This research investigates the dynamic interplay between financial technology, information and communication technology, energy consumption, and economic growth on environmental sustainability within Emerging and Growth-Leading Economies (EAGLEs) from 2005 to 2020. Utilizing advanced econometric techniques, such as Fully Modified Least Squares (FMOLS) and Vector Autoregressive Error Correction Model (VECM), the investigation scrutinizes the hypothesized relationships among these variables. Panel unit root tests were deployed to assess stationarity, while panel least squares methodology was employed to determine the presence of co-integration among the variables under study. The analysis reveals that internet usage, GDP, and renewable energy consumption exhibit a notable influence in diminishing CO₂ emissions within EAGLE economies. Additionally, the findings substantiate the existence of long-term causality originating from these variables and impacting CO₂ emissions. Conversely, the role of ATM networks in CO₂ emissions remains ambiguous, implying that financial technology’s influence on environmental sustainability is inconclusive. Consequently, the research posits that environmental sustainability in EAGLE economies is chiefly determined by factors such as internet usage, economic expansion, and renewable energy consumption, with financial technology demonstrating no discernable impact. In light of these findings, the study advocates for the reevaluation and adaptation of existing policies and strategies to account for shifting climatic conditions. By doing so, decision-makers can better align their efforts with the pursuit of environmental sustainability in the context of rapidly evolving economies.

Introduction: Environmental, Social, and Governance (ESG) uncertainty has emerged as an important source of financial risk, particularly for pollution‐intensive industries that are highly exposed to sustainability‐related policy and regulatory changes. However, evidence on how ESG uncertainty is associated with sectoral volatility remains limited. This study examines the relationship between the U.S. ESG Uncertainty Index (U.S. ESGUI) and the volatility dynamics of the six S&P 500 sectors with the highest historical TRI‐based pollution rankings: Energy, Utilities, Consumer Discretionary, Consumer Staples, Materials, and Industrials. Methods: The study employs the GARCH–MIDAS framework, which integrates high‐frequency daily stock returns with the low‐frequency U.S. ESGUI to capture short‐term volatility dynamics and long‐term ESG‐related effects. Forecasting performance is evaluated against GARCH(1,1), EGARCH(1,1), and RV‐GARCH–MIDAS benchmark models. Diebold–Mariano (DM) tests are used to compare predictive performance, and additional crisis‐period out‐of‐sample analyses examine forecasting performance during the COVID‐19 pandemic and the Russia–Ukraine conflict. Results: The results reveal that U.S. ESG uncertainty is significantly associated with long‐run volatility across all six sectors, while short‐run volatility remains highly persistent. Sectoral heterogeneity is evident: Energy, Utilities, Consumer Discretionary, and Materials exhibit negative long‐run associations with U.S. ESG uncertainty, whereas Consumer Staples and Industrials exhibit positive long-run associations. The ESGUI‐GARCH–MIDAS model generally achieves lower forecast losses than the GARCH(1,1), EGARCH(1,1), and RV‐GARCH–MIDAS benchmark models across the six sectors. DM tests further show that the ESGUI‐GARCH–MIDAS model significantly outperforms the RV‐GARCH–MIDAS benchmark under both the TSR and TSQ criteria across all six sectors (p < 0.001), indicating that the forecasting gains are not attributable solely to the mixed‐frequency framework. Compared with GARCH(1,1) and EGARCH(1,1), the DM results provide stronger and more consistent evidence under the TSR criterion, although relative forecasting performance varies across loss functions and sectors. The additional crisis‐period out‐of‐sample analyses provide further evidence on the model’s forecasting performance during periods of heightened market uncertainty. Discussion: The findings suggest that the U.S. ESGUI contains useful information for understanding and forecasting sectoral volatility, while its association with long‐run volatility varies across the most polluted sectors. The study contributes to the growing ESG–finance literature by highlighting the relevance of sustainability‐related uncertainty in sector‐level volatility modelling and forecasting.