The continuous advancement and promotion of green energy products are gradually reducing consumers’ dependence on traditional energy sources. Due to the limitations of big data applications and constraints of AI technology diffusion, the adoption of green energy has yet to achieve widespread implementation or full consumer acceptance. Having employed a tripartite game-theoretic approach, this study selected governments, enterprises, and consumers as key players to analyze the evolutionary game strategies in AI-driven green energy innovation. A three-party evolutionary game model was constructed and adopted, in order to reveal critical factors that affect the decisions of these stakeholders on green energy innovation and consumption. The findings indicated a positive correlation between the intensity of consumer preference for green energy products and enterprises’ decisions to pursue the related AI-enhanced innovation. Although firms still weigh the impact incurred by the costs of research and development on profitability, the likelihood of enterprises engaging in AI-driven green energy innovation increases significantly alongside the elevating consumer preference for green energy products.
Environmental pollution is a headache nowadays. It is observed that waste from industries plays a vital role in environmental pollution. So, in this study, it is tried to reduce the amount of waste materials and try to make it suitable for reuse. In today's market, it is impossible to survive in the market without the help of advertising. Therefore, advertisement for quick information about a product to its customer is used in this study. The demand for 3D printing techniques is increasing day by day for their utilization in the fields of agriculture, education, medicine, automobiles, mobile phones, aircraft, spaceships, etc. In today's era, with the advancement of technology, 3D printing is becoming more and more popular in manufacturing, health care, and many other sectors. Therefore, a production model with waste minimization and utilization of 3D printing is formulated in this study. This study primarily aims to determine optimal cycle length, production rate, and the optimal amount of inventory model technique by synthesizing the carbon emission rate with minimizing the total cost. A numerical example is presented to illustrate the characteristics and progress of the economic production quantity (EPQ) model.
This study explored the governance structure that supported the implementation of the European plastic circular economy by examining the relationships among legal requirements, technical standards, industrial practices, and digital infrastructure. In recent years, the European Union (EU) has strengthened its circular economy policies through measures such as the Packaging and Packaging Waste Regulation (PPWR), the Ecodesign for Sustainable Products Regulation (ESPR), and the Digital Product Passport (DPP). Researchers initially analyzed the impact of environmental regulations on global markets, global value chain (GVC) governance, and digital traceability systems as separate topics. Relatively little attention has been paid to how these elements incorporated as a single governance system. To bridge this research gap, this study introduced the concept of Institutional Translation and proposed an analytical framework encompassing three institutional domains, i.e., law, standardization, and industrial practice, to be supported by a cross-cutting digital infrastructure. Using qualitative mechanism mapping, the study explored how policy objectives were translated into operational practices. The findings underscored the effectiveness of the EU circular economy framework though it depended not simply on regulation, standards, or digital systems in isolation. The way these elements interacted was indeed the critical impetus. Policy objectives were translated into compulsory market conditions through legal mandates, operationalized through standards developed in interaction with the industry, and supported by digital infrastructures that facilitated traceability and compliance verification across value chains. In summary, the European circular economy framework extended beyond conventional environmental regulation by combining legal requirements, standardization processes, industrial practices, and digital infrastructures in a coherent governance arrangement. By introducing the concept of Institutional Translation, this study explained how circular economy policies were put into practice across complex value chains.
The Yellow River Basin plays a crucial part in China’s plan to meet the “Dual Carbon” targets since reduction of carbon emissions has a profound impact on the national path towards low-carbon development. This research adopted the Tapio decoupling model and the GM(1,1) grey forecasting model to examine how carbon emissions interacted among nine provinces and regions within the Basin from 2006 to 2021. It also predicted future emission tendencies and obtained the following key results: (1) From 2006 to 2021, total carbon emissions in the Yellow River Basin grew at an average annual rate of 4.17%, with the upper and middle reaches driving most of the increase while the lower reaches continued to account for the majority of emissions; (2) The decoupling relationship between economic growth and carbon emissions demonstrated different patterns over space and time. Mostly the upper areas had a type of expansive negative decoupling, the middle areas fluctuated extensively, and the lower areas mostly encountered a weak decoupling situation. During the study period, there was a shift from extensive growth to more differentiation; and (3) According to the projections of the GM(1,1) grey forecasting model, carbon emissions in these nine provinces along the Yellow River would keep increasing but at a slower pace. Therefore, the paper put forward targeted suggestions including promoting industrial green transformation in different regions, developing inter-regional cooperation mechanisms for emission reduction, and optimizing the basin-wide energy system so as to promote sustainable, green, and low-carbon development in the Yellow River Basin.
Artificial intelligence is a transformative enabler of sustainable business practices, offering substantial opportunities to enhance the environmental and operational performance of micro, small, and medium enterprises. Despite growing global interest in artificial intelligence-driven sustainability, limited attention has been directed toward its application in resource-constrained and conflict-affected economies. This study aims to examine the role of artificial intelligence in facilitating sustainable practices among green micro, small, and medium enterprises and to evaluate the applicability of successful artificial intelligence adoption experiences from India to Afghanistan. A qualitative research design based on a structured literature review was employed, and relevant secondary data were systematically collected from peer-reviewed publications indexed in Scopus, Science Direct, and Google Scholar. The reviewed evidence demonstrates that artificial intelligence can significantly improve resource efficiency, energy management, waste reduction, supply chain optimization, environmental monitoring, green marketing, and eco-innovation. The Indian experience indicates that successful artificial intelligence adoption has been facilitated by supportive policy frameworks, expanding digital infrastructure, and continuous capacity development for micro, small, and medium enterprises. In contrast, artificial intelligence adoption in Afghanistan is constrained by inadequate digital infrastructure, limited technological readiness, and insufficient financial resources. Nevertheless, the findings suggest that these barriers can be progressively mitigated through context-specific implementation strategies. It is therefore concluded that artificial intelligence can serve as a practical catalyst for accelerating sustainable micro, small, and medium enterprise transformation and promoting green economic development in Afghanistan when integrated with coordinated investments in digital infrastructure, technical capacity development, supportive policy frameworks, and financial support mechanisms. By synthesizing existing knowledge and translating successful practices from India into the Afghan context, this study contributes to the growing literature on artificial intelligence-driven sustainable entrepreneurship and provides evidence-based policy recommendations for fostering environmentally sustainable and economically resilient micro, small, and medium enterprises in developing economies.
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