Are there industrial SO₂ convergences in China’s prefecture-level cities? New evidence from a spatial econometric perspective

Abstract

As the world’s largest emitter of sulfur dioxide, China is facing mounting domestic and international pressures to tackle the increasingly serious atmospheric pollution. Convergence is an important inherent characteristic of sulfur dioxide discharge. This study examines the convergence of per capita sulfur dioxide emissions across 280 Chinese prefecture-level cities from 2003 to 2016. Due to the spatial autocorrelation of air pollutants, conventional estimation methods for β convergence ignore the spatial effects and produce biased results. Consequently, spatial econometric models with different weight matrices are employed to control for spatial effects. The empirical results indicate that per capita sulfur dioxide emissions exhibit both absolute β convergence and conditional β convergence, and spatial effect and other socioeconomic factors accelerate the convergence speed. In addition, this study verifies the environmental Kuznets curve hypothesis between sulfur dioxide and gross domestic product. The results highlight the importance of regional cooperation and coordination when formulating environmental and industrial policies.

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“…However, there are other contexts that have deserved the consideration of academics who have carried out studies related to spatial dynamics, such as those from the following countries: Russia (Balash et al, 2020), Belarus (Celbis et al, 2018), Mexico (German-Soto & Brock, 2015, Romania (Goschin, 2017), China (He et al, 2017), Great Britain (Henley, 2005), Tunisia (Labidi, 2019), the Iberian Peninsula , the United States (Rey & Montouri, 1999), Colombia (Royuela & Adolfo Garcia, 2015) and Brazil (Silveira-Neto & Azzoni, 2006). Spatial autocorrelation approaches have also been considered in other assessments, such as the following: personal insolvency (Bishop, 2013), ripple effect on housing values (de la Paz et al, 2017), technical efficiency (Ezcurra, Iraizoz, & Rapun, 2008), transport infrastructures (Gao et al, 2019), economic growth efficiency with low carbon (Ju & Zhang, 2020), pollutant emissions (Li et al, 2018), food inflation (Liontakis & Kremmydas, 2014), eco-efficiency , rental housing , transport efficiency (Ma, Wang, Sun, Liu, & Li, 2018), sulphur dioxide emissions (Nan et al, 2020), fertility rate (Salvati et al, 2020), homicides and personal damages (Santos-Marquez & Mendez, 2020), interregional migration (Sardadvar & Rocha-Akis, 2016), diabetes incidence/ prevalence (Shrestha et al, 2016), carbon emissions (Su, 2020), educational standards (Tselios, 2008) and energy efficiency (Zhang et al, 2017). Some of the analyses related to spatial issues found increasing returns to scale (Dall'Erba et al, 2008), based on the developments associated with the Verdoorn law (Angeriz et al, 2008), and identify processes which promote spatial asymmetries (Cracolici et al, 2007) through agglomeration or polarization dynamics.…”

“…Spatial autocorrelation approaches have also been considered in other assessments, such as the following: personal insolvency (Bishop, 2013), ripple effect on housing values (de la Paz et al, 2017), technical efficiency (Ezcurra, Iraizoz, & Rapun, 2008), transport infrastructures (Gao et al, 2019), economic growth efficiency with low carbon (Ju & Zhang, 2020), pollutant emissions (Li et al, 2018), food inflation (Liontakis & Kremmydas, 2014), eco‐efficiency (Liu et al, 2020), rental housing (Liu et al, 2020), transport efficiency (Ma, Wang, Sun, Liu, & Li, 2018), sulphur dioxide emissions (Nan et al, 2020), fertility rate (Salvati et al, 2020), homicides and personal damages (Santos‐Marquez & Mendez, 2020), interregional migration (Sardadvar & Rocha‐Akis, 2016), diabetes incidence/prevalence (Shrestha et al, 2016), carbon emissions (Su, 2020), educational standards (Tselios, 2008) and energy efficiency (Zhang et al, 2017).…”