Journal of Hunan University Natural Sciences

This study evaluates the environmental sustainability of the municipality of Palmira, Colombia, using the emergy accounting approach. It provides an updated assessment of the territory’s social-ecological metabolism for 2023 and offers a quantitative basis for planning in intermediate agro-industrial cities. The research is motivated by the increasing pressure that urbanization, agro-industrial expansion, and metropolitan integration place on the natural resources of intermediate municipalities, whose sustainability depends on the balance between the local ecological base and socioeconomic demands. In this context, the objective of the study is to evaluate the environmental performance of Palmira’s territorial system using emergy indicators that integrate ecological and economic flows.
The methodology is based on Odum’s emergy accounting framework. The system boundary was defined as the municipal territory of Palmira, and the main flows of energy, materials, food, goods, services, and economic transactions were identified. A system flow diagram was developed to represent these interactions. Physical and economic data for 2023 were obtained from official statistical sources and converted into solar emergy units (seJ) using emergy transformities reported in the scientific literature.
The results show a total emergy use of 3.20 × 10²² seJ/year, of which 41% corresponds to renewable resources, 34% to local non-renewable resources, and 25% to imports. The calculated indicators were EYR = 4.0, ELR = 1.44, and ESI = 2.78. These values suggest that Palmira has a relatively strong contribution from local resources and a moderate environmental loading level.
The study concludes that Palmira operates as an agro-industrial social-ecological system with a significant ecological base supporting its territorial metabolism. However, dependence on external inputs and soil degradation remain important challenges for its long-term sustainability.

Keywords: emergy accounting; territorial sustainability; urban metabolism; agro-industrial systems; intermediate cities; environmental performance; social-ecological systems.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.7

This study investigates how humble leadership, intrinsic motivation, and employee positive affect influence work engagement and creative performance among Millennial employees in Jakarta’s digital banking sector. As creativity becomes increasingly important in a rapidly changing digital environment, understanding the psychological and leadership-related factors that support innovation is essential. Using a quantitative survey approach, data were collected from Millennial employees working in three independent digital banks in Jakarta. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a two-stage approach to assess reliability, validity, and structural relationships. The results show that humble leadership, intrinsic motivation, and employee positive affect each have a significant positive effect on work engagement. Furthermore, intrinsic motivation, employee positive affect, and work engagement significantly enhance creative performance. However, humble leadership does not directly affect creative performance, indicating that its influence operates indirectly through work engagement. Mediation analysis confirms that work engagement plays a key role in linking leadership humility, internal motivation, and employees’ emotional states to creative outcomes. These findings highlight the importance of fostering work engagement as a pathway to strengthening creativity among Millennial employees in the digital banking sector. The study contributes empirical evidence from the Indonesian digital banking context and provides practical insights for leaders seeking to build an innovative and psychologically empowered workforce.

Keywords: humble leadership; intrinsic motivation; employee positive affect; work engagement; creative performance; Millennial employees; digital banking; Indonesia.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.6

Predicting event outcomes, particularly in sports, has attracted increasing research attention due to the growing availability of historical and performance-related data. Football match outcome prediction has traditionally relied on expert judgment, statistical analysis of past results, and qualitative assessments of team strengths and weaknesses; however, such approaches may be limited by subjectivity, incomplete feature representation, and restricted predictive consistency. This study develops and compares predictive models for football match outcomes using ensemble learning and deep learning algorithms applied to tabular sports data. A publicly available football match dataset obtained from Kaggle was used, and five algorithms were implemented: Deep Neural Network (DNN), TabTransformer, Neural Oblivious Decision Ensembles (NODE), XGBoost, and LightGBM. Model performance was evaluated using standard classification metrics, including precision, recall, F1-score, and accuracy. The results show that the deep learning models achieved moderate predictive performance, with accuracies ranging from 78% for NODE to 87% for the best-performing deep learning model. In contrast, XGBoost demonstrated strong performance across all metrics, achieving 0.96 precision, 0.96 recall, 0.95 F1-score, and 96% accuracy. LightGBM achieved the highest overall performance, with 0.98 precision, 0.98 recall, 0.98 F1-score, and 99% accuracy. These findings indicate that LightGBM is the most effective model for this tabular classification task, followed closely by XGBoost. Although the deep learning models, particularly TabTransformer, show potential, they did not outperform the boosting algorithms in this evaluation. The study recommends the use of ensemble-based algorithms for football match outcome prediction, especially when working with structured tabular datasets. Future research may extend this work by applying advanced hyperparameter optimization techniques, such as grid search, random search, or Bayesian optimization, to further improve the performance of LightGBM and XGBoost.

Keywords: football match prediction; match outcome classification; ensemble learning; deep learning; XGBoost; LightGBM; tabular data.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.5

Firm value remains a central indicator of corporate performance. However, empirical evidence on how internal financial decision-making processes contribute to firm value remains limited, particularly in manufacturing firms operating in emerging economies and in studies based on primary managerial data. This study addresses this gap by conceptualizing financial decision-making quality as a multidimensional managerial capability and examining its effect on perceived firm value. A quantitative research design was employed using survey data collected from managers and financial executives of manufacturing firms. Financial decision-making quality was measured through investment analysis, financing structure decisions, and financial risk evaluation, while perceived firm value was assessed using indicators related to competitiveness, growth prospects, and sustainability. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that financial decision-making quality has a positive and statistically significant effect on perceived firm value. Firms with higher-quality financial decision-making processes tend to report stronger perceived firm value. However, the findings should be interpreted with caution due to the cross-sectional design and the use of perception-based measures, which may be subject to respondent bias. This study contributes to the literature by providing direct empirical evidence on the role of managerial financial decision-making quality as an internal determinant of perceived firm value in emerging economy contexts. The findings highlight the importance of strengthening managerial financial capabilities as a strategic lever for enhancing firm performance.

Keywords: financial decision-making quality; perceived firm value; manufacturing firms; emerging economies; managerial financial capability; PLS-SEM.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.4

Hotelling’s T² test is a foundational multivariate statistical method widely used for hypothesis testing involving mean vectors. However, its classical formulation relies on strong assumptions, including multivariate normality, low dimensionality relative to sample size, and the absence of outliers. In recent decades, a growing body of literature has proposed robust extensions of Hotelling’s T² test to address violations of these assumptions, particularly in high-dimensional and contaminated data settings. Despite rapid methodological development, simulation-based evidence on the performance of these robust extensions has not yet been systematically synthesized. Guided by the PRISMA framework, this study conducts a systematic literature review of simulation studies published between 1974 and 2025 that examine robust variants of Hotelling’s T² test and related multivariate tests. Searches were conducted in Scopus and Web of Science, resulting in 35 eligible studies for qualitative synthesis. Using thematic analysis, three major themes were identified: robustness in high-dimensional and small-sample regimes; robustness to distributional deviations and outlier contamination; and calibration and computational robustness through resampling and adaptive procedures. The review highlights the consistent performance advantages of robust methods over the classical Hotelling’s T² test under assumption violations, identifies methodological gaps in simulation design, and provides recommendations for future research.

Keywords: Hotelling’s T² test; robust statistics; high-dimensional data; multivariate tests; simulation study; systematic literature review.

DOI https://doi.org/10.55463/issn.1674-2974.53.5.3