This research article examines the relationship between the level of social welfare expenditure and economic growth rates, based on unbalanced panel data from 38 OECD countries covering the period from 1985 to 2022. Four hypotheses are formulated regarding the impact of social expenditure on economic growth rates. Through multiple iterations of regression model building, employing various combinations of dependent and independent variables, and conducting tests for stationarity and causality, compelling empirical evidence was obtained on the negative influence of social welfare spending on economic growth rates. The study takes into account both government and non-governmental expenditures on social welfare, a novelty in this field. This approach allows for a detailed examination of the effects of different components on economic growth and provides a more comprehensive understanding of the relationships. The findings indicate that countries with high levels of social welfare spending experience a slowdown in economic growth rates. This is associated with increasing demands on social security systems, their growing inclusivity, and the escalating required levels of financing, which are increasingly covered by debt sources. The research highlights the need to strike a balance between social expenditures and economic growth rates and proposes a set of measures to ensure economic growth outpaces the indexing of social expenditures. The abstract underscores the relevance of the study in light of the widespread recognition of the necessity to combat inequality, poverty, and destitution, and calls on OECD countries’ governments to pay increased attention to social policy in order to achieve sustainable and balanced economic growth.

Background: Bitcoin mining, an energy-intensive process, requires significant amounts of electricity, which results in a particularly high carbon footprint from mining operations. In the Republic of Kazakhstan, where a substantial portion of electricity is generated from coal-fired power plants, the carbon footprint of mining operations is particularly high. This article examines the scale of energy consumption by mining farms, assesses their share in the country’s total electricity consumption, and analyzes the carbon footprint associated with bitcoin mining. A comparative analysis with other sectors of the economy, including transportation and industry is provided, along with possible measures to reduce the environmental impact of mining operations. Materials and methods: To assess the impact of bitcoin mining on the carbon footprint in Kazakhstan, electricity consumption from 2016 to 2023, provided by the Bureau of National Statistics of the Republic of Kazakhstan, was used. Data on electricity production from various types of power plants was also analyzed. The Life Cycle Assessment (LCA) methodology was used to analyze the environmental performance of energy systems. CO₂ emissions were estimated based on emission factors for various energy sources. Results: The total electricity consumption in Kazakhstan increased from 74,502 GWh in 2016 to 115,067.6 GWh in 2023. The industrial sector’s electricity consumption remained relatively stable over this period. The consumption by mining farms amounted to 10,346 GWh in 2021. A comparative analysis of CO₂ emissions showed that bitcoin mining has a higher carbon footprint compared to electricity generation from renewable sources, as well as oil refining and car manufacturing. Conclusions: Bitcoin mining has a significant negative impact on the environment of the Republic of Kazakhstan due to high electricity consumption and resulting carbon dioxide emissions. Measures are needed to transition to sustainable energy sources and improve energy efficiency to reduce the environmental footprint of cryptocurrency mining activities.

This study aims to analyse the impact of Brexit on London’s housing market, exploring socio-economic and regional disparities. By examining property transaction data from 2012 to 2022, the research seeks to understand how Brexit has influenced real housing prices across different boroughs of London. The methodology involves aggregating transaction data from the Her Majesty (HM) Price Paid database and normalizing prices using the Consumer Price Index (CPI) to obtain real price variations. These data were segmented into three distinct periods: pre-Brexit (2012–2016), post-plebiscite Brexit (2016–2019), and post-implementation Brexit (2020–2022). Spatial analysis was conducted using the software Quantum Geographic Information System (QGIS), transforming point data (postcodes) into polygonal data (wards) for better visualization and comparison. The findings reveal significant socio-economic impacts, with traditionally affluent areas such as Westminster, Kensington, and Chelsea experiencing notable declines in real housing prices. Conversely, certain outer boroughs like Newham and Barnet showed resilience, with positive real price variations despite decreased sales. This geographical disparity underscores the uneven distribution of Brexit’s economic consequences, highlighting the critical role of localized economic policies and development projects in mitigating adverse effects. The results confirm existing literature on the polarization and regional inequalities exacerbated by Brexit while providing new insights into the complex interplay of local and global factors affecting housing markets. The findings emphasize the need for targeted policy interventions to address the diverse challenges posed by Brexit, ensuring both affluent and disadvantaged areas receive adequate support. This research is crucial for informing public policy, urban planning, and housing market strategies in a post-Brexit context, promoting equitable and sustainable development across London.

