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Environmental benefits of electronic commerce over the conventional retail trade? A case study in Shenzhen, China
Science of the Total Environment 679 (2019) 378–386
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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Environmental benefits of electronic commerce over the conventional retail trade? A case study in Shenzhen, China Yi-Bo Zhao a, Guang-Zhou Wu b, Yong-Xi Gong c,d,e, Ming-Zheng Yang a,f, Hong-Gang Ni a,⁎ a
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China The Smart City Research Institution of China Electronic Technology Group Corporation, Block C, Shenzhen International Innovation Center, Shenzhen 518003, China School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China d Shenzhen Key Laboratory of Urban Planning and Decision Making, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China e Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China f Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China b c
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• City-level CO2 emission from retail trade was estimated using average package unit. • Environmental cost of conventional retail is higher than that of electronic commerce. • Packaging is responsible for CO2 emission in electronic commerce. • CO2 is mainly emitted from buildings and consumer trips in conventional retail trade. • The CO2 emission difference between the two channels in China was 124 Mt in 2016.
a r t i c l e
i n f o
Article history: Received 28 February 2019 Received in revised form 29 April 2019 Accepted 6 May 2019 Available online 8 May 2019 Editor: Deyi Hou Keywords: Retail industry Electronic commerce Conventional retail Carbon emission
a b s t r a c t Electronic commerce has been becoming the new driver of the retail industry. The large-scale expansion of electronic commerce with additional packaging certainly increases stress on the environment. However, a comparative analysis of environmental impacts of electronic commerce and conventional retail trade channels is unavailable. In this study, an Average Package Difference Model (APDM) was developed to evaluate CO2 emissions difference via the two retail channels in Shenzhen, China based on a life-cycle perspective. In the meanwhile, the national emission was estimated by the above results. Our results suggest that conventional retail has a higher environmental cost than that of electronic commerce, especially during shopping trips. Specifically, average CO2 emission difference per package in terms of product returns, packaging, buildings and transportation were 0.14 ± 0.03, 0.84 ± 0.08, 0.67 ± 0.04, 1.3 ± 0.26 kg, respectively. CO2 is mainly emitted from buildings and consumer trips in conventional retail trade, whereas packaging is mainly responsible for CO2 emission in ecommerce. In China, the total CO2 emission difference between conventional retail and electronic commerce was 124 million tons in 2016. Growth of the proportion of electronic commerce will contribute to lower CO2 emissions induced by the entire retail industry. Actually, carbon emissions can be reduced in both conventional retail and electronic commerce, such as the reusable packaging, opening shopping centers in dense population zones and promoting the usage of public transportation. © 2019 Elsevier B.V. All rights reserved.
Abbreviations: E-commerce, electronic commerce; CNY, Chinese Yuan; APDM, Average Package Difference Model; O-D, origin-destination; CV, coefficient of variance. ⁎ Corresponding author. E-mail address: [email protected] (H.-G. Ni).
https://doi.org/10.1016/j.scitotenv.2019.05.081 0048-9697/© 2019 Elsevier B.V. All rights reserved.
Y.-B. Zhao et al. / Science of the Total Environment 679 (2019) 378–386
1. Introduction In China, the retail industry comprises 8% of national energy consumption and continues to grow (Y. Li et al., 2016). Therefore, the commercial sector, including internet marketing and traditional stores, is crucial for controlling carbon emission and transforming the economy in China (Wang and Lin, 2017). Within the commercial sector, online shopping has become increasingly prevalent in recent years because of the convenience of online business in China. For example, during 2016 alone, web merchandise sales were estimated at 5.16 trillion Chinese Yuan (CNY), with an increase of 26.2% from 2015 (Department of Electronic Commerce and Informatization, 2017). Also in 2016, electronic commerce retail accounted for 14.9% of the total retail turnover in China, greatly increasing from 6.3% in 2012 (China e-Business Research Center, 2017). This growth in internet shopping may create considerable environmental problems (Fan et al., 2017). However, it is unclear whether online shopping is more detrimental to the environment, especially when compared with conventional trade channels. Electronic commerce is perceived as potentially energy saving and cost effective (Sivaraman et al., 2007). However, this is not the case according to other studies (Williams and Tagami, 2002). In this context, which category (internet marketing or traditional stores) has higher environment cost is currently indeterminate. A comprehensive comparative analysis regarding environmental effects would provide insights into these two trade categories and implications for policy makers. Cities are the main emitters of CO2 throughout the world. This is the case for China (cities contribute 85% of the total national CO2 emissions). Therefore, cities are the key areas for CO2 emission mitigation. Recently, the comprehensive emission inventories of city-level by fossil fuels and socioeconomic sectors in China were made to understand the city-level emission and explore the emission reduction (Shan et al., 2018; Shan et al., 2019). It suggests that technological improvement is a practical and effective for reducing emissions in terms of city level. As an important component of city industry, the retail industry's CO2 emissions need to be examined. However, the lack of an assessment model of the environmental impacts of the retail industry persists (Mangiaracina et al., 2015). In particular, investigations of city-level
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emissions are challenging (Kang et al., 2014; Shan et al., 2017). A possible reason may be that specific data on carbon emissions from internet shopping and conventional shopping are not available at the city level. By integrating the main energy-consumption factors of internet marketing and traditional stores, including product returns, buildings, packaging and transportation (Palsson et al., 2017; Weber et al., 2009), we aim to offer a comprehensive understanding of these two trade channels regarding their environmental effects. To our knowledge, the current studies mainly focus on certain specific retail goods, such as books (Williams and Tagami, 2002) and clothing (Wiese et al., 2012), but not on the entirety of online shopping. Recently, the concept of “functional unit” was introduced to facilitate the allocation and reconstruction of footprint at item level (undistinguished retail goods) (van Loon et al., 2015). To overcome the challenges for city-level data calculation limitations, an Average Package Difference Model (APDM) from a life-cycle perspective comparing the environment costs (CO2 emission) of the two trade categories was developed in this study (Fig. 1). That is, the subtraction calculation between the CO2 emission level of an average package (with equivalent weight and value from the same retail category) via the two trade channels is used to offset several stages of the life cycle of an average package (e.g., manufacturing, rural delivery and waste disposal). The corresponding comparison conducted within the same retail category (e.g. same clothes from same brand via two channels) each time, avoids the consideration of heterogeneity across different goods. In this study, we defined a package as goods and packaging as packaging materials (corrugated box and plastic bag) for online shopping. On one hand, the data about these stages, especially energy and material flow among cities, is not required; on the other hand, we can also conduct a comparative analysis using other stages including within city transportation, buildings and packaging. In addition, the Average Package Difference Model (APDM) is also applicable considering the fact that many deliveries (0.584 billion packages, 28.54%) occur from origin to destination within the boundaries of Shenzhen city in 2016 (Shenzhen Municipal Postal Administration, 2017). The concept of an average package also simplifies the comparison despite the complex categories of retail products. In this study, the megacity Shenzhen, with a population of 14.8
Fig. 1. Primary framework of the Average Package Difference Model (in green circle, and details in red circle). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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million, has been selected for developing the comparison model due to its large share of service sector activities (52.7%) (Shan et al., 2017) and higher proportion of online shopping (47.0%) (Shenzhen Bureau of Statistics, 2017b; Shenzhen China, 2017). The major aim of the present study is to estimate the city-level CO2 emission difference between online business and offline business by proposed Average Package Difference Model (APDM) in Shenzhen from a life-cycle perspective (including production returns, buildings, packaging and transportation). A national estimation was established and spatialized based on the calculation of CO2 emission in Shenzhen. The obtained results facilitated insights and policy implications regarding CO2 emission and reduction. 2. Method and data The environmental impacts of both retail categories from production to consumption in Shenzhen are considered, including fuel consumption during transportation, electricity consumption in buildings and energy consumption induced by delivery packages (research boundary). It is noted that the production process and waste disposal are excluded based on the concept of Average Package Difference Model (APDM). Then CO2 emission is extrapolated to the national scale based the CO2 emission estimation in Shenzhen via two retail categories. Considering product returns, the total CO2 emissions are assigned to ordered packages (returns are excluded). The delivery of goods in Shenzhen is primarily conducted by road, accounting for 76.3% of deliveries in 2016 (Shenzhen Transportation Committee, 2017). The specific transportation modes and ratios for the delivery of retail goods are unknown and the emission factors of other transportations largely vary compared with that of road transportation. Therefore, only road transportation is considered. 2.1. Modeling the CO2 emission difference in Shenzhen The Average Package Difference Model (APDM) was used to calculate the CO2 emission induced by the retail industry in Shenzhen from a life-cycle perspective. The model can be used to conduct a comparative analysis of CO2 emissions for the first time, as shown in the following equation: ΔC ¼ B þ P þ T þ R
ð1Þ
where ΔC is the CO2 emission difference between conventional trade and electronic commerce; B, P, T, and R are the CO2 emission differences regarding buildings, packaging, transportation, and product returns between the two categories, respectively. To facilitate the difference calculation, the following assumptions were made: (a) The production processes are equal for both trade categories; (b) The same manufacturers/warehouses and transport routes to Shenzhen are utilized for the two trade categories; (c) After arrival of the goods at expressway toll stations in Shenzhen, the transport routes diverge for the two trade categories; (d) Products are sold via the two categories with similar average weights and volumes; (e) The shortest routes are selected for the two categories, except for customer routes to shopping centers and commercial streets.
