This paper provides a unique empirical analysis of the effects of political factors on the adoption of PPP contracts in Brazil. As such, it innovates along two different lines: first, political factors behind the adoption of PPPs have been largely ignored in the vast body of empirical literature, and second, there is scant work done on the motives of any kind behind the adoption of PPPs in Brazil. Various economic and financial reasons have been evoked to justify the use of PPPs in general. These include the goal of promoting socio-economic development in a tight public budgetary framework or of improving the quality of public services through the use of economically efficient and cost-effective mechanisms. Any possible underlying political motives, however, have been overlooked in the PPP research. And yet, there is abundant literature suggesting a link between the adoption of PPPs and the ideology of the governing body or the political cycles associated with elections. This study examines the impact of ideological commitment and opportunistic political behavior on the process of PPP contracting in Brazil, including the stages of public consultation, the publication of tender, and the signature of the contract, using federative-level data for the period between 2005 and 2022. Consistent with the outstanding literature, the two hypotheses are tested: first, conservative parties tend to celebrate more PPP contracts than left-leaning parties, and second, the electoral calendar has a significant effect in the process, allowing for opportunistic behaviors. Empirical results suggest that there is little evidence for the relevance of ideological leanings in the process of adopting PPPs in Brazil. Additionally, regardless of ideology, parties significantly choose to enter PPPs at specific points in the electoral cycle, suggesting decisions are influenced by political considerations and electoral strategy rather than by purely financial or ideological considerations. This may pose severe constraints on the efficiency and cost-effectiveness of the contracts, negatively impacting public governance and leading to protracted costs for taxpayers.

This study conducts a comprehensive analysis of the aquaculture industry across 11 coastal regions in eastern China from 2017 to 2021 to assess their adaptability and resilience in the face of climate change. Cluster analysis was employed to examine regional variations in aquaculture adaptation by analyzing data on annual average temperatures, annual extreme high/low temperatures, annual average relative humidity, annual sunshine duration, and total yearly precipitation alongside various aquaculture practices. The findings reveal that southern regions, such as Fujian and Guangdong, demonstrate higher adaptability and resilience due to their stable subtropical climates and advanced aquaculture technologies. In contrast, northern regions like Liaoning and Shandong, characterized by more significant climatic fluctuations, exhibit varying degrees of cluster changes, indicating a continuous need to adjust aquaculture strategies to cope with climatic challenges. Additionally, the study explores the specific impacts of climate change on species selection, disease management, and water resource utilization in aquaculture, emphasizing the importance of developing region-specific strategies. Based on these insights, several strategic recommendations are proposed, including promoting species diversification, enhancing disease monitoring and control, improving water quality management techniques, and urging governmental support for policies and technical guidance to enhance the climate resilience and sustainability of the aquaculture sector. These strategies and recommendations aim to assist the aquaculture industry in addressing future climate challenges and fostering long-term sustainable development.

The incorporation of artificial intelligence (AI) into language education has created new opportunities for improving the instruction and acquisition of Chinese characters. Nevertheless, the cognitive difficulties linked to the acquisition of Chinese characters, such as their intricate visual features and lack of clear meaning, necessitate thoughtful deliberation when developing AI-supported learning interventions. The objective of this project is to explore the capacity of a collaborative method between humans and machines in teaching Chinese characters, utilising the advantages of both human expertise and AI technology. We specifically investigate the utilisation of ChatGPT, a substantial language model, for the creation of instructional materials and evaluation methods aimed at teaching Chinese characters to individuals who are not native speakers. The study utilises a mixed-methods approach, which involves both qualitative examination of lesson plans created by ChatGPT and quantitative evaluation of student learning outcomes. The results indicate that the suggested framework for human-machine collaboration can successfully tackle the cognitive difficulties associated with learning Chinese characters, resulting in enhanced learner involvement and performance. Nevertheless, the research also emphasises the constraints of AI-generated material and the significance of human involvement in guaranteeing the accuracy and dependability of educational interventions. This research adds to the expanding collection of literature on AI-assisted language learning and offers practical insights for educators and instructional designers who aim to use AI tools into Chinese language curriculum. The results emphasise the necessity of employing a multi-disciplinary strategy in AI-supported language learning, incorporating knowledge from cognitive psychology, educational technology, and second language acquisition.

The widespread adoption of digital technologies in tourism has transformed the data privacy landscape, necessitating stronger safeguards. This study examines the evolving research environment of digital privacy in tourism management, focusing on publication trends, collaborative networks, and social contract theory. A mixed-methods approach was employed, combining bibliometric analysis, social contract theory, and qualitative content analysis. Data from 2004 to 2023 were analyzed using network visualization tools to identify key researchers and trends. The study highlights a significant increase in academic attention after 2015, reflecting the industry's growing recognition of digital privacy as crucial. Social contract theory provided a framework emphasizing transparency, consent, and accountability. The study also examined high-impact articles and the role of publishers like Elsevier and Wiley. The findings offer practical insights for policymakers, industry leaders, and researchers, advocating for ongoing collaboration to address privacy challenges in tourism.

Conspiracy theories during Covid-19 pandemic spread worldwide, including in Indonesia. What political and religious factors explain their spread in Indonesia with particular reference to the DKI Jakarta province, its surrounding municipalities, and West Sumatera province? This study aimed to answer the questions. It employed a qualitative approach with multi-data collection methods, including those from media, documents, and interviews. The spread of Conspiracy theories benefited from the democratic system that promotes the freedom of information in using social media. First, the government officials initially spread conspiracy theories to satisfy people’s anxiety about the obscured Pandemic. However, they resulted in the government’s ambiguous, controversial, and reckless policies leading to people’s distrust of the government. Jokowi-Makruf Amien, political opponents capitalized on the government’s poor policies to spread conspiracy theories which partly discredited the Jokowi-Amien administration. Both government officials and the opposition capitalized on politics and religious teaching or supra-natural pretexts to posit their conspiracy theories.