Global climate change’s pervasive impacts extend beyond the environment, significantly affecting mental health across diverse regions. This study offers a comprehensive multi-regional analysis spanning Asia, Africa, Oceania, Europe, and the Americas, addressing three critical gaps in existing research: (i) the necessity of a global scope given climate change’s widespread impact, (ii) the under-researched mental health dimension compared to general health effects, and (iii) the integration of climate and mental health data. Using data from 1970 to 2020, we found a strong correlation between climate change and rising mental disorders globally. Regional patterns emerged, with Asia, Africa, and Oceania showing broader associations with various mental health issues, while Europe and the Americas saw increases in anxiety and depression. This study contributes to a more comprehensive understanding of the interconnectedness between climate change, mental health, and sustainability. By addressing the mental health impacts of climate change, we can identify sustainable solutions that promote both environmental well-being and human well-being. Our findings highlight the urgent need for global action to mitigate climate change’s mental health effects and provide insights for tailored interventions and public health strategies. Additionally, socio-economic factors like unemployment, urbanisation, GDP growth, and globalisation are incorporated to explore the intricate interplay between climate change, mental health, and societal contexts, offering a clearer understanding of the mechanisms at play.

The term “dual-carbon goals” refers to the commitment of nations or regions to achieving carbon peaking and carbon neutrality. In this context, this study specifically denotes reducing carbon emissions and promoting the transformation and upgrading of energy markets through green innovation and entrepreneurial activities. This study explores the dynamic relationships among key variables in the energy market. It focuses on the impact of investments in green technological innovation on the balance of energy supply and demand, as well as market demand. The findings aim to provide scientific evidence to support the achievement of sustainable development goals. Through system dynamics modeling, this study analyzes the intricate relationships among energy demand, supply, green technology innovation investment, and market demand. In the realm of complex network analysis, market participants are categorized into conservative, moderate, and innovative groups, and their market behavior patterns are examined. In the data mining and machine learning phases, this study constructed a predictive model with an accuracy rate of 85%, forecasting the impact of future green innovation and entrepreneurship on market demand and energy supply. The results indicate that, in the dual-carbon era, investment in green innovation and entrepreneurship plays a significant role. This study enhances the proportion of renewable energy in the market, reduces carbon emissions, and accelerates the transformation of energy demand. Conservative market participants dominate the energy market, but the rise of innovative market participants suggests a positive influence of green innovation and entrepreneurship on market structure. The accuracy of the predictive model provides a reliable basis for policy formulation and strategic planning, offering scientific guidance for achieving sustainable energy development goals.

This study investigates the feasibility and effectiveness of integrating Attribute-Based Encryption (ABE) into smart healthcare networks, with a particular focus on its role in enhancing anti-corruption mechanisms. The study provides a comprehensive analysis of current vulnerabilities in these networks, identifying potential data security risks. An anti-corruption mechanism is designed to ensure data integrity and reliability. The ABE approach is then empirically compared to other prominent encryption algorithms, such as Identity-Based Encryption, Data Encryption Standard, Advanced Encryption Standard, and Rivest-Shamir-Adleman algorithms. These methods are evaluated based on access latency, data transmission speed, system stability, and anti-corruption capabilities. Experimental results highlight the strengths of the ABE algorithm, demonstrating an average access latency of 31.6 ms, a data transmission speed of 3.56 MB/s, and an average system stability of 98.74 %. Furthermore, when integrated into anti-corruption mechanisms, ABE effectively protects against data tampering and misuse, ensuring secure data transmission. Compared to alternative algorithms, ABE offers a more efficient, secure, and stable solution for data management within smart healthcare networks, supported by its robust anti-corruption capabilities. This positions ABE as an optimal choice for safeguarding the integrity and security of healthcare data.

Enterprise financial risk prediction is a fundamental task in financial risk management, yet its performance is often hindered by severe class imbalance, cross-enterprise heterogeneity, and the limited availability of labeled risky samples. These challenges are particularly pronounced in few-shot settings, where conventional machine learning and deep classification models tend to suffer from unstable representation learning, feature collapse, and weak decision boundaries. To address this issue, this study proposes a hierarchical metric learning framework for few-shot enterprise financial risk prediction on imbalanced tabular data. The framework integrates a state-space feature embedding network, an Adaptive Spectral Decomposition and Multi-Scale State Embedding module, and a Hierarchical Metric Manifold Alignment mechanism to enhance risk-sensitive representation learning, preserve geometric consistency across embedding levels, and improve prototype-based discrimination in the metric space. Experiments are conducted on three public datasets, namely American Bankruptcy, Corporate Financial Risk Assessment, and Enterprise Financial Network, under a unified 2-way 20-shot setting. The proposed method consistently achieves the best overall performance across Precision, Recall, Accuracy, F1-score, and AUC, with AUC values of 0.9526, 0.9687, and 0.9716 on the three datasets, respectively. Ablation studies and visual analyses further show that the proposed framework improves intra-class compactness, inter-class separability, and classification robustness under highly imbalanced conditions. These findings indicate that the proposed method provides an effective and robust machine learning solution for enterprise financial risk prediction and early warning in data-scarce financial scenarios.