Quantitative Approaches for Analyzing the Potential Effectiveness of Vietnam’s Emissions Trading System: A Systematic Review

Abstract

Carbon pricing initiatives have been developed globally in recent decades. Among carbon pricing initiatives, direct carbon pricing, particularly the emissions trading system (ETS), has attracted the interest of stakeholders and nations. With its ambitious potential for climate change mitigation, Vietnam has considered establishing a domestic ETS. However, studies on the effectiveness and impact of Vietnam’s ETS are scarce. This study reviews the quantitative approaches to assessing Vietnam’s ETS and evaluates the methodologies, limitations, and challenges of current quantitative assessments such as the simple reduced-form emission projection method, computable general equilibrium (CGE), input–output analysis (IOA), and multi-criteria analysis. CGE and IOA are highly recommended to obtain comprehensive results. However, IOA is more capable of avoiding numerous assumptions, complicating the use of CGE. This study recommends directions for possible innovative improvements for utilizing IOA in future research on Vietnam’s ETS; thus, suggesting a competent approach for effectively analyzing ETS.

1. Introduction

Human activities, through greenhouse gas (GHG) emissions, have induced adverse climate change effects [1]. The world has witnessed more severe extreme heat events, heavy precipitation, and droughts in the past century than in the pre-industrial period, which have prompted nations to implement mitigation strategies. Practical climate action requires mobilizing financial resources for such activities and developing low-carbon technology [2]. A prevalent solution for both financial resource mobilization and technology transfer is carbon pricing. It involves raising the price of carbon content or the cost of carbon emissions in proportion to the products and fossil fuels used [3,4,5,6,7]. By increasing prices through carbon pricing, households, firms, and governments are incentivized to opt for carbon-intensive products [8]. Carbon pricing is categorized into two types: direct and indirect, each carrying distinct advantages and disadvantages that are tailored towards specific contexts of application.

1.1. Indirect Carbon Pricing

The indirect carbon pricing approach indirectly imposes additional charges for carbon emissions associated with products [7]. This price increase is an incentive for phasing out emission-intensive products. Typical indirect carbon pricing methods include taxes and subsidies related to fossil fuels, which affect energy consumption habits. For instance, fuel excise per unit of fuel, such as gasoline and diesel, represent the costs of burning these fuels. However, this charge does not directly correlate with the emissions generated from burning or even the carbon content of the fuels [7]. In contrast, fossil fuel subsidies reduce fuel costs, creating an adverse signal in indirect carbon pricing that promotes an increased consumption and production of energy with high emissions [7].

Figure 1 illustrates an example of indirect carbon pricing through a gasoline subsidy. The fossil fuel price comprises three cost components: cost to consumers, the cost of paying taxes to the government, and external costs, which include impacts on society such as healthcare expenses, traffic accidents, lower productivity due to transport congestion, and the cost of global warming. The red arrows indicate the basis for the subsidy, highlighting the exemption of pretax and forgone value-added tax. This type of subsidy can be seen as covering the social costs of fossil fuel combustion impacts. The grey arrows represent the external costs, as a basis for estimating corrective environmental and carbon taxes. This example demonstrates a negative indirect carbon price signal because it reduces gasoline prices, thereby encouraging higher consumption and leading to increased carbon emissions from gasoline combustion.

1.2. Direct Carbon Pricing

Direct carbon pricing involves creating a specific price signal to mitigate GHG emissions associated with products [7]. These instruments represent the dollar cost for each metric ton of carbon dioxide equivalent (tCO₂e). Ordinary direct carbon pricing mechanisms include carbon taxes and emissions trading systems (ETSs). Figure 2 illustrates the current direct carbon pricing operation. The map shows that carbon taxes and ETSs are mostly implemented in high-income countries, such as America, Europe, and Central Asia. Among lower-middle-income countries, Vietnam is one of two countries considering the implementation of carbon taxes and ETSs, notwithstanding the social, economic, legal, and political barriers [7]. Forecasted to remain the fastest-growing region in the coming years [10], South Asia does not contribute to the GHG emissions coverage from carbon pricing initiatives. Therefore, an enormous potential to foster the development of carbon taxes and ETSs exists in this region.

Carbon tax is an economic climate policy that imposes a charge on specific entities for their GHG emissions, serving as an incentive to reduce emissions [7]. The carbon tax rate, which represents the social cost of emissions, is typically determined by the government. However, the effectiveness of emissions reduction is based on the responsive mitigation activities of the emitters.

