Spatial correlation analysis of low-carbon innovation: A case study of manufacturing patents in China

Yang, Cuiwei; Liu, Shuning

doi:10.1016/j.jclepro.2020.122893

Cited by 53 publications

(35 citation statements)

References 72 publications

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“…Bai et al (2020) verified the reliability of the modified gravity model by demonstrating the significant correlation between the modified carbon emission spatial association matrix and the related variables in the analysis of the spatial correlation data over the years [26]. Yang and Liu (2020) studied the spatial association of low carbon innovation in China by using a modified gravity model through the analysis of manufacturing patents data [36]. In this paper, a construction carbon emission network was constructed by constructing a spatial correlation matrix obtained from a modified gravity model.…”

Section: Discussionmentioning

confidence: 99%

“…According to the findings of the QAP regression, it can be found that the spatial association of CO 2 emissions for the construction sector would be significantly influenced by geographical adjacency and the differences in energy intensity and industrial structure. Qin et al (2019) [22] and Yang and Liu (2020) [36] showed that geographical proximity is the main factor of spatial relationships, and industrial structure, technology level, and other factors also significantly affect spatial correlation.…”

Section: Discussionmentioning

confidence: 99%

“…Second, the other category of studies employs the SNA approach to analyze the carbon emissions spatial correlation properties [34][35][36][37][38]. These studies define the spatial association between sample regions by the vector autoregression (VAR) approach or the gravity model, then create a network of spatial associations across all regions and investigate the features of CO 2 emissions spatial associations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Relational data have the advantage of directly reflecting association features, which is beneficial to the construction of spatial correlation networks and can give concise and intuitive visual analysis. Second, the QAP analysis method in SNA allows correlation analysis among the spatial association matrix and possible driven ingredients, which facilitates the identification of influence factors that significantly affect spatial associations [36]. It is worth mentioning that QAP obtains the estimation results by multiple permutations of the random permutation matrix, thus avoiding the multicollinearity problem that often occurs when using the traditional ordinary least squares (OLS) method [40].…”

Section: Literature Reviewmentioning

confidence: 99%

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Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry

You

et al. 2021

Sustainability

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As the world’s largest carbon emitter, China is under enormous pressure to decrease carbon emissions. With the economic development in recent years, China has increased its investment in infrastructure, and the construction industry has become an essential source of carbon emissions. Using the social network analysis (SNA) methodology, this article analyzes the evolutionary characteristics of the spatial correlation network for carbon emissions in the construction industry from 2003–2017 and its affecting factors. The results of the empirical analysis in this paper are: (1) the spatial association of carbon emissions in Chinese inter-provincial construction industry shows an intuitive network layout and the spatial network has gradually stabilized since 2014; (2) according to the results of degree centrality, betweenness centrality and closeness centrality, it can be concluded that the regions with higher level of association with other provinces are the central and the eastern regions (Henan, Hubei, Hunan, Guangdong, Jiangsu, etc.) and Xinjiang; the linkage of construction-related carbon emissions was mainly achieved through the regions of Henan, Anhui, Shanxi, Hebei, Guangdong, and Inner Mongolia; the regions with higher level of construction industry development (Jiangsu, Henan, Hunan, Guangdong, etc.) are more closely associated with other provinces; (3) geographical proximity and reduction of difference in energy intensity and in industrial structure have substantial positive effects on the carbon emission association of the construction industry. Finally, based on the research results, this article proposes corresponding policy recommendations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry

You

et al. 2021

Sustainability

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“…As mentioned above, China’s low-carbon innovation is especially active in the past few decades (Yang and Liu 2020 ). However, its carbon emissions are still growing.…”

Section: Literature Review and Research Hypothesesmentioning

confidence: 99%

Do carbon emissions accelerate low-carbon innovation? Evidence from 285 Chinese prefecture-level cities

Pan

Zhang

Xie

et al. 2021

Environ Sci Pollut Res

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Climate change caused by carbon emissions has a strong influence on the economy and human society. Though numerous previous studies have emphasized the importance of low-carbon innovation on curbing or mitigating carbon emissions, not much attention has been given to the reverse effect. We used a panel of 285 Chinese prefecture-level cities from 2005 to 2016 and Cooperative Patent Classification (CPC)-Y02 patents as low-carbon innovation indicators. The results show that the increasing carbon emissions accelerate cities’ low-carbon innovation in China, and the predicted effect varies across low-carbon innovation types. As carbon emissions rise, more low-carbon innovation will occur in activities with higher carbon emissions. Besides, we explore environmental awareness as the mediation channel for carbon emissions to impact low-carbon innovation. With the help of media, government, and enterprises, the growing carbon emissions promote public environmental awareness and change consumers’ behaviors, motivating companies to speed up low-carbon innovation.

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Unveiling evolution characteristics of inventive activity on climate change mitigation technologies in China

Liu

Wang²,

Xiao³

2022

Environ Dev Sustain

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Spatial correlation analysis of low-carbon innovation: A case study of manufacturing patents in China

Cited by 53 publications

References 72 publications

Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry

Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry

Do carbon emissions accelerate low-carbon innovation? Evidence from 285 Chinese prefecture-level cities

Unveiling evolution characteristics of inventive activity on climate change mitigation technologies in China

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