Abstract
The Beijing-Tianjin-Hebei (B-T-H) region, who captures the national strategic highland in China, has drawn a great deal of attention due to the fog and haze condition and other environmental problems. Further, the high carbon emissions generated by energy consumption has restricted its further coordinated development seriously. In order to accurately analyze the potential influencing factors that contribute to the growth of energy consumption carbon emissions in the B-T-H region, this paper uses the carbon emission coefficient method to measure the carbon emissions of energy consumption in the B-T-H region, using a weighted combination based on Logarithmic Mean Divisia Index (LMDI) and Shapley Value (SV). The effects affecting carbon emissions during 2001–2013 caused from five aspects, including energy consumption structure, energy consumption intensity, industrial structure, economic development and population size, are quantitatively analyzed. The results indicated that: (1) The carbon emissions had shown a sustained growth trend in the B-T-H region on the whole, while the growth rates varied in the three areas. In detail, Hebei Province got the first place in carbon emissions growth, followed by Tianjin and Beijing; (2) economic development was the main driving force for the carbon emissions growth of energy consumption in B-T-H region. Energy consumption structure, population size and industrial structure promoted carbon emissions growth as well, but their effects weakened in turn and were less obvious than that of economic development; (3) energy consumption intensity had played a significant inhibitory role on the carbon emissions growth; (4) it was of great significance to ease the carbon emission-reduction pressure of the B-T-H region from the four aspects of upgrading industrial structure adjustment, making technological progress, optimizing the energy structure and building long-term carbon-emission-reduction mechanisms, so as to promote the coordinated low-carbon development.
Highlights
With the rapid development of the economy, China is facing a severe energy and environment situation with issues such as increasing energy consumption, enormous carbon emissions, serious fog and haze conditions and frequent extreme weather disasters
The data of energy consumption comes from the Beijing Statistical Yearbook [37], Tianjin Statistical Yearbook [38], Hebei Economic Yearbook [39] and the Chinese Energy Statistics Yearbook during 2001–1014; the data of industrial output value, gross domestic product (GDP) and population quantity comes from the China statistical Yearbook [40] during 2001–2014
To accurately analyze the potential influencing factors affecting carbon emissions, in this paper, based on the measurement of carbon emissions in the B-T-H region by the direct carbon emissions coefficient method, the weighted-combination decomposition analysis model, who was made up by Logarithmic Mean Divisia Index (LMDI) and Shapley Value (SV), was applied to quantitatively analyze the effects on carbon emissions during 2001–2013 caused by five factors, including energy consumption structure, energy consumption intensity, industrial structure, economic development and population size
Summary
With the rapid development of the economy, China is facing a severe energy and environment situation with issues such as increasing energy consumption, enormous carbon emissions, serious fog and haze conditions and frequent extreme weather disasters. The LMDI decomposition method is more advanced because it focuses on multiple factors, and because its decomposition results can be in additive form or in multiplied form without residual error It can be used for some incomplete data sets, effectively improving the interpretability of influencing factors, which brings the great facility in the field of energy and environmental problems analysis [27,28]. The rest of the paper is structured as follows: Section 2 estimated the carbon emissions from energy consumption in the B-T-H region; Section 3 established the carbon emission decomposition model; thorough analysis was made aiming at the decomposition results and corresponding measures and suggestions were proposed for energy saving and emission reduction in Section 4; at last, Section 5 summarized the research results
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