This study aims to develop and validate a strategic model tailored to the unique challenges and contexts faced by micro, small, and medium-sized enterprises (MSMEs) in Ecuador, enhancing their operational efficiency and access to financing. Employing a quantitative approach, the research utilized a non-experimental, cross-sectional design to gather data from a sample of 358 companies. The study revealed that MSMEs are significantly hindered by limited access to financing, lack of managerial skills, and technological gaps. Despite these challenges, MSMEs demonstrated considerable adaptability and resilience, underscoring their critical role in the local economy. The strategic model proposed leverages Porter’s Diamond Model to identify and address the specific competitive and operational challenges encountered by these enterprises. Key findings include the necessity for enhanced financial literacy, simplified regulatory frameworks, and the integration of digital technologies to improve competitiveness. The proposed model focuses on strategic training, fostering innovation, and creating a more supportive financing environment. The implications of this study are profound, suggesting that policymakers and practitioners should streamline regulatory processes, enhance financial and technological support frameworks, and provide tailored training programs. These strategies are intended to bolster the sustainability and growth of MSMEs, contributing to broader economic development. This research contributes to the academic literature by providing empirical evidence on the challenges faced by MSMEs in developing economies and proposing a contextually adapted strategic model to mitigate these challenges, thereby enhancing their economic impact and sustainability.

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.

This paper discusses the concept of creating a new reality using the approaches of smart cities to develop eco-cities, in which the necessary balance between nature and progress can be maintained. The authors propose that the concept of smart cities should be used as a tool for the creation of eco-cities, and argue that the positive synergies between the two will be strongest if the smart concept acts as a tool for the creation of eco. The core elements of a smart eco-city are identified as smart sustainable use of resources, a smart sustainable healthy community, and a smart sustainable economy. The results of the article were the foundation for the development concept for Vision Bratislava 2050—the vision and strategy for the development of the capital of the Slovak Republic. The authors also discuss the challenges of transforming cities into smart eco-formats, including the need for digital resilience in the face of potential cataclysms. They suggest that this is a promising area for further research into the concept of smart eco-cities.

This paper aims to contribute with a literature review on the use of AI for cleaner production throughout industries in the consideration of AI’s advantage within the environment, economy, and society. The survey report based on the analysis of research papers from the recent literature from leading database sources such as Scopus, the Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar identifies the strategic strengths of AI in optimizing the resources, minimizing the carbon footprint and eradicating wastage with the help of machined learning, neural networks and predictive analytics. AI integration presents vast aspects of environmental gains, including such enhancements as a marked reduction concerning the energy and materials consumed along with enhanced ways of handling the resulting waste. On the economic aspect, AI enhances the processes that lead to better efficiency and lower costs in the market on the other hand, on the social aspect, the application of any AI influences how people are utilized as workers/clients in the community. The following are some of the limitations towards AI adoption as proposed by the review of related literature; The best things that come with AI are yet accompanied by some disadvantages; there are implementation costs, data privacy, as well as system integration that may be a major disadvantage. The review envisages that with the continuation of the AI development in the following years, the optic is going to be the accentuation on the enhancement of the process of feeding the data in real-time mode, IoT connections, and the implementation of the proper ethical approaches toward the AI launching for all segments of the society. The conclusions provide precise suggestions to the people working in the industry to adopt the AI advancements appropriately and at the same time, encourage the lawmakers to create favorable legal environments to enable the ethical uses of AI. This review therefore calls for more targeted partnerships between the academia, industry, and government to harness the full potential of AI for sustainable industrial practices worldwide.

This academic paper explores the impact of multi-entity cooperation on the effectiveness of public service provision in China. It examines the social governance pattern proposed by the 19th National Congress of the CCP and the emphasis on co-building, co-governing, and sharing. The paper highlights the need for collaboration among various entities and the transition from sole government provision to improve urban public services. It aims to investigate the moderating effects of institutions, policies, and public participation. The study will involve quantitative and qualitative phases in three cities in Guangdong Province and target governmental departments, commercial organizations, non-profit social organizations, and local residents. The research aims to provide policy recommendations, innovate institutional policies, enhance public engagement, and improve multi-party cooperation and urban public services. It seeks to contribute practical models and measures for effective government public management and service implementation.

Kampar Regency, as the largest pineapple producer in Riau Province, has yet to provide significant added value for the surrounding SMEs. The limitations in technology and innovation, infrastructure support, and market access have prevented this potential from being optimally utilized. A Technopark can provide the necessary facilities and infrastructure to enhance production efficiency, innovation, and product quality, thus driving local economic growth. The objective of this study is to identify and determine potential locations for the development of a pineapple-based Technopark in Kampar Regency. This study is crucial as a fundamental consideration in selecting the technopark location and assessing the effectiveness and success of the technopark area. The method used in this study is AHP-GIS to analyze relevant parameters in the site selection process for the technopark area. Parameters considered in this study include slope, land use, availability of raw materials, accessibility of roads, access to water resources, proximity to universities, market access, population density, and landfill. The analysis results indicate that the percentage of land highly suitable for the technopark location is 0.78%, covering an area of 8943 hectares. Based on the analysis, it is recommended that potential locations for the development of a pineapple SMEs-based technopark in Kampar Regency are dispersed in Tambang District, encompassing three villages: Rimbo Panjang, Kualu Nenas and Tarai Bangun. The findings of this study align with the spatial planning of Kampar Regency.