Mapping ESG Structures Across Business Sectors in Emerging Markets: A Quantitative Exploration of Pillars and Subcategories

To provide a structured foundation for the study, this section outlines the theoretical and empirical gaps identified in the current ESG literature, particularly in the context of emerging markets. Despite increasing attention to ESG practices, many studies remain limited to aggregate-level analysis, overlooking sectoral nuances and internal ESG structures [21, 32-35]. To address these limitations, the study formulates specific research questions aligned with clear objectives and appropriate methodologies. The research framework is summarized in Table 1.

4.1 Overview of ESG scores across emerging markets

Over the past five years, the average total ESG score of companies in emerging markets reached 55.35, indicating that, in general, corporate sustainability performance is above average and relatively sound (Table 3). However, the relatively high standard deviation of 18.77 reflects substantial variation among companies, both in terms of ESG disclosure and implementation.

When examined by pillar, the Social pillar recorded the highest average score compared to the Environmental and Governance pillars. This is further supported by two key subcategories within the Social pillar—Community and Workforce—which both showed high average values and relatively stable data distributions. These findings suggest that companies in emerging markets demonstrate greater commitment to social issues, such as community engagement and workforce management.

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Figure 1. Distribution of EGS total score across sector for 5 years

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Table 3. Descriptive statistics and correlation analysis

Table 3(a). Partial spearman and distance correlation (dCor) controlling for sector fixed effects

Note: Partial correlations control for sector-specific effects to remove inter-industry bias. √ denotes significance after Benjamini–Hochberg FDR correction (α = 0.05).

The distribution of total ESG scores across business sectors also reveals significant performance disparities (Figure 1). The Banking & Investment Services sector ranks highest, with a median ESG score exceeding 75, indicating high consistency and quality in reporting. In contrast, sectors such as Food & Beverages, Retailers, and Cyclical Consumer Services exhibit wide score distributions with low medians, potentially indicating inconsistent ESG practices and risks of greenwashing. Although the Mineral Resources and Energy – Fossil Fuels sectors display relatively good ESG performance, their broad score ranges indicate internal disparities in ESG implementation within the same sector.

Furthermore, the average ESG score trends from 2020 to 2024 (Figure 2) show that most sectors experienced gradual improvement over time, reflecting progressive integration of ESG into corporate strategies. The Real Estate (RE) and Telecommunications Services (TS) sectors recorded consistently increasing trends, suggesting enhanced awareness and more substantive ESG adoption. On the other hand, sectors such as Food & Beverages, Cyclical Consumer Services, and Retailers show stagnant or declining trends, possibly indicating symbolic ESG adoption or structural challenges. Meanwhile, Banking & Investment Services (BIS) and Personal & Household Products & Services (PHPS) consistently demonstrate strong and stable ESG performance, indicating sectoral advantages in governance and sustainability compliance.

Table 3(a) reports the results of the robustness test using partial correlations controlling for sector fixed effects. The findings confirm that the relationships between the ESG pillars and the overall ESG score remain highly consistent after accounting for sectoral composition. The Social pillar continues to exhibit the strongest association with ESG Total (ρ = 0.83; dCor = 0.83; p < 0.001), followed by Environmental (ρ = 0.78; dCor = 0.75) and Governance (ρ = 0.70; dCor = 0.68). All subcategories remain statistically significant under the Benjamini–Hochberg correction, suggesting that ESG linkages are robust and firm-intrinsic rather than driven by sectoral bias. Particularly, the strong within-sector contributions of Resource Use and Human Rights underscore the salience of social and environmental efficiency as universal determinants of ESG performance across firms.

4.2 Pillar-level and subcategory analysis

Most business sectors in emerging markets still position the Social (S) pillar as the dominant dimension within their ESG structure. As shown in Figure 3, the contribution of the social pillar across various sectors exceeds 30%, and in some cases even approaches or surpasses 40%–50%. Sectors such as Cyclical Consumer Services (49.3%), Applied Resources (49.4%), and Telecommunications Services (47.3%) exhibit particularly high proportions of the social pillar. This finding is consistent with the earlier descriptive results indicating that the highest average ESG scores derive from the social dimension, highlighting companies’ focus on issues such as labor relations, human rights, and product responsibility.

