This study examines the socioeconomic, institutional, technological, and behavioral determinants of carbon dioxide (CO
2
) emissions across the G7 countries over the period 2011–2024. Specifically, it investigates how self‐reported life satisfaction (SRLS), human development (HDI), total factor productivity (TFP), environmental policy stringency (EPS), government effectiveness (GE), and real expenditure per capita (REPC) are associated with CO
2
emissions while accounting for the nonlinear effect of HDI and the moderating role of household expenditure. Following diagnostic tests for slope heterogeneity, cross‐sectional dependence, stationarity, cointegration, multicollinearity, and model specification, the study employs the FE‐DKSE as the primary estimation technique, with PCSE and FGLS estimations used to assess robustness. The findings reveal that HDI is consistently associated with lower CO
2
emissions, while its significant quadratic term indicates a nonlinear relationship that does not support the conventional EKC hypothesis. EKC exhibits a positive association with CO
2
emissions, whereas environmental policy stringency contributes to emission reductions. Moreover, the SRLS × REPC interaction is positive and significant, suggesting that the association between life satisfaction and CO
2
becomes stronger as household expenditure increases. The robustness analyses confirm the stability of these relationships across alternative estimators. By integrating nonlinear human development effects and the behavioral interaction between subjective well‐being and household expenditure within a unified empirical framework, this study delivers updated and robust indications on the complex drivers of carbon emissions in advanced economies and contributes to a more inclusive understanding of the development–environment nexus in the G7 countries.
Muhammad Khizar Saeed, Abdulateif A. Almulhim, Abdullah A. Aljughaiman· Sustainable Development· 0 citations
The role of artificial intelligence (AI) in project evaluation, capital allocation, and sustainability performance has gained attention, yet the firm-level channel through green investment remains underexplored. This study examines whether AI-driven decision capability increases green investment intensity and whether such investment improves environmental sustainability and financial performance under the moderating role of governance quality. The analysis uses panel data for 750 firms from 2015 to 2024 and applies descriptive statistics, panel regression, mediation and moderation tests, dynamic robustness checks, and machine learning models. The results show that AI-driven decision capability significantly increases green investment intensity. Green investment positively affects environmental sustainability and financial performance and mediates the link between AI capability and firm outcomes. Governance quality strengthens the effect of green investment on both outcomes. The findings indicate that AI creates sustainable value through strategic capital allocation, especially when firms maintain effective governance mechanisms.
M. Qamruzzaman, A. Almulhim, Syed Nazmus Sakib et al.· Journal of Intelligent Decis...· 0 citations
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