For the cost-saving purpose, the above assumptions can be realistic in two retail channels. The detailed sensitivity and uncertainty of the assumptions was complicated due to various variables and limited data, which was excluded in this study. Generally, the logistics system for electronic commerce contains distribution centers and delivery points. The locations of 9 distribution centers and 957
delivery points for the five main express companies in China are captured with Python from Geohey (https://geohey.com/public-data) (Fig. 2, detailed in Supporting data, SD). For the conventional trade category, the goods are delivered by trucks entering Shenzhen via expressway toll stations and transferred via logistics centers to physical stores. Specifically, physical stores in Shenzhen consist of 129 shopping centers (including extra-large shopping centers, shopping centers and department stores), 98 commercial streets and 2727 supermarkets (Fig. 3). Location verification was conducted through random field survey and online street view. The locations of expressway toll stations and logistics centers are obtained from the road network and LOGINK (http://resource.logink.org/trade/web/index. html), respectively, while those of physical stores are captured with Python from Geohey. In terms of product returns, electronic commerce may have a different potential for producing wasted unsellable products compared with conventional retail (Williams and Tagami, 2002). A product return rate (~8%) occurred via electronic commerce in China, whereas conventional retail has a product return rate of 6% based on the news (SD). However, as official data regarding an accurate product return rate was not available, we assumed the same product return rate (6%) in both trade categories. Each product return indicates delivering two more times (from and to Shenzhen). Specifically, 94% of product consumption leads to 106% of the total CO2 emission level from transportation and packaging and buildings. The specific data for the proportion of product returns requiring within city transportation was not available. Therefore, we have not included within city transportation for product returns. From a life-cycle perspective, operational energy is dominant in the total energy consumption of a building (75–86%) (Chang et al., 2013). In general, electricity is the main power source in the eastern area (N70%) for operating building functions, and the related CO2 emission level is increasing rapidly, especially in commercial buildings (Lin and Liu, 2015). To our knowledge, the estimated annual energy consumption intensity of a warehouse is not available for China. Annual energy consumption intensity of a warehouse is approximately 1/2 of a store in the US (Howard et al., 2012), and the leading value of annual energy use intensity of a store is 80 kWh/m2 in the hot summer and warm winter areas in China (Yan et al., 2017). Hence, we assumed that the energy consumption intensity of a warehouse is 40 kWh/m2, and the energy use intensities of other buildings are summarized in Table S1. The total business area for offline shopping (including self-management, joint management and leases) is 0.885 million m2 (Shenzhen Bureau of Statistics, 2017a), and the total warehouse area is 0.204 million m2 (SD). In contrast, the floor area for online trade, including distribution centers and delivery points, is 0.196 million m2 (Table S2, SD). In this study, we adopted the latest CO2 emission factor for electricity generation of 0.8676 kg CO2/kWh (National Development and Reform Commission of China, 2017). The common materials and size and weight used for one delivery good are a corrugated box (25 cm ∗ 18.5 cm ∗ 12.5 cm, 160 g) and a plastic bag (38 cm ∗ 28 cm, 10 g), with a total of 0.8362 kg CO2 emissions (Yi et al., 2017). Due to the 6% product return rate, 94% of inflow packages were ordered by consumers and a total 106% package volume was generated. Therefore, the delivery packaging (1.4 billion, SD) for electronic commerce contributes to an additional emission of 1.17 billion kg CO2. The average CO2 emission level for an ordered package was 0.944 kg. We partitioned Shenzhen into population zones (administrative-region based) and established origin-destination (O-D) traffic zones. Since the transportation sector greatly affects CO2 emissions (Seebauer et al., 2016), we modeled transportation with detailed data. Transportation includes passenger trips and freight transport. The trips of consumers, vehicles and packages were assigned to O-D traffic zones with a population weighted method (SD). In practice, the majority of actual trips to and from shopping are optimal and shortest (Jia et al., 2013). Based on assumption (e), the travel distance distribution obtained from a
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Fig. 2. Electronic commerce in Shenzhen, China.
previous study (Yan et al., 2014) was used to simulate the real-world trips to shopping centers and commercial streets. Transportation modes (e.g., walking, bus, metro and private car) induced by physical
shopping are categorized by distance (SD). CO2 emission intensities (g/person·km) for transport modes are adopted from a previous study (Li et al., 2015). CO2 emissions induced by offline shopping were
Fig. 3. Conventional shopping in Shenzhen, China.
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calculated with the following equation: 3 XXX X P ijk Lij α k Lij þ β1 Q mj Dmj Coffline ¼ 2 j k¼1 j Xi þ β2 T nm Rnm
ð2Þ
m
where Pijk(Lij) represents trips to shopping mall j via trip mode k (k = 1, 2, 3 designate car, bus and metro, respectively) from population zone i, which is the function of travel distance Lij and decreases as distance increases; αk is the CO2 emission intensity by transport mode (g/ person·km); β1 and β2 are CO2 emissions per km during delivery; Qmj is the package volume between shopping mall j closest to logistics center m; Dmj is the distance from shopping mall j to the closest logistics center m; Tnm is the package volume between highway toll station n closest to logistics center m; and Rnm is the distance from highway toll station n closest to logistics center m. Notably, Lij, Dmj, Tnm and Rnm were obtained with the Dijkstra algorithm (Murota and Shioura, 2014) in MATLAB, which is a well-known algorithm calculating the shortest path among points. These terms were calculated with the following formula: Lij Dmj Rnm ¼ F min fGk ðV; EÞ; ij jjm; jjmjng
ð3Þ
where Gk(V, E) is the road network topology of trip mode k, and Fmin is the shortest path operator. The CO2 emission level induced by electronic commerce was calculated with the following equation:
C offline ¼
6 X X w¼1
þ
φ1 Aiw lwih þ
i
6 X X
w¼1 g
6 X X
φ2 Bwhg dwhg
w¼1 g
φ3 C wgn r wgn
ð4Þ
where Aiw is the package volume from delivery point w to population zone m; lwih is the distance between population zone i and delivery point h of express company w closest to population zone i; φ1, φ2 and φ3 are the CO2 intensities per kilometer from vehicles (Table S3); Bwhg is the package volume from distribution center g of express company w to closest delivery point h; dwhg is the distance between distribution center g of express company w and closest delivery point h; Cwgn is the package volume delivered by express company w via highway toll station n to distribution center g; rwgn is the distance between distribution center g of express company w and closest highway toll station n. Similarly, lwih, dwhg and rwgn were calculated with the Dijkstra algorithm. The schematic diagram of the calculation method regarding transportation is also present in Fig. 4. The emission parameters of fossil fuel combustion and vehicle usage can be found in previous studies (Table S3). There are large uncertainties in estimating CO2 emissions, caused by emission factors (Mi et al., 2017; Mi et al., 2019), and activity data (Zhao et al., 2011). Due to the large uncertainties of all the variables, the Monte Carlo simulation was chosen to interpret the variation range of environmental costs in Shenzhen (Weber et al., 2010; Zhao et al., 2012). Both emission factors (electricity (coefficient of variance (CV): 3%) and diesel oil (CV: 18%)) and activity data (electricity sector (CV: 5%), transportation sector (CV: 16%) and Industry (CV: 10%)) were assumed to have normal distributions (Table S4) (Shan et al., 2017; Zhao et al., 2011). 2.2. Nationwide estimation A monotonic relationship exists between the total GDP and total CO2 emission level via electronic commerce or conventional retail trade in each city. That is, the total CO2 emission level increases with growing total GDP in cities. Per capita GDP is the most important driving factor of CO2 emissions in the commercial sector (Wang and Lin, 2017). CO2 emissions from increasing average GDP per capita should be a mixed indicator of physical energy efficiency and economic structure (Price et al., 2013). Due to data availability, 10 districts in Shenzhen with calculated
Fig. 4. The schematic diagram of the calculation method regarding transportation.
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CO2 emissions and GDP were used to explore the relationship between CO2 emissions via the two retail channels. The CO2 emission and GDP were assigned to a population zone level (57 population zones) in Shenzhen, and spatial heterogeneity was investigated to ensure the rationality of the relationship. Based on the relationship, an estimated national result was obtained and spatialized (specified below). 3. Results and discussion 3.1. Product returns The product returns accounted for 19.6% of total transportation emission (Table 1). In particular, the CO2 emission from product returns in e-commerce was over four times larger than that from transportation. It implies that an efficient management regarding goods quality sold via e-commerce is needed for less product returns. In terms of CO2 emission difference, it is twice more in conventional retail (Table S5). Generally, product returns contribute to the smallest part in CO2 emission difference between two retail channels. 3.2. Buildings As expected, the CO2 emission from e-commerce is far lower than that from conventional retail (Table 1), due to no demand in commercial buildings via e-commerce. Clearly, buildings are the second biggest factor affecting CO2 difference. Nonetheless, commercial buildings in China are undergoing expansion with growing income levels (Yu et al., 2014), possibly due to the competition with electronic commerce (Ministry of Commerce of the People's Republic of China, 2015). More integrated shopping centers and energy-saving measures in lighting and air conditioning are needed (Ministry of Commerce of the People's Republic of China, 2015). More broadly, increasing energy intensity and improving the energy structure (e.g., reducing the use of coal) are strategies to reduce CO2 emission in China (Wang and Lin, 2017). 3.3. Packaging Surprisingly, packaging is responsible for the main CO2 emission via e-commerce, which makes e-commerce less attractive for environmental reasons (Table 1). Previous study has emphasized the considerable environmental impact due to a large consumption of packaging materials (Fan et al., 2017). Based on an available survey (China e-Business Research Center, 2016), only 30.8% of online shopping consumers discard the packaging directly after receiving packages. Therefore, the environmental impact of packaging can be reduced due to reuse by consumers (i.e., longer life cycle). Additionally, improving energy efficiency and material management can also reduce CO2 emissions from electronic commerce packaging (Hekkert et al., 2000a; Hekkert et al., 2000b). Tape-free and biodegradable packaging has been used since 2017, cardboard boxes are recyclable (Central People's Government of the People's Republic of China, 2017), and the proportion of degradable packaging will be increased to 50% in 2020 (State Postal Bureau of China, 2017). The usage of reusable packaging is effective material Table 1 Package volume and product return and simulated total CO2 emission from various sectors via the two retail channels in Shenzhen base on the proposed Average Package Difference Model (APDM).
