Unlocking sustainable development in East Asia Pacific and South Asia: An econometric exploration of ESG initiatives

ABSTRACT

This study employs a rigorous econometric approach to investigate the intricate interrelationships among environmental, social, and governance (ESG) factors and their impact on GDP growth and the attainment of Sustainable Development Goals (SDGs) in East Asia-Pacific and South Asian countries. Utilizing panel data spanning the period from 2003 to 2021 sourced from the World Bank, our study diverges from previous research by adopting a comprehensive and inclusive stance in selecting relevant variables. Robust econometric methods, including cross-sectional dependence tests, unit root tests, Granger causality tests, and cointegration analyses, have been applied to scrutinize these relationships. The findings substantiate the significant long-run associations between ESG factors, GDP growth, and SDGs. This research emphasizes the distinctive aspect of meticulously assessing the interconnections of each country. Notably, this study innovates by considering environmental, social, and governance indicators alongside GDP growth to comprehensively evaluate their impact on SDGs. The application of advanced econometric methodologies confirms cross-sectional dependence, a long-run symmetric relationship, and stationary variables with high significance. Autoregressive Distributed Lag (ARDL) estimates revealed a positive long-run association between governance factors (GOVNf) and SDGs, indicating a negative relationship between environmental factors (ENVf) and SDGs. To further test the robustness of the findings we used AMG and CCEMG estimations. The study establishes significant relationships between environmental, social, and governance factors, GDP growth, and SDGs, providing valuable insights for policymakers and researchers alike.

KEYWORDS:

• ESG
• sustainable development
• SDGs
• sustainable growth
• economic growth

JEL CLASSIFICATION:

• F63
• Q5
• Q56

1. Introduction

Environmental, Social, and Governance (ESG) is commonly associated with the concept of sustainability (Abdullah et al., 2023). From this perspective, sustainability refers to how a country approaches its activities in a manner that minimizes the adverse effects on the environment and society (Rahman et al., 2023). This encompasses factors such as decreasing carbon emissions, responsibly handling waste, promoting fairness, and maintaining corporate governance practices. A country actively oversees its operations to mitigate their effects (Gazi, 2020; Hill, 2020). ESG factors encompass a collection of principles created for weighing as well as overseeing organizations’ environmental, social, and governance activities. ESG assists in evaluating a firm’s meticulousness and commitment to social progress, environmental safety, and corporate responsibility (Escrig-Olmedo et al., 2019).

The environment, resources, and society are facing pressure owing to the continuous and rapid growth of industrial and service sector activities. However, despite the scale of this problem, individuals or organizations engage in initiatives that promote ecological friendliness (Schroeder et al., 2019). The public’s receptivity to the social and environmental requirements of economic activities has been growing (Abdullah et al., 2023). This responsiveness extends to government and business organizations, which have begun to rank objectives beyond financial profits. These goals encompass assurances of environmental safeguarding along with social growth (Işık, Ongan, Islam, Jabeen, et al., 2024; Rasoolimanesh et al., 2023)

Sustainable Development Goals (SDGs) include a collection of 17 objectives officially accepted by member countries of the United Nations (UN) in 2015, forming part of the 2030 Agenda for Development. The agenda consists of 17 SDGs with 169 targets. These 17 objectives were divided into three groups: social, environmental, and economic. In plants, people, peace, prosperity, and partnership, the 5Ps are also associated with the SDGs (Gazi et al., 2022; Herrero et al., 2021).

While governments play a vital role in interpreting international policies into action, non-governmental organizations and private corporations also contribute significantly. The 17 SDGs were drawn by the United Nations, which several countries have adopted for sustainable development. These objectives cover a broad range of areas, including the eradication of poverty, the guarantee of zero or no hunger, the promotion of good health and well-being, the enhancement of educational standards, the guarantee of access to clean or pure water, the encouragement of innovation and cutting-edge technology, the elimination of inequality, the promotion of sustainable cities and communities, the promotion of ethical production and consumption, the resolution of macroclimate issues, the preservation of life under the sea and on land, the assurance of justice and strong institutions, and the promotion of partnerships for these objectives. Scholars such as Swain and Yang-Wallentin (2020), Islam et al. (2024), Islam et al. (2020), Islam et al. (2021), Dabbous and Tarhini (2021), Dimian et al. (2021), Swain and Yang-Wallentin (2020), and Ma’ruf and Aryani (2019) have intricately underscored the supreme significance of these goals for achieving global betterment, not only for individual countries but also for the entire world. One of the objectives of implementing the SDGs is to foster a sustainable economy that is advanced and people-oriented, ensuring employment opportunities and raising living standards. The focus is on the well-being of all existing species, which encompasses people, ensuring humanity’s improvement and recognizing the long-term nature of progress in the economy (Trung et al., 2021; Vaio et al., 2020). It involves not only inspecting or overseeing the conscientiousness of corporations but also measuring the efficacy of the policies framed in this context. The 2030 sustainability agenda of the United Nations Assembly contains SDGs that have been linked and developed from the symbolic relationships existing among ESG factors (Khaled et al., 2021; Zhao et al., 2021). SDGs have been accomplished through the collective efforts of a nation’s business entities. Companies, businesses, and corporations cooperate to defend the environment, enhance the prosperity of society, and lift their performance. This combined approach is crucial for bringing about positive changes on a larger scale (Consolandi et al., 2020; Islam et al., 2022; Islam, Rana, et al., 2023; Islam, Rahman, et al., 2023; Rojek-Adamek, 2021; Vveinhardt & Sroka, 2021). Significant positive impacts arise as organizations or businesses conscientiously conduct operations, adhere to norms to mitigate harmful effects, ensure health and hygiene, provide access to clean water, preserve natural resources, and promote the principles of sustainability (Nurwani et al., 2020; Pashkevich & Haftor, 2020; Saetra, 2021). Likewise, evaluating companies’ social performance helps the nation accomplish the SDGs, which leads to long-term growth (Praja et al., 2023). Successful execution of SDGs can be supported by company policies and practices that foster strong, cooperative partnerships with stakeholders while assuring their rights and providing positive benefits (Betti et al., 2018; Drebee et al., 2020; Piligrimiene et al., 2022). A country’s progress is driven by foreign direct investment (FDI). Chauhan and Kumar (2019) find that foreign investors are interested in investing in firms that disclose more non-financial information or ESG activities. By addressing the literature review of previous studies, we attempted to answer questions about ESG and SDGs. The research questions (RQ) were as follows:

RQ1:

What are the long- and short-term effects of environmental factors on SDGs in East Asia Pacific and South Asian countries?

