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Characterizing the generation and spatial patterns of carbon emissions from urban express delivery service in China
Environmental Impact Assessment Review 80 (2020) 106336
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Environmental Impact Assessment Review journal homepage: www.elsevier.com/locate/eiar
Characterizing the generation and spatial patterns of carbon emissions from urban express delivery service in China
T
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Peng Kanga, Guanghan Songa, Dongjie Chena, Huabo Duana, , Ruoyu Zhongb a b
College of Civil and Transportation Engineering, Shenzhen University, 518060 Shenzhen, China China Center for Special Economic Zone Research, Shenzhen Univeristy, 518060 Shenzhen, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Express delivery Transportation Carbon emission Spatial analysis China
Express delivery services, as an indispensable part of our daily life, have grown rapidly because of the booming e-commerce and logistics industries. Accordingly, there is increasing concern about the environmental load from delivering letters and parcels, such as road freight transportation emissions and packaging waste, which have not been seriously considered in previous work. In this study, a spatially based dynamic model has been created to quantify the impacts (measured in carbon emissions, CO2eq.) from the express delivery sector in China. Specifically, intracity (urban) express delivery services—delivery and pick-up services located within the same city—was chosen for analysis. The results indicated that the carbon emissions from the transportation phase of the express delivery sector in Chinese cities varied from 20 t to 4000 t in 2017, of which 18% was attributable to the weight of extra packaging materials. Carbon emission intensities for all cities showed a close relationship with their socioeconomic status. For example, the spatial pattern of intracity express delivery volumes and associated carbon emissions showed a significant clustering property: high-value cities were clustered in eastern China and low-value cities in western China. Furthermore, the carbon hotspots were mainly located in the Yangtze River Delta and Pearl River Delta urban agglomerations. Overall, our research method and preliminary findings could be helpful for the green development of the booming express delivery sector in China and beyond.
1. Introduction Express delivery refers to parcel and letter delivery activities (business to customer, customer to customer, and business to business, but excluding large and industrial delivery or logistic service) (YZ/ T0128-2007, National Standard for Express Delivery Service in China). Generally, express delivery services are guaranteed to be finished promptly, within a stipulated time frame, and these can be divided into warehouse storage, the sorting and delivery process, independent facilities, equipment, and delivery channels (Wang et al., 2016). Based on the scope of this sector, express delivery services can be categorized into three types: intracity express delivery (inland express service: delivery and pick-up services are within the same city); intercity express delivery (urban express delivery: delivery and pick-up services are in different cities); and external: Hong Kong, Macao, Taiwan and international express delivery service (delivery and pick-up services are external to mainland China) (DRC, 2017). Since the emergence of “Internet plus warehousing,” the online shopping provided by e-commerce enterprises like Alibaba Group and JD.com has introduced a new level of convenience and is changing the
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public's way of life (Liu et al., 2016; Meng and Zhou, 2016). Meanwhile, city logistics distribution departments have been developing rapidly in almost all cities in China, with strong support from all levels of government, because these services provide employment and improve a city's overall competitiveness (Zhe, 2011; Xu et al., 2020). Besides, urbanization requires a variety of materials to flow through urban areas, and the energy required for this flow is concentrated in these areas. This demand for material is the primary impetus behind express logistics development. According to the ‘Report on China's Express Development Index in 2018’ issued by the State Post Bureau of China, China's express delivery sector exceeded 50 billion pieces in that year, and the annual growth rate reached 51.4% in the past decade(SPBC, 2019). Accompanied by the advances in ‘Internet plus and Artificial Intelligence,’ the logistics industry has been making steady progress in its service capability, operations level, technical equipment, and management level. Moreover, the express volume is facing exponential growth and could reach 100 billion pieces in 2020 (SPBC, 2017). Thus, it is imperative to determine the environmental burden of the express industry and to take some effective measures to mitigate it.
Corresponding author. E-mail address: [email protected] (H. Duan).
https://doi.org/10.1016/j.eiar.2019.106336 Received 28 June 2019; Received in revised form 14 October 2019; Accepted 2 November 2019 Available online 19 November 2019 0195-9255/ © 2019 Elsevier Inc. All rights reserved.
Environmental Impact Assessment Review 80 (2020) 106336
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2.2. Carbon emission quantification model for intracity express delivery service
It is well known that the most active type of intracity express delivery is in urban areas. Some research has shown that logistics delivery has become one of the most important sources of global carbon emissions, because of the large energy consumed in freight transportation (Yang and Guo, 2012; Chen and Lin, 2017). Currently, a few research studies have already focused on the environmental impacts of the logistic system, such as the release of pollutants, intensive energy and resource consumption, and carbon emissions (Bouchery and Fransoo, 2015; Fan et al., 2017). However, these evaluation models and methods have mainly focused on a specific geographic scope or logistic process, and are not suitable for the evaluation of the total impact of urban express delivery services (Fei et al., 2013; Christine et al., 2013). Therefore, finding an appropriate assessment model for carbon emissions from intracity express delivery has become a strategic imperative for any organization, considering the complexity and connectivity of cities (Sun et al., 2015). Actually, the urban environment is deteriorating in many cities, and they are all facing challenges of unsustainable development. Hence an energy-saving green express delivery system will eventually have to be established, and is part of the development plan of the express delivery industry (Yang et al., 2016; Liu et al., 2018;Hao et al., 2019). And control of carbon emissions from intracity express delivery will not only help address the climate-change issue but will also satisfy regulatory requirements (He et al., 2017; Zuev et al., 2019). Considering the importance of intracity express delivery and the characteristics of the urban environment, the main goal of this research was the construction of a universal model of carbon emissions assessment for intracity express delivery services in China. Specifically, the carbon emissions of intracity express delivery services within important cities were evaluated, and the correlating relationships among city clusters were analyzed based on socioeconomic factors. In addition, the spatial pattern of carbon emissions attributable to the intracity express delivery services in China was analyzed.
Human activities, especially economic behavior, depend predominantly on urban geographic form and population density. Some research on human economic activities has been analyzed by focusing on a combination of urban form and population density. In this study, in order to calculate carbon emissions from intracity express delivery services, a systematic model for quantification of carbon emissions was constructed by considering these two factors—urban form and population density—to express the complexity, heterogeneity, and connectivity of the urban ecosystem. Specifically, the model contained three main parts: (1) quantification of the transportation distance for intracity express delivery service, based on the transportation mode and phase; this was the key component of the model; (2) characterization of the delivery volume and distribution information of parcels and letters, as well as their weight data; and (3) extraction of carbon emission factors from a commercial Chinese-context life cycle assessment (LCA) database (Gabi) of various transportation modes related to express delivery (in kg CO2 eq./tkm). It should be noted that the urban ecosystem in this model was configured as having one or more centers and a few surrounding mini patches, because of the fragmented character of urban areas in China. Regarding the hierarchical relationship between the center(s) and other satellite patches in the urban form, transportation among these patches can be divided into three phases: (1) the transportation phase for a single patch; (2) the transportation phase between the center and surrounding patches, and (3) the transportation phase between surrounding patches. Finally, the ratios among these three transportation distances were ascertained based on the patch area and population density. According to the practice of optimal delivery efficiency, the transportation phase for a single patch was designed as a circle, and all points within the circle were within the transportation range for that patch. Therefore, the transportation distance for each patch was the radius of the circle. The transportation distance was then revised based on the compactness ratio of the patch. It is undeniable that population density and patch area are the two primary factors for a transportation model. The population density for each patch was therefore extracted, and the product of the patch area and its population density obtained. Finally, the transportation distance for each patch was calculated based on the ratio of the product for the patch. The process for quantification of the transportation distance of a single patch was as follows. First, the transportation radius of a single patch was calculated with Eq. (1).
2. Method 2.1. Study case As mentioned in Section 1, intracity express delivery service has been widely promoted in each city of China. Sixty-two cities were selected for this study. These included the top 50 cities in terms of intracity express delivery volume, along with some provincial capitals. Together, these two types of cities totaled 91% of those studied. The locations and express delivery volumes for those sampling cities are depicted in Fig. 1. In this study, the intracity express delivery volume was found to be dominated by Beijing, Shanghai, Guangzhou, and Shenzhen, whose collective volume came to > 700 million pieces annually, while by contrast the delivery volume of some provincial capitals in the northwest was lower than 20 million pieces each. Most of these cities were in 13 urban agglomerations in China. The number of cities in the Yangtze River Delta urban agglomeration, the Pearl River Delta urban agglomeration and the Beijing-Tianjin-Hebei (BTH) urban agglomeration were 17, 8 and 5, respectively. Other urban agglomerations contained an average of two cities each. Moreover, six of the cities do not belong to any urban agglomeration. These statistics indicate that the delivery volume in the eastern region was massive, while that in the western region was considerably lower. In addition, this study focused only on the impacts (measured by carbon emissions, kg CO2 eq.) from the transportation phase; the impacts of warehouse storage and receiving and distribution centers were excluded. We also did not consider the impacts of packaging waste. The impacts of the transportation of express deliveries out of the cities (intercity delivery) was, as stated above, also outside the scope of this study.