Package volume (billion) Product returns (billion) Product returns (billion kg CO2) Transportation (billion kg CO2) Packaging (billion kg CO2) Building (billion kg CO2)
E-commerce
Conventional
CO2 difference
1.24 0.16 0.14 0.03 1.04 0.02
1.58 0.2 0.41 2.22 0 1.09
0.27 2.19 −1.04 1.07
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management, which leads to an estimated 16% reduction in CO2 emissions (Hekkert et al., 2000a). 3.4. Transportation Non-work trips tend to release less CO2 than work-related trips (Ma et al., 2015), which indicates that the shortest routes for shopping, most frequently non-work trips, assumed in our study were rational. In general, goods transportation is more efficient than consumer driving, resulting in reductions of fuel consumption and CO2 emissions (Carling et al., 2015; Xu et al., 2009). Interestingly, we determined that the delivery distances of packages via electronic commerce were eight times longer and that 429% of CO2 was released during within city delivery (Table S6). This determination was consistent with the finding from a previous study suggesting that energy use increased in logistics and freight transport in online shopping (Zhang and Zhao, 2017). However, online retail only released 0.06% of the total CO2 emission level, compared with emissions via conventional retail trade. The main contributor of CO2 emissions in conventional retail is the consumer trips with a distance of 25.8 km (motorized). The CO2 emissions induced by consumer trips were highly significant (99.5% of the total CO2 emissions from conventional retail) due to highly heterogeneous shopping centers and dense consumer trips (Table S7). A 1.97 package equivalent was taken for each trip to physical stores in 2016 (SD). More packages purchased during each consumer trip can significantly reduce CO2 emissions and even release less CO2 emissions than conventional retail trade (van Loon et al., 2015). Reductions in CO2 emission can be achieved via the electronic commerce channel, such as through optimizations in goods delivery and more efficient and practicable logistics systems (Kim et al., 2009). The introduction of electric vehicles can aid in emission reduction only through energy-savings in the electricity sector (Hofmann et al., 2016). 3.5. CO2 emissions and uncertainty analysis We presented a spatial variation of CO2 emission differences in population zones to identify zones with high CO2 emission differences and related causes (Fig. 5). The CO2 emission difference per package between the two retail channels was mapped within population zones. Clearly, the southern area is highly accessible for consumers via the two channels, which results in fewer CO2 emission differences per package. This result also indicated that CO2 emission level per trip was much higher when shopping in a less developed area (Figs. S2 and S3) (J. Li et al., 2016). The CO2 emission level per package via the two retail trade channels was nearly equal if CO2 emissions from consumer trips were excluded. However, 138% more CO2 was released for each package via conventional retail trade (Table S5). We also examined the uncertainty (two standard deviations divided by the mean) of the total CO2 emission differences and sectors' emissions in the estimation model with Monte Carlo analysis (10,000 iterations). Except for CO2 emissions induced by packaging, conventional retail released more CO2 (Fig. 6). The uncertainty fell in the range of 12% ~ 43%, and the building sector's emission level and total emission difference contributed to the smallest and largest uncertainties, respectively. Moreover, real-world parameters, such as product return rates, energy use intensity in buildings, number of trips, travel distance distribution, modal split and package delivery, were not available, which contributed to uncertainties in estimation. Based on the Shenzhen Statistical Yearbook (Shenzhen Bureau of Statistics, 2017a), end-use energy consumption in the retail trade was approximately 2.29 million tons of standard coal equivalent (TCE, 1 TCE = 29.39 GJ, provided in SD), which accounted for 5.6% of the total energy consumption of all industries in Shenzhen. Because the energy structure of the retail industry was not available, we assumed that coal and diesel oil (two dominant fuels in Shenzhen) were the only fuels consumed. The estimated CO2 emission value based on the Statistical
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Fig. 5. CO2 emission differences induced by population zones between conventional retail trade and electronic commerce retail.
Yearbook was 4.99–6.50 million tons (SD), whereas the total CO2 emission level calculated in our study was 4.95 million tons (Table 1). Considering the inconsistency in energy statistics (Zhang et al., 2007) and use of clean energy and within city transportation, our results were comparable to the statistical data. 3.6. CO2 emission differences To understand the environmental impact of the two retail trade channels in China, we aggregated the city-level estimations and obtained national-level results. We determined that the CO2 emissions per unit of GDP for various cities in China were quite different (Shan
et al., 2017). Particularly, the level of CO2 emissions per unit of GDP was much lower in mega cities due to their enormous GDP values. Clearly, a proper scale is crucial for examining the relationship between commerce CO2 emissions and GDP. Given the city-level administrative management, the operation of city-level retail company and online package delivery, and retail trade activities depends on every district or county (units that form a city) own socioeconomic activities, rather than that of whole city. In this sense, the district or county is a reasonable unit for calculating CO2 emission at a national scale. Therefore, 10 districts in Shenzhen were selected to develop an emission model according to the relationship between GDP and CO2 emissions, rather than using the total CO2 emission level in Shenzhen as the average value in China. That is, the CO2 emissions at a district or county scale were calculated using the following model and then were summed up into the city-level CO2 emissions. Examining several function forms (Tables S8 and S9), we obtained the relationship at a district or county scale through the following equation: ln y ¼ k ln x þ b
Fig. 6. Cumulative probability of CO2 emission difference per package between conventional retail trade and electronic commerce retail in 2016 in Shenzhen. The insert plots are CO2 emissions differences (mean ± standard deviation) with a 95% confidence interval (gray: conventional minus electronic commerce; yellow: electronic commerce minus conventional). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
ð5Þ
where x is the total GDP (10,000 CNY), y is the total CO2 (kg) emission via either retail trade channel. In particular, k is 0.7881 and 1.019 for electronic commerce and conventional retail trade, respectively; b is 6.325 and 1.382 for the two categories, respectively. The uncertainty of the equation was examined using CO2 emission and GDP assigned to a population zone level (Table S10 and Fig. S4). No spatial heterogeneity was observed using White Heteroskedasticity Test at a significance level of 0.05 (Table S11) and the uncertainty was 15.48% (SD). It indicated a reasonable relationship between CO2 emission and GDP at a district level. Interestingly, the CO2 emission level induced by electronic commerce is much lower than that of conventional retail trade, although CO2 emissions for both categories increase with the GDP. In addition, the CO2 emission difference between these two retail channels expands with GDP (less than ~20 trillion CNY) for one city (Fig. S5). All
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Fig. 7. CO2 emission differences between the two retail trade channels for cities in China.
of the results suggest the apparent environmental benefits of electronic commerce over conventional retail trade. By applying the model to cities in mainland China, the conventional retail trade channel released an estimated 124 million tons CO2 more than electronic commerce, which was comparable to the total consumption emission level from Beijing in 2007 (142 million tons) (Mi et al., 2016). Most cities (86.9%) released b0.60 million tons CO2 emissions more in conventional retail trade (Fig. 7). Currently, the CO2 emission difference is larger in cities with higher GDPs, especially in Beijing, Shanghai, Chongqing, Guangzhou and Shenzhen in China. Because of various socioeconomic conditions in cities, such as population distribution, industrial structure and urbanization rate, the national estimation may generate large uncertainties. Admittedly, the CO2 emission of retails is related to purchasing behaviors, living standard, added value in retail sales. Nonetheless, a general evaluation was obtained for understanding electronic commerce and conventional retail trade at a national scale.
responsible for CO2 emission. Nonetheless, the CO2 emission difference between the two retail categories is expected to decrease because of the scale economic effect of physical stores. In addition, there are several corresponding strategies to curb the CO2 emission from two retail channels, such as opening shopping centers in dense population zones and promoting the usage of public transportation and recyclable packages. Acknowledgement This study was financially supported by the National Key Research and Development Program of China (2017YFC0505701) and the National Natural Science Foundation of China (No. 41771169 and 41371169). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.05.081.
4. Conclusion References In this study, by introducing the concept of average package unit, an Average Package Difference Model was developed to evaluate CO2 emissions via the two retail channels in China based on a life-cycle perspective. It indicated conventional retail has a higher CO2 emission than that of electronic commerce, and the CO2 emission difference per package was 1.28 ± 0.31 kg. A national estimation model was investigated, and the CO2 emission difference between conventional retail and electronic commerce in China is approximately 124 million tons. In the conventional retail trade, CO2 is mainly emitted from buildings and consumer trips. In contrast, packaging is the biggest factor
Carling, K., Han, M., Hakansson, J., Meng, X., Rudholm, N., 2015. Measuring transport related CO2 emissions induced by online and brick-and-mortar retailing. Transp. Res. PT. D-Transp. Enviro. 40, 28–42. Central People's Government of the People's Republic of China, 2017. “Double 11” express delivery is expected to reach 1 billion this year and enable “green warehouse” and carton recycling for the first time. http://www.gov.cn/xinwen/2017-09/20/content_ 5226459.htm, Accessed date: January 2018. Chang, Y., Ries, R.J., Wang, Y., 2013. Life-cycle energy of residential buildings in China. Energ. Policy 62, 656–664. China e-Business Research Center, 2016. The survey of Chinese online consumption and guideline of online shopping in 2016. http://www.100ec.cn/zt/upload_data/wlxf. pdf, Accessed date: February 2018.
