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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 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 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.

The paper aims to examine the environmental challenges of artificial intelligence (AI) in EU law that regard both illicit uses of the technology, i.e. overuse or misuse of AI and its possible underuses. The aim of the paper is to show how such regulatory efforts of legislators should be understood as a critical component of the Green Deal of the EU institutions, that is, to save our planet from impoverishment, plunder and destruction.

To illustrate the different ways in which AI can represent a game-changer for our environmental challenges, attention is drawn to a multidisciplinary approach, which includes the analysis of the initiatives on the European Green Deal; the proposals for a new legal framework on data governance and AI; principles of environmental and constitutional law; the interaction of such principles and provisions of environmental and constitutional law with AI regulations; other sources of EU law and of its Member States.

Most recent initiatives on AI, including the AI Act (AIA) of the European Commission, have insisted on a human-centric approach, whereas it seems obvious that the challenges of environmental law, including those triggered by AI, should be addressed in accordance with an ontocentric, rather than anthropocentric stance. The paper provides four recommendations for the legal consequences of this short-sighted view, including the lack of environmental concerns in the AIA.

The environmental challenges of AI suggest complementing current regulatory efforts of EU lawmakers with a new generation of eco-impact assessments; duties of care and disclosure of non-financial information; clearer parameters for the implementation of the integration principle in EU constitutional law; special policies for the risk of underusing AI for environmental purposes. Further research should examine these policies in connection with the principle of sustainability and the EU plan for a circular economy, as another crucial ingredient of the Green Deal.

The paper provides a set of concrete measures to properly tackle both illicit uses of AI and the risk of its possible underuse for environmental purposes. Such measures do not only concern the “top down” efforts of legislators but also litigation and the role of courts. Current trends of climate change litigation and the transplant of class actions into several civil law jurisdictions shed new light on the ways in which we should address the environmental challenges of AI, even before a court.

A more robust protection of people’s right to a high level of environmental protection and the improvement of the quality of the environment follows as a result of the analysis on the legal threats and opportunities brought forth by AI.

The paper explores a set of issues, often overlooked by scholars and institutions, that is nonetheless crucial for any Green Deal, such as the distinction between the human-centric approach of current proposals in the field of technological regulation and the traditional ontocentric stance of environmental law. The analysis considers for the first time the legal issues that follow this distinction in the field of AI regulation and how we should address them.

The purpose of this study is to shed light on the twin transition in China in the organization of innovation processes in artificial intelligence (AI) and green technology (GT) development and to understand the role of foreign multinationals in Chinese innovation systems.

A qualitative research approach is used by interviewing executives from German multinationals with expertise in AI and GT development and organization of innovation processes in China. In total, 11 semi-structured interviews were conducted with companies, and the data were analysed with a thematic qualitative text analysis.

The findings show that AI applications for GT are primarily developed in cross-company projects that are led by local and regional authorities through the organization of industrial districts and clusters. German multinationals are either being integrated, remaining autonomous or being excluded from these twin transition innovation processes.

This paper aims to fill the gap in the literature by providing one of the first qualitative approach towards twin transition innovation processes in China and exploring the integration of multinational enterprises in cluster organizations. To the best of the author’s knowledge, this is one of the first twin transition studies from this perspective in emerging economies.

This study aims to investigate the significance of an emerging concept – green talent management (TM) and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.

Two hundred and thirty-five structured questionnaires were administered to the academic staff in five universities located in Northern Cyprus, and the data was analyzed using partial least square structural equation modeling with the aid of WarpPLS (7.0).

This study provides evidences that green hard and soft TM exerts significant influence on employees’ innovative work behavior. Similarly, transformational leadership and artificial intelligence were confirmed to have a significant impact on employees’ innovative work behavior. Moreover, the study found transformational leadership and artificial intelligence to significantly moderate the relationship between green hard TM and employees’ innovative work behavior.

The study provides theoretical and managerial implications of findings that will assist the leaders in higher educational institutions in harnessing the potential of green TM in driving their employees’ innovative work behavior toward the achievement of sustainable competitive advantage in the market where they operate.

The attention of researchers in the recent time has been on the way to address the challenge facing organizational leaders on how to develop and retain employee that will contribute to the sustainability of their organization toward the achievement of sustainable competitive advantage in the market they operate. Meanwhile, the studies exploring these concerns are limited. In view of this, this study investigates the significance of an emerging concept – green talent management and its influence on employees’ innovative work behavior, together with the moderating roles of transformational leadership and artificial intelligence within the context of higher educational institutions.

This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI).

Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model.

This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP.

This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

The research study aims to investigate green supply chain management (GSCM) elements as part of a complete system. It aims to understand the special properties of the GSCM system under the moderating effects of product complexity and purchasing structure.

A thorough literature review led to the building of the conceptual framework. Six constructs were identified using systems theory. These constructs include green supply chain technological dimensions (particularly, Artificial Intelligence (AI) based), green supply chain strategy, green supply chain process, product complexity, purchasing structure, and firm performance. The instrument was scientifically developed for gathering survey responses using complete design test methods. The conceptual model was eventually tested based on survey data collected from 250 automotive components and allied manufacturers in the emerging economy of South Africa.

The results indicate that GSCM technological dimensions (AI-based) positively influence GSCM strategy. Further, GSCM strategy was found to positively influence the GSCM process. The GSCM processes have significant effects on environmental performance, social performance, and financial performance. The product complexity has a significant moderation effect on the paths GSCM strategy and GSCM process.

The findings from multivariate data analysis provide a better understanding of GSCM system dynamics and are helpful to key decision-makers. This unique model has elevated GSCM theory to a new level. There are limited studies available in the existing GSCM literature using systems theory. This study will offer an advanced/comprehensive understanding to readers in this relatively new concept.

The purpose of this paper is to raise awareness on the need for a more comprehensive approach on the interdependence between artificial intelligence (AI) and environmental sustainability. It provides an overview of existing sustainable AI policy initiatives at the national and regional level. More precisely, it discusses whether existing European Union (EU) environmental policies are suitable for the AI era or whether new regulations are needed in this field. Finally, this paper assesses cross-fertilisation opportunities between the EU and non-EU countries.

This study is based on a qualitative analysis of sustainable applications of AI and the sustainability of AI. Emphasis is laid on the latter, and a “sustainable by design” approach is proposed, which in essence is a prerequisite for transparent, responsible and human-centred AI systems. The analysis primarily focuses on environmental sustainability.

The majority of studies focus on how to use AI to protect the environment with very little attention paid to sustainable design of AI. On the other hand, the EU’s comprehensive approach towards sustainable AI is closest to promoting “sustainable by design” AI. Several ways have been identified in which the EU’s actions can be translated beyond its borders.

One of the largest limitations of this study is its moderate scope. This paper is confined to the EU and as such provides a limited assessment of global policies and measures on the interplay between sustainability and AI. Consequently, the paper did not provide an in-depth analysis of environmental policies worldwide that could help provide a better picture of possible cooperation areas or common grounds. Another limitation of this study is that it primarily focuses on environmental aspects and as such accords little attention to the economic and social pillars of sustainability.

With less than 10 years to go before reaching the sustainable development goal deadline, this study can help stakeholders better understand what is being done worldwide in terms of sustainable AI. Moreover, given that the technology is still in its early phase, this study can inspire a “sustainable by design” approach to the development of AI technologies.

All national AI strategies published by 1 June 2021 were analysed to identify whether and to what extent they prioritise the interplay between environment and AI. Furthermore, the authors also looked at the EU policy and how it aims to address AI from a sustainable perspective.