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Abstract

By using methods of carbon footprint analysis, and impacts of population, affluence and technology (IPAT), this article analyzes the carbon footprints of residence and travel, and their influential factors for different urban residential incomes, using 1,500 household questionnaires in Shijiazhuang city, Hebei province. The results show that the process of urban residents improving their living standards is also a driving factor in the increase of their carbon footprints; at the same time, the progress in technology has a positive impact on reducing the carbon footprints of urban residence. This article suggests that some measures, such as promoting energy-saving buildings and central heating supply, and establishing the convenient and comfortable public transport system, should be taken to reduce the carbon footprints of residence and travel in Shijiazhuang city.

Citation

Zhuang, X., K. Jiang, and X. Zhao, 2011: Analysis of the carbon footprint and its environmental impact factors for living and travel in Shijiazhuang city. Adv. Clim. Change Res., 2(3), doi: 10.3724/SP.J.1248.2011.00159.

Keywords

carbon footprint ; residential living ; energy ; greenhouse gas

1. Introduction

With the increasing urbanization in China, more people have been moving into cities over the recent years. This has resulted in increased demand on public service infrastructures and greater consumption of large amount of energy resources, causing serious environmental concerns. Solving various urban problems has become a challenging task for the administration.

It has been shown that current urban development and the residential way of life have resulted in negative impact on global ecology, environment, and climate. If the conventional method of development continues, there will be definite deviation from the direction of sustainability. To cope with the situation, there must be sustained low carbon development in China and in many other countries of the world. Many international organizations including the World Wide Fund for Nature (WWF) and researcher organizations have began to assess carbon footprint as a method to determine whether a country’s or region’s greenhouse gas (GHG) emissions due to human activities can be limited within the climate system threshold. The most representative results are the Global Footprint Network’s 2008 National Footprint Accounts Guidelines and 2008 National Footprint Accounts Calculation Methods. The Global Footprint Network’s core research calculates both the ecological footprint, the demand on nature and biocapacity, the capacity to meet this demand, of 201 countries, territories, and regions. In the 2003 China Ecological Footprint Report , jointly issued by the China Environment and Development International Cooperation Commission and WWF, relations between China’s ecological footprint and its biocapacity were thoroughly analyzed, and suggestions were provided to mitigate the conflicts among them. Their conclusions have provided very strong references for China to evaluate and improve its environment, to make polices for sustainable development, and to transform its economy into a low carbon one.

Based on the available core research results, this article aims to study findings on the carbon footprints of residential living and transportation behaviors in Shijiazhuang, Hebei province, a typical North China city. On the basis of household questionnaires, the article has analyzed the carbon footprints of residences and travel habit and their influential factors under different urban household incomes. The purpose of the study is to stimulate concerns on energy saving and environmental protection while enjoying the modernized way of life, and to encourage lower carbon footprints while improving life quality, as well as to mitigate future urban development problems due to present insufficient environmental protection. It also calls for the Chinese government departments to implement stringent regulations, in order to deal with higher energy demand and pollution problems in residential life and travel at the high rate of development and realize a sustainable low carbon development.

2. Methodology

The term “carbon footprint” refers to a person or organization’s carbon consumption, or the CO₂ or GHG emitted directly or indirectly during the life cycle of an activity or a product [ TERI, 2008 ]. Therefore, carbon footprint can be used to evaluate an object’s (including a region, an organization, or a product) impact on environment. Based on the original concept of the footprint, this study developed a method and computation model on urban resident carbon footprints. According to the original footprint calculations, energy consumption of human activities are generally converted to bioproductive area so as to evaluate the sustainability of consumption from an ecological point of view. However because this study focuses on GHG emissions of residential lifestyle and transportation and their impact on environment, we have made some revisions on original footprint calculations as follows.