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China e-Business Research Center, 2017. Monitoring report for China e-commerce market in 2016. http://www.100ec.cn/zt/16jcbg/ (accessed January 2018). Department of Electronic Commerce and Informatization, 2017. The main data of China's e-commerce in 2016. http://dzsws.mofcom.gov.cn/article/dzsw/tjjc/201706/ 20170602591873.shtml (accessed December 2017). Fan, W., Xu, M., Dong, X., Wei, H., 2017. Considerable environmental impact of the rapid development of China's express delivery industry. Resour. Conserv. Recycl. 126, 174–176. Hekkert, M.P., Joosten, L.A.J., Worrell, E., 2000a. Reduction of CO2 emissions by improved management of material and product use: the case of transport packaging. Resour. Conserv. Recycl. 30 (1), 1–27. Hekkert, M.P., Joosten, L.A.J., Worrell, E., Turkenburg, W.C., 2000b. Reduction of CO2 emissions by improved management of material and product use: the case of primary packaging. Resour. Conserv. Recycl. 29 (1–2), 33–64. Hofmann, J., Guan, D., Chalvatzis, K., Huo, H., 2016. Assessment of electrical vehicles as a successful driver for reducing CO2 emissions in China. Appl. Energy 184, 995–1003. Howard, B., Parshall, L., Thompson, J., Hammer, S., Dickinson, J., Modi, V., 2012. Spatial distribution of urban building energy consumption by end use. Energ. Build. 45, 141–151. Jia, T., Carling, K., Håkansson, J., 2013. Trips and their CO2 emissions to and from a shopping center. J. Transp. Geogr. 33, 135–145. Kang, J., Zhao, T., Liu, N., Zhang, X., Xu, X., Lin, T., 2014. A multi-sectoral decomposition analysis of city-level greenhouse gas emissions: case study of Tianjin, China. Energy 68, 562–571. Kim, J., Xu, M., Kahhat, R., Allenby, B., Williams, E., 2009. Designing and assessing a sustainable networked delivery (SND) system: hybrid business-to-consumer book delivery case study. Environ. Sci. Technol. 43 (1), 181–187. Li, J., Zhang, P., Lo, K., Guo, M., Wang, M., 2015. Reducing carbon emissions from shopping trips: evidence from China. Energies 8 (9), 10043–10057. Li, J., Lo, K., Zhang, P., Guo, M., 2016a. Consumer travel behaviors and transport carbon emissions: a comparative study of commercial centers in Shenyang, China. Energies 9 (10), 765–778. Li, Y., Tan, W., Sha, R., 2016b. The empirical study on the optimal distribution route of minimum carbon footprint of the retail industry. J. Clean. Prod. 112, 4237–4246. Lin, B., Liu, H., 2015. CO2 emissions of China's commercial and residential buildings: evidence and reduction policy. Build. Environ. 92, 418–431. Ma, J., Liu, Z., Chai, Y., 2015. The impact of urban form on CO2 emission from work and non-work trips: the case of Beijing, China. Habitat Int 47, 1–10. Mangiaracina, R., Marchet, G., Perotti, S., Tumino, A., 2015. A review of the environmental implications of B2C e-commerce: a logistics perspective. Int. J. Phys. Distrib. Logist. Manage. 45 (6), 565–591. Mi, Z., Zhang, Y., Guan, D., Shan, Y., Liu, Z., Cong, R., Yuan, X.-C., Wei, Y.-M., 2016. Consumption-based emission accounting for Chinese cities. Appl. Energy 184, 1073–1081. Mi, Z., Meng, J., Guan, D., Shan, Y., Song, M., Wei, Y.-M., Liu, Z., Hubacek, K., 2017. Chinese CO2 emission flows have reversed since the global financial crisis. Nat. Commun. 8 (1), 1712. Mi, Z., Zheng, J., Meng, J., Zheng, H., Li, X., Coffman, D.M., Woltjer, J., Wang, S., Guan, D., 2019. Carbon emissions of cities from a consumption-based perspective. Appl. Energy 235, 509–518. Ministry of Commerce of the People's Republic of China, 2015. Green book on energy conservation and environmental protection in China's retail industry in 2015. http://images.mofcom.gov.cn/ltfzs/201603/20160315140507770.pdf (accessed April 2018). Murota, K., Shioura, A., 2014. Dijkstra's algorithm and L-concave function maximization. Math. Program. 145 (1–2), 163–177. National Development & Reform Commission of China, 2017. A notice concerning the public consultation on the emission factor (draft) of China regional power grid in 2016. http://qhs.ndrc.gov.cn/gzdt/201704/t20170414_844347.html, Accessed date: February 2018. Palsson, H., Pettersson, F., Hiselius, L.W., 2017. Energy consumption in e-commerce versus conventional trade channels - insights into packaging, the last mile, unsold products and product returns. J. Clean. Prod. 164, 765–778. Price, L., Zhou, N., Fridley, D., Ohshita, S., Lu, H., Zheng, N., Fino-Chen, C., 2013. Development of a low-carbon indicator system for China. Habitat Int 37, 4–21. Seebauer, S., Kulmer, V., Bruckner, M., Winkler, E., 2016. Carbon emissions of retail channels: the limits of available policy instruments to achieve absolute reductions. J. Clean. Prod. 132, 192–203. Shan, Y., Guan, D., Liu, J., Mi, Z., Liu, Z., Liu, J., Schroeder, H., Cai, B., Chen, Y., Shao, S., Zhang, Q., 2017. Methodology and applications of city level CO2 emission accounts in China. J. Clean. Prod. 161, 1215–1225.
Shan, Y., Guan, D., Hubacek, K., Zheng, B., Davis, S.J., Jia, L., Liu, J., Liu, Z., Fromer, N., Mi, Z., Meng, J., Deng, X., Li, Y., Lin, J., Schroeder, H., Weisz, H., Schellnhuber, H.J., 2018. Citylevel climate change mitigation in China. Sci. Adv. 4 (6), eaaq0390. Shan, Y., Liu, J., Liu, Z., Shao, S., Guan, D., 2019. An emissions-socioeconomic inventory of Chinese cities. Scientific Data 6, 190027. Shenzhen Bureau of Statistics, 2017a. Shenzhen statistical yearbook 2016. http://www.sz. gov.cn/sztjj2015/xxgk/tjsj/tjnj/201712/W020171219625244452877.pdf, Accessed date: January 2018. Shenzhen Bureau of Statistics, 2017b. Statistical bulletin of Shenzhen national economic and social development in 2016. http://www.sztj.gov.cn/xxgk/tjsj/tjgb/201705/ t20170502_6199402.htm, Accessed date: January 2018. Shenzhen China, 2017. E-commerce transactions in Shenzhen exceeded 2 trillion yuan in China. http://www.sz.gov.cn/cn/xxgk/zfxxgj/zwdt/201703/t20170329_6101767. htm, Accessed date: January 2018. Shenzhen Municipal Postal Administration, 2017. Postal industry development statistics bulletin in 2016. http://xxgk.gdsz.spb.gov.cn/extranet/detail.html?yc_id=d31da7f18f51-47b8-bdc5-6501f2eda263, Accessed date: April 2019. Shenzhen Transportation Committee, 2017. Annual report of traffic and transportation operation index in 2016. http://www.sztb.gov.cn/xxgk/xxgkml/tjxx/201702/ t20170206_5949703.htm, Accessed date: January 2018. Sivaraman, D., Pacca, S., Mueller, K., Lin, J., 2007. Comparative energy, environmental, and economic analysis of traditional and e-commerce DVD rental networks. J. Ind. Ecol. 11 (3), 77–91. State Postal Bureau of China, 2017. Guidance on promoting green packaging for express delivery industry. http://www.gov.cn/xinwen/2017-11/02/content_5236573.htm, Accessed date: January 2018. van Loon, P., Deketele, L., Dewaele, J., McKinnon, A., Rutherford, C., 2015. A comparative analysis of carbon emissions from online retailing of fast moving consumer goods. J. Clean. Prod. 106, 478–486. Wang, A., Lin, B., 2017. Assessing CO2 emissions in China's commercial sector: determinants and reduction strategies. J. Clean. Prod. 164, 1542–1552. Weber, C.L., Hendrickson, C.T., Matthews, H.S., Nagengast, A., Nealer, R., Jaramillo, P., 2009. Life cycle comparison of traditional retail and e-commerce logistics for electronic products: a case study of buy.com. 2009 IEEE International Symposium on Sustainable Systems and Technology, pp. 1–6. Weber, C.L., Koomey, J.G., Matthews, H.S., 2010. The energy and climate change implications of different music delivery methods. J. Ind. Ecol. 14 (5), 754–769. Wiese, A., Toporowski, W., Zielke, S., 2012. Transport-related CO2 effects of online and brick-and-mortar shopping: a comparison and sensitivity analysis of clothing retailing. Transp. Res. PT. D-Transp. Enviro. 17 (6), 473–477. Williams, E., Tagami, T., 2002. Energy use in sales and distribution via E-commerce and conventional retail: a case study of the Japanese book sector. J. Ind. Ecol. 6 (2), 99–114. Xu, M., Allenby, B., Kim, J., Kahhat, R., 2009. A dynamic agent-based analysis for the environmental impacts of conventional and novel book retailing. Environ. Sci. Technol. 43 (8), 2851–2857. Yan, X.-Y., Zhao, C., Fan, Y., Di, Z., Wang, W.-X., 2014. Universal predictability of mobility patterns in cities. J. R. Soc. Interface 11 (100). Yan, D., Hong, T., Li, C., Zhang, Q., An, J., Hu, S., 2017. A thorough assessment of China's standard for energy consumption of buildings. Energ. Build. 143, 114–128. Yi, Y., Wang, Z., Wennersten, R., Sun, Q., 2017. Life cycle assessment of delivery packages in China. In: Yan, J., Sun, F., Chou, S.K., Desideri, U., Li, H., Campana, P., Xiong, R. (Eds.), 8th International Conference on Applied Energy, pp. 3711–3719. Yu, S., Eom, J., Zhou, Y., Evans, M., Clarke, L., 2014. Scenarios of building energy demand for China with a detailed regional representation. Energy 67, 284–297. Zhang, M., Zhao, P., 2017. The impact of land-use mix on residents' travel energy consumption: new evidence from Beijing. Transp. Res. PT. D-Transp. Enviro. 57, 224–236. Zhang, Q., Streets, D.G., He, K., Wang, Y., Richter, A., Burrows, J.P., Uno, I., Jang, C.J., Chen, D., Yao, Z., Lei, Y., 2007. NOx emission trends for China, 1995–2004: the view from the ground and the view from space. J. Geophys. Res.-Atmos. 112 (D22). Zhao, Y., Nielsen, C.P., Lei, Y., McElroy, M.B., Hao, J., 2011. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos. Chem. Phys. 11 (5), 2295–2308. Zhao, Y., Nielsen, C.P., McElroy, M.B., 2012. China's CO2 emissions estimated from the bottom up: recent trends, spatial distributions, and quantification of uncertainties. Atmos. Environ. 59, 214–223.
