Divergent Leading Factors in Energy-Related CO2 Emissions Change among Subregions of the Beijing–Tianjin–Hebei Area from 2006 to 2016: An Extended LMDI Analysis

Abstract

In recent decades, the Beijing–Tianjin–Hebei (BTH) region has experienced rapid economic growth accompanied by increasing energy demands and CO2 emissions. Understanding the driving forces of CO2 emissions is necessary to develop effective policies for low-carbon economic development. However, because of differences in the socioeconomic systems within the BTH region, it is important to investigate the differences in the driving factors of CO2 emissions between Beijing, Tianjin, and Hebei. In this paper, we calculated the energy-related industrial CO2 emissions (EICE) in Beijing, Tianjin, and Hebei from 2006 to 2016. We then applied an extended LMDI (logarithmic mean Divisia index) method to determine the driving forces of EICE during different time periods and in different subregions within the BTH region. The results show that EICE increased and then decreased from 2006 to 2016 in the BTH region. In all subregions, energy intensity, industrial structure, and research and development (R&D) efficiency effect negatively affected EICE, whereas gross domestic product per capita effect and population had positive effects on EICE. However, R&D intensity and investment intensity had opposite effects in some parts of the BTH region; the effect of R&D intensity on EICE was positive in Beijing and Tianjin but negative in Hebei, while the effect of investment intensity was negative in Beijing but positive in Tianjin and Hebei. The findings of this study can contribute to the development of policies to reduce EICE in the BTH region.

Economic Structure Transformation and Low-Carbon Development in Energy-Rich Cities: The Case of the Contiguous Area of Shanxi and Shaanxi Provinces, and Inner Mongolia Autonomous Region of China

“…Cities are the key areas in which to implement carbon emission popular alternative method. Index decomposition analysis (IDA) [30][31][32][33][34][35] and structural decomposition analysis (SDA) [36][37][38][39][40][41] are mainly employed. IDA can only measure the direct production effect, while SDA can capture the direct and indirect production effects by taking inter-industrial interactions into consideration [6].…”

Industrial Energy-Related CO2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016

“…In more than half of these provinces, the effect changed from promotion to inhibition from 2008–2012 to 2012–2016. It was reported that R&D intensity presented an obvious mitigating effect in SH [ 14 ], HB [ 19 ] and on the national level [ 15 ]. Taken together, the effect of R&D intensity varied considerably, which may be associated with changes in the performers of R&D activities [ 17 ] and technical barriers.…”

“…An extended LMDI model [ 19 ] was used to break down the IECE changes into the following nine factors: …”

“…However, most of these studies only considered several conventional factors on CO 2 emissions, including carbon emission coefficient, energy structure, energy intensity, industrial structure, economic output, population and urbanization [ 6 , 13 ], while technological factors, namely R&D efficiency and R&D intensity, were largely ignored. Recently, the LMDI model has been extended by introducing the three factors to explain changes in carbon emissions [ 14 , 15 , 16 , 17 , 18 , 19 ] and it was suggested that investment intensity displays a promotional effect, whereas R&D efficiency and R&D intensity can bring either positive or negative effects on carbon emissions.…”