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The industrial sector of China is critical to the country's economic growth. On the other side, industrialisation has resulted in a high rate of emissions, pushing China to spend extensively on industrial pollution remediation. As a result, this study looks at the relationship between investment completed in the treatment of industrial pollution and economic development. Initially, the study used the global Moran's I test (Queen's contiguity matrix) to find spatial autocorrelation for the 'investment completed in the treatment of industrial pollution' factor, where the study found a positive association across Chinese provinces, and suggest the existence of spatial autocorrelation. Thereafter, a time-fixed effect spatial error model was used due to the lowest Akaike information criterion and Bayesian information criterion to analyse regional data of China from 1999 to 2018. The data reveal a positive association between investment completed in the treatment of industrial pollution and regional economic growth, both in the short and long term. Furthermore, the negative consequences of urban wages and foreign investment on investment completed in the treatment of industrial pollution are having the reverse effect on regional green development, necessitating ecologically friendly actions to mitigate the negative environmental effects of both. The results highlight the need for policymakers in other countries to review their plans for economic expansion and create environmentally friendly legislation. By implementing the Chinese green economic growth model, policymakers in industrially polluting nations can reduce industrial pollution and foster green growth in their nation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Wearable payment devices (WPD) are gaining acceptance fast and transforming everyday life and commercial operations in China. Limited research works were conducted on customers' adoption intentions to obtain a real image of the evolution of WPD in China. This study aims to investigate the effects of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Perceived Trust (PT), and Lifestyle Compatibility (LC) on the intention to adopt WPD among Chinese consumers by expanding unified theory of acceptance and use of technology with two impelling determinants (i.e. PT and LC). Using an online survey, empirical data were collected from 298 respondents in China. In a two-stage data analysis, partial least squares structural equation modelling (PLS-SEM) were employed to analyse the causal effects and associations between independent and dependent variables, whereas artificial neural networks (ANN) were used to evaluate the research model prediction capability. The (PLS-SEM) findings indicated that PE, SI, FC, HM, LC, and PT had substantial positive impacts on adoption intention, whilst EE had no impact on adoption intention among Chinese consumers. The ANN analysis proved the high prediction accuracy of data fitness, with ANN findings highlighting the importance of PT, FC, and PE on the intention to adopt WPD. It was suggested that the study findings assist WPD service providers and the smart wearable device industry practitioners in developing innovative products and implementing efficient marketing strategies to attract the existing and potential WPD users in China., Competing Interests: The authors have declared that no competing interests exist.