero, the integration is not easy. Therefore, if the shape of the consumer surplus is not a linear demand function derived from a triangle that is easy to obtain, it is difficult to calculate the consumer surplus in the form of a formula and its approximation is required. In this regard, Alexander et al. [51] attempted to approximate the consumer surplus using a first-order Taylor expansion, which can be useful in this study. If we denote the consumer surplus as $CS$, the approximation formula for the consumer surplus can be derived as follows:

$$CS=-{\displaystyle \frac{P_0{Q}_o}{2ϵ}}$$

(7)

where $Q_o$ represents a certain level of demand, $P_o$ is the price level at which the demand is $Q_o$, and $ϵ$ is the price elasticity of the demand. In other words, obtaining information on the price elasticity of the demand is essential in calculating the consumer surplus.

Thus, the WTP for consuming $Q_o$ kWh of electricity, $WTP$, and the WTP for consuming 1 kWh of electricity, $WTPU$, are computed as

$$WTP=P_0{Q}_o+CS=P_0{Q}_o-{\displaystyle \frac{P_0{Q}_o}{2ϵ}}=P_0{Q}_o\left(1-{\displaystyle \frac{1}{2ϵ}}\right)$$

(8)

and

$$WTPU={\displaystyle \frac{WTP}{Q_o}}=P_0\left(1-{\displaystyle \frac{1}{2ϵ}}\right),$$

(9)

respectively.

Researchers can select and utilize either Equation (9) or Equation (10) according to their research purposes. Since $P_0$ is generally known, the key to estimating $WTP$ or $WTPU$ is to find $ϵ$. The variables included in the model described so far are summarized in

Table 3. Description of the variables used in measuring the consumer surplus and economic benefits.

Variables Used	Definitions
$Q$	Consumption of a good under consideration
$G$	Composite good except for $Q$
$P_Q$	Price for the good under consideration
$P_G$	Price for the composite good
$M$	Consumer’s income
$Q_o$	Current level of consumtion
$P_o$	Price level at which the demand is $Q_o$
$ϵ$	Price elasticity of the demand

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4. Data, Results, and Discussion

This section will provide a description of the data used to apply the methodology presented in Section 3, the results found from its application, and a discussion of these results.

4.1. Data

4.1.1. Data for Delving into the Economic Effects

To conduct IOA, an IOT issued by a reputable institution should be utilized. In this regard, the present study utilizes the Vietnam IOT extracted from Organization for Economic Cooperation and Development [52] on 18 November 2023, based on the data from 2018. This table was published by the Vietnam Statistical Office in the most recent year, 2021. In other words, the most recent IOT available at the time the authors conducted their research was from 2018. Of course, the IOT is several years old at this point, but as discussed earlier, this study uses the input inverse matrix rather than an IOT itself. The input inverse matrix is known to be stable over time. In addition, since unit values representing ratios are used instead of monetary unit values directly in IOA, external adjustments such as separate interpolation or extrapolation are unnecessary.

The number of sectors originally provided by the Vietnam Statistical Office is 44. Among them, “electric equipment” and “construction” are combined to form the NGCCPP construction sector. “Electricity, gas, steam, and air conditioning supply” is renamed as the NGCCPP operation sector. The number of sectors modified by the authors is 43. Therefore, as indicated in

Table 4. Sector classification by the Vietnam Statistical Office and the authors.

