Driving forces analysis of energy-related carbon dioxide (CO2) emissions in Beijing: an input–output structural decomposition analysis
Introduction
CO2 emission, which has emerged as a major concern to be addressed, has attracted increasingly more widespread attention all over the world. Currently, due to the increasing concerns of the environmental impact of CO2 emissions, there are many facts that prove that greenhouse gas emissions, mainly CO2 emissions, reflect a large quantity of environmental issues, such as the rise of the global temperature and the heat-transfer conductance of urban areas and fog and haze (Wang et al., 2012). Therefore, reducing CO2 emissions has become a new economic development research direction world-wide (Wang et al., 2012).
China, the largest developing country in the world, has already become the top primary energy consumer as well as the top CO2 emitter in the world (Boden et al., 2009, Yuan et al., 2013). In January of 2012, the regulation of CO2 emissions was embodied in the 12th Five Year Plan, which was issued by China's State Council (Wang and Liang, 2013). The reduction standard noted that CO2 emissions should decrease by 17% from 2010 to 2015 to reverse the domination of both production-based and consumption-based CO2 emissions, which have increased rapidly in recent decades (Peters et al., 2011).
China is the most populous country in the world, and the metropolitan population represents more than half of the Chinese population. Due to the rapid development of urbanization and industrialization, Beijing, the governmental and economic center in China, unquestionably has played an important role in producing CO2 emissions in the industrialization and socialization processes during the last few decades (Satterthwaite, 2008, Wei and Yu, 2006). Both the operation of different industries and human actions produced CO2 emissions in Beijing, which have negatively affected the use of energy and the development of the country (Kennedy et al., 2010). Beijing has a permanent population of approximately 20 million. With the improvement of people's living standard, the sharply increasing household income may be the result of well-run industries and an expansion of the economic scale. Compared with other cities, Beijing provides an emblematic example of different types of consumption that are heavily dependent on plenty of products and resources that are provided by different industrial sectors such as agriculture, industrial departments and services (Li et al., 2015). Apparently, these sectors would lead to the result that the reduction of CO2 emissions still needs in-depth research. It is vital to highlight the research regarding CO2 emissions to have it placed on the agenda.
By analyzing the contributory factors to urban CO2 emissions, many previous studies have explored the reasons for the CO2 emission differences and proposed the optimal means of reducing CO2 emissions. Frequently used methods include the IPCC method (IPCC, 2006), the IPAT method (Di et al., 2011), the STIRPAT method (Fu et al., 2015, York et al., 2003), the LMDI method (Ang, 2005, Cansino et al., 2015), the GFI method (Wang et al., 2012; Ze et al., 2006) the Kaya Index method (Mavromatidis et al., 2016, Zhang and Zhou, 2007), etc. These methods strive to examine the differentiated impact factors on energy-related CO2 emission and argue that differentiated measures should be adopted according to regions (Wang and Zhao, 2015). However, these previous studies have simply adopted a single CO2 emissions index and have failed to comprehensively reflect the linkages between the different industrial sector and the effects of the sectoral connection and economic structure factors on CO2 emissions.
This paper used the input–output structural decomposition analysis (IO-SDA) method to calculate the direct, indirect and gross CO2 emission and the CO2 emission intensity of the different sectors in Beijing during the years of 2000–2010 and analyzed the driving forces underlying the changes in CO2 emissions evolution. The 10 years of research is of great significance. One of the major trends of international economic development in the 21st century is the globalization of the economy. During this period, China joined the World Trade Organization (WTO) in December 11, 2001. In Beijing, as the representative of the rapid development of China's economy, the Gross Domestic Product (GDP) has been increased by 5 times from 2000 to 2010. Meanwhile, there is an increasing number of people who tend to work and live in Beijing due to its better social resources in this period (Wang et al., 2013). This rapid growth in economic development has been considered as the hallmark of the Chinese economy, and this is unprecedented (Chen, 2015, Huang et al., 2015, Zhang et al., 2013).
