The increase in energy consumption is closely linked to environmental pollution. Healthcare spending has increased significantly in recent years in all countries, especially after the pandemic. The link between healthcare spending, greenhouse gas emissions and gross domestic product has led many researchers to use modelling techniques to assess this relationship. For this purpose, this paper analyzes the relationship between per capita healthcare expenditure, per capita gross domestic product and per capita greenhouse gas emissions in the 27 EU countries for the period 2000 to 2020 using Error Correction Westerlund, and Westerlund and Edgerton Lagrange Multiplier (LM) bootstrap panel cointegration test. The estimation of model coefficients was carried out using the Augmented Mean Group (AMG) method adopted by Eberhardt and Teal, when there is heterogeneity and cross-sectional dependence in cross-sectional units. In addition, Dumitrescu and Hurlin test has been used to detect causality. The findings of the study showed that in the long run, per capita emissions of greenhouse gases have a negative effect on per capita health expenditure, except from the case of Greece, Lithuania, Luxembourg and Latvia. On the other hand, long-term individual co-integration factors of GDP per capita have a positively strong impact on health expenditure per capita in all EU countries. Finally, Dumitrescu and Urlin’s causality results reveal a significant one-way causality relationship from GDP per capita and CO₂ emissions per capita to healthcare expenditure per capita for all EU countries.

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.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

To investigate the possible role of arbuscular mycrrhizal fungi (AMF) in alleviating the negative effects of salinity on Stevia rebaudiana (Bert.), the regenerated plantlets in tissue culture was transferred to pots in greenhouse and inoculated with Glomus intraradices. Salinity caused a significant decrease in chlorophyll content, photosynthesis efficiency and enhanced the electrolyte leakage. The use of AMF in salt –affected plants resulted in improved all above mentioned characteristics. Hydrogen peroxide and malondialdehyde (MDA) contents increased in salt stressed plants while a reduction was observed due to AMF inoculation. CAT activity showed a significant increase up to 2 g/l and then followed by decline at 5 g/l NaCl in both AMF and non-AMF treated stevia, however, AMF inoculated plants maintained lower CAT activity at all salinity levels (2 and 5 g/l). Enhanced POX activities in salt- treated stevia plants were decreased by inoculation of plants with AMF. The addition of NaCl to stevia plants also resulted in an enhanced activity of SOD whilst, AMF plants maintained higher SOD activity at all salinity levels than those of non-AMF inoculated plants. AMF inoculation was capable of alleviating the damage caused by salinity on stevia plants by reducing oxidative stress and improving photosynthesis efficiency. 

COVID-19 has amplified existing imbalances, institutional and financing constraints associated with a development strategy that did not take sufficient account of challenges with emissions, environmental damage and health risks associated with climate change in a number of countries, including China. The recovery from the pandemic can be combined with appropriately designed investments that take into account human, social, natural and physical capital, as well as distributional objectives, that can also address commitments under the Paris agreement. An important criterion for sustainable development is that the tax regimes at the national and sub-national levels should reflect the same criteria as the investment strategy. Own-source revenues, are essential to be able to access private financing, including local government bonds and PPPs in a sustainable manner. Governance criteria are also important including information on the buildup of liabilities at all levels of government, to ensure transparent governance.
Despite differences in political systems, the Chinese experiences are relevant in a wide range of emerging market countries as the measures utilize institutions and policies reflecting international best practices, including modern tax administrations for the VAT, and income taxes, and benefit-linked property taxes, as well as utilization of balance sheets information consistent with the IMF’s Government Financial Statistics Manual, 2014. The options have significant implications for policy advice and development cooperation for meeting global climate change goals while ensuring sustainable employment generation with transparency and accountability.