Search

China currently is the largest emitter of carbon dioxide (CO 2 ) in the world. Moreover, total energy consumption and CO 2 emissions in China will continue to increase due to the rapid advance of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO 2 emissions at the moment or in the future. However, most of the existing literature analyzes the impact of the high–tech industry on emission mitigation from an aggregate perspective. Few studies have focused on regional differences in China. Based on 1999–2015 panel data of China's 30 provinces, this study uses the STIRPAT model to explore the influence of the high–tech industry on CO 2 emission reduction in China from a regional perspective. The results show that the high–tech industry is beneficial to reduce CO 2 emissions. Moreover, the impact intensity of the high–tech industry in the eastern region is higher than those in the central and western regions due to significant differences in R&D funding, R&D personnel investments and high–tech purchase expenditure. The study's findings not only contribute to the existing literature, but also worthy of adequate attention from China's policy makers. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO 2 ) emission in China is currently the largest in the world. Considering that the transport sector, it is one of the major contributors, its CO2 emissions and their main driving factors have become the focus of many scholars. However, majority of the existing studies usually investigates CO2 emissions using the averaging method (i.e., ordinary least squares method). In fact, the data distribution of socio-economic variables is often non–normal, with the tail having hidden important information. In order to overcome the shortcomings of existing research, this paper uses a quantile regression approach to explore the main driving forces of the difference in CO 2 emissions under high, medium and low level of development. The results show that the effect of economic growth on CO 2 emissions in the 25th–50th quantile provinces is higher than those in the other quantile provinces due to the differences in fixed–asset investment and motor vehicles. Energy intensity has a similar story owing to different R&D funding and R&D personnel investments. The influences of urbanization in the upper 90th and 75th−90th quantile provinces are higher than those in the other quantile provinces because of the differences in human capital accumulation and patented technology. Freight turnover produces the same effect, which is due to the differences in freight transportation and transport mode. However, the impacts of passenger turnover in the lower 10th and 10th−25th quantile provinces are the highest in all the quantile provinces. Therefore, in the process of emission reduction, the relevant departments should be concerned about the heterogeneous effects of these driving forces on the different quantile provinces, rather than adopting the “one–size–fits–all approach”. [ABSTRACT FROM AUTHOR]

China has become one of the most heavily polluted countries in the world with China's air pollution attracting claims from some international media of its spread to neighboring countries such as Korea and Japan, in order to bring to the concern of the international community. PM 2.5 (fine particles) pollution is one of the main sources of China's air pollution. The detrimental effect PM 2.5 pollution poses on health of residents and its hindrances to transportation has attracted the attention of many scholars who have conducted a wide range of investigations on PM 2.5 pollution. However, in spite of the avalanche of research on PM 2.5 pollution in China, majority of the existing studies in terms of methodology has usually investigated air pollution using the averaging method. In fact, the data distribution of socio-economic variables is often non-normal, with the tail having hidden important information. In order to overcome the shortcomings of existing research, this paper uses a quantile regression approach to explore the main driving forces of the difference in PM 2.5 pollution under high, medium and low emission levels. The results show that the effect of economic growth on PM 2.5 pollution in the upper 90th quantile provinces is the highest in all the quantile provinces due to the differences in fixed–asset investment and export trade. The impacts of energy efficiency in the 75th–90th and upper 90th quantile provinces are stronger than those in the lower 10th, 10th–25th, 25th–50th, and 50th–75th quantile provinces because of a big differences in research and development (R&D) funding and R&D personnel investment. The case of industrialization was similar on account of the differences in the industrial and building sectors. However, the empirical evidence showed that influences of urbanization in the 25th–50th and 50th–75th quantile provinces were lower than those in the other quantile provinces owing to the differences in motor vehicle and real estate industry. Thus, the heterogeneous effects of these driving forces on the different quantile provinces should be taken into consideration when discussing the mitigation of PM 2.5 pollution in China. [ABSTRACT FROM AUTHOR]

PM 2.5 emissions not only have serious adverse health effects, but also impede transportation activities, especially in air and highway transport. As a result, PM 2.5 emissions have become a public policy concern in China in recent years. Currently, the vast majority of existing researches on PM 2.5 are based on natural science perspective. Very few economic studies on the subject have been conducted with linear models. This paper adopts provincial panel data from 2001 to 2012, and uses the STIRPAT model and nonparametric additive regression models to examine the key driving forces of PM 2.5 emissions in China. The results show that the nonlinear effect of economic growth on PM 2.5 emissions is consistent with the Environmental Kuznets Curve (EKC) hypothesis. The nonlinear impact of urbanization exhibits an inverted “U-shaped” pattern due to the rapid development of urban real estate in the early stages and the strengthening of environmental protection measures in the latter stage. Coal consumption follows an inverted “U-shaped” relationship with PM 2.5 emissions owing to massive coal consumption at the beginning and efforts to optimize the energy structure as well as technological progress in clean energy in the latter stages. The nonlinear inverted “U-shaped” impact of private vehicles may be due to the different roles of scale, structural and technical effects at different stages. However, energy efficiency improvement follows a positive “U-shaped” pattern in relation to PM 2.5 emissions because of differences in the scale of the economy and the speed of technological progress at different times. As a result, the differential dynamic effects of the driving forces of PM 2.5 emissions at different times should be taken into consideration when initiating policies to reduce PM 2.5 emissions in China. [ABSTRACT FROM AUTHOR]

Pollution emissions have become a matter of public concern in recent years. However, the vast majority of existing researches on PM 2.5 pollution are from the natural science perspective, and few studies have been conducted from an economic point of view. This paper adopts provincial panel data from 2001 to 2012 and panel data models to analyze the key driving forces of PM 2.5 emissions at the regional level in China. The results show that economic growth is a decisive factor of PM 2.5 emissions. The impacts of urbanization on PM 2.5 emissions vary across regions and decrease continuously from the central region to the western and eastern regions. The effects of private cars and coal consumption on PM 2.5 emissions in the eastern region are greater than that in the central and western regions because of significant differences in R&D investment, private car ownership and total coal consumption. Energy efficiency improvement has greater potential to mitigate PM 2.5 emissions in the central and western regions than that in the eastern region due to its low level of technology. Hence, in order to effectively reduce emissions, the Chinese government should consider all these factors as well as regional heterogeneity in developing appropriate mitigation policies. [ABSTRACT FROM AUTHOR]