The development of creative industries is considered one of the areas of structural transformation of resource‑dependent and single‑industry cities, whose economic development is limited by the existing sectoral specialization and spatial remoteness. The aim of the study is to identify the spatial patterns of new creative industrialization and to examine the economic factors influencing the development of creative industries in resource‑oriented and single‑industry cities, as well as in certain regional centers of the Russian Far East. Methods of spatial panel econometrics, global and local Moran indices, Lagrange multiplier tests, and SAR, SEM, SDM, and GSPRE models were applied. The initial data were compiled based on the financial statements of enterprises from the SPARK information and analytical system for 2013– 2022. The sample includes 26 settlements (17 single‑industry towns and 9 administrative centers), or 260 annual observations. The SDM_fe1 model (AIC = 913.0; BIC = 934.4) revealed positive direct effects of ICT dynamics (0.020) and the concentration of core assets in creative industries (1.131). The concentration of wages is characterized by a statistically insignificant direct effect (0.215) and a significant negative spatial effect (−2.933). The results indicate that the business climate in single‑industry towns, which allows enterprises to potentially understate wages by leveraging their status as small and micro‑enterprises, becomes a negative factor in spatial development, manifesting itself in a decline in the growth rates of creative industries in nearby single‑industry towns.
Migrant remittances are an important source of external financing for developing economies, but their impact on economic growth remains ambiguous. This study aims to examine the long-run effects of remittances, foreign direct investment (FDI), trade openness, and tax revenues on real GDP and to identify causal relationships among these variables in five Turkic Republics (Azerbaijan, Kazakhstan, Kyrgyzstan, Türkiye, and Uzbekistan) and Tajikistan over the period 2005–2023. The analysis employed tests of cross‑country dependence and coefficient homogeneity, panel unit root tests, the Durbin–Hausman cointegration test, FMOLS and DOLS estimates, as well as the Dumitrescu–Hurlin panel causality test. The results showed a positive long‑term relationship between remittances and real GDP: the coefficient was 10.7917 for FMOLS (p = 0.0137) and 3.8556 for DOLS (p = 0.023). Trade openness also has a positive and statistically significant long-term relationship with real GDP: the corresponding coefficients were 1.1260 and 1.2183 (p = 0.000 in both cases). At the same time, the coefficients for foreign direct investment turned out to be negative: −1.7361 for FMOLS (p = 0.051) and −2.2875 for DOLS (p = 0.088). The results obtained confirm the significant role of remittances in the economic dynamics of the countries under study and indicate the feasibility of improving the mechanisms for attracting them into the formal financial sector and for their productive use. The findings underscore the need to develop formal financial channels, improve the efficiency of using money transfers, and direct external financial flows toward productive investments and sustainable economic growth.
Predicting stock market dynamics remains challenging, especially in emerging and frontier markets, where high uncertainty, limited liquidity, and information asymmetries can reduce the robustness of predictive models. This study aims to compare the forecasting performance of Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models for the TUNINDEX20 against a naïve persistence benchmark. The information base consisted of daily data on the TUNINDEX20 index of the Tunis Stock Exchange from February 6, 2012, to December 31, 2024, including 1,915 observations. The sample was divided chronologically into a training set of 1,532 observations and a test set of 383 observations. The results showed that the artificial neural network slightly outperformed the long short‑term memory model in terms of key accuracy metrics: for the first model, MAE was 12.06, RMSE was 15.92, MAPE was 0.31%, MASE was 1.0769, R² was 0.9912, and the accuracy of predicting the direction of index movement was 53.91%. At the same time, the naive model showed slightly lower forecast errors (MAE = 11.76; RMSE = 15.82), and the Diebold–Mariano test did not reveal a statistically significant advantage of ANN over the naive model (p = 0.7285). The results obtained show that a more complex architecture does not automatically provide an advantage in forecasting TUNINDEX20. Future research could include adding technical indicators and macro‑financial variables, testing models in other emerging markets, and applying adaptive and hybrid forecasting methods.
Women play an important role in domestic enterprises in the fishing sector, but the mechanisms through which institutional support contributes to sustainable livelihoods remain poorly understood. This study aims to develop and validate a sequential capability-building model explaining how institutional support contributes to livelihood resilience among women operating household-based smoked fish enterprises in Indonesia. The study uses a consistent research design of mixed methods combining historical analysis and structural equation modeling using the partial least squares (PLS-SEM) method. The study analyzed materials from 2014-2024 from 40 documentary sources and 20 interviews, and surveyed 368 women employed in domestic enterprises in Tegalsari, Central Java. The results showed that institutional support positively affects women's economic agency (β = 0.697, p < 0.001) and entrepreneurial orientation (β = 0.164, p = 0.005). Women's economic agency enhances entrepreneurial orientation (β = 0.727, p < 0.001) and the ability to market innovation (β = 0.444, p < 0.001), but it has no statistically significant direct impact on the sustainability of livelihoods (β = 0.138, p = 0.149). Entrepreneurial orientation has the strongest direct impact on livelihood sustainability (β = 0.588, p < 0.001), while market innovations also have a significant impact (β = 0.203, p = 0.005). The results obtained confirm a consistent process of opportunity formation, in which institutional support is transformed into sustainable livelihoods primarily through women's economic agency, entrepreneurial orientation, and the ability to innovate in the market.
The rapid introduction of artificial intelligence into smart city management systems is changing approaches to urban planning, public service provision, and management decision-making, but scientific research in this area remains thematically and disciplinarily fragmented. The primary aim of this study is to systematically map the scientific literature on the role of artificial intelligence in the digital transformation of smart city governance. The paper uses methods of bibliometric and network analysis, including analyses of publication and citation dynamics, co-authorship, bibliographic coupling among organizations, and the co-occurrence of keywords. The selection of publications was conducted in accordance with the PRISMA 2020 guidelines, and the construction and visualization of bibliometric networks were performed using the VOSviewer program. The initial data was obtained from the Scopus database on October 5, 2025. An initial search revealed 34,548 documents. After applying the criteria of the subject area, publication period, document type, and language, the final sample included 1,865 English-language journal articles published in 2015-2025. The largest number of publications is in China — 236 articles (12.7%), Great Britain — 230 (12.3%), and the USA — 216 (11.6%). Kazakhstan ranks 54th with nine publications. The most productive journal is Sustainability, with 254 articles, followed by Sustainable Cities and Society with 134 and Cities with 122. The results show a steady increase in scientific interest in the topic after 2019 and, at the same time, indicate a lack of development in interregional scientific cooperation and research on the ethical, institutional, and societal aspects of the use of artificial intelligence in urban governance.
