Study on the Spatial Pattern of the Carbon Footprint of China’s E-Commerce Express Packaging Considering Embodied Carbon Transfer

Abstract

With the rapid development of e-commerce in China, carbon emissions from express packaging have become increasingly prominent, and the division of inter-regional emission responsibilities has emerged as a key research focus. Based on the principle of shared responsibility between producers and consumers, this study integrates life cycle assessment (LCA) and spatial decomposition analysis to quantify the full-life-cycle carbon footprint of China’s e-commerce express packaging across the raw material, production, and disposal stages and calculates the inter-provincial embodied carbon transfer. The findings show that: (1) in 2022, total emissions reached 41.209 million t CO₂e, exhibiting a “more in the east, less in the west” spatial pattern, with Guangdong Province as the largest source; (2) plastic packaging generates roughly twice the upstream emissions of paper packaging, while paper packaging surpasses plastic during disposal; and (3) significant inter-provincial disparities exist in embodied carbon transfer, with seven southeastern coastal provinces as net exporters and a net-import pattern of “more in the east, less in the west; more in the south, less in the north,” accounting for 40 % of the total transfer. Based on this, it is recommended that the government attach great importance to the issue of responsibility allocation arising from the embodied carbon transfer of e-commerce express packaging.

1. Introduction

In recent years, with the express development of e-commerce in China [1], the volume of express business has grown rapidly. As of 2024, the annual volume of China’s postal industry has reached 193.7 billion pieces of mail delivery business, an increase of 19.2% year-on-year [2]. At the same time, the growth of express business volume has also brought about a blowout in the use of express packaging, which in turn has brought about a significant increase in carbon emissions [3]. Packaging such as boxes and bags used in e-commerce express delivery generates carbon footprints in the life cycle stages of raw materials, production, and disposal [4], but under the current system, carbon emissions from commodities are mostly attributed to the manufacturing side. The so-called implied carbon is to separate part of the carbon emissions attributable to the consumption end from the hidden production end to reasonably allocate the responsibility for regional carbon emissions at different stages [5]. The study shows that the total net implied carbon flow of carbon from express packaging boxes in China in 2020 will be 248,100 t CO₂e, accounting for about 24% of the total emissions from packaging boxes, exposing a significant imbalance in the allocation of carbon responsibility between regions. Suppose the carbon emissions from express packaging continue to be accounted for in the traditional way. In that case, it will not only hide the responsibility of the consumption side but also may lead to the economically developed provinces and cities in the east of China as the main consumers of express parcels, driving the high-carbon production activities of the resource provinces in the central and western parts of the country through inter-regional purchasing, without taking the responsibility of the corresponding carbon emissions. More urgently, China’s landfill rate for express packaging waste is over 60%, with a reuse rate of less than 5%, further locking implied carbon into the end-of-pipe treatment stage. Even more critically, under the policy directive from China’s National Development and Reform Commission—Opinions on Further Strengthening Plastic Pollution Control, which states that by 2025, e-commerce parcels should no longer be subject to secondary packaging, the prevailing accounting logic of “producer pays, consumer exempt” continues to exacerbate regional inequities in climate governance. If the traditional accounting model persists, embodied carbon from express packaging could lead to an additional billion-ton-scale increase in emissions by 2030. This would impose substantial cost pressures on certain regions (and courier enterprises), further intensifying the inequity in carbon responsibility allocation.

Moreover, since the Paris Agreement, the issue of global plastic pollution has become increasingly prominent, underscoring the real-world relevance of this study’s focus on the carbon footprint of e-commerce express packaging [6,7]. Therefore, there is an urgent need to track the carbon footprint of the whole life cycle [8], strip the implied carbon of express packaging from the production side, incorporate it into the accounting system of the consumption side, and promote the accurate allocation of regional carbon emission quotas to provide a scientific basis for cross-regional co-operation in emission reduction and climate justice.

At present, current domestic and foreign studies focus on the express carbon footprint, express packaging decarbonization, carbon emission responsibility allocation, inter-trade implied carbon transfer, and other aspects that have yielded significant results.

The existing research is mainly focused on the following aspects:

(i) Express packaging carbon footprint calculation. The main methods include: calculating the total carbon footprint of express packaging based on the principle of full life cycle evaluation (Wu Yuchun et al. (2018)) [9], the carbon emissions of individual express shipments (Zhou Yang et al. (2021) [10]), and the emissions generated from the recycling of recycled containers (Ren Shuhang et al. (2023) [11]), etc.; and calculating the carbon emissions of the whole logistics process based on the unit of pieces by adopting the statistical approach (Zhang Hao et al. (2018) [12]).

(ii) Green governance of express packaging. The main studies analyzed the pollution caused by express packaging, proposed relevant pollution control suggestions (Yan Jiaqi et al. (2022) [13], Zhang Bin et al. (2023) [14]); proposed design strategies for express packaging recycling devices in the recycling stage to achieve the purpose of saving energy and reduce emissions (Jing Xiaohua (2018) [15]), and so on.

(iii) Regional carbon emission responsibility weight allocation method. There are four main ways to allocate the weight of carbon emission responsibility. First, the allocation method based on technological differences; that is, to determine the boundary of carbon emission responsibility between the production region and the consumption region through technological differences (Marques et al. (2012) [16]); second, the equal allocation method, which advocates for the producer and the consumer to each bear half of the responsibility, and balances the responsibility for carbon emissions upstream and downstream of the supply chain in the ratio of 1:1 (Ferng (2003 [17]); Wiedmann et al. (2006) [18]; Xu Yugao and He Jiankun (2000) [19]). The third is the cumulative carbon emission method, i.e., the proportion of carbon emission responsibility borne by a link in the industry chain should be the ratio of the cumulative carbon emission increase of the upstream link and the current link to the cumulative total emissions of the whole industry chain (Bastianoni et al. (2004) [20]; Lenzen et al. (2007) [21]). The final method is the economic value-added method, in which the carbon emission responsibility of each production link in the production chain is allocated according to its own share of value-added, and the remaining responsibility is passed on to the next link (Lenzen et al. (2007); Peters et al. (2008) [22]; Zhao Dingtao et al. (2013) [23]).

(iv) Research on inter-country (provincial) implied carbon transfer. The main contents include exploring the evolution of the origin, measurement methods, and influencing factors of trade-implied carbon (Xing Yuanyuan et al. (2023) [24]); calculating the inter-provincial (urban and rural) trade-implied carbon emissions, and analyzing the spatial pattern and circulation paths of trade-implied carbon (Xing Zhencheng (2023) [25], Luo Huili et al. (2023) [26]).

