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The purpose of this paper is to present the legal conditions under which governments may use green artificial intelligence (AI) in city planning. Although Japan was one of the early countries to release its general AI principles, it has been relatively slow in establishing conditions where administrative agencies may use AI. Granted, there have been some recent scholarship that discusses the usage of AI in general under Japanese administrative law, but the use of green AI in city planning under Japanese law has not yet been discussed. Hence, this paper intends to focus on green AI in city planning and discuss the conditions for usage based on different categories of AI.

This paper conducts a legal analysis on the utilization of AI for the purpose of sustainable city planning and administration in Japan. The approach of this paper is to summarize the existing scholarship in Japanese administrative law and analyse the new elements in the new field of green AI in city planning. This paper is not a natural science paper. The social science method of jurisprudence is used. This paper cites only public sources, and no informal literature has been referenced.

This paper establishes the conditions where Japanese central and local government may use green AI in city planning from a legal viewpoint based on three categories. The categories are green AI usage in city planning concerning things, green AI usage in city planning concerning people and green AI usage in city planning concerning automated decision-making.

This research is limited to an analysis of Japanese law, which means that issues other than law are not included in this paper. Further, although general legal issues are discussed, this paper is intended to discuss Japanese law issues only, and foreign laws are not discussed. Therefore, this paper mostly cites Japanese language papers published in domestic journals.

The intended practical implication of this paper is to allow central and local governments to determine – based on the proposed categories – whether green AI can be used for city planning purposes and under which conditions. The authors hope that this will assist the Japanese government in establishing rules on the usage of AI by governmental agencies and allow for the greater actual usage by Japanese central and local governments of green AI in future city planning.

As the theme of this paper deals with governmental use (and the function of a government is to serve society), the social implications at issue can be said to be equivalent to the practical implication.

There have been articles discussing Japanese administrative law restrictions on AI in general. However, as of now, to the best of the authors’ knowledge, there have been no articles published focusing on green AI used for city planning. The authors note that the green AI used for city planning would have different legal implications from AI’s usage by the government in general, such as the chatbot used by the agencies or lethal autonomous weapons by the military force. Therefore, this paper is original in focusing on green AI used for city planning.

The growth and promotion of green tourism destinations can have many potential benefits from artificial intelligence (AI). The literature on AI and applications of AI in promoting green destinations is very less. The major areas of research in this direction are related with nature-based tourism or sustainable tourism. There is a great potential to research in this area as AI can play an important role in promoting green destinations. Simultaneously, AI can play the role of enabler to achieve environmental targets by promoting various green destinations. The major finding of this chapter is that the research in this area is majorly revolving around tourist destinations and sustainable development. Another area of research where AI is used is eco-tourism and sustainable tourism. With the help of various decision support systems, sustainable tourism can be promoted. Social media platforms and digitalization of tourism is a great enabler of using AI in the field of tourism.

The purpose of this paper is to identify the key roles of transparency in making artificial intelligence (AI) greener (i.e. causing lesser carbon dioxide emissions) during the design, development and manufacturing stages or processes of AI technologies (e.g. apps, systems, agents, tools, artifacts) and use the “explicability requirement” as an essential value within the framework of transparency in supporting arguments for realizing greener AI.

The approach of this paper is argumentative, which is supported by ideas from existing literature and documents.

This paper puts forward a relevant recommendation for achieving better and sustainable outcomes after the reexamination of the identified roles played by transparency within the AI technology context. The proposed recommendation is based on scientific opinion, which is justified by the roles and importance of the two approaches (compliance and integrity) in ethics management and other areas of ethical studies.

The originality of this paper falls within the boundary of filling the gap that exists in sustainable AI technology and the roles of transparency.

This book is essential for anyone in artificial intelligence (AI) and destination management in the tourism industry or government. The book includes both theoretical and practical writings for stakeholders. In all chapters, we provide titles including AI, regenerative and green destinations, sustainable tourism, tourist motivations and expectations, good examples of smart destinations and regions, the regeneration of the tourism industry via AI, rethinking tourism activities and products, current issues in robots, self-service technology, effect of pandemic on smart destinations, sustainable gastronomy and regenerative tourism and tourism issues are discussed in the management plans of the centralisation. This book provides cases and empirical studies that deal in depth with the current situation, challenges, solutions and future strategies after technological development of tourism and increasing interest on smart destinations from a responsible perspective, for readers with an equitable interest or involvement with the organizations in inquiry.

Due to the rapid increase in global warming and environmental disasters, destination management and tourists' environmental awareness have increased. This increase in environmental awareness has led destinations to prioritize green practices that reduce environmental pollution. Moreover, in recent years, with the rapid development of technology, artificial intelligence technology has also been used in applications that reduce environmental pollution in destinations. This is because environmentally friendly products and services offered by destinations have started to have an impact on tourists' travel choices. Additionally, tourists' awareness and loyalty towards environmentally friendly destinations have started to increase, resulting in the formation of brand value for destinations. Therefore, green practices and AI technologies play a role in the formation of consumer-based destination green brand value.

This study was commissioned by DA-AI Technology Co. Ltd. and used the outcome expectancy theory from the social cognitive framework and concept of planned behavior to structure an outside-inside user expectancy model. The purpose of this study is to identify the elements that influence internal customers to select green products.

The model reflected the outside expectancy of users regarding three aspects: perceived benefits, barriers and perceived corresponding value of green products as the stimulus of a user-perceptive process. The trained onsite interviewers collected 438 completed questionnaires focused on the volunteers of Tzu Chi as the main subjects of this study.

The volunteers emphasized the meaningfulness and superiority of products much more than they emphasized the enterprise image and brand image when they were trying to adopt green products. The volunteers did not express an unwillingness to adopt green products, even if they had to face the complexity of the products and pay an extra learning cost.