Contents lists available at ScienceDirect
Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv
Environmental benefits of electronic commerce over the conventional retail trade? A case study in Shenzhen, China Yi-Bo Zhao a, Guang-Zhou Wu b, Yong-Xi Gong c,d,e, Ming-Zheng Yang a,f, Hong-Gang Ni a,⁎ a
School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China The Smart City Research Institution of China Electronic Technology Group Corporation, Block C, Shenzhen International Innovation Center, Shenzhen 518003, China School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China d Shenzhen Key Laboratory of Urban Planning and Decision Making, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China e Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China f Key Laboratory for Earth Surface Processes, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China b c
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• City-level CO2 emission from retail trade was estimated using average package unit. • Environmental cost of conventional retail is higher than that of electronic commerce. • Packaging is responsible for CO2 emission in electronic commerce. • CO2 is mainly emitted from buildings and consumer trips in conventional retail trade. • The CO2 emission difference between the two channels in China was 124 Mt in 2016.
a r t i c l e
i n f o
Article history: Received 28 February 2019 Received in revised form 29 April 2019 Accepted 6 May 2019 Available online 8 May 2019 Editor: Deyi Hou Keywords: Retail industry Electronic commerce Conventional retail Carbon emission
a b s t r a c t Electronic commerce has been becoming the new driver of the retail industry. The large-scale expansion of electronic commerce with additional packaging certainly increases stress on the environment. However, a comparative analysis of environmental impacts of electronic commerce and conventional retail trade channels is unavailable. In this study, an Average Package Difference Model (APDM) was developed to evaluate CO2 emissions difference via the two retail channels in Shenzhen, China based on a life-cycle perspective. In the meanwhile, the national emission was estimated by the above results. Our results suggest that conventional retail has a higher environmental cost than that of electronic commerce, especially during shopping trips. Specifically, average CO2 emission difference per package in terms of product returns, packaging, buildings and transportation were 0.14 ± 0.03, 0.84 ± 0.08, 0.67 ± 0.04, 1.3 ± 0.26 kg, respectively. CO2 is mainly emitted from buildings and consumer trips in conventional retail trade, whereas packaging is mainly responsible for CO2 emission in ecommerce. In China, the total CO2 emission difference between conventional retail and electronic commerce was 124 million tons in 2016. Growth of the proportion of electronic commerce will contribute to lower CO2 emissions induced by the entire retail industry. Actually, carbon emissions can be reduced in both conventional retail and electronic commerce, such as the reusable packaging, opening shopping centers in dense population zones and promoting the usage of public transportation. © 2019 Elsevier B.V. All rights reserved.
Abbreviations: E-commerce, electronic commerce; CNY, Chinese Yuan; APDM, Average Package Difference Model; O-D, origin-destination; CV, coefficient of variance. ⁎ Corresponding author. E-mail address: [email protected] (H.-G. Ni).
https://doi.org/10.1016/j.scitotenv.2019.05.081 0048-9697/© 2019 Elsevier B.V. All rights reserved.
Y.-B. Zhao et al. / Science of the Total Environment 679 (2019) 378–386
1. Introduction In China, the retail industry comprises 8% of national energy consumption and continues to grow (Y. Li et al., 2016). Therefore, the commercial sector, including internet marketing and traditional stores, is crucial for controlling carbon emission and transforming the economy in China (Wang and Lin, 2017). Within the commercial sector, online shopping has become increasingly prevalent in recent years because of the convenience of online business in China. For example, during 2016 alone, web merchandise sales were estimated at 5.16 trillion Chinese Yuan (CNY), with an increase of 26.2% from 2015 (Department of Electronic Commerce and Informatization, 2017). Also in 2016, electronic commerce retail accounted for 14.9% of the total retail turnover in China, greatly increasing from 6.3% in 2012 (China e-Business Research Center, 2017). This growth in internet shopping may create considerable environmental problems (Fan et al., 2017). However, it is unclear whether online shopping is more detrimental to the environment, especially when compared with conventional trade channels. Electronic commerce is perceived as potentially energy saving and cost effective (Sivaraman et al., 2007). However, this is not the case according to other studies (Williams and Tagami, 2002). In this context, which category (internet marketing or traditional stores) has higher environment cost is currently indeterminate. A comprehensive comparative analysis regarding environmental effects would provide insights into these two trade categories and implications for policy makers. Cities are the main emitters of CO2 throughout the world. This is the case for China (cities contribute 85% of the total national CO2 emissions). Therefore, cities are the key areas for CO2 emission mitigation. Recently, the comprehensive emission inventories of city-level by fossil fuels and socioeconomic sectors in China were made to understand the city-level emission and explore the emission reduction (Shan et al., 2018; Shan et al., 2019). It suggests that technological improvement is a practical and effective for reducing emissions in terms of city level. As an important component of city industry, the retail industry's CO2 emissions need to be examined. However, the lack of an assessment model of the environmental impacts of the retail industry persists (Mangiaracina et al., 2015). In particular, investigations of city-level
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emissions are challenging (Kang et al., 2014; Shan et al., 2017). A possible reason may be that specific data on carbon emissions from internet shopping and conventional shopping are not available at the city level. By integrating the main energy-consumption factors of internet marketing and traditional stores, including product returns, buildings, packaging and transportation (Palsson et al., 2017; Weber et al., 2009), we aim to offer a comprehensive understanding of these two trade channels regarding their environmental effects. To our knowledge, the current studies mainly focus on certain specific retail goods, such as books (Williams and Tagami, 2002) and clothing (Wiese et al., 2012), but not on the entirety of online shopping. Recently, the concept of “functional unit” was introduced to facilitate the allocation and reconstruction of footprint at item level (undistinguished retail goods) (van Loon et al., 2015). To overcome the challenges for city-level data calculation limitations, an Average Package Difference Model (APDM) from a life-cycle perspective comparing the environment costs (CO2 emission) of the two trade categories was developed in this study (Fig. 1). That is, the subtraction calculation between the CO2 emission level of an average package (with equivalent weight and value from the same retail category) via the two trade channels is used to offset several stages of the life cycle of an average package (e.g., manufacturing, rural delivery and waste disposal). The corresponding comparison conducted within the same retail category (e.g. same clothes from same brand via two channels) each time, avoids the consideration of heterogeneity across different goods. In this study, we defined a package as goods and packaging as packaging materials (corrugated box and plastic bag) for online shopping. On one hand, the data about these stages, especially energy and material flow among cities, is not required; on the other hand, we can also conduct a comparative analysis using other stages including within city transportation, buildings and packaging. In addition, the Average Package Difference Model (APDM) is also applicable considering the fact that many deliveries (0.584 billion packages, 28.54%) occur from origin to destination within the boundaries of Shenzhen city in 2016 (Shenzhen Municipal Postal Administration, 2017). The concept of an average package also simplifies the comparison despite the complex categories of retail products. In this study, the megacity Shenzhen, with a population of 14.8
Fig. 1. Primary framework of the Average Package Difference Model (in green circle, and details in red circle). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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million, has been selected for developing the comparison model due to its large share of service sector activities (52.7%) (Shan et al., 2017) and higher proportion of online shopping (47.0%) (Shenzhen Bureau of Statistics, 2017b; Shenzhen China, 2017). The major aim of the present study is to estimate the city-level CO2 emission difference between online business and offline business by proposed Average Package Difference Model (APDM) in Shenzhen from a life-cycle perspective (including production returns, buildings, packaging and transportation). A national estimation was established and spatialized based on the calculation of CO2 emission in Shenzhen. The obtained results facilitated insights and policy implications regarding CO2 emission and reduction. 2. Method and data The environmental impacts of both retail categories from production to consumption in Shenzhen are considered, including fuel consumption during transportation, electricity consumption in buildings and energy consumption induced by delivery packages (research boundary). It is noted that the production process and waste disposal are excluded based on the concept of Average Package Difference Model (APDM). Then CO2 emission is extrapolated to the national scale based the CO2 emission estimation in Shenzhen via two retail categories. Considering product returns, the total CO2 emissions are assigned to ordered packages (returns are excluded). The delivery of goods in Shenzhen is primarily conducted by road, accounting for 76.3% of deliveries in 2016 (Shenzhen Transportation Committee, 2017). The specific transportation modes and ratios for the delivery of retail goods are unknown and the emission factors of other transportations largely vary compared with that of road transportation. Therefore, only road transportation is considered. 2.1. Modeling the CO2 emission difference in Shenzhen The Average Package Difference Model (APDM) was used to calculate the CO2 emission induced by the retail industry in Shenzhen from a life-cycle perspective. The model can be used to conduct a comparative analysis of CO2 emissions for the first time, as shown in the following equation: ΔC ¼ B þ P þ T þ R
ð1Þ
where ΔC is the CO2 emission difference between conventional trade and electronic commerce; B, P, T, and R are the CO2 emission differences regarding buildings, packaging, transportation, and product returns between the two categories, respectively. To facilitate the difference calculation, the following assumptions were made: (a) The production processes are equal for both trade categories; (b) The same manufacturers/warehouses and transport routes to Shenzhen are utilized for the two trade categories; (c) After arrival of the goods at expressway toll stations in Shenzhen, the transport routes diverge for the two trade categories; (d) Products are sold via the two categories with similar average weights and volumes; (e) The shortest routes are selected for the two categories, except for customer routes to shopping centers and commercial streets.