Rrad =
A/π
(1)
where Rrad is the transportation radius and A is the area of the patch. The index on urban form measurement was applied to the transportation distance. The compactness ratio of the patch's outer contour is a key index reflecting the city form, and is computed as follows:
CoI = 2
πA P
(2)
where CoI is the compactness ratio of the patch, A is the area of the patch, and P is the perimeter of the patch contour. CoI ranges from 0 to 1. A higher value indicates a more compact shape and a value closer to 1 indicates that the shape is closer to a circle, and vice versa. A circle is the most compact shape, and thus the compactness ratio of a long narrow shape is far smaller than 1. Based on the calculation of CoI and Rrad, the transportation distance can be calculated with Eq. (3).
2 ⎞ Rone = Rrad ∗ ⎛ ⎝ 1 + 2 ∗ CoI ⎠ where Rone is the transportation distance for one patch. 2
(3)
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Fig. 1. Delivery volume (in pieces) of intracity express delivery for selected cities. Note: The top 50 cities in China for intracity express delivery service, and some provincial capitals (62 in total). Legend refers to pieces of express mail, annually. Data source: Local postal bureau (in 2017).
Then the first transportation distance was computed with Eq. (4): n
Rtran =
∑ i=1
popk ∗ Ak n
∑1 popi ∗ Ai
∗ R (one) k
2.3. Spatial analysis of carbon emissions from intracity express delivery In order to analyze the relationship between carbon emissions and socioeconomic factors, eight primary factors were selected from the general economy and the express economy. The analysis of carbon emission factors in the transportation industry needs to consider the socioeconomic development and relevant logistical industries (Lin and Benjamin, 2017). (1) The general economy included GDP (gross domestic product), the proportion of the tertiary industry and the population; (2) the economy of the express delivery service, consisting of delivery income, service sites, express delivery volume per person, express delivery expenditure per person, and road length. The Pearson correlation index was applied to analyze the correlations among these indexes. Moreover, based on the selected indexes, the K-Means cluster method was applied in order to categorize the 62 cities into five grades. In this study, the carbon emissions of intracity delivery services in China were calculated based on the systematic model. Specifically, a universally used spatial autocorrelation indicator - Moran'I - was used to analyze the global distribution pattern of carbon emissions (Anselin, 1995). A value close to 1 indicates a completely clustered pattern, a value close to −1, a completely dispersed pattern. Additionally, the local spatial statistic G combined with a Bonferroni-type test was used to identify the hotspots of carbon emissions in the form of a Z-value. Higher positive values represent higher volumes of carbon emitted by intracity express delivery services (Ord and Getis, 1995). The Z-value was further divided into six grades by the natural breaks (Jenks) method, to present various levels of hotspots.
(4)
where Rtran is the transportation distance for a single patch; R(one)k is the transportation distance for patch k; popk is the population density for patch k; Ak is the area of patch k; and ∑1npopi ∗ Aiis the total population of all the researched patches. The secondary transportation phase involves the distance between the center patch and its surrounding patches. Initially, the gravity center of each patch was determined, and the major patch was then identified. Based on the two aforementioned processes, the transportation distances between the center patch and its surrounding patches were calculated using the Euclidean distance function, and the mean distance was then obtained. Meanwhile, the calculation of the distance for the surrounding patches was similar to that for the second transportation type. The flowchart of the three types of transportation distance for intracity express delivery is depicted in Fig. 2. In order to comprehensively calculate the transportation distance for intracity express delivery, the ratios among the three types of transportation phases were determined and combined with the weighting indexes for the patches. First, the number of patches was counted, and the ratio of the major patch was computed as the product of the patch area and its population density. Then the patch threshold was identified for patches where the ratio was > 85% of the total area. Based on the identification of patch number and the ratio of the major patch, the ratios of the three types of transportation phases were ascertained and are listed in Table 1. Finally, the transportation distance was computed using the ratio and distance for the three types of transportation phases.
2.4. Data acquisition and quality evaluation Four different types of data, acquired from various sources, were used for this analysis: package information, urban images, socioeconomic data, and environmental impact factors. The survey mainly 3
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Fig. 2. Modeling for the three types of transportation phases for intracity express delivery service - a case study of Beijing: (a) land cover and land use of Beijing; (b) built-up area of Beijing; (c) three types of transportation phases and their distances.
ecosystem. Initially, the buffer for each patch was set to 0.8 km. Then urban patches were identified based on the condition that the area of a patch had to be > 7 km2. Finally, the major patch and surrounding patches were ascertained and adjusted, for each city. Population density data for China was acquired from the environmental and resource data cloud platform of the Chinese Academy of Sciences. The population of patches varied from 0 to 67,000 persons per km2. The patch with the mean population density was extracted based on the population density of the images (Fig. S1.b). The intracity express delivery volume, number of express sites, delivery volume per person, and delivery expenditure per person were acquired from the local urban Postal Statistics Annual Report for 2017. Then GDP, PTI, and population figures were obtained from the city statistical yearbook of China and the National Social and Economic Development Annual Report on each city. In this study, we defined two main transportation phases in intracity express delivery: truck transportation for the beginning phase, and electric-vehicle transportation for the end phase, because these two modes of delivery were given high priority for investigation. Using the emission factors for each transportation mode, adjusted to the Chinese context, exported from Gabi software (Version 8.0, a commercial professional life cycle assessment software package with an embodied database), the carbon emissions could be easily calculated.
Table 1 Ratios for three types of transportation phases and their distances. TPA
FPR
Type I
Type II
Type III
1
1 0.95–1 0.90–0.95 0.85–0.90 0.80–0.85 0.7–1 < 0.7 0.5–1 0–0.5
1 0.98 0.95 0.93 0.90 0.80 0.76 0.75 0.72 0.65
0 0.015 0.03 0.05 0.07 0.18 0.21 0.22 0.24 0.30
0 0.005 0.02 0.02 0.03 0.02 0.03 0.03 0.04 0.05
2 3–5 >5
Note: TPA: patch threshold; FPR: ratio of the major patch.
included package weight and packaging material weight. In this study, four types of packaging were considered: corrugated box, plastic bag, file envelope, and other packaging. The corrugated box category included four sizes (oversized, large, medium and small); plastic bags included white, pure, variegated, black, and gray, and plastic bag cartons, all in both large and small sizes; file envelopes included two sizes (large and small); ‘other’ packaging included woven bags, air bubble bags and foam materials, all in both large and small sizes (Duan et al., 2019). In order to systematically investigate package information, a comprehensive investigation of package types and their weights was carried out in Shenzhen, by a series of survey teams, in May and June of 2018. Sample batches of each packaging material category, of different sizes and materials, were weighed, and the mean values calculated. Finally, the ratios of the various categories of packaging material were calculated. Land use and land cover data for China, in 2015, were acquired from the environmental and resource data cloud platform of the Chinese Academy of Sciences. The resolution of images was 90 m. The land cover and land use were categorized into 11 classes: forest, shrub, grassland, water body, farmland, residential, desert, bare land, urban forest, urban grass, and traffic area (Fig. S1.a). Of these, residential, urban forest, urban grass, and traffic area were classified as the urban
3. Result and discussions 3.1. Selection and description of sampled cities A total of 62 cities were selected for this study, mainly distributed in 13 urban agglomerations. The urban agglomerations and relevant cities are listed in Table 2. The average delivery volumes for the Pearl River Delta (PRD), the Beijing-Tianjin-Hebei (BTH) and the Yangtze River Delta (YRD) urban agglomerations came to > 300 million pieces each, while the corresponding volume for the Hu-Bao-E-Yu (HBEY) urban agglomeration was < 20 million pieces, annually. The land areas of the selected cities ranged from 400 to 80,000 km2. Within this range, the area of Chongqing was highest: > 80,000 km2, 4
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Table 2 Express delivery volumes and urban agglomerations. Urban agglomeration
Cities
Express delivery volume (million pieces)
Beibu Gulf (BBG) Beijing-Tianjin-Hebei (BTH) Central Plains (CPU) Chengdu-Chongqing (CYU) Hu-Bao-E-Yu (HBEY) Harbin-Changchun (HCU) Liaozhongnan (LZN) Pearl River Delta (PRD)
Nanning (NN), Haikou (HK) Beijing (BJ), Tianjin (TJ), Shijiazhuang (SJZ), Baoding (BD), Langfang (LF) Zhengzhou (ZEZ), Xinxiang (XX) Chengdu (CD), Chongqing (CQ) Huhehaote (HHH), Yinchuan (YC) Haerbin (HEB), Changchun (CC) Dalian (DL), Shenyang (SY) Guangzhou (GZ), Shenzhen (SZ), Dongguan (DG), Foshan (FS), Jieyang (JY), Shantou (ST), Huizhou (HuZ), Zhongshan (ZS) Jinan (JN), Qingdao (QD), Linyi (LY) Fuzhou (FZ), Xiamen (XM), Quanzhou (QZ), Zhangzhou (ZAZ) Shanghai (SH), Hangzhou (HZ), Nanjing (NJ), Suzhou (SuZ), Wuxi (WX), Ningbo (NB), Changzhou (CZ), Wenzhou (WZ), Nantong (NT), Suqian (SQ), Jinhua (JH), Huaian (HA), Shaoxing (SX).Jiaxing (JX), Xuzhou(XZ), Yancheng (YAC), Taizhou(TZ), Changzhou (CZ), Hefei (HF) Changsha (CS), Wuhan (WH), Nanchang (NC)
48 1324 287 390 38 78 133 2735
Guiyang (GY), Kunming (KM), Lasa (LS), Lanzhou (LZ), Taiyuan (TY), Xining (XN), Wulumuqi (WM)
110
Shandong Peninsula (SDP) west of the Straits (WCS) Yangtze River Delta (YRD)
The middle reaches of the Yangtze River of Urban Agglomeration Other provincial capital (OTH)
181 393 3818
299
Note: The locations, urban areas, and built-up areas for cities in the urban agglomerations can be found in Fig. S2–S3.
