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.

This study investigates the relationship between inter-organizational knowledge-sharing and innovation performance based on the resource-based theory and network embedded theory. It aims to examine the mediating effect of network characteristics in the relationship between inter-organizational knowledge-sharing and innovation performance. Through quantitative study, data is collected from 275 firms and analyzed through regression analysis. The results reveal that inter-organizational knowledge-sharing has a positive effect on enterprise innovation capability. Innovation capability has a positive effect on enterprise innovation performance. The link between inter-organizational knowledge-sharing and innovation performance is mediated by enterprise innovation capability. Evidence in support of full mediation is found. Connection strength and network scale play a positive moderator role in the relationship between inter-organizational knowledge-sharing and innovation capability. The findings provide a theoretical basis for inter-organizational knowledge-sharing and help enterprises establish innovative advantages. These also guide the inter-organizational knowledge-sharing among members in practice.

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.

The new energy demonstration city policy is a significant pilot measure to promote the transition of China’s energy system, aiming at developing new, green, and low-carbon sources of energy. In this paper, the Non-radial Directional Distance Function (NDDF) was adopted to calculate the Energy-Carbon Performance Index (ECPI) of Chinese 182 cities, for measuring the Energy-Carbon Performance (ECP) level of each city. On this basis, it is possible to empirically analyse the impact that the policy orientation of constructing new energy demonstration cities has had on urban energy carbon performance by using a combination of Propensity Score Matching and Difference-in-Difference. Moreover, a mediating effect model is utilised to test the mediating effect of technological innovation. The results show that the new energy demonstration city policy can significantly improve the ECP. Technological innovation has a partial mediating effect between the policy orientation of new energy demonstration city construction and ECP, which accounts for 12.92% of the total effect. Optimising the industrial structure, improving the level of economic development, increasing carbon sink resources, and attracting foreign direct investment all have significant impacts on the improvement of China’s ECP, while the urbanisation process has an inhibitory effect on the improvement of ECP. Heterogeneity analysis shows that policy orientation has a better driving effect on eastern cities and western cities in promoting the improvement of ECP. The policy implications of this paper are that 1) The government should expand the scope of new energy city pilots in an orderly manner; 2) The lasting and long-term influence of policy orientation on ECP should make use of technological innovation intermediary channels; 3) Support policies are supposed to formulate according to local conditions.

Taking the data of various sectors and three industries from 1980 to 2019 as the research object, the LMDI-I (Logarithmic Mean Divisia Index) multiplicative decomposition model, which is based on the principle of decomposing the change in energy consumption into the contribution of each factor, was used to decompose the carbon emission intensity into technological progress effect and economic structure changing effect. Meanwhile, quantitative econometric models of energy price, economic growth, energy consumption structure, and the two effects were also established. The empirical results showed that energy price, economic growth, and energy consumption structure significantly influenced the reduction in carbon emission intensity. A positive U-shaped relationship between energy prices and carbon emission intensity was overserved, and the rise of energy prices mainly drive the decline of carbon emission intensity through the effect of technological progress. However, the effect of economic structure driven by the rise of energy prices was limited; thus, further optimization of economic structure is needed. Additionally, the proportion of coal consumption was positively correlated with the technological progress effect and economic structure change effect, while the decrease in coal consumption proportion promoted the decline of carbon emission intensity. Finally, three recommendations based on the analysis were proposed.