Assessing the impact of population, income and technology on energy consumption and industrial pollutant emissions in China

Abstract

a b s t r a c tElucidating the complex mechanism of the impact of demographic changes, economic growth, and technological advance impacts on energy consumption and pollutant emissions is fundamentally necessary to inform effective strategies on energy saving and emission reduction in China. Here, based on a balanced provincial panel dataset in China over the period 1990-2012, we used an extended STIRPAT model to investigate the effects of human activity on energy consumption and three types of industrial pollutant emissions (exhaust gases, waste water and solid waste) at the national and regional levels and tested the environmental Kuznets curve (EKC) hypothesis. Empirical results show that a higher population density would result in a decrease in energy consumption in China as a whole and in its eastern, central and western regions, but the extent of its effect on the environment depends on the type of pollutants. Higher population density increased wastewater discharge but decreased solid waste production in China and its three regions. The effect of economic development on the environment was heterogeneous across the regions. The proportion of industrial output had a significant and positive influence on energy consumption and pollutant emissions in China and its three regions. Higher industrial energy intensity resulted in higher levels of pollutant emissions. No strong evidence supporting the EKC hypothesis for the three industrial wastes in China was found. Our findings further demonstrated that the impact of population, income and technology on the environment varies at different levels of development. Because of the regional disparities in anthropogenic impact on the environment, formulating specific region-oriented energy saving and emission reduction strategies may provide a more practical and effective approach to achieving sustainable development in China.

China’s Contributions to Global Green Energy and Low-Carbon Development: Empirical Evidence under the Belt and Road Framework

“…The energy consumption is much lower than the energy output, indicating that energy has not been fully utilized for export or storage, or that the energy demand is saturated at the current economic and technological level, so the industrial structure and energy utilization technologies require transformation and upgrading. China has good development experience in the transformation and upgrading of the energy industry and the use of technology, which can help those countries to break the bottleneck of energy development and reach a green energy path [37]. According to Figure 3 which shows the relationship between per capita GDP and per capita energy consumption in 27 major countries and regions in 2013, and to Figure 4 which represents the relationship between per capita GDP and per capita carbon emissions in those countries and regions in 2013, there are three economic development modes for major international powers: the USCanadian mode (high energy consumption, high carbon emissions and high GDP, H-H-H), the EUJapan mode (low energy consumption, low carbon emissions and high GDP, L-L-H), and the ChinaRussia mode (high energy consumption, high carbon emissions and low GDP, H-H-L).…”

“…The energy consumption is much lower than the energy output, indicating that energy has not been fully utilized for export or storage, or that the energy demand is saturated at the current economic and technological level, so the industrial structure and energy utilization technologies require transformation and upgrading. China has good development experience in the transformation and upgrading of the energy industry and the use of technology, which can help those countries to break the bottleneck of energy development and reach a green energy path [37].…”

Time and space model of urban pollution migration: Economy-energy-environment nexus network

“…Unlike economic relations, the direct pollution linkage between cancer villages and their adjacent cities is a mutually affected relationship, rather than a one-way impact of big cities on counties. For example, (sd(12, 1), sd(1, 12)) = (À, +) indicates an ''exploited relationship," and (sd (22,5), sd(5, 2)) = (+, À); (sd(6, 4) sd(4, 6)) = (0, 0) shows a ''neutral relationship." In other words, Wenshang's pollution is affected by Jining and Luoshan's by Shenqiu, but there is no direct pollution interaction between Xiping and Luoshan.…”

“…Second, when a node's relationship changes, the entire network change must be considered, as with the previous case about economic influence. For example, (su (22,5), su (5, 22)) = (À, À) indicates a competitive relationship. Considering the indirect relationship between Luohe and Shenqiu, the two maintain a competitive relationship and are both affected by other cities' or counties' pollution.…”

“…In their estimation, an increasing number of scholars will study the relocation of pollution-causing enterprises after the appearance of a new data collection strategy and solution to the problem of data sources. Liu et al [22] used an extended STIRPAT model to investigate the effects of human 1 http://s.weibo.com/weibo/%25E7%2599%258C%25E7%2597%2587%25E6%259D% 2591?topnav=1&wvr=6&b=1. activity on energy consumption and three types of industrial pollutant emissions (exhaust gases, waste water and solid waste) at the national and regional levels and tested the environmental Kuznets curve (EKC) hypothesis based on a balanced provincial panel dataset in China over the period 1990-2012.…”