Firstly, a life cycle assessment method was used to analyze the direct and indirect GHG emissions resulting from residential lifestyles and transportation in the city of Shijiazhuang. Compared with only focusing on CO₂ emission, this method can be more precise in reflecting the residential lifestyle footprint impact on the environment. Secondly, the footprint was converted to CO₂ equivalent (CO₂ -eq), which helps better understanding of the footprint associated with daily life. Thirdly, the footprint was calculated according to Chinese energy emission factors and the factors were substituted by IPCC factors if there are data missing. Finally, based on the carbon footprint analysis, the methods of carbon footprint determination, impacts of population, affluence and technology (IPAT) was used to further analyze the carbon footprints of residential lifestyle and travel, and their influential factors associated with different urban residential incomes, resulting in suggested measures to reduce the carbon footprint. Detailed analysis method is summarized as follows.

(1) Conducting 1,500 household questionnaires in Shijiazhuang city. The questionnaires were distributed in 103 residential communities in Chang’an, Xinhua, Qiaoxi, Qiaodong, and Yuhua districts of Shijiazhuang. Families were randomly selected. After trial questionnaires, the questions and format were revised or supplemented as needed. The questionnaires covered information on family members, occupation, annual income, energy consumption from household appliances and equipment, average travel mileage, fuel types, and energy consumption, etc. In the questionnaires, residential lifestyle energy consumption is further divided into in-house energy consumption and transportation related energy consumption. The family in-house energy consumptions include cooking, lighting, heating, hot water, air conditioning, entertainment (TV, audio), and other electric appliances (washing machine, computer, electric ovens), a total of 38 kinds of energy using equipment. While the transportation energy consumption is classified into 9 different travel purposes, including shopping, sightseeing, visits, and entertainment, ect. The transport purposes also include travel within the city and travel to other Chinese cities and abroad.

(2) Data grouping and statistics are shown as following.

a) The surveyed families are divided into 5 groups according to their annual family incomes: low income group (< 17,999 RMB), below average income group (18,000–34,999 RMB), average income group (35,000–49,999 RMB), upper average income group (50,000–74,999 RMB), and high income group (> 75,000 RMB).

b) To make the study results more informative, household average and per capita are used as calculation units for the 2008 for survey data.

c) In this study, energy consumed for transport is only accounted for non-work related purposes, such as transport for shopping, entertaining, tours, and social visits, while commuting to work and transport for business activities are considered part of service industry actions but not residential energy consumption. Similarly, energy use for self-employed working should be deducted from residential living energy consumption. However, since many self-employed business workers received the questionnaire, some work related energy consumption is counted in residential energy uses, for example, the electricity used for home-run groceries. On the other hand, some home business energy uses are separated, such as household garment factories, home printing mills, and household livestock farms. Therefore it is difficult to find out how much home business energy has been accounted for in the total residential living energy consumption. For this situation, the study has made a special comparison between energy consumptions of households with selfemployed business and those without. The comparison shows that among the income groups, there is no significant variance between the two sample types, which means that self-employed business energy consumption contributes an insignificant part in the total residential living energy used, and can be neglected.

d) To get an overview of income factors, household average energy consumption is calculated based on total households in each income group without considering whether a family consumes energy in practice.

(3) Annual consumption of each energy product E_x (GJ) per each household for living and travel is calculated for different income levels. The energy products x=1, 2, 3, …, 7 represent coal, gas, diesel, aviation fuel, LPG, natural gas, and electricity respectively. Power supplied from hydroelectric power is deducted from the total electricity consumed according to the utility structure in Shijiazhuang power grid.

(4) The household average CO₂ emission T_i (kg) is computed according to Eq. (1) , where P_x represents CO₂ emission during the production of each energy product consumed by residential living and travel, and C_x is the CO₂ emission during the consumption of each energy product by residents in living and travel. P_x and C_x can be calculated according to Eq. (2) . V_x represents a CO₂ emission factor (kg CO₂ GJ^–1 ) during energy production such as coal exploitation and processing, the refining process, and power generation, etc. F_x is an emission factor (kg CO₂ GJ^–1 ) associated with energy consumption (living and travel).

