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People who identify as transgender face stigma, isolation and harassment while often struggling to come to terms with their gender identity. They also disproportionately experience mental health difficulties. The purpose of this paper is to present the voices of transgender people in the Republic of Ireland (RoI) in regard to the issues they are facing, improvements they would like to see made to schools, workplaces, services and society in general and whether mental health supports fulfil their needs.

Ten open questions were embedded within a quantitative online survey (LGBTIreland study) on factors impacting social inclusion, mental health and care. These open questions were re-analysed with exclusive focus on the transgender participants (n = 279) using content/thematic analysis.

The participants in this study reported significant signs of mental distress. The following themes emerged: impact of stigma, deficiencies in mental health services, need for education on transgender identity, importance of peer support, achieving self-acceptance and societal inclusion questioned.

Efforts to recruit young participants have led to a possible over-representation in this study.

The findings suggest the need for improvement in mental health support services, including further education in how to meet the needs of transgender individuals.

Transgender people in Ireland experience social exclusion. The need for more inclusivity was emphasised most in secondary schools. Education on transgender identities in all contexts of society is recommended by the participants.

This study reports on the largest group of transgender participants to date in RoI. Their voices will affect perceptions on social inclusion and mental health care.

Anti-racism has been practiced in various ways, with varying degrees of effectiveness. This chapter engages with the body of scholarship that focuses on approaches aimed at promoting anti-racist actions, policies and social change. It discusses some of the main anti-racism strategies that have been deployed across different countries and examines anti-racism practices in interpersonal, intergroup and community settings. These approaches encompass civil rights campaigns, legislative and policy interventions, affirmative action, diversity and inclusion training, prejudice reduction, intergroup contact, organisational development and holistic anti-racism approaches. Some anti-racism practices and policies, such as awareness campaigns, social marketing and diversity training, also extend to digital platforms, with social media and multimedia networks deployed to broaden the reach and impact of anti-racist endeavours. This chapter specifically engages with local anti-racism movements and draws principles for broader implementation of anti-racism policy and practice. It concludes with a brief discussion of the effectiveness of contemporary anti-racism approaches.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

This study aims to explore the diverse sources of electricity generation (coal, natural gas, oil and hydroelectricity) and their respective associations with economic growth and environmental quality.

This study uses static panel data analysis with a random effects model for six selected ASEAN countries (Indonesia, Malaysia, Filipina, Thailand, Vietnam and Myanmar) from 1994 to 2014.

This study reveals that economic growth in six selected ASEAN countries is enhanced by electricity generation from all sources, while the contribution of electricity production from hydroelectricity remains the largest and strongest. There is no environmental impact of electricity production from hydroelectric, whereas fossil fuel-based electricity production emits carbon dioxide, with coal sources being the largest contributor, followed by natural gas and oil.

Based on the results, these six ASEAN countries should invest more in hydropower projects, reduce the coal mix in power generation and promote clean coal technology to improve economic efficiency and environmental sustainability.

To the best of the authors’ knowledge, no research has examined the relationship between electricity production, environmental quality and economic growth in Southeast Asian nations. Therefore, the outcome of this study is expected to provide insightful results to supplement the framing and implementation of national and collective regional strategies for sustainable electricity generation in ASEAN countries.

The purpose of this chapter is to highlight how universities got into the predicament in which they currently find themselves in, or somewhat planned to be in, in the 2020s. The historical account outlines the purpose of higher education and who it was for throughout the last few centuries, before a more in-depth analysis of the last few decades will highlight how, and why, neoliberal and subsequently managerial aspects of leadership and performance metrics crept into universities before an analysis of the last 5–10 years, including the onset and consequences of COVID, will demonstrate that many ‘COVID recovery plans’ around staff cuts and course reforms were already in place before COVID, but it was COVID that allowed these plans in most cases to be escalated.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

This study aims to define a “technological statecraft” concept to distinguish tech-based measures/sanctions from an array of economic measures ranging from restrictions of rare earth elements and natural gas supplies to asset freezes under the wider portfolio of economic statecraft. This concept is practically intended to reveal the USA’s “logic of choice” in its employment of technology as an efficient instrument to deal with China in the context of the great power rivalry.

This study follows David A. Baldwin’s statecraft definition and conceptualization methodology, which relies on “means” rather than “ends.” In addition to Baldwin and as an incremental contribution to his economic statecraft analysis, this study also combines national political economy with statecraft analysis with a particular focus on the utilization of technological measures against China during the Trump administration.

The US rationale for choosing technology, namely, emerging and foundational technologies, in its rivalry against China is caused at least by two factors: the nature of the external challenge and the characteristics of the US innovation model based largely on radical innovations. To deal with China, the USA practically distinguished the role of advanced technology and followed a grammer of technological statecraft as depicted in the promulgated legal texts during the Trump administration.

Despite a growing volume of literature on economic statecraft and technological competition, studies focusing on countries’ “logic of choice” with regard to why and under what conditions they choose financial, technological or commodity-based sanctions/measures/controls are lacking. Inspired from Baldwin’s account on the “logic of choice” from among alternative statecrafts (i.e. diplomacy, military, economic statecraft, and propaganda). This study will contribute to the literature with a clear lens to demonstrate the “logic of choice” from among a variety of economic statecraft measures in the case of the US technological statecraft toward China.