This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

The Mass Rapid Transit (MRT) Purple Line project is part of the Thai government’s energy- and transportation-related greenhouse gas reduction plan. The number of passengers estimated during the feasibility study period was used to calculate the greenhouse gas reduction effect of project implementation. Most of the estimated numbers exceed the actual number of passengers, resulting in errors in estimating greenhouse gas emissions. This study employed a direct demand ridership model (DDRM) to accurately predict MRT Purple Line ridership. The variables affecting the number of passengers were the population in the vicinity of stations, offices, and shopping malls, the number of bus lines that serve the area, and the length of the road. The DDRM accurately predicted the number of passengers within 10% of the observed change and, therefore, the project can help reduce greenhouse gas emissions by 1289 tCO₂ in 2023 and 2059 tCO₂ in 2030.

This paper uses existing studies to explore how Artificial Intelligence (AI) advancements enhance recruitment, retention, and the effective management of a diverse workforce in South Africa. The extensive literature review revealed key themes used to contextualize the study. This study uses a meta-narrative approach to literature to review, critique and express what the literature says about the role of AI in talent recruitment, retention and diversity mapping within South Africa. An unobtrusive research technique, documentary analysis, is used to analyze literature. The findings reveal that South Africa’s Human Resource Management (HRM) landscape, marked by a combination of approaches, provides an opportunity to cultivate alternative methods attuned to contextual conditions in the global South. Consequently, adopting AI in recruiting, retaining, and managing a diverse workforce demands a critical examination of the colonial/apartheid past, integrating contemporary realities to explore the potential infusion of contextually relevant AI innovations in managing South Africa’s workforce.

Maps of forest stand condition—the current phase of the forest-forming process—will be useful for foresters in their forest management in addition to the forest planning and cartographic materials. The mapping methodology was applied in the test area of the Bolshemurtinsky forest district of the Krasnoyarsk region, which is typical for the southern taiga forests of East Siberia. Source data for mapping was obtained on the basis of descriptions of the forest subcompartments on the GIS attribute table of the forest district. Forest stand confinement to the terrain relief indicators was identified on the basis of the SRTM 55-01 digital terrain model data. Spatial analysis has been performed using the ArcGIS Spatial Analyst module. Mapping capability has been shown not only for the year of forest inventory but also for the earlier period of time. To determine the predominant species and the age of the 100-year-old forest stand, a scheme was proposed in which the conceivable options are typified depending on the succession trend, the forest stand age prior to disturbance, and the period of reforestation. Map fragments of the test area as of 2006—the year of forest inventory—and as of 1906—the year of the intensive colonization beginning in southern Siberia—are demonstrated. Maps of forest condition in the test area represent successions that are typical in the southern taiga forests of Siberia: post-harvest, pyrogenic, and biogenic. The methodology of forest condition mapping is universal.

In the context of globalization and urbanization, rural development faces many challenges, such as population loss and uneven distribution of resources. This paper analyzes the similarities and differences in sustainable rural development strategies between China and Europe through a comparative perspective. China has optimized land use by relying on land policy innovations, such as the household contract responsibility system and the “separation of three rights”, as well as the construction of small towns; while Europe focuses on private ownership and market mechanisms, and supports agricultural and rural development through the Common Agricultural Policy (CAP). Using literature review, comparative research and policy analysis, the study shows that the policy innovations in China and Europe, each with its own focus, have been effective in promoting agricultural output and rural social development. Particularly noteworthy is that the “three rights” policy has increased agricultural productivity through the liberalization of management rights, while the European CAP has contributed to the diversification of the rural economy and environmental protection through continuous reforms. This study emphasizes that through policy innovation and international cooperation, combining the strengths of China and Europe, it is possible to provide a new model of sustainable development for the global countryside. Specifically, through the establishment of Sino-European R&D centers for agricultural science and technology, exchange of talents, and cooperation in green infrastructure development, technology transfer and application can be accelerated, cultural exchange and understanding can be promoted, and the sustainable development agenda for global rural areas can be jointly advanced.

China established pilot carbon markets in 2013. In 2020, it set targets for carbon peaking in 2030 and carbon neutrality by 2050. China’s national carbon market officially commenced operations in 2021. Based on the national market and seven pilot markets, this study established the factors influencing carbon trading prices by examining market participants, macroeconomics, energy prices, carbon prices in other markets, etc. Asymmetrical development among the seven pilot cities, for which the study employed a mixed-effects model, was the primary factor impacting carbon prices. The carbon prices in the pilot cities cannot be extrapolated to the entire country. In the national carbon market, where the study employed a multiple regression lag model, the SSE index was positively correlated with carbon prices, whereas the Dow Jones index had no significant effect on carbon prices in terms of macroeconomics. Coal and natural gas prices were negatively correlated with carbon prices, whereas oil prices were positively correlated with energy prices. The EU market prices have a positive correlation with prices in other markets. The significance of this study is that it covers the largest national Emissions Trading System (ETS) in the world and allows for comparing the characteristics of the Chinese market with those of other ETS markets. Additional studies, including more sectors, should be conducted as China’s ETS coverage increases.