Spatial correlation of factors affecting CO2 emission at provincial level in China: A geographically weighted regression approach

Wang, Yanan; Chen, Wei; Kang, Yanqing; Li, Wei; Guo, Feng‐Biao

doi:10.1016/j.jclepro.2018.03.002

Cited by 106 publications

(39 citation statements)

References 46 publications

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“…On the other hand, tourism has been attributed to strong economic growth (Diamond 2005), which has led to massive urbanization and industrialization. There is no doubt that industrialization is expected to continue to rise, and this possibility is composite in itself because more industrialization implies more global carbon emission (Schubert et al 2011;Ouyang and Lin 2017;Wang et al 2018;Lin and Benjamin 2019;Wang and Su 2019;Yang et al 2017;Bekhet and Othman 2017;Liu et al 2018;Pan et al 2019;Nie et al 2019;Haug and Ucal 2019;Zameer et al 2020;Shahbaz et al 2020).…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Modelling the interaction between tourism, energy consumption, pollutant emissions and urbanization: renewed evidence from panel VAR

Adedoyin

Bekun

2020

Environ Sci Pollut Res

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In less than two decades, the global tourism industry has overtaken the construction industry as one of the biggest polluters, accounting for up to 8% of global greenhouse gas emissions as reported by the United National World Trade Organization (UNWTO 2018). This position resonates the consensus of the United Nations Framework Convention on Climate Change (UNFCCC). Consequently, research into the causal link between emissions and the tourism industry has increased significantly focusing extensively on top earners from the industry. However, few studies have thoroughly assessed this relationship for small island economies that are highly dependent on tourism. Hence, this study assessed the causal relationship between CO2 emissions, real GDP per capita (RGDP) and the tourism industry. The analysis is conducted for seven tourism-dependent countries for the period 1995 to 2014 using panel VAR approach, with support from fully modified ordinary least square and pooled mean group–autoregressive distributed lag models. Unit root tests confirm that all variables are stationary at first difference. Our VAR Granger causality/block exogeneity Wald test results show a unidirectional causality flowing from tourism to CO2 emission, RGDP and energy consumption, but a bi-directional causality exists between tourism and urbanization. This implies that in countries that depend on tourism, the behaviour of CO2 emission, RGDP and energy consumption can be predicted by the volume of tourist arrivals, but not the other way around. The impulse response analysis also shows that the responses of tourism to shocks in CO2 appear negative within the 1st year, positive within the 2nd and 3rd years but revert to equilibrium in the fourth year. Finally, the reaction of tourism to shocks in energy consumption is similar to its reaction to shocks in RGDP. Tourism responds positively to shocks in urbanization throughout the periods. These outcomes were resonated by the Dumitrescu and Hurlin causality analysis where the growth-induced tourism hypothesis is validated as well as feedback causality observed between tourism and pollutant emission and urbanization and pollutant emission in the blocks over the sampled period. Consequently, this study draws pertinent energy and tourism policy implications for sustainable tourism on the panel over their growth trajectory without compromise for green environment.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Modelling the interaction between tourism, energy consumption, pollutant emissions and urbanization: renewed evidence from panel VAR

Adedoyin

Bekun

2020

Environ Sci Pollut Res

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“…Except for the spatial weight matrix which represents the geographical relationship among provinces, other variable data cannot describe the inter-provincial correlation effect properly [26]. For instance, by introducing the global and the local Moran I exponents, researchers notice the evident positive spatial association between Chinese regional CO 2 emissions, and spatial agglomeration features exist [27][28][29][30][31][32][33]. However, Han et al (2018) concluded that a gravity model combining geographic and economic distances presented significantly higher spatial correlations of carbon emissions than a simple matrix of geographic and economic distances [18].…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…The GWR model uses local weighted ordinary least squares to estimate point parameters, whose weight is the distance function from the position of the regression point to the locations of other sample points. [56]. The GWR model could be explained as:…”

Section: Local Regression Modelmentioning

confidence: 99%

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Zhao

2019

Sustainability

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Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

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Spatial correlation of factors affecting CO2 emission at provincial level in China: A geographically weighted regression approach

Cited by 106 publications

References 46 publications

RETRACTED ARTICLE: Modelling the interaction between tourism, energy consumption, pollutant emissions and urbanization: renewed evidence from panel VAR

RETRACTED ARTICLE: Modelling the interaction between tourism, energy consumption, pollutant emissions and urbanization: renewed evidence from panel VAR

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

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

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