Sector Numbers	Sectors Originally Provided by the Vietnam Statistical Office	Sectors Modified by the Authors
1.	Agriculture, hunting, forestry	Agriculture, hunting, forestry
2.	Fishing and aquaculture	Fishing and aquaculture
3.	Mining and quarrying, energy-producing products	Mining and quarrying, energy-producing products
4.	Mining and quarrying, non-energy-producing products	Mining and quarrying, non-energy-producing products
5.	Mining support service activities	Mining support service activities
6.	Food products, beverages and tobacco	Food products, beverages and tobacco
7.	Textiles, textile products, leather, and footwear	Textiles, textile products, leather, and footwear
8.	Wood and products of wood and cork	Wood and products of wood and cork
9.	Paper products and printing	Paper products and printing
10.	Coke and refined petroleum products	Coke and refined petroleum products
11.	Chemical and chemical products	Chemical and chemical products
12.	Pharmaceutical, medicinal, chemical, and botanical products	Pharmaceutical, medicinal, chemical, and botanical products
13.	Rubber and plastic products	Rubber and plastic products
14.	Other non-metallic mineral products	Other non-metallic mineral products
15.	Basic metals	Basic metals
16.	Fabricated metal products	Fabricated metal products
17.	Computer, electronic, and optical equipment	Computer, electronic, and optical equipment
18.	Machinery and equipment, not elsewhere classified	Machinery and equipment, not elsewhere classified
19.	Motor vehicles, trailers, and semi-trailers	Motor vehicles, trailers, and semi-trailers
20.	Other transport equipment	Other transport equipment
21.	Manufacturing not elsewhere classified; repair and installation of machinery and equipment	Manufacturing not elsewhere classified; repair and installation of machinery and equipment
22.	Water supply; sewage, waste management, and remediation activities	Water supply; sewage, waste management, and remediation activities
23.	Wholesale and retail trade; repair of motor vehicles	Wholesale and retail trade; repair of motor vehicles
24.	Land transport and transport via pipelines	Land transport and transport via pipelines
25.	Water transport	Water transport
26.	Air transport	Air transport
27.	Warehousing and support activities for transportation	Warehousing and support activities for transportation
28.	Postal and courier activities	Postal and courier activities
29.	Accommodation and food service activities	Accommodation and food service activities
30.	Publishing, audiovisual, and broadcasting activities	Publishing, audiovisual, and broadcasting activities
31.	Telecommunications	Telecommunications
32.	IT and other information services	IT and other information services
33.	Financial and insurance activities	Financial and insurance activities
34.	Real estate activities	Real estate activities
35.	Professional, scientific, and technical activities	Professional, scientific, and technical activities
36.	Administrative and support services	Administrative and support services
37.	Public administration and defense; compulsory social security	Public administration and defense; compulsory social security
38.	Education	Education
39.	Human health and social work activities	Human health and social work activities
40.	Arts, entertainment, and recreation	Arts, entertainment, and recreation
41.	Other service activities	Other service activities
42.	Electrical equipment	Construction of natural gas combined cycle power plant
43.	Construction	Operation of natural gas combined cycle power plant
44.	Electricity, gas, steam, and air conditioning supply

, a domestic transaction IOT composed of a total of 43 sectors is used in this study. The NGCCPP construction sector and the NGCCPP operation sector are treated as sectors 42 and 43, respectively, in this paper.

The first column of this table presents the original 43 sectors compiled by the Vietnam Statistical Office, while the second column contains the 43 sectors utilized by the authors in this study. The NGCCPP construction and operation sectors are located at the bottom of the table. Although it would be desirable to obtain an IOT containing more sectors to extract the NGCCPP construction and operation sectors more accurately, it is impossible to obtain an IOT containing more than 43 sectors, according to an inquiry with the Vietnam Statistical Office. Since sectors 42 or 43 will be exogenized in IOA, all subsequent analysis results will be presented in the form of impacts on the 42 sectors.

4.1.2. Data for Delving into the Economic Benefits

In this study, instead of directly estimating the price elasticity of demand by estimating Vietnam’s electricity demand, the authors aim to utilize the figures presented in previous studies. The reason is that the authors were unable to obtain sufficient reliable data as they do not exist in Vietnam. It was difficult to locate sufficient data for estimating the electricity demand function. The authors were able to find a total of four previous studies in the literature, and their main results are reported in

Table 5. Summary of four previous studies dealing with the electricity demand function in Vietnam.

Sources	Methods	Main Findings
Nguyen [19]	Ordinary least squares model using lagged prices as instrument variables during the period 2012–2016	Price elasticity: −1.53 in the marginal price model and −2.03 in the average price model Income elasticity: 0.24
Phu [20]	Two-stage least squares model using 5000-household data for five large cities Data obtained from the Vietnam Household Registration Survey in 2015	Price elasticity: −1.33 to −0.94 in the average price model and −1.48 to −1.12 in the marginal price model
Trung and Tuyet [21]	Cobb–Douglas type of demand function Data obtained from the Vietnam Household Living Standard Survey in 2019	Income elasticity: 0.4493
Do and Le [22]	Two-stage least squares model Data obtained from the Vietnam Household Registration Survey in 2015	Price elasticity: −1.152 Income elasticity: 0.046

. The data applied and major findings of each piece of research are described in the following.

