Sustainable Risk Governance in Maritime Transport: Embodied Carbon Emissions and Responsibility Distribution Across BRICS Coastal Economies

Abstract

Maritime carbon responsibility allocation can guide sea level rise and storm surge mitigation in BRICS coastal zones by addressing emissions-driven climate risks. This study analyzes the characteristics of and differences in embodied carbon emissions in the Maritime Transport Industry of the BRICS countries from the perspectives of producer responsibility, consumer responsibility, and shared responsibility, based on a global value chain framework. Using non-competitive input–output data from the OECD and introducing a processing trade adjustment mechanism, the study calculates the carbon emissions of the five countries from 1995 to 2018. The empirical results show that under producer responsibility, carbon emissions in China and South Africa’s maritime transport sectors are mainly driven by exports, with production-side emissions significantly higher than consumption-side emissions. Under consumer responsibility, emissions in India and Brazil are driven by the demand for imported goods, reflecting their high reliance on external markets. In shared responsibility accounting, China’s cumulative carbon emissions account for 66.81% of the total emissions from the five countries, highlighting its central role in global supply chains. The study also finds that the differences in carbon emissions among the countries are mainly due to differences in economic structures, trade dependencies, and consumption patterns. Different responsibility accounting methods have a significant impact on carbon emissions, with export-oriented countries tending to weaken producer responsibility, while import-oriented countries seek to avoid consumer responsibility. The shared responsibility mechanism, through the dynamic allocation coefficient $\alpha$, provides a practical approach to balancing efficiency and equity in global carbon governance.

1. Introduction

The allocation of carbon responsibilities in maritime transport is critically linked to coastal hazard mitigation strategies. As greenhouse gas emissions from maritime transport contribute significantly to ocean warming and rising sea levels, these processes intensify coastal hazards—such as flooding, erosion, and storm surges—in BRICS coastal regions. By implementing equitable carbon responsibility frameworks, such as shared responsibility models, policymakers can create incentives for reducing emissions in the maritime sector. Such reductions not only lessen global greenhouse gas emissions, but also help to mitigate the associated risks to vulnerable coastal communities, thereby strengthening overall coastal resilience.

The maritime industry plays a pivotal role in global trade, carrying approximately 90% of the world’s commerce. However, this significant contribution to economic activity also comes with a substantial environmental cost, as it is a major source of greenhouse gas emissions that drive climate change [1]. Most scientists believe that climate change is influenced by human activities, so reducing carbon dioxide emissions is crucial. In view of this, the maritime transport sector must assume its due responsibility and adopt innovative measures on a global scale to mitigate its impact on climate change [2]. To this end, the global maritime industry has made concerted efforts to reduce its carbon footprint throughout the entire supply chain, including ship fleets, ports, terminals, and inland transportation [3]. The BRICS coalition, comprising Brazil, Russia, India, China, and South Africa, is energetically engaged in tackling the world’s most pressing challenges [4].

Embodied carbon in trade refers to the total amount of greenhouse gases emitted during the production of goods and services and throughout their entire supply chain, from the place of production to the place of consumption [5]. Current approaches to measuring carbon emissions can be broadly classified into three main categories: (1) the bottom-up Life Cycle Assessment (LCA) approach [6], which is well suited for nations with high-quality data or specific products and services, despite posing challenges when applied on a broader societal scale; (2) the Intergovernmental Panel on Climate Change (IPCC) methodology [7], which utilizes standard coal conversion and carbon emission coefficients to estimate industry-wide emissions, making it ideal for macro-level regional analyses; and (3) the Input–Output Analysis (IOA) method [8], which adopts a top-down perspective to calculate total industrial emissions and examine inter-industry correlations, and has gained widespread application in energy and environmental research.

