The diminution of natural resource exploration, the retrieval of waste, and the structural modification of polymers by additives are the main contributions to sustainable development. The properties of bitumen are enhanced by the crumb rubber through effective bitumen modification techniques, which have environmental and economic advantages. In this study, plastic waste, plastic-coated aggregate (PCA), and bitumen were blended in order to enhance the engineering properties of the flexible pavement. In order to compute the composition of crumb rubber modified bitumen (CRMB), the adopted materials were subjected to the relevant experiments. PCA was a very effective material when compared to the standard bitumen road pavement. The recycling of waste crumb rubber and plastic was tested by adding them into the hot mix asphalt. The Marshall properties of standard (virgin) bituminous mix, CRMB grade 55, and plastic mix asphalt were studied in detail to explore the solutions for a sustainable environment. The comparison was performed between these two materials with the standard bitumen, which resulted in the CRMB and plastics being found as the most effective additions with robust properties such as low-cost material, high strength, long life usage, and un-harmful to nature. The optimal bitumen content was found to be 6.166%, 6.1%, and 5.833% for standard bitumen, crumb rubber modified bitumen, and plastic-coated aggregate, respectively.

The study examines the impact of artificial intelligence (AI) adoption on poverty reduction and inclusive economic growth in BRICS economy in the 2008–2023 period through the lenses of the endogenous growth theory and the capability approach developed by Sen. The research adopts second-generation econometric methods including the Pesaran cross-sectional dependence test, CIPS panel unit root test, Westerlund cointegration and the Cross-Sectionally Augmented Autoregressions Distributed Lag (CS-ARDL) model. The empirical findings indicate a statistically significant and strong connection between AI adoption and poverty reduction, with 1% of AI adoption resulting in 0.14 and 0.39% poverty reduction in the short run and long run, respectively, indicating that the poverty-reducing benefits of AI increase over time. Poverty reduction is further reinforced by human development, access to clean cooking fuels, economic growth, and effective governance, but income inequality has the opposite effect. The error correction value is negative and highly significant, indicating the stability of the long-run equilibrium relationship. Robustness checks confirm the consistency of the findings on an alternative measure of poverty. In the course of the research, AI can support poverty alleviation as a general-purpose technology, provided robust governance systems and supplementary funding for human development are in place. The results provide significant policy implications for using AI to achieve SDG 1 in developing economies.