Purpose: This paper explores the contribution of Industry 4.0 technologies—including artificial intelligence (AI), the Internet of Things and blockchain—to sustainability across sectors such as energy, manufacturing, agriculture, urban development and healthcare. It assesses how these technologies enhance resource efficiency, minimize waste and optimize operational performance. Design/methodology/approach: This study combines a review of scholarly literature with sector-specific case studies to investigate how Industry 4.0 technologies support sustainable practices. It identifies practical applications and examines key challenges, including implementation costs, regulatory constraints and concerns over data privacy. The paper concludes with policy recommendations and suggestions for future research. Findings: Industry 4.0 technologies play a significant role in advancing sustainability. In the energy sector, smart grids and blockchain support renewable energy integration and enable decentralized transactions. In manufacturing, these technologies improve production efficiency, reduce waste and lower energy consumption. Precision agriculture enhances input efficiency and increases crop yields. However, challenges such as high implementation costs and resistance to change remain. Overcoming these obstacles requires joint efforts from both the public and private sectors, along with enabling policy frameworks. Research limitations/implications: This review uniquely combines insights across multiple sectors, offering a cohesive view of how Industry 4.0 technologies can drive sustainable development. It also highlights challenges and provides a roadmap for stakeholders to integrate these technologies effectively for both environmental and economic benefits. Practical implications: The findings offer practical guidance for organizations and policymakers in adopting Industry 4.0 technologies that align with sustainability objectives. They also provide strategies to overcome adoption barriers and promote a digital transformation that fosters environmental sustainability and social equity. Originality/value: This review offers an integrated perspective by synthesizing insights from multiple sectors, presenting a comprehensive view of how Industry 4.0 technologies contribute to sustainable development. It also identifies key challenges and outlines a strategic roadmap for stakeholders to effectively adopt these technologies for both environmental and economic gains.

As a bridge connecting economic activities and ecological protection, green finance is inherently not merely a tool for capital allocation but also embodies profound ethical values. From the perspectives of environmental ethics, social justice, and intergenerational equity, this paper analyzes the inherent ethical connections between green finance and sustainable development. Through a literature review, it sorts out the status of relevant research, identifies issues in current green finance practices, including the "expansion of instrumental rationality" and "ambiguity of ethical boundaries"—and proposes solutions by constructing a "Three-Dimensional Ethical Balance Model for Green Finance." It also offers suggestions for path optimization from three aspects: value reshaping, institutional design, and subject synergy, aiming to provide theoretical references for green finance to return to the essence of sustainable development.

Effective management of energy and water resources is essential for mitigating environmental impacts and enhancing sustainability. This paper proposes a multiple-objective linear program tailored to accommodate energy-water applications in diverse climatic conditions in the Netherlands. It introduces an innovative approach that combines few-shot learning, designed to expand the datasets effectively, and sophisticated machine learning architectures such as Deep Autoregression to allow for more precise and reliable predictions. The method implements machine learning to enhance multi-objective operations research optimisation problems and significantly reduces the reliance on extensive datasets typically necessary for accurate predictive modelling. Experimental results show a notable increase in prediction accuracy, with the integrated approach surpassing traditional models by up to about 33 %. Additionally, we achieve an extension of the 8 solved scenarios to 800 similar scenarios so that the operational efficiency and sustainability of resource management are significantly improved, demonstrating the potential of machine learning technologies in this field. The strategic application of the proposed method compensates for the limitations associated with small datasets and ensures the scalability and effectiveness of the predictive models across various environmental scenarios.

Introduction: China’s urban and rural residents social endowment insurance (URRSEI) plays a key role in ensuring the quality of life of the older adult and promoting social stability.Methods: Based on the data of 31 provinces in China from 2012 to 2022, this study evaluates the efficiency of URRSEI and analyzes its influencing factors through DEA method and Tobit Model.Results and discussion: The overall efficiency of China is high. Eastern region has the highest efficiency. The efficiency of central region is relatively stable. Western region has the lowest average efficiency. The level of local fiscal social security expenditure is negatively correlated with efficiency, while the increase of institutional maintenance rate and educational resources significantly improves the performance of URRSEI. The urban registered unemployment rate and the number of unemployment insurance recipients play a positive role in the security function.

Climate shocks are increasingly recognized as systemic stressors that disrupt financial stability and undermine sustainable development. Using a comprehensive panel of Chinese listed firms from 2007 to 2023, this study examines how physical climate shocks propagate through the financial system. Specifically, we investigate their impact on elevating stock price crash risk and impairing corporate sustainability. We construct a firm-level physical climate risk indicator by applying machine learning and text analysis to annual reports. The empirical evidence demonstrates that climate shocks significantly increase stock price crash risk, indicating heightened systemic vulnerability within the financial system. Mechanism analysis identifies two key transmission channels linking climate shocks to crash risk: tightened liquidity constraints and diminished risk-taking capacity. Furthermore, we find that firms with stronger green transformation efforts exhibit lower sensitivity to climate-induced crash risk. This highlights the crucial role of green initiatives in enhancing institutional and financial resilience. Additional analyses reveal that the rise in crash risk subsequently weakens corporate sustainable development performance. Overall, these findings provide micro-level evidence of how climate shocks generate asymmetric effects within the financial system. The study concludes with policy implications for strengthening climate resilience, stabilizing capital markets, and advancing sustainability in emerging economies.

Graph Fraud Detection (GFD) has become a critical task in online systems such as financial networks, review platforms, and social media, where fraudulent behaviors are inherently rare and often embedded within benign communities. Graph Neural Networks (GNNs) have shown promise for GFD by modeling relational dependencies; however, their effectiveness is severely limited by two persistent challenges: extreme label imbalance and the coexistence of homophilic and heterophilic connections. Most existing approaches attempt to mitigate these issues by modifying graph structures or suppressing heterophilic neighbors, which may introduce bias and scalability limitations. In this work, we address GFD from a frequency-domain perspective and propose a Frequency-aware Graph Neural Network (F-GNN). The core insight is that heterophilic interactions often manifest as high-frequency signals that are diluted by dominant low-frequency homophilic patterns in conventional message passing. F-GNN explicitly decouples node representations in the graph frequency domain and employs node-adaptive spectral gating to selectively emphasize informative high-frequency components. In addition, a fraud-aware representation fusion mechanism is introduced to counteract label imbalance during neighborhood aggregation. Extensive experiments on four benchmark datasets-Yelp, Amazon, T-Finance, and T-Social-demonstrate that F-GNN consistently outperforms state-of-the-art GNN-based fraud detection methods under both supervised and semi-supervised settings, achieving up to 99.81% AUC and 96.65% F1-Macro. These results highlight the effectiveness of frequency-aware modeling as a principled alternative to structure-based heuristics for graph fraud detection.