(v) Calculation methods for embodied carbon footprint. The embodied carbon accounting framework mainly comprises four categories: Multi-Regional Input–output models (MRIO), Environmentally Extended Input–Output models (EEIO), Life Cycle Assessment (LCA), and decomposition methods (e.g., LMDI, KAYA-LMDI). Among these, MRIO and EEIO are widely used to trace embodied carbon flows in global or regional trade. For example, Zihan Yang et al. (2025) analyzed the global aluminum trade’s embodied carbon emissions via MRIO [27]; X.D. Wu et al. (2020) distinguished carbon transfers in intermediate and final trade using MRIO [28]; Hongguang Liu et al. (2015) used MRIO to quantify China’s consumption and export carbon emissions [29]; Ting Jie et al. (2023) constructed a China multi-regional embodied carbon network based on EEIO [30]. LCA focuses on full-cycle embodied carbon accounting in the construction sector, such as Theophilus Frimpong Adu et al. (2025) comparing two building materials’ embodied carbon in Ghana [31]; Ana Karolina Santos et al. (2025) proposed an LCA-based strategy for building embodied carbon calculation [32]; Paul Moran et al. (2025) evaluated embodied carbon in Irish student accommodations per EN 15978 [33]. Decomposition methods analyzed drivers of embodied carbon emissions: Liang Xiangyao et al. (2025) applied LMDI to decompose factors affecting embodied carbon in China’s paper industry [34]; Li Xiang (2024) combined KAYA-LMDI and HEM models to explore the logistics sector’s emission linkages [35]; and Shukuan Bai et al. (2025) employed Structural Decomposition Analysis (SDA) to study carbon emission drivers within China’s global value chains [36].

Although existing studies have made progress in areas such as express delivery carbon footprints, quantitative research on the spatial flow mechanisms of embodied carbon in e-commerce packaging and on shared responsibility remains insufficient. First, most work has focused on carbon accounting for the packaging material production stage without systematically covering all life-cycle phases—raw material procurement, transportation and distribution, use, and waste treatment—resulting in incomplete footprint estimates. Second, the current embodied-carbon accounting frameworks lack systematic integration: traditional studies use input–output models (MRIO/EEIO) or life-cycle assessment (LCA) to calculate embodied carbon, but the former, while able to track inter-regional carbon flows, ignores emissions from packaging waste handling, and the latter, although chain-wide, has not yet been applied to the e-commerce express packaging sector. Third, the critical role of embodied carbon transfers in regional responsibility allocation is widely overlooked; existing research lacks spatial analysis of inter-provincial carbon flows and thus fails to reveal the structural inequity whereby economically developed regions shift their emission responsibilities through cross-regional procurement. Fourth, systematic study of this “low-value, high-throughput” packaging category is clearly lagging: most existing models are adapted from manufacturing sectors (e.g., electronics, textiles) and do not account for the high-frequency, wide-area characteristics of e-commerce logistics.

Therefore, there is an urgent need for systematic research that integrates Life Cycle Assessment (LCA) with spatial analysis methods to decouple the full life cycle carbon footprint of courier packaging from the production side and incorporate it into the accounting on the consumption side. Based on this, this study innovatively integrates the producer–consumer co-responsibility principle with spatial analysis methods. Constructing a comprehensive LCA model systematically deconstructs the carbon footprint composition of China’s e-commerce courier packaging, maps the interprovincial embodied carbon flows, and designs differentiated carbon responsibility allocation schemes. This provides both theoretical support and practical pathways for improving China’s carbon governance system in the courier industry. It is also the first study to analyze the embodied carbon flows of e-commerce courier packaging at the interprovincial level, serving as both a validation of international theories such as Wiedmann (2006) [18] in the Chinese context and a typical case from emerging markets for global courier packaging carbon governance.

2. Objectives and Hypotheses

2.1. Research Objectives

This study aims to comprehensively analyze the carbon footprint of e-commerce courier packaging in China and its interprovincial transfer characteristics by combining Life Cycle Assessment (LCA) and embodied carbon analysis, providing a scientific basis for achieving regional coordinated emission reduction. The specific objectives are:

1. To establish a full-cycle carbon accounting framework for China’s e-commerce express packaging. This includes quantifying the life-cycle carbon footprint of e-commerce express packaging across the raw material, production, and disposal stages; building a provincial-level carbon inventory database; and revealing the spatial distribution of emissions across different life-cycle phases.

2. To analyze the spatial pattern of the carbon footprint of e-commerce express packaging in China. Using Geographic Information System (GIS) spatial analysis methods, this study identifies the clustering characteristics and gradient differences in provincial carbon footprints, as well as key high-emission hotspots.

3. To investigate the interprovincial embodied carbon transfer characteristics of e-commerce express packaging. This involves identifying net inflow and outflow provinces of embodied carbon from e-commerce packaging and quantifying the volume of such transfers.

2.2. Research Hypotheses

Based on the principle of shared responsibility between producers and consumers, the following hypotheses are proposed:

1. Material heterogeneity hypothesis: Different types of e-commerce express packaging materials (e.g., paper-based and plastic-based) exhibit significant differences in carbon emission intensity across life-cycle stages, and their contributions to the total carbon footprint also vary.

2. Spatial differentiation hypothesis: Differences in economic development, industrial structure, and express delivery volumes across provinces result in spatial variations in the carbon footprint of e-commerce packaging, thereby influencing the pattern of embodied carbon transfers.

3. Materials and Methods

3.1. Data Sources

The region of this study is selected as the provincial-level administrative regions (hereinafter referred to as ‘provinces and regions’) of China except for Hong Kong Special Administrative Region (HKSAR), Macao Special Administrative Region (MSAR), and Taiwan, including 22 provinces, five autonomous regions, and four municipalities directly under the central government. The core data comes from the ‘Operation of Postal Industry in 2022 by the State Post Bureau’, the 51st Statistical Report on the Development of the Internet in China [37], and related statistics available. These sources cover China Post Group Corporation, nationwide express delivery operators within China, and other logistics service providers.

It should be noted that because of the lack of data on the number of goods received in each province in China, according to the sampling results of Yu Jinyan (2022) [38], the correlation coefficient between the number of goods received and the amount of Internet users in each province and region in China reaches 0.97 [38], and at the same time, the author’s team combined the total amount of China’s express delivery business in 2022 and the total population in China in 2022 to arrive at the per capita express delivery use in China in 2022 is 78.44 pieces. Therefore, in this study, when calculating the carbon footprint of China’s e-commerce express packaging at the disposal stage, the data on the number of receipts is mainly based on the number of Internet users and the number of expresses per capita in each province and region of China for calculation and research (for details, see Equation (2) in Section 3.2.1). (It should be noted that the estimated parcel receipt volumes based on the number of internet users may contain potential biases.)

Other data required for this study are mainly from various statistical yearbooks in China and will not be repeated. According to China’s State Post Bureau, China’s e-commerce express volume is about 80% of China’s total express volume [39], so the number of e-commerce express parcels used in this study is 80% of China’s total express business.

