A Study on Sustainability Indicators for Energy Companies in Viet Nam

Abstract

The energy sector is a cornerstone of Viet Nam’s economic growth, providing critical contributions to development and employment. However, ensuring its long-term sustainability remains a pressing challenge. This study leverages the United Nations’ ESG framework to develop a comprehensive structure for sustainable management indicators tailored to Viet Nam’s energy industry. Through expert interviews and systematic analysis using the modified Delphi and DEMATEL methods, the study identified “Cost Management” as the most critical sustainability indicator, influencing other key areas. Additionally, “Innovation Management”, “Renewable Energy”, “Vocational Training”, and “Human Capital Development” emerged as pivotal for driving sustainability. These findings underscore the importance of aligning sustainability practices with operational efficiency and innovation. The study highlights the urgent need for energy companies to adopt targeted solutions such as cost optimization, investments in renewable technologies, and workforce development to foster sustainable growth. By offering actionable insights and a prioritized framework, this research provides energy companies and policymakers with a practical roadmap to enhance Viet Nam’s energy sustainability and support its economic recovery post-COVID-19.

1. Introduction

The electricity industry plays a crucial role in supplying energy required for contemporary society. It includes the generation, transmission, distribution, and retail of electricity, involving a variety of technologies. Over recent years, the global energy demand has consistently risen. The worldwide energy consumption index rose by 2.1% in 2022. Despite being affected by the COVID-19 pandemic, this increase was more rapid than the average from 2010 to 2019. Global power consumption is predicted to accelerate and rise by an average of 3.4% per year until 2026 [1].

Due to a stronger economic outlook, it will fuel the gains, resulting in a greater increase in energy consumption in both advanced and emerging nations. Viet Nam is one of the emerging countries that have rapid economic expansion and has emerged as one of Asia’s fastest-growing energy markets. Viet Nam’s electricity demand is anticipated to increase by an average of 7% annually from 2024 to 2026.

In Viet Nam during this period, industry is expected to consume the most energy, followed by transportation, and then civil and commercial services [2]. Those energy demands are not only a big contributor to electricity demand but also a foundation for economic growth. The rapid urbanization process in recent years with the development of high-rise buildings in cities requires a lot of energy for lighting and cooling, along with the expansion of the energy network to remote areas, also causes electricity demand to increase sharply. Previously, high energy demand caused power shortages in Northern Viet Nam, incurring a cost of USD 1.4 billion [1].

To address the rapidly increasing demand for energy in recent years, together with the development of sustainable energy in the long term, Viet Nam has taken several actions. These initiatives aim to address environmental problems, reduce greenhouse gas emissions, and improve energy security while fostering long-term economic growth. They also support the adoption of electric vehicles (EVs). Not only the Viet Nam government but also energy companies in Viet Nam work toward sustainability. Globalization, environmental pollution, and limited resources have increased the demand for energy companies to shift their focus away from financial performance and toward sustainability and responsible corporate performance. Acknowledging the significance of upholding sustainability in energy development, this article examines sustainable management indexes for energy companies in Viet Nam.

Sustainability in the energy sector is a critical global concern, especially for rapidly developing economies like Viet Nam. Sovacool and Griffiths [3] have explored the role of renewable energy and sustainability indicators at a global level but lack specificity for Viet Nam’s unique socio-economic and industrial contexts. The challenges of managing the country’s rapid urbanization, energy-intensive industries, and the growing gap in energy storage solutions remain underexplored. Studies like those by Hahn and Kühnen [4] emphasize the importance of ESG frameworks but fall short of addressing their practical application in Viet Nam’s energy industry.

While some work has highlighted individual indicators such as renewable energy adoption [5] and energy efficiency [6], there is limited research analyzing the interdependencies between these indicators in the Vietnamese context. Moreover, the prioritization of these indicators remains unclear, leaving energy companies without actionable guidance to balance economic growth with sustainability goals. This study aims to develop a tailored framework for analyzing the interdependencies between sustainability indicators in the context of energy companies in Viet Nam and determining priority between the indicators.

2. Background of Research

2.1. Overview of Energy Sector in Viet Nam

In 2023, world electricity generated by solar power recorded a 24.5% increase, and the growth of wind energy production was 9.8% [7]. In Viet Nam, electricity generated by solar slightly decreased by 0.2% from 25.75 TWh to 25.7 TWh, and wind had an astonishing growth of 25.1% from 9.09 TWh to 11.37 TWh. With a total of 13.41% of the Vietnamese market share for electricity generation, wind and solar energy hit a new high in 2023. Despite the continuous phenomenal growth of renewables such as solar and wind, Viet Nam still heavily relies on coal, which accounts for 46.87%, much higher than the rate 35.38% from the world [7].

Total system power generation in October 2023 was 24.28 billion kWh (an average of 783.2 million kWh/day), an increase of 11.3% over the same period in October 2022. The highest daily output was 857.9 million kWh (6 October 2023), and the highest capacity was 41,183 MW (5 October 2023). The system’s production for the first 10 months of 2023 was 234.13 billion kWh, a 3.9% increase over the same time last year. In 2023, coal thermal power is the dominant source, accounting for 46.87% of electricity generated. Hydro power comes second with 29.26%, followed by solar and wind at a total of 13.41%. According to Power Development Plan 8, the goal is to increase utility-scale solar PV capacity to approximately 13 GW and wind capacity to about 28 GW by the year 2030. Coal-fired capacity is intended to reach its peak at 30 GW [8].

Viet Nam’s coal-fired power plants typically serve as baseload plants due to their stable output and limited ability to adjust quickly to fluctuations in demand. In contrast, hydroelectric power plants are more flexible and can be rapidly mobilized to respond to changes in energy demand, making them a key resource for managing peak loads and grid stability. However, there is an increasing demand for diversification due to environmental concerns and the dependence on imported coal. As shown in Figure 1, solar and wind energy developed rapidly in recent years. Although conventional sources such as hydropower and coal have high shares, the future is expected to be more balanced, with renewables playing an increasingly important role.

In the first ten months of 2023, Viet Nam Electricity (EVN) and its units began construction on 62 projects and completed 71 power grid works ranging from 110 kV to 500 kV (including one 500 kV project, nine 220 kV projects, and 61 110 kV projects), with the Nam Dinh 1—Thanh Hoa 500 kV line (line section from VT96 to VT117) being the most urgent. In September and October 2023, EVN leaders and units collaborated with the People’s Committees of many Northern provinces to undertake investment in power plant development and electricity supply in the province. Simultaneously, they collaborated with the co-sponsor delegation to analyze the Bac Ai Pumped Storage Hydropower project and secure funding.

Viet Nam’s electricity generation has a high CO₂ equivalent (CO₂e) emission factor of 0.56 kg CO₂e/kWh, positioning it among countries with high emission intensities. This is mainly due to its significant reliance on fossil fuels, particularly coal, for energy production. In comparison, China has a slightly higher emission factor at 0.62 kg CO₂e/kWh, driven by its own heavy dependence on coal. India also faces a high emission intensity of 0.65 kg CO₂e/kWh, again due to coal usage. On the other hand, the United States has a lower emission factor of 0.40 kg CO₂e/kWh, thanks to its more diversified energy mix, which includes significant contributions from renewable energy sources. Germany and France have much lower emission factors, at 0.27 kg CO₂e/kWh and 0.05 kg CO₂e/kWh, respectively. This is primarily attributed to their investments in renewable energy and nuclear power, which significantly reduce their carbon footprints. The high emission intensity in Viet Nam presents both challenges and opportunities, particularly when it comes to electrification, such as the transition to electric vehicles.

Viet Nam has established an implementation strategy for its National Power Development Master Plan covering the years 2021 to 2030, and it has sanctioned a new system that permits renewable project developers to sell electricity directly to major consumers [8]. Emerging renewable energy firms have the chance to grow, while established energy companies can enhance their sustainable electricity offerings. Nonetheless, these energy companies encounter numerous challenges, and identifying key indicators to boost sustainability will be essential.

2.2. Recent Research on Corporate Sustainability Indicators

Corporate sustainability, also known as business sustainability, involves managing the financial, social, and environmental challenges of a company to ensure ongoing success. This includes evaluating factors such as Environment, Social, and Governance (ESG). Environmental sustainability focuses on energy efficiency, reducing carbon footprints, and managing waste and water consumption. Social sustainability emphasizes corporate social responsibility, including fair labor practices and community participation. Economic sustainability refers to long-term profitability and involves corporate governance, risk management, ethical practices, and transparency.

Dun & Bradstreet [9], a global company of business data and analytics, plays a crucial role in this area by providing comprehensive commercial data and insights that help businesses evaluate and implement ESG factors effectively.

Dow Jones Sustainability Indices (DJSI) [10] is a set of global sustainability standards that analyze and quantify a company’s sustainability performance. DJSI, which was created by S&P Dow Jones Indices and RobecoSAM, assesses a variety of ESG variables to identify companies that will show future leadership and sustainability in their sector. These indices provide a complete framework for investors, corporations, and stakeholders to analyze and compare sustainability performance. By addressing sustainability issues and adjusting to DJSI criteria, companies can proactively manage their competitiveness, risks related to climate change, resource scarcity, reputational damage, and other sustainability-related challenges.

Dočekalová and Kocmanová [11] found that nine aggregate indicators were created in total. In addition to the complex performance indicator, four indicators relate to performance categories, namely, the environmental performance indicator, the social performance indicator, the economic performance indicator, and the corporate governance performance indicator.

Rodrigues et al. [12] have compiled a collection of sustainability indicators. They are classified into three categories: environmental, social, and economic, and are weighted based on their prevalence in the systems identified in the literature study. Interviews in Rodrigues et al. with subject matter experts should be examined, and decision-making procedures should be used to select and align the most relevant indicators to overcome the absence of empirical research when using first sustainability evaluation grading systems such as the Building Research Establishment Environmental Assessment Method (BREEAM) and Leadership in Energy and Environmental Design (LEED).

Moreover, in the study of Turcu [13], it is surprisingly difficult to identify an “integrated” set of sustainability impact scores (SIs) in a literature otherwise besieged with lists of indicators, not due to a lack of valid sets of SIs, but rather to the abundance of them. This study discovered that some indicators were “valued” more than others in their respective communities, which appears to be underappreciated in indicator formulation and sustainability evaluation.

Valenti et al. [14] define sustainable aquaculture as the cost-effective production of aquatic organisms. At the same time, businesses must be resilient to survive over time. There were 20 sustainability indicators; 7 are classified as effect indicators, while the remaining indicators are classified as cause indicators in Li et al. [15]. In the study, the ’resource conservation potential’ was ranked first in the cause group, and practically all environmental indicators in the cause group.

Stamford and Azapagic [16] have established a different sustainability framework, which includes 43 techno-economic, environmental, and social variables that are evaluated on a life cycle basis when applicable. Although the framework was created largely to address concerns about nuclear energy in the United Kingdom, it also allows for sustainability analyses and comparisons of other energy technologies and is relevant to other nations.

Li, Pinto, and Kumar [15] found that among the sustainability assessment factors mentioned in the article, the most influential factors include the following. Ability to pay, willingness to pay, electric tariff, total revenue, investment cost, savings, staff salary and workload, percentage of productive income to total revenue, number of customers, natural disaster impact: flood and landslide and water discharge availability. The most often used indicator in Alabbasi et al. [17] is “Capital cost”, followed by “Impact on emission level” and “Job creation”.

Lastly, in Deng et al. [18], the triple bottom line theory is the most widely used theoretical framework to analyze the performance of energy systems from a sustainability perspective. Furthermore, while the indicators used in each article are not the same, there are commonalities in these indicators that suggest which indicators are more widely used and appreciated. Using the literature, we collected, organized, and removed some unimportant indicators for energy companies. The list of indicators is shown in

Table 1. Sustainable indicators for energy companies.

Aspects	Indicators	Explanation	Reference
Corporate governance	Supply chain management	Include the purchase of physical items, information, services, and other resources that a business needs to continue operating and growing.	[9,10,12,18]
	Cost management	Include cost data collection, analysis, and reporting to improve budgeting, forecasting, and cost monitoring.	[11,12,16,17,18,19,20]
	Innovation management	Include the decisions, activities, and practices to design and implement an innovation strategy.	[10,11,18]
	Corporate governance development	The fundamental concepts of corporate governance include accountability, transparency, fairness, responsibility, and risk management.	[9,10,11,14,19]
	Marketing practice	Include advertising, selling, and delivering products to consumers or other businesses.	[10,12]
	Shareholder rights	Common shareholders should have six rights: voting power, ownership, the right to transfer ownership, a claim to dividends, the right to view corporate papers, and the right to sue for unlawful conduct.	[9,11,12]
Environmental Protection	Climate strategy	Reduce and stabilize the amounts of greenhouse gases that trap heat in the atmosphere, as well as react to existing climate change scenarios.	[9,10,12,17,20]
	Water-related risks and sources management	Recognizing and resolving possible water-related concerns, as well as assessing water availability and quality, are all important considerations when considering new development locations.	[11,12,13,14,15,16,17,18,20]
	Environmental policy and management system	A system that incorporates policies, procedures, and processes for training workers, monitoring, summarizing, and reporting specialized environmental performance information to a firm’s internal and external stakeholders.	[9,10,11]
	Fuel efficiency and low carbon strategy	These methods aim to achieve social, economic, and environmental development goals while lowering long-term greenhouse gas emissions and strengthening resilience to climate change.	[9,10,11,14,19]
	Materials and raw materials	In a circular economy, producers develop items that can be reused. Products and raw materials are reused as much as possible.	[11,12,19]
	Renewable energy	Include wind power, solar power, biofuel (organic materials used as fuel) and hydroelectricity.	[12,13,14,15,16,17,18,19]
Social responsibility	Vocational training	Refers to instructional programs or courses that teach the skills needed for a certain job function or trade.	[9,11,13,15,19,20]
	Occupational health and safety	A practice that deals with the safety, health, welfare, and wellbeing of people when they are at work.	[9,10,13,14,20]
	Talent attraction	Include employer branding, recruiting marketing, company culture, and benefits.	[9,10,13,18]
	Development of human capital	The process of enhancing and improving the skills, knowledge, abilities, and overall potential of individuals within a society or organization.	[9,10,16]
	Human rights	Include the right to life, liberty, freedom of expression, employment, and education.	[9,11,16]
	Customers health and safety protection	It is best protected by means of strict and common safety rules and standards for consumer products.	[9,10,14,15,17]

.

2.3. Research Method

As mentioned in the previous section, Viet Nam’s electricity industry has innovated to be more sustainable recently. Due to the ongoing changes and the complex and unclear network of interaction between criteria, determining important criteria becomes difficult. Experts’ opinions are valuable to assess problems, and many multi-criteria decision-making (MCDM) methods are widely used to analyze the opinions.

