Purpose: The purpose of this paper is to evaluate the impact of corruption on the environmental quality in Tunisia. Indeed, the post-revolution period is characterized by a remarkable increase in the rates of corruption.Design/methodology/approach: The direct and indirect effects of control corruption on economic growth and CO₂emissions in Tunisia have been examined using the autoregressive distributed lag (ARDL) cointegration framework among corruption, growth and CO₂emissions.Findings: Results substantiate a positive and significant relationship between control of corruption and economic growth, a negative and significant relationship between control of corruption and environmental quality (CO₂) and a negative and significant relationship between control of corruption and energy consumption. The findings suggest that while the control of corruption contributes to economic growth, its positive effect could be transposed indirectly via its impacts on environmental quality.Originality/value: A strategy against corruption will reduce CO₂emissions; however, its positive effect on economic growth indirectly contributes to reverse this relationship.

Artificial intelligence (AI) technologies can significantly influence the employment and career development of people with disabilities, presenting both opportunities and challenges. On the one hand, AI can enhance accessibility, create new job opportunities, and assist in skill development. On the other hand, it can lead to potential job displacement and exacerbate barriers if not implemented inclusively. The objective of this research is to explore the implications of different artificial intelligence measures (artificial intelligence, machine learning, data science, big data) and unemployment among disabled people with different educational levels (primary, secondary, tertiary). The first difference generalized method of moments with threshold model confirms the nonlinear linkages between AI and unemployment. More specifically, AI reduces the unemployment rate among people with disability only in the upper regime, particularly for those with secondary and tertiary educational levels. For unemployed disabled people with primary education, there is little evidence of the role of AI in lowering unemployment in both regimes. The findings of the present empirical study are important for designing suitable policies and strategies for reducing the unemployment rates and achieving a sustainable future for people with disability.