Carbon implications of China s urbanization

Transcription

1 Energ. Ecol. Environ. (2016) 1(1):39 44 DOI /s x RESEARCH ARTICLE Carbon implications of China s urbanization Kuishuang Feng 1 Klaus Hubacek 1 1 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA Received: 15 February 2016 / Revised: 15 February 2016 / Accepted: 15 February 2016 / Published online: 25 February 2016 Joint Center on Global Change and Earth System Science of the University of Maryland and Beijing Normal University and Springer-Verlag Berlin Heidelberg 2016 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 CO 2 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 CO 2 emissions would be induced by moving 100 million rural residents to cities by Rural urban migration plans of such scale need to go hand in hand with urban planning and climate policies to mitigate the effects on CO 2 emissions and other environmental issues. Keywords Rural urban migration Input output analysis CO 2 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 & Kuishuang Feng kfeng@umd.edu & Klaus Hubacek Hubacek@umd.edu development, which requires significant product, energy materials and other natural resource inputs and results in huge waste streams and emissions such as CO 2 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 CO 2 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 CO 2 emissions per unit of gross domestic production (GDP) (i.e., carbon intensity) by % 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

2 40 K. Feng, K. Hubacek 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 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 9 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 ¼ A x þ y ð1þ where y denotes final demand. Then, the total economic output x required to supply the final demand is calculated by x ¼ ði AÞ 1 y ð2þ where I denotes the n 9 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 CO 2 emissions incurred to produce one monetary unit of output of each economic sector. Therefore, the total direct and indirect emissions are calculated by g ¼ e ði AÞ 1 y þ hh ð3þ where g is the total direct and indirect CO 2 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 CO 2 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 CO 2 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 CO 2 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 CO 2 emissions data were obtained from Liu et al. (2015b). Liu et al. (2015a, b) published estimates of CO 2 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 CO 2 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 CO 2 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 CO 2 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

3 Carbon implications of China s urbanization 41 will migrate from rural to urban areas by 2020 with migration of 16.7 million per year between 2015 and 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 CO 2 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 CO 2 emissions. 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 Per capita CO2 emissions (ton) Rural Urban Na onal average Fig. 1 Per capita CO 2 emissions of Chinese household (ton) 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 CO 2 emissions in their upstream supply chains. In addition, it is interesting to note that spending on real estate may trigger a large amount of CO 2 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 CO 2 emissions to produce these goods and services. In addition, once residents are locked into urban lifestyles, the accumulative CO 2 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 CO 2 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 CO 2 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 CO 2 emissions could reach 1 gigaton in about 5 years which is about 17 % of China s total territorial CO 2 emissions and more than the total annual CO 2 emissions of Germany. 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 CO 2 emissions, thus imposing a big

4 42 K. Feng, K. Hubacek Fig. 2 Structural decomposition of carbon footprint for rural and urban households Million CO2 emissions Cumula ve addi onal CO2 emissions Fig. 3 Cumulative additional CO 2 emissions from moving 100 million rural residents to urban by 2020 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 CO 2 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 CO 2 emissions during production processes and resource extraction. Given the fact that the average lifespan of a Chinese building is only 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 CO 2 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 CO 2 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