To get consistent and valuable information from a group of experts, the Delphi approach has been used in research [16]. The goal of the Delphi method is to achieve agreement among a panel of experts in the relevant field to form a unified perspective on an issue or set of challenges [21]. The Delphi method is a structured communication framework that is based on the findings of many rounds of surveys presented to an expert panel. Although multiple surveys in the classic Delphi approach can result in a final agreement, the implementation cost may be prohibitively expensive, and the response rate may be reduced due to the time-consuming procedure. Therefore, the modified Delphi is adopted to reduce costs. Instead of being fully anonymous, the modified Delphi method may contain some amount of supervised contact amongst experts, allowing for debate or explanation of ideas while not divulging or identifying individual or personal viewpoints. A more targeted selection of participants helps address the drawbacks of traditional techniques by ensuring a varied variety of knowledge and opinions. Rather than the typical numerous rounds, the modified Delphi method can condense the process into fewer rounds to expedite consensus building. In this research, modified Delphi was adopted to get consensus on sustainability indicators between experts.

Many methods in MCDM have been proposed to investigate the network structure or the importance of criteria. Technique for order preference by similarity to ideal solution (TOPSIS) selects the best alternative that is the farthest from the negative ideal point and the shortest from the position ideal point [22]. The VIKOR method establishes a compromise ranking list based on the measure of closeness to the ideal solution [23]. Grey relational analysis (GRA) tackles issues involving complex interrelationships among numerous variables by utilizing incomplete and uncertain data [24]. Analytic hierarchy process (AHP) method conducts pairwise comparisons to assess the relative significance of components at each tier of the hierarchy and examines different options at the lowest level to determine the optimal choice among several alternatives [25]. However, AHP assumes the structure of criteria follows a hierarchy and limits interrelationship between criteria. Analytic network process (ANP) is capable of handling dependencies, but it presumes that each segment has equal weights when forming a super weighted matrix [26].

DEMATEL (decision-making trial and evaluation laboratory) is recognized as an effective approach to determine chain components of cause and effect in a complex system. DEMATEL analyzes the dependent interactions among components and determines the key components through a visual structural representation [27]. This method not only uses matrices to turn interdependency connections into cause-and-effect groups but also employs a direct impact diagram to identify critical variables in a complex structural system. DEMATEL has been enhanced to improve decision making in a variety of scenarios, as many real-world systems provide inaccurate and misleading information [27]. Due to its advantages, this research adopted DEMATEL to examine the relationship between sustainability indicators.

3. Methodology

The goal of the methods presented is to simplify the process of acquiring expert perspectives, improving those opinions based on feedback, and eventually reaching a consensus or convergence of thoughts on a given subject. The overview of our research process is shown in Figure 2.

3.1. Modified Delphi Method

The process of performing the modified Delphi method can be summarized in the following 5 steps:

Step 1. Problem Identification and Question Formulation: The problem or topic of interest is defined, and clear, short, and detailed questions are generated.

Step 2. Selection of the expert panel: A varied team of specialists with relevant experience, backgrounds, skills, and perspectives in the specified issue or subject is selected.

Step 3. Initial round of input: Experts are asked to give anonymous feedback, thoughts, forecasts, or ideas about the identified topic through surveys, questionnaires, or other data-gathering tools.

Step 4. Feedback and Iteration: A summary of the initial responses is provided to the experts, allowing them to reevaluate or modify their positions based on the overall feedback. This process will be repeated in subsequent rounds if needed.

Step 5. Consensus Analysis and Reporting: The ultimately gathered expert responses are examined, and a report detailing the extent of consensus or alignment among expert viewpoints, along with the reasoning, is produced.

3.2. Decision Making and Trial Evaluation Laboratory (DEMATEL)

Based on Murugan and Marisamynathan [28], Shieh, et al. [29], and Tsai, et al. [30], the DEMATEL technique procedure is presented below.

Step 1: Formulate the average matrix $A$. Denote $x_{ij}^k$ the extent to which factor $i$ influences factor $j$ from expert $k$. For each expert $k$, a $n\times n$ non-negative matrix, such as $x^k={\left[x_{ij}^k\right]}_{n\times n}$, can be generated, where $1\le i\le n$, $1\le j\le n$, and $1\le k\le H$. To synthesize all the responses from $H$ experts, the average matrix $A={\left[a_{ij}\right]}_{n\times n}$ can be formulated as follows:

$$a_{ij}={\displaystyle \frac{1}{H}}\sum _{k=1}^H{x}_{ij}^k$$

(1)

Step 2: Calculate the normalized initial direct-relation matrix $D$ by multiplying the average matrix $A$ with $S$, which is defined by the following formula.

$$D=A\times S$$

(2)

$$S={\displaystyle \frac{1}{\underset{1\le i\le n}{\mathrm{m}\mathrm{a}\mathrm{x}} \sum _{j=1}^n a_{ij}}}$$

(3)

