Issue #1 (Volume 17 2026)

Type of the article: Research Article

Abstract
This paper aims to examine the role of artificial intelligence (AI) training effectiveness in achieving a green growth strategy in Jordan, particularly at the Jordanian Electric Power Company, which represents the Jordanian energy sector. The analysis is supported by the multifaceted program evaluation framework by Daniel Stufflebeam (CIPP). AI training is considered a strategic intangible asset that promotes the growth of rare and invaluable intangible human capital. Quantitative cross-sectional research design was applied, targeting employees of the Jordanian Electric Power Company. Using a simple random sampling, 178 valid responses were directly engaged. The assessment of the theoretical and structural models was done using SPSS and SmartPLS. The results indicated that AI training effectiveness is a significant predictor of the green growth strategy’s outcomes (β = 0.562, t = 8.990, p < 0.001), explaining 31.6% of the variance. The strongest predictor among the CIPP dimensions was the dimension of input (β = 0.556, R2 = 0.310), then the dimensions of context (β = 0.532, R2 = 0.283), process (β = 0.516, R2 = 0.266), and product (β = 0.487, R2 = 0.237). These findings indicate that highly developed training programs, when designed according to the organizational context, resource-rich, and executed effectively, yield quantifiable skills development and play an influential role in meeting the goals of the national green growth strategy.

Acknowledgment
Gratitude is expressed to the Middle East University, Amman, Jordan, for the financial support to cover this article’s publishing fee.

Type of the article: Research Article

Abstract
This study examines the effect of ESG reporting on the financial performance of green companies listed in Indonesia between 2020 and 2024, totaling 85 companies with 425 observations. Using ESG scores and corporate financial data from Bloomberg, three panel models were estimated: a random-effects model for ROA, a fixed-effects model for ROE, controlling for size, leverage, growth, and cash flow, and a test for sectoral differences between energy and non-energy companies. The results indicate that governance scores are positively associated with ROA (β = 0.011, p = 0.092), whereas ESG scores are weakly positively associated with ROA (β = 0.020, p = 0.080). Environmental and social scores are not statistically significant to ROA or ROE. For ROE, firm size is the main significant predictor (β = 2.126, p = 0.042). The results observe significant differences between the energy and non-energy sectors, with the energy sector reporting higher financial performance after controlling for ESG. Finally, this study indicates that ESG reporting policies that promote good governance can yield faster returns to shareholders.

Acknowledgment
This study was supported by the Ministry of Research, Technology, and Greater Education and Al-Madani School of Economic Sciences. 

Type of the article: Research Article

Abstract
This study aims to empirically examine the dynamic effects of geopolitical risk, economic policy uncertainty, and climate policy uncertainty on CO₂ emissions in G7 economies, utilizing annual data from 1990 to 2022. To account for cross-sectional dependence and parameter heterogeneity, the analysis employs a cross-sectional autoregressive distributed lag (CS-ARDL) model. Diagnostic tests confirm significant cross-sectional dependence and slope heterogeneity among the variables. All variables are integrated of order one, I (1), confirmed by unit root tests. In contrast, the cointegration test provides a strong indication of a stable long-run relationship among geopolitical risk, policy uncertainty measures, and CO₂ emissions. The outcomes show that a 1% rise in the geopolitical risk index leads to a statistically significant long-run rise of 0.042% in per capita CO₂ emissions. In addition, a 1% increase in economic policy uncertainty and climate policy uncertainty is associated with long-run increases of 0.028% and 0.015%, respectively. These results remain robust across alternative estimators. Overall, the evidence suggests that heightened geopolitical risk and policy-related uncertainties significantly exacerbate environmental degradation in G7 economies, highlighting the necessity for strategies that improve stability, reduce uncertainty, and encourage renewable energy adoption as part of a long-term environmental strategy.