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A mixed-method study was performed to determine the impact of integrating systems thinking (ST) into an electronic learning module for junior high school teachers in the Philippines. The study aims to assess how an ST approach to pedagogy compared against a conventional approach in terms of contribution to the participants’ global climate change content knowledge, holistic thinking and depth and accuracy of knowledge and reasoning.

The study implemented e-learning modules using an ST approach versus a conventional approach in teaching climate science to junior high school teachers. The paper presents quantitative data obtained from pre and posttests results of the 20 teacher-participants and qualitative data obtained during the focus-group discussion (FGD) after the implementation of the study.

The results from the statistical analysis indicated that the ST group obtained a significant increase in their assessment scores compared to the non-ST group, according to predetermined criteria. Content knowledge, depth and accuracy of knowledge and reasoning increased the most. The participants mentioned during the FGD that the module helped deepen their understanding of climate change.

The study was limited to 20 teacher-participants, but it has relevance for public school teachers in the country given that participants had raised concerns regarding the lack of training in their schools for teaching climate science. They admitted that they lacked critical information to include in climate change topics in their classes.

This paper shows how the ST approach can be used to teach climate change to junior high students. The e-learning module can provide teachers with better understanding, knowledge and reasoning to teach climate change to high school students more effectively.

This study aims to explore how the three types of human resource (HR) practices, encapsulated in the ability, motivation and opportunity (AMO) model, foster a learning organizational culture (LOC). In doing so, the authors evaluate the centrality of knowledge sharing (KS) in mediating this relationship.

A quantitative survey is undertaken to collect data from managers working in organizations operating in the UK. The authors use several statistical techniques to assess the psychometric properties of the measures and test the hypotheses using multiple regression executed with Preacher and Hayes’ Process macro.

The findings show that the AMO HR practices significantly facilitate the development of a LOC in the workplace, and KS among organizational members amplifies the effects of these HR practices in the process.

A LOC functions as an important source of organizational performance and effectiveness. It enhances the absorptive capacity of the organization to capture, share and transfer knowledge to optimize work. Hence, developing a culture that nurtures organizational learning could be a priority for managing HR. This study, therefore, extends the understanding of the role of AMO HR practices in fostering a learning culture – thus, providing managers with the essential knowledge to improve performance. The study also enriches the literature on HR practices, KS and LOC by integrating these three variables into a unifying framework.

Although training is essential to continuous improvement, scant literature examines post-training facilitators for continuous improvement. The study aims to explore the relationship between training and continuous improvement, the mediating role of self-efficacy and the moderate role of training transfer climate.

This study utilizes the questionnaire survey of 455 Vietnamese employees to test the link between continuous improvement training and continuous improvement, the moderate role of the training transfer climate and the mediating role of self–efficacy.

Research results reveal that training positively influences continuous improvement. Furthermore, self-efficacy fully intervenes in the link between training and continuous improvement. Finally, the training transfer climate positively moderates this link.

Although the link between training and continuous improvement is suspicious, there is scant research on post-training facilitators of continuous improvement applications. To the best of the author's knowledge, this study is one of the first to explore the moderation role of transfer climate and the mediation role of self-efficacy in the relationship between training and continuous improvement.

Climate change is the biggest global health threat of the 21st century. Its impacts scatter through every stratum of society placing the marginalized clusters at the highest vulnerability and calling for action at all levels. Adaptation enhances resilience, whereas mitigation minimizes the anthropogenic forces of climate change; these are crucial initiatives for climate-resilient sustainable development. The aim of this study was to determine the knowledge and attitudes on climate change adaptation and mitigation at individual level and assess related factors.

A descriptive study is done to assess the knowledge and attitudes of school teachers in three educational zones of the Kalutara district using random cluster sampling. A self-administered questionnaire including 25 equally weighted questions to assess knowledge and 10 statements to assess attitudes was used.

