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This study aims to clarify the following research questions: to what extent do people consider natural disaster risks as important for residential selection? what personal demographics and attitudes toward natural disaster risks are associated with the relative importance of natural disasters for residential selection? and to what extent do the associated personal attributes influence the relative importance of natural disasters for residential selection?

An internet-based survey was performed to collect 2,000 responses from residents of Osaka Prefecture, Japan, to gauge people’s relative importance of safety against natural disasters regarding residential preference. The obtained results were analysed using two types of statistical analysis, specifically chi-square test and multivariable logistic regression analyses.

It was found that 37.3% of the respondents in Osaka Prefecture, Japan, considered the “safety against natural disasters” relatively important when selecting a residential location. The statistical analysis also demonstrated that those having a relatively higher level of disaster awareness and preparedness were 1.41 times more likely to prefer to live in a place that is safer from natural disasters. Thus, it was suggested that disaster education aimed at raising the level of people’s disaster awareness could be effective to increase the number of people who choose to live in a safer place from natural disasters.

Living in an area that is safer from natural disasters can effectively minimize human and property damage. Recently, several measures have been taken in Japan to guide people to live in a safer place. The clarification of the extent to which people consider natural disaster risks as important for residential selection and the understanding of the categories of the people who are likely to do so is important to develop more effective natural disaster measures; however, there has been less attention on such investigation. Therefore, this study conducted an internet-based survey and examined it.

This study aims to develop an extended social attachment model for expatriates, integrating a multiple stakeholder perspective, to understand evacuation decisions during disasters.

Through interviews with 12 Tokyo-based expatriates who experienced the 2011 Tohoku earthquake, tsunami and nuclear disasters, this study collects the lived experiences of a diverse set of expatriates. This data is analyzed abductively to map relevant evacuation factors and to propose a reaction typology.

While the 2011 Tohoku disasters caused regional destruction and fears of nuclear fallout, Tokyo remained largely unscathed. Still, many expatriates based in Tokyo chose to leave the country. Evacuation decisions were shaped by an interplay of threat assessment, location of attachment figures and cross-cultural adjustment. The study also discusses the influence of expatriate types.

Disaster planning is often overlooked or designed primarily with host country nationals in mind. Expatriates often lack the disaster experience and readiness of host country nationals in disaster-prone regions in Asia and beyond, and thus might need special attention when disaster strikes. This study provides advice for how to do so.

By unpacking the under-researched and complex phenomenon of expatriate reactions to disasters, this study contributes to the fields of international human resource and disaster management. Specifically, seven proposition on casual links leading to expatriate evacuation are suggested, paving the way for future research.

This research explores the intricate dynamics of national interests realised through Japan's official development assistance (ODA) to China. It aims to deepen the understanding of these mechanisms, detailing the extent to which Japan has accomplished its national interests.

The paper applies the role theory and narrative analysis to elucidate Japan's national role conception and its categories of national interests with regards to its ODA policy. It utilises both qualitative and quantitative methods to examine the success rate in achieving Japan's diplomatic objectives and how those interests have manifested over time.

The findings suggest a mixed outcome. Whilst Japan's ODA to China has helped in expanding trade and fostering mutual understanding and cooperation, it has been less successful in promoting democratic governance in China or effectively counterbalancing China's regional power. Hence, the realisation of national interests through ODA is a complex process contingent upon numerous factors.

This study stands out for its multifaceted approach in examining Japan's ODA policy towards China, integrating both quantitative and qualitative methodologies and applying the role theory in the context of international development aid. It fills a significant gap in the literature by analysing the interplay between national interests and foreign aid, providing nuanced insights into the successes and challenges of Japan's pursuit of its diplomatic objectives. The study's findings have important implications for understanding the complexity of international aid dynamics and can inform future policy decisions in the realm of international relations and foreign aid.

In 2017, it was a challenge to assess the future of global trade. It was an open question whether the US financial crisis and the recession that it triggered would mark a turning point for the liberal post–World War II world order. If one looked toward Europe, China, Latin America, and Japan, there was a flurry of activity. New trade agreements were being completed and pursued. In Washington, DC, on the other hand, President Donald Trump seemed set on ripping apart and/or renegotiating any trade deal the United States was ever part of.