Nguyen [19] estimated the electricity demand function for the period from 2012 to 2016 and found that the price elasticity of demand was −1.53 and −2.03 in the average price model and the marginal price model, respectively. The income elasticity of demand was 0.24. Phu [20] analyzed data collected through a survey of 5000 households in five major cities in Vietnam and obtained price elasticities of demand ranging from −1.33 to −0.94 in the average price model and from −1.48 to −1.12 in the marginal price model. Trung and Tuyet [21] also estimated the electricity demand function, but they did not incorporate the price variable as an independent variable. Thus, the price elasticity of demand was not obtained. Do and Le [22] also estimated the average price model using data collected through a survey of 5000 households in five major cities in Vietnam and analyzed the price elasticity of electricity demand, which was −1.152, and income elasticity, which was 0.046.

In other words, the price elasticity information presented in the three previous studies can be utilized. Rather than arbitrarily choosing one of these price elasticity values, this study aims to calculate their average value. Thus, the average of the seven values, −1.53, −2.03, −1.33, −0.94, −1.48, −1.12, and −1.152, is −1.339, indicating that Vietnam’s electricity demand is elastic to price changes. While it is common for the absolute value of the price elasticity of demand for essential goods to be less than 1, Vietnam exhibits a relatively high price elasticity of demand for electricity. In this study, the price elasticity of the electricity demand for Vietnam, which will be substituted into Equation (6), is −1.339.

4.2. Results

4.2.1. Economic Effects

As explained above, ${\left(I^e-A^e\right)}^{-1}{A}^{NG}$ in Equation (4) represents how much production is induced in each sector when one dollar is invested in the construction or operation phase of the NGCCPP. Therefore, the sum of its columns shows how much of a production increase is brought about by a one-dollar investment in the construction or operation phase of the NGCCPP in the national economy as a whole. Multiplying this value by the investment amount for each year produces the total production-inducing effects. Similarly, ${\widehat{A_V^e}\left(I^e-A^e\right)}^{-1}{A}^{NG}$ in Equation (5) represents how much value-added is created in each sector when one dollar is invested in the construction or operation phase of the NGCCPP. The sum of its columns indicates how much value-added increase is brought about by a one-dollar investment in the construction or operation phase of the NGCCPP in the national economy as a whole. Multiplying this value by the investment amount for each year becomes to be the total value-added creation effects.

The production-inducing and value-added creation effects generated by the construction and operation of the NGCCPP in Vietnam, as previously explained, are presented in

Table 6. Effects of one-dollar production or investment in constructing or operating a natural gas combined cycle power plant on inducing sectors’ production and value-added.