In an ETS, the government imposes a limit on GHG emissions for entities within its jurisdiction [7]. These entities must submit emission trajectories, known as “allowances,” to track their emissions over a specified compliance period. Each emission unit is an authorization to release a specific amount of emissions, typically measured as one metric ton of carbon dioxide equivalent (tCO₂e). These units can be traded among covered entities or, in some cases, with other traders. ETSs exist in various forms, such as “cap-and-trade” and “rate-based” approaches, each employing distinct emission unit terminology. The interplay between the supply and demand for emission units influences carbon prices within these systems.

A carbon credit mechanism is a framework in which tradeable credits (tCO₂e) are generated through voluntary initiatives to reduce emissions [7]. These credits signify reductions achieved by preventing emissions, such as by capturing methane from landfills, extracting carbon from the atmosphere through afforestation, or employing direct capture and storage techniques. In contrast to carbon taxes and ETSs, carbon crediting mechanisms operate distinctively. Businesses and organizations can earn carbon credits and revenue by demonstrating reductions or sequestration compared to a predefined baseline scenario.

Unlike carbon taxes and ETSs, which adhere to the “polluter pays” principle and necessitate payment for emissions, carbon credit mechanisms provide a supply source but do not inherently create demand [7]. The value of carbon credits depends on external demand sources, which may stem from regulated emitters seeking to offset their obligations within an ETS or carbon tax, or from corporations striving to fulfill voluntary emission reduction targets.

1.3. Current Status and Patterns of Global Carbon Pricing

Backed by scientific reports from several international organizations, including the UNFCCC, IPCC, IMF, and OECD, carbon pricing has garnered attention as an effective tool for facilitating the transition to sustainable development [3]. Since the ratification of the Paris Agreement on Climate Change at COP26 in 2021, the implementation of carbon pricing initiatives at national and subnational levels has been strengthened. Figure 3 illustrates a dramatic upward trend in the percentage of global GHG emissions covered in 2021, demonstrating an approximately 150 percent increase by 2023 compared to 2019. Over the past five years, the number of carbon pricing initiatives has gradually increased from 57 to 73, with half of these initiatives being emissions trading systems (ETSs).

Despite the significant impacts of the pandemic from 2019 to 2023, there remains strong attention and interest among nations in carbon pricing initiatives. After proper enforcement from intergovernmental unions, countries facing challenges in mobilizing financial resources for climate change mitigation have gradually shown interest in participating in the carbon market, fostering the involvement of private actors. Carbon pricing appears to be a powerful instrument for encouraging cleaner production technologies and strengthening mutual collaboration between countries in attaining global climate commitments.

Carbon prices exhibit significant variation, ranging from less than US$1/tCO₂e to approximately US$156/tCO₂e, as illustrated in Figure 4 [7]. To effectively align with the Paris Agreement targets, a minimum carbon price of US$40 to US$80 per ton of CO₂ equivalent is recommended [3]. However, in 2019 and 2020, half of emissions were priced below US$10/tCO₂e [3,4]. Since 2021, the recommended rate for a carbon price has been between US$50 and US$100 per ton of CO₂ equivalent by 2030 [3]. For direct comparison with current carbon prices, adjusted for inflation from 2017 to 2023, the necessary price ranges from US$61 to US$122 per ton of CO₂ equivalent by 2030, indicating the light grey stripe shown in Figure 4 [7].

Figure 4 also reveals that only nine out of 60 carbon pricing initiatives, less than one fifth, are priced within the required range for the climate target. The highest-priced carbon credits and taxes are primarily implemented in Europe and the United Kingdom, with six out of the nine highest-priced initiatives enforced through mandatory carbon tax schemes. This issue implies that governments could raise the price of carbon credits with an appropriate top-down intervention. However, owing to the impact of the COVID-19 pandemic, carbon prices briefly dipped before recovering by the beginning of 2022 [7]. As of 1 April 2023, less than 5 percent of global GHG emissions are covered by direct carbon prices at or above the recommended range by 2030 (in 2023 USD), primarily in Europe [7].

Overall, carbon prices must increase over an extended period to ensure sufficient budget allocation for climate commitments. From 2019 to 2023, global revenue from direct carbon pricing doubled from US$44 billion in 2019 to US$95 billion by 2023 [3,7]. Figure 5 shows the global revenue from ETSs and carbon taxes spanning 17 years. For a continuous decade from 2006, the carbon tax revenue remained dominant at around US$10 billion per year. A milestone was reached in 2018 when ETS revenue nearly matched carbon tax revenue. Furthermore, after only three years, the revenue from ETSs in 2021 was double the revenue from carbon taxes, highlighting the high potential for ETSs to fund mitigation actions and sustainable development.

Given the effectiveness of carbon pricing, even developing countries such as Vietnam are exploring options for future ETSs [7]. Aligned with its commitments to the 2016 Paris Agreement and the new zero emissions target outlined in COP26 by 2050, Vietnam is motivated to transition from conventional development strategies to more innovative approaches [11,12]. The mission to construct the carbon market to improve the progression of emission targets in international commitment is declared under Clause 1, Article 41 of the Law on Environmental Protection 2014 and under Clause 2, Article 91 of the Law on Environmental Protection 2020 [13,14]. Regulations on the carbon market in the Law on Environmental Protection 2020 are detailed under the Decree 06/2022/ND-CP [15]. Accordingly, the domestic carbon market includes exchanging greenhouse gas emission allowances and carbon credits from appropriate domestic and international carbon credit exchange and offset mechanisms, following the provisions of law and international treaties to which Vietnam is a member. The Ministry of Finance is the agency in charge of establishing the domestic carbon market and the Ministry of Natural Resources and Environment organizes the operation of the domestic carbon market and participates in the carbon market. The development roadmap and timing for implementing the domestic carbon market are divided into two phases: (i) the Pilot phase from 2025 to 2027 and (ii) the Official operation period after 2028.

Until now, Vietnam has developed the administration mechanism for internationally transferred carbon credit through the Clean Development Mechanism (CDM). Decision No. 1775/QD-TTg 2012 approved the project management of gas emissions causing greenhouse effect and the management of carbon credit trading to the market by the Prime Minister (Government of Vietnam, 2012). For the coming period, Vietnam aims to formulate the domestic emission trading system (ETS) with the appropriate regulating intervention from the government. The operation of the carbon market is Vietnam’s primary focus among the climate policies. The carbon market is a powerful instrument to mobilize financial resources to mitigate climate change and ensure sustainable development. Thus, the decision-making process should involve exploring the effectiveness and impact of Vietnam’s ETS.

In the development of earlier ETSs worldwide, various techniques have been applied to assess the feasibility and potential impacts of ETSs on socioeconomic and environmental factors [16]. However, only a few quantitative methodologies have been utilized for Vietnam’s ETSs, exacerbating the uncertainty in the country’s policymaking procedures and hindering stakeholders’ implementation in the market. Therefore, a systematic review of the quantitative approaches in existing studies on Vietnam’s ETSs is needed. This study aims to summarize relevant concepts of ETS development in existing literature, identify patterns and possible gaps in methodologies raised in current studies, and synthesize competent approaches for future research.

The remainder of this paper is organized as follows: Section 2 summarizes the core elements of the ETS with multiple options for the policymaking process. Section 3 describes the systematic search methodology for this study. Finally, Section 4 identifies the pattern of existing literature and the limitations of this study; it also discusses the best approaches to quantitatively assess Vietnam’s ETS.

2. Determinants Influencing Carbon Market Development

2.1. ETS Capacity

The ETS cap establishes the upper limit on government-issued allowances permitted within a specified period [17]. This restriction curtails the capacity of the covered sources to contribute to global emissions and significantly influences allowance prices. A lower cap signifies a more stringent restriction on total allowed emissions, resulting in increased scarcity of carbon allowances and, subsequently, higher carbon prices. ETS caps are categorized into two types: absolute and intensity. The total cap refers to the maximum emissions allowances allocated to regulated market participants. Setting an ETS cap involves balancing emissions reduction goals and macroeconomic costs.

Two methods exist for establishing the cap: top-down and bottom-up approaches. Under the top-down approach, the government sets a cap by considering broad emission reduction objectives and comprehensively assessing the mitigation potential and costs across sectors subject to the cap [17]. This approach facilitates aligning ETS ambitions with mitigation goals and the influence of other policies and measures within its jurisdiction. However, the bottom-up cap setting relies on aggregating the mitigation potentials of participating sectors and subsectors [17]. While this aggregated approach is advantageous for considering the specific circumstances of entities and participants, it may not necessarily align with the national emissions reduction target.

