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

Zou Zou,Wu Wu,Tang Tang

doi:10.3390/su11184929

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.

Highlights

Global warming is one of the most serious environmental issues across the globe, and carbon dioxide (CO2) emissions are the major contributor to global warming
Energy-related industrial CO2 emissions (CE) were determined as activity data (AD; i.e., fossil fuel consumption) multiplied by net calorific value (NCV; i.e., the heat value produced per physical unit of fossil fuel combusted), the emission factor of CO2 (EF; i.e., the CO2 emissions per net caloric value), and oxygenation efficiency (O; i.e., the oxidation ratio when burning fossil fuels): CEij = ADij × NCVj × EFj × Oj where subscripts i and j refer to the specific sector and energy type, respectively
Total industrial energy consumption and its structure is crucial for energy-related industrial CO2 emissions (EICE) owing to the fact that different types of energy have very different CO2 emission coefficients, which is obvious given that the CO2 emission coefficient of coal and coke is much higher than that of oil and gas

Summary

Introduction

Global warming is one of the most serious environmental issues across the globe, and carbon dioxide (CO2) emissions are the major contributor to global warming. China has become the largest carbon emitter since 2006 and the largest energy consumer since 2009. China has committed to mitigating its CO2 emissions. During the Copenhagen Climate Change Conference in 2009, the Chinese government proposed to reduce China’s CO2 emission intensity by 40–45% in 2020 compared with the 2005 level [2]. During the Paris Climate Conference in 2015, China further made a commitment of reducing its CO2 emission intensity by 60–65% by 2030 compared to the 2005 level. The Chinese government aims to decrease its total CO2 emissions after 2030 [3]

Methods

Results

Conclusion