Sectors a	Production-Inducing Effects				Value-Added Creation Effects
	Construction		Operation		Construction		Operation
	Values	Ranks	Values	Ranks	Values	Ranks	Values	Ranks
1.	0.00486	18	0.00259	13	0.00107	16	0.00057	11
2.	0.00040	34	0.00025	35	0.00006	37	0.00004	37
3.	0.00866	11	0.04529	1	0.00283	7	0.01482	1
4.	0.00330	22	0.00048	29	0.00078	19	0.00011	30
5.	0.00009	42	0.00043	31	0.00002	42	0.00010	31
6.	0.00270	23	0.00169	18	0.00020	29	0.00013	29
7.	0.00134	28	0.00131	21	0.00026	28	0.00026	19
8.	0.00737	12	0.00355	11	0.00046	23	0.00022	23
9.	0.00722	15	0.00204	16	0.00065	20	0.00018	24
10.	0.01808	5	0.01018	5	0.00101	17	0.00057	12
11.	0.01189	6	0.00254	14	0.00111	15	0.00024	20
12.	0.00019	39	0.00014	41	0.00003	41	0.00002	39
13.	0.01089	7	0.00093	23	0.00166	11	0.00014	27
14.	0.10398	1	0.00331	12	0.01529	1	0.00049	13
15.	0.06671	2	0.00536	7	0.00565	3	0.00045	14
16.	0.02615	4	0.00637	6	0.00289	6	0.00070	8
17.	0.00383	20	0.00041	32	0.00053	21	0.00006	34
18.	0.00114	30	0.00044	30	0.00020	30	0.00007	32
19.	0.00030	37	0.00015	38	0.00003	40	0.00002	41
20.	0.00073	31	0.00015	39	0.00009	35	0.00002	40
21.	0.00726	14	0.02731	2	0.00133	13	0.00500	2
22.	0.00134	29	0.00134	20	0.00043	25	0.00043	16
23.	0.03305	3	0.01133	4	0.01083	2	0.00371	4
24.	0.01062	9	0.00532	8	0.00198	10	0.00099	7
25.	0.00348	21	0.00160	19	0.00052	22	0.00024	21
26.	0.00171	25	0.00091	24	0.00027	27	0.00014	26
27.	0.00409	19	0.00214	15	0.00115	14	0.00060	9
28.	0.00061	32	0.00031	34	0.00012	33	0.00006	33
29.	0.00567	17	0.00364	10	0.00157	12	0.00101	6
30.	0.00041	33	0.00015	40	0.00009	34	0.00003	38
31.	0.00153	26	0.00129	22	0.00031	26	0.00026	18
32.	0.00026	38	0.00016	36	0.00008	36	0.00005	36
33.	0.01080	8	0.00398	9	0.00295	5	0.00109	5
34.	0.00194	24	0.00058	26	0.00091	18	0.00027	17
35.	0.00733	13	0.00203	17	0.00209	9	0.00058	10
36.	0.00136	27	0.00052	27	0.00045	24	0.00017	25
37.	0.00590	16	0.00051	28	0.00261	8	0.00023	22
38.	0.00034	36	0.00081	25	0.00019	31	0.00045	15
39.	0.00015	40	0.00037	33	0.00005	38	0.00013	28
40.	0.00011	41	0.00003	42	0.00005	39	0.00001	42
41.	0.00036	35	0.00016	37	0.00013	32	0.00005	35
42.			0.02680	3			0.00490	3
43.	0.01013	10			0.00298	4
Sum (A)	0.38826		0.00426		0.06588		0.03962
Effects on the sector (B)	1.00000		1.00000		0.18277		0.29388
Totals (A + B)	1.38826		1.00426		0.24865		0.33349

^a They are described in Table 4.

. The numbers in this table represent the production or value-added amount that each sector is affected by when one dollar’s worth of investment or production is made in the construction and operation sectors of the NGCCPP. Given the nature of IOA, investment or production in the construction or operation sectors of the NGCCPP affects not only the production and value-added of these sectors but also those of other sectors. The former is shown in Table 6 as “Effects on the sector”. The production-inducing effect on sector $NG$ is precisely 1, and the value-added creation effect on sector $NG$ becomes the value-added coefficient of sector $NG$, which is the proportion of value -added of sector $NG$ to the total input of sector $NG$. The sum of the effects on other sectors and the effects on the sector itself are also reported in the table.

In the construction phase, the sector with the largest production-inducing effects and value-added creation effects is “Sector 14. Other non-metallic mineral products”. This is likely to be because the output of Sector 14 is heavily used in the construction of the power plant. “Sector 15. Basic metals” and “Sector 23. Wholesale and retail trade; repair of motor vehicles” also show significant production-inducing effects and value-added creation effects. Conversely, in the operation phase, “Sector 3. Mining and quarrying, energy-producing products” has the highest production-inducing effects and value-added creation effects. This is as expected as fuel is likely to be the largest cost incurred in operating the power plant. “Sector 21. Manufacturing not elsewhere classified; repair and installation of machinery and equipment” and “Sector 42. Construction of natural gas combined power plant” also exhibit relatively large production-inducing effects and value-added creation effects.

4.2.2. Economic Benefits

As explained above, the authors failed to obtain sufficient data to estimate the electricity demand function for Vietnam, a socialist country. The authors estimated the price elasticity of demand as −1.339 using the results of previous studies presented in Table 5. This value is assumed to remain constant throughout the analysis period (2024–2053). Of course, assuming that price elasticity remains unchanged and fixed during the analysis period can be restrictive. However, all previous studies that estimated the price elasticity of electricity demand in Vietnam assumed fixed price elasticity by estimating a double-log demand function rather than variable price elasticity. Therefore, the authors found it challenging to obtain information on variable price elasticity. Moreover, estimating future variable price elasticity is nearly impossible. The estimation of future variable price elasticity for electricity demand in Vietnam is left as a topic for future research.

Using Equation (9), which represents consumer surplus, the economic benefits obtained by Vietnamese consumers from consuming 1 kWh of electricity can be calculated as follows:

$$\begin{gathered} \mathrm{E}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{o}\mathrm{m}\mathrm{i}\mathrm{c} \mathrm{b}\mathrm{e}\mathrm{n}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{t}\mathrm{s}=\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{e}\mathrm{r} \mathrm{p}\mathrm{r}\mathrm{i}\mathrm{c}\mathrm{e}+\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{e}\mathrm{r} \mathrm{s}\mathrm{u}\mathrm{r}\mathrm{p}\mathrm{l}\mathrm{u}\mathrm{s} \\ =P_0+\left(-{\displaystyle \frac{P_0}{2ϵ}}\right)=\left(1-{\displaystyle \frac{1}{2ϵ}}\right)P_0 \\ =\left(1-{\displaystyle \frac{1}{2\ast (-1.339)}}\right)P_0=1.373\times P_0 \end{gathered}$$

(10)

Therefore, the economic benefits of electricity consumption in Vietnam can be said to be 1.373 times the price. According to the Vietnam Ministry of Industry and Trade, in 2021, the electricity price in Vietnam was USc 8.14 per kWh [53]. The estimated consumer surplus in Vietnam in 2021 was computed to be USc 3.04 per kWh. The estimated benefits of electricity consumption in Vietnam in 2021 was USc 11.18 per kWh. Assuming that future real electricity rates remain unchanged for convenience, this USc estimate of the economic benefits remains the same for the future.

4.3. Discussion

So far, quantitative analyses have been provided for the effect of one-dollar investment or production in the construction and operation stages of the NGCCPP on each sector or the overall economy as well as the economic benefits of supplying 1 kWh of electricity. Now, it is necessary to combine these results with the overview of the NGCCPP project presented in Table 1 to calculate the annual values of the economic effects and benefits generated by the project. First, the economic effects are calculated by multiplying the total effects presented in Table 6 by the total construction costs and annual operating costs invested in the project. The second column of

Table 7. Economic effects and benefits of constructing and operating the proposed natural gas combined cycle power plant.

Years	Economic Effects (Unit: USD One Million)		Economic Benefits
	Production- Inducing Effects	Value-Added Creation Effects	Electricity Consumed (unit: GWh)	Consumption Benefits a (Unit: USD One Million)
2025–2028	2315.60	414.7
2029	816.91	231.09	8753	1147
2030	833.36	235.75	8719	1164
2031	848.41	240.00	8708	1181
2032	863.78	244.35	8698	1199
2033	879.50	248.80	8711	1218
2034	895.53	253.33	8677	1235
2035	911.92	257.97	8667	1254
2036	928.66	262.71	8656	1273
2037	945.78	267.55	8669	1293
2038	963.24	272.49	8635	1312
2039	981.09	277.54	8625	1332
2040	999.33	282.70	8615	1353
2041	1017.97	287.97	8629	1376
2042	1036.99	293.35	8594	1396
2043	1056.43	298.85	8584	1418
2044	1076.30	304.47	8573	1441
2045	1096.61	310.22	8587	1465
2046	1117.32	316.08	8553	1488
2047	1138.50	322.07	8543	1512
2048	1160.14	328.19	8532	1537
2049	1047.60	296.35	8546	1563
2050	1070.17	302.74	8512	1588
2051	1093.24	309.26	8502	1614
2052	1116.81	315.93	8492	1641
2053	1021.81	289.06	8570	1676
Totals	27,233.01	7463.56	215,347	34,676

^a They are calculated using the information presented in Table 1.

contains the figures for the economic effects generated over a period of 5 years from 2024 to 2028, when the power plant will be constructed, and 25 years from 2029 to 2053, when the power plant will be in operation. The third column of Table 5 presents estimates of the power consumption benefits that will occur during the period of operation of the NGCCPP.