The volunteers would decrease the consumption of green products when the price was high and would increase their consumption when their ecological values encouraged them to do so. This consumptive value implies that green product adoption was perceived to enhance the social image, self-assessed value and bodily happiness of the users because their inside expectancies were fulfilled.

This paper reviews recent research on the expected economic effects of developing artificial intelligence (AI) through a survey of the latest publications, in particular papers and reports issued by academics, consulting companies and think tanks.

Our paper represents a point of view on AI and its impact on the global economy. It represents a descriptive analysis of the AI phenomenon.

AI represents a driver of productivity and economic growth. It can increase efficiency and significantly improve the decision-making process by analyzing large amounts of data, yet at the same time it creates equally serious risks of job market polarization, rising inequality, structural unemployment and the emergence of new undesirable industrial structures.

This paper presents itself as a building block for further research by introducing the two main factors in the production function (Cobb-Douglas): labor and capital. Indeed, Zeira (1998) and Aghion, Jones and Jones (2017) suggested that AI can stimulate growth by replacing labor, which is a limited resource, with capital, an unlimited resource, both for the production of goods, services and ideas.

Our study contributes to the previous literature and presents a descriptive analysis of the impact of AI on technological development, economic growth and employment.

This systematic literature review (SLR) aims to critically analyze the current academic research on the adoption of artificial intelligence (AI) in supply chain management (SCM) and develop a theoretical framework and future research agenda.

Through a comprehensive review of 68 relevant papers, this study synthesizes the findings to identify key themes based on extended technology-organization-environment (TOE) theory.

This study analyzes AI integration in SCM based on the TOE framework, identifying drivers (technological, organizational, environmental and human), barriers (technical, organizational, economic and human) and outcomes (operational, environmental, social and economic) of AI adoption. It emphasizes AI's potential in improving SCM practices like resilience, process improvement and sustainable operations, contributing to better decision-making, efficiency and sustainable practices. The study also provided a novel framework that offers insights for strategic AI integration in SCM, aiding policymakers and managers in understanding and leveraging AI's multifaceted impact.

The originality of the study lies in the development of a theoretical framework that not only elucidates the drivers and barriers of AI in SCM but also maps the operational, financial, environmental and social outcomes of AI-enabled practices. This framework serves as a novel tool for policymakers and managers, offering specific, actionable insights for the strategic integration of AI in supply chains (SCs). Furthermore, the study's value is underscored by its potential to guide policy formulation and managerial decision-making, with a focus on optimizing SC efficiency, sustainability and resilience through AI adoption.

This study aims to investigate connections between the development of robotic and artificial intelligence (AI) and green crypto investments. The author also explores the influences of global uncertainty shocks like the COVID-19 pandemic and international conflicts on the role of each channel.

In this research, the author uses a cutting-edge model-free connectedness approach to investigate the relationships between the development of Global X Robotics and AI (BOTZ) and the volatility of green crypto investments from November 9, 2017 to March 24, 2023.

In the sample duration, the findings reveal a two-way link between AI and green/nongreen cryptocurrencies. Throughout the examined period, BOTZ has been a net receiver of shocks as determined by the net total connectedness. Among the main spillover shock carriers in the system, green cryptocurrencies are the most significant. The net pairwise directional connectivity reveals that green cryptocurrencies controlled BOTZ throughout the analyzed time, particularly during the COVID-19 era as well as the Ukraine–Russia crisis. According to the findings, the proposed system is vulnerable to a high level of indication influence.

The results have important policy implications for investors and governments, as well as methods from the spillovers across the various indicators and their interconnections. Sharp information on the primary contagions among these indicators aids politicians in designing the most appropriate policies.

To the best of the authors’ knowledge, this paper is the first to look at the link between AI, technological advancement and green cryptocurrency investing. Second, this study developed a methodology for examining instability links between various factors that is more appropriate for investigating these linkages. This study investigates the links between AI, technical advancement and green digital currencies using a cutting-edge model-free connectivity method. This work is also the first to examine the interconnection between volatility derived from AI, technological development and green cryptocurrency investments in light of unknown events, such as the COVID-19 pandemic and the Ukrainian–Russian conflict. Finally, this study includes a daily database from the BOTZ fund, which attempts to invest in firms that stand to gain from rising robotics and AI use. Cardano (ADA), IOTA, NANO (XNO), Stellar Lumens and Tron are examples of green cryptocurrencies, whereas Bitcoin is an example of a nongreen cryptocurrency. These virtual currencies are being used to investigate the relationship between investor mood and green and nongreen digital currencies. The data set spans the period from November 9, 2017 to March 24, 2023.

The theoretical background bases on the big data analytics-artificial intelligence (BDA-AI) technologies and supply chain ambidexterity (SCAX) in the firms to assess their sustainability endeavors such as green supply chain management (GSCM) to improve their green communication and corporate image.

Around 220 participants in the manufacturing firms are participants' industry expertise, diverse roles, and representation as key stakeholders.

The results show BDA-AI and SCAX affected on GSCM and found the significant relationships with green communication and corporate image. Green communication was discovered to impact corporate image significantly.

Prior studies are neglected to address the relationship among the AI, powered by rapid computational and BDA breakthroughs, redefines cognitive tasks, achieving feats previously deemed impossible-making implicit judgments, simulating emotions, and driving operations. This study selects manufacturing firms as respondents due to their forefront of BDA-AI and supply chain ambidexterity adoption to benefit the operational efficiency and competitiveness. The firms intricate supply chains, diverse stakeholders, and strategic emphasis on corporate image make it an ideal context to examine the nuanced impact of these technologies.