RQ2:

How do East Asia Pacific and South Asian countries’ social factors affect the long- and short-term SDGs?

RQ3:

In what way do (long-term) East Asia-Pacific and South Asian countries’ governance factors affect SDGs?

In this study, we aim to investigate the interconnection between ESG practices and SDG scores in East Asia Pacific and South Asian countries. Here, we examine how ESG factors affect a country’s ability to achieve the SDGs. We use evidence from 18 East Asia Pacific countries along with eight South Asian countries, totaling 26 countries. Several statistical models, such as the modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and Autoregressive Distributed Lag (ARDL) models, have been run to examine whether ESG factors have any impact on the SDG scores of the 26 counties. Among the statistical models, the Augmented Mean Group (AMG) and Common Correlated Effect Mean Group (CCEMG) models are a unique set of statistical models used to assess how well ESG components satisfy expected short- and long-term relationships with SDG scores over a 20-year timeframe.

To fill a vacuum in the literature, this study examines ESG variables and SDG scores in a total of 26 East Asia-Pacific and South Asian nations. By examining a variety of emerging, underdeveloped, and developed nations in the East Asia-Pacific and South Asian areas, this study closes this gap and offers insights into the interactions between ESG variables, economic growth, and SDGs in this particular setting. Consequently, this study’s findings will provide new insight into how to reevaluate the sustainability of the economy in this part of the world.

This study contributes significantly to the body of knowledge in two key ways. Firstly, it distinguishes itself as the inaugural research endeavor conducted across nations in South Asia and East Asia Pacific, concurrently analyzing the impact of GDP growth, and government, societal, and environmental metrics on the Sustainable Development Goals (SDGs). This unique approach ensures a comprehensive and holistic evaluation of pertinent factors, thereby offering a well-rounded perspective. Secondly, the study employs robust econometric methods, including Granger causality tests, Cross-Sectional Dependence tests, first and second-generation unit root tests, as well as estimations such as Fully Modified Ordinary Least Squares, Dynamic Ordinary Least Squares, and Autoregressive Distributed Lag/Pooled Mean Group. Furthermore, to test the robustness of the study we employed AMG and CCEMG which showed the validity of the study. These rigorous methodologies yield conclusive results that transcend local contexts, thereby guiding the development of comprehensive policies with broader implications.

The rest of the paper is structured as follows: Section 2 presents a comprehensive review of the relevant literature about the study. Section 3 outlines the methodology employed in conducting the research. Section 4 presents and analyzes the results obtained from the study. In Section 5, a discussion of the findings is provided, offering insights and interpretations. Section 6 offers concluding remarks, including policy implications derived from the study’s outcomes. Finally, Section 7 addresses the limitations inherent in the study’s methodology and findings.

2. Literature review

Sustainable development goals (SDGs) have gained prominence as a global priority over the last several decades. Both developed and developing nations are making every effort to achieve their goals for sustainable development. Because the one-eyed development without a holistic consideration of the ESG factors could not comply with the sustainability of the development in the long run. Consequently, these areas also got the spotlight of the researchers and became one of the key aspects of the SDGs for nations of any type.

Caiado et al. (2018), Farelnik et al. (2021), and Streimikiene and Ahmed (2021) reported that simple economic growth establishes a nation’s place in the global marketplace; however, the goal of sustainable development is to safeguard such circumstances across all nations. While sustainable development primarily focuses on economic progress, it is also intricately linked to resource availability, higher quality, healthy living creatures, and positive social outcomes within a nation.

The Asia-Pacific Sustainable Development Goal Progress Report 2022 by The Economic and Social Commission for Asia and the Pacific (ESCAP), across Asia and the Pacific, despite governments aiming at the SDGs and the major targets of reaching the farthest behind first, the speed of development has not kept up and has even slowed. It has been noted that the rise in the development of the SDGs in the Asia-Pacific region and South Asia has slowed down owing to the intensified development challenges caused by the COVID-19 pandemic along with climate change. Moreover, it has been indicated that the region is not on track to achieve any of the 17 SDGs within the designated timeframe of 2030. The SDGs Progress Report 2022 by ESCAP also added that the growth in achieving SDGs in South Asia and East Asia Pacific countries represents a complex situation, where SDG (Goal-1), no poverty, has been achieved by South Asian and some East Asian Pacific countries, but they were disappointed in meeting 2022 total SDGs targets. Notably, this region shows the highest ratio of multidimensional poverty (29.2%), in contrast to other subregions. For instance, although the income poverty percentages are relatively low (3.9% and 1.5%, respectively) in Pakistan and Bhutan, there is a significant frequency of multidimensional poverty (38.3% and 37.3%, respectively).¹ Additionally, limited progress has been made in several SDGs, including good health, well-being, and safety (Goal 3), reasonable and clean energy (Goal 7), industry, new innovations, infrastructure (Goal 9), life on land (Goal 15), and partnership for the goals (Goal 17). Nevertheless, it has been observed that East Asia-Pacific and South Asian countries have recently maintained their focus on environmental, social, and governance issues that directly impact the SDGs.