3.2. Research Methods

3.2.1. Calculating the Carbon Footprint of E-Commerce Express Packaging in China

At present, carbon footprint accounting primarily follows three methodological approaches: Life Cycle Assessment (LCA), Input–Output Analysis (IOA), and hybrid methods. Among them, while IOA benefits from the framework of national economic accounting, it faces limitations such as outdated benchmark tables (updated every five years) and insufficient longitudinal comparability, making it difficult to capture dynamic changes in specific express packaging products [40]. The hybrid method (which combines LCA and IOA for carbon footprint calculation [41]) improves system boundaries by supplementing with process inventories but is still constrained by the sectoral averaging inherent in IO tables, which fails to distinguish the carbon emission differences between paper- and plastic-based packaging materials within the same sector [42]. Given that e-commerce express packaging generates carbon emissions in stages such as raw material extraction and production, the LCA method—by constructing product-specific life cycle inventory databases—can avoid the data timeliness and resolution limitations of IOA and enable refined accounting of differences in packaging material types and production processes. Therefore, this study adopts the Life Cycle Assessment (LCA) approach to calculate the carbon footprint of e-commerce express packaging in China.

In China’s e-commerce express packaging materials, paper-based materials and plastic-based materials are the two main types of packaging materials, with paper-based materials mainly including corrugated cardboard boxes and document bags, while plastic-based materials cover plastic bags, woven bags, plastic bubble bags, and plastic foam boxes. When studying the specific share of each material consumption for e-commerce express packaging, the authors’ team compared the “Research Report on the Carbon Emission Reduction Potential of Green Packaging in China’s Express Industry in 2021–2030” published by Sinopec on 30 September 2022 and the “Characteristics of China’s Express Packaging Waste Generation and Status of its Management” published by Greenpeace, a global environmental protection organization, on November 2019 [43], and found that the respective year, the proportion of packaging materials consumed in the express industry has not changed much. Hence, this paper directly adopts the data on the proportion of consumption of various types of packaging materials in packaging from the industry’s public data in 2020 when calculating the carbon footprint of e-commerce express packaging. That is, among the various materials consumed by China’s e-commerce express packaging, corrugated boxes account for 52.10%, plastic bags account for 35.40%, woven bags account for 2.80%, plastic bubble bags account for 4.20%, plastic foam boxes account for 0.50%, and document bags account for 5.00% (as shown in Figure 1).

The whole life cycle of the carbon footprint of e-commerce express packaging is divided into the carbon footprint of the raw material stage, the carbon footprint of the production stage, the carbon footprint of the processing stage, and the carbon footprint of the transport stage. The carbon footprint of the raw material stage of e-commerce express packaging is the carbon emissions generated in the upstream manufacturing process of the primary raw materials input in the production process of express packaging, the carbon footprint of the production stage of e-commerce express packaging is the carbon emissions generated by the production and processing of raw materials into express packaging, and the carbon footprint of the processing stage of e-commerce express packaging is the carbon emissions generated by the recycling, reuse and final disposal of the used express packaging materials. The carbon emissions generated by the used express packaging materials are recycled, reclaimed, and finally disposed of. Since Ren Shuhang (2023) found that the carbon footprint of express packaging in the transport stage only accounts for a relatively small proportion (0.04%) of its full life cycle carbon footprint [11], only the carbon footprint of e-commerce express packaging in the raw material stage, the carbon footprint of e-commerce express packaging in the production stage, and the carbon footprint of e-commerce express packaging in the processing stage are considered in this study. It should be noted that in the treatment stage, the treatment methods of e-commerce express packaging waste are specifically divided into four ways: recycling, landfill, incineration, and other methods, and the author’s team found that by comparing the ‘Characteristics of China’s Express Packaging Waste Generation and the Current Status of its Management’ published by Zhang Bin (2023) and Greenpeace (for a global environmental protection organization) in 2019, in each year of 2018–2022, the express packaging Waste disposal methods and their proportions have not changed much [14], so the e-commerce express packaging waste disposal methods and their proportions in this paper refer to ‘China’s Express Packaging Waste Generation Characteristics and Management Status Quo’ published by Greenpeace in 2019, as shown in Figure 2, and the proportions of these four methods in the e-commerce express packaging waste disposal chain are as follows. The four disposal methods for e-commerce express packaging exhibit significant structural differences. Among them, the divergence in waste flows between paper-based and plastic-based packaging is particularly pronounced: the recycling rate of paper packaging (81.12%) is 39 times higher than that of plastic packaging (2.08%), while the proportion of plastic packaging sent to landfill (58.74%) exceeds that of paper packaging (10.82%) by 47.92 percentage points. This disparity stems from multiple factors: first, paper packaging benefits from mature recycling technologies and a well-established collection system, whereas plastic packaging faces bottlenecks such as high recycling costs, low regeneration value, and an underdeveloped recycling network; second, there is sustained market demand for recycled paper products in industries like papermaking and packaging, while the downstream applications for recycled plastic products remain relatively limited; and finally, existing policies have established incentive mechanisms for the circular use of paper packaging, but the recycling of plastic packaging waste still lacks effective regulatory mechanisms and a comprehensive system of technical standards [44].

In summary, the calculation of carbon emissions from e-commerce express packaging in this study is shown in Equations (1)–(5).

$${\mathrm{E}}_1={ \mathrm{E}}_2+{\mathrm{E}}_3+{\mathrm{E}}_4$$

(1)

$${\mathrm{R}}_{\mathrm{i}}={\mathrm{P}}_{\mathrm{i}} \times \mathrm{I} \times (\mathrm{A} ÷ \left(\mathrm{P} \times \mathrm{I}\right))$$

(2)

Among them:

$${\mathrm{E}}_2={\sum }_{\mathrm{i}=1}^6\mathrm{L} \times \mathrm{N} \times {\mathrm{F}}_{\mathrm{i} }\times {\mathsf{\mu }}_{\mathrm{i}}$$

(3)

$${\mathrm{E}}_3={\sum }_{\mathrm{k},\mathrm{i}=1}^6\mathrm{L} \times \mathrm{N} \times {\mathrm{F}}_{\mathrm{k}} \times { \mathsf{\mu }}_{\mathrm{i}}$$

(4)

$${\mathrm{E}}_4={\sum }_{\mathrm{m},\mathrm{i}=1}^4\mathrm{L} \times \mathrm{N} \times \mathrm{X} \times { \mathrm{F}}_{\mathrm{m}} \times { \mathsf{\mu }}_{\mathrm{m}}+{\sum }_{\mathrm{n}=1}^4\mathrm{L} \times \mathrm{N} \times \mathrm{Y} \times {\mathrm{F}}_{\mathrm{n}} \times { \mathsf{\mu }}_{\mathrm{n}}$$

(5)

Among them (see

Table 1. Meaning of the symbols in the formula.