2.2. Allowance Distribution

When policymakers impose an emissions cap, they introduce scarcity, creating a “climate rent”. This scarcity results in elevated prices for products associated with high emissions, devalues specific assets, and negatively affects workers. The method of allocating allowances is crucial in determining the distribution of climate rent and related expenses across society. Although the overall economic influence of an ETS is relatively minor, outcomes can significantly differ, with winners and losers depending on the strategic allocation of allowances. Allocation approaches significantly influence the reaction of entities to an ETS. For instance, they shape companies’ production levels, strategies for new investments, and the extent to which they transfer their carbon prices to consumers. By employing these approaches, allocation methods can ultimately affect the overall economic cost of an ETS.

Two primary allocation methods exist: free allowance distribution by the government, encompassing various distribution methods, and the sale of allowances through auctions. We examined four specific options: auctioning allowances for sale, freely allocating allowances through a grandparenting approach, freely allocating allowances with fixed sector benchmarking and occasional output-based updating (OBA), and freely allocating allowances with OBA and annual updating. Each allocation method requires primary data to establish the allocation baseline. Figure 6 illustrates the specific data requirements for different allocation methods. Among the four allocation methods, auctioning requires the least data for estimation [17]. For the auctioning approach, the allocations are determined by the market, eliminating the need for specific data analysis. The government’s role is limited to conducting frequent auctions with restricted sale values to prevent market manipulation [17]. Another three approaches correspond to three phases of EU ETSs: the first phase—applying the grandparenting approach—only considers current emission intensity; the second phase—with fixed sector benchmarking—incorporates the future emissions intensity reduction potential, known as the emissions benchmark; and the third phase considers fluctuations of the firms’ output [17].

2.3. Offsets

Offsets play a critical role in ETSs by offering credits to reduce or remove emissions from sources not covered by the ETS. Once credits are approved, offsets are considered comparable to allowances within the ETS for compliance purposes. Including offsets in an ETS expands the range of options for reducing emissions in the market, enabling new entities to qualify for selling emission reductions. These offset options may surpass the abatement option for compliance with the emissions cap, potentially reducing compliance costs for entities and allowing for greater mitigation ambitions within the ETS. Moreover, accepting offsets can lead to economic, social, and environmental co-benefits, fostering investment in sustainable development, technology sharing and transfer, and the participation of uncovered sectors.

However, there are potential drawbacks to accepting offsets in an ETS. While it offers greater compliance flexibility for covered sectors and may lower carbon prices, it could temporarily constrain investments in sustainable development within those sectors. The careful design and implementation of offset approaches are essential to ensuring the environmental integrity of these units. Additionally, mitigating the risk of double counting, such as by releasing sequestered carbon from established carbon sinks such as forests, is crucial for certain offset types. Using offsets may also raise concerns about well-proportioned sustainability across different sectors because of budget mobilization to specific industries or jurisdictions for investment in low-emission production and the co-benefits of climate change mitigation.

3. Methodology

A review of studies analyzing Vietnam’s ETS was conducted in December 2023. We utilized the Web of Science Direct and Google Scholar to identify documents containing the keywords “Vietnam”, “carbon market”, “emissions trading scheme”, and “emissions trading system”. We explicitly focused on empirical case studies published in English peer-reviewed journals from 2010 to 2023. The initial search yielded 171 articles, which we then narrowed down to seven after screening for relevance. The screening process involved considering titles and abstracts, and removing duplicates. Throughout this process, we ensured that each of the seven papers constituted an empirical case study of Vietnam’s ETS. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist (see in the Supplementary Materials).

As the number of quantitative studies conducted on Vietnam’s ETS is limited, a quantitative assessment was not applicable to this review. Instead, the research applied a qualitative approach summarizing the evolution and trends of methodologies employed in simulating Vietnam’s ETS while also analyzing its limitations and challenges. For each approach, the study discusses the foundational definition of the concepts, including the input of the analysis and the corresponding output. Additionally, we also discuss the advantages and disadvantages of each methodology and the specific case study’s circumstances.