A literature review of the impact of ESG on SDGs is widespread. However, the issue of East Asia Pacific and South Asian countries is still unsettled. The results of the earlier studies are detailed in the following section.

Swain and Yang-Wallentin (2020) and Chien et al. (2021) studied the strategies and challenges associated with accomplishing SDGs by utilizing multiple metrics. Their findings underlined the significant supportive role of ESG aspects in achieving the SDGs. De Franco et al. (2021) studied the United States and European Countries on sustainable investment (ESG vs. SDG) and found that the factors of ESG and SDG do not overlap significantly, so high ESG-rated firms are not necessarily the top contributors. This method is equally accurate in the opposite manner. Vveinhardt and Sroka (2020) highlighted in their research that while ASEAN countries have developed aggregates to accomplish certain economic and social indicators of the SDGs, negative trends persist in environmental factors, particularly concerning the 13th goal, climate action. The rates of missing individuals affected individuals and death rates due to climate-related disasters remained higher.

Environmental factors are a crucial aspect of ESG considerations, focused on fostering the commitment and initiatives of business entities to enhance their environmental practices. This is vital for attaining the SDGs related to environmental quality and its desired outcomes (Mahmood et al., 2021; Roscoe et al., 2019). The formulation and implementation of rules intended to safeguard the environment from the hostile effects of business activities constitute environmental practices. In the framework of ESG implementation, businesses rank both enhancing revenue and market share, while mitigating environmental issues raised by the public and averting harm to the environment. This approach aims to attain the SDGs, which include relatively inexpensive clean energy, clean water, climate action, living below and on land, and public health (Vega-Muñoz et al., 2021). Vollmer et al. (2020) examined the influence of environmental governance on reaching the third SDG (good health) along with the sixth SDG (clean water and sanitation). Through stakeholder surveys, they applied the Freshwater Health Index to three river basins in Latin America to assess environmental governance on a 0–100 scale. This research demonstrated how companies that use environmentally friendly products and efficient methods for waste disposal produce less hazardous waste, thus minimizing water pollution. This study underscores the importance of environmental governance for preserving water quality and defending public health. When businesses and organizations work in a manner that does not damage the environment, such as mitigating air, water, and land pollution, a country’s capacity to achieve the objectives outlined in the Sustainable Development Agenda is strengthened. Integrating operational and environmental practices contributes to making the world a better place while achieving the SDGs (Pahl-Wostl et al., 2018). In their study, Işık, Ongan, Islam, Pinzon, et al. (2024) similarly identified a long-term negative relationship and a short-term positive relationship between environmental factors and sustainable development goals. Consequently, the first hypothesis of this study is as follows:

H1_a:

Environmental factors have a long-term relationship with SDGs in East Asia Pacific and South Asian countries.

According to Işık, Ongan, Islam, Pinzon, et al. (2024), social factors exhibit a significant and positive relationship with sustainable development goals in the newly expanded BRICS countries. Social governance in business refers to laws and regulations aimed at enhancing interactions with stakeholders and defending their rights, good health, and achievement. It helps enterprises and stakeholders, who are part of the public, align with the main priorities of the SDGs. Effective governance guided by companies, especially under ESG principles, plays a significant role in attaining SDGs by prioritizing the health, human rights, and affluence of the public (Criado-Gomis et al., 2020; Li et al., 2018). A quantitative study conducted in Northeast Brazil by Siakwah et al. (2020) with 829 participants utilized surveys and employed Structural Equation Modeling (SEM) to identify the connection between SDG achievement and social governance, focusing on smart cities. The results indicate that in smart cities with healthy social governance, there is an upgrade in quality of life. Effective communication and information systems enable employers to meet employees’ basic needs, inspiring them to achieve better work performance and a raised standard of living. This study found that social governance is crucial in accomplishing the SDGs, which include industry, respectable employment, zero hunger, no poverty, good health, and well-educated well-being. The inclusion of social governance in business organizations’ ESG dimension contributes to the overall success of a country in procuring its SDGs by enhancing the business’s stake in this cooperative effort (Siakwah et al., 2020). Enterprises operating within the Chinese economy have revealed a trend of forming policies based on the assessment and guidelines of social governance. Therefore, management actively designs strategies that encourage employee engagement in business activities. These policies incorporate nurturing smooth communication channels with employees, providing training, offering sensitive and financial backup, inspiring open expressions of feelings for better quality performance, and prioritizing employee health and security (Xue et al., 2018). The developed version of CSR, currently known as social governance, focuses on developing diversity among employees, preserving stakeholders’ rights, and ensuring consumer and environmental safety in organizations. When businesses obey guidelines and regulations that build positive relationships with stakeholders, they may make responsible judgments, proving the stakeholders’ health safety, economic rights, and emotions. This commitment contributes to organizational success and ensures the security and equal rights of stakeholders (Menton et al., 2020). Thus, the second hypothesis is as follows:

H2_a:

Social factors have a long-term relationship with SDGs in East Asia Pacific and South Asian countries.