${\displaystyle T_{1}=\sum _{x=1}^{7}P_{x}+\sum _{x=1}^{7}C_{x}}$

( 1)

${\displaystyle \sum _{x=1}^{7}P_{x}=\sum _{x=1}^{7}\left(E_{x}v_{x}\right),\sum _{x=1}^{7}C_{x}=}$${\displaystyle \sum _{x=1}^{7}\left(E_{x}F_{x}\right)}$

( 2)

Therefore we can see that the annual household average CO₂ emission for residential living and travel includes not only the emission during energy consumption, but also during energy production, so that the calculation can be more integrated. For CO₂ emission from the consumption of electricity and heat, the total refers to the CO₂ emitted from coal production to power and heat generation. The CO₂ emission from residential fuel consumption of gasoline, diesel, and aviation fuel includes both fuel consumption and production, with the estimated hidden energy consumption of each energy product is based on existing research results [ Han et al., 2009  and Zhuang and Jiang, 2009 ]. The carbon emission factors and carbon oxidation of each energy product are taken from the research result of China National GHG Inventories [ CNCGO , 2008 ], while the CH₄ and N₂ O emission factors of the energy products refer to the India Energy Institute’s IPCC factors that are adjusted according to Asian country practices.

(5) The total annual carbon footprint T for each household is calculated according to Eq. (3) , where T represents household average annual CO₂ emission, T₂ is the total CH₄ emission, and T₃ means total N₂ O emission. Other GHG emissions such as SF₆ , HFCs (hydrofluorocarbons), and PFCs (perfluorocarbons) are not considered in this study due to their small amounts. Finally, the emissions of CO₂ , CH₄ , and N₂ O are converted into CO₂ -eq according to their global warming potentials. The total annual household carbon footprint is calculated as:

${\displaystyle T=T_{1}+T_{2}+T_{3}}$

( 3)

(6) The major factors affecting the carbon footprint and their relationship is analyzed. Based on the residential footprint results, we use the IPAT method to further investigate the footprint impact by influencing factors such as household incomes and technology improvement such as GHG emission per income.

3. Investigation results

3.1. CO₂ is the main GHG of residential lifestyle and transportation emissions

Each year, a family discharges 11,995.0 kg CO₂ , 49.0 kg CH₄ , and 0.03 kg N₂ O for living and transportation energy uses in Shijiazhuang city. If these CH₄ and N₂ O emissions are converted to CO₂ -eq, a family would emit 1,242.0 kg CO₂ -eq CH4, and 10.0 kg CO₂ -eq N₂ O each year, totaled 13,247.0 kg CO₂ -eq. Therefore, CO₂ is the major GHG emission from resident lifestyle and transportation in Shijiazhuang, which accounts for 90.6% of the total emission, followed by CH₄ , accounting for 9.4%, and N₂ O, which only contributes 0.1%. The above emission components are almost the same across the income groups showing very little difference.

3.2. Improved lifestyle quality has increased carbon footprint

Table 1 shows the carbon footprint for different income groups in Shijiazhuang, which suggests that improved quality of life due to increased family income results in the increased demand for coal and electricity accordingly. For example, a high income family would consume 23.3 GJ more coal than low income families, and 10.8 GJ more electricity, 0.9 GJ more natural gas, and 0.4 GJ more LPG. This means that high income families would emit 4,605.3 kg CO₂ -eq more GHGs than the low income group. With increasing family income, the improved quality of life would result in an increased carbon footprint accordingly.