4. Quantitative Approaches for Simulating Vietnam’s Carbon Market

4.1. Evolution and Trends of Methodologies in Simulating Vietnam’s ETS

As the implementation of the ETS in Vietnam is currently under consideration [18], most relevant studies comprise ex-ante analyses based on the system’s design assumptions. Common quantitative approaches applied to Vietnam’s ETS include computable general equilibrium (CGE) [19,20,21,22], input–output analysis (IOA) [23], and multi-criteria analysis (MCA) [24,25]. Despite the potential contribution of Vietnam’s ETS to global emissions reduction targets, quantitative research on Vietnam’s ETS is scarce [21]. The authors of [12] indicate that China, the USA, Australia, Germany, and England are among the leading contributors to ETS research utilizing quantitative models.

Table 1. Quantitative approaches for simulating the effectiveness of Vietnam’s ETS.

Approach	Model Components	Model Outputs	Advantages	Disadvantages
Simple Reduced-form Emission Projection Method	The 2020 GHG emission trajectories; an assumption of BAU emission growth rate for 2021–2030; Vietnam’s mitigation targets for energy industries; the effect of a carbon price on emissions from the literature review; an assumption on the annual growth rate of the carbon price	The base of carbon pricing for the achievement of the NDC; budget generated from carbon prices, changes in downstream energy prices, and the deviation of non-GHG emissions	Simple formulation with desired component indicators	Strong data assumptions; low accuracy
Computable General Equilibrium	World economy database; macroeconomic projections; emission levels; energy demand; capitals	ETS factors, including carbon price	Ex-ante analysis of different ETS designs	Local optima; low accuracy
Input–Output Analysis	Fitness function: optimal profit, mitigation cost, maximal mitigation effect Variables: Numeric data on ETS-related policies Constraints: regulations in an ETS, relationship among stakeholders, and impact factors	Simulated economic outlook and emission levels of different policy options	Ex-ante analysis of different ETS designs, incorporating mature economic theories	Local optima
Multi-criteria Analysis	Multiple criteria with impact scores; weighting of criteria; preference and indifference indexes	The results correspond to the budget flow, which ranges between −1 and 1; the balance between each alternative’s positive and negative flow	Simple structure with direct, decisive quantification	Subjectivity

lists recent case studies that have employed quantitative approaches to simulate Vietnam’s ETS.

Among these approaches, multi-criteria analysis is the most straightforward and practicable method for identifying the most preferred options among complex indicators. The core function of multi-criteria decision analysis (MCDA) is to synthesize multiple criteria with conflicting interests. The results of the MCDA are the optimal policy options that reflect the trade-offs between different factors [26]. Another advantage of this method is the inclusiveness of both quantitative and qualitative databases in numerical analysis, which creates an inter-consultant relationship among stakeholders, policymakers, and scientists [26]. However, the case study by [26] includes only an ex-post analysis of the effects and impacts of the Belt and Road Initiative on carbon pricing. In developing countries, including Vietnam, that have not implemented direct carbon pricing, conducting an ex-ante analysis is essential to assessing the policy’s potential benefits and impacts.

Another case study on Vietnam’s carbon market applies a multi-criteria analysis to identify the most promising policy option among various carbon pricing initiatives. The authors of [21] simplify the analysis implementation by providing scoring points for three criteria and primarily offer an overview of the current legal framework for introducing carbon pricing options. As the definition of the assessment criteria is simplified, the output of the ex-ante analysis is limited, with vague arguments regarding the potential for participation in the carbon market.

The reduced-form emissions projection method differs in assessing Vietnam’s ETSs. The authors of [20] employ the influence rate of additional charges on GHG emissions obtained from secondary data from previous relevant research. This approach has a substantial advantage; it provides a straightforward estimation of the emissions reduction effect when a specific carbon price rate is applied. However, the selection of preferred policy options is based only on multi-criteria analysis, which experts generally consult without in-depth evidence-based analysis. Moreover, the perceived effect of a carbon price is extrapolated from research on the European ETS from 2012 to 2017 [27] for an even broader outbound projection period from 2020 to 2030.