This case explores Trump's opinions and emerging policy stance on trade, bilateralism, and the global economy, among others. It also gives an overview of the World Trade Organization (WTO) and the General Agreement on Tariffs and Trade (GATT) and asks whether the Trump presidency would constitute a major challenge to the WTO and what it stood for in 2017.

Effective post-disaster communication is essential for destination marketing organizations to encourage visitors following natural disasters. This research aimed to analyze the impact of two typical types of post-disaster communication messages – solidarity messages and testimonial messages – on post-disaster visiting intention. This research proposes effective communication strategies for post-disaster destination marketing.

For the case of Jiuzhai Valley, China, which experienced market recovery after two years of rebuilding following a 2017 earthquake, this study designed three contextual experiments based on post-disaster communication scenarios on the Internet and social media to test the causal chain between recovery message types and post-disaster visiting intentions. The data were analyzed using analysis of variance, independent samples t-tests, and the bootstrap method.

The results indicate that both solidarity messages and testimonial messages evoked higher visiting intention than the no-message group. However, solidarity messages (vs testimonial messages) were more effective when shared on the destination management organization's official account than when they were shared on an influencer's account, with their effects being mediated by the symbolic – as opposed to the hedonic – value of tourist behavior.

Destination management organizations should actively carry out marketing communication through the Internet and social media for areas that have experienced disasters. Crisis communicators should consider inviting popular social media influencers to visit the destination and share their experiences to enhance market confidence, while also paying more attention to the operation of official social media accounts.

This study sheds light on the use of the Internet and social media as tools for post-disaster marketing. By expanding on post-disaster communication theory, this study fills a research gap regarding the effectiveness of tourism marketing strategies after a crisis.

Natural disasters profoundly influence agricultural trade sustainability. This study investigates the effects of natural disasters on agricultural production imports in China within 2002 and 2018. This exploration estimates the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations.

This investigation uses Probit, Logit, Cloglog and Ordinary Least Squares (OLS) models.

The results confirm the mediating role of transportation infrastructure and agriculture value-added and the moderating role of government effectiveness and diplomatic relations in China. According to the findings, natural disasters in trading partners heighten the risk to the agricultural imports. This risk raises, if disasters damage overall agricultural yield or transportation infrastructure. Moreover, governments’ effective response or diplomatic ties with China mitigate the risk. Finally, the effect of disasters varies by the developmental status of the country involved, with events in developed nations posing a greater risk to China’s imports than those in developing nations.

China should devise an early warning system to protect its agricultural imports by using advanced technologies such as data analytics, remote sensing and artificial intelligence. In addition, it can leverage this system by improving its collaboration with trading partners, involvement in international forums and agreement for mutual support in crisis.

This study aims to investigate the quality of disclosure of a cutting-edge reporting tool – integrated reporting (<IR>) – in terms of its effectiveness to report on COVID-19 pandemic information, its ability to provide forward-looking information and risk impact implications, and its quality determinants in challenging times.

Thanks to a content analysis of 247 <IR> for FY20, an integrated reporting disclosure score was developed to assess the disclosure quality provided by the sampled companies. Three research questions were tested through logistic regressions.

Non-financial disclosure activities struggle to provide adequate information in terms of potential future scenarios, risk assessment and forward-looking analyses. However, companies incorporated in “Anglo-Saxon” territories drafted integrated reports of higher quality. More recently, incorporated companies have made a greater effort to measure and report COVID-19 pandemic impacts on environmental, social and governance and business activities, also increasing their risk assessment and mitigation efforts. Concerning the determinants of disclosure quality, leverage, corporate governance structures, country of incorporation and belonging to “high impact” industries all lead to a higher quality of <IR> disclosure.

Examining in detail corporate social responsibility activities and corporate governance integrity is pivotal to orienting strategy towards sustainable trajectories: to do so, corporate reporting and disclosure practices are essential tools. In this context, corporate governance systems that emphasize board diversity are proven, even in disruptive circumstances, to play a crucial role in providing corporate reports of higher quality. High disclosure quality that goes beyond mere financial results is considered to be necessary to remain competitive strategically, socially and environmentally.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.