For the cost-saving purpose, the above assumptions can be realistic in two retail channels. The detailed sensitivity and uncertainty of the assumptions was complicated due to various variables and limited data, which was excluded in this study. Generally, the logistics system for electronic commerce contains distribution centers and delivery points. The locations of 9 distribution centers and 957
delivery points for the five main express companies in China are captured with Python from Geohey (https://geohey.com/public-data) (Fig. 2, detailed in Supporting data, SD). For the conventional trade category, the goods are delivered by trucks entering Shenzhen via expressway toll stations and transferred via logistics centers to physical stores. Specifically, physical stores in Shenzhen consist of 129 shopping centers (including extra-large shopping centers, shopping centers and department stores), 98 commercial streets and 2727 supermarkets (Fig. 3). Location verification was conducted through random field survey and online street view. The locations of expressway toll stations and logistics centers are obtained from the road network and LOGINK (http://resource.logink.org/trade/web/index. html), respectively, while those of physical stores are captured with Python from Geohey. In terms of product returns, electronic commerce may have a different potential for producing wasted unsellable products compared with conventional retail (Williams and Tagami, 2002). A product return rate (~8%) occurred via electronic commerce in China, whereas conventional retail has a product return rate of 6% based on the news (SD). However, as official data regarding an accurate product return rate was not available, we assumed the same product return rate (6%) in both trade categories. Each product return indicates delivering two more times (from and to Shenzhen). Specifically, 94% of product consumption leads to 106% of the total CO2 emission level from transportation and packaging and buildings. The specific data for the proportion of product returns requiring within city transportation was not available. Therefore, we have not included within city transportation for product returns. From a life-cycle perspective, operational energy is dominant in the total energy consumption of a building (75–86%) (Chang et al., 2013). In general, electricity is the main power source in the eastern area (N70%) for operating building functions, and the related CO2 emission level is increasing rapidly, especially in commercial buildings (Lin and Liu, 2015). To our knowledge, the estimated annual energy consumption intensity of a warehouse is not available for China. Annual energy consumption intensity of a warehouse is approximately 1/2 of a store in the US (Howard et al., 2012), and the leading value of annual energy use intensity of a store is 80 kWh/m2 in the hot summer and warm winter areas in China (Yan et al., 2017). Hence, we assumed that the energy consumption intensity of a warehouse is 40 kWh/m2, and the energy use intensities of other buildings are summarized in Table S1. The total business area for offline shopping (including self-management, joint management and leases) is 0.885 million m2 (Shenzhen Bureau of Statistics, 2017a), and the total warehouse area is 0.204 million m2 (SD). In contrast, the floor area for online trade, including distribution centers and delivery points, is 0.196 million m2 (Table S2, SD). In this study, we adopted the latest CO2 emission factor for electricity generation of 0.8676 kg CO2/kWh (National Development and Reform Commission of China, 2017). The common materials and size and weight used for one delivery good are a corrugated box (25 cm ∗ 18.5 cm ∗ 12.5 cm, 160 g) and a plastic bag (38 cm ∗ 28 cm, 10 g), with a total of 0.8362 kg CO2 emissions (Yi et al., 2017). Due to the 6% product return rate, 94% of inflow packages were ordered by consumers and a total 106% package volume was generated. Therefore, the delivery packaging (1.4 billion, SD) for electronic commerce contributes to an additional emission of 1.17 billion kg CO2. The average CO2 emission level for an ordered package was 0.944 kg. We partitioned Shenzhen into population zones (administrative-region based) and established origin-destination (O-D) traffic zones. Since the transportation sector greatly affects CO2 emissions (Seebauer et al., 2016), we modeled transportation with detailed data. Transportation includes passenger trips and freight transport. The trips of consumers, vehicles and packages were assigned to O-D traffic zones with a population weighted method (SD). In practice, the majority of actual trips to and from shopping are optimal and shortest (Jia et al., 2013). Based on assumption (e), the travel distance distribution obtained from a
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Fig. 2. Electronic commerce in Shenzhen, China.
previous study (Yan et al., 2014) was used to simulate the real-world trips to shopping centers and commercial streets. Transportation modes (e.g., walking, bus, metro and private car) induced by physical
shopping are categorized by distance (SD). CO2 emission intensities (g/person·km) for transport modes are adopted from a previous study (Li et al., 2015). CO2 emissions induced by offline shopping were
Fig. 3. Conventional shopping in Shenzhen, China.
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calculated with the following equation: 3 XXX X P ijk Lij α k Lij þ β1 Q mj Dmj Coffline ¼ 2 j k¼1 j Xi þ β2 T nm Rnm
ð2Þ
m
where Pijk(Lij) represents trips to shopping mall j via trip mode k (k = 1, 2, 3 designate car, bus and metro, respectively) from population zone i, which is the function of travel distance Lij and decreases as distance increases; αk is the CO2 emission intensity by transport mode (g/ person·km); β1 and β2 are CO2 emissions per km during delivery; Qmj is the package volume between shopping mall j closest to logistics center m; Dmj is the distance from shopping mall j to the closest logistics center m; Tnm is the package volume between highway toll station n closest to logistics center m; and Rnm is the distance from highway toll station n closest to logistics center m. Notably, Lij, Dmj, Tnm and Rnm were obtained with the Dijkstra algorithm (Murota and Shioura, 2014) in MATLAB, which is a well-known algorithm calculating the shortest path among points. These terms were calculated with the following formula: Lij Dmj Rnm ¼ F min fGk ðV; EÞ; ij jjm; jjmjng
ð3Þ
where Gk(V, E) is the road network topology of trip mode k, and Fmin is the shortest path operator. The CO2 emission level induced by electronic commerce was calculated with the following equation:
C offline ¼
6 X X w¼1
þ
φ1 Aiw lwih þ
i
6 X X
w¼1 g
6 X X
φ2 Bwhg dwhg
w¼1 g
φ3 C wgn r wgn
ð4Þ
where Aiw is the package volume from delivery point w to population zone m; lwih is the distance between population zone i and delivery point h of express company w closest to population zone i; φ1, φ2 and φ3 are the CO2 intensities per kilometer from vehicles (Table S3); Bwhg is the package volume from distribution center g of express company w to closest delivery point h; dwhg is the distance between distribution center g of express company w and closest delivery point h; Cwgn is the package volume delivered by express company w via highway toll station n to distribution center g; rwgn is the distance between distribution center g of express company w and closest highway toll station n. Similarly, lwih, dwhg and rwgn were calculated with the Dijkstra algorithm. The schematic diagram of the calculation method regarding transportation is also present in Fig. 4. The emission parameters of fossil fuel combustion and vehicle usage can be found in previous studies (Table S3). There are large uncertainties in estimating CO2 emissions, caused by emission factors (Mi et al., 2017; Mi et al., 2019), and activity data (Zhao et al., 2011). Due to the large uncertainties of all the variables, the Monte Carlo simulation was chosen to interpret the variation range of environmental costs in Shenzhen (Weber et al., 2010; Zhao et al., 2012). Both emission factors (electricity (coefficient of variance (CV): 3%) and diesel oil (CV: 18%)) and activity data (electricity sector (CV: 5%), transportation sector (CV: 16%) and Industry (CV: 10%)) were assumed to have normal distributions (Table S4) (Shan et al., 2017; Zhao et al., 2011). 2.2. Nationwide estimation A monotonic relationship exists between the total GDP and total CO2 emission level via electronic commerce or conventional retail trade in each city. That is, the total CO2 emission level increases with growing total GDP in cities. Per capita GDP is the most important driving factor of CO2 emissions in the commercial sector (Wang and Lin, 2017). CO2 emissions from increasing average GDP per capita should be a mixed indicator of physical energy efficiency and economic structure (Price et al., 2013). Due to data availability, 10 districts in Shenzhen with calculated
Fig. 4. The schematic diagram of the calculation method regarding transportation.
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CO2 emissions and GDP were used to explore the relationship between CO2 emissions via the two retail channels. The CO2 emission and GDP were assigned to a population zone level (57 population zones) in Shenzhen, and spatial heterogeneity was investigated to ensure the rationality of the relationship. Based on the relationship, an estimated national result was obtained and spatialized (specified below). 3. Results and discussion 3.1. Product returns The product returns accounted for 19.6% of total transportation emission (Table 1). In particular, the CO2 emission from product returns in e-commerce was over four times larger than that from transportation. It implies that an efficient management regarding goods quality sold via e-commerce is needed for less product returns. In terms of CO2 emission difference, it is twice more in conventional retail (Table S5). Generally, product returns contribute to the smallest part in CO2 emission difference between two retail channels. 3.2. Buildings As expected, the CO2 emission from e-commerce is far lower than that from conventional retail (Table 1), due to no demand in commercial buildings via e-commerce. Clearly, buildings are the second biggest factor affecting CO2 difference. Nonetheless, commercial buildings in China are undergoing expansion with growing income levels (Yu et al., 2014), possibly due to the competition with electronic commerce (Ministry of Commerce of the People's Republic of China, 2015). More integrated shopping centers and energy-saving measures in lighting and air conditioning are needed (Ministry of Commerce of the People's Republic of China, 2015). More broadly, increasing energy intensity and improving the energy structure (e.g., reducing the use of coal) are strategies to reduce CO2 emission in China (Wang and Lin, 2017). 3.3. Packaging Surprisingly, packaging is responsible for the main CO2 emission via e-commerce, which makes e-commerce less attractive for environmental reasons (Table 1). Previous study has emphasized the considerable environmental impact due to a large consumption of packaging materials (Fan et al., 2017). Based on an available survey (China e-Business Research Center, 2016), only 30.8% of online shopping consumers discard the packaging directly after receiving packages. Therefore, the environmental impact of packaging can be reduced due to reuse by consumers (i.e., longer life cycle). Additionally, improving energy efficiency and material management can also reduce CO2 emissions from electronic commerce packaging (Hekkert et al., 2000a; Hekkert et al., 2000b). Tape-free and biodegradable packaging has been used since 2017, cardboard boxes are recyclable (Central People's Government of the People's Republic of China, 2017), and the proportion of degradable packaging will be increased to 50% in 2020 (State Postal Bureau of China, 2017). The usage of reusable packaging is effective material Table 1 Package volume and product return and simulated total CO2 emission from various sectors via the two retail channels in Shenzhen base on the proposed Average Package Difference Model (APDM).