Notation	Significance
E1	Total Life Cycle Carbon Footprint of E-commerce Express Packaging Materials
E2	E-commerce express packaging materials raw materials stage carbon footprint
E3	Carbon footprint of e-commerce express packaging materials at the production stage
E4	Carbon Footprint of E-commerce Express Packaging Material Disposal Stage
Ri	Express Receipts in Provincial Administrative Regions, 2022 (pieces)
P	Size of total resident population in provincial administrative districts (persons)
Pi	Size of resident population in each provincial administrative district (persons)
I	Internet penetration rate (uniform value of 0.756), data quoted from the 51st Statistical Report on the Development of the Internet in China by CNNIC.
A	Total annual business volume of national e-commerce courier (pieces)
N	Total annual business volume of national e-commerce courier (pieces)
X	Share of paper-based e-commerce express packaging materials (57.1 percent)
Y	Plastic packaging 42.9 percent
L	Consumption of packaging materials for a single piece of express delivery (0.0002 tonnes/piece), determined based on analysis of historical data for 2018–2020
μi	Proportion of each type of material in China’s e-commerce express packaging in the overall packaging material
μm	Proportion of treatment methods used in the paper packaging waste treatment chain for each paper type of material
μn	Proportion of treatment methods used for each plastic material in the plastic packaging waste treatment chain
Fi/Fk	Carbon emission factor of a material at the raw material/production stage in China’s e-commerce express packaging
Fm/Fn	Carbon emission factors of a paper/plastic material in China’s e-commerce delivery at the waste disposal stage

):

Additionally, the carbon emission factors used in this study are shown in

Table 2. Life-cycle carbon emission factors of express packaging materials (unit: kg CO₂/kg).

Types	Raw Material		Production		Landfill	Incineration	Recycling
	EFs	Data Source	Efs	Data Source	EFs			Data Source
Corrugated box	0.88	China express packaging standard	0.257	China express packaging standard	1.084	0.855	−0.03	GaBi database
envelope	2.52		0.008		1.084	0.855	−0.049
Plastic bag	2.68		0.56		0.104	0.949	−0.068
Woven bag	1.96		0.537		0.096	1.72	−0.068
Foam box	3.04	Calculation based on GaBi software	0.758	Calculation based on GaBi software	0.117	1.652	−0.061
Bubble pack	2.15		0.363		0.012	0.563	−0.068

. The data is sourced from an authoritative report jointly released by official institutions such as the Petrochemical Division of Sinopec—“Sinopec: 2021–2030 Carbon Emission Reduction Potential of Green Packaging in China’s Express Delivery Industry” (2022) [4]. The carbon emission factors in the report are based on industry standards for express packaging in China, professional databases (such as eBalance(Standard version) and GaBi(7.0) software), and input–output analysis tables from selected express packaging enterprises, ensuring high authority and applicability.

3.2.2. Carbon Footprint Calculation of Chinese E-Commerce Express Packaging Considering Implied Carbon

Currently, academic research predominantly employs the full life-cycle method [45] and input–output method [46] to dissect the dynamics of implied carbon flows across industries and geographies [47]. In terms of industry, researchers typically examine the transmission along the industrial chain and shifts in industrial location to elucidate the patterns of implied carbon flow. Spatially, scholars have established through pertinent analyses that the energy structure, level of urbanization, foreign trade, and industrial composition are pivotal determinants of the spatial distribution of carbon emissions. While these studies have largely adopted a macroscopic lens to analyze implied carbon emission flows, such as on an international or provincial scale, they have not adequately quantified the carbon emission flows stemming from the movement of products between provinces and regions. In this paper, we opt for a holistic life-cycle approach to quantitatively analyze the carbon footprint of China’s e-commerce courier packaging, incorporating the concept of implied carbon. This method allows for a more comprehensive understanding of the environmental impact associated with the e-commerce packaging life-cycle and the inter-regional flow of goods.

This paper delineates the complete life-cycle of China’s e-commerce express packaging into three distinct stages: the raw material stage, the production stage, and the treatment stage. Notably, the raw material and production stages are situated geographically at the destination of the e-commerce express delivery, whereas the treatment stage occurs at the origin of the shipment. This implies that the responsibility for the carbon emissions across the life-cycle of e-commerce express packaging is apportioned between the dispatch and destination locations. Following the principle of shared responsibility, the carbon emissions throughout the entire process are divided between the production and consumption sectors. That is, a region’s emissions may originate from either the production or consumption side. Determining the precise allocation of responsibility between the production and consumption sides involves the use of a responsibility-sharing coefficient.

As early as 2003, Ferng proposed a model for shared responsibility between producers and consumers, cautiously suggesting a 50–50 split reflective of the era’s constraints. More recently, Yang Jun et al. 2022 suggested that the shared responsibility coefficient on the production side could range from 39.98% to 47.73% and on the consumption side from 52.27% to 60.02% [48,49]. Drawing on the work of numerous scholars—and considering that while e-commerce express packaging brings benefits to both producers and consumers, its existence fundamentally stems from consumer demand—this study assumes that consumers should bear slightly more responsibility than producers. Therefore, a 60% responsibility allocation to the consumption side is adopted, referencing the “Benefit Principle” proposed by Ferng (2003) [17], which suggests that consumers who gain convenience from using packaging should assume primary responsibility. This is consistent with the Consumer Responsibility Extension theory put forward by Wiedmann et al. (2006) [18] in their multi-country input–output studies and also aligns with the consumption-side responsibility range of 52.27–60.02% found by Yang Jun (2022) [48] in his research on carbon emission allocation across Chinese provinces.

The specific calculation formulas are shown in Equations (6)–(8).

$${\mathrm{Er}}_5={\mathrm{Er}}_{6 }+{ \mathrm{Er}}_7+{\mathrm{Er}}_8$$

(6)

$${\mathrm{Er}}_9=\left({\mathrm{Er}}_6+{\mathrm{Er}}_7+{\mathrm{Er}}_8\right) \times 0.4+\left({\mathrm{Er}}_{10}+{ \mathrm{Er}}_{11}+{\mathrm{Er}}_{12}\right) \times 0.6$$

(7)

$${\mathrm{Er}}_{13}={ \mathrm{Er}}_5-{\mathrm{Er}}_9$$

(8)

where: Er₅ is the carbon emission without considering the implied carbon; Er₆, Er₇, and Er₈ are the carbon emission caused by the products produced in the region in the raw material, production and treatment stages, respectively; Er₉ is the carbon emission considering the implied carbon; Er₁₀, Er₁₁ and Er₁₂ are the carbon emission caused by all the products consumed in the region in the raw material, production and treatment stages; Er₁₃ is the net implied carbon flow; Er₁₃ > 0 indicates a net implied carbon outflow, i.e., the local region bears the carbon emissions of other regions, and Er₁₃ < 0 indicates a net implied carbon inflow, i.e., the carbon emissions of the local region are transferred to other regions.

4. Carbon Footprint Decomposition at Each Stage

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.

4.1. Carbon Footprint of E-Commerce Express Packaging Raw Material Stage

The authors’ team calculated the carbon footprint of China’s e-commerce express packaging at the raw material stage by using Equation (3) (see

Table A1. Carbon footprint of various materials at the stage of raw materials for e-commerce express packaging (unit: 10⁴ t CO₂e).

