The aim of this article is to investigate the impediments to creativity perceived by managers, the levels of creativity, its indicators, and personal characteristics conducive to creativity, as well as to elucidate the correlations among them. An experimental study was conducted involving 300 participants. Methods employed include surveying, testing, and mathematical statistical analysis. As the level of creativity increases, participants tend to assess their opportunities more favorably. The expression of creativity depends on the interconnection among the barriers to creativity, indicators of creativity, and personal qualities of creativity. A high level of creativity is manifested when there are fewer barriers and personal qualities such as Imagination and a propensity for Risk-taking. Conversely, the level of expression of creativity is low when there is an interconnection between Creativity and Complexity, Imagination, and creativity barriers such as lack of confidence and conformity to majority opinion.

The emission trading scheme (ETS) is arguably one of the most effective approaches for encouraging industries to transition to a low-carbon economy and, as a result, assisting nations in meeting their goals under the United Nations Framework Convention on Climate Change to mitigate the challenge of climate change. ETS is gaining popularity as more governments throughout the world contemplate implementing it, particularly in developing countries. Much of the existing research has concentrated on debates concerning ETS operations in developed nations. This study is to give a discourse of the success criteria for ETS implementation that have been identified in the literature and then cross-referenced in the context of Malaysia. For this, the research used an integrated approach of scoping review of existing literature and in-depth interviews with Malaysian stakeholders. Using Narassimhan et al. (2018)’s ETS assessment framework, the scoping review identified five major attributes that lead to successful ETS implementation in a global context that are environmental effectiveness, economic efficiency, market management, stakeholder engagement, and revenue management. In-depth interviews with several groups of discovered stakeholder engagement as an essential attribute that would play a critical role in advancing ETS implementation in Malaysia. The study concludes by proposing a complete strategy based on empirical information and first-hand narratives, providing useful insights for politicians, industry players, and environmental activists. The recommendation is especially important as Malaysia strives to improve its commitment to sustainable and responsible development in light of the challenges posed by climate change.

Increasing number of smart cities, the rise of technology and urban population engagement in urban management, and the scarcity of open data for evaluating sustainable urban development determines the necessity of developing new sustainability assessment approaches. This study uses passive crowdsourcing together with the adapted SULPiTER (Sustainable Urban Logistics Planning to Enhance Regional freight transport) methodology to assess the sustainable development of smart cities. The proposed methodology considers economic, environmental, social, transport, communication factors and residents’ satisfaction with the urban environment. The SULPiTER relies on experts in selection of relevant factors and determining their contribution to the value of a sustainability indicator. We propose an alternative approach based on automated data gathering and processing. To implement it, we build an information service around a formal knowledge base that accumulates alternative workflows for estimation of indicators and allows for automatic comparison of alternatives and aggregation of their results. A system architecture was proposed and implemented with the Astana Opinion Mining service as its part that can be adjusted to collect opinions in various impact areas. The findings hold value for early identification of problems, and increasing planning and policies efficiency in sustainable urban development.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.