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Arising carbon emissions and accelerating industrialization have intensified concerns regarding sustainable development in emerging markets. This study examines whether innovation functions as a direct mechanism for decarbonization and whether innovation-driven environmental dynamics follow the Environmental Kuznets Curve (EKC) framework. Using panel data from emerging market economies over the period 2000–2020, the study applies fixed-effects estimation techniques to investigate both the linear and nonlinear effects of innovation on CO₂ emissions. Innovation is measured through research and development (R&D) expenditure, while industrialization, urbanization, trade openness, financial development, and ICT indicators are incorporated as control variables. The findings demonstrate that innovation significantly reduces CO₂ emissions, suggesting that technological advancement supports environmental sustainability through cleaner production systems and efficiency-enhancing mechanisms. However, the analysis does not support the existence of an innovation-driven EKC, as the nonlinear innovation term remains statistically insignificant. The study contributes to environmental sustainability literature by extending the EKC framework beyond conventional income-centered explanations and positioning innovation as a direct decarbonization mechanism in emerging economies. The findings further provide practical implications for policymakers seeking to accelerate low-carbon transitions through innovation-oriented industrial and technological policies.

This study finds that green loans and green mortgages are positively and statistically significantly associated with CO₂ emissions in Nigeria, contrary to their intended role in climate mitigation. Using annual time-series data from 2012 to 2024 obtained from the Central Bank of Nigeria (CBN) Statistical Bulletin and the World Bank World Development Indicators (WDI), the study examines the effect of loan-based climate finance on carbon dioxide (CO₂) emissions, with gross domestic product per capita included as a control variable. The Autoregressive Distributed Lag (ARDL) framework is employed due to its suitability for small samples and mixed orders of integration. The Pesaran et al. (2001) bounds testing approach confirms the existence of a long-run relationship, and the model is re-specified as an error-correction model (ECM) to capture short-run dynamics and long-run adjustment. The results show that green loans and green mortgages increase emissions in the long run, while short-run effects are weak and largely insignificant. The error-correction term is negative and statistically significant, indicating adjustment toward the long-run equilibrium. These findings suggest that existing green credit instruments in Nigeria have not yet translated into measurable emissions reduction, reflecting weaknesses in credit targeting and monitoring. The study highlights the need for clearer classification standards, stronger regulatory oversight, and improved monitoring frameworks to ensure that climate finance effectively supports emissions reduction objectives.