Province	Corrugated Box	Plastic Bag	Woven Bag	Plastic Bubble Bag	Plastic Foam Box	Document Pocket	The Total	Paper	Plastics
nationwide	811.2	1678.6	97.6	159.8	26.9	222.9	2996.9	1034.1	1962.8
Anhui	25.9	53.6	3.1	5.1	0.9	7.1	95.7	33.0	62.7
Beijing	14.4	29.7	1.7	2.8	0.5	3.9	53.0	18.3	34.7
Fujian	31.3	64.7	3.8	6.2	1.0	8.6	115.6	39.9	75.7
Gansu	1.4	3.0	0.2	0.3	0.0	0.4	5.3	1.8	3.5
Guangdong	221.1	457.5	26.6	43.5	7.3	60.8	816.7	281.8	534.9
Guangxi	7.7	16.0	0.9	1.5	0.3	2.1	28.6	9.9	18.7
Guizhou	3.6	7.5	0.4	0.7	0.1	1.0	13.3	4.6	8.7
Hainan	1.2	2.5	0.1	0.2	0.0	0.3	4.5	1.5	2.9
Hebei	38.7	80.0	4.7	7.6	1.3	10.6	142.8	49.3	93.5
Henan	32.7	67.6	3.9	6.4	1.1	9.0	120.7	41.6	79.0
Heilongjiang	5.3	11.0	0.6	1.0	0.2	1.5	19.7	6.8	12.9
Hubei	23.6	48.8	2.8	4.6	0.8	6.5	87.1	30.0	57.0
Hunan	17.0	35.2	2.0	3.3	0.6	4.7	62.8	21.7	41.1
Jilin	4.3	8.8	0.5	0.8	0.1	1.2	15.8	5.4	10.3
Jiangsu	63.9	132.2	7.7	12.6	2.1	17.6	236.1	81.5	154.6
Jiangxi	13.4	27.7	1.6	2.6	0.4	3.7	49.4	17.0	32.4
Liaoning	12.6	26.0	1.5	2.5	0.4	3.5	46.4	16.0	30.4
Inner Mongolia	1.8	3.7	0.2	0.3	0.1	0.5	6.6	2.3	4.3
Ningxia	0.7	1.5	0.1	0.1	0.0	0.2	2.7	0.9	1.8
Qinghai	0.2	0.5	0.0	0.0	0.0	0.1	0.8	0.3	0.6
Shandong	42.3	87.6	5.1	8.3	1.4	11.6	156.4	54.0	102.4
Shanxi	5.2	10.7	0.6	1.0	0.2	1.4	19.1	6.6	12.5
Shaanxi	8.3	17.1	1.0	1.6	0.3	2.3	30.6	10.6	20.0
Shanghai	21.0	43.4	2.5	4.1	0.7	5.8	77.4	26.7	50.7
Sichuan	21.0	43.6	2.5	4.1	0.7	5.8	77.8	26.8	50.9
Tianjin	8.9	18.5	1.1	1.8	0.3	2.5	32.9	11.4	21.6
Tibet	0.1	0.2	0.0	0.0	0.0	0.0	0.3	0.1	0.2
Xinjiang	1.2	2.5	0.1	0.2	0.0	0.3	4.4	1.5	2.9
Yunnan	6.5	13.5	0.8	1.3	0.2	1.8	24.1	8.3	15.8
chekiang	168.0	347.7	20.2	33.1	5.6	46.2	620.7	214.2	406.5
Chongqing	8.0	16.6	1.0	1.6	0.3	2.2	29.6	10.2	19.4

in Appendix A.1) and found that the carbon footprint of China’s e-commerce express packaging at the raw material stage shows a spatial pattern of “more in the south, less in the north, more in the east, and less in the west” (see Figure 3). China’s Guangdong Province is the largest province in terms of carbon emissions at the raw material stage, with its carbon emissions at the raw material stage amounting to 8.167 million tCO₂e, accounting for 27.25% of the total national carbon emissions at the raw material stage.

Among these, Guangdong Province, serving as China’s dual hub for both manufacturing and logistics, has, since the onset of the Reform and Opening Up, leveraged the Pearl River Delta’s robust industrial base and export-oriented economic model to rapidly attract a large concentration of e-commerce, express delivery, and packaging enterprises. In 2022, Guangdong led the nation in both e-commerce order volume and express shipment volume, dispatching over 24.1 billion parcels—more than 12 percent of the national total. Situated on the southern coast, Guangdong boasts a dense port network (e.g., the Shenzhen Yantian Port and Guangzhou Port) that links all regions of China and handles a significant share of export-trade shipments. Moreover, Guangdong’s packaging supply chain enjoys a high degree of local integration—cities such as Dongguan and Foshan are among the country’s principal production bases for corrugated cartons and plastic packaging materials—endowing the province with a pronounced advantage in the e-commerce express-packaging value chain. Zhejiang Province, meanwhile, has become home to the headquarters of major e-commerce and logistics platforms such as Alibaba and Cainiao Network, forming the most densely clustered e-commerce ecosystem in the country. Since the 2000s, Zhejiang has proactively fostered the growth of small and micro e-commerce enterprises, giving rise to express-sorting hubs and packaging supply centers in places like Yiwu (the world’s largest small-commodity wholesale market), Jiaxing, and Jinhua. In 2022, Zhejiang’s express shipment volume reached 18.46 billion parcels, second only to Guangdong. As a result, Zhejiang occupies an irreplaceable position in the e-commerce supply chain.

It is followed by Zhejiang Province of China (A core region of the e-commerce industry cluster, home to the headquarters of well-known e-commerce companies such as Alibaba), whose carbon emissions at the feedstock stage amount to 6.207 million tCO₂e, accounting for 20.71 percent of the national total carbon emissions at the raw stage. Zhejiang Province followed closely, with raw material stage emissions of 6.207 million tCO₂e, constituting 20.71% of the national total. Jiangsu, Shandong, and Hebei provinces collectively accounted for an additional 17.86%. As detailed in Table A1 (see Appendix A.1), the carbon footprint of plastic e-commerce express packaging at the raw material stage substantially surpassed that of paper-based packaging, nearly doubling the latter’s emissions. Specifically, plastic bags were identified as a major source of emissions, with a significant figure of approximately 16,789,000 tCO₂e, making them the primary contributors to the emissions profile of plastic packaging at this stage.

In contrast, plastic foam boxes had a comparatively lower impact, with emissions of only 269,000 tCO₂e, marking the lowest contribution among the plastic packaging types. On the other hand, within the realm of paper-based e-commerce express packaging, corrugated boxes led in carbon emissions, with a substantial figure of 8,112,000 tCO₂e, clearly positioning them as the main contributors to paper-based emissions. Conversely, document bags had a notably lower carbon footprint, with emissions of 2.229 million tCO₂e, placing them at the bottom of the list for paper-based packaging emissions. This analysis underscores the importance of considering both regional and material-specific factors when assessing the environmental impact of e-commerce packaging, highlighting the need for targeted strategies to mitigate emissions across different stages of the supply chain. This material-dominated emission pattern is rooted in the material substitution revolution during the industrialization process. Since the mid-20th century, corrugated cardboard boxes have systematically replaced wooden packaging to become the mainstay of logistics, thanks to their combined advantages of compressive strength and foldability. During the same period, plastic products functionally replaced traditional containers due to their lightweight and moisture-resistant properties. Although new materials such as degradable polyethylene (PE) have overcome key technological barriers, their penetration rate across the entire industrial chain remains below 12%, and a large-scale low-carbon substitution effect has yet to emerge.