Fig. 3. Estimates of transportation distance for intracity express delivery for 62 cities. Note: City and urban agglomeration abbreviations can be found in the abbreviation table.
while that of Haikou was lowest: < 500 km2. In general, the areas of most cities fell into the range of 10,000 to 20,000 km2. The urban land areas and the ratios of built-up portions are depicted in Fig. S4. The built-up ratio of most cities was approximately 30%. The ratios for the
Yangtze River Delta (YRD) and the Pearl River Delta (PRD) were obviously higher than those for other regions. In particular, the ratios of Shenzhen, Dongguan, and Shanghai were all over 50%. However, the ratios of some of the northwest provincial capitals were lower than 5
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Fig. 4. Carbon emission estimates from the transportation phase of intracity express delivery services. Note: City and urban agglomeration abbreviations can be found in the abbreviation table. The carbon emissions of packaging materials are depicted in Fig. S9.
patches > the transportation distance between the center and the surrounding patches > the transportation distance within a single patch (Fig. S7). The proportional configuration for the three types of transportation phases, however, can be interpreted as follows: the ratio of the first transportation type (the transportation distance within a single patch) was largest; the ratio of the second transportation type (the transportation distance between the center and the surrounding patches) was of medium length; and the ratio of the third transportation type (the transportation distance between surrounding patches) was smallest. Looking at the transportation distance within a single patch, the range of the transportation distance for these cities was from 6 to 40 km, and the average value was 16.8 km. Specifically, the distances for Shanghai, Beijing, Wushan, and Foshan were all longer than 30 km, while the distances for Haikou, Lasa, and Huizhou were in the range of 5 to 8 km. The transportation distance between the center and surrounding patches of these cities ranged from 0 to 80 km, and the average value was 43.9 km. The transportation distances between the center and surrounding patches in Xinxiang, Yancheng, and Changchun were all > 70 km, while the corresponding distances for Shenzhen, Lasa, Haikou, and Wulumuqi were all 0 km, because these cities consisted of only one patch. The transportation distance between surrounding patches in these cities ranged from 0 to 140 km, with an average value of 60 km. The transportation distance between surrounding patches in Chongqing was 139 km, while this distance for Harbin, Yancheng, and Shenyang was > 80 km. The accuracy of assessment model is shown in Table S1 and Fig. S8. The transportation distances of intracity express for these cities ranged from 5 km to 40 km (see Fig. 3). The distances in Shanghai and Beijing were > 35 km, while the distances in Haikou and Xining were < 10 km. Comparing the cities by urban agglomeration, the distances for the Beijing-Tianjin-Hebei (BTH) and the Yangtze River Delta
10%. This phenomenon suggests that the high built-up ratio in urban areas generally resulted in more highly developed express delivery services and more massive express volumes. 3.2. Transportation distance estimates for intracity express delivery In order to match the ratios of the three types of phases, their indexes were calculated based on the total number of patches, the desired patch threshold, and the ratio of the major patch. Specifically, the desired patch threshold represented the required minimum number of patches and included those patches where the product of the patch area and population density was > 85% of the total. The total number of patches for each of these cities ranged from 1 to 30 (Fig. S5), while the mean number of patches for these cities was approximately seven. Nevertheless, Chongqing had 29 patches, and Yanchang had 18, while Shenzhen and Haikou had only one patch each. In this study, the mean threshold of the desired number of patches was approximately two. In 67.3% of the cities the threshold of the desired number of patches was one. However, the thresholds of the desired number of patches were extremely high (5, 5 and 7) for Baoding, Chongqing and Langfang, respectively, suggesting that these cities had several highly developed centers. As for the ratio of the major patch, in 52.6% of the cities, it was over 90%. In 22.7% of the cities the ratio of the major patch fell into the range of 80% to 90% (Fig. S6). However, in a fraction (16%) of these cities the ratio was below 60%, and the ratios of the major patches in Langfang and Huizhou were 24% and 34%, respectively (see Fig. S6). These ratios suggest that most cities had one major center, and the development pattern of most cities was a pie shape, with development radiating out from the center to the more outlying areas. Consequently, the three types of transportation phases were ranked as follows: the transportation distance between surrounding 6
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Fig. 5. Correlations among socioeconomic factors and carbon emissions from intracity express delivery transportation. Data sources: Socioeconomic data were taken from the Statistical Yearbooks from local statistical offices, and the express statistics were acquired from the 2017 Postal Statistics Bulletin from each city.
lengths in the city, and urban population—were all higher than 0.5, indicating that carbon emissions had a close relationship with other express delivery factors. At the same time, other express delivery factors had a negative relationship with GDP. Nevertheless, there was a mutually positive relationship among those factors. Five grade levels were identified in these cities (Fig. S10). The proportions of grade levels I, II, III, IV, and V were 11.3%, 20.3%, 32.3%, 17.7%, and 17.7%, respectively. The cities in the BeijingTianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations were in grades I, II and III, respectively.
(YRD) urban agglomerations were both > 25 km, while the distances for the Central Plains (CPU), the Chengdu-Chongqing (CCY), the Liaozhongnan (LZN) and the Pearl River Delta (PRD) urban agglomerations were all > 20 km. 3.3. Carbon emission estimates for intracity express delivery transportation In this study, the packaging material accounted for 18.2% of the total carbon emissions. The carbon emissions of packaging fell into the range of 20 to 4000 t (see Fig. 4), although the corresponding weight of the packaging materials ranged from only 4 to 796 t (Fig. S9). The highest carbon emissions were from Beijing and Shanghai, both > 4000 t each, and the carbon emissions from Guangzhou and Shenzhen were > 2500 t each. However, the carbon emissions from Haikou, Lasa and Xining were all below 50 t each. The average value of carbon emissions in these seven cities was 563 t. As for urban agglomerations, the carbon emissions from the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) urban agglomerations were all significantly higher than those from other agglomerations.
3.5. Spatial analysis of carbon emissions The carbon emissions from intracity express delivery transportation in China are depicted in Fig. 6a. The carbon emissions in the eastern areas of China were higher than those in the western areas. The strongest intensities of carbon emissions were mainly located in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations. The Moran'I value was 0.34, indicating that the carbon emissions in China represented a cluster pattern, with the higher values clustered in the east and lower values clustered in the west. The hotspot of carbon emissions was similar to the overall carbon emissions for China, with the highest values distributed in Beijing, Shanghai, Shenzhen, and Guangzhou (Fig. 6b). Obviously, the intracity express delivery is accelerating, for two reasons: (1) rapid urbanization, including an increase in population and an expansion of urban areas; (2) equally rapid development of logistics facilities and warehouses; these maintain the conditions for express delivery. It is generally accepted that urban areas are the major sources for carbon emissions, and the emissions from express delivery in urban areas are far from being a negligible contributor to this problem. Overall, our study revealed significant regional disparities from the carbon emission perspective, in China. It is well known that China faces
3.4. Correlation and cluster analysis The correlation between carbon emissions from intracity express delivery services and the selected index are depicted in Fig. 5. There is a positive correlation between carbon emissions and both the general economy and the express economy, except for GDP values; the correlation between carbon emissions and GDP is negative. This phenomenon can be interpreted as follows: in some cities—such as Yanchang, Jieyang and Taizhou and so on—the express delivery service industry is highly developed, a mainstay industry in this area. The correlated indexes between carbon emissions and other express delivery factors—delivery service income, personal express delivery expenditures, road 7
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Fig. 6. Carbon emissions and hotspot analysis from intracity express delivery transportation in China. Note: a) Carbon emissions from intracity express delivery in China; b) Hotspot analysis in China. A higher level represents stronger carbon emissions from express delivery.
