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Energy, Ecology and Environment

, Volume 1, Issue 1, pp 39–44 | Cite as

Carbon implications of China’s urbanization

  • Kuishuang FengEmail author
  • Klaus HubacekEmail author
Research Article

Abstract

China recently announced a plan to move an unprecedented large number of rural residents to cities over a relative short period of time; i.e., potentially more than 100 million people would move to China’s cities by 2020 potentially leading to large increases in energy consumption and CO2 emissions. By applying environmentally extended input–output analysis, in this study we estimate the carbon footprint of Chinese urban and rural residents and assess the carbon implications of China’s urban migration plan. Our results show that more than 1 gigaton cumulative additional CO2 emissions would be induced by moving 100 million rural residents to cities by 2020. Rural–urban migration plans of such scale need to go hand in hand with urban planning and climate policies to mitigate the effects on CO2 emissions and other environmental issues.

Keywords

Rural–urban migration Input–output analysis CO2 emissions Mitigation 

1 Introduction

China’s urban population has sharply increased from less than 200 million to more than 700 million in the past three decades. More than half of China’s population is currently living in urban areas (National Bureau of Statistics of China 2013). Large-scale urbanization in China has led to unprecedented urban expansion and infrastructure development, which requires significant product, energy materials and other natural resource inputs and results in huge waste streams and emissions such as CO2 emissions. Migration from rural and urban areas leads to higher incomes and thus a substantial increase in household consumption and more carbon intensive lifestyles (Feng et al. 2009; Guan et al. 2009; Hubacek et al. 2007, 2009; Xu et al. 2015; Zhu and Wei 2015).

China recently announced a plan to move more rural residents to cities (XINHUANET 2014). Studies predict that more than 100 million people will move to China’s cities by 2020—one of the greatest migrations in history (Johnson 2014). The Chinese government sees this as an opportunity to bolster economic growth based on domestic consumption. This vast migration at a historically unprecedented scale in a relative short period of time will have many social and economic effects and a host of unintended environmental side-effects. Transplanting more than 100 million relatively poor rural residents to cities will induce a huge amount of energy consumption and CO2 emissions to create infrastructure and housing as well as producing the additional goods and services that come with higher incomes and urban lifestyles. This rapid change on such large scale and in relatively short time may further impose pressure on climate mitigation targets such as reducing its CO2 emissions per unit of gross domestic production (GDP) (i.e., carbon intensity) by 40–45 % from 2005 levels by 2020 announced by the Chinese government (Su 2010), thus jeopardizing the global agreement of keeping warming to well below 2 °C above pre-industrial levels (UNFCCC 2015).

In this study, we first estimate carbon footprints of Chinese rural and urban residents by applying environmentally extended input–output approach. And then, we assess carbon implications of China’s urban migration plan, moving 100 million rural residents to cities by 2020.

2 Materials and methods

Environmentally extended input–output models have been frequently applied to estimate carbon footprints (Barrett et al. 2013; Davis and Caldeira 2010; Druckman and Jackson 2009; Feng et al. 2012, 2013; Guan et al. 2008; Hertwich and Peters 2009; Huppes et al. 2006; Feng et al. 2014; Guan et al. 2009; Liang et al. 2007; Liu et al. 2015a; Meng et al. 2013; Minx et al. 2011; Peters et al. 2007; Zhang et al. 2009).

Input–output analysis originally developed by Leontief describes how sectors are interrelated through producing and consuming intermediate economic outputs that are represented by monetary transactions between economic sectors (Miller and Blair 2009). In an input–output model, it is assumed that each industry consumes outputs of various other industries in fixed ratios in order to produce its own unique and distinct output (Miller and Blair 2009; Suh and Huppes 2005).