4.2. Carbon Footprint of E-Commerce Courier Packaging at the Production Stage

The author team calculated the carbon footprint of China’s e-commerce express packaging in the production phase using Formula (4) (see Appendix A.2: Table A1). They found that the carbon footprint in the production phase presents a “coastal encircling the inland” spatial pattern (see Figure 4). Guangdong Province is the largest emitter in the production phase, with a carbon emission of 1.768 million tCO₂e, accounting for 27.25% of the total carbon emissions from the production phase nationwide. Zhejiang Province is the runner-up, contributing 1.343 million tCO₂e to the national total, or 20.71%. China’s Jiangsu Province also plays a notable role, with its production stage emissions of 511,000 tCO₂e, constituting 7.88% of the national total. Together, the economic powerhouses of Guangdong, Zhejiang, and Jiangsu form the backbone of China’s carbon emissions in the production stage, with their emissions figures holding a dominant position.

In contrast, the geographically remote provinces of Tibet and Qinghai have minimal carbon emissions at the production stage, approaching nearly zero. At the production stage, the carbon emissions from plastic materials continue to outpace those from paper materials, nearly doubling the latter’s emissions. Specifically, the primary source of carbon emissions for paper materials is corrugated cardboard boxes, which emit 2.369 million tCO₂e. For plastic materials, the emissions are predominantly from plastic bags, with a substantial emission figure of 3.507 million tCO₂e.

Meanwhile, the carbon emissions from plastic foam boxes and document bags remain relatively minor, contributing less significantly to the overall carbon footprint. It is important to highlight that while there is a significant disparity in carbon emissions between plastic and paper materials at the raw material stage, the main contributors—plastic bags and corrugated cardboard boxes—do not exhibit a proportional relationship in their emissions at the production stage. This observation underscores the intricate and variable nature of carbon emissions across different materials and production processes, indicating the need for a nuanced approach to emission reduction strategies. In contrast, the emissions from plastic foam boxes and document bags are relatively small, having a limited overall impact. This difference stems from the phase characteristics within the full life-cycle carbon accounting system. Compared to the raw material acquisition phase, carbon emission factors in the production phase typically decrease by 30–45%. This essentially reflects the carbon intensity differences in the processing technologies of different packaging materials: the raw material pre-treatment complexity and energy consumption intensity of plastic products are significantly higher than those of paper products, resulting in the carbon emission advantage in the production phase being offset by the fossil energy dependency in the raw material acquisition phase.

4.3. Carbon Footprint of E-Commerce Express Packaging Disposal Stage

The authors’ team measured the carbon footprint of China’s e-commerce express packaging in the processing stage through Equations (2) and (5) (see Appendix A.3:

Table A3. Carbon emissions of various materials in the packaging utilization stage of e-commerce express delivery (unit: 10⁴ t CO₂e).

Province	Corrugated Box	Plastic Bag	Woven Bag	Plastic Bubble Bag	Plastic Foam Box	Document Pocket	The Total	Paper	Plastics
nationwide	129.8	258.5	20.5	30.7	3.7	12.5	455.6	142.3	313.3
Anhui	2.1	4.1	0.3	0.5	0.1	0.2	7.3	2.3	5
Beijing	1.1	2.3	0.2	0.3	0	0.1	4	1.3	2.8
Fujian	2.5	5	0.4	0.6	0.1	0.2	8.8	2.7	6
Gansu	0.1	0.2	0	0	0	0	0.4	0.1	0.3
Guangdong	17.7	35.2	2.8	4.2	0.5	1.7	62.1	19.4	42.7
Guangxi	0.6	1.2	0.1	0.1	0	0.1	2.2	0.7	1.5
Guizhou	0.3	0.6	0	0.1	0	0	1	0.3	0.7
Hainan	0.1	0.2	0	0	0	0	0.3	0.1	0.2
Hebei	3.1	6.2	0.5	0.7	0.1	0.3	10.9	3.4	7.5
Henan	2.6	5.2	0.4	0.6	0.1	0.3	9.2	2.9	6.3
Heilongjiang	0.4	0.8	0.1	0.1	0	0	1.5	0.5	1
Hubei	1.9	3.8	0.3	0.4	0.1	0.2	6.6	2.1	4.6
Hunan	1.4	2.7	0.2	0.3	0	0.1	4.8	1.5	3.3
Jilin	0.3	0.7	0.1	0.1	0	0	1.2	0.4	0.8
Jiangsu	5.1	10.2	0.8	1.2	0.1	0.5	17.9	5.6	12.3
Jiangxi	1.1	2.1	0.2	0.3	0	0.1	3.8	1.2	2.6
Liaoning	1	2	0.2	0.2	0	0.1	3.5	1.1	2.4
Inner Mongolia	0.1	0.3	0	0	0	0	0.5	0.2	0.3
Ningxia	0.1	0.1	0	0	0	0	0.2	0.1	0.1
Qinghai	0	0	0	0	0	0	0.1	0	0
Shandong	3.4	6.7	0.5	0.8	0.1	0.3	11.9	3.7	8.2
Shanxi	0.4	0.8	0.1	0.1	0	0	1.5	0.5	1
Shaanxi	0.7	1.3	0.1	0.2	0	0.1	2.3	0.7	1.6
Shanghai	1.7	3.3	0.3	0.4	0	0.2	5.9	1.8	4
Sichuan	1.7	3.4	0.3	0.4	0	0.2	5.9	1.8	4.1
Tianjin	0.7	1.4	0.1	0.2	0	0.1	2.5	0.8	1.7
Tibet	0	0	0	0	0	0	0	0	0
Xinjiang	0.1	0.2	0	0	0	0	0.3	0.1	0.2
Yunnan	0.5	1	0.1	0.1	0	0.1	1.8	0.6	1.3
chekiang	13.4	26.8	2.1	3.2	0.4	1.3	47.2	14.7	32.4
Chongqing	0.6	1.3	0.1	0.2	0	0.1	2.2	0.7	1.5

) and found that the carbon footprint of China’s e-commerce express packaging in the processing stage showed significant geographical characteristics, i.e., a spatial pattern of “decreasing from the southeast to the northwest” (Figure 5): China’s Guangdong province ranks first in terms of carbon emissions, with 1.33 million tonnes of CO₂ equivalent, making it one of the top five in the country, along with Zhejiang, Jiangsu, Shandong and Hebei. In contrast, carbon emissions from e-commerce express packaging in western China are generally low, especially in the regions of China represented by Tibet and Qinghai, where carbon emissions from e-commerce express packaging processing are almost close to zero. This finding provides an important geographical distribution reference for carbon emission control in the e-commerce express industry.