Table 1. Average household GHG emissions of living and transportation in Shijiazhuang city (unit: kg CO₂ -eq)

Fuel type	Annual household GHG emissions
	Low income group	Below average income group	Average income group	Upper average income group	High income group	Mean household emission
LPG	239.2	299.5	292.7	277.9	276.0	277.1
Gasoline	4.9	12.8	60.3	60.7	287.5	85.2
Diesel	3.3	3.4	7.3	8.3	7.2	5.9
Natural gas	227.4	217.5	239.8	278.6	290.1	250.7
Electricity	2,270.5	2,817.6	3,222.1	3,295.8	4,021.4	3,125.5
Coal	8,191.7	9,522.2	8,822.2	10,266.1	10,639.3	9,488.3
Aviation fuel	6.7	15.6	11.2	10.5	27.5	14.3
Total	10,943.6	12,888.6	12,655.6	14,197.8	15,548.9	13,246.9

3.3. Coal heating is the major factor affecting household carbon footprint

According to the investigated families of various income groups, the annual household average GHG emission is 13,246.9 kg CO₂ -eq, of which 9,488.3 kg CO₂ -eq, or 72%, emission is from coal combustion, a determinant GHG emission contributor. Electricity (from coal power generation) contributes to 3,125.5 kg CO₂ -eq emissions, or 24%, while 277.1 kg CO₂ -eq is from LPG and 250.7 kg CO₂ -eq is from natural gas, both contributing 2% of the total GHG emissions. Consumption of gas, diesel, and aviation fuel result in emissions of 85.3 kg CO₂ -eq, 5.9 kg CO₂ -eq, and 14.3 kg CO₂ -eq, which are marginal comparatively.

From low income groups to high income groups, average home footprints are depicted in Table 2 . The discrepancies over different income groups show that compared with the lowest income families, the highest income families have a bigger carbon footprint by emitting 2,302.0 kg CO₂ -eq more due to larger living space heating, private car dominated transport, and more electrical consumption for entertainment purposes. Comparatively, the footprint of low income group is 2,303.3 kg CO₂ -eq less than the overall average due to the less energy uses in the above aspect. In the average income families, because of improved cooking, hot water supply and frequent uses of electric appliances, their living pattern is quite close to the average. However, because of their small living space and unimproved transport conditions, their footprint is 591.3 kg CO₂ -eq less than the overall household average. Therefore, coal heating can be a determinant factor affecting household carbon footprint in the city.

Table 2. GHG emissions by source and income group in Shijiazhuang city (unit: kg CO₂ -eq)

Emission source	Values under each group are the differences from household average
	Household average	Low income group	Below average income group	Average income group	Upper average income group	High income group
Cooking & hot water	523.8	–59.5	–9.0	3.8	27.2	37.5
Transport	109.9	–92.6	–75.3	–25.6	–24.5	218.0
Lighting & appliance	3,124.9	–854.7	–307.8	96.7	170.4	895.4
Heating	9,488.3	–1,296.6	33.9	–666.1	777.8	1,151.0
Total	13,246.9	–2,303.3	–358.3	–591.3	950.9	2,302.0

3.4. Emissions from gasoline and aviation kerosene for transportation are the most sensitive to family income

Along with increased income level, GHG emissions are generally increased due to more energy consumption over different income groups. Of the mean household GHG emissions, the high income families discharge more GHGs than the low income families: 0.2 times more from LPG, 58 times more from gasoline, 1.2 times more from diesel, 0.8 times more from electricity, 0.3 times more from coal, 0.3 times more from natural gas, and 3.1 times more from aviation kerosene. The above data show that GHG emissions from gas and aviation kerosene can be the most sensitive factors to resident income. Due to the higher income, residents have increased demand for frequent and comfortable transport. This is also supported by a residential travel pattern and energy consumption analysis. In the low income group, public transportation contributes to 32% of the total energy consumption, while in other income groups this contribution is no larger than 16% and only 4% for high income group. Energy consumption by private car and taxi by the high income group is 5.0 times and 8.0 times higher than by low income group respectively. In average the high income group consumes 4.0 times more transportation energy than the low income group. Therefore we can see that seeking convenient, comfortable local transport and farther travels can be major reason to a higher household footprint in the high income families.