high in megacities and low in small and medium-sized cities. The eastern part of China accounts for about 90% of the country's carbon emissions. Accordingly, the management of regional express delivery should consider geographical features. Based on our results, regionally oriented measures are needed for the eastern, central, and western
significant imbalances among its regional economies, despite the benefits generated by its rapidly growing economy. The carbon emissions generated by intracity express delivery transportation show a strong interlink with socioeconomic factors. The spatial distribution of carbon emissions was high in eastern coastal cities and low in inland cities;
8
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10 km. (2) The carbon emissions of the express package delivery service fell into the range of 20 to 4000 t, while those of the packaging materials varied from 4 to 796 t. Moreover, there is a positive relationship between carbon emissions and both the general economy and the express delivery economy. (3) China's carbon emissions represented a cluster pattern, with higher values clustered in the east and lower values clustered in the west. The hotspot of carbon emissions was similar to the overall carbon emissions of China, with the highest values distributed in Beijing, Shanghai, Shenzhen, and Guangzhou. (4) Regional transportation management must consider geographical features and relevant socioeconomic factors. The results of this study proved that our model has applicability for the carbon emissions of the transportation phase of intra city express delivery service in urban areas; this contribution is not negligible. Reducing the volume of packaging material can play an important role in minimizing carbon emissions from the express delivery sector. (5) Finally, the management of regional express delivery should consider geographical features. Regionally oriented measures are advocated for the eastern, central, and western regions, as well as for cities of different sizes. In total, to improve the environmental performance of express delivery, China's logistics companies need to adopt advanced management and decision methods, enabling logistics enterprises to optimize delivery routes and design effective service and delivery networks.
regions, as well as for cities of different sizes. In the past decade, China has made large investments of financial and other resources in the construction of urban public transportation, intelligent networks, and various modes of delivery (Li et al., 2019a). Considering the massive delivery volume and longer delivery distance in the eastern coastal megacities, low-carbon development in this area should improve the efficiency of facilities and infrastructure in the logistics industry, especially when combined with advances in urban public transportation (Li et al., 2019b). Site selection and design of logistics centers has been optimized with artificial intelligence systems. In addition, the coordination of express waybills, as well as the selection of delivery routes and mode, have been based on the analysis of large volumes of data (Yan et al., 2019). Meanwhile, considering the lower delivery volumes and shorter transportation distances in the western region, low-carbon development should be emphasized there: for example, more environmentally friendly vehicles (Stolaroff et al., 2018). In other words, to improve the environmental performance and reduce the costs of express delivery, China's logistics companies need to adopt advanced management and decision methods, in order for logistics enterprises to optimize delivery routes and design effective service and delivery networks (Yang and Guo, 2012). Because the proportion of packing material made up 18.2% of the total weight of packages, the reduction of packaging material is an important measure for low-carbon development. In our survey on the weights of packages and packaging materials, the proportion of packaging material in corrugated boxes, file envelopes, and foam materials was usually > 20%. In recent years, various measures have been put forward for reducing the weight of express packages, by relevant management departments of the state, such as SPBC and the Ministry of Ecological Environment Protection. The benefits of these measures lie not only in reducing resource consumption but also in minimizing carbon emissions from transportation (Yi et al., 2017). China's cities vary greatly in population, function, location, and economic development. In consideration of their significant differences and the deficiencies this study has identified in this field, an evaluation model was constructed based on urban landscape patterns and relevant population densities. The total transportation distance was then calculated from these values and from the ratios of the three types of transportation phases. The advantage of our model is that it can provide a uniform evaluation perspective and produce clear results for individual Chinese cities. Considering the large volume and complexity of data related to intracity express delivery services, our model has limited applicability for dealing with a single delivery event. It can, however, help logistics enterprises promote their services to a precise target market, using environmental protection as a goal. The design of a lowcarbon strategy is subject to dynamic forecasting of express delivery behavior, and the environmental account of an enterprise depends on the capability of acquiring and analyzing large quantities of data. Thus, the viability and profitability of logistics enterprises can be improved in line with advances in service quality and environmental performance.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was supported by the Scientific Research Fund of Introduced High Talent of Shenzhen University (827-000044). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.eiar.2019.106336. References Anselin, L., 1995. Local indicators of spatial association. Geogr. Anal. 27, 93–115. Bouchery, Y., Fransoo, J., 2015. Cost, carbon emissions and modal shift in intermodal network design decisions. Int. J. Prod. Econ. 164, 388–399. Chen, T., Lin, C.W., 2017. A fuzzy collaboration system for ubiquitous loading/unloading space recommendation in the logistics industry. Robot. Comput. Integr. Manuf. 45, 86–98. Christine, W., Ebba, B., Claudia, R., 2013. Planning for climate change in urban areas: from theory to practice. J. Clean. Prod. 50, 68–81. Deloitte Research China (DRC), 2017. China E-Retail Market Report 2016. Deloitte Research China, Shanghai, China. Duan, H., Song, G., Qu, S., Dong, X.B., Xu, M., 2019. Post-consumer packaging waste from express delivery in China. Resour. Conserv. Recycl. 144, 137–143. Fan, W., Xu, M., Dong, X., Wei, H., 2017. Considerable environmental impact of the rapid development of China express delivery industry. Resour. Conserv. Recycl. 126, 174–176. Fei, L., Klimont, Z., Qiang, Z., Cofala, J., Zhao, L., Hong, H., Nguyen, B., Schöpp, W., Sander, R., Zheng, B., Hong, C., He, K., Amann, M., Heyes, C., 2013. Integrating mitigation of air pollutants and greenhouse gases in Chinese cities: development of GAINS-City model for Beijing. J. Clean. Prod. 58, 23–33. Hao, Y., Liu, H., Chen, H., Sha, Y., Ji, H., Fan, J., 2019. What affect consumers’ willingness to pay for green packaging? Evidence from China. Resour. Conserv. Recycl. 141, 21–29. He, Z., Chen, P., Liu, H., Guo, Z., 2017. Performance measurement system and strategies for developing low-carbon logistics: a case study in China. J. Clean. Prod. 156, 395–405. Li, X., Lee, S., Liu, B., Wang, L., 2019a. Contemporary Logistics in China. Springer, Singapore. https://doi.org/10.1007/978-981-13-7816-4(b).
4. Conclusion A universal model of carbon emissions assessment was established for intracity express delivery service in China. This consisted of three parts: clarification of the transportation mode and phase, characterizing the delivery volume and distribution information of parcels and letters, and exportation of carbon emission factors. The carbon emissions of intracity express delivery service within important cities in China was evaluated. Finally, spatial analysis and spatial patterns of carbon emissions in China were analyzed. The main conclusions of this study are as follows: (1) The transportation distances of intracity express for these cities ranged from 5 km to 40 km. The mean distances in Shanghai and Beijing were > 35 km, while those in Haikou and Xining were < 9
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Energy use and life cycle greenhouse gas emissions of drones for commercial package delivery. Nat. Commun. 9, 409. Sun, L., Li, J., Hao, S., 2015. Restriction factors and countermeasure for the development of low-carbon logistics. In: LISS 2014. Springer, pp. 49–54. Wang, J., Yang, Y., Sun, D., 2016. Analysis of the development of multimodal transport hubs. Compr. Transp. 10, 42–45. Xu, Z., Xiong, X., Zhao, Y., Xiang, W., Wu, C., 2020. Pollutants delivered every day: Phthalates in plastic express packaging bags and their leaching potential. 384, 121282. Yan, Z., Ismail, H., Chen, L., Zhao, X., Wang, L., 2019. The application of big data analytics in optimizing logistics: a developmental perspective review. In: J. Data. Info & Manag, pp. 1–11. Yang, B., Guo, Y.Y., 2012. Research on the energy consumption evaluation model of container terminal logistics system. Appl. Mech. Mater. 157, 1224–1229. Yang, J., Guo, J., Ma, S., 2016. Low-carbon city logistics distribution network design with resource deployment. J. Clean. Prod. 119, 223–228. Yi, Y., Wang, Z., Wennersten, R., Sun, Q., 2017. Life cycle assessment of delivery packages in China. Energy Procedia 105, 3711–3719. Zhe, Z., 2011. Preliminary analysis of external diseconomy of city logistics. Pract. Foreign Econ. Relat. Trade. 5, 86–88. Zuev, D., Tyfield, D., Urry, J., 2019. Where is the politics? E-bike mobility in urban China and civilizational government. Environ Inniv. Soc Tr. 30, 19–32.