Step 3: Calculate the total-relation matrix $T$ using the following formula, where $I$ is an $n\times n$ identity matrix.

$$T=D{\left(I-D\right)}^{-1}$$

(4)

The total-relation matrix $T$ has a matrix representation as follows.

$$T={\left[\begin{array}{ccc}{t}_{11}& \cdots & t_{1n}\\ ⋮& \ddots & ⋮\\ t_{n1}& \cdots & t_{nn}\end{array}\right]}_{n\times n}$$

(5)

Step 4: Calculate the degree of influence strength. The influence generated and received by factors $i$, $R_i$, and $C_i$ respectively, is calculated as follows.

$$R_i=\sum _{j=1}^n t_{ij}, i=\mathrm{1,2},3,\dots n$$

(6)

$$C_i=\sum _{j=1}^n t_{ji}, i=\mathrm{1,2},3,\dots n$$

(7)

The value of $\left(R_i+C_i\right)$ shows all the effects generated and received by factor $i$ indicating the impact of both factor $i$ on the whole system as well as the influence of other system factors on factor $i$. The $\left(R_i-C_i\right)$ indicator, on the other hand, displays the net influence that factor $i$ has on the system. If the matching value of $\left(R_i-C_i\right)$ for the factor $i$ is positive, the factor $i$ is a causal factor. Otherwise, the factor $i$ is an effect factor.

4. Results and Discussions

4.1. Design and Implement Questionnaires

This study explores sustainable management indicators applicable to energy companies in Viet Nam. The study invited 10 experts holding management positions in Viet Nam’s energy companies to provide insights on sustainability indicators. Experts were selected based on their educational qualifications, professional experience, and industry representation to ensure diverse perspectives. All participants held at least a bachelor’s degree, with most possessing master’s qualifications in fields like business management or energy market analysis. They had an average of over 10 years of professional experience in areas such as operations, supply chain, and logistics management. To ensure representation across the energy sector, participants came from various roles, including business development managers, operations directors, and chief accountants, and from different regions in Viet Nam, reflecting the country’s varied energy challenges and opportunities.

The questionnaire was meticulously designed to collect both quantitative and qualitative data. It featured two main sections: a quantitative evaluation of the importance of sustainability indicators using a 0–10 rating scale and open-ended questions for qualitative feedback. A pilot test ensured the questionnaire’s clarity and relevance before distribution. The surveys were disseminated via email and online platforms to accommodate experts’ schedules, achieving a 100% response rate. Data were analyzed using the modified Delphi method and DEMATEL to identify and refine key indicators, ensuring consensus and actionable insights for sustainable energy practices. Details on the experts’ demographics are shown in

Table 2. Demographic information of the experts.

Experts	Education	Positions	Area	Number of Working Years
Expert A	Master	Business development manager	Energy market	12 years
Expert B	Bachelor	Planning supervisor	project scheduling	6 years
Expert C	Master	Chief accountant	Finance	5 years
Expert D	Master	Operation director	Facility operation	16 years
Expert E	Master	Manager	Supply chain	7 years
Expert F	Master	Transaction manager	Logistic	10 years
Expert G	Bachelor	Operation director	Operations management	6 years
Expert H	Bachelor	Material planning manager	IT	13 years
Expert J	Master	Business development manager	Marketing	15 years
Expert K	Bachelor	Manager	IT	21 years

.

In the first section, the modified Delphi method was used, and questionnaires were distributed to experts from Viet Nam’s energy companies to fill in, and their expert suggestions were also collected to increase or decrease the prototype structure and converge the questionnaire on importance.

Table 3. Evaluation and rating on criteria from experts.

Aspects	Indicators	Approval (Yes/No)	Importance *
Corporate governance	Supply chain management	Yes	8.40
	Cost management	Yes	8.30
	Innovation management	Yes	7.45
	Corporate governance development	Yes	8.30
	Marketing practice	No	6.70
	Shareholder rights	No	6.85
Environmental protection	Climate strategy	No	6.45
	Water-related risks and sources management	No	6.80
	Environmental policy and management system	Yes	7.80
	Fuel efficiency and low-cross strategy	Yes	7.75
	Materials and raw materials	No	6.50
	Renewable energy	Yes	8.50
Social responsibility	Vocational training	Yes	7.70
	Occupational health and safety	No	6.95
	Talent attraction	No	6.60
	Development of human capital	Yes	7.40
	Human rights	Yes	7.95
	Customer health and safety protection	No	6.60

* Scale of 0–10: 0 is the least important, 10 is the most important.

shows the evaluation on criteria from the experts.

Experts believe that a coefficient of indicators equal to or greater than 7 is suitable to consider the indicators. According to Table 3, marketing practice, shareholder rights, climate strategy, water-related risks and sources management, materials and raw materials, occupational health and safety, talent attraction, and customer health and safety protection are removed because their scores are less than 7. Experts recommend categorizing detailed and comprehensive information as related sub-criteria due to their similar implications. The final structure of the sustainability indicators after using the modified Delphi method can be seen in Figure 3.