The innovation and contribution of this research mainly lies in the following aspects: First, this paper used the IO-SDA combined method to study the technology, sectoral connection, economic structure and economic scale – these significant driving factors in 25 different sectors. This will be more effective in seeking the underlying causes of CO2 emissions and the driving mechanism. Moreover, compared to the calculation of IPCC Guidelines for national greenhouse gas inventories and other models, during the supply chains or during the cycling of products, the IO-SDA method can evaluate the resource flows and transfer of environmental burdens (Aviso et al., 2011). This top-down economic technique uses sectoral monetary transaction data to explain the complex interdependencies of industries in modern economies (Leontief, 1970). In view of Beijing as a center for China with rapid economic growth, this model can be used to study the implicit CO2 emissions among the trade of the sectors. Namely, the model can clearly quantify the intersectoral CO2 emissions flows in different sectors, and the total CO2 emissions can be counted according to this. Second, based on the IO-SDA method, technology and economic factors have been analyzed deeply. Although there has been research on CO2 emissions in Beijing, nearly all studies focused on area urban trades, urban residential consumption and government consumption instead of concentrating on industrial departments. Based on the input–output table and the energy balance table, this article divided the industrial sectors in Beijing into detail. In addition to agriculture and the third industry sector, the secondary industry sector was separated into 23 detailed sectors. Finally, taking advantage of different energy consumptions in the calculation of CO2 emissions, such as gasoline, coal, and fuel oil, it is easy to make a comparison of different industries' CO2 emissions under the same background conditions. The analysis can provide a guiding significance to a low CO2 transformation such that the relevant departments can enact statutes and provide relevant policy guidance to reduce CO2 emissions.
Section snippets
Direct CO2 emissions accounting framework
The calculation of direct CO2 emissions (DCE) is based on the energy consumption tables of different sectors in Beijing. DCE studied the CO2 emission of energy consumption to satisfy the final demand of industries. The energy consumption of industries consists of 10 categories: 1 – coal, 2 – coke, 3 – gasoline, 4 – kerosene, 5 – diesel fuel oil, 6 – fuel oil, 7 – liquefied petroleum, 8 – natural gas, 9 – electric power and 10 – heating power. The DCE accounting framework can be expressed as:
CO2 emissions of different sectors over 2000–2010
Fig. 1 displays the calculation results of input–output analysis. We can obtain the changes in CO2 emissions among different sectors in Beijing. Agriculture, industry and the tertiary sectors present a rising trend in CO2 emissions during this period. Among these sectors, the heavy industrial sectors remains the highest CO2 emissions. In the industrial sectors, the petroleum and natural gas exploitation (3-EPG), metal ores mining industry (4-MDM), manufacturing and processing of foods and
CO2 emissions of representative sectors in Beijing
With the development of the integration all around the world, the growth of China has become an indispensable part in terms of the development of the world economy. Generally speaking, the CO2 emissions of different sectors in Beijing act as a true portrayal of environmental pollution in China (Bai et al., 2016). In Beijing, the energy consumption of industrial sectors accounts for a total energy consumption of approximately 70% at present. Compared to other sectors, the second industry is the
Conclusions
In this study, the IO model is applied to calculate the CO2 emissions of different sectors in Beijing during the 2000–2010 period. During this period, the total average growth rate is 54%; the sole decline of 33% occurs from 2007 to 2010. The CO2 emission intensity in Beijing showed a total downward trend. The highest proportion that DCI, accounting for TCI emergence in 22-EGW, achieved 55%; the lowest proportion is 2% in 19-COM. Many sectors should be called into action to reduce CO2
Acknowledgments
This research was supported by National Natural Science Foundation of China (41301636, 51474033), National Social Science Foundation of China (13CJY051) and Hunan Provincial Natural Science Foundation of China (2015JJ3083).
References (56)
The LMDI approach to decomposition analysis: a practical guide
Energy Policy
(2005)- et al.
Fuzzy input–output model for optimizing eco-industrial supply chains under water footprint constraints
J. Clean. Prod.
(2011) - et al.
An inquiry into inter-provincial carbon emission difference in China: aiming to differentiated KPIs for provincial low carbon development
Ecol. Indic.
(2016) - et al.
Drivers of greenhouse gas emissions in the Baltic States: a structural decomposition analysis
Ecol. Econ.
(2014) - et al.
Driving forces of Spain's CO2 emissions: a LMDI decomposition approach
Renew. Sustain. Energy Rev.
(2015) Environmental pollution emissions, regional productivity growth and ecological economic development in China
China Econ. Rev.
(2015)- et al.
Scenario analysis of China's primary energy demand and CO2 emissions based on IPAT model
Energy Proc.
(2011) - et al.
The strategy of a low-carbon economy based on the STIRPAT and SD models
Acta Ecol. Sin.
(2015) - et al.
Regional application of ground source heat pump in China: a case of Shenyang
Renew. Sustain. Energy Rev.
(2013) - et al.
Regional initiatives on promoting cleaner production in China: a case of Liaoning
J. Clean. Prod.
(2010)