Li, Y., Cai, B., Ye, Z., Wang, Z., Zhang, W., Zhou, P., Chen, J., 2019b. Changing patterns and determinants of transportation carbon emissions in Chinese cities. Energy 174, 562–575 (a). Lin, B., Benjamin, N.I., 2017. Influencing factors on carbon emissions in China transport industry: anew evidence from quantile regression analysis. J. Clean. Prod. 150, 175–187. Liu, B.L., Wang, L., Lee, S.J., Liu, J., Qin, F., Jiao, Z.L., 2016. Contemporary Logistics in China. Springer DOI 10.1007/978-3-662-47721-2. Liu, S., Lin, B., Wu, J., Zhao, Y., 2018. Modeling the service network design problem in railway express shipment delivery. Symmetry. 10 (9), 391. Meng, Q., Zhou, Q., 2016. Assessment of express delivery service based on SERVQUAL model. In: WHICEB 2016 Proceedings, pp. 441–449. Ord, J.K., Getis, A., 1995. Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306. State Post Bureau of China (SPBC), 2017. The National 13th Five-Year Plan for Express Delivery Sector. available at. State Post Bureau of China, Beijing, China (In Chinese). available at. http://www.spb.gov.cn/zc/ghjbz_1/201702/ W020170213328753560937.pdf. State Post Bureau of China (SPBC), 2019. Report on China’s Express Development Index in 2018. available at. http://www.spb.gov.cn/xw/dtxx_15079/201904/t20190417_ 1814716.html. Stolaroff, J.K., Samaras, C., O’Neill, E.R., Lubers, A., Mitchell, A.S., Ceperley, D., 2018.
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Environmental Impact Assessment Review journal homepage: www.elsevier.com/locate/eiar
Characterizing the generation and spatial patterns of carbon emissions from urban express delivery service in China
T
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Peng Kanga, Guanghan Songa, Dongjie Chena, Huabo Duana, , Ruoyu Zhongb a b
College of Civil and Transportation Engineering, Shenzhen University, 518060 Shenzhen, China China Center for Special Economic Zone Research, Shenzhen Univeristy, 518060 Shenzhen, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Express delivery Transportation Carbon emission Spatial analysis China
Express delivery services, as an indispensable part of our daily life, have grown rapidly because of the booming e-commerce and logistics industries. Accordingly, there is increasing concern about the environmental load from delivering letters and parcels, such as road freight transportation emissions and packaging waste, which have not been seriously considered in previous work. In this study, a spatially based dynamic model has been created to quantify the impacts (measured in carbon emissions, CO2eq.) from the express delivery sector in China. Specifically, intracity (urban) express delivery services—delivery and pick-up services located within the same city—was chosen for analysis. The results indicated that the carbon emissions from the transportation phase of the express delivery sector in Chinese cities varied from 20 t to 4000 t in 2017, of which 18% was attributable to the weight of extra packaging materials. Carbon emission intensities for all cities showed a close relationship with their socioeconomic status. For example, the spatial pattern of intracity express delivery volumes and associated carbon emissions showed a significant clustering property: high-value cities were clustered in eastern China and low-value cities in western China. Furthermore, the carbon hotspots were mainly located in the Yangtze River Delta and Pearl River Delta urban agglomerations. Overall, our research method and preliminary findings could be helpful for the green development of the booming express delivery sector in China and beyond.
1. Introduction Express delivery refers to parcel and letter delivery activities (business to customer, customer to customer, and business to business, but excluding large and industrial delivery or logistic service) (YZ/ T0128-2007, National Standard for Express Delivery Service in China). Generally, express delivery services are guaranteed to be finished promptly, within a stipulated time frame, and these can be divided into warehouse storage, the sorting and delivery process, independent facilities, equipment, and delivery channels (Wang et al., 2016). Based on the scope of this sector, express delivery services can be categorized into three types: intracity express delivery (inland express service: delivery and pick-up services are within the same city); intercity express delivery (urban express delivery: delivery and pick-up services are in different cities); and external: Hong Kong, Macao, Taiwan and international express delivery service (delivery and pick-up services are external to mainland China) (DRC, 2017). Since the emergence of “Internet plus warehousing,” the online shopping provided by e-commerce enterprises like Alibaba Group and JD.com has introduced a new level of convenience and is changing the
⁎
public's way of life (Liu et al., 2016; Meng and Zhou, 2016). Meanwhile, city logistics distribution departments have been developing rapidly in almost all cities in China, with strong support from all levels of government, because these services provide employment and improve a city's overall competitiveness (Zhe, 2011; Xu et al., 2020). Besides, urbanization requires a variety of materials to flow through urban areas, and the energy required for this flow is concentrated in these areas. This demand for material is the primary impetus behind express logistics development. According to the ‘Report on China's Express Development Index in 2018’ issued by the State Post Bureau of China, China's express delivery sector exceeded 50 billion pieces in that year, and the annual growth rate reached 51.4% in the past decade(SPBC, 2019). Accompanied by the advances in ‘Internet plus and Artificial Intelligence,’ the logistics industry has been making steady progress in its service capability, operations level, technical equipment, and management level. Moreover, the express volume is facing exponential growth and could reach 100 billion pieces in 2020 (SPBC, 2017). Thus, it is imperative to determine the environmental burden of the express industry and to take some effective measures to mitigate it.
Corresponding author. E-mail address: [email protected] (H. Duan).
https://doi.org/10.1016/j.eiar.2019.106336 Received 28 June 2019; Received in revised form 14 October 2019; Accepted 2 November 2019 Available online 19 November 2019 0195-9255/ © 2019 Elsevier Inc. All rights reserved.
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2.2. Carbon emission quantification model for intracity express delivery service
It is well known that the most active type of intracity express delivery is in urban areas. Some research has shown that logistics delivery has become one of the most important sources of global carbon emissions, because of the large energy consumed in freight transportation (Yang and Guo, 2012; Chen and Lin, 2017). Currently, a few research studies have already focused on the environmental impacts of the logistic system, such as the release of pollutants, intensive energy and resource consumption, and carbon emissions (Bouchery and Fransoo, 2015; Fan et al., 2017). However, these evaluation models and methods have mainly focused on a specific geographic scope or logistic process, and are not suitable for the evaluation of the total impact of urban express delivery services (Fei et al., 2013; Christine et al., 2013). Therefore, finding an appropriate assessment model for carbon emissions from intracity express delivery has become a strategic imperative for any organization, considering the complexity and connectivity of cities (Sun et al., 2015). Actually, the urban environment is deteriorating in many cities, and they are all facing challenges of unsustainable development. Hence an energy-saving green express delivery system will eventually have to be established, and is part of the development plan of the express delivery industry (Yang et al., 2016; Liu et al., 2018;Hao et al., 2019). And control of carbon emissions from intracity express delivery will not only help address the climate-change issue but will also satisfy regulatory requirements (He et al., 2017; Zuev et al., 2019). Considering the importance of intracity express delivery and the characteristics of the urban environment, the main goal of this research was the construction of a universal model of carbon emissions assessment for intracity express delivery services in China. Specifically, the carbon emissions of intracity express delivery services within important cities were evaluated, and the correlating relationships among city clusters were analyzed based on socioeconomic factors. In addition, the spatial pattern of carbon emissions attributable to the intracity express delivery services in China was analyzed.
Human activities, especially economic behavior, depend predominantly on urban geographic form and population density. Some research on human economic activities has been analyzed by focusing on a combination of urban form and population density. In this study, in order to calculate carbon emissions from intracity express delivery services, a systematic model for quantification of carbon emissions was constructed by considering these two factors—urban form and population density—to express the complexity, heterogeneity, and connectivity of the urban ecosystem. Specifically, the model contained three main parts: (1) quantification of the transportation distance for intracity express delivery service, based on the transportation mode and phase; this was the key component of the model; (2) characterization of the delivery volume and distribution information of parcels and letters, as well as their weight data; and (3) extraction of carbon emission factors from a commercial Chinese-context life cycle assessment (LCA) database (Gabi) of various transportation modes related to express delivery (in kg CO2 eq./tkm). It should be noted that the urban ecosystem in this model was configured as having one or more centers and a few surrounding mini patches, because of the fragmented character of urban areas in China. Regarding the hierarchical relationship between the center(s) and other satellite patches in the urban form, transportation among these patches can be divided into three phases: (1) the transportation phase for a single patch; (2) the transportation phase between the center and surrounding patches, and (3) the transportation phase between surrounding patches. Finally, the ratios among these three transportation distances were ascertained based on the patch area and population density. According to the practice of optimal delivery efficiency, the transportation phase for a single patch was designed as a circle, and all points within the circle were within the transportation range for that patch. Therefore, the transportation distance for each patch was the radius of the circle. The transportation distance was then revised based on the compactness ratio of the patch. It is undeniable that population density and patch area are the two primary factors for a transportation model. The population density for each patch was therefore extracted, and the product of the patch area and its population density obtained. Finally, the transportation distance for each patch was calculated based on the ratio of the product for the patch. The process for quantification of the transportation distance of a single patch was as follows. First, the transportation radius of a single patch was calculated with Eq. (1).