4.2. Decision-Making Trial and Evaluation Laboratory (DEMATEL)

In this section, the DEMATEL approach is used to administer a questionnaire on the effect of indicators to determine the degree of mutual influence between indicators and extract key indicators from it. Ten questionnaires are collected as input for DEMATEL.

According to the calculation process described in Section 3,

Table 4. The average direction-relation matrix A for Vietnamese energy companies.

	GO1	GO2	GO3	GO4	EN1	EN2	EN3	SO1	SO2	SO3
GO1	0	7.24	7.05	7.34	6.51	7.25	7.85	7.86	7.22	7.11
GO2	7.32	0	7.87	7.58	7.47	7.83	8.17	7.57	8.17	7.53
GO3	7.11	7.34	0	7.35	7.54	7.04	7.97	7.32	7.78	7.06
GO4	7.47	8.25	8.12	0	6.58	7.03	7.34	7.64	7.36	7.38
EN1	6.41	7.32	6.55	6.47	0	7.88	7.95	6.76	6.62	6.78
EN2	7.18	7.47	6.88	6.35	8.05	0	8.08	6.87	6.75	6.64
EN3	6.83	7.12	7.78	6.20	8.18	7.82	0	7.71	7.18	7.15
SO1	6.55	7.71	8.12	7.43	6.05	5.67	7.75	0	8.15	7.74
SO2	6.21	7.85	7.25	7.36	5.56	6.84	7.63	8.43	0	7.39
SO3	6.43	6.07	5.63	6.05	7.12	6.68	6.15	7.75	7.62	0

shows the average matrix A.

Subsequently, the average matrix $A$ is normalized. As a result, the normalized direct-relation matrix $X$ is calculated and shown in

Table 5. The normalized direct-relation matrix (X) for Vietnamese energy companies.

	GO1	GO2	GO3	GO4	EN1	EN2	EN3	SO1	SO2	SO3
GO1	0.000	0.104	0.101	0.106	0.094	0.104	0.113	0.113	0.104	0.102
GO2	0.105	0.000	0.113	0.109	0.108	0.113	0.118	0.109	0.118	0.108
GO3	0.102	0.106	0.000	0.106	0.109	0.101	0.115	0.105	0.112	0.102
GO4	0.108	0.119	0.117	0.000	0.095	0.101	0.106	0.110	0.106	0.106
EN1	0.092	0.105	0.094	0.093	0.000	0.113	0.114	0.097	0.095	0.098
EN2	0.103	0.108	0.099	0.091	0.116	0.000	0.116	0.099	0.097	0.096
EN3	0.098	0.102	0.112	0.089	0.118	0.113	0.000	0.111	0.103	0.103
SO1	0.094	0.111	0.117	0.107	0.087	0.082	0.112	0.000	0.117	0.111
SO2	0.089	0.113	0.104	0.106	0.080	0.098	0.110	0.121	0.000	0.106
SO3	0.093	0.087	0.081	0.087	0.102	0.096	0.089	0.112	0.110	0.000

.

Then, the total influence matrix T is calculated and shown in

Table 6. Total influence matrix (T) for Vietnamese energy companies.

	GO1	GO2	GO3	GO4	EN1	EN2	EN3	SO1	SO2	SO3
GO1	0.325	0.514	0.492	0.433	0.442	0.468	0.570	0.552	0.525	0.483
GO2	0.494	0.499	0.580	0.510	0.529	0.552	0.656	0.630	0.616	0.565
GO3	0.437	0.536	0.420	0.453	0.474	0.486	0.593	0.567	0.552	0.503
GO4	0.455	0.561	0.539	0.371	0.476	0.500	0.600	0.585	0.562	0.520
EN1	0.359	0.461	0.433	0.372	0.305	0.424	0.516	0.484	0.463	0.426
EN2	0.396	0.493	0.467	0.399	0.438	0.352	0.549	0.516	0.495	0.454
EN3	0.422	0.521	0.509	0.428	0.470	0.484	0.478	0.559	0.533	0.492
SO1	0.406	0.515	0.500	0.430	0.432	0.445	0.564	0.446	0.531	0.486
SO2	0.391	0.504	0.478	0.417	0.414	0.447	0.550	0.542	0.414	0.470
SO3	0.297	0.380	0.356	0.305	0.334	0.345	0.426	0.428	0.408	0.272

.

The results of the degree of influence strength of the indicators are presented in

Table 7. The importance of each indicator for Vietnamese energy companies.

Indicator	${\mathit{C}}_{\mathit{j}}$	${\mathit{R}}_{\mathit{i}}$	Prominence ${\mathit{C}}_{\mathit{j}}+{\mathit{R}}_{\mathit{i}}$	Relation ${\mathit{C}}_{\mathit{j}}-{\mathit{R}}_{\mathit{i}}$	Ranking of ${\mathit{C}}_{\mathit{j}}+{\mathit{R}}_{\mathit{i}}$
GO1	4.8027	3.9807	8.7834	0.822	8
GO2	5.6289	4.9844	10.6133	0.6445	1
GO3	5.0199	4.7729	9.7928	0.247	5
GO4	5.1694	4.1179	9.2873	1.0515	6
EN1	4.2444	4.3124	8.5568	−0.068	9
EN2	4.5596	4.5023	9.0619	0.0573	7
EN3	4.8940	5.5008	10.3948	−0.6068	2
SO1	4.7539	5.3086	10.0625	−0.5547	3
SO2	4.6255	5.0992	9.7247	−0.4737	4
SO3	3.5513	4.6704	8.2217	−1.1191	10

below.