Governance factors demonstrate a notable and adverse correlation with sustainable development goals within the recently expanded BRICS countries (Işık, Ongan, Islam, Pinzon, et al., 2024), Corporate governance is a collection of rules, procedures, methods, and relationships utilized by various stakeholders to monitor, manage, and administer an organization. Governance structures and principles play an important role in shaping decision-making techniques. The aforementioned structures and concepts delineate the entitlements and responsibilities of several stakeholders including regulators, creditors, auditors, directors, and shareholders. Corporate governance is essential because stakeholders, stockholders, and senior executives may have conflicts of interest (Naciti, 2019). Corporate governance is an integral part of the wider ESG framework, and the SDGs are achieved through effective execution (Dahlmann et al., 2019). To examine the European sample for 2016–2017, Martínez-Ferrero and García-Meca (2020) determined whether internal governance efficiency is responsible for meeting the SDGs in European organizations. The research, conducted through multiple regression analyses, found that when internal corporate governance efficiency improves, corporations are more likely to prioritize SDGs in their sustainability reports. This finding indicates a significantly positive correlation between internal corporate governance and the inclusion of SDGs in reporting. A study explores 153 Italian public-interest entities to determine how businesses respond to the 17 SDGs noted in the 2030 Agenda. When organizations have strong corporate governance, such as having a right-sized board, competent members, independence, and a thorough organizational arrangement, they influence diverse elements of the organizations efficiently. In summary, executing governance principles as part of ESG activities not only endorses financial development but also advances social and environmental performance (Pizzi et al., 2021). Therefore, the third hypothesis is as follows:

H3_a:

Governance factors have a long-term relationship with the SDGs in East Asia Pacific and South Asian countries.

Inspiring the attainment of SDGs is aided by economic or GDP growth throughout business cycles. Robust economic development is globally significant because it provides a common standard for assessing and identifying investment possibilities. The potential and successful achievement of the SDGs is greatly enhanced by the direct impact of GDP growth (Huang et al., 2021). Ward et al. (2016) examined the sustainability of constant GDP growth and its strong correlation with carbon production are examined by (Ward et al., 2016). This study explores the misleading idea that GDP decouples from emissions, highlighting situations in which resource substitution occurs, financial flows diverge from energy production, and the environmental effects of financial activity are exported or moved offshore. The study concludes that it would be deceptive to depict a decoupling of GDP from sustainability. It is suggested that countries experiencing transition acknowledge the physical constraints on their resources and aim to achieve GDP levels and sustainable growth rates. Blagov and Petrova-Savchenko (2021) focused on the effects of GDP growth on achieving SDGs. According to this study, countries with high rates of economic growth also have fast-moving technical developments, which drive innovation in the fields of transportation, industry, infrastructure, and communication systems. Consequently, the achievement of the 9th SDG, which comprises infrastructure, industry, and innovation, is facilitated by economic development or GDP growth. In general, rapid economic expansion increases the production of goods and services produced by the economy. A substantial workforce is needed for an upsurge in productive activities, which also promotes job possibilities and raises employee earnings and benefits to improve their living conditions. Consequently, a higher economic growth (GDP) rate helps achieve the SDGs, which include the goals of eradicating hunger and poverty (Shahbaz et al., 2021). Hence, this study proposes the following hypotheses:

H4_a:

GDP growth has a long-term relationship with the SDGs in East Asia Pacific and South Asian countries.

In light of the review of the relevant previous studies, we decided to focus our research on ESG factors and SDG scores in a total of 26 East Asia-Pacific and South Asian countries to address a gap in existing relevant research for four reasons. First, we consider that ESG factors deserve attention owing to their immense importance for the betterment of the world, and in the global agenda, SDGs play a pivotal role as one of the significant and positive issues that require extensive study to explore in a greater horizon. Second, Jelinkova et al. (2021) and Karobliene and Pilinkiene (2021) examined the connection between the sharing economy and SDGs, and Ma’ruf and Aryani (2019) tested the connection between SDGs and finances, while the current study included additional factors related to environmental, social, and governance (ESG) factors. Third, Hieu and Hai (2023) analyzed the nexus of SDGS in BRICS countries; McBride et al. (2019) studied SDGs in G7 and G20 countries, and Sadiq et al. (2023) explored the effects of ESG in achieving SDG in China, whereas the current study intended to determine the impact of ESG factors on SDGs in the East Asia Pacific and South Asian countries. Fourth, the projected models examining the relationships between Environmental, Social, and Governance (ESG) factors and Sustainable Development Goals (SDGs) have not been previously tested within the 26 East Asia-Pacific and South Asian countries. While some notable studies, such as those conducted by Işık, Ongan, and Islam (2024), have addressed the impact of ESG on SDGs and economic growth, they have not specifically focused on this diverse group of countries. This study fills this gap by analyzing a mix of developing, underdeveloped, and developed countries in the East Asia-Pacific and South Asian regions, providing insights into the interplay between ESG factors, economic growth, and SDGs within this unique context. Therefore, the results of the present study will uncover a way to rethink economic sustainability in this region of the world.

3. Methods

3.1. Variable selection

The selection of countries from the East Asia-Pacific and South Asian regions for analysis is driven by the constraints imposed by the unavailability of the dataset for specific variables and the chosen timeframe. The dataset encompasses estimated results spanning 2003 to 2021, representing the most extensive available data period. This temporal scope allows for a comprehensive examination of the trends and patterns over time. By focusing on these regions, the analysis aimed to provide insights into the dynamics of the selected variables within a specified geographical context. The decision to concentrate on East Asia Pacific and South Asia acknowledges the uniqueness of these regions and the potential variation in factors influencing the variables of interest. This targeted approach ensures that the analysis is tailored to the available data and specific characteristics of the chosen regions, enhancing the relevance and applicability of the findings. The ESG indicators justified by (Işık et al., 2024, 2024, 2024) and selected countries are listed in Tables 1 and 2, respectively.

