Studies on the effect of biospheric value, eco-design, and green management intransigence on perceived green performance in the tourism and hospitality industry are gradually emerging. However, more evidence is needed from the aviation industry or airport context, especially in Africa. This cross-sectional study aims to probe and demonstrate the effect of biospheric value on green management intransigence and perceived green performance, the mediating role of management intransigence and biospheric value, and the influence on pro-environmental behavior among airport management and employees. The extended theory of planned behavior (TPBe) and triple bottom line theory (TBL)/sustainable economic development theory (SED) (TBL/SED) set the foundation for this research study. With the case study approach, data were collected through online questionnaires from employees and management staff of two international airports in Lagos and Abuja, Nigeria. This scientific study contributes to the literature on green energy by shedding light on the importance of integrating green practices into airport operations with environmentally friendly programs. Its focus on green management intransigence and its implications on employees’ behavior has received little or no attention. The data were analyzed using PLS-SEM and Importance–performance matrix analysis (IPMA). The IPMA is innovative as it helps to extend the results of PLS-SEM by also taking the importance and performance of each construct into account graphically as it relates to green airport management. IPMA posits that management tends to take actions to improve conditions that enhance factors of most significant concern to stakeholders. Our results reveal the effect of biospheric value and the behaviors of management and nonmanagement staff of the selected airports on the green performance with apparent differences in the group-specific performance. In practice, this implies an urgent need for airport management to review their approach and strategy to sustainable practices, airports’ resilience, and adaptation to climate change for sustainable tourism development. This study advances scientific and practical knowledge of eco-design of airport buildings (EAB), biospheric-value (BV), and green management intransigence (GMI). The findings can assist decision makers and practitioners in embracing green technologies and practices in airport management and operations.

Artificial intelligence is increasingly viewed as both a catalyst for clean-energy development and a potential constraint on green innovation. This study examines the quantile-specific relationships between artificial intelligence, clean-energy market performance, and green innovation using daily market data from 15 June 2018 to 29 June 2026. The analysis employs Quantile Kernel Regularized Least Squares (QKRLS), which captures nonlinear and heterogeneous associations across different market conditions. The findings reveal a positive and statistically significant association between artificial intelligence and clean-energy-market performance across all examined quantiles, with the strongest estimated marginal associations occurring in the lower quantiles. Conversely, artificial intelligence is negatively and significantly associated with green innovation throughout the distribution, with the strongest negative associations observed at the 0.10 and 0.90 quantiles. The study therefore highlights the need for policies that align artificial-intelligence growth with the sustained development of green innovation.