Carbon emission efficiency in China: A spatial panel data analysis

Industrialization, urbanization, and carbon emission efficiency of Yangtze River Economic Belt—empirical analysis based on stochastic frontier model

“…Huo et al (2020) believed that for the construction industry, the increase in urban population and building area will have a negative impact on carbon emissions. Wang et al (2019c) calculated the carbon emission efficiency of 31 provinces in China, and proposed that the improvement of urbanization level has a positive effect on efficiency. Sun and Huang (2020) found that the impact of urbanization on carbon emission efficiency is inverted U-shaped and there is a critical value for urbanization to promote carbon emission efficiency.…”

Revisiting the empirical relationship among the main targets of sustainable development: Growth, education, health and carbon emissions

“…In general words, the study argued that increase in body mass index (BMI) of population of 116 countries in studied period has reduced economic growth from 3.5 to 5.8%. In empirical literature, (Arnold, 2002; Groot & van den Brink, 2006; Li & Wang, 2018; Wang et al, 2019) also reached at similar conclusion regarding health problems and economic growth.…”

“…Later, a large number of literature have analysed the impact of several factors on economic growth for single and multiple countries which concluded that labour, capital and education play significant role in economic growth (Arnold, 2002;Brunello & Comi, 2004;Inklaar & Timmer, 2006;Mankiw, Romer, & Weil, 1992;Paolo, 2013;Sarwar, Shahzad, Chang, & Tang, 2019b;Shahbaz, Sarwar, Chen, & Malik, 2017;Teixeira & Queirós, 2016). The existing literature and endogenous growth theory highlighted the key determinants of economic growth such as; FDI, government consumption, geography, foreign trade, capital, energy, institutional and political stability (Teixeira & Queirós, 2016;Wang, Deng, Wang, Zhang, & Liang, 2019). Nevertheless, education and health issues are stand as the most left out and inconclusive determinants which are included in such analysis.…”

Spatial Distribution and Regional Difference of Carbon Emissions Efficiency of Industrial Energy in China

“…Wang et al (2018) find that CO2 emissions of China have shown a stable spatial agglomeration effect from global and local perspectives because of the similarity and connectivity of spatial units. Provinces and cities with high carbon emissions efficiency in China cluster in mid-east coastal regions (Wang et al 2019), while provinces and cities with low carbon emissions efficiency cluster in northern part of China (Tian and Zhou 2019). Since the clustering effect is found to be quite obvious in China, some other researchers even initially partitioned clustering areas based on carbon emissions efficiency by using the quantitative methodology.…”