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Population aging and housing price can affect household consumption, and population aging can indirectly affect household consumption through housing price. This study uses panel data from 31 provinces and cities in China between 2001 and 2018 to construct a panel vector autoregression model for analyzing the impact of population aging and housing price on household consumption level. We determine that population aging and housing price positively impact household consumption level, and population aging also has a positive effect on housing price. The mediating effect model using time series data from 2001 to 2018, with housing price as the mediating variable reveals that population aging directly increases household consumption level, and indirectly increases it through housing price. Finally, this study proposes suggestions such as optimizing the population structure, improving the housing system, and advancing the development of the older adult industry. [ABSTRACT FROM AUTHOR]

Even though the coronavirus disease (COVID-19) has limited consumption, individuals continue to plan post-pandemic consumption activities to get rid of the stress caused by consumption repression. Building on Maslow's theory of needs and Herzberg's two-factor theory, our research categorizes consumption into fundamental ("must-have" products that fulfill the physical needs of individuals), hygiene (maintaining the security needs of consumers), and motivational consumption (enhancing well-being of individuals). Based on empirical data of purchase behavior and consumption expectation before, during, and after the pandemic in China, we identify how consumption repression induces psychological distress, via a sense of feeling threatened, lacking control, or lacking freedom, and how the expectation of future consumption alleviates that stress. Results show that fundamental consumption leads to psychological distress; hygiene consumption can both result in and reduce stress; and motivational consumption can reduce stress. Our findings provide new insights into the relationship between consumption and psychological distress through new theoretical formulations. The results can be applied by marketers attempting to understand purchase decision-making and by policymakers supporting both citizens and commerce during social emergencies. [ABSTRACT FROM AUTHOR]

The importance of energy in modern life is self-evident. Forecasting future energy consumption can help governments and businesses formulate reasonable energy supply and demand policies to ensure energy security and economic development. To this end, a novel adaptive fractional grey model with fractional derivative was established. Firstly, a novel fractional cumulative operator is proposed that operates in a fractional-order domain and has the potential to alternate between giving priority to new or old information. This method facilitates the effective utilization of data when working with a limited number of samples. Secondly, the model's adaptability and flexibility were improved through the introduction of a nonlinear term in the whitening equation; and the fractional derivative was introduced into the whitening equation to solve the problem of poor adaptability of existing integer-order derivative to nonlinearity and volatility. To enhance the model's performance, the study utilized the Grey Wolf Optimization (GWO) algorithm to optimize the model parameters. Furthermore, the robustness of the proposed model was verified using Monte Carlo simulations and probability density analysis; and the experimental results indicated that the proposed model exhibits better robustness. Finally, three actual cases of China's total energy consumption, total crude oil consumption and domestic heat consumption are predicted. • A novel structure adaptive fractional derivative grey model is proposed. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other models. [ABSTRACT FROM AUTHOR]