4.3. Analysis

Based on the classification by Si, You, Liu and Zhang [27] and data from Table 7, Figure 4 is drawn to illustrate the influential network relationship between indicators. The average prominence $R+C$ is 9.450. If the prominence of an indicator is greater than the average, the indicator can be considered as perceived benefits; otherwise, indicators are considered as perceived risks [18]. When the company evaluates its sustainable operations, indicators that are perceived benefits are GO₂, GO₃, SO₁, SO₂, and EN₃. In contrast, GO₁, GO₄, EN₁, EN₂, and SO₃ are considered perceived risks that have a less total effect than perceived benefits.

In addition, if the relation $R-C$ is a positive number, it means that this indicator is classified as a cause, which will affect the performance of other indicators; conversely, if its value is a negative number, it means that the indicator is classified as a result, and it will be affected. If indicators with positive relations are addressed, other factors can also benefit. Therefore, companies should emphasize improvements to critical indicators such as GO₄, GO₁, GO₂, GO₃, and EN₂ in this area.

Based on prominence and relation, we can divide the network into quadrants. Core indicators, driving indicators, impact indicators, and independent indicators. Core indicators are indicators that have a high total effect and a positive net effect on the network. Prioritizing core indicators in general and GO₂ and GO₃ in specific will drive the network toward the goal of sustainability.

The second quadrant of driving indicators includes GO₄, GO₁, and EN₂. Although they are not as significant as the key variables in terms of prominence, the study findings show that this indicator is distinctive and will influence the performance of other indicators. They significantly influence performance and should be prioritized after addressing the indicators in the first quadrant.

According to the findings, the third quadrant independent indicators include SO₃ and EN₁, which are independent factors. Because the interaction and effect of other indicators are minimal, it is recommended that such indicators are handled individually and have the lowest priority in the allocation of management resources.

The indicators in the fourth quadrant impact indicators are SO₁, SO₂, and EN₃. The relationships between SO₁, SO₂, and EN₃ are negative; they are not as prioritized as the first and second quadrants. SO₁, SO₂, and EN₃ have high prominence; they need to be given enough managerial resources to keep the interaction between indicators even though the required resources are not as many as indicators in the first and second quadrants.

Based on the findings from the research, Vietnamese energy businesses can adopt a series of targeted solutions aimed at enhancing operational efficiency, driving innovation, and fostering sustainable growth. The first critical area of focus is improving cost management (GO₂). A comprehensive cost management system is essential to collect and analyze data systematically across key cost components such as inputs (fuel, materials, labor), operating expenses, maintenance, and capital costs. Detailed analysis over time, by category, and against competitors will enable businesses to identify cost bottlenecks and implement optimization measures. Advanced technologies such as enterprise resource planning (ERP) systems, financial management software, and business intelligence (BI) tools can further enhance this process by automating data collection, processing, and analysis, thereby providing real-time cost insights for strategic decision making. Furthermore, conducting periodic internal audits will help evaluate the effectiveness of cost management systems, ensure regulatory compliance, and proactively address errors, fraud, or inefficiencies. Together, these measures can significantly enhance the financial health and competitiveness of energy businesses in Viet Nam.

Another essential area for energy businesses is promoting innovation and renewable energy (GO3 and EN3), which are pivotal for achieving long-term sustainability. Investment in Research and Development (R&D) should be prioritized, with a focus on renewable energy technologies such as solar, wind, and biomass. Specific research objectives can include improving energy conversion efficiency, reducing production costs, and developing effective energy storage solutions. Collaborative efforts are equally crucial—businesses should seek partnerships with domestic and international research institutions, universities, and technology companies. These partnerships enable access to cutting-edge technology, shorten research timelines, and mitigate investment risks through shared resources and expertise. Participation in technology transfer programs and forming joint ventures with foreign partners can further accelerate the adoption of advanced technologies. Additionally, energy businesses should work closely with policymakers to advocate for supportive frameworks, such as tax incentives, credit schemes, land use policies, and favorable electricity pricing. Such initiatives not only reduce financial barriers but also create a conducive environment for renewable energy development.

