This study analyzes the role of the green economy and the impact of investments in green projects on sustainable economic development in Albania, focusing on employment creation, economic growth, and environmental sustainability. The study combines theoretical analysis with empirical analysis of statistical data for the period 2018–2024, using linear regression models in IBM SPSS Statistics. The findings indicate that investments in green sectors, such as renewable energy, waste management, and energy efficiency, have a positive impact on economic development and green employment. The comparative analysis with Western Balkan countries highlights that Albania has made progress in the green transition; however, it still faces challenges related to project financing, the development of carbon markets, and institutional strengthening. The study concludes that the green economy represents an important instrument for sustainable development and supports Albania’s European integration process.
This paper analyses the inflationary phenomenon in Romania throughout 2026, a period characterized by significant upward pressure on consumer prices. Drawing on official data from the National Institute of Statistics, the National Bank of Romania and Eurostat, the study documents annual inflation rising from 9.62% in January to 10.71% in April 2026, placing Romania among the most severely affected EU member states. The research identifies the main drivers - structural fiscal imbalances, rising energy prices, leueuro depreciation, and labor market pressures — and evaluates the NBR's monetary policy response. Conclusions emphasize that macroeconomic stabilization depends on fiscal consolidation, accelerated EU funds absorption, and coordinated monetary-fiscal policy.
Foreign Direct Investment (FDI) has acted as a primary catalyst for China’s economic growth, transforming the nation from an isolated agrarian economy into a global manufacturing powerhouse. While historical data demonstrates that foreign direct investments fundamentally drove China's capital deepening, export expansion, and technology transfer, contemporary trends show a critical shift: China is transitioning from relying on high-volume manufacturing investments to targeting high-quality, innovation-driven FDI. The research paper investigates the multidimensional impact of Foreign Direct Investment (FDI) on China’s macro-economic trajectory through a comprehensive mixed-methods framework. The results demonstrate that China's domination on critical raw materials market, innovation and technology exerts a significant effect on economic growth. Due to the regional disparities in FDI distribution, the paper offers a significant contribution to the literature, formulating strategic guidelines for policymakers to leverage foreign direct investment for a more balanced economic growth in China.
Armed forces are among the most resource intensive institutions in modern economies, but their material flows are still structurally organised around a linear extract-produce-dispose model, which generates significant strategic vulnerabilities. In this paper we investigate the potential of circular economy (CE) principles, including reuse, remanufacturing, reverse logistics and closed-loop material recovery, as a resource security tool for military organisations. The research uses an explanatory mixed-methods design. It combines a descriptive quantitative analysis of critical raw material price volatility, based on USGS Mineral Commodity Summaries for 2016–2025, with an institutional analysis of NATO and European Defence Agency policy documents. SIPRI military expenditure data for 21 NATO member states during 2016–2024 are used to contextualise the budgetary relevance of resource shocks.
Financial forecasting is a widely used tool in accounting and business management, as it provides estimates generally based on past financial statements and supports the prediction of future revenue and expense movements, which may significantly influence strategic objectives. One of the main challenges companies face in revenue forecasting is related to the large volume of available data and the extent to which these data are relevant for estimation models. The study aims to examine whether machine learning techniques can improve revenue growth forecasting in automotive companies compared with traditional methods. Based on a sample of 1.272 companies from the automotive sector over the period 2010 – 2024, the results indicate that the Random Forest and XGBoost models achieve better performance in forecasting revenue growth compared with traditional statistical methods, as well as with other machine learning approaches.
