Journal
POLISH JOURNAL OF ENVIRONMENTAL STUDIES
Volume 26, Issue 6, Pages 2507-2521Publisher
HARD
DOI: 10.15244/pjoes/70894
Keywords
construction industry; carbon intensity; spatial autocorrelation; Moran's I; geographically weighted regression
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Funding
- National Social Science Foundation of China [16CJY028]
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Climate change continuously threatens sustainable development. As the largest energy consumer and carbon emitter in the world, China is facing increasing pressure to cut carbon emissions. Based on Moran's index I and geographically weighted regression, this paper investigates the spatiotemporal characteristics and the dominating factors of China's province-level carbon intensity in the construction industry from 2005 to 2014, which is aimed at providing a scientific basis for government while implementing a regional-oriented carbon emissions reduction strategy. The empirical results are shown as follows. Firstly, carbon intensity in the construction industry of each province has been decreasing in the past 10 years. Secondly, provincial carbon intensity in this sector shows significant positive spatial autocorrelation characteristics and the degree of spatial clustering of carbon intensity tended to weaken in this period. Third, according to the analysis of the geographically weighted regression (GWR) model, carbon intensity is positively affected by energy intensity while the labor input and production efficiency both have negative effect. Particularly the regression coefficient of labor input is almost twice as large as the other two factors. The results reveal that there is a significant spatial disparity of these three factors in different provinces.
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