For example, it is expected that during the 5-year period of constructing a 1.5 GW capacity NGCCPP, production and value-added of USD 2315.60 million and USD 414.75 million, respectively, will be generated in the Vietnamese economy. During that period, no power consumption benefits will be generated, only production and value-added. In 2030, the second year after the plant begins operating, the plant’s operation will not only induce USD 833.36 million in production and USD 235.75 million in value-added but also generate power consumption benefits of USD 1164.33 million.

There is a need to discuss one issue regarding accepting the economic effects: whether it is appropriate to predict the economic effects until 2053 based on the analysis using IOT data from 2018. The answer in the literature regarding this is “yes”. Equations (4) and (5), which represent the economic effects, utilize the input coefficient matrix $A$. The $X_i$ and $z_{ij}$ required to derive $A$ vary each year. However, the elements of $A$, $a_{ij}$’s, can be stable over time. In other words, the stability of the input coefficients is a key issue, and it is known to be relatively stable. Moreover, in fact, we use ${(I-A)}^{-1}$ rather than $A$ as ${(I-A)}^{-1}$ is known to be more stable than $A$ [48]. Therefore, the authors believe that the results presented earlier can represent the economic effects until 2053 without much difficulty.

Lastly, a cost–benefit analysis for the NGCCPP construction project was attempted using the findings of this study. While the construction costs are evident, the operating costs are uncertain and rely solely on predictions. Only the electricity consumption benefits obtained earlier are reflected in the economic benefits. This is because the production-inducing effects and value-added creation effects obtained earlier are not considered as benefits. The analysis covers the period from 2025 to 2053. The social discount rate used in the analysis needs to be determined. Determining an appropriate discount rate is quite important in cost–benefit analysis. Rather than researchers arbitrarily determining the level of discount rate, it would be better to utilize the one officially used by the State Bank of Vietnam, a central bank of Vietnam. In this regard, the State Bank of Vietnam reduced the discount rate from 4.5% to 3.5% in March 2023 and has maintained it since then [54]. Because 3.5% is used by the country’s central bank, this study also applies a discount rate of 3.5%. The values and present values of the costs and benefits from 2024 to 2053 are presented in

Table 8. Costs and benefits of the proposed natural gas-fired power plant.

Years	Costs a		Benefits a		Net Benefits a
	Values	Present Values	Values	Present Values	Values	Present Values
2024	70	68			−70	−68
2025	170	159			−170	−159
2026	700	631			−700	−631
2027	551	480			−551	−480
2028	178	150			−178	−150
2029	693	564	1147	933	454	369
2030	707	556	1164	915	457	359
2031	720	547	1181	897	462	351
2032	733	538	1199	880	466	342
2033	746	529	1218	863	472	335
2034	760	521	1235	846	476	326
2035	774	512	1254	830	480	318
2036	788	504	1273	814	485	310
2037	802	495	1293	799	491	303
2038	817	488	1312	783	495	295
2039	832	480	1332	768	500	288
2040	848	473	1353	754	506	282
2041	863	465	1376	741	512	276
2042	880	458	1396	726	517	269
2043	896	450	1418	713	522	262
2044	913	443	1441	700	528	256
2045	930	436	1465	687	535	251
2046	948	430	1488	674	540	245
2047	966	423	1512	662	546	239
2048	984	416	1537	650	553	234
2049	889	363	1563	639	675	276
2050	908	359	1588	627	680	269
2051	927	354	1614	616	687	262
2052	947	349	1641	605	694	256
2053	867	309	1676	597	809	288
Totals	22,807	12,948	34,676	18,720	11,873	5774

^a The values are expressed in USD one million.

. The base year for the present values is set to be the year 2023.

The results of the cost–benefit analysis conducted using the values reported in Table 8 are summarized in

Table 9. Results of cost–benefit analysis of the proposed natural gas combined cycle power plant.

Indicators	Values
Benefit–cost ratio	1.45
Net present value	USD 5580 million
Internal rate of return	21.6%

. The benefit-to-cost ratio is 1.45, which exceeds 1.0. The net present value is greater than 0, and the internal rate of return exceeds the social discount rate of 3.5%. Applying the three indicators for cost–benefit analysis, the project secures economic feasibility. Therefore, the project should be implemented promptly. This result can be utilized by various institutions. First, the Vietnamese government can refer to the fact that the project is socially beneficial when determining whether to approve the project. Since the beneficiaries of the project are clearly the Vietnamese people, the electricity consumption benefits become the economic benefits of the project. The project provides greater benefits to the Vietnamese people than the costs incurred. Second, those who promote the project need to raise funds, and, when approaching international organizations such as the Asian Development Bank, they can provide investors with the results of the cost–benefit analysis.