Table 1. ESG indicators

ESG Indicators
Environmental Factors (ENVf)	Social Factors (SOCf)	Governance Factors (GOVNf)
Renewable energy consumption	Life expectancy	Control of corruption
Forest area	Population density	Regulatory quality
CO2 emissions	Female to male ratio	Rule of law
Methane emissions		Government effectiveness
Nitrous oxide emissions

Table 2. Selected countries

Region	Countries	Number of Countries
East Asia Pacific	Australia, Brunei, Cambodia, China, Fiji, Indonesia, Japan, Korea Rep., Laos, Malaysia, Mongolia, Myanmar, New Zealand, Papua New Guinea, Philippines, Singapore, Thailand, and Vietnam	18
South Asia	Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka	8
	Total	26

3.2. Model

This study introduces a basic production function that models SDGs and ESG indicators, GDPG (Gross Domestic Product Growth), which influences it as follows:

$\mathit{SDG}{s}_{\mathit{it}}=\mathit{f}(\mathit{ENV}{f}_{\mathit{it},}\mathit{SOC}{f}_{\mathit{it}}\mathit{GOVN}{f}_{\mathit{it}}\mathit{GDP}{G}_{\mathit{it}})$

The subscript i represents a specific country and t represents the corresponding time period. where SDGs is the SDG score, GDPG is the growth of GDP (annual %), ENV is the Environmental factor (Table 1), SOC is the Social factor (Table 1), and GOVNf is the Governance factor (Table 1).

3.3. Econometric approach

For our panel data analysis, we adhered to the methodology outlined in Figure 1. We assessed the presence of the cross-sectional dependence (CSD) test (Table 4), based on Pesaran (2007). This involved examining the association coefficients between time-series data for each country. Recognizing the potential impact of CSD on traditional panel unit root tests, we opted for the second-generation unit root test (Table 5) proposed by Pesaran (2007). This method combines the t-statistics for panel roots using the Cross-sectionally augmented Im-Pesaran-Shin (CIPS) test for individual units within the cross-section and the cross-section augmented Dickey-Fuller (CADF) unit root test for the average individual unit.

Figure 1. Pathway of panel data analysis.

To ascertain causal relationships in the data, Granger causality tests (Table 6) supplemented by a bootstrap procedure were conducted. This analysis assumed stationarity in the studied variables. Unit root tests were employed to confirm integration, and a panel cointegration test proposed by Westerlund (2007) was conducted (Table 7) to identify long-term symmetric relationships among the series variables.

To estimate only long-run effects, we applied the FMOLS and DOLS models (Table 8) to address issues such as serial correlation and endogeneity (Ward et al., 2016). To account for both long- and short-run effects, we utilized pooled mean group (PMG) estimators (Table 9) for dynamic heterogeneous panel cointegration analysis, as recommended by Pesaran (2007).

To validate the coefficient estimation, we utilize AMG and CCEMG estimators in our empirical examination. Nevertheless, these methods are limited to forecasting long-term elasticities and assessing robustness exclusively over extended periods. Bond and Eberhardt (2013) introduced the AMG model, which is a two-step procedure designed to address concerns regarding cross-sectional dependence and heterogeneity in slopes.

The methods employed in our analysis were carefully selected to ensure robustness and reliability in our investigation. The CSD test was utilized to examine cross-sectional dependence, crucial for valid panel data analysis across multiple countries. To address potential issues of cross-sectional dependence in traditional unit root tests, we adopted the second-generation unit root test (CIPS and CADF). Granger causality tests were conducted to explore causal relationships between variables, providing insights into dynamic interactions. The Westerlund panel cointegration test was employed to identify long-term symmetric relationships among variables. For estimating long-run effects while mitigating issues like serial correlation and endogeneity, we employed the FMOLS and DOLS models. The PMG estimators facilitated dynamic heterogeneous panel cointegration analysis, capturing both long- and short-run effects. To validate coefficient estimation, the AMG and CCEMG estimators were utilized. These methods were chosen for their suitability in panel data settings and their ability to provide robust estimates of variable relationships, ensuring comprehensive and reliable results in our analysis.

4. Results

4.1. Descriptive statistics

The descriptive statistics provide a comprehensive overview of the key variables in Table 3. For the variable SDGs, which presumably represent the achievement of Sustainable Development Goals, the mean value is 63.733, with a standard deviation of 8.499, indicating a moderate level of variability around the mean. The minimum and maximum values were 36.69 and 79.525, respectively, suggesting a broad range of observed values. Environmental factors (ENVf) exhibited a negligible mean of −0.031 with a standard deviation of 1.005, indicating limited variability around the mean. The range, from −1.247 to 2.226, suggests a relatively narrow distribution of values. Social factors (SOCf) and governmental factors (GOVf) had mean values of 0.035 and 0.006, respectively, with standard deviations of 1.003 and 1.018. Both variables showed varying degrees of variability around their means, with SOCf ranging from −2.247 to 1.587 and GOVf ranging from −2.412 to 2.416. The GDP growth variable (GDPG) had a mean of 4.742 and a larger standard deviation of 4.995, indicating substantial variability. The observed range for GDPG is wide, ranging from −33.493 to 41.745, highlighting significant fluctuations in GDP growth within the dataset.

Table 3. Descriptive statistics

Variable	Mean	Std. Dev.	Min	Max
SDGs	63.733	8.499	36.69	79.525
ENVf	−.031	1.005	−1.247	2.226
SOCf	.035	1.003	−2.247	1.587
GOVf	.006	1.018	−2.412	2.416
GDPG	4.742	4.995	−33.493	41.745

4.2. Cross-sectional dependence test

Table 4 illustrates the test statistics for evaluating Cross-sectional Dependence (CSD) using various methods for the key variables: Sustainable Development Goals (SDGs), Environmental Factors (ENVf), Social Factors (SOCf), Governance Factors (GOVNf), and GDPG. The Breusch-Pagan LM, Pesaran Scaled LM, Bias-correlated Scaled LM, and Pesaran CSD tests all exhibit high significance levels at 99%, indicating a robust presence of cross-sectional dependence among these variables. Particularly noteworthy is the Pesaran CSD test, which offers an alternative assessment perspective that also reports statistically significant results at the 99% confidence level. These findings emphasize consistent cross-sectional dependence within the dataset, shedding light on the interconnected relationships among the examined variables.

Table 4. Cross-sectional dependence

	Breusch-Pagan LM	Pesaran Scaled LM	Bias-correlated scaled LM	Pesaran CSD
SDGs	5389.996***	198.6655***	197.9433***	73.1883***
ENVf	3107.070***	109.1218***	108.3995***	7.98096***
SOCf	2552.893***	87.38513***	86.66291***	3.44667***
GOVNf	1609.618***	50.38688***	49.66466***	12.4414***
GDPG	1698.818***	53.88558***	53.16336***	36.0108***

***represents 99%, **represents 95%, and *represents 90% significance level.

4.3. Unit root test

To add the CSD to our dataset, we used a second-generation unit root test (CIPS and CADF) (Pesaran, 2007). Table 5 shows the outcomes of the unit root tests applied to diverse variables, employing both first-generation tests, namely Im-Pesaran-Shin (IPS) and Fisher-Augmented Dickey-Fuller (FADF), and second-generation approaches, denoted as Cross-section IPS (CIPS) and Cross-section Augmented Dickey-Fuller (CADF). While second-generation tests are conventionally recommended in the presence of pronounced cross-sectional dependence in the dataset, both sets of tests are executed to obtain a comprehensive understanding of stationarity properties. The IPS outcomes indicate a lack of stationarity across the variable set, except for the GDPG. Similarly, the FADF test revealed stationarity at the level solely for GOVNf and GDPG. While conducting second-generation tests (CIPS and CADF), it is discerned that notwithstanding the absence of stationarity at the level for certain variables, compelling evidence of stationarity at the first difference emerges.

Table 5. Unit root test (first & second generation)

Variables	I(0)		I(1)		I(0)		I(1)
	IPS	FADF	IPS	FADF	CIPS	CADF	CIPS	CADF
SDGs	8.89	42.17	−7.13***	157.05***	−2.34***	−2.00*	−4.45***	−3.16***
ENVf	2.14	44.23	−6.64***	139.28***	−2.28**	−1.82	−4.15***	−2.67***
SOCf	2.51	44.79	−6.12***	149.79***	−1.56	−1.89	−3.02***	−2.10**
GOVNf	−1.19	91.61***	−9.14***	215.42***	−2.18**	−2.05**	−4.23***	−2.98***
GDPG	−2.89***	124.76***	−12.06***	246.47***	−2.91***	−1.95	−4.97***	−3.32***

***represents 99%, **represents 95%, and *represents 90% significance level.

4.4. Causality test

Table 6 shows the causality tests conducted using Dumitrescu’s and Hurlin (2012) methodology, with variables ENVf, SOCf, GOVNf, and GDPG. The statistics provided are W-bar and Z-bars for each independent variable. The W-bar statistic assesses the null hypothesis of no Granger causality running from the independent to the dependent variable. Similarly, the Z-bar statistic evaluates the null hypothesis of no reverse causality:

Table 6. Dumitrescu Hurlin panel causality tests

Independent Variable	W-bar	Z-bar
ENVf	2.91966	4.89104
SOCf	2.90825	4.85943
GOVNf	1.65139	1.37812
GDPG	1.45601	0.83696

***represents 99%, **represents 95%, and *represents 90% significance level.

4.5. Cointegration test

To enhance the reliability of the p-values and address potential cross-member correlation effects, we employed a rigorous bootstrap resampling procedure with 100 estimations for the panel cointegration test. This yielded four key statistics: Gt and Pt for the group mean tests, and Ga and Pa for the panel mean tests. Examination of the results in Table 7 reveals support for the null hypothesis, indicating a long-term association among the variables. This leads to the conclusion that the model details are cointegrated, thus affirming a sustained relationship. To further explore the long-term impact of the SDGs, we conducted FMOLS and DOLS tests, followed by the utilization of the ARDL/PMG model to assess the enduring relationship.

Table 7. Westerlund cointegration tests

Statistics	Value	Z-value
Gt	−1.314	6.041
Ga	−4.135	5.871
Pt	−3.272	7.092
Pa	−2.041	4.886

***represents 99%, **represents 95%, and *represents 90% significance level.

4.6. The results of long-run estimations

Table 8 demonstrates the results derived from the comprehensive FMOLS and DOLS analyses, which aimed to elucidate the intricacies of the long-term relationships among the considered variables. Both FMOLS and DOLS methodologies converge to reveal consistent patterns, emphasizing the substantial influence of environmental factors (ENVf), social factors (SOCf), governance factors (GOVNf), and GDP growth (GDPG) on Sustainable Development Goals (SDGs). The FMOLS model’s revelation of a significant and positive correlation between SOCf and SDGs is particularly noteworthy, attaining an impressive 99% significance level. According to the FMOLS, a 1% increase in SOCf corresponds to a substantial 3.92% increase in SDGs, supporting hypothesis H2a. Conversely, negative relationships emerged between the SDGs and both ENVf and GDPG in the FMOLS estimation. A 1% increase in ENVf led to a considerable 9.23% decrease in SDGs, whereas a similar increase in GDPG resulted in a noteworthy 0.17% decrease. The DOLS estimations mirror these findings, supporting Hypotheses H1a and H4a. Furthermore, DOLS revealed a positive relationship between SOCf and SDGs, with a 1% increase in SOCf corresponding to a significant 2.5% increase in SDGs, reinforcing hypothesis H2a. Finally, our analysis revealed a noteworthy positive relationship between governance factors (GOVNf) and SDGs in the DOLS estimations, supporting hypothesis H3a. However, it is important to note that, in the case of FMOLS estimations, no significant relationship was observed between GOVNf and SDGs.

Table 8. The result of the long-term relationship

	FMOLS		DOLS
Independent Variables	Coeff	Std Err	Coeff	Std Err
ENVf	−9.2307***	0.7865	−6.5858***	1.223
SOCf	3.9244***	0.8941	2.5698*	1.427
GOVNf	0.8336	0.6791	2.1235**	1.009
GDPG	−0.1718***	0.0289	−0.4583***	0.1038

***represents 99%, **represents 95%, and *represents 90% significance level.

4.7. Ardl/Pmg

Table 9 provides the ARDL/PMG model, reflecting the relationships between the dependent variable, likely associated with Sustainable Development Goals (SDGs), and several independent variables in both the long and short run. In the long run, the negative coefficient of −27.126 for environmental factors (ENVf) suggests that, as environmental considerations increase, the dependent variable SDGs decrease. Conversely, the positive coefficient of 26.042 for governmental influence (GOVNf) implies that a higher level of governmental influence corresponds to an increase in SDGs. However, social factors (SOCf) do not exhibit a statistically significant long-term impact in the long run. In the short run, none of the coefficients of ENVf, SOCf, GOVNf, or GDP growth (GDPG) are statistically significant. This lack of significance in the short run implies that, at least in the timeframe considered, there are no discernible relationships between the dependent variable and specified independent variables. The ARDL/PMG model yielded outcomes comparable to those of FMOLS and DOLS when investigating the association between ENVf and SDGs (Accepting H1a) and GOVNf and SDGs (Accepting H3a).

Table 9. ARDL/PMG model

	Long-run Relationship		Short-run Relationship
Variables	Coefficient	Std. Error	Coefficient	Std. Error
ENVf	−27.126***	9.6392	−40.2034	42.2218
SOCf	−5.997	6.5818	−0.4762	1.6955
GOVNf	26.042***	9.7985	−0.2478	0.2526
GDPG	1.362	0.8584	0.0111	0.0105

***represents 99%, **represents 95%, and *represents 90% significance level.

4.8. Robustness check

In our study, we utilized both the AMG and CCEMG models (refer to Table 10) to thoroughly assess the reliability of the long-term estimates derived from the autoregressive distributed lag (ARDL) methodology. The application of the AMG and CCEMG tests not only verified the robustness of our findings but also bolstered the credibility of our ARDL results. The coherence observed among the outcomes from the AMG, CCEMG, and ARDL approaches underscored the dependability of our empirical evidence.

Table 10. AMG and CCEMG model

	AMG	CCEMG
Variables	Coefficient	Coefficient
ENVf	−54.859	−19.231
SOCf	0.7023	0.327
GOVNf	−0.8255*	−0.777*
GDPG	0.0049	−0.008

*Represents 90% significance level.

In the AMG and CCEMG analyses, we identified a significant correlation between the governance factor and sustainable development goals. Although the direction of this relationship differs when compared to the ARDL results, the significance observed further attests to the robustness of our findings. Moreover, these analyses revealed no significant impact of social factors and GDP growth on sustainable development goals.

5. Discussion

In pursuit of our objectives, we formulated a model to scrutinize SDG attainment in East Asia-Pacific and South Asian countries, examining the impact of ESG factors and GDP growth. The outcomes substantiate the acceptance of hypotheses H1a, H2a, H3a, and H4a, indicating a significant long-run relationship between ESG, GDPG, and SDG achievement. It is imperative to meticulously assess the interconnections between variables for each country, which is a distinctive aspect of our study compared to other research endeavors. The primary innovation of this research lies in its comprehensive consideration of variables, encompassing environmental, social, and governance indicators, and GDP growth, to evaluate their impact on SDGs. Furthermore, this study employs reliable and robust econometric methodologies, yielding definitive and conclusive results. This research represents a unique endeavor to examine the relationships among these variables by utilizing econometric techniques known for their efficiency and reliability in generating meaningful outcomes. The application of the CIPS and CADF tests confirmed the presence of cross-sectional dependence in our panel variables, consistent with the findings of prior studies. Additionally, these tests suggest the likelihood of a long-run symmetric relationship because all variables exhibit integration of the same order. Furthermore, our results indicate that the SDGs, ENVf, SOCf, GOVNf, and GDPG are stationary in their levels or first differences, with high significance levels (90%, 95%, or 99%) across various cases. These collective findings imply a lasting impact of ESG factors and GDP growth on the SDGs. Notably, each variable has a significant impact on the SDGs, indicating cointegration and long-term relationships.

The Autoregressive Distributed Lag (ARDL) estimates derived from the entire panel data indicate a notable and positive long-term association between governance factors (GOVNf) and Sustainable Development Goals (SDGs). This finding contrasts with the results reported by Işık, Ongan, Islam, Pinzon, et al. (2024), who identified a negative relationship between governance factors and sustainable development goals. Similarly, Işık, Ongan, Islam, Pinzon, et al. (2024) found a negative relationship between environmental factors (ENVf) and SDGs, which aligns with our study’s finding of a negative relationship between environmental factors and sustainable development goals. Therefore, our study presents divergent results regarding the relationship between governance factors and sustainable development goals compared to the findings of Işık, Ongan, Islam, Pinzon, et al. (2024), while reinforcing the negative association between environmental factors and SDGs. The FMOLS model established a significant relationship between ENVf, SOCf, and GDPG on SDGs, and the DOLS model identified a significant relationship between ESG and GDPG on SDGs. Moreover, the AMG and CCEMG models validated the significance of the ARDL findings.

6. Conclusion

Our investigation delves into the intricate interrelationships among environmental, social, and governance factors, and the attainment of GDP growth and SDGs in East Asia Pacific and South Asian countries. This study spans the timeframe from 2003 to 2021, utilizing the latest available data from the World Bank. Our approach diverges from previous research, which has predominantly focused on isolated associations among these elements. Instead, our research adopts a comprehensive and inclusive stance in the selection of pertinent variables by employing robust econometric methods for rigorous analysis.

The results reveal a significant long-run relationship between ESG, GDPG, and the achievement of SDGs in East Asia-Pacific and South Asian countries. Careful evaluation of the relationships between the factors for every nation is essential, which sets our study apart from previous studies.

