Many green financial products currently have a low financial return level; even so, these products are spreading dynamically. In our study, we explored Hungarian green financial investment preferences and separated consumers of green financial products into homogeneous groups, which were characterized on the basis of sociodemographic characteristics. In the case of investments with a similar risk, using the sample we examined we proved that there is a homogenous group (C2) in Hungary which prefers green aspects to higher financial returns in the course of its investment decisions. We separated a group (C3) which can be considered influenceable, and we concluded that, with the application of appropriate marketing activities, this group could be a potential target consumers for national banks and traders of green financial products in the future. Young females are the main target consumers for green financial products in Hungary, and they are the largest majority of the C2 group, for whom financial rationality takes a backseat to green aspects. Based on the results of our study, national banks and traders of financial products can create a more accurate and effective marketing strategy for their products on the Hungarian market.

The evaluation of sustainability is complex, involving several factors and expert opinions. In our research, we analyzed and evaluated the activities of a premium packaging material manufacturer with the participation of three relevant and professional groups. Various expectations have been defined in the form of plan values. From these plan values, we measured and evaluated the sustainability level of the company with the help of plan-fact ratios, fuzzy logic and triangular functions, enabling the subjective evaluation and integration of different opinions into the model. The purpose of our model is to support enterprise decision-making by taking into account sustainability aspects and the different expectations of interested parties. Our model helps identify intervention points and manages subjectivity in the field so that the decisions of enterprise managers better reflect the expectations and perspectives of those involved. The model we built is significantly different from previously used and developed indexes, as it functions as an enterprise-controlling model and index during sustainability evaluation. The developed model can also integrate company-specific and global sustainability indicators. Our research contributes to the development of sustainability evaluation methodology and the scientific examination of enterprise decision-making models. The controlling model we developed offers an integrated approach to managing the subjectivity of sustainability evaluation and the different expectations of stakeholder groups.

Accurate spray timing is essential for reducing unnecessary pesticide use in irrigated cotton production. This study developed and evaluated a locally calibrated six-stage Spraying Phase (SP) scale for the Karshi/Qashqadaryo production context. The scale was established through a two-round moderated consensus process involving 16 expert panelists representing this production context. A screened dataset of 14,400 non-standardized smartphone images was used to train and evaluate a ResNet-50 convolutional neural network (CNN) for SP-stage classification. Field validation was conducted at the Karshi Engineering and Economics Institute during the 2023 and 2024 seasons using an internally controlled randomized complete block design (RCBD)-style paired comparison of SP-based and BBCH-based spray timing. The CNN achieved 93.0% test accuracy. The mean number of pesticide applications was descriptively lower under SP-guided scheduling than under BBCH-based scheduling (3.75 versus 4.88 applications per season; −23.1%). For the inferentially evaluated outcomes, crop-protection cost decreased by 21.2%, the Environmental Risk Index decreased by 21.6%, and plot-level lint-equivalent yield increased by 4.5%. These findings support SP-guided timing as a promising locally calibrated decision-support approach under the tested Karshi/Qashqadaryo conditions; broader use requires multi-site, multi-cultivar, multi-season, device-stratified, and BBCH-level validation, together with technical deployment testing and implementation-cost assessment.

Nowadays, one of the key areas of sustainable agriculture is increasing animal welfare. However, in the absence of generally accepted measurement criteria and systems, measuring animal welfare can be considered a subjective area that makes measuring animal welfare complex. As a result, both increasing welfare and making intervention decisions are not clear for farm management. In our research, we develop a fuzzy logic-based decision support system that is able to handle the subjectivity arising from determining animal welfare. During focus group interviews, experts pointed out that animal welfare assessment systems do not provide adequate support in decision-making. However, the integration of different benchmarks (past, best values and competitors) and the triangular membership functions assigned to them in the assessment significantly supports decision-making. The models were tested with data collected with the Welfare Quality Assessment System of three dairy farms (Austrian, Hungarian, and Slovak). In our result the models show different assessment results; therefore, an aggregate assessment model was created by aggregating the results of the models. The aggregate model incorporates the value judgments and importance of the different models by applying the Choquet integral, thereby providing a more accurate assessment according to the criteria that meet the expectations of decision-makers. Our research shows that animal welfare assessment systems should be based on fuzzy logic and the application of multi-criteria benchmarks until standards reduce the uncertainty in measuring animal welfare levels.