At the disposal stage, the carbon footprints of the two major categories of paper-based and plastic-based e-commerce express packaging are of particular interest due to the diversified treatment methods involved in waste disposal. However, the total amount of paper-based and plastic-based e-commerce express packaging used across the country is comparable. However, it is worth noting that the carbon footprint of paper-based e-commerce express packaging at the disposal stage significantly exceeds that of plastic-based e-commerce express packaging at the disposal stage, reaching almost four times the latter. This finding highlights the specificity and importance of paper packaging in terms of carbon emissions at the handling stage. It provides an important basis for further optimizing the management of carbon emissions from e-commerce express packaging. This difference primarily arises from the characteristics of the disposal methods: paper packaging generates methane during landfill degradation (with a greenhouse effect 28 times that of CO₂, according to IPCC standards), and incineration requires additional energy to purify the flue gas due to unstable calorific value; while plastic packaging has lower carbon emissions during inert landfill disposal, and its incineration has stable calorific value and may allow for energy recovery.

5. Carbon Footprint of E-Commerce Courier Packaging Considering Implicit Carbon

5.1. Analysis of Total Carbon Emission

The authors’ team measured the carbon footprint of China’s e-commerce express packaging in each province without considering implied carbon through Equations (6)–(8), and further quantified the implied carbon of e-commerce express packaging in each province by combining it with the results of the carbon footprint calculations that considered implied carbon. The national implicit carbon net flow has an error of Er₁₃-19.7 million tCO₂e, which is specifically due to the systemic bias in estimating delivery volumes based on the number of internet users. The industry generally considers an error within ±5% to be a reasonable range. The error margin in this study is only 0.47%, which is significantly better than the industry average, further validating the reliability of the data. Moreover, this error margin has not had a substantial impact on the core findings of this study.

As shown in

Table 3. Summary of carbon emissions of e-commerce express packaging (unit: 10⁴ tCO₂e).

Province	E2	E3	E4	Er1	Er5	Er13
nationwide	2996.9	648.6	455.6	4101.2	4120.9	−19.7
Anhui	95.7	20.7	15.6	137.7	160.6	−28.6
Beijing	53	11.5	8.6	72.1	67.7	5.5
Fujian	115.6	25	18.8	155.1	137.4	22
Gansu	5.3	1.1	0.9	15.1	46.8	−39.5
Guangdong	816.7	176.8	133.1	1037.4	673.3	453.3
Guangxi	28.6	6.2	4.7	52.3	104.6	−65.1
Guizhou	13.3	2.9	2.2	29.6	75.2	−56.8
Hainan	4.5	1	0.7	9	20.5	−14.4
Hebei	142.8	30.9	23.3	199.4	209.3	−12.4
Henan	120.7	26.1	19.7	181	240.3	−73.8
Heilongjiang	19.7	4.3	3.2	34.7	65.4	−38.2
Hubei	87.1	18.8	14.2	126.2	150.9	−30.8
Hunan	62.8	13.6	10.2	99.3	150.8	−64.2
Jilin	15.8	3.4	2.6	27.3	50	−28.3
Jiangsu	236.1	51.1	38.5	316.7	280.1	45.6
Jiangxi	49.4	10.7	8.1	75.8	106.9	−38.8
Liaoning	46.4	10	7.6	71	99.4	−35.4
Inner Mongolia	6.6	1.4	1.1	16.3	45.9	−36.8
Ningxia	2.7	0.6	0.4	5.8	14.3	−10.6
Qinghai	0.8	0.2	0.1	3.1	10.9	−9.8
Shandong	156.4	33.9	25.5	225.5	265.1	−49.3
Shanxi	19.1	4.1	3.1	35.3	71.8	−45.4
Shaanxi	30.6	6.6	5	50.9	86.5	−44.3
Shanghai	77.4	16.8	12.6	102.8	86.3	20.6
Sichuan	77.8	16.8	12.7	123.6	190.2	−83
Tianjin	32.9	7.1	5.4	44.8	42.2	3.3
Tibet	0.3	0.1	0.1	1.7	6.6	−6.1
Xinjiang	4.4	1	0.7	14.3	47.9	−41.9
Yunnan	24.1	5.2	3.9	45.5	95.8	−62.7
Chekiang	620.7	134.3	101.2	777.9	458.2	398
Chongqing	29.6	6.4	4.8	47.1	72.9	−32

, the total carbon footprint of China’s e-commerce express packaging in 2022, taking into account the implied carbon, will be about 41.209 million tonnes of CO₂e. Figure 6 shows that the carbon footprint of China’s e-commerce express packaging in all provinces has obvious geographic characteristics, presenting the characteristics of ‘more in the east and less in the west’, and decreasing all the way from the southeast to the northwest. At the same time, the total carbon emissions of e-commerce express packaging materials in all provinces of China are polarised, with the total carbon emissions of the top two provinces accounting for 27.46% of the national total carbon emissions, while the total carbon emissions of the bottom five provinces account for only 2.29%, showing a relatively concentrated spatial trend in the total carbon emissions.

Specifically, Guangdong Province is the province with the largest carbon emissions from e-commerce express packaging in China, with a life-cycle carbon emission of 6.733 million tonnes of CO₂e, accounting for 16.34 percent of the country’s total carbon emissions; Zhejiang Province and Jiangsu Province ranked second and third with 11.12 percent and 6.80 percent of the total, respectively. In contrast, the provinces with the lowest carbon emissions are mainly concentrated in western China, with Xinjiang, Hainan, Ningxia, Qinghai, and Tibet China’s five provinces together accounting for only 2.29 percent. This spatial distribution feature not only reflects the differences in regional economic development levels and e-commerce activity but also provides an important basis for developing regionally differentiated carbon emission reduction policies.

5.2. China’s E-Commerce Express Packaging Hidden Carbon

As shown in Figure 7, there are only seven provinces (Guangdong, Zhejiang, Jiangsu, Fujian, Shanghai, Beijing, and Tianjin) with net implied carbon outflows, and the net implied carbon outflows are mainly concentrated along the southeast coast of China, while the net implied carbon inflows extend from east to west, showing a spatial pattern of “more in the east, less in the west, and more in the south and less in the north”.

Among the provinces with net implied carbon outflows, Guangdong and Zhejiang occupy a prominent position. With net implied carbon outflows of 4,533,000 tCO₂e and 3,980,000 tCO₂e, Guangdong and Zhejiang together contributed 89.79 percent of the total implied carbon outflows. The main reason for this phenomenon is that the number of shipments far exceeds the number of receipts in the two provinces. Specifically, Guangdong ranks first in both shipments and receipts, but its shipments (24.109 billion units) significantly exceed its receipts (9.92815 billion units). Zhejiang followed closely behind, with the second-highest volume of shipments in the country, but lagging in terms of receipts, ranking only eighth in the country.

Meanwhile, although the remaining 24 provinces are all net implied carbon outflows, Sichuan is particularly prominent, with 830,000 tCO₂e. This is mainly because Sichuan, as a province with a large number of received shipments (ranked fifth), has shifted the carbon emissions that should have been borne by it to other provinces. In addition, provinces such as Guangxi, Henan, and Hunan also ranked among the top provinces in terms of net implied carbon outflow, a phenomenon also attributed to the fact that the volume of receipts in these provinces is much larger than the volume of shipments. It is worth mentioning that Sichuan Province serves as a key hub for express-delivery consumption and transit in western China. As one of the country’s most populous provinces (with a permanent population exceeding 83 million), Sichuan’s vast consumer market has made it the most important receiving area for e-commerce platforms in the southwest. The Chengdu–Chongqing city-cluster integration development strategy—elevated to a national strategy in 2020—has further reinforced Sichuan’s role as a logistics hub and transit center. In 2022, Sichuan ranked fifth nationwide in express-delivery receipt volume, surpassing several eastern coastal provinces. Moreover, Sichuan’s strategic location makes it the principal corridor from East and South China to western provinces such as Tibet and Xinjiang, playing a vital role in receiving and redistributing express shipments.

Under the principle of shared responsibility between producers and consumers, the implied carbon transfer phenomenon reveals the uneven distribution of responsibility for carbon emissions. Specifically, provinces with net implied carbon outflows, such as Guangdong and Zhejiang, bear a higher level of carbon emission responsibility than they should for the emissions they produce, suggesting that they have additionally borne part of the carbon emission responsibility of other regions, whereas provinces with net implied carbon inflows, such as Sichuan, Guangxi, and Henan, actually emit less carbon than they should, reflecting that these provinces have shifted part of their carbon emission responsibility to other regions. This finding not only highlights the imbalance in the distribution of carbon emission responsibilities among regions but also provides an important basis for improving the carbon emission responsibility-sharing mechanism and promoting regional cooperation in emission reduction.

The core mechanism of implicit carbon transfer stems from multiple structural contradictions: (i) “Production–consumption” geographical separation: The raw material and production stages of e-commerce express packaging are concentrated in eastern China (e.g., Guangdong and Zhejiang account for 40% of national shipments), while the consumption side covers the entire country, forming a spatial mismatch of “eastern production, national consumption”. (ii) Responsibility allocation rules: According to the producer–consumer 6:4 split, major shipping provinces (e.g., Guangdong’s shipping/receiving ratio is 24.1 billion—9.9 billion) retain 40% of the production carbon and transfer 60% to the consumption areas, while receiving provinces (e.g., Sichuan) bear the external transfer responsibility. (iii) Scale effect of logistics networks and consumption demand: The logistics network in southeastern coastal provinces is concentrated (the implicit carbon outflow of seven provinces accounts for 89.79% of the national total), while large consumer provinces in central and western China (such as Henan and Sichuan) experience a demand-driven net inflow due to capacity gaps.

6. Conclusions and Discussion

6.1. Discussion

This study constructs a producer–consumer shared responsibility model, revealing disparities in carbon emission responsibilities between eastern consumer regions and western production regions. Under the “demand-driven responsibility-sharing” framework, the consumer side is found to bear approximately 60% of the emissions, aligning with the “Extended Producer Responsibility” principle highlighted in the United Nations Global E-waste Mon571itor 2023 [49]. Compared with existing carbon footprint studies on supply chains that mostly focus on upstream, midstream, and downstream sectors [50], this study further refines the analysis to the interprovincial level, clarifying the spatial distribution of responsibilities in e-commerce express packaging carbon emissions. This helps identify responsible parties more precisely and provides targeted insights for policy formulation.

In terms of policy practice, China has preliminarily established the foundation of a carbon responsibility offset system. The Ministry of Ecology and Environment’s Interim Regulations on Carbon Emissions Trading Management issued in 2021 stipulates that key emitters may offset quota settlements using verified emission reductions [51]. However, the current mechanism still suffers from inadequate policy coordination and limited coverage, indicating the urgent need to improve market-based regulatory tools. It is recommended that while exploring carbon credit markets, special attention should be paid to their institutional fragility: the carbon price fluctuations triggered by the EU’s Carbon Border Adjustment Mechanism (CBAM) have already had ripple effects, and the dynamic allocation of domestic regional quotas may worsen the “emission reduction suppression” phenomenon in underdeveloped areas [52]. The pattern of net embodied carbon outflows resulting from industrial transfers to Southeast Asian countries mirrors China’s past development trajectory, revealing that the disjunction between production and consumption responsibilities has become a common challenge in global climate governance.

According to statistics from China’s State Post Bureau, the number of express deliveries in China reached 193.7 billion in 2024 [53]. Meanwhile, this study estimates that carbon emissions from e-commerce express packaging in 2022 totaled 41.209 million tCO₂e. As key actors in life cycle packaging management, the emission reduction performance of express delivery companies directly influences the industry’s carbon peaking progress. It is therefore recommended that express enterprises be included in carbon trading markets and regulatory watchlists as soon as possible. By leveraging market mechanisms, courier companies can be incentivized to reduce energy consumption and emissions and promote greener, reduced, and more circular packaging, thereby effectively lowering the carbon emissions of the express delivery sector.

6.2. Conclusions

This paper provides a comprehensive life cycle analysis of the carbon footprint of e-commerce express packaging in China, quantifying the carbon emissions associated with different stages and packaging types. It further calculates provincial-level carbon footprints under two scenarios: with and without considering embodied carbon. Based on these findings, the study explores embodied carbon transfers and draws the following conclusions:

• In 2022, the carbon footprint of China’s e-commerce express packaging reached approximately 41.209 million tCO₂e. Geographically, the carbon footprint shows a clear pattern of “more in the east, less in the west”, gradually decreasing from the southeast coast to the northwest inland regions. Guangdong Province, with its large volumes of shipments and receipts, recorded the highest carbon footprint, while Tibet had the lowest due to its relatively small scale of e-commerce activity.
• The carbon footprint of China’s e-commerce packaging displays distinct spatial characteristics. At the raw material stage, the emissions show a “more in the south and east, less in the north and west” pattern. During the production stage, emissions are mainly concentrated in coastal areas, forming a “coast-surrounding-inland” structure. In the disposal stage, the footprint shows a gradual decrease from the southeast to the northwest.
• Significant differences exist in the carbon footprints of different packaging materials: plastic packaging generated 29.969 million tCO₂e during the raw material stage and 4.11 million tCO₂e during the production stage—2.9 and 1.7 times higher than those of paper packaging (10.341 million and 2.376 million tCO₂e, respectively). However, in the disposal stage, paper surpassed plastic, with emissions reaching 3.133 million tCO₂e, which is 2.2 times greater than plastic’s 1.423 million tCO₂e.
• There is a clear pattern of embodied carbon transfer between provinces in China. Net embodied carbon outflows are concentrated in seven provinces, including Guangdong and Zhejiang, while net inflows follow a “more in the east and south, less in the west and north” distribution. Embodied carbon transfers account for as much as 40% of the total. Specifically, Guangdong and Zhejiang contributed 4.533 million and 3.98 million tCO₂e, respectively, accounting for 89.79% of the total net outflow.

Future research can be further expanded in the following two directions: First, by enriching data sources to obtain more granular, province-level data on parcel receipt volumes and disposal methods, thereby enhancing the accuracy of the results; Second, by incorporating policy simulations to assess the dynamic changes in carbon footprints under various intervention measures, providing decision-making support for the formulation of effective emission reduction policies.