2. Method 2.1. Study case As mentioned in Section 1, intracity express delivery service has been widely promoted in each city of China. Sixty-two cities were selected for this study. These included the top 50 cities in terms of intracity express delivery volume, along with some provincial capitals. Together, these two types of cities totaled 91% of those studied. The locations and express delivery volumes for those sampling cities are depicted in Fig. 1. In this study, the intracity express delivery volume was found to be dominated by Beijing, Shanghai, Guangzhou, and Shenzhen, whose collective volume came to > 700 million pieces annually, while by contrast the delivery volume of some provincial capitals in the northwest was lower than 20 million pieces each. Most of these cities were in 13 urban agglomerations in China. The number of cities in the Yangtze River Delta urban agglomeration, the Pearl River Delta urban agglomeration and the Beijing-Tianjin-Hebei (BTH) urban agglomeration were 17, 8 and 5, respectively. Other urban agglomerations contained an average of two cities each. Moreover, six of the cities do not belong to any urban agglomeration. These statistics indicate that the delivery volume in the eastern region was massive, while that in the western region was considerably lower. In addition, this study focused only on the impacts (measured by carbon emissions, kg CO2 eq.) from the transportation phase; the impacts of warehouse storage and receiving and distribution centers were excluded. We also did not consider the impacts of packaging waste. The impacts of the transportation of express deliveries out of the cities (intercity delivery) was, as stated above, also outside the scope of this study.
Rrad =
A/π
(1)
where Rrad is the transportation radius and A is the area of the patch. The index on urban form measurement was applied to the transportation distance. The compactness ratio of the patch's outer contour is a key index reflecting the city form, and is computed as follows:
CoI = 2
πA P
(2)
where CoI is the compactness ratio of the patch, A is the area of the patch, and P is the perimeter of the patch contour. CoI ranges from 0 to 1. A higher value indicates a more compact shape and a value closer to 1 indicates that the shape is closer to a circle, and vice versa. A circle is the most compact shape, and thus the compactness ratio of a long narrow shape is far smaller than 1. Based on the calculation of CoI and Rrad, the transportation distance can be calculated with Eq. (3).
2 ⎞ Rone = Rrad ∗ ⎛ ⎝ 1 + 2 ∗ CoI ⎠ where Rone is the transportation distance for one patch. 2
(3)
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Fig. 1. Delivery volume (in pieces) of intracity express delivery for selected cities. Note: The top 50 cities in China for intracity express delivery service, and some provincial capitals (62 in total). Legend refers to pieces of express mail, annually. Data source: Local postal bureau (in 2017).
Then the first transportation distance was computed with Eq. (4): n
Rtran =
∑ i=1
popk ∗ Ak n
∑1 popi ∗ Ai
∗ R (one) k
2.3. Spatial analysis of carbon emissions from intracity express delivery In order to analyze the relationship between carbon emissions and socioeconomic factors, eight primary factors were selected from the general economy and the express economy. The analysis of carbon emission factors in the transportation industry needs to consider the socioeconomic development and relevant logistical industries (Lin and Benjamin, 2017). (1) The general economy included GDP (gross domestic product), the proportion of the tertiary industry and the population; (2) the economy of the express delivery service, consisting of delivery income, service sites, express delivery volume per person, express delivery expenditure per person, and road length. The Pearson correlation index was applied to analyze the correlations among these indexes. Moreover, based on the selected indexes, the K-Means cluster method was applied in order to categorize the 62 cities into five grades. In this study, the carbon emissions of intracity delivery services in China were calculated based on the systematic model. Specifically, a universally used spatial autocorrelation indicator - Moran'I - was used to analyze the global distribution pattern of carbon emissions (Anselin, 1995). A value close to 1 indicates a completely clustered pattern, a value close to −1, a completely dispersed pattern. Additionally, the local spatial statistic G combined with a Bonferroni-type test was used to identify the hotspots of carbon emissions in the form of a Z-value. Higher positive values represent higher volumes of carbon emitted by intracity express delivery services (Ord and Getis, 1995). The Z-value was further divided into six grades by the natural breaks (Jenks) method, to present various levels of hotspots.
(4)
where Rtran is the transportation distance for a single patch; R(one)k is the transportation distance for patch k; popk is the population density for patch k; Ak is the area of patch k; and ∑1npopi ∗ Aiis the total population of all the researched patches. The secondary transportation phase involves the distance between the center patch and its surrounding patches. Initially, the gravity center of each patch was determined, and the major patch was then identified. Based on the two aforementioned processes, the transportation distances between the center patch and its surrounding patches were calculated using the Euclidean distance function, and the mean distance was then obtained. Meanwhile, the calculation of the distance for the surrounding patches was similar to that for the second transportation type. The flowchart of the three types of transportation distance for intracity express delivery is depicted in Fig. 2. In order to comprehensively calculate the transportation distance for intracity express delivery, the ratios among the three types of transportation phases were determined and combined with the weighting indexes for the patches. First, the number of patches was counted, and the ratio of the major patch was computed as the product of the patch area and its population density. Then the patch threshold was identified for patches where the ratio was > 85% of the total area. Based on the identification of patch number and the ratio of the major patch, the ratios of the three types of transportation phases were ascertained and are listed in Table 1. Finally, the transportation distance was computed using the ratio and distance for the three types of transportation phases.
2.4. Data acquisition and quality evaluation Four different types of data, acquired from various sources, were used for this analysis: package information, urban images, socioeconomic data, and environmental impact factors. The survey mainly 3
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Fig. 2. Modeling for the three types of transportation phases for intracity express delivery service - a case study of Beijing: (a) land cover and land use of Beijing; (b) built-up area of Beijing; (c) three types of transportation phases and their distances.
ecosystem. Initially, the buffer for each patch was set to 0.8 km. Then urban patches were identified based on the condition that the area of a patch had to be > 7 km2. Finally, the major patch and surrounding patches were ascertained and adjusted, for each city. Population density data for China was acquired from the environmental and resource data cloud platform of the Chinese Academy of Sciences. The population of patches varied from 0 to 67,000 persons per km2. The patch with the mean population density was extracted based on the population density of the images (Fig. S1.b). The intracity express delivery volume, number of express sites, delivery volume per person, and delivery expenditure per person were acquired from the local urban Postal Statistics Annual Report for 2017. Then GDP, PTI, and population figures were obtained from the city statistical yearbook of China and the National Social and Economic Development Annual Report on each city. In this study, we defined two main transportation phases in intracity express delivery: truck transportation for the beginning phase, and electric-vehicle transportation for the end phase, because these two modes of delivery were given high priority for investigation. Using the emission factors for each transportation mode, adjusted to the Chinese context, exported from Gabi software (Version 8.0, a commercial professional life cycle assessment software package with an embodied database), the carbon emissions could be easily calculated.
Table 1 Ratios for three types of transportation phases and their distances. TPA
FPR
Type I
Type II
Type III
1
1 0.95–1 0.90–0.95 0.85–0.90 0.80–0.85 0.7–1 < 0.7 0.5–1 0–0.5
1 0.98 0.95 0.93 0.90 0.80 0.76 0.75 0.72 0.65
0 0.015 0.03 0.05 0.07 0.18 0.21 0.22 0.24 0.30
0 0.005 0.02 0.02 0.03 0.02 0.03 0.03 0.04 0.05
2 3–5 >5
Note: TPA: patch threshold; FPR: ratio of the major patch.
