Investigating economic growth, energy consumption and their impact on CO₂emissions targets in China

Abstract

This article aims to analyse economic growth, energy consumption and carbon dioxide (CO 2 ) emissions data in China comparing with the US and Japanese data. Then, we try to evaluate the Chinese government's targets in reducing energy use and carbon intensity. Economic growth is a key factor that determines the Chinese's ability to meet these targets. If we suppose that China's economic growth is maintained at 9.54% per year, and total primary energy supply (TPES) growth is stabilized at 3.82% on average, the country would be able to cut energy consumption per unit amount of gross domestic product (GDP) by 23.5% in 2010: if carbon-GDP intensity could decrease by 4.5% on average, China would be able to achieve a reduction in carbon-GDP emissions target by 49.87% or above the target in 2020. We suggest that China needs to stabilize CO 2 per total primary energy supply intensity. This target can also push government to implement clean coal technology and promote renewable energy target more seriously. Finally, we argue that binding target on CO 2 emissions has worked effectively in the case of Japan, but we have to be careful when analysing economic-energy-CO 2 emissions in Japan due to 'the lost decade' of Japan's economy. Finally, we expect that developed countries such as Japan and the United States can help China not only in transferring technology but also in strengthening the institutional capacity such as in harmonizing regulations, in energy planning and in developing human capability.

Study of the impact of energy consumption structure on carbon emission intensity in China from the perspective of spatial effects

“…Factor decomposition models tend to use the logarithmic mean decomposition method (LMDI) and the modified Laspeyres exponential decomposition method, and the research results show that energy intensity, the energy consumption structure, the carbon emission coefficient, industrial structure, and other factors have a great impact on carbon emission intensity (Torvanger 1991;Barnali Nag 2000;Nag and Parikh 2000;Ebohon and Ikeme 2006;Lu et al 2007;Bhattacharyya and Matsumura 2010;Chen 2011;Hammond and Norman 2012;Alves and Moutinho 2013;Li and Ou 2013;Zhang and Da 2015;Liu et al 2015;Chen and Lin 2015;Ang and Su 2016;Su and Ang 2016;Zhang et al 2016;Jiang 2017;Sun et al 2017;Tian et al 2018). Econometric models primarily use time series data models, panel data models, and other models to study the impact of economic development, foreign direct investment, technological progress, population size, urbanization, and other factors on carbon emission intensity (Giblin and Mcnabola 2009;Sambodo and Oyama 2011;Fisher-Vanden et al 2012;Fang et al 2013;Wang and Ren 2013;Chik et al 2013;Zhang et al 2014;Ren et al 2014;Wei and Zhou 2014;Ohlan 2015;Wang et al 2015;Jovanović et al 2015;Lin et al 2015;Dong et al 2016;Köne and Büke 2016;Lin and Nelson 2017;…”

Impacts of Energy Structure on Carbon Emissions in China, 1997–2019

“…All non-fossil fuel energy sources-hydro-, nuclear, wind, and solar power-consisted of two parts: (18) electricity and ( 19) other energy. Based on provincial-level statistical yearbooks published throughout China, hydro-and nuclear power consumption were included under (18) electricity, while wind, solar, and biomass consumption were placed within (19) other energy. Notably, electricity consumption, represented here solely by hydro-and nuclear power consumption, excluded fossil-fuel power consumption.…”

“…For example, Bhattacharyya et al studied 15 European Union member nations using LMDI-based factor decomposition, finding that changes in energy structure, reduced energy intensity, and improved production processes were the primary drivers of carbon emissions [7]. Alternatively, econometric analyses typically involve the use of time series, cross-sectional, and panel data models to study the correlations of technological progress, population size, urbanization, and other factors with carbon emissions [13][14][15][16][17][18][19][20]. Moreover, the geographic detector method (i.e., geodetector), has become a dominant technique for investigating While studies regarding the impacts of individual energy components on carbon emissions remain limited, there has been an abundance of research on factors influencing carbon emissions, the majority of which indicating that energy structure is one of the most important factors influencing carbon emissions.…”