Spatiotemporal heterogeneity in associations of national population ageing with socioeconomic and environmental factors at the global scale. (1st November 2022)

• Record Type: Journal Article
• Authors: Wan, Qin; Tang, Zhangying; Pan, Jay; Xie, Mingyu; Wang, Shaobin; Yin, Hao; Li, Junming; Liu, Xin; Yang, Yang; Song, Chao
• Is Part Of: Journal of cleaner production. Volume 373(2022)
• Publication Date: 2022-11-01
• Subjects: Global ageing -- Spatiotemporal nonstationarity -- Socioeconomic and environment -- Space-time relative importance of factors -- Spatiotemporal variance partitioning index -- Bayesian STVC model; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.133781
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Global concerted and sustained action is required under a rapid population ageing trend, while global ageing varies across countries in space and time. To support global action on sustainable development and healthy ageing, we investigated the spatiotemporal heterogeneity toward associations between national population ageing (the share of the population aged 65 and older) and various socioeconomic and environmental factors for 189 countries and territories from 2001 to 2020. We adopted Bayesian Spatiotemporally Varying Coefficients (STVC) model to fit the spatial and temporal heterogeneous associations among variables. The concept of variance partitioning was innovatively integrated into Bayesian STVC modeling to propose a spatiotemporal variance partitioning index (STVPI) for identifying the explainable percentage of influencing factors considering their spatiotemporal heterogeneous impacts. The results showed that global ageing had increased rapidly over the past 20 years, especially after 2009, and exhibited a clear geospatial agglomeration, with Europe and Africa possessing the highest and lowest regional ageing levels. The total explainable percentages of socioeconomic and environmental factors for global ageing were 61.85% [95% credible intervals (CIs): 58.57%–64.9%] and 37.40% (95% CIs: 34.38%–40.65%), respectively. Specifically, the cumulative explainable percentage of the five factors, male-to-female ratio, gross national income (GNI), particulate matter 2.5 (PM2.5 ), normalized difference vegetation index (NDVI), and temperature, exceeded 90%. Over time, the annual impacts of education, male-to-female ratio, and physicians were increasing year by year; in contrast, the annual impacts of hospital beds, GNI, NDVI, PM2.5, and precipitation showed downward trends. Geospatially, the country-scale impacts of all factors showed substantial geographical disparities globally but significant clusters regionally. According to country subgroups (not-ageing, ageing, aged, and hyper-aged society), sex ratio, national income, air quality, greenness, and climate consistently played essential roles across the subgroups of four ageing stages. Our findings focusing on spatiotemporal disparities toward ageing and its influencing factors are expected to inform the formation of differentiated policies tailored for different national contexts in response to global ageing. The STVC-based STVPI is promising to be used in broader natural and social sciences to determine the relative importance of potential influencing factors within spatiotemporal dimensions to real-world phenomena. Graphical abstract: Image 1 Highlights: Temporal nonlinearity and geospatial agglomeration are found in heterogeneous associations of national ageing with factors. STVPI is proposed to evaluate the relative importance (%) of explanatory factors considering spatiotemporal heterogeneity. Global ageing is associated with the social economy (61.85% explanation) and the natural environment (37.40% explanation). Five specific factors explain more than 90%, related to sex ratio, national income, air quality, greenness, and climate. Subgroups of countries at four types of different stages of ageing show similar patterns in terms of influencing factors.