Conversely, the Environmental (E) pillar tends to receive lower proportions in many sectors, particularly in service-based sectors such as Cyclical Consumer Services (12.2%) and Telecommunications Services (18.7%). However, several sectors demonstrate relatively stronger attention to environmental concerns, such as Personal & Household Products & Services (31.7%), Chemicals (31.2%), and Food & Beverages (30.2%). This variation likely reflects differing industry characteristics and external pressures related to environmental impact management. Overall, these findings suggest that attention to environmental aspects remains uneven across business sectors.

Meanwhile, the Governance (G) pillar displays relatively stable distributions across most sectors, with proportions ranging from 21% to 38%. This indicates that governance structures and strategic policies—such as CSR Strategy and Shareholder Rights—remain essential elements in corporate ESG management, even if they are not the primary focus in every sector.

A deeper analysis at the subcategory level reveals more specific contribution patterns across sectors (Figure 4). Subcategories such as Workforce and Community emerge as dominant contributors in various sectors, reinforcing the earlier observation of the social dimension’s dominance. For instance, the Cyclical Consumer Services sector records a 24% share for the Community subcategory, while the Industrial & Commercial Services sector highlights Workforce as a key ESG component.

In contrast, environmental subcategories like Emissions and Resource Use show more varied distributions. The Utilities sector, for example, records a notable 14% proportion for Emissions, suggesting more measurable efforts in managing environmental impact in resource-intensive industries.

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Figure 3. Average proportion of ESG pillars per sector

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Figure 4. Average proportion of ESG subcategories per sector

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Figure 5. Distance corelation matrix (heatmap)

Other subcategories such as Innovation, Management, and CSR Strategy also show significant proportions in certain sectors. For example, the Chemicals and Personal & Household Products & Services sectors report Innovation proportions of 15% and 11%, respectively, reflecting their emphasis on developing sustainable solutions. Meanwhile, Cyclical Consumer Services stands out in the Management subcategory (18%), and Applied Resources demonstrates a high share in CSR Strategy (14%).

Taken together, these findings indicate that ESG sustainability patterns in emerging markets are sector-specific and layered. The primary ESG focus varies not only across pillars but also across subcategories. These nuanced patterns are crucial for regulators, investors, and companies to consider when designing more targeted and contextual ESG strategies aligned with the characteristics of each business sector.

4.3 Correlation patterns among ESG dimensions

Correlation analysis was conducted to understand the relationships between each ESG pillar and subcategory with the overall ESG Total score. To ensure robustness and account for potential non-linear relationships, Distance Correlation (dCor) was employed as the primary test, complemented by Spearman rank correlation to provide directionality of associations.

 At the pillar level, all three ESG pillars show strong and positive associations with ESG Total (Table 3). The Social pillar demonstrates the highest distance correlation (dCor = 0.8875; ρ = 0.8930), followed by Governance (dCor = 0.7888; ρ = 0.8237) and Environmental (dCor = 0.7585; ρ = 0.7739). This ranking is consistent across both methods, confirming the social pillar’s dominant role in ESG structures for companies in emerging markets.

At the subcategory level, the variables Emissions (dCor = 0.7792; ρ = 0.7976), Workforce (dCor = 0.7495; ρ = 0.7740), and Management (dCor = 0.7194; ρ = 0.7526) show the highest correlations with ESG Total. This suggests that environmental emissions control, human capital development, and internal governance quality are critical drivers of ESG performance overall. Conversely, subcategories such as Innovation (dCor = 0.2912; ρ = 0.2725) and Shareholders (dCor = 0.3562; ρ = 0.3791) demonstrate much weaker correlations, indicating limited aggregate contribution.

The within-dimension analysis (Figure 5) reveals distinct internal correlation patterns. Within the Environmental dimension, Emissions and Resource Use exhibit a very high mutual association, reflecting their complementary contribution to environmental performance. In Governance, Management shows an exceptionally strong alignment with the overall governance construct, indicating that governance scores in the dataset are predominantly driven by management-related practices. Within the Social dimension, most subcategories demonstrate strong interconnections—particularly Workforce and Human Rights—whereas Innovation remains weakly linked to other subcategories, underscoring its marginal role and relative independence from the core social structure.