Package volume (billion) Product returns (billion) Product returns (billion kg CO2) Transportation (billion kg CO2) Packaging (billion kg CO2) Building (billion kg CO2)
E-commerce
Conventional
CO2 difference
1.24 0.16 0.14 0.03 1.04 0.02
1.58 0.2 0.41 2.22 0 1.09
0.27 2.19 −1.04 1.07
383
management, which leads to an estimated 16% reduction in CO2 emissions (Hekkert et al., 2000a). 3.4. Transportation Non-work trips tend to release less CO2 than work-related trips (Ma et al., 2015), which indicates that the shortest routes for shopping, most frequently non-work trips, assumed in our study were rational. In general, goods transportation is more efficient than consumer driving, resulting in reductions of fuel consumption and CO2 emissions (Carling et al., 2015; Xu et al., 2009). Interestingly, we determined that the delivery distances of packages via electronic commerce were eight times longer and that 429% of CO2 was released during within city delivery (Table S6). This determination was consistent with the finding from a previous study suggesting that energy use increased in logistics and freight transport in online shopping (Zhang and Zhao, 2017). However, online retail only released 0.06% of the total CO2 emission level, compared with emissions via conventional retail trade. The main contributor of CO2 emissions in conventional retail is the consumer trips with a distance of 25.8 km (motorized). The CO2 emissions induced by consumer trips were highly significant (99.5% of the total CO2 emissions from conventional retail) due to highly heterogeneous shopping centers and dense consumer trips (Table S7). A 1.97 package equivalent was taken for each trip to physical stores in 2016 (SD). More packages purchased during each consumer trip can significantly reduce CO2 emissions and even release less CO2 emissions than conventional retail trade (van Loon et al., 2015). Reductions in CO2 emission can be achieved via the electronic commerce channel, such as through optimizations in goods delivery and more efficient and practicable logistics systems (Kim et al., 2009). The introduction of electric vehicles can aid in emission reduction only through energy-savings in the electricity sector (Hofmann et al., 2016). 3.5. CO2 emissions and uncertainty analysis We presented a spatial variation of CO2 emission differences in population zones to identify zones with high CO2 emission differences and related causes (Fig. 5). The CO2 emission difference per package between the two retail channels was mapped within population zones. Clearly, the southern area is highly accessible for consumers via the two channels, which results in fewer CO2 emission differences per package. This result also indicated that CO2 emission level per trip was much higher when shopping in a less developed area (Figs. S2 and S3) (J. Li et al., 2016). The CO2 emission level per package via the two retail trade channels was nearly equal if CO2 emissions from consumer trips were excluded. However, 138% more CO2 was released for each package via conventional retail trade (Table S5). We also examined the uncertainty (two standard deviations divided by the mean) of the total CO2 emission differences and sectors' emissions in the estimation model with Monte Carlo analysis (10,000 iterations). Except for CO2 emissions induced by packaging, conventional retail released more CO2 (Fig. 6). The uncertainty fell in the range of 12% ~ 43%, and the building sector's emission level and total emission difference contributed to the smallest and largest uncertainties, respectively. Moreover, real-world parameters, such as product return rates, energy use intensity in buildings, number of trips, travel distance distribution, modal split and package delivery, were not available, which contributed to uncertainties in estimation. Based on the Shenzhen Statistical Yearbook (Shenzhen Bureau of Statistics, 2017a), end-use energy consumption in the retail trade was approximately 2.29 million tons of standard coal equivalent (TCE, 1 TCE = 29.39 GJ, provided in SD), which accounted for 5.6% of the total energy consumption of all industries in Shenzhen. Because the energy structure of the retail industry was not available, we assumed that coal and diesel oil (two dominant fuels in Shenzhen) were the only fuels consumed. The estimated CO2 emission value based on the Statistical
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Fig. 5. CO2 emission differences induced by population zones between conventional retail trade and electronic commerce retail.
Yearbook was 4.99–6.50 million tons (SD), whereas the total CO2 emission level calculated in our study was 4.95 million tons (Table 1). Considering the inconsistency in energy statistics (Zhang et al., 2007) and use of clean energy and within city transportation, our results were comparable to the statistical data. 3.6. CO2 emission differences To understand the environmental impact of the two retail trade channels in China, we aggregated the city-level estimations and obtained national-level results. We determined that the CO2 emissions per unit of GDP for various cities in China were quite different (Shan
et al., 2017). Particularly, the level of CO2 emissions per unit of GDP was much lower in mega cities due to their enormous GDP values. Clearly, a proper scale is crucial for examining the relationship between commerce CO2 emissions and GDP. Given the city-level administrative management, the operation of city-level retail company and online package delivery, and retail trade activities depends on every district or county (units that form a city) own socioeconomic activities, rather than that of whole city. In this sense, the district or county is a reasonable unit for calculating CO2 emission at a national scale. Therefore, 10 districts in Shenzhen were selected to develop an emission model according to the relationship between GDP and CO2 emissions, rather than using the total CO2 emission level in Shenzhen as the average value in China. That is, the CO2 emissions at a district or county scale were calculated using the following model and then were summed up into the city-level CO2 emissions. Examining several function forms (Tables S8 and S9), we obtained the relationship at a district or county scale through the following equation: ln y ¼ k ln x þ b
Fig. 6. Cumulative probability of CO2 emission difference per package between conventional retail trade and electronic commerce retail in 2016 in Shenzhen. The insert plots are CO2 emissions differences (mean ± standard deviation) with a 95% confidence interval (gray: conventional minus electronic commerce; yellow: electronic commerce minus conventional). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
ð5Þ
where x is the total GDP (10,000 CNY), y is the total CO2 (kg) emission via either retail trade channel. In particular, k is 0.7881 and 1.019 for electronic commerce and conventional retail trade, respectively; b is 6.325 and 1.382 for the two categories, respectively. The uncertainty of the equation was examined using CO2 emission and GDP assigned to a population zone level (Table S10 and Fig. S4). No spatial heterogeneity was observed using White Heteroskedasticity Test at a significance level of 0.05 (Table S11) and the uncertainty was 15.48% (SD). It indicated a reasonable relationship between CO2 emission and GDP at a district level. Interestingly, the CO2 emission level induced by electronic commerce is much lower than that of conventional retail trade, although CO2 emissions for both categories increase with the GDP. In addition, the CO2 emission difference between these two retail channels expands with GDP (less than ~20 trillion CNY) for one city (Fig. S5). All
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385
Fig. 7. CO2 emission differences between the two retail trade channels for cities in China.
of the results suggest the apparent environmental benefits of electronic commerce over conventional retail trade. By applying the model to cities in mainland China, the conventional retail trade channel released an estimated 124 million tons CO2 more than electronic commerce, which was comparable to the total consumption emission level from Beijing in 2007 (142 million tons) (Mi et al., 2016). Most cities (86.9%) released b0.60 million tons CO2 emissions more in conventional retail trade (Fig. 7). Currently, the CO2 emission difference is larger in cities with higher GDPs, especially in Beijing, Shanghai, Chongqing, Guangzhou and Shenzhen in China. Because of various socioeconomic conditions in cities, such as population distribution, industrial structure and urbanization rate, the national estimation may generate large uncertainties. Admittedly, the CO2 emission of retails is related to purchasing behaviors, living standard, added value in retail sales. Nonetheless, a general evaluation was obtained for understanding electronic commerce and conventional retail trade at a national scale.
responsible for CO2 emission. Nonetheless, the CO2 emission difference between the two retail categories is expected to decrease because of the scale economic effect of physical stores. In addition, there are several corresponding strategies to curb the CO2 emission from two retail channels, such as opening shopping centers in dense population zones and promoting the usage of public transportation and recyclable packages. Acknowledgement This study was financially supported by the National Key Research and Development Program of China (2017YFC0505701) and the National Natural Science Foundation of China (No. 41771169 and 41371169). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.05.081.
4. Conclusion References In this study, by introducing the concept of average package unit, an Average Package Difference Model was developed to evaluate CO2 emissions via the two retail channels in China based on a life-cycle perspective. It indicated conventional retail has a higher CO2 emission than that of electronic commerce, and the CO2 emission difference per package was 1.28 ± 0.31 kg. A national estimation model was investigated, and the CO2 emission difference between conventional retail and electronic commerce in China is approximately 124 million tons. In the conventional retail trade, CO2 is mainly emitted from buildings and consumer trips. In contrast, packaging is the biggest factor
Carling, K., Han, M., Hakansson, J., Meng, X., Rudholm, N., 2015. Measuring transport related CO2 emissions induced by online and brick-and-mortar retailing. Transp. Res. PT. D-Transp. Enviro. 40, 28–42. Central People's Government of the People's Republic of China, 2017. “Double 11” express delivery is expected to reach 1 billion this year and enable “green warehouse” and carton recycling for the first time. http://www.gov.cn/xinwen/2017-09/20/content_ 5226459.htm, Accessed date: January 2018. Chang, Y., Ries, R.J., Wang, Y., 2013. Life-cycle energy of residential buildings in China. Energ. Policy 62, 656–664. China e-Business Research Center, 2016. The survey of Chinese online consumption and guideline of online shopping in 2016. http://www.100ec.cn/zt/upload_data/wlxf. pdf, Accessed date: February 2018.