A common methodology for quantitatively assessing Vietnam’s ETS is CGE. CGE models are characterized by equilibrium equations with linkages between economic entities via commodity and price flow balances [21]. The authors of [17] observe that setting a cap for each industry has led to the trading of emission permits between industries, inducing a change in product prices. This price change disrupts the balance between product supply and demand. Hence, the optimization models elaborate on the new equilibrium for the fitness function for optimum production and emission levels [21]. The CGE model employed in this study comprises two main constraints [28]: the market clearing theory, which states that all income will be spent on initial input factors and different commodities; and side constraints, including the availability of other technologies, expansion limits of new technologies, and decline limits of old technologies. Each industry’s substitution elasticity is based on these constraints [28]. The operation of an ETS, an externality, shocks the economy, necessitating a new economic equilibrium [21]. The fitting function of this new equilibrium aims to optimize total surplus [28]. The CGE model is solved numerically using sophisticated nonlinear programming [28]. The output of the GTAP-E [21] includes the deviations in the volume and value of carbon credits, marginal abatement costs, energy prices, emissions reduction, production levels of sectors, welfare, consumer price index, and gross domestic product among different policy options, the baseline scenario starting from 2020, and marginal abatement costs of sectors under different scenarios. The model’s output comprehensively envisions the entire economy of the regions and countries examined. Therefore, this methodology has become a common approach for ex-ante policy analysis. However, the numerical output of the study is based on several assumptions in the model formulation.

Another robust quantitative approach for the ex-ante analysis of policies is IOA. The authors of [25] indirectly analyze the impact of decarbonization policies in ASEAN countries. IOA is based on primary stable technical coefficients, the perfect elasticity of sectors’ production, and the supply of capital, labor, and land [29,30]. Employing the ordinary IOA methodology, [25] flexibly utilize theories with constraints on the target growth of each region. Similar to the CGE approach, IOA also uses an optimization problem with ordinary balance equations and factor constraints. Its output is similar to that of CGE, including changes in sector production, regional emission levels, and proportional use of fuels. IOA is claimed to aid in analyzing conflicting objectives between economic and environmental targets [31]. Its cautious approach to the assumptions in the model is argued to mitigate bias in predicting economic outcomes [30]. Moreover, an extension of ordinary IOA integrates it with structural decomposition analysis to define the determinants of specific issues [32]. The mainstream economic perspective of the original IOA yields concrete results for national economies [33].

4.2. Limitations and Challenges

The specific original features of each quantitative approach introduce limitations and challenges in analyzing previous case studies on Vietnam. In [20], employing a simple reduced-form emission projection method, a vague understanding is presented of the average reduced emissions resulting from an additional carbon price. Moreover, the average data are derived from a European case study, overlooking the significant differences between developed and developing countries, such as Vietnam. The simplicity of the secondary reference for estimating emissions reductions from carbon pricing hinders the coverage of comparable research. Therefore, applying this method to a random study area is challenging. A simple linear extrapolation aids study areas in the literature. The simplicity of the extrapolation supports the extension of the research scale. Reduced-form emission projections can assess the effects of Vietnam’s ETS on the regional carbon market. Various studies forecasting emissions in neighboring economies, such as Thailand, China, and Malaysia [34,35,36], can be referenced for reduced-form emission projections. Simple, reduced-form emission projections can be developed using regression models and statistical approaches by applying machine learning [35]. Additionally, emission projections must consider relevant factors, including climate variables [36]. Ref. [34] forecasted future carbon emissions based on technological innovations and plausible policy options. Synthesizing this literature can support future research on the effects of Vietnam’s ETS on future emission projections. A simple emissions projection method for Vietnam’s ETS can be advanced by integrating regression models with relevant influencing factors identified in previous regional studies.

Similarly, MCDA with simplified numerical policy impacts is widely used despite its limitations. This approach aids in selecting among various choices based on multiple criteria [37]. Nonetheless, it has two common disadvantages: the controversy surrounding the weight of coefficients and inconsistencies between judgment and ranking criteria [38]. Therefore, to eliminate these biases, various weighting methods for MCDA have been developed. Most MCDA research has utilized the fusion of fuzzy numbers and linguistic responses to problems to avoid uncertainty in determining the significance of criteria and competence of alternatives [37]. MCDA is more commonly used to support decision-making between comparable policy options than to assess different ETS modification scenarios. It supports policy options based on the humanistic perceptions of experts. The ranking or weighting of criteria depends on existing referable policy visions for estimating impacts and effects [39].

Both CGE and IOA share specific similarities, particularly in facing common challenges: the data availability of national account systems and related macroeconomic databases. The more advanced the national general statistics, the more favorable the construction of the models. Additionally, both approaches require comprehensive secondary data for the assumptions of related components and modules, such as energy development and welfare [20,21,22,29]. IOA and CGE are employed to assess the economic impacts of specific interventions on regional or national economies [22], requiring assumptions regarding the stability of all other irrelevant factors [31,40]. Therefore, most uncertainties in IOA originate from inherent assumptions and incoherent data sources [31]. Researchers can mitigate these limitations by making appropriate adjustments to obtain more realistic results [31]. However, CGE models require numerous assumptions, which complicate their usage [30].