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Figure 6. Heatmap correlation of ESG subcategories

The sector-level heatmap (Figure 6) reveals substantial variations across industries. In the Mineral Resources sector, Emissions (dCor = 0.93) and Resource Use (dCor = 0.85) dominate, reflecting the environmental intensity of extractive activities. A similar pattern is observed in the Pharmaceuticals & Medical Research sector. For Retailers, Community shows a very strong correlation with ESG Total (dCor = 0.96), whereas Innovation remains relatively marginal (dCor = 0.75). In the Energy – Fossil Fuels sector, Community (dCor = 0.78) stands out despite lower contributions from other social dimensions, indicating a sector-specific focus on community relations.

Overall, these findings highlight that while ESG pillars generally exhibit strong and consistent relationships with overall ESG performance, the influence of specific subcategories is highly sector dependent. The use of Distance Correlation provides additional insight by capturing both linear and non-linear associations, revealing patterns that conventional correlation tests might overlook. Consequently, ESG strategies should be tailored to industry-specific drivers, acknowledging that certain dimensions may be more influential—or even counterproductive—depending on sectoral context.

4.4 Sectoral ESG strengths and weaknesses

Following the descriptive and correlational analysis, the next step is to map the strengths and weaknesses of each sector in relation to the three ESG pillars. This identification is essential for understanding which sustainability dimensions are most and least prominent across sectors, uncovering potential greenwashing practices, and formulating more contextual and evidence-based ESG strategies.

The results indicate that ESG strength in each sector is not solely determined by average scores or structural proportions but also by the extent to which each subcategory contributes significantly to the total ESG score. Table 4 summarizes the main strengths and notable weaknesses for each sector, based on the most dominant subcategories and their correlation with the ESG Total score.

Table 4. Summary of sectoral ESG strengths and weaknesses

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Figure 7. Bubble chart: Average proportion vs. Correlation of ESG subcategories

To gain deeper insights into sectoral ESG strengths and weaknesses, a combined analysis of the proportion and correlation of ESG subcategories across sectors was conducted. Figure 7 presents a bubble chart mapping the relationship between average subcategory proportions and their linear correlations with ESG Total. The visualization reveals an asymmetric relationship between structural dimensions (i.e., how prominently a subcategory is reported) and functional dimensions (i.e., its actual impact on sustainability performance).

Based on this analysis, ESG subcategories can be classified into four strategic groups according to their average proportion (reporting relevance) and correlation (impact relevance):

•High proportion – High impact

Subcategories that are dominant in reporting and significantly contribute to ESG performance, namely: Workforce, Emissions, Product Responsibility, Management, and Resource Use. These subcategories should be the primary focus of cross-sector ESG strategies due to their proven positive impact on ESG scores.

•High proportion – moderate impact

Subcategories frequently reported but with suboptimal contribution to ESG Total, such as: Community, CSR Strategy, and Shareholders. Notably, Community has the highest structural proportion among this group, yet only moderate correlation. This suggests that community-related disclosures are often symbolic or lack integration into core sustainability strategies. Enhancing implementation quality and impact measurement is necessary in sectors dominated by this subcategory.

•Moderate proportion – high impact

Subcategories that are less dominant in reporting but have significant influence on ESG Total, exemplified by Human Rights. This indicates a potential for quick strategic gains if strengthened, despite limited current attention.

•Low proportion – low impact

Subcategories with both low reporting prevalence and minimal impact, represented by Innovation. This finding signals a critical gap, suggesting that sustainable innovation has yet to be integrated into corporate ESG agendas in emerging markets. There is an urgent need for policy support and incentives to elevate innovation as a core pillar of sustainability.

Within the Retailers sector, the Emissions subcategory exhibits a negative association with ESG Total. This pattern contrasts with materiality expectations that emphasize emissions salience in retail supply chains. Several non-causal mechanisms can reconcile this result: (i) scope composition, whereby the subscore places greater weight on Scopes 1–2 while material exposure resides in Scope 3 upstream; (ii) a disclosure visibility effect, in which firms with broader reporting reveal higher intensity levels in the short run; (iii) business-model heterogeneity within retail (e-commerce, specialty, grocery) that can yield pooled sign reversals; and (iv) normalization effects when intensity uses volatile denominators (e.g., revenue). Consistent with our correlational design, we interpret this finding as descriptive co-variation rather than causal impact.