Many scholars have conducted relevant research on embodied carbon in trade. Zhang and Li [9] analyzed how “WTO+” and “WTO-X” deep trade agreements shape global embodied carbon emission networks, finding WTO-X provisions more impactful, and proposed policy adjustments favoring developing countries to align trade governance with climate goals. Li et al. [10] constructed a matching index model to analyze disparities between embodied carbon flows and value-added trade across China’s eight regions and 31 provinces in 2012 and 2015, revealing mismatches (e.g., northwestern regions exporting carbon, but importing value-added from northern coasts, and Beijing–Xinjiang’s inverse carbon value flows), and proposed ecological compensation mechanisms to address regional/provincial imbalances. Yu et al. [11] quantified China–Russia trade embodied carbon flows (2007–2015), revealing China’s net export decline despite trade expansion, and identified the metal/chemical sectors as major emission sources, attributed emission reduction to carbon coefficients and growth to trade scale, and advocated for bilateral energy structure optimization and technological upgrades. Zheng et al. [12] employed a multi-regional input–output (MRIO) model to quantify and compare trade-embodied carbon emissions within the forest industry across BRICS nations (Brazil, Russia, India, China, and South Africa), analyzing the cross-border environmental impacts of sectoral trade activities.

The marked spatial disparities in carbon emissions present a serious challenge to coordinated regional carbon reduction initiatives [13]. Recently, a contentious debate has emerged concerning the basis on which national CO₂ emission inventories should be established, pitting territory-related production against consumption as the primary criterion [14]. Hence, the matter of responsibility sharing for embodied carbon emissions in trade becomes an inescapable issue. Some scholars have conducted research on carbon emissions under the producer responsibility system. Li et al. [15] applied the method of moment quantile regression to OECD economies (1990–2020), demonstrating that production-based emissions are primarily driven by natural resource exploitation and economic growth, while they can be effectively mitigated through environmental regulations, carbon taxation, and renewable energy adoption, and they urged targeted carbon pricing and accelerated clean transitions to decouple production systems from emission-intensive pathways. Wang and Yang [16] employed structural decomposition analysis of production-based emissions in China and Germany (2000–2014), revealing divergent trajectories: China’s 194% emission surge driven by consumption/production structures contrasts with Germany’s 17% reduction through sectoral decoupling; they urged China to adopt German-style structural optimization and carbon intensity mitigation strategies to align production systems with Paris Agreement commitments. Some scholars have also studied carbon emissions from the perspective of consumer responsibility. For example, Gao et al. [17] addressed the gap in research on China’s consumption-based greenhouse gas (GHG) emissions by analyzing trends and factors driving changes using a global multi-regional input–output model, revealing that non-CO₂ GHG emissions like CH₄, N₂O, and F-gases have increased more rapidly than CO₂ emissions, with investment-based emissions growing fastest and household consumption having the most significant impact on non-CO₂ GHG emissions. Due to the different perspectives of the producer responsibility system and the consumer responsibility system, how to share responsibility for greenhouse gas emissions between consumers and producers is a highly sensitive issue in international climate policy negotiations [18].

At present, research on embodied carbon emissions in water transport is still very limited [19], especially since maritime transport trade among BRICS countries accounts for a large proportion of international trade. This study selects the BRICS countries as its research subjects, primarily due to their critical roles in global trade and the diversity of their economic development stages, thereby providing a multidimensional perspective for analyzing trade-embedded carbon emissions. The maritime transport industry, owing to its dominant role in international trade and the prominence of its associated carbon emission issues, has become the specific focus of this research. Moreover, the emphasis on shared responsibility is grounded in the scientific recognition that coordinated efforts among countries and industries are essential for addressing global climate change; it also reflects the pursuit of fairness in the allocation of carbon emission responsibility, thereby transcending the limitations of a single-responsibility approach and promoting the achievement of emission reduction targets through a more comprehensive and balanced strategy.

2. Methods and Data

2.1. Embodied Carbon Accounting Framework

From the perspective of global value chains, this study classifies embodied carbon emissions in the maritime transport industry into four categories:

(1) Category I (Domestic Products for Domestic Consumption, DPDCs): Emissions generated by domestically produced goods directly consumed within the country.