3.5. The carbon footprint of the high income families is 1.4 times that of the low income families

The GHG emissions data from various income groups demonstrate consumer carbon footprints of different lifestyles, as shown in Table 3 . Comparing the GHG emissions from the high income families with the low income families, families in the high-income group emit 1.2 times more GHG for cooking and hot water, 18.7 times more for traffic, 1.3 times more for heating, and 1.8 times more for electricity. Overall, the carbon footprint for the high income families is 1.4 times that of the low income families.

Table 3. Residential household GHG emissions by activity in Shijiazhuang city (unit: kg CO₂ -eq)

Emission source	Average household emissions
	Low income group	Below average income group	Average income group	Upper average income group	High income group
Cooking & hot water	464.3	514.8	527.6	551.0	561.3
Transport	17.3	34.6	84.3	85.5	327.9
Lighting & appliance	2,270.2	2,817.1	3,221.6	3,295.3	4,020.3
Heating	8,191.7	9,522.2	8,822.2	10,266.1	10,639.3

3.6. Improving energy efficiency will be an effective measure to manage and reduce residential carbon footprints

By using the IPAT equation to assess the IPAT factors in Shijiazhuang city and by considering annual income, household average total energy consumption, household total emissions, energy consumption of unit income, and unit income GHG emission, the following equation can be used to determine an average household carbon footprint.

${\displaystyle I=P\times A\times T_{js}}$

( 4)

I is average home GHG emission. P illustrates one home (P = 1). A is average household income. T_js = T_{js 1} × T_{js 2} . T_{js 1} is energy consumption per household average income. T_{js 2} is unit energy consumption’s GHG emission.

According to Eq. (4) : 1) using current technology (unit income energy consumption and unit energy GHG emission), the higher the average income of a family is will determine how large the associated negative environmental impact will be (e.g., increased GHG emission). The questionnaire analysis indicates that families in the high income group emit more GHGs, when average household income increases by one time, 6% more GHG emission is emitted. This conclusion reflects a significant relation between residential income level and GHG emission (Table 3 ). Therefore, average household income is an important factor affecting the family’s carbon footprint, which contributes 81% to the average home GHG emission increase rate. 2) Under the current income levels, technical innovation (for better energy efficiency, clean technology, renewable energy, and low carbon technologies) will have positive impact on GHG emission cuts. Generally, technical improvement and energy efficiency will contribute –19% to the emission reduction rate. 3) When considering both income and technological factors, the positive impact on the environment due to technologies can only be 1/4 of the negative impact due to income increase factors. Nevertheless, the improvement of technology and energy efficiency will still be considered an important and effective measure to reduce the carbon footprint due to a high quality life.

4. Suggestions to reduce carbon footprint in residential lifestyle and transportation

(1) While significant efforts have been made in the industrial sector energy efficiencies, attention should still be paid to energy saving measures in residence and transportation systems. Improved awareness of low carbon life-styles can help direct public attention to reducing carbon footprints in daily life and taking responsibility to protect the environment for current and later generations.

(2) Promoting energy saving buildings and central heating systems will be important measures to reduce the footprint from heating. We suggest that in the city of Shijiazhuang, higher energy efficient standards need to be adopted for new construction projects, so that their heating footprint can be reduced by 50% or more. We also suggest that combined heat and power and central heating systems be promoted over home heating and small boilers and the installation of enduser meter systems (home adjusted heating meters) to reduce energy consumption by 35%–40% [ Jiang, 2000 ] while meet different heating demands. This can be an effective measure for improving heating efficiency and for lowering residential carbon footprint.

(3) To develop convenient and comfortable public transportation system. With increasing income, private cars become a substitute for public transportation because of their convenience and comfort, which has become a big challenge for controlling carbon footprint in residential travel behavior. To address this issue, it is recommended to develop safe, comfortable, attractive and reliable public transport systems, and this could be done through effective planning and development by giving it a high priority.

Acknowledgements

This research is supported under the French Institute of Veolia Environment (IVE) program.

References

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