This study’s complete analysis of environmental, social, and governance indicators and GDP growth’s influence on SDGs is its main originality. The research also used solid econometric methods to obtain clear conclusions. These factors were examined in this study using econometric methods recognized for their efficiency and dependability in producing relevant results. Additionally, these tests suggest the likelihood of a long-run symmetric relationship because all variables exhibit integration of the same order. Furthermore, our results indicate that the SDGs, ENVf, SOCf, GOVNf, and GDPG are stationary in their levels or first differences, with high significance levels (90%, 95%, or 99%) across various cases. These collective findings imply a lasting impact of ESG factors and GDP growth on the SDGs. Notably, each variable has a significant impact on the SDGs, indicating cointegration and long-term relationships. Furthermore, the estimates of Autoregressive Distributed Lag (ARDL) obtained from the complete panel data validate a substantial and positive long-term correlation between GOVNf and SDGs. Conversely, these estimates reveal an inverse relationship between ENVf and SDGs. Both the FMOLS and DOLS models established a significant correlation between ENVf, SOCf, and GDPG on SDGs, as well as between ESG and GDPG on SDGs. In light of these results, the current study has also contributed by proposing unique policy implications for key stakeholders.

6.1. Policy implications

To apply the research findings to actionable strategies, policymakers should carefully consider the nuanced insights derived from the study’s analysis of the complex interaction among environmental, social, governance, and GDPG factors concerning the dynamics of sustainable development goals across East Asia Pacific and South Asian nations. The following policy implications are provided based on these insights:

• Policy Focus on Governance Factors: Policies aimed at enhancing governance effectiveness should be prioritized, as our study indicates a significant and positive long-run association between governance factors (GOVNf) and Sustainable Development Goals (SDGs). Strengthening governance frameworks and promoting transparency and accountability can contribute to the attainment of SDGs.

• Addressing Environmental Challenges: Policymakers need to address environmental challenges effectively, given the negative relationship observed between environmental factors (ENVf) and SDGs. Strategies focused on environmental conservation, pollution reduction, and sustainable resource management are crucial for advancing sustainable development agendas.

• Promoting Social Inclusivity: Our findings underscore the importance of promoting social inclusivity (SOCf) in achieving SDGs. Policies aimed at reducing inequalities, improving access to education, healthcare, and social services, and fostering inclusive economic growth can facilitate progress towards sustainable development objectives.

• Integration of ESG Considerations in Economic Policies: Economic policies should integrate environmental, social, and governance (ESG) considerations to foster sustainable development (Işık, Ongan, & Islam, 2024). Işık et al. (2024) introduced a novel concept termed ECON-ESG, which integrates economic considerations with ESG (Environmental, Social, and Governance) factors. Encouraging businesses and industries to adopt responsible environmental and social practices, alongside promoting good governance principles, can contribute to sustainable economic growth and SDG attainment.

• Long-term Planning and Monitoring: Policymakers should adopt a long-term perspective in planning and monitoring efforts to achieve SDGs. Continual monitoring and evaluation of policies, along with adaptive management strategies, are essential to address evolving challenges and ensure progress towards sustainable development objectives over time.

7. Limitations and future guidelines

This study acknowledges certain limitations that will shape future research efforts. The current study concentrated exclusively on East Asia Pacific and South Asian countries. However, in future investigations, further analysis should be conducted to encompass a wider range of nations in these diverse categories. In order to determine the impact on the Sustainable Development Goals (SDGs), this research solely takes into account the three most important categories of environmental, social, and governance aspects. In addition to these considerations, other elements, such as green financing, the generation of renewable energy, and energy efficiency, may have an impact on the sustainable development of nations. However, none of these aspects have been investigated in this research. To assess sustainable development, it is advised that upcoming writers broaden the scope of research by investigating these crucial aspects. Elements such as macroeconomic indicators, demographic trends, social dynamics, environmental conditions, and health-related variables can also significantly impact the SDGs. Subsequent studies could integrate these aspects into our analysis. Additionally, although we utilized the ARDL, FMOLS, and DOLS methodologies, we acknowledge that our research did not include asymmetric analysis and quantile regression techniques. Future research could explore additional analytical tools and techniques to offer a more reliable understanding of the determinants and implications of ESGs and SDGs.

Author contributions

Conceptualization, Md. Abu Issa Gazi and Hasibul Islam; Data curation, Md. Abu Issa Gazi, Abdul Rahman S Senathirajah and Rejaul Karim; Formal analysis, Hasibul Islam and Sadikatul Mawa Momo; Funding acquisition, Md. Aminul Islam and Md. Abu Issa Gazi; Investigation, Md. Aminul Islam, Md. Abu Issa Gazi, Hasibul Islam, Abdul Rahman S Senathirajah and Rejaul Karim; Methodology, Hasibul Islam and Sadikatul Mawa Momo; Project administration, Md. Abu Issa Gazi; Resources, Md. Aminul Islam, Md. Abu Issa Gazi, Abdul Rahman S Senathirajah, Sadikatul Mawa Momo and Rejaul Karim; Supervision, Md. Aminul Islam; Validation, Md. Abu Issa Gazi, Abdul Rahman S Senathirajah, Sadikatul Mawa Momo and Rejaul Karim; Writing—original draft, Rejaul Karim, Hasibul Islam and Sadikatul Mawa Momo; Writing—review & editing, Md. Aminul Islam, Md. Abu Issa Gazi and Abdul Rahman S Senathirajah.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [M.A.I. Gazi], upon reasonable request.

Additional information

Funding

This project was supported by the own funds of the authors and partially supported by INTI International University, Malaysia.

Notes

1. Asia and the Pacific SDG Progress Report 2022: Widening Disparities Amid COVID-19. Available at: https://www.unescap.org/kp/2022/asia-and-pacific-sdg-progress-report-2022#; (Accessed on 10^th December, 2023)