The development of human resources (SO₁ and SO₂) is a foundational pillar for sustaining growth and addressing challenges in the energy sector. Tailored vocational training programs should be designed to equip workers with the knowledge and skills needed to operate, maintain, and repair complex energy systems, with an emphasis on renewable energy technologies. Higher education institutions also play a vital role; updating university curricula to include emerging fields such as energy management, energy economics, and sustainable development will better prepare graduates for industry demands. Collaborative partnerships between universities and businesses can enhance this process by creating relevant training modules, internships, and opportunities for applied research. Beyond education and training, businesses must focus on attracting and retaining talent by fostering a professional, dynamic, and creative work environment. Competitive compensation packages, clear career pathways, and opportunities for professional growth are essential to building a skilled and motivated workforce. By prioritizing human resource development, energy businesses can not only enhance productivity but also ensure a steady pipeline of talent to support future innovations and expansions.

By implementing these solutions across cost management, innovation, renewable energy, and human resource development, Vietnamese energy businesses can significantly enhance their operational and strategic capabilities. These efforts will not only improve their competitiveness in a rapidly evolving energy market but also position them as leaders in sustainability. Moreover, the adoption of these strategies aligns with Viet Nam’s broader socio-economic goals, contributing to environmental preservation, energy security, and inclusive economic growth. Through proactive measures and collaboration across stakeholders, the energy sector can play a transformative role in Viet Nam’s journey toward a sustainable and prosperous future.

The transition toward sustainability in Viet Nam’s energy sector presents a range of challenges and opportunities across technological, economic, and policy aspects. The adoption of renewable energy technologies, such as solar and wind power, is crucial for meeting sustainability goals. However, significant upgrades are required to the existing energy infrastructure to accommodate sustainable technologies and integrate renewable energy sources into the grid. Additionally, the lack of expertise and resources among companies limits the widespread adoption of these technologies.

The high initial costs associated with renewable energy solutions remain a significant barrier, despite the long-term economic benefits. To encourage investment, economic incentives like feed-in tariffs and tax breaks are critical. As Viet Nam shifts toward a renewable-based energy system, companies may face price volatility in energy markets, which will require effective risk management strategies.

Regulations such as the National Electricity Development Plan (i.e., Power Development Plan 8) approved by the Viet Nam government set ambitious renewable energy integration targets but also introduce compliance challenges. To drive sustainable investments, effective government incentives and consistent policy implementation are essential. Active governance and stakeholder engagement are crucial for the success of sustainability initiatives, necessitating strong analytical capabilities and adaptable policies.

Unlike previous studies [3,4,5,6] that focused solely on individual indicators or overlooked the unique socio-economic and industrial contexts of Viet Nam, this research explores the interdependencies between these indicators and prioritizes them within the specific context of Viet Nam. While the journey toward sustainability in Viet Nam’s energy sector is challenging, the potential environmental and economic benefits make it a worthwhile endeavor. Continuous collaboration between local businesses, international partners, and the government will be key to overcoming obstacles and achieving sustainability goals.

5. Conclusions

5.1. Theoretical and Practical Implications

This study developed a comprehensive framework of sustainability indicators tailored to Viet Nam’s energy sector, addressing the need for a structured approach to promote sustainable practices. By leveraging the United Nations’ ESG framework and utilizing the modified Delphi and DEMATEL methods, the research identified and prioritized critical indicators, such as cost management, innovation, renewable energy, vocational training, and human capital development. These findings emphasize the importance of aligning sustainability practices with operational efficiency and innovation to achieve long-term sustainability for energy companies in Viet Nam.

This study is one of a few studies investigating sustainability in Viet Nam’s energy sector and enriching the literature by providing a tailored framework that integrates global sustainability goals. It advances academic understanding of energy transitions in Viet Nam, demonstrating how interdependencies among sustainability indicators can be prioritized to inform decision making. Additionally, the study’s application of the modified Delphi and DEMATEL methods offers a replicable approach for other industries and countries seeking to address similar challenges.

5.2. Research Limitations and Future Research

Despite its contributions, this study has limitations. First, the research relied on expert opinions from Viet Nam’s energy sector, which may not fully capture the perspectives of other stakeholders such as consumers or policymakers. Second, the focus was limited to Viet Nam, and cross-country comparisons would help generalize the framework. Lastly, quantitative methods such as regression analysis or simulation modeling could complement the qualitative insights, offering deeper empirical validation.

Future studies could address these limitations by adopting a more inclusive and diverse approach to stakeholder engagement. Expanding the pool of participants to include policymakers, community representatives, and energy consumers would provide a more comprehensive view of sustainability priorities. Additionally, extending the scope to cross-country comparisons would help validate the framework’s applicability in different energy systems and economic contexts.

Integrating quantitative methodologies, such as regression analysis, econometric modeling, or simulation techniques, would complement the qualitative insights from the Delphi and DEMATEL methods. These approaches would enable researchers to empirically test the relationships among indicators and quantify their contributions to sustainability goals.

Longitudinal studies tracking the implementation and outcomes of the proposed indicators over time would provide valuable data on their real-world performance and adaptability. Moreover, future research could explore combining the Delphi and DEMATEL methods with advanced technologies such as machine learning, big data analytics, or artificial intelligence to enhance indicator prioritization and uncover new sustainability trends.

By addressing these limitations and pursuing these research avenues, future studies can contribute to a deeper understanding of sustainability in energy systems and provide more effective solutions for global energy transitions.