Although the smart home industry is rapidly emerging, it faces the risk of privacy security that cannot be neglected. As this industry now has a complex combination system involving multiple subjects, it is difficult for the traditional risk assessment method to meet these new security requirements. In this study, a privacy risk assessment method based on the combination of system theoretic process analysis–failure mode and effect analysis (STPA–FMEA) is proposed for a smart home system, considering the interaction and control of ‘user-environment-smart home product’. A total of 35 privacy risk scenarios of ‘component-threat-failure-model-incident’ combinations are identified. The risk priority numbers (RPN) was used to quantitatively assess the level of risk for each risk scenario and the role of user and environmental factors in influencing the risk. According to the results, the privacy management ability of users and the security state of the environment have significant effects on the quantified values of the privacy risks of smart home systems. The STPA–FMEA method can identify the privacy risk scenarios of a smart home system and the insecurity constraints in the hierarchical control structure of the system in a relatively comprehensive manner. Additionally, the proposed risk control measures based on the STPA–FMEA analysis can effectively reduce the privacy risk of the smart home system. The risk assessment method proposed in this study can be widely applied to the field of risk research of complex systems, and this study can contribute to the improvement of privacy security of smart home systems.

The central region is an important strategic area that encompasses the east and the west and connects the south and the north. Promoting high-quality urban development in the central region plays a positive role in comprehensively upgrading the central rising strategy and realizing coordinated regional development. Based on the measurement index system result of the level of high-quality urban development in the central region, this study describes the regional gap and its dynamic evolution through the Dagum Gini coefficient and the kernel density function. In addition, it analyzes the causes of the gap in high-quality development of cities in the central region from the perspective of problem area identification. The result shows that the overall high-quality development of cities in the central region is increasing, with high-level cities clustering around the core cities. The relative regional disparities continue to narrow, but the absolute differences tend to expand. The super-variable density tends to be the main source of the overall difference, and the high-quality development of cities in each region is positively spatial correlated with each other. At present, the lagging economic development and outcomes sharing are the main obstacles to the high-quality development of cities in the central region.

Enterprises engaged in transnational operations are confronted with increasingly complex environmental policies and compliance challenges. A critical hurdle to achieving sustainable development lies in rapidly and accurately extracting environmental protection requirements from vast volumes of policy. To address this, our study introduces an automated framework for knowledge graph construction, termed iteration–extraction–optimization (IEO), driven by a large language model (LLM). Diverging from conventional linear extraction methods, the IEO framework employs an iterative enhancement process to progressively build and refine a policy knowledge network, capturing the intricate relationships among legislation, institutions, and environmental obligations. As a case study, we applied the framework to Niger’s environmental policy, constructing a large-scale knowledge graph with 61,912 entities and 81,389 relations. Preliminary evaluations demonstrate the framework’s high performance in knowledge capture completeness, achieving a recall of 0.93 and an F1-score of 0.84. This research presents a novel paradigm for the intelligent parsing of environmental policy texts, providing a knowledge graph that serves as a vital decision-support tool for corporate environmental risk management and strategic sustainability planning.