The response rate was 98.8% [n = 618] and 23% of the study population had poor knowledge of climate change adaptation and mitigation at the individual level. Favorable attitudes were observed among more than 65% of participants for all the statements on climate change adaptation and mitigation. Average family income, working duration, involvement in school environmental societies and highest education qualification showed a significant positive association, whereas age and current grade had a significant negative association with good knowledge. The level of knowledge had no significant variation among male and female teachers.

Overall knowledge was poor among more than 20% of the teachers highlighting the need for school and community-based awareness programs to be implemented to address the issue.

By drawing on the perceived organizational support (POS) theory and the extended job demands-resources model (JD-R model), the aim of this study is to investigate the influence of different levels of organizational support during change (i.e. organizational level, supervisory level, coworker level) on firm financial performance and to explore the role of employee work engagement.

Data were collected during the second outbreak of the COVID-19 pandemic in two waves, approximately 2 weeks apart. A total of 291 employees in the Greek telecommunication industry completed questionnaires examining the POS during change and consequently, their supervisors evaluated their work engagement. The research model was tested with the use of structural equation modeling.

The research findings note the importance of different levels of organizational support during change; they describe how each level influences employees' work engagement as well as they confirm that employee work engagement mediates the relationship between POS during change and firm financial performance. Theoretical and practical implications of these findings are discussed.

The results indicate that should firms manage to influence positively employees' work engagement by providing support at all levels during change, they may boost their financial performance.

The research findings provide new insights into how POS and work engagement may influence firm financial performance. The originality of this study lies in the finding that employees' work engagement mediates the relationship between POS during change and firm financial performance. Further, the study was carried out in the Greek telecommunication industry during the second outbreak of the COVID-19 pandemic.

The purpose of this research was to discover the impacts of taught environmental sustainability-focused geography postgraduate programmes on student attitudes, behaviours and practices in relation to environmental awareness within two research-intensive universities in the UK.

A case study involved online surveys to measure environmental attitudes, behaviours and practices at the start and end of four taught geography postgraduate programmes.

There was widespread attitudinal change and an increasing prioritisation of environmental issues reported among participants after they had completed their programme. However, behavioural change was limited, and there was little evidence of greater awareness being translated into changed practices. The learning benefits included a greater focus on interdisciplinarity, holistic thinking and critical self-reflection.

The findings demonstrate that postgraduate taught (PGT) programmes in sustainability improve student awareness and concern about environmental issues but do not necessarily lead to widespread behavioural change. This raises questions for programme convenors about how education for sustainability can be truly transformational and avoid leading students to develop eco-anxieties over the scale of change required.

There is a lack of research engaging with students on PGT programmes, especially in understanding their impacts on environmental attitudes, behaviours and practices. The research provides an evidence base for understanding the effects of PGT programmes in challenging student values, attitudes and practices and by implication knowledge transfer post-graduation, with the potential to help protect the environment and identify ways of living better with the ever-changing planet.

Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

Higher education institutions can utilize sustainability reporting tools as a means of increasing organizational learning and strengthening sustainability. Finding ways to deepen and broaden the nature of learning will help better equip these institutions to address complex challenges like climate change.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

In response to the lack of connection between employee development and employee engagement, this study explores the existing empirical findings regarding these two concepts. Based on the conservation of resources theory, the authors propose a novel theoretical framework that can better leverage the identified antecedents and relationships for future research.

An integrative literature review of 64 empirical studies published in peer-reviewed journals was conducted.

Three different levels of antecedents, including the work environment, social exchange and individual characteristics, are identified. Employee development and employee engagement exhibit reciprocal relationships. Considering the role of job performance as a catalyst, the authors propose an upward gain spiral model to advance both research and practice.

This integrative literature review aims to facilitate discussions based on three distinct sub-dimensions: physical, emotional and cognitive energies, relevant to both employee development and employee engagement. Through this distinction, a more comprehensive understanding of the connection between employee development and employee engagement can be cultivated.