included package weight and packaging material weight. In this study, four types of packaging were considered: corrugated box, plastic bag, file envelope, and other packaging. The corrugated box category included four sizes (oversized, large, medium and small); plastic bags included white, pure, variegated, black, and gray, and plastic bag cartons, all in both large and small sizes; file envelopes included two sizes (large and small); ‘other’ packaging included woven bags, air bubble bags and foam materials, all in both large and small sizes (Duan et al., 2019). In order to systematically investigate package information, a comprehensive investigation of package types and their weights was carried out in Shenzhen, by a series of survey teams, in May and June of 2018. Sample batches of each packaging material category, of different sizes and materials, were weighed, and the mean values calculated. Finally, the ratios of the various categories of packaging material were calculated. Land use and land cover data for China, in 2015, were acquired from the environmental and resource data cloud platform of the Chinese Academy of Sciences. The resolution of images was 90 m. The land cover and land use were categorized into 11 classes: forest, shrub, grassland, water body, farmland, residential, desert, bare land, urban forest, urban grass, and traffic area (Fig. S1.a). Of these, residential, urban forest, urban grass, and traffic area were classified as the urban
3. Result and discussions 3.1. Selection and description of sampled cities A total of 62 cities were selected for this study, mainly distributed in 13 urban agglomerations. The urban agglomerations and relevant cities are listed in Table 2. The average delivery volumes for the Pearl River Delta (PRD), the Beijing-Tianjin-Hebei (BTH) and the Yangtze River Delta (YRD) urban agglomerations came to > 300 million pieces each, while the corresponding volume for the Hu-Bao-E-Yu (HBEY) urban agglomeration was < 20 million pieces, annually. The land areas of the selected cities ranged from 400 to 80,000 km2. Within this range, the area of Chongqing was highest: > 80,000 km2, 4
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Table 2 Express delivery volumes and urban agglomerations. Urban agglomeration
Cities
Express delivery volume (million pieces)
Beibu Gulf (BBG) Beijing-Tianjin-Hebei (BTH) Central Plains (CPU) Chengdu-Chongqing (CYU) Hu-Bao-E-Yu (HBEY) Harbin-Changchun (HCU) Liaozhongnan (LZN) Pearl River Delta (PRD)
Nanning (NN), Haikou (HK) Beijing (BJ), Tianjin (TJ), Shijiazhuang (SJZ), Baoding (BD), Langfang (LF) Zhengzhou (ZEZ), Xinxiang (XX) Chengdu (CD), Chongqing (CQ) Huhehaote (HHH), Yinchuan (YC) Haerbin (HEB), Changchun (CC) Dalian (DL), Shenyang (SY) Guangzhou (GZ), Shenzhen (SZ), Dongguan (DG), Foshan (FS), Jieyang (JY), Shantou (ST), Huizhou (HuZ), Zhongshan (ZS) Jinan (JN), Qingdao (QD), Linyi (LY) Fuzhou (FZ), Xiamen (XM), Quanzhou (QZ), Zhangzhou (ZAZ) Shanghai (SH), Hangzhou (HZ), Nanjing (NJ), Suzhou (SuZ), Wuxi (WX), Ningbo (NB), Changzhou (CZ), Wenzhou (WZ), Nantong (NT), Suqian (SQ), Jinhua (JH), Huaian (HA), Shaoxing (SX).Jiaxing (JX), Xuzhou(XZ), Yancheng (YAC), Taizhou(TZ), Changzhou (CZ), Hefei (HF) Changsha (CS), Wuhan (WH), Nanchang (NC)
48 1324 287 390 38 78 133 2735
Guiyang (GY), Kunming (KM), Lasa (LS), Lanzhou (LZ), Taiyuan (TY), Xining (XN), Wulumuqi (WM)
110
Shandong Peninsula (SDP) west of the Straits (WCS) Yangtze River Delta (YRD)
The middle reaches of the Yangtze River of Urban Agglomeration Other provincial capital (OTH)
181 393 3818
299
Note: The locations, urban areas, and built-up areas for cities in the urban agglomerations can be found in Fig. S2–S3.
Fig. 3. Estimates of transportation distance for intracity express delivery for 62 cities. Note: City and urban agglomeration abbreviations can be found in the abbreviation table.
while that of Haikou was lowest: < 500 km2. In general, the areas of most cities fell into the range of 10,000 to 20,000 km2. The urban land areas and the ratios of built-up portions are depicted in Fig. S4. The built-up ratio of most cities was approximately 30%. The ratios for the
Yangtze River Delta (YRD) and the Pearl River Delta (PRD) were obviously higher than those for other regions. In particular, the ratios of Shenzhen, Dongguan, and Shanghai were all over 50%. However, the ratios of some of the northwest provincial capitals were lower than 5
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Fig. 4. Carbon emission estimates from the transportation phase of intracity express delivery services. Note: City and urban agglomeration abbreviations can be found in the abbreviation table. The carbon emissions of packaging materials are depicted in Fig. S9.
patches > the transportation distance between the center and the surrounding patches > the transportation distance within a single patch (Fig. S7). The proportional configuration for the three types of transportation phases, however, can be interpreted as follows: the ratio of the first transportation type (the transportation distance within a single patch) was largest; the ratio of the second transportation type (the transportation distance between the center and the surrounding patches) was of medium length; and the ratio of the third transportation type (the transportation distance between surrounding patches) was smallest. Looking at the transportation distance within a single patch, the range of the transportation distance for these cities was from 6 to 40 km, and the average value was 16.8 km. Specifically, the distances for Shanghai, Beijing, Wushan, and Foshan were all longer than 30 km, while the distances for Haikou, Lasa, and Huizhou were in the range of 5 to 8 km. The transportation distance between the center and surrounding patches of these cities ranged from 0 to 80 km, and the average value was 43.9 km. The transportation distances between the center and surrounding patches in Xinxiang, Yancheng, and Changchun were all > 70 km, while the corresponding distances for Shenzhen, Lasa, Haikou, and Wulumuqi were all 0 km, because these cities consisted of only one patch. The transportation distance between surrounding patches in these cities ranged from 0 to 140 km, with an average value of 60 km. The transportation distance between surrounding patches in Chongqing was 139 km, while this distance for Harbin, Yancheng, and Shenyang was > 80 km. The accuracy of assessment model is shown in Table S1 and Fig. S8. The transportation distances of intracity express for these cities ranged from 5 km to 40 km (see Fig. 3). The distances in Shanghai and Beijing were > 35 km, while the distances in Haikou and Xining were < 10 km. Comparing the cities by urban agglomeration, the distances for the Beijing-Tianjin-Hebei (BTH) and the Yangtze River Delta
10%. This phenomenon suggests that the high built-up ratio in urban areas generally resulted in more highly developed express delivery services and more massive express volumes. 3.2. Transportation distance estimates for intracity express delivery In order to match the ratios of the three types of phases, their indexes were calculated based on the total number of patches, the desired patch threshold, and the ratio of the major patch. Specifically, the desired patch threshold represented the required minimum number of patches and included those patches where the product of the patch area and population density was > 85% of the total. The total number of patches for each of these cities ranged from 1 to 30 (Fig. S5), while the mean number of patches for these cities was approximately seven. Nevertheless, Chongqing had 29 patches, and Yanchang had 18, while Shenzhen and Haikou had only one patch each. In this study, the mean threshold of the desired number of patches was approximately two. In 67.3% of the cities the threshold of the desired number of patches was one. However, the thresholds of the desired number of patches were extremely high (5, 5 and 7) for Baoding, Chongqing and Langfang, respectively, suggesting that these cities had several highly developed centers. As for the ratio of the major patch, in 52.6% of the cities, it was over 90%. In 22.7% of the cities the ratio of the major patch fell into the range of 80% to 90% (Fig. S6). However, in a fraction (16%) of these cities the ratio was below 60%, and the ratios of the major patches in Langfang and Huizhou were 24% and 34%, respectively (see Fig. S6). These ratios suggest that most cities had one major center, and the development pattern of most cities was a pie shape, with development radiating out from the center to the more outlying areas. Consequently, the three types of transportation phases were ranked as follows: the transportation distance between surrounding 6
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Fig. 5. Correlations among socioeconomic factors and carbon emissions from intracity express delivery transportation. Data sources: Socioeconomic data were taken from the Statistical Yearbooks from local statistical offices, and the express statistics were acquired from the 2017 Postal Statistics Bulletin from each city.
lengths in the city, and urban population—were all higher than 0.5, indicating that carbon emissions had a close relationship with other express delivery factors. At the same time, other express delivery factors had a negative relationship with GDP. Nevertheless, there was a mutually positive relationship among those factors. Five grade levels were identified in these cities (Fig. S10). The proportions of grade levels I, II, III, IV, and V were 11.3%, 20.3%, 32.3%, 17.7%, and 17.7%, respectively. The cities in the BeijingTianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations were in grades I, II and III, respectively.
(YRD) urban agglomerations were both > 25 km, while the distances for the Central Plains (CPU), the Chengdu-Chongqing (CCY), the Liaozhongnan (LZN) and the Pearl River Delta (PRD) urban agglomerations were all > 20 km. 3.3. Carbon emission estimates for intracity express delivery transportation In this study, the packaging material accounted for 18.2% of the total carbon emissions. The carbon emissions of packaging fell into the range of 20 to 4000 t (see Fig. 4), although the corresponding weight of the packaging materials ranged from only 4 to 796 t (Fig. S9). The highest carbon emissions were from Beijing and Shanghai, both > 4000 t each, and the carbon emissions from Guangzhou and Shenzhen were > 2500 t each. However, the carbon emissions from Haikou, Lasa and Xining were all below 50 t each. The average value of carbon emissions in these seven cities was 563 t. As for urban agglomerations, the carbon emissions from the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) urban agglomerations were all significantly higher than those from other agglomerations.