386
Y.-B. Zhao et al. / Science of the Total Environment 679 (2019) 378–386
China e-Business Research Center, 2017. Monitoring report for China e-commerce market in 2016. http://www.100ec.cn/zt/16jcbg/ (accessed January 2018). Department of Electronic Commerce and Informatization, 2017. The main data of China's e-commerce in 2016. http://dzsws.mofcom.gov.cn/article/dzsw/tjjc/201706/ 20170602591873.shtml (accessed December 2017). Fan, W., Xu, M., Dong, X., Wei, H., 2017. Considerable environmental impact of the rapid development of China's express delivery industry. Resour. Conserv. Recycl. 126, 174–176. Hekkert, M.P., Joosten, L.A.J., Worrell, E., 2000a. Reduction of CO2 emissions by improved management of material and product use: the case of transport packaging. Resour. Conserv. Recycl. 30 (1), 1–27. Hekkert, M.P., Joosten, L.A.J., Worrell, E., Turkenburg, W.C., 2000b. Reduction of CO2 emissions by improved management of material and product use: the case of primary packaging. Resour. Conserv. Recycl. 29 (1–2), 33–64. Hofmann, J., Guan, D., Chalvatzis, K., Huo, H., 2016. Assessment of electrical vehicles as a successful driver for reducing CO2 emissions in China. Appl. Energy 184, 995–1003. Howard, B., Parshall, L., Thompson, J., Hammer, S., Dickinson, J., Modi, V., 2012. Spatial distribution of urban building energy consumption by end use. Energ. Build. 45, 141–151. Jia, T., Carling, K., Håkansson, J., 2013. Trips and their CO2 emissions to and from a shopping center. J. Transp. Geogr. 33, 135–145. Kang, J., Zhao, T., Liu, N., Zhang, X., Xu, X., Lin, T., 2014. A multi-sectoral decomposition analysis of city-level greenhouse gas emissions: case study of Tianjin, China. Energy 68, 562–571. Kim, J., Xu, M., Kahhat, R., Allenby, B., Williams, E., 2009. Designing and assessing a sustainable networked delivery (SND) system: hybrid business-to-consumer book delivery case study. Environ. Sci. Technol. 43 (1), 181–187. Li, J., Zhang, P., Lo, K., Guo, M., Wang, M., 2015. Reducing carbon emissions from shopping trips: evidence from China. Energies 8 (9), 10043–10057. Li, J., Lo, K., Zhang, P., Guo, M., 2016a. Consumer travel behaviors and transport carbon emissions: a comparative study of commercial centers in Shenyang, China. Energies 9 (10), 765–778. Li, Y., Tan, W., Sha, R., 2016b. The empirical study on the optimal distribution route of minimum carbon footprint of the retail industry. J. Clean. Prod. 112, 4237–4246. Lin, B., Liu, H., 2015. CO2 emissions of China's commercial and residential buildings: evidence and reduction policy. Build. Environ. 92, 418–431. Ma, J., Liu, Z., Chai, Y., 2015. The impact of urban form on CO2 emission from work and non-work trips: the case of Beijing, China. Habitat Int 47, 1–10. Mangiaracina, R., Marchet, G., Perotti, S., Tumino, A., 2015. A review of the environmental implications of B2C e-commerce: a logistics perspective. Int. J. Phys. Distrib. Logist. Manage. 45 (6), 565–591. Mi, Z., Zhang, Y., Guan, D., Shan, Y., Liu, Z., Cong, R., Yuan, X.-C., Wei, Y.-M., 2016. Consumption-based emission accounting for Chinese cities. Appl. Energy 184, 1073–1081. Mi, Z., Meng, J., Guan, D., Shan, Y., Song, M., Wei, Y.-M., Liu, Z., Hubacek, K., 2017. Chinese CO2 emission flows have reversed since the global financial crisis. Nat. Commun. 8 (1), 1712. Mi, Z., Zheng, J., Meng, J., Zheng, H., Li, X., Coffman, D.M., Woltjer, J., Wang, S., Guan, D., 2019. Carbon emissions of cities from a consumption-based perspective. Appl. Energy 235, 509–518. Ministry of Commerce of the People's Republic of China, 2015. Green book on energy conservation and environmental protection in China's retail industry in 2015. http://images.mofcom.gov.cn/ltfzs/201603/20160315140507770.pdf (accessed April 2018). Murota, K., Shioura, A., 2014. Dijkstra's algorithm and L-concave function maximization. Math. Program. 145 (1–2), 163–177. National Development & Reform Commission of China, 2017. A notice concerning the public consultation on the emission factor (draft) of China regional power grid in 2016. http://qhs.ndrc.gov.cn/gzdt/201704/t20170414_844347.html, Accessed date: February 2018. Palsson, H., Pettersson, F., Hiselius, L.W., 2017. Energy consumption in e-commerce versus conventional trade channels - insights into packaging, the last mile, unsold products and product returns. J. Clean. Prod. 164, 765–778. Price, L., Zhou, N., Fridley, D., Ohshita, S., Lu, H., Zheng, N., Fino-Chen, C., 2013. Development of a low-carbon indicator system for China. Habitat Int 37, 4–21. Seebauer, S., Kulmer, V., Bruckner, M., Winkler, E., 2016. Carbon emissions of retail channels: the limits of available policy instruments to achieve absolute reductions. J. Clean. Prod. 132, 192–203. Shan, Y., Guan, D., Liu, J., Mi, Z., Liu, Z., Liu, J., Schroeder, H., Cai, B., Chen, Y., Shao, S., Zhang, Q., 2017. Methodology and applications of city level CO2 emission accounts in China. J. Clean. Prod. 161, 1215–1225.
Shan, Y., Guan, D., Hubacek, K., Zheng, B., Davis, S.J., Jia, L., Liu, J., Liu, Z., Fromer, N., Mi, Z., Meng, J., Deng, X., Li, Y., Lin, J., Schroeder, H., Weisz, H., Schellnhuber, H.J., 2018. Citylevel climate change mitigation in China. Sci. Adv. 4 (6), eaaq0390. Shan, Y., Liu, J., Liu, Z., Shao, S., Guan, D., 2019. An emissions-socioeconomic inventory of Chinese cities. Scientific Data 6, 190027. Shenzhen Bureau of Statistics, 2017a. Shenzhen statistical yearbook 2016. http://www.sz. gov.cn/sztjj2015/xxgk/tjsj/tjnj/201712/W020171219625244452877.pdf, Accessed date: January 2018. Shenzhen Bureau of Statistics, 2017b. Statistical bulletin of Shenzhen national economic and social development in 2016. http://www.sztj.gov.cn/xxgk/tjsj/tjgb/201705/ t20170502_6199402.htm, Accessed date: January 2018. Shenzhen China, 2017. E-commerce transactions in Shenzhen exceeded 2 trillion yuan in China. http://www.sz.gov.cn/cn/xxgk/zfxxgj/zwdt/201703/t20170329_6101767. htm, Accessed date: January 2018. Shenzhen Municipal Postal Administration, 2017. Postal industry development statistics bulletin in 2016. http://xxgk.gdsz.spb.gov.cn/extranet/detail.html?yc_id=d31da7f18f51-47b8-bdc5-6501f2eda263, Accessed date: April 2019. Shenzhen Transportation Committee, 2017. Annual report of traffic and transportation operation index in 2016. http://www.sztb.gov.cn/xxgk/xxgkml/tjxx/201702/ t20170206_5949703.htm, Accessed date: January 2018. Sivaraman, D., Pacca, S., Mueller, K., Lin, J., 2007. Comparative energy, environmental, and economic analysis of traditional and e-commerce DVD rental networks. J. Ind. Ecol. 11 (3), 77–91. State Postal Bureau of China, 2017. Guidance on promoting green packaging for express delivery industry. http://www.gov.cn/xinwen/2017-11/02/content_5236573.htm, Accessed date: January 2018. van Loon, P., Deketele, L., Dewaele, J., McKinnon, A., Rutherford, C., 2015. A comparative analysis of carbon emissions from online retailing of fast moving consumer goods. J. Clean. Prod. 106, 478–486. Wang, A., Lin, B., 2017. Assessing CO2 emissions in China's commercial sector: determinants and reduction strategies. J. Clean. Prod. 164, 1542–1552. Weber, C.L., Hendrickson, C.T., Matthews, H.S., Nagengast, A., Nealer, R., Jaramillo, P., 2009. Life cycle comparison of traditional retail and e-commerce logistics for electronic products: a case study of buy.com. 2009 IEEE International Symposium on Sustainable Systems and Technology, pp. 1–6. Weber, C.L., Koomey, J.G., Matthews, H.S., 2010. The energy and climate change implications of different music delivery methods. J. Ind. Ecol. 14 (5), 754–769. Wiese, A., Toporowski, W., Zielke, S., 2012. Transport-related CO2 effects of online and brick-and-mortar shopping: a comparison and sensitivity analysis of clothing retailing. Transp. Res. PT. D-Transp. Enviro. 17 (6), 473–477. Williams, E., Tagami, T., 2002. Energy use in sales and distribution via E-commerce and conventional retail: a case study of the Japanese book sector. J. Ind. Ecol. 6 (2), 99–114. Xu, M., Allenby, B., Kim, J., Kahhat, R., 2009. A dynamic agent-based analysis for the environmental impacts of conventional and novel book retailing. Environ. Sci. Technol. 43 (8), 2851–2857. Yan, X.-Y., Zhao, C., Fan, Y., Di, Z., Wang, W.-X., 2014. Universal predictability of mobility patterns in cities. J. R. Soc. Interface 11 (100). Yan, D., Hong, T., Li, C., Zhang, Q., An, J., Hu, S., 2017. A thorough assessment of China's standard for energy consumption of buildings. Energ. Build. 143, 114–128. Yi, Y., Wang, Z., Wennersten, R., Sun, Q., 2017. Life cycle assessment of delivery packages in China. In: Yan, J., Sun, F., Chou, S.K., Desideri, U., Li, H., Campana, P., Xiong, R. (Eds.), 8th International Conference on Applied Energy, pp. 3711–3719. Yu, S., Eom, J., Zhou, Y., Evans, M., Clarke, L., 2014. Scenarios of building energy demand for China with a detailed regional representation. Energy 67, 284–297. Zhang, M., Zhao, P., 2017. The impact of land-use mix on residents' travel energy consumption: new evidence from Beijing. Transp. Res. PT. D-Transp. Enviro. 57, 224–236. Zhang, Q., Streets, D.G., He, K., Wang, Y., Richter, A., Burrows, J.P., Uno, I., Jang, C.J., Chen, D., Yao, Z., Lei, Y., 2007. NOx emission trends for China, 1995–2004: the view from the ground and the view from space. J. Geophys. Res.-Atmos. 112 (D22). Zhao, Y., Nielsen, C.P., Lei, Y., McElroy, M.B., Hao, J., 2011. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos. Chem. Phys. 11 (5), 2295–2308. Zhao, Y., Nielsen, C.P., McElroy, M.B., 2012. China's CO2 emissions estimated from the bottom up: recent trends, spatial distributions, and quantification of uncertainties. Atmos. Environ. 59, 214–223.