The results of the economic feasibility analysis conducted in this study can be utilized to obtain a construction permit for the NGCCPP from the Vietnamese government. This is because it is reasonable for the government to grant permits only to projects with secured economic feasibility for the continuity and stability of the project. Therefore, the structure adopted in this study can be usefully utilized for similar situations in other countries. Applying the IOA technique presented in this study to the IOT for a specific country can measure the economic effects on that country. The IOT is usually published by the country’s statistical office. The economic benefits of electricity consumption can be estimated by using Equations (8) and (9), and information on the price elasticity of demand can be obtained by directly estimating the electricity demand function or utilizing existing studies. At this time, the electricity demand and price related to a specific power generation project must be appropriately forecasted.

As the final task, a sensitivity analysis is performed on the results of the cost–benefit analysis from three perspectives: changes in the discount rate, changes in benefits, changes in costs, and changes in price elasticity of electricity demand. The results are presented in

Table 10. Benefit–cost ratio values from the results of the sensitivity analysis according to changes in the discount rate.

Discount Rate	PV of Costs a	PV of Benefits a	Benefit–Cost Ratio
2.00%	16,314	24,135	1.48
2.50%	15,076	22,139	1.47
3.00%	13,959	20,341	1.46
3.50%	12,948	34,676	1.45
4.00%	12,033	17,255	1.43
4.50%	11,202	15,931	1.42
5.00%	10,447	14,730	1.41

^a The values are expressed in USD one million, PV means present value, and the base year for the PV is set to be the year 2023.

,

Table 11. Benefit–cost ratio values from the results of the sensitivity analysis according to changes in the benefits.

Change Rate	PV of Costs a	PV of Benefits a	Benefit–Cost Ratio
−30.00%	12,948	13,184	1.01
−20.00%	12,948	14,976	1.16
−10.00%	12,948	16,848	1.3
0%	12,948	18,720	1.45
10.00%	12,948	20,592	1.59
20.00%	12,948	22,464	1.73
30.00%	12,948	24,336	1.88

^a The values are expressed in USD one million, PV means present value, and the base year for the PV is set to be the year 2023.

,

Table 12. Benefit–cost ratio values from the results of the sensitivity analysis according to changes in the costs.

Change Rate	PV of Costs a	PV of Benefits a	Benefit–Cost Ratio
−30.00%	9064	18,720	2.07
−20.00%	10,358	18,720	1.81
−10.00%	11,653	18,720	1.61
0%	12,948	18,720	1.45
10.00%	14,243	18,720	1.31
20.00%	15,538	18,720	1.2
30.00%	16,832	18,720	1.11

^a The values are expressed in USD one million, PV means present value, and the base year for the PV is set to be the year 2023.

and

Table 13. Benefit–cost ratio values from the results of the sensitivity analysis according to changes in price elasticity of electricity demand.

Price Elasticity	PV of Costs a	PV of Benefits a	Benefit–Cost Ratio
−0.33	12,948	34,282	2.65
−0.66	12,948	23,956	1.85
−1	12,948	20,445	1.58
−1.339	12,948	18,720	1.45
−1.66	12,948	17,736	1.37
−1.99	12,948	17,055	1.32
−2.33	12,948	16,555	1.28

^a The values are expressed in USD one million, PV means present value, and the base year for the PV is set to be the year 2023.

, respectively. The discount rate was changed by 0.5% points from 2.0% to 5.0%, and the benefits and costs were decreased or increased by 30%, 20%, and 10%, respectively. The price elasticity of electricity demand was changed by approximately −0.33. The lowest benefit–cost ratio was 1.01 in Table 11. Therefore, the conclusion from these four tables is that the implications of the cost–benefit analysis do not change. In other words, the results of the sensitivity analysis show that the project still has economic feasibility.

5. Conclusions

It is now time to report the concluding remarks of this paper. Vietnam is a developing country with rapidly increasing electricity consumption. Using a stable power supply while reducing GHG emissions instead of coal-fired power plants will play an important role in the country’s future sustainable development. This article attempted to quantitatively explore the economic effects and benefits generated from the project to construct an NGCCPP with a capacity of 1.5 GW in Vietnam to supply electricity. IOA and the demand function approach were applied as methods for obtaining the economic effects and benefits, respectively. More specifically, the production-inducing effects and value-added creation effects were analyzed separately for the construction and operation of the power plant, and the electricity consumption benefits were estimated as the sum of the electricity price and consumer surplus. The economic effects were calculated for the period from the construction and operation of the power plant in 2024 until 2053. The electricity consumption benefits were estimated for the 25-year period from 2029 to 2053.

During the construction period of the NGPP, it is expected to induce USD 2315.60 million of production and USD 414.75 million of value-added for the Vietnamese economy. The production-inducing effects, value-added creation effects, and economic benefits ensuing from the operation of the NGPP in 2030 were estimated to be USD 833.36 million, USD 235.75 million, and USD 1164.33 million, respectively. The cost–benefit analysis revealed a benefit-to-cost ratio of 1.45, which is higher than 1, indicating the economic feasibility of construction. Therefore, the project passed the cost–benefit analysis. The project should be smoothly implemented in Vietnam. This study estimated the economic effects and benefits of constructing an NGCCPP in Vietnam for the first time in the literature. This indicates the significance of this paper’s contribution.

The main policy implication of this study is that the implementation of the project is socially beneficial. The study presented three additional policy implications. First, this study measured the economic effects in terms of production and value-added growth, and the results can be directly applied to assess the economic effects of various NG-fired power projects currently being promoted throughout Vietnam. As various power plant construction projects are expected to increase in Vietnam in the future, the results of this study can be usefully applied to evaluate these projects. Of course, if a more recent IOT is published, updated results can be obtained by applying the framework presented in this study to the IOT.

Second, it was found that the project provides benefits that outweigh the costs in Vietnam, making its implementation socially desirable. However, the recent domestic and global situations raise some potential risk factors. As NG produced in Vietnam becomes depleted, a large amount of LNG must be imported from abroad, which could significantly increase electricity prices. This may face backlash from the public and industry. Therefore, it is essential to secure social consensus on using LNG, which is more expensive than coal, to reduce GHG emissions. In addition, efforts should be made to select cheaper LNG products in the international LNG market and import them.

Third, basic financing for the implementation of the project should come from South Korea, but the situation in South Korea can be variable. Recently, a political event occurred in which the President of South Korea was impeached, slightly hindering the South Korean economy. Especially since the project was officially approved by the Vietnamese government in January 2025 and is now on track, any hindrance to financing could delay or derail the project. Therefore, political risks in South Korea need to be removed quickly.

This study has three potential limitations and needs to be improved in three aspects through future research. First, this study focused only on the NGCCPP due to data limitations; since various types of power plants, such as solar power generation, onshore wind power generation, and offshore wind power generation, are being proposed in Vietnam, it is necessary to produce analysis results for each type of power plant using more secure data in the future. In addition, electricity consumption benefits can be estimated by usage, such as residential, industrial, commercial, and agricultural.

Second, since the IOA used in the study is limited to statistical analysis, it should be expanded to dynamic analysis. For example, conducting IOA with IOTs from various years to examine how the economic effects change over time could yield new insights. In addition, dynamic analysis can be used to predict the future IOT. In fact, estimating the future IOT is almost impossible because it requires a huge amount of data. However, if a certain trend is observed through the dynamic analysis, a rough outline of the future IOT can be drawn. Meanwhile, IOA using intra-regional and inter-regional IOTs in Vietnam will enable the derivation of differentiated economic effects by region.

Third, due to difficulties in data acquisition, the study was unable to estimate the electricity demand function in Vietnam and relied on results from previous research. However, after securing sufficient data in the future, it will be necessary to properly estimate the demand function. At this time, the application of a demand function model that can reflect variable price elasticity can be considered. Based on this model, future variable price elasticity can also be reasonably predicted. This estimation can provide us with accurate information on the price elasticity of electricity demand.