3.5. Spatial analysis of carbon emissions The carbon emissions from intracity express delivery transportation in China are depicted in Fig. 6a. The carbon emissions in the eastern areas of China were higher than those in the western areas. The strongest intensities of carbon emissions were mainly located in the Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations. The Moran'I value was 0.34, indicating that the carbon emissions in China represented a cluster pattern, with the higher values clustered in the east and lower values clustered in the west. The hotspot of carbon emissions was similar to the overall carbon emissions for China, with the highest values distributed in Beijing, Shanghai, Shenzhen, and Guangzhou (Fig. 6b). Obviously, the intracity express delivery is accelerating, for two reasons: (1) rapid urbanization, including an increase in population and an expansion of urban areas; (2) equally rapid development of logistics facilities and warehouses; these maintain the conditions for express delivery. It is generally accepted that urban areas are the major sources for carbon emissions, and the emissions from express delivery in urban areas are far from being a negligible contributor to this problem. Overall, our study revealed significant regional disparities from the carbon emission perspective, in China. It is well known that China faces
3.4. Correlation and cluster analysis The correlation between carbon emissions from intracity express delivery services and the selected index are depicted in Fig. 5. There is a positive correlation between carbon emissions and both the general economy and the express economy, except for GDP values; the correlation between carbon emissions and GDP is negative. This phenomenon can be interpreted as follows: in some cities—such as Yanchang, Jieyang and Taizhou and so on—the express delivery service industry is highly developed, a mainstay industry in this area. The correlated indexes between carbon emissions and other express delivery factors—delivery service income, personal express delivery expenditures, road 7
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Fig. 6. Carbon emissions and hotspot analysis from intracity express delivery transportation in China. Note: a) Carbon emissions from intracity express delivery in China; b) Hotspot analysis in China. A higher level represents stronger carbon emissions from express delivery.
high in megacities and low in small and medium-sized cities. The eastern part of China accounts for about 90% of the country's carbon emissions. Accordingly, the management of regional express delivery should consider geographical features. Based on our results, regionally oriented measures are needed for the eastern, central, and western
significant imbalances among its regional economies, despite the benefits generated by its rapidly growing economy. The carbon emissions generated by intracity express delivery transportation show a strong interlink with socioeconomic factors. The spatial distribution of carbon emissions was high in eastern coastal cities and low in inland cities;
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10 km. (2) The carbon emissions of the express package delivery service fell into the range of 20 to 4000 t, while those of the packaging materials varied from 4 to 796 t. Moreover, there is a positive relationship between carbon emissions and both the general economy and the express delivery economy. (3) China's carbon emissions represented a cluster pattern, with higher values clustered in the east and lower values clustered in the west. The hotspot of carbon emissions was similar to the overall carbon emissions of China, with the highest values distributed in Beijing, Shanghai, Shenzhen, and Guangzhou. (4) Regional transportation management must consider geographical features and relevant socioeconomic factors. The results of this study proved that our model has applicability for the carbon emissions of the transportation phase of intra city express delivery service in urban areas; this contribution is not negligible. Reducing the volume of packaging material can play an important role in minimizing carbon emissions from the express delivery sector. (5) Finally, the management of regional express delivery should consider geographical features. Regionally oriented measures are advocated for the eastern, central, and western regions, as well as for cities of different sizes. In total, to improve the environmental performance of express delivery, China's logistics companies need to adopt advanced management and decision methods, enabling logistics enterprises to optimize delivery routes and design effective service and delivery networks.
regions, as well as for cities of different sizes. In the past decade, China has made large investments of financial and other resources in the construction of urban public transportation, intelligent networks, and various modes of delivery (Li et al., 2019a). Considering the massive delivery volume and longer delivery distance in the eastern coastal megacities, low-carbon development in this area should improve the efficiency of facilities and infrastructure in the logistics industry, especially when combined with advances in urban public transportation (Li et al., 2019b). Site selection and design of logistics centers has been optimized with artificial intelligence systems. In addition, the coordination of express waybills, as well as the selection of delivery routes and mode, have been based on the analysis of large volumes of data (Yan et al., 2019). Meanwhile, considering the lower delivery volumes and shorter transportation distances in the western region, low-carbon development should be emphasized there: for example, more environmentally friendly vehicles (Stolaroff et al., 2018). In other words, to improve the environmental performance and reduce the costs of express delivery, China's logistics companies need to adopt advanced management and decision methods, in order for logistics enterprises to optimize delivery routes and design effective service and delivery networks (Yang and Guo, 2012). Because the proportion of packing material made up 18.2% of the total weight of packages, the reduction of packaging material is an important measure for low-carbon development. In our survey on the weights of packages and packaging materials, the proportion of packaging material in corrugated boxes, file envelopes, and foam materials was usually > 20%. In recent years, various measures have been put forward for reducing the weight of express packages, by relevant management departments of the state, such as SPBC and the Ministry of Ecological Environment Protection. The benefits of these measures lie not only in reducing resource consumption but also in minimizing carbon emissions from transportation (Yi et al., 2017). China's cities vary greatly in population, function, location, and economic development. In consideration of their significant differences and the deficiencies this study has identified in this field, an evaluation model was constructed based on urban landscape patterns and relevant population densities. The total transportation distance was then calculated from these values and from the ratios of the three types of transportation phases. The advantage of our model is that it can provide a uniform evaluation perspective and produce clear results for individual Chinese cities. Considering the large volume and complexity of data related to intracity express delivery services, our model has limited applicability for dealing with a single delivery event. It can, however, help logistics enterprises promote their services to a precise target market, using environmental protection as a goal. The design of a lowcarbon strategy is subject to dynamic forecasting of express delivery behavior, and the environmental account of an enterprise depends on the capability of acquiring and analyzing large quantities of data. Thus, the viability and profitability of logistics enterprises can be improved in line with advances in service quality and environmental performance.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments This study was supported by the Scientific Research Fund of Introduced High Talent of Shenzhen University (827-000044). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.eiar.2019.106336. References Anselin, L., 1995. Local indicators of spatial association. Geogr. Anal. 27, 93–115. Bouchery, Y., Fransoo, J., 2015. Cost, carbon emissions and modal shift in intermodal network design decisions. Int. J. Prod. Econ. 164, 388–399. Chen, T., Lin, C.W., 2017. A fuzzy collaboration system for ubiquitous loading/unloading space recommendation in the logistics industry. Robot. Comput. Integr. Manuf. 45, 86–98. Christine, W., Ebba, B., Claudia, R., 2013. Planning for climate change in urban areas: from theory to practice. J. Clean. Prod. 50, 68–81. Deloitte Research China (DRC), 2017. China E-Retail Market Report 2016. Deloitte Research China, Shanghai, China. Duan, H., Song, G., Qu, S., Dong, X.B., Xu, M., 2019. Post-consumer packaging waste from express delivery in China. Resour. Conserv. Recycl. 144, 137–143. Fan, W., Xu, M., Dong, X., Wei, H., 2017. Considerable environmental impact of the rapid development of China express delivery industry. Resour. Conserv. Recycl. 126, 174–176. Fei, L., Klimont, Z., Qiang, Z., Cofala, J., Zhao, L., Hong, H., Nguyen, B., Schöpp, W., Sander, R., Zheng, B., Hong, C., He, K., Amann, M., Heyes, C., 2013. Integrating mitigation of air pollutants and greenhouse gases in Chinese cities: development of GAINS-City model for Beijing. J. Clean. Prod. 58, 23–33. Hao, Y., Liu, H., Chen, H., Sha, Y., Ji, H., Fan, J., 2019. What affect consumers’ willingness to pay for green packaging? Evidence from China. Resour. Conserv. Recycl. 141, 21–29. He, Z., Chen, P., Liu, H., Guo, Z., 2017. Performance measurement system and strategies for developing low-carbon logistics: a case study in China. J. Clean. Prod. 156, 395–405. Li, X., Lee, S., Liu, B., Wang, L., 2019a. Contemporary Logistics in China. Springer, Singapore. https://doi.org/10.1007/978-981-13-7816-4(b).
4. Conclusion A universal model of carbon emissions assessment was established for intracity express delivery service in China. This consisted of three parts: clarification of the transportation mode and phase, characterizing the delivery volume and distribution information of parcels and letters, and exportation of carbon emission factors. The carbon emissions of intracity express delivery service within important cities in China was evaluated. Finally, spatial analysis and spatial patterns of carbon emissions in China were analyzed. The main conclusions of this study are as follows: (1) The transportation distances of intracity express for these cities ranged from 5 km to 40 km. The mean distances in Shanghai and Beijing were > 35 km, while those in Haikou and Xining were < 9
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