Important modifications are occurring in temperate forests due to climate change; in polar latitudes their distribution area is increasing, while in tropical latitudes it is decreasing due to temperature increase and droughts. One of the biotic regulators of temperate forests are the debarking insects that cause the mortality of certain trees. These insects have increased in number, favored by climate change, and the consequences on forests have not been long in coming. In recent times in the northern hemisphere, the massive mortality of conifers due to the negative synergy between climate change and debarking insects has been evident. In Mexico, we have also experienced infestations by bark stripping insects never seen before; therefore, we are trying to understand the interactions between climate change, forest health and bark stripping insects, to detect the areas with greater susceptibility to attack by these insects and propose management measures to reduce the effects.

Every plant is significantly important in tackling climate change, including Makila (Litsea angulata BI) an endemic wood species found in the forest of Moluccas Provinces. Therefore, this research aimed to examine the role of the Makila plant in tackling climate change by measuring biomass content using constructing an allometric equation. The method used was a destructive sampling, where 40 units of Makila plant at the sampling level were felled, and sorted according to root, stem, branch, rating, and leaf segments. Each segment was weighed both at wet and after drying, followed by a classical assumption test in data processing, and the formulation of an allometric equation. The regression model was examined for normality and suitability in predicting independent variables, ensuring there were no issues with multicollinearity, heteroscedasticity, and autocorrelation. The results yielded a multiple linear regression, namely: Y = −1131.146 + 684.799X₁ + 4.276X₂, where Y is biomass, X₁ is the diameter, and X₂ is the tree height. Based on the results of the t-test: variable X₁ partially affected Y while variable X₂ partially had no effect on Y. The F-test indicated that variables X₁ and X₂ jointly affected Y with R Square: 0.919 or 91.9% and the rest was influenced by other unexplored factors. To simplify biomass prediction and field measurement, a regression equation that used only 1 independent variable, namely tree diameter, was used for the experiment. Allometric equation only used 1 variable, Y = −1,084,626 + 675,090X₁, where X₁ = tree diameter, Y = Total biomass with R = 0.957, and R² = 0.915. Considering the potential for time, cost, and energy savings, as well as ease of measurement in the field, the biomass of young Makila trees was simply predicted by measuring the tree diameter and avoiding the height. This method used the strong relationship between biomass, plant diameter, and height to facilitate the estimation of biomass content accurately by entering the results of field measurements.

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.

Climate change plays a vital role in shaping the knowledge construction of farmers for managing their agricultural land. Therefore, this study aims to analyze the coffee farmers’ knowledge construction process regarding climate change. This research utilizes qualitative methods. This research approach uses the grounded theory, which can help researchers uncover the relationship between the coffee farmers’ knowledge construction and climate change. The data were collected through semi-structured interviews and analyzed using constant comparative methods. The transcription of the field notes was analyzed using NVivo version 12, a program for analyzing qualitative data. There were 33 informants in the study. This study found that the conditions and situations of wind speed and uncertain whether strongly influence the farmers’ construction of climate knowledge. Coffee farmers are looking for new ways to respond to climate change, such as increasing the intensity of the care they give to their coffee plants, gradually harvesting according to the ripeness of the coffee fruits, finding alternative ways to dry the coffee beans, and reducing the use of fertilizer. However, coffee farmers are also starting to adapt old knowledge from their parents to the latest perceived climate phenomena, so that they can look for alternative sources of livelihood outside their farms. This knowledge construction process serves as a form of adaptation by the coffee farmers to climate change, and reflects the dynamic between traditional knowledge and current experience. Understanding this knowledge construction helps coffee farmers to cope with climate change and to design appropriate policy strategies to support the sustainability of coffee farming in an era of climate change. Further research is needed at the regional level.

Consumer satisfaction can be defined as the user’s response to a service or experience compared to the user’s expectations and perceived practical benefits. After reviewing consumer satisfaction models, it can be argued that there is no single model of consumer satisfaction assessment that is suitable for every service and every region of the world, as the causes and outcomes of satisfaction often vary. The research is original in its methodology: at the beginning, a theoretical research model is presented, then hypotheses are formulated, and correlation, factorial, regression analyses were made, which results confirmed hypotheses. The crop insurance system consists of relations between the state institution regulates insurance activities, farmers, insurers and insurance intermediaries. The aim of this article is to identify the factors that determine consumer satisfaction with crop insurance and to assess their impact. The empirical study found that consumer satisfaction is determined by the factors of recognizable value, functional (process) and technical (result) quality, consumer expectations, and image. The most important factors that determine consumer satisfaction of crop insurance are recognizable value, functional quality, and consumer expectations. Consumer satisfaction can be assessed by the cost paid and the quality received, the quality expected, and the consumers’ evaluation of the services. It was found that the socio-demographic elements of consumers do not have a decisive influence on the factors that determine service satisfaction and consumer satisfaction. It is also established that socio-demographic elements of consumers (farmer experience and insurance experience) have direct statistically significant but weak links with consumer satisfaction.

Climate change is one of the most critical global challenges, driven primarily by the rapid increase in greenhouse gas concentrations. Carbon sequestration, the process by which ecosystems capture and store carbon, plays a key role in mitigating climate change. This study investigates the factors influencing carbon sequestration in subtropical planted forest ecosystems. Field data were collected from 100 randomly sampled plots of varying sizes (20 m² × 20 m² for trees, 5 m² × 5 m² for shrubs, and 1 m² × 1 m² for herbs) between February and April 2022. A total of 3,440 plants representing 36 species were recorded, with Prosopis juliflora and Prosopis cineraria as the dominant tree species and Desmostachya bipinnata as the dominant herb. Regression analysis, Pearson correlation, and structural equation modeling were performed using R software to explore relationships between carbon sequestration and various biotic and abiotic factors. Biotic factors such as diameter at breast height (DBH; R=0.94), tree height (R=0.83), and crown area (R=0.98) showed strong positive correlations with carbon sequestration. Abiotic factors like litter (R=0.37), humus depth (R=0.43), and electrical conductivity (E.C; R=0.11) also positively influenced carbon storage. Conversely, pH (R=-0.058), total dissolved solids (TDS; R=-0.067), organic matter (R=-0.1), and nitrogen (R=-0.096) negatively impacted carbon sequestration. The findings highlight that both biotic and abiotic factors significantly influence carbon sequestration in planted forests. To enhance carbon storage and mitigate climate change, efforts such as afforestation, reforestation, and conservation of subtropical forest ecosystems are essential.