Employing simulation models for Vietnam’s case studies presents significant data processing challenges owing to the asynchronous nature of the GHG emissions inventory data and national accounts statistical data [23]. The industry categories for GHG emissions inventory are based on the IPCC inventory guidelines. Nevertheless, sectors within the national accounts database are assembled into the system of national accounts of the United Nations Statistics Division. Discrepancies in categories complicate data processing and undermine the accuracy of the GHG emission intensity data for individual industries.

5. Discussion and Conclusions

There is a strong global trend in the implementation of various carbon pricing initiatives. Indirect carbon pricing increases the prices of products to discourage the consumption of emission-intensive products. However, indirect carbon pricing is claimed to generate a negative carbon price signal [7]. Owing to the limitations of indirect carbon pricing, direct carbon pricing initiatives, such as carbon taxes and ETSs, have been implemented as more effective measures for climate change mitigation. Global ETS initiatives have made the international carbon market the most active in history. In 2023, global revenue from direct carbon pricing reached US$95 billion, with most originating from ETSs [7]. Concerning the potential of ETSs, Vietnam is considering establishing a domestic carbon market in the near future [18]. Nevertheless, few studies have quantitatively assessed Vietnam’s ETS. Studies have primarily analyzed partial ETS implementations, focusing on the energy sector [21,24].

The author’s of [12] indicate that most studies on ETS designs are conducted using simulation models, with the most frequently used methodologies being CGE and IOA. Given the detailed comparison between these two methodologies and the aim for comprehensive simulation results, the preferable method for assessing Vietnam’s ETS is IOA. IOA is superior to CGE because of its simplicity and expected output, making it a competent approach for quantitatively assessing Vietnam’s economic and environmental policies [23,33]. Extended IOA, with appropriate adjustments, can be applied to simulate economies and yield more realistic results [30]. Although IOA has limited capacity due to a lack of price-induced substitution effects [30], it can still simulate these effects resulting from relative price changes [33,41]. Moreover, with simple linear programming, IOA can be easily adjusted by incorporating additional percentage revisions to the input multipliers for more practical results [30].

Our review of current studies on Vietnam’s ETS reveals two significant gaps: insufficient research and an overfocus on emission-intensive sectors. Future research should expand the impact assessment of the nationwide ETS, ensuring full participation from all of the economic sectors. Evaluating an economy-wide ETS provides insights into the market’s total capacity. This analysis offers policymakers an overview of the sectors that are most prioritized and require additional regulation. However, given the limitations of IOA [30], subsequent studies should advance the extended IOA for environmental economic policy assessment, incorporating various theorems such as price change and the substitution theorem.

Furthermore, [12] highlights the effectiveness of ensemble methodologies, which involve combining several models to enhance the advantages of individual models. For instance, statistical models with fitting functions can emerge from economic simulation models to better project exogenous variables. Moreover, machine learning and artificial intelligence can be utilized to solve optimization problems in simulation models, leading to better results and capabilities for relatively complex models [16]. Simulation models have also been integrated with optimization models, such as data envelopment analysis (DEA), to assess the mitigation potential of specific climate policies [29].

The gaps identified in current studies on Vietnam’s ETS underscore the necessity for future research to broaden the impact assessment beyond emission-intensive sectors. A call for an economy-wide ETS evaluation, encompassing participation from all industries, is essential to provide policymakers with a holistic overview of market capacity and identify sectors requiring additional regulations.

Incorporating these methodologies promises a more nuanced understanding of the complexities associated with ETS implementation, ensuring that future research addresses these gaps and leverages cutting-edge techniques for a more comprehensive and accurate evaluation of Vietnam’s ETS and similar global environmental policies.

Even though this study puts a significant effort into assessing Vietnam’s ETS quantitatively, potential biases could not be avoided because of the search engine and the inaccessibility of some literature. On the one hand, the use of specific search engine databases such as Google Scholar and Web of Science Direct may affect the scope of the results. The search engines used are the two most common platforms for compiling scientific papers. However, other platforms may have access to other environmental science publications. On the other hand, owing to the limited resources for the study, we exclude inaccessible publications with relevant terms.