5 Carbon implications of China s urbanization 43 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 CO 2 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 CO 2 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 CO 2 emissions and other environmental issues. References Barrett J, Peters G, Wiedmann T, Scott K, Lenzen M, Roelich K, Le Quéré C (2013) Consumption-based GHG emission accounting: a UK case study. Clim Policy 13: BPIE (2011) Europe s buildings under the microscope a countryby-country review of the energy performance of buildings. Buildings Performance Institute Europe Chivakul M, Lam WR, Liu X, Maliszewski W, Schipke A (2015) Understanding residential real estate in China. International Monetary Fund, Washington Davis SJ, Caldeira K (2010) Consumption-based accounting of CO 2 emissions. Proc Natl Acad Sci 107: Druckman A, Jackson T (2009) The carbon footprint of UK households : a socio-economically disaggregated, quasi-multiregional input output model. Ecol Econ 68: Feng K, Hubacek K, Guan D (2009) Lifestyles, technology and CO 2 emissions in China: a regional comparative analysis. Ecol Econ 69: Feng K, Siu YL, Guan D, Hubacek K (2012) Analyzing drivers of regional carbon dioxide emissions for China. J Ind Ecol 16: Feng K, Davis SJ, Sun L, Li X, Guan D, Liu W, Liu Z, Hubacek K (2013) Outsourcing CO2 within China. Proc Natl Acad Sci 110: Feng K, Hubacek K, Sun L, Liu Z, (2014) Consumption-based CO 2 accounting of China s megacities: the case of Beijing, Tianjin, Shanghai and Chongqing. Ecol Indic 47:26 31 Guan D, Hubacek K, Weber CL, Peters GP, Reiner DM (2008) The drivers of Chinese CO 2 emissions from 1980 to Glob Environ Change 18: Guan D, Peters GP, Weber CL, Hubacek K (2009) Journey to world top emitter: an analysis of the driving forces of China s recent CO 2 emissions surge. Geophys Res Lett 36:1 5 Hertwich EG, Peters GP (2009) Carbon footprint of nations: a global, trade-linked analysis. Environ Sci Technol 43: Hubacek K, Sun L (2001) A scenario analysis of China s land use and land cover change: incorporating biophysical information into input output modeling. Struct Change Econ Dyn 12:367 Hubacek K, Guan D, Barua A (2007) Changing lifestyles and consumption patterns in developing countries: a scenario analysis for China and India. Futures 39: Hubacek K, Guan D, Barrett J, Wiedmann T (2009) Environmental implications of urbanization and lifestyle change in China: ecological and water footprints. J Clean Prod 17: Huppes G, de Koning A, Suh S, Heijungs R, van Oers L, Nielsen P, Guinee JB (2006) Environmental impacts of consumption in the European Union: high-resolution input output tables with detailed environmental extensions. J Ind Ecol 10:129 Johnson I (2014) China releases plan to incorporate farmers into cities. The New York Times, Beijing Lenzen M (2011) Aggregation versus disaggregation in input output analysis of the environment. Econ Syst Res 23:73 89 Lenzen M, Treloar GJ (2005) Endogenising Capital a comparison of two methods. J Appl Input Output Anal 10:1 11 Liang QM, Fan Y, Wei EM (2007) Multi-regional input output model for regional energy requirements and CO 2 emissions in China. Energy Policy 35: Liu Z, Davis SJ, Feng K, Hubacek K, Liang S, Anadon LD, Chen B, Liu J, Yan J, Guan D (2015a) Targeted opportunities to address the climate-trade dilemma in China. Nat Clim Change 6: Liu Z, Guan D, Wei W, Davis SJ, Ciais P, Bai J, Peng S, Zhang Q, Hubacek K, Marland G, Andres RJ, Crawford-Brown D, Lin J, Zhao H, Hong C, Boden TA, Feng K, Peters GP, Xi F, Liu J, Li Y, Zhao Y, Zeng N, He K (2015b) Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature 524: Meng B, Xue J, Feng K, Guan D, Fu X (2013) China s inter-regional spillover of carbon emissions and domestic supply chains. Energy Policy 61: Miller RE, Blair PD (2009) Input output analysis: foundations and extensions, 2nd edn. Cambridge University Press, New York Minx JC, Baiocchi G, Peters GP, Weber CL, Guan D, Hubacek K (2011) A carbonizing dragon : China s fast growing CO 2 emissions revisited. Environ Sci Technol 45: National Bureau of Statistics of China (2013) China Statistics Yearbook National Statistical Bureau of China, Beijing National Bureau of Statistics of China (2015) National input output tables. National Bureau of Statistics of China, Beijing Peters GP, Weber CL, Guan D, Hubacek K (2007) China s growing CO 2 emissions: a race between increasing consumption and efficiency gains. Environ Sci Technol 41: Su W (2010) Letter from SU Wei Director General of the Department of Climate Change, NDRC, to Yves de Boer, Executive Secretary of the UNFCCC. Department of Climate Change, National Development & Reform Commission of China Suh S, Huppes G (2005) Methods for life cycle inventory of a product. J Clean Prod 13: UNFCCC (2015) Historic Paris Agreement on climate change 195 nations set path to keep temperature rise well below 2 degrees Celsius. United Nations Framework Convention on Climate Change Wang Q (2010) Short-lived buildings create huge waste. China Daily htm Wang A, Liao W, Hein A-P (2012) Bigger, better, broader: a perspective on China s auto market in McKinsey & Company, New York

6 44 K. Feng, K. Hubacek XINHUANET (2014) State New Urbanization Plan ( 国家新型城镇化规划 ). XINHUANET, Beijing Xu X, Tan Y, Chen S, Yang G, Su W (2015) Urban household carbon emission and contributing factors in the Yangtze River Delta, China. PLoS ONE 10:e Zhang M, Mu H, Ning Y, Song Y (2009) Decomposition of energyrelated CO 2 emission over in China. Ecol Econ 68: Zhu Q, Wei T (2015) Household energy use and carbon emissions in China: a decomposition analysis. Environ Policy Gov 25: