Addressing environmental degradation has become a pressing priority in the era of global sustainability. As carbon dioxide (CO ₂ ) emissions continue to escalate—particularly in emerging economies—identifying effective policy and technological pathways for emissions reduction is essential. This study investigates how economic growth, governance mechanisms, and technological advancement influence environmental sustainability in Mexico during the period 1980–2023. Mexico, a key emerging economy with ambitious climate goals, serves as a critical case for exploring these dynamics. Using annual time series data, the study employs the autoregressive distributed lag (ARDL) bounds testing method, supported by robustness checks using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS), and canonical cointegration regression (CCR) techniques. The findings reveal that fiscal decentralization and economic growth significantly contribute to environmental degradation, while renewable energy consumption and globalization are associated with reductions in CO ₂ emissions. Although ICT expansion holds long‐term efficiency potential, it currently exacerbates emissions due to its dependence on fossil‐fuel‐based infrastructure. These results underscore the complex trade‐offs involved in aligning fiscal, technological, and energy policies with sustainability goals. The study concludes with clear policy recommendations: Mexico should reform its fiscal decentralization frameworks, invest in renewable‐powered ICT infrastructure, and accelerate clean energy development to fulfill its climate commitments. The findings offer actionable insights to guide low‐carbon transitions in emerging economies.

This study investigates the determinants of ecological sustainability across G7 nations by analyzing the factors influencing the load capacity factor (LCF)—a comprehensive measure comparing biocapacity and ecological footprint. The uses the STIRPAT model as a basis of analysis, and the main results are found using a novel Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model; the study explores both short-run and long-run dynamics between LCF and key macroeconomic indicators—economic growth (ECG), population (POP), structural change (STC), service sector development (SERV), green innovation (GI), and research and development expenditure (R&D). The empirical analysis reveals that population growth and structural change have positive but mixed effects; the service sector contributes positively in the short term but negatively in the long run. Green innovation significantly reduces environmental pressure over time, confirming its role as a key driver of sustainability. R&D, although insignificant in the baseline model, shows significant long-run effects in robustness checks. The error correction term is negative and highly significant, indicating rapid adjustment toward long-run equilibrium. Robustness checks using AMG, MG, and CCEMG estimators confirm the validity and consistency of most results. Findings highlight the complex interplay between economic and environmental variables, underscoring the need for green innovation, sustainable service sector models, and targeted R&D investment. However, the study underscores that merely increasing R&D funding is insufficient to address environmental challenges. Instead, fostering the service sector and promoting green innovation are essential for achieving long-run economic growth with sustainability in the G7 and other developed nations alike. AbstractSection Graphical abstract