This research seeks to identify the value of a few common factors determining the speed of economic growth in Baltic states and analyzes their impact in detail on Latvia’s lagging. Latvia’s economic starting point after regaining independence because of the collapse of the Soviet Union was at least comparable to its neighbors. Still, after the implementation of liberal reforms towards a free market’ economy and 20 years of operation as an EU full member, Latvia is lagging in growth, prosperity, and innovation. Within the analysis, this scientific paper pays special attention to the three less discussed factors, namely, the impact of post-Soviet mind-set effects as a part of local innovation culture, lasting since regaining independence in 1991; the importance of the availability of talent pull, its density, diversity, and accessibility; and readiness and capability to capture external knowledge and technology adoption. The overall approach is the systemic assessment of the national innovation system and/or innovation ecosystem, trying to understand the differences between these two models. Research is performed by analysis of the performance of the local innovation ecosystem in connection with export- and Foreign Direct Investment (FDI) policies. The authors present a novel method for visually representing economic growth and its application in analyzing process development within transitional economic nations. The study uses an analytical and synthetical literature review. It offers a new GDP data visualization method useful for monitoring economic development and forecasting potential economic crises—the outcomes from aggregative literature analysis in a consolidated concept are provided for required talent policy proposals. The post-Soviet mindset is seen as a heritage and devious underdog that has left incredibly diverse consequences on today’s society, power structures, economic growth potential, and the emergence of healthy, well-managed, and sustainable innovation ecosystems. The post-Soviet mindset is a seemingly hidden and, at the same time, an intriguing factor that has a significant impact on the desire to make and implement the right decisions related to innovation, education, and other policies promoting business development. The key outcome of the article is that sociocultural aspects and differences in innovation culture led to a slow-down of Latvia’s economic growth compared to Estonia’s and Lithuania’s slightly more successful economic reforms.

The rapid advancement of artificial intelligence (AI) technology is profoundly transforming the information ecosystem, reshaping the ways in which information is produced, distributed, and consumed. This study explores the impact of AI on the information environment, examining the challenges and opportunities for sustainable development in the age of AI. The research is motivated by the need to address the growing concerns about the reliability and sustainability of the information ecosystem in the face of AI-driven changes. Through a comprehensive analysis of the current AI landscape, including a review of existing literature and case studies, the study diagnoses the social implications of AI-driven changes in information ecosystems. The findings reveal a complex interplay between technological innovation and social responsibility, highlighting the need for collaborative governance strategies to navigate the tensions between the benefits and risks of AI. The study contributes to the growing discourse on AI governance by proposing a multi-stakeholder framework that emphasizes the importance of inclusive participation, transparency, and accountability in shaping the future of information. The research offers actionable insights for policymakers, industry leaders, and civil society organizations seeking to foster a trustworthy and inclusive information environment in the era of AI, while harnessing the potential of AI-driven innovations for sustainable development.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

Root turnover is a key process of terrestrial ecosystem carbon cycle, which is of great significance to the study of soil carbon pool changes and global climate change. However, because there are many measurement and calculation methods of root turnover, the results obtained by different methods are quite different, and the current research on root turnover of forest ecosystem on the global regional scale is not sufficient, so the change law of root turnover of global forest ecosystem is still unclear. By collecting literature data and unifying the calculation method of turnover rate, this study integrates the spatial pattern of fine root turnover of five forest types in the world, and obtains the factors affecting fine root turnover of forest ecosystem in combination with soil physical and chemical properties and climate data. The results showed that there were significant differences in fine root turnover rate among different forest types, and it gradually decreased with the increase of latitude; the turnover rate of fine roots in forest ecosystem is positively correlated with annual average temperature and annual average precipitation; fine root turnover rate of forest ecosystem is positively correlated with soil organic carbon content, but negatively correlated with soil pH value. This study provides a scientific basis for revealing the law and mechanism of fine root turnover in forest ecosystem.

The affectations caused by extreme events of natural origin such as droughts and floods in traditional homes in the province of Gran Chaco, in Bolivia, are frequent. These aspects compromise the habitat of the populations that occupy them, as is the case of the original Weenhayek people, as an alternative for the improvement of the human habitat of this town. Through theoretical and empirical methods, five variables used for the development of the adaptation model were determined, from the bases of planned adaptation as a component of urban-territorial resilience, in search of an improvement of socio-environmental systems in the face of the effects of climate change, exemplified in the Weenhayek native people. The model establishes the improvements of traditional dwellings, from a current trend of deterioration to one of preservation, conservation and growth in the Weenhayek culture, through various features, such as: Respects the cultural design of the house that integrates local patterns of the environment, ecosystem and contemporary construction elements without affecting its image, the materials and construction techniques used are of a traditional nature, but with contemporary elements that improve their application, durability, stability, as an articulated construction system, commits governments in all instances to the technical-constructive study of the rural areas of the human settlements of the Weenhayek people, and establishes a starting point towards new studies focused on native peoples.

State-owned enterprises (SOEs) manage significant portion of world economy, including in the developing countries. SOEs are expected to be active and play significant role in improving the country’s economic performance and welfare through enhancing innovation performance. However, closed innovation process and lack of collaboration hinders SOEs to reach satisfying innovation performance level. This paper explores the construction and role of innovation ecosystem in the strategic entrepreneurship process of SOEs, of which is represented by dynamic capability framework, business model innovation, and collaborative advantage. Based on the analysis, this paper concluded that the collaboration between actors in the Innovation Ecosystem (IE) has positive effect to strengthening SOE’s Sensing Capabilities (SC) related to the process of exploring and identifying innovation opportunities. The increase of Sensing Capabilities (SC) will play significant role as input or antecedent on formulating proactive Innovation Strategy (IS) in orchestrating SOE’s innovation process. SOEs which has implementing proactive Innovation Strategy (IS) will be able to build collaboration and finding right Business Model Innovation (BMI). Finally, by building collaboration with other actors through the innovative business model has significant role to increase SOE’s Collaborative Advantage (CA), which considered as a proxy for competitiveness of SOEs.