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The impact of LCTI on China's low-carbon transformation from the spatial spillover perspective.

ABSTRACT: China has conducted a long-term low-carbon technology innovation (LCTI), but there was a faster increase of CO2 emission in 2017 and 2018 than in 2016, which has lead scholars to doubt the effect of LCTI on CO2 emission. This paper builds a spatial auto regression (SAR) model with provincial panel data from 2011 to 2017 to calculate the spatial spillover effect of China's LCTI on regional CO2 emission. The results show that regional LCTI can reduce the local CO2 emission, but will increase the CO2 emission of adjacent regions due to spatial spillover effect. This produces the uncertainty of the promotion effect of LCTI on China's low-carbon transformation. Therefore, this paper suggests innovation resources should be appropriately and evenly distributed among regions to avoid their agglomeration in few regions.

SUBMITTER: Li W

PROVIDER: S-EPMC7682850 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The impact of LCTI on China's low-carbon transformation from the spatial spillover perspective.

Li Wenchao W Xu Jian J Wang Zhengming Z Yang Jialiang J

PloS one 20201123 11

China has conducted a long-term low-carbon technology innovation (LCTI), but there was a faster increase of CO2 emission in 2017 and 2018 than in 2016, which has lead scholars to doubt the effect of LCTI on CO2 emission. This paper builds a spatial auto regression (SAR) model with provincial panel data from 2011 to 2017 to calculate the spatial spillover effect of China's LCTI on regional CO2 emission. The results show that regional LCTI can reduce the local CO2 emission, but will increase the C ...[more]

PMID: 33226980

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Project description:BackgroundEven though China launched a series of measures to alleviate several financial burdens (including health insurance scheme, increased government investment, and so on), the economic burden of health expenditure has still not been alleviated. Out-of-pocket payments (OPPs) show not only a time correlation but also some degree of spatial correlation. The aims of the current study were thus to identify the spatial cluster of OPPs, to investigate the main factors affecting variation, and to explore the spatial spillover sources of China's OPP.MethodsGlobal and local spatial autocorrelation tests were validated to identify the spatial cluster of OPPs using the panel data of 31 provinces in China from 2005 to 2016. The Spatial Durbin Model, established in this paper, measured the spatial spillover effect of OPPs and analyzed the possible spillover sources (demand, supply, and socio-economic factors.ResultsOPPs were found to have a significant and positive spatial correlation. The results of the Spatial Durbin Model showed the direct and indirect effects of demand, supply, and socio- economic factors on China's OPPs. Among the demand factors, the direct and indirect correlation (elasticity) coefficients were positive. Among the supply factors, the direct and indirect effects of the share of primary health beds on residents' OPPs were negative. The ratio of health technicians in hospitals to those in primary health institutions on per capital OPPs had a significant indirect effect. Among the socio-economic factors, the direct effects of GDP, government health expenditure, and urbanization on OPPs were found to be positive. There were no significant indirect effects of socio-economic factors on OPPs.ConclusionThis paper finds that China's OPPs are not randomly distributed but, overall, present a positive spatial cluster, even though a series of measures have been launched to promote health equity. Socio-economic factors and those associated with demand were found to be the main influences of variation in OPPs, while demand was seen to be the driver of the positive spatial spillover of OPPs, whereby effective supply could inhibit these positive spillover effects.

Dataset Information

The impact of LCTI on China's low-carbon transformation from the spatial spillover perspective.

Publications

The impact of LCTI on China's low-carbon transformation from the spatial spillover perspective.

Similar Datasets

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