Based on this assumption, we define a n × n matrix A of which each column of A shows domestic and imported intermediate economic outputs in monetary values which are required to produce one unit of monetary output of another sector. We define x as the total economic output, where x is equal to the summation of the economic outputs consumed by intermediate economic sectors final consumers (e.g., household, government, capital investment and export). For the economy as whole, the input–output model can be shown by
$$ x = A * x + y $$
(1)
where y denotes final demand. Then, the total economic output x required to supply the final demand is calculated by
$$ x = \left( {I - A} \right)^{ - 1} *\,y $$
(2)
where I denotes the n × n identity matrix with 1s in the main diagonal and 0s elsewhere.
The total direct and indirect emissions by domestic and import sectors to deliver a certain amount of economic output can be calculated by the environmentally extended input–output (EIO) model which assumes that the amount of emissions generated by a sector are proportional to the amount of output of the sector; and the identity of the emissions and the ratio between them are fixed. Vector e shows the amount of CO2 emissions incurred to produce one monetary unit of output of each economic sector. Therefore, the total direct and indirect emissions are calculated by
$$ g = e * \left( {I - A} \right)^{ - 1} * \,y + hh $$
(3)
where g is the total direct and indirect CO2 emissions associated with the final consumption of a nation; y is a vector that shows the final consumption of products from different economic sectors; hh denotes direct household emissions (e.g., emissions from heating and driving). To capture the total CO2 emissions of rural and urban household consumption, we replace the total final consumption vector, y, with the rural or urban household consumption vector.

It is important to note that about one-third of China’s CO2 emissions are associated with capital investments, which are crucial for providing the infrastructure and production facilities required to produce goods and services consumed by households. To allocate the CO2 emissions associated with capital formation to household consumption, we close the IO model for capital investment using the augmentation method in the absence of a detailed capital flow matrix for China (Lenzen and Treloar 2005). In the closed model, we treat capital as an input to production rather than final demand.

In this study, the China’s 2012 input–output table was collected from the National Bureau of Statistics of China (National Bureau of Statistics of China 2015). The Chinese IO table for 2012, which contains 139 economic sectors, is the latest IO table published by the Chinese government. Capital investment data were collected from the China Statistical Yearbook 2013 (National Bureau of Statistics of China 2013). Sectoral level CO2 emissions data were obtained from Liu et al. (2015b). Liu et al. (2015a, b) published estimates of CO2 emissions from fossil fuel combustion and industrial processes for 45 economic sectors as well as direct emissions from rural and urban households. Studies show that disaggregation of environmental data or input–output sectors may improve the accuracy of the input–output analytical result (Lenzen 2011). Therefore, in this study we disaggregate CO2 emissions of 45 economic sectors into 139 national IO sectors by assuming sectors in the same industrial categories having the same emission intensity (emission per unit of total economic output). Therefore, the CO2 emissions of 139 IO sectors can be estimated based on the emission intensities of the 45 economic sectors and the total output of the 139 IO sectors.

In this study, we carry out a scenario analysis to estimate the additional CO2 emissions due to the announced plan of moving 100 million rural residents to cities in China. In this scenario, we assume that 100 million rural residents will migrate from rural to urban areas by 2020 with migration of 16.7 million per year between 2015 and 2020. The rural residents are assumed to adopt urban lifestyles and income when they move to urban areas; thus, their carbon footprint will be similar to the one of urban residents.

3 Results

Figure 1 shows per capita carbon footprints of rural and urban households as well as the national average. From Fig. 1, we can see that carbon footprints vary significantly between rural and urban residents. On average, rural residents in China cause 1.8 ton of CO2 emissions per year, while the per capita footprint of urban residents is close to three times (5.2 tons) the one for rural residents. Although urban residents account for 52 % of the total Chinese population, they are responsible for more than three quarters of the total household consumption-related CO2 emissions.
Fig. 1

Per capita CO2 emissions of Chinese household (ton)

It has been widely discussed that urban residents on average enjoy more luxury lifestyles than rural residents, in particular in a developing country like China (Feng et al. 2009; Guan et al. 2008; Hubacek et al. 2007; Hubacek and Sun 2001), which may explain the big gap in per capita carbon footprints between rural and urban residents. Figure 2 shows a structural decomposition of the carbon footprint for rural and urban residents in China. The figure shows that more than half of the carbon footprint of rural households is associated with consumption of goods and services for their basic needs, such as food and drinks, clothes and utilities (including direct emissions from heating). However, the share of the carbon footprint for the basic needs of urban households is about 37 %; thus, the share of other luxury goods and services becomes dominant in the total carbon footprint of urban households. For example, the shares of equipment and machinery and petroleum products for urban households together account for about 10 % of the total carbon footprint as urban households in China tend to have a higher rate of car ownership and consume more gasoline products than rural households. In addition, urban households may have easier access to services, such as banking, recreation and health facilities, but also tend to eat out more often than rural residents, and all these activities require inputs of goods from manufacturing, energy and resource extracting sectors, thus causing a large amount of CO2 emissions in their upstream supply chains.
Fig. 2

Structural decomposition of carbon footprint for rural and urban households

In addition, it is interesting to note that spending on real estate may trigger a large amount of CO2 emissions as real estate is a capital intensive sector requiring a huge amount of construction and building materials. Emissions associated with real estate spending accounts for more than 20 % of the total carbon footprint for both rural and urban households. This is consistent with the China’s real estate investment data which show a growth from 4 % of GDP in 1997 to 15 % of GDP in 2014 with high share of residential real estate investment (close to 10 % of GDP) (Chivakul et al. 2015).