Freshwater problems in coastal areas include the process of salt intrusion which occurs due to decreasing groundwater levels below sea level which can cause an increase in salt levels in groundwater so that the water cannot be used for water purposes, human consumption and agricultural needs. The main objective of this research is to implementation of RWH to fulfill clean water needs in tropical coastal area in Tanah Merah Village, Indragiri Hilir Regency, with the aim of providing clean water to coastal communities. The approach method used based on fuzzy logic (FL). The model input data includes the effective area of the house’s roof, annual rainfall, roof runoff coefficient, and water consumption based on the number of families. The BWS III Sumatera provided the rainfall data for this research, which was collected from the Keritang rainfall monitoring station during 2015 and 2021. The research findings show that FL based on household scale RWH technology is used to supply clean water in tropical coastal areas that the largest rainwater contribution for the 144 m² house type for the number of residents in a house of four people with a tank capacity of 29 m² is 99.45%.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

Countries employ various strategies to strengthen their soft power through education, public campaigns, mandatory service, and community involvement, essential for building a well-informed, prepared, and resilient citizenry. In Indonesia, the Civic Awareness for State Defence (CASD) program is designed to instil state defence awareness among citizens. This study introduces the Indonesia State Defence Index (SDI), a novel metric grounded in theoretical constructs such as national identity, nationalism, patriotism, and national pride. Differentiating from previous indices, our SDI employs advanced methodologies including Principal Component Analysis (PCA) and Structural Equation Modeling (SEM) to enhance measurement accuracy. Unlike earlier approaches that used traditional aggregation methods, our use of PCA ensures the reduction of dimensions for each state defence indicator, thereby guaranteeing that only the intended dimensions are measured. Utilising data from the State Defence Survey conducted by the Indonesian Ministry of Defence from 1 March to 26 June 2024, we aim to measure and benchmark SDI values across Indonesian regions, thereby elucidating the civic awareness profile in the context of state defence. The refined SDI provides critical insights for policymakers, highlighting regions that require focused interventions to bolster state defence preparedness.

This research examines the influence of virtual community platform attributes on luxury consumers’ purchase intentions, with a specific focus on the role of policy innovation in digital infrastructure. The study aims to 1) identify key factors affecting purchase intentions toward luxury products in virtual environments; 2) develop and validate a structural equation model to analyze these intentions; and 3) provide actionable insights for luxury goods marketers to refine their strategies within these platforms. Utilizing a structural equation model, the study investigates the interactions among various determinants of consumer behavior in virtual communities, highlighting the impact of policy innovation. Data was collected through purposive sampling from 1142 respondents in China’s top 10 high-spending cities on luxury goods, ensuring data relevance. The findings emphasize the significance of knowledge sharing, interactive communication, and leaders’ opinions in virtual communities in building consumer trust and shaping perceptions of online reviews. These elements influence purchase intentions directly and indirectly, with consumer trust serving as a crucial mediator. The study reveals the substantial impact of virtual community attributes on fostering consumer trust and shaping buying decisions for luxury items, underlining the contribution of social development processes. Moreover, the role of policy innovation is found to be significant in enhancing these virtual community dynamics, suggesting that regulatory changes can positively influence consumer engagement and trust. The conclusions offer valuable implications for marketers, proposing strategies to boost consumer engagement and drive sales in virtual settings. This research contributes to the theoretical understanding of digital consumer behavior and provides practical strategies for innovation and growth within the luxury goods sector, emphasizing the critical role of policy innovation in shaping these dynamics.

This study aims to identify factors related to the impact of social capital on happiness among multicultural families using the 2019 Community Health Survey, which represents the South Korean population. The study utilized data from the 2019 Korea Community Health Survey, and the study participants, aged 20 years or older, included 3524 members of multicultural families from a total of 229,099 adult households. The study found a significant difference in happiness scores across different age groups (t = 57.00, p < 0.01). Based on the median value of happiness, significant relationships were found with the independent variables: Physical Environment of Trust (t = −5.13, p < 0.001), Social Networks (t = −5.51, p < 0.001), and Social Participation (t = −5.47, p < 0.001). Happiness was found to have a positive correlation with the Physical Environment of Trust (r = 0.12, p < .001), Social Participation (r = 0.11, p < 0.001), and Social Network (r = 0.13, p ≤ 0.001). In contrast, Age (r = −0.13, p ≤ 0.001) and Stress (r = −0.14, p ≤ 0.001) showed negative correlations with happiness (r = 0.57, p < 0.001). The analysis identified a positive community physical environment (t = 3.85, p < 0.01), increased social networks (t = 4.27, p < 0.01), and higher social participation (t = 6.88, p < 0.01) as significant predictors of happiness. This model suggests that the explanation power is 15%, which is statistically significant (R² = 0.15, F = 57.72, p < 0.001). This study highlights the influence of social capital on the happiness of multicultural families living in Korea. Given the increasing number of multicultural families in the country, strategic interventions aimed at enhancing social networks and participation are necessary to promote their happiness.