Assessing the impact of Environmental, Social and Governance (ESG) framework on the performance of listed real estate companies in Malaysia
Bibliographic record
Abstract
Purpose This study investigates the financial impact of Environmental, Social, and Governance (ESG) practices on the performance of publicly listed property companies in Malaysia. Amid growing regulatory and investor pressure, the research aims to assess how ESG dimensions influence operational efficiency, profitability, and market valuation in an emerging market context, where ESG integration remains uneven and underexplored. Design/methodology/approach Using panel data from 91 Malaysian property firms over the period 2012–2023, the study applies Ordinary Least Squares (OLS) regression models and descriptive statistical analyses to evaluate the relationship between ESG scores—both overall and disaggregated by pillar—and a firm’s performance indicators, including Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q. The models incorporate firm-level and macroeconomic control variables to ensure robustness and contextual relevance. Findings The findings reveal that overall ESG performance positively correlates with financial outcomes, with governance and social pillars exerting the strongest influence. Governance quality significantly enhances market valuation, while social initiatives are positively associated with profitability. Environmental performance shows limited short-term impact. Asset growth emerges as a critical driver of financial success, whereas high financial leverage undermines performance. A firm’s size and Gross Domestic Product (GDP) have marginal effects, suggesting internal strategies outweigh macroeconomic conditions in this context. Practical implications The results provide actionable insights for corporate leaders, investors, and policymakers in Malaysia and similar emerging markets. They highlight the importance of embedding ESG into core business strategies rather than treating it as a compliance exercise. The study supports recent regulatory efforts to strengthen ESG reporting and urges firms to prioritise governance structures, stakeholder engagement, and sustainable growth over debt-driven expansion. Originality/value This study offers one of the first large-scale, sector-specific analyses of ESG performance in Malaysia’s real estate industry. It contributes original empirical evidence to the global ESG literature by contextualising ESG-financial linkages in an emerging economy. The findings underscore the strategic value of ESG integration and demonstrate how localised ESG maturity can influence financial outcomes in capital-intensive sectors like real estate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".