Moving 100 million rural residents to cities may not only change their lifestyles and increase their consumption of goods and services, but may also cause a huge amount of additional CO2 emissions to produce these goods and services. In addition, once residents are locked into urban lifestyles, the accumulative CO2 emissions associated with their lifestyle change may impose a huge challenge to global climate mitigation targets, for instance holding the increase in global average temperature to well below 2 degrees C above pre-industrial levels (UNFCCC 2015). Figure 3 shows a scenario result on cumulative additional CO2 emissions (the difference between rural and urban carbon footprints) by moving 100 million rural residents to cities in China. From the figure, we see that about 1200 million tons additional CO2 emissions are required to meet the needs of 100 million rural residents changing to urban lifestyle and consumption patterns by 2020, assuming no change in technology. The total cumulative additional CO2 emissions could reach 1 gigaton in about 5 years which is about 17 % of China’s total territorial CO2 emissions and more than the total annual CO2 emissions of Germany.
Fig. 3

Cumulative additional CO2 emissions from moving 100 million rural residents to urban by 2020

4 Conclusions and policy implications

Our IO-based scenario analysis shows a huge gap in per capita carbon footprints between rural and urban residents in China. In this study, our EIO analysis shows a huge gap in per capita carbon footprint between rural and urban residents in China; urban resident has per capita carbon footprints three times the size of rural residents. The implementation of China’s rural–urban migration plan, moving more than 100 million rural residents to cities, may cause more than 1 gigaton of additional CO2 emissions, thus imposing a big challenge to China’s national climate mitigation policies and the global agreement to prevent 2 degrees temperature increase. Therefore, careful urban planning is necessary to mitigate the effects of the large-scale rural–urban migration on CO2 emissions and other environmental issues such as air pollution and water scarcity.

A large-scale migration from rural to urban area will probably trigger another wave of carbon intensive residential real estate and other investments to meet the demand of the new urban residents. The increasing demand of residential buildings will lead to an increasing production of building materials and can be eventually translated to a large amount of CO2 emissions during production processes and resource extraction. Given the fact that the average lifespan of a Chinese building is only 25–30 years (Wang 2010), it is crucial to establish building codes that extend the lifespan of residential buildings. For example, renovating old buildings may improve energy efficiency and living quality (BPIE 2011); at the same time mitigate CO2 emissions, rather than entirely rebuilding the old building. In addition, setting up binding codes for energy efficiency for new residential development may significantly reduce the effects of urban expansion on material and energy use and CO2 emissions.

Our results also show that urban residents tend to have a much higher share of carbon emissions for their consumption of vehicles and petroleum products as part of their overall carbon budget. In an emerging economy like China, higher incomes in urban areas means more spare money for the purchase of private vehicles and thus higher fuel consumption. China’s passenger car market had an average annual increase of about 24 % from 2005 to 2011, and this is dominantly driven by the purchasing power of urban residents (Wang et al. 2012). There are existing car restriction policies in some megacities in China, such as Beijing, Shanghai and Hangzhou. However, the new urban migration may largely occur in small- and medium-sized cities which might lead to a significant increase in China’s car ownership. Increasing public transport accessibility in urban and suburban areas along with car restriction policies may be more effective to prevent the fast growth of using private transport, thus slowing down the increasing CO2 emissions from urban expansion. This requires large-scale investments in urban transport infrastructure, which will have medium-term carbon implications due to construction but will have longer-term impacts guiding the choices of residents toward low carbon mobility.

Overall, our results indicate that there is a large potential for additional CO2 emissions caused by the ambitious rural–urban migration plans in China. Rural–urban migration plans need to go hand in hand with urban planning and climate policies to mitigate the effects on CO2 emissions and other environmental issues.

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Copyright information

© Joint Center on Global Change and Earth System Science of the University of Maryland and Beijing Normal University and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Geographical SciencesUniversity of MarylandCollege ParkUSA

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