Innovation shows a comparatively weak association with ESG Total in 2020–2024. Across the full sample, Innovation exhibits the smallest correlations with ESG Total (rank-based and distance measures). When accounting for sectoral composition, the association remains modest. In this dataset and horizon, the Innovation indicators used by the rating provider appear less aligned with the composite ESG score than other subcategories. This finding should not be interpreted as evidence that Innovation is ineffective or unimportant for sustainability outcomes. Rather, several non-causal explanations are plausible: (i) measurement and construct alignment (Innovation is often captured as inputs, plans, or early-stage actions that may not register in composite scores); (ii) timing and lags (effects may materialize beyond the study window); (iii) sectoral heterogeneity (innovation salience and disclosure vary across industries); and (iv) weighting/variance (lower variance or weights can attenuate observable associations). Consistent with our correlational design, we refrain from prescriptive claims.

4.5 Implications for policy and practice

The sectoral mapping of ESG performance underscores that effective ESG strategies must move beyond disclosure quantity and instead emphasize the thematic relevance and impact of each subcomponent. The observed mismatch between subcategory disclosure levels and their actual correlation with ESG Total scores highlights the need to assess not only what is reported, but how much it contributes to sustainability performance.

This has several practical implications. First, both companies and regulators must adopt sector-specific ESG strategies. A uniform approach is insufficient given the diverse subcategory dominance and effectiveness across industries. By integrating the joint analysis of proportion and correlation, stakeholders can prioritize subcategories that both matter and perform—such as Workforce, Emissions, Product Responsibility, Management, and Resource Use.

Second, the findings point to the risk of greenwashing, especially in subcategories like Community or CSR Strategy, which often receive high disclosure yet have weak ties to overall ESG performance. This raises concerns about symbolic compliance. Regulators should therefore implement mechanisms to evaluate disclosure quality, not merely quantity, ensuring that reported efforts reflect real-world sustainability outcomes.

Third, the innovation subcategory remains underrepresented and low in impact across most sectors. Despite its potential to drive long-term ESG resilience, it is often overlooked. Incorporating Innovation more strategically into ESG frameworks is crucial, especially for industries facing rapid transformation or resource constraints. Ultimately, ESG in emerging markets should be grounded in impact-oriented and context-aware policies. The framework presented in this study offers practical guidance for evidence-based ESG planning, enabling tailored improvements in sustainability reporting, regulatory design, and investor decision-making.

4.6 Limitations and future work

While this study offers a comprehensive mapping of the structural composition of ESG and its sectoral dynamics, several limitations should be acknowledged. First, the research relies solely on secondary ESG score data from a single provider. Although the research methodology is sound, the use of secondary data entails potential biases in data collection, weighting schemes, and sectoral disclosure practices. This dependence also limits the exploration of qualitative ESG dimensions. Second, the study adopts a descriptive–correlational and cross-sectoral approach. While this is useful for identifying patterns, it cannot establish causal relationships between ESG subcategories and overall sustainability outcomes. Third, despite covering multiple sectors in an emerging market context, differences in regulatory maturity and institutional environments may influence ESG disclosure behavior, potentially constraining cross-sector comparability.

To address these limitations, future research could integrate primary data sources—such as stakeholder interviews, field observations, or sustainability audits—to complement quantitative findings with richer qualitative insights. Extending the temporal scope to include longer historical periods would help detect structural shifts in ESG priorities and assess sectoral resilience over time. In addition, advanced analytical techniques, including predictive modeling, network analysis, or machine learning–based ESG classification, could enhance the capacity to forecast sector-specific risks, identify early signs of greenwashing, and examine evolving interactions among ESG subcategories. Comparative studies between emerging and developed markets would further refine ESG theory and strengthen the applicability of sector-specific policy recommendations. Patterns consistent with misalignment between Innovation disclosures and composite scoring merit further investigation. Future studies should assess causal pathways and measurement design (e.g., alternative indicators, sector-specific weights, longer horizons) using identification strategies suited to causal inference (e.g., panel designs with exogenous shocks, instrumental variables, or matched natural experiments).