Weather and climate services are essential tools that help farmers make informed choices, such as choosing appropriate crop varieties. These services depend considerably on the availability of adequate investments in infrastructure related to weather forecasting, which are often provided by the State in most countries. Zimbabwean farmers generally have limited access to modern weather and climate services. While extensive attempts have been made to investigate farmers’ socioeconomic factors that influence access to and use of weather and climate services, comparative political economy analysis of weather and climate service production and use is limited. To address this knowledge gap, this study examines the production, dissemination, and usage of modern seasonal weather services through a political economy analysis perspective. The findings of this study highlight considerable discrepancies in access and use of seasonal weather forecasts between male and female farmers, those who practise African Traditional Religions versus Christians, and the minority group (Ndau tribe) and the majority group (Manyika tribe). This result suggested the presence of social marginalization. For example, minority Ndau members living in remote areas with limited radio signals and a weak mobile network have limited access to modern seasonal weather forecasts, forcing them to rely much more on indigenous weather forecasts. Further, due to unequal power relations, a greater proportion of male farmers participated in agricultural policy formation processes than their female counterparts. To promote inclusive development and implementation, deliberate efforts need to be made by State authorities to incorporate adherents of African traditional religions, members of minority tribes and female farmers in agricultural policymaking processes, including seasonal weather forecast delivery policies. Further, the study suggests the relaxation or elimination of international sanctions on Zimbabwe by the European Union, United Kingdom and the United States of America, given that they are considerably affecting marginalized groups of farmers in their climate change adaptation practices, including the use of modern weather and climate services. The vast majority of these marginalized farmers never benefitted from the land reform programme and were also not responsible for the design and implementation of this programme which triggered these sanctions.

The purpose of the study is to create proposals and recommendations to improve the system evaluating the quality of governance and efficient use of budget funds in order to improve public welfare and sustainable development. The research methodology included application of statistical methods to review scientific articles, legislative acts and other documents, study models for evaluating the quality of governance and efficient use of budget funds. Mathematical modeling and forecasting methods were also used to assess aspects of governance and predict the results when changes are made, including building a trend model and determining the forecast values of accrued taxes and mandatory payments for 2024–2026. The conclusions highlight there is a positive correlation between the accrued taxes and mandatory payments to the budget of the Republic of Kazakhstan, and an economic growth and changes in tax legislation. The key factors influencing the quality of governance and efficient use of budget funds were identified. Recommendations were developed to improve the quality assessment system and governance of budget funds in order to increase efficiency and responsibility in financial management. The results of the study can be used by public administration bodies and financial institutions to optimize the governance of budget funds.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The challenge of developing cadastral infrastructure in Africa is inextricably linked to the global issues of sustainable development. Indeed, in light of the constraints inherent to conventional cadastral systems, alternative systems developed through land regulation programmes (LRPs) are compelled to align with the tenets of sustainable development. A discursive study, conducted through a semisystematic literature review, enabled the selection of 53 documents on cadastral systems deployed in multiple countries across the African continent. A number of systems were identified and grouped into four categories: urban, rural, participatory and hybrid cadastral systems. These systems are developed on the basis of standards and sociotechnical approaches, including the LADM, STDM, and FFP, as well as innovative technologies such as blockchain. However, their sustainability is limited by the fact that they are not multipurpose cadastral systems. Consequently, there is an urgent need for studies to develop a global framework that will produce truly significant and sustainable results for all sections of society.

Interest in the impact of environmental innovations on firms’ financial performance has surged over the past two decades, but studies show inconsistent results. This paper addresses these divergences by analyzing 74 studies from 1996 to 2022, encompassing 4,390,754 firm-year observations. We developed a probability-based meta-analysis approach to synthesize existing knowledge and found a generally positive impact of environmental innovations on financial performance, with a probability range of 0.85 to 0.97. Manufacturing firms benefit more from environmental innovations than firms in other industries, and survey-based studies report a more favorable relationship than those using secondary data. This study contributes to existing knowledge by providing a comprehensive aggregation of data, supporting the resource-based view (RBV) and the Porter hypothesis. The findings suggest significant policy implications, highlighting the need for tailored incentives and information-sharing mechanisms, and underscore the importance of diverse data sources in research to ensure robust results.

In Industry 4.0, the business model innovation plays a crucial role in enabling organizations to stay competitive and capitalize on the opportunities presented by digital transformation. Industry 4.0 is driven by digitalization and characterized by integrating various emerging technologies. These technologies can potentially change traditional business models and create new value propositions for customers. This paper aims to analyze and review the research papers through a bibliometric approach scientifically. The data were extracted from reputable Clarivate Web of Science (WoS) Core Collection sources from 2010 to 2023 (June). However, the publication started in 2018 for the research fields. The results show that scientific publications on research domains have increased significantly from 2020. VOSviewer, R Language, and Microsoft Excel were utilized for analysis. Bibliometric and Scientometric approaches conducted to determine and explore the publication patterns with significant keywords, topical trends, and content clustering better discussions of the publication period. The visualization of the data set related to research trends of Industry 4.0 in relation to Business Model Innovation resulted in several co-occurrence clusters namely: 1) Business Model Innovation; 2) Industry 4.0; 3) Digital transformation; and 4) Technology implementation and analysis. The study results would identify worldwide research trends related to the research domains and recommendations for future research areas.