Abdulla All Noman

Abdulla All noman, Umma Habiba Akter, Tahmid Hasan Pranto et al.

With unorganized, unplanned and improper use of limited raw materials, an abundant amount of waste is being produced, which is harmful to our environment and ecosystem. While traditional linear production lines fail to address far-reaching issues like waste production and a shorter product life cycle, a prospective concept, namely circular economy (CE), has shown promising prospects to be adopted at industrial and governmental levels. CE aims to complete the product life cycle loop by bringing out the highest values from raw materials in the design phase and later on by reusing, recycling, and remanufacturing. Innovative technologies like artificial intelligence (AI) and machine learning(ML) provide vital assistance in effectively adopting and implementing CE in real-world practices. This study explores the adoption and integration of applied AI techniques in CE. First, we conducted bibliometric analysis on a collection of 104 SCOPUS indexed documents exploring the critical research criteria in AI and CE. Forty papers were picked to conduct a systematic literature review from these documents. The selected documents were further divided into six categories: sustainable development, reverse logistics, waste management, supply chain management, recycle & reuse, and manufacturing development. Comprehensive research insights and trends have been extracted and delineated. Finally, the research gap needing further attention has been identified and the future research directions have also been discussed.

Abdullah Al Abrar Chowdhury, Azizul Hakim Rafi, Adita Sultana et al.

The escalating challenge of climate change necessitates an urgent exploration of factors influencing carbon emissions. This study contributes to the discourse by examining the interplay of technological, economic, and demographic factors on environmental sustainability. This study investigates the impact of artificial intelligence (AI) innovation, economic growth, foreign direct investment (FDI), energy consumption, and urbanization on CO2 emissions in the United States from 1990 to 2022. Employing the ARDL framework integrated with the STIRPAT model, the findings reveal a dual narrative: while AI innovation mitigates environmental stress, economic growth, energy use, FDI, and urbanization exacerbate environmental degradation. Unit root tests (ADF, PP, and DF-GLS) confirm mixed integration levels among variables, and the ARDL bounds test establishes long-term co-integration. The analysis highlights that AI innovation positively correlates with CO2 reduction when environmental safeguards are in place, whereas GDP growth, energy consumption, FDI, and urbanization intensify CO2 emissions. Robustness checks using FMOLS, DOLS, and CCR validate the ARDL findings. Additionally, Pairwise Granger causality tests reveal significant one-way causal links between CO2 emissions and economic growth, AI innovation, energy use, FDI, and urbanization. These relationships emphasize the critical role of AI-driven technological advancements, sustainable investments, and green energy in fostering ecological sustainability. The study suggests policy measures such as encouraging green FDI, advancing AI technologies, adopting sustainable energy practices, and implementing eco-friendly urban development to promote sustainable growth in the USA.

Adita Sultana, Abdullah Al Abrar Chowdhury, Azizul Hakim Rafi et al.

The current paper investigates the influences of AI innovation, GDP growth, renewable energy utilization, the digital economy, and industrialization on CO2 emissions in the USA from 1990 to 2022, incorporating the ARDL methodology. The outcomes observe that AI innovation, renewable energy usage, and the digital economy reduce CO2 emissions, while GDP expansion and industrialization intensify ecosystem damage. Unit root tests (ADF, PP, and DF-GLS) reveal heterogeneous integration levels amongst components, ensuring robustness in the ARDL analysis. Complementary methods (FMOLS, DOLS, and CCR) validate the results, enhancing their reliability. Pairwise Granger causality assessments identify strong unidirectional connections within CO2 emissions and AI innovation, as well as the digital economy, underscoring their significant roles in ecological sustainability. This research highlights the requirement for strategic actions to nurture equitable growth, including advancements in AI technology, green energy adoption, and environmentally conscious industrial development, to improve environmental quality in the United States.