(2) Category II (Domestic Products for Foreign Consumption, DPFCs): Emissions resulting from domestically produced goods exported to meet foreign demand.

(3) Category III (Foreign Products for Domestic Consumption, FPDCs): Emissions associated with imported intermediate goods that are processed domestically and used for final domestic consumption.

(4) Category IV (Foreign Products for Foreign Consumption, FPFCs): Emissions that are entirely generated and consumed abroad (excluded from this study’s accounting).

Building on the traditional input–output model, this study introduces a processing trade correction mechanism to better capture the embodied carbon emission effects of intermediate goods circulation. Specifically, emissions from imported intermediate goods that are processed domestically are calculated using domestic production technology coefficients, while emissions from directly imported final consumer goods are determined based on the carbon emission coefficients of the country of origin.

2.2. Responsibility Accounting Model Construction

(1) Producer and Consumer Responsibility Accounting

Emissions Embodied in Production (EEPs) include Categories I (DPDCs) and II (DPFCs), reflecting the contribution of domestic production activities to global supply chain carbon footprints. The formula is as follows:

$$EEP=I+\mathit{II}=e^d{\left(I-A^d\right)}^{-1}Y$$

(1)

where $e^d$ is the direct carbon emission coefficient vector of the home country; ${\left(I-A^d\right)}^{-1}$ is the domestic total requirement coefficient matrix; and $Y$ is the total output vector.

Emissions Embodied in Consumption (EECs) encompass Categories I (DPDCs) and III (FPDCs), representing life cycle emissions driven by domestic final demand. The formula is as follows:

$$EEC=I+\mathit{III}=E^d{Y}_D+E^d{A}^m{\left(I-A^d\right)}^{-1}{Y}_D+E^{im}{Y}_m$$

(2)

where $Y_D$ is the domestic final demand vector; $A^m$ is the direct consumption coefficient matrix of imported intermediates; $E^d{A}^m{\left(I-A^d\right)}^{-1}$ represents the total domestic carbon emission coefficient of imports; $Y_m$ is the portion of imported products directly used for final consumption; and $E^{im}$ is the total carbon emission coefficient of the importing country, calculated as follows:

$$E^{im}=e_{im}^d{\left(I-A_{im}^d\right)}^{-1}$$

(3)

where $e_{im}^d$ is the direct carbon emission coefficient of the importing country, and ${\left(I-A_{im}^d\right)}^{-1}$ is the domestic total requirement coefficient matrix of the importing country.

Note that, since matrix multiplication is associative but not commutative, and considering that $E^d=e^d{\left(I-A^d\right)}^{-1}$ represents the complete carbon emission coefficient, this study employs the formula $E^d\mathrm{Y}=[e^d{\left(I-A^d\right)}^{-1}]Y$ to calculate production-side carbon emissions. This method produces higher estimates compared to the production-based carbon emission calculation by the OECD, which is computed as $e^d[{\left(I-A^d\right)}^{-1}Y]$.

(2) Embodied Carbon Responsibility Sharing Model

This study develops a carbon responsibility sharing model based on the “principle of shared responsibility”, aiming to achieve an equitable distribution between producer responsibility and consumer responsibility to jointly promote energy conservation and emission reduction. According to Ferng’s [20] weighted average responsibility sharing model, the total carbon emission responsibility is normalized to 1, with the producer’s share denoted as $\alpha$ and the consumer’s share as $1–\alpha .$ The basic expression of the trade-embedded carbon responsibility sharing model is given as follows:

$$SR=\alpha EEP+\left(1-\alpha \right)EEC$$

(4)

Expanding this yields the following:

$$SR={\underbrace{E^d{Y}_D}}_1+{\underbrace{\alpha E^d{Y}_E}}_2+{\underbrace{\left(1-\alpha \right){[E}^d{A}^m{\left(I-A^d\right)}^{-1}{Y}_D+E^{im}{Y}_m]}}_3$$

(5)

where $Y_E$ is the export vector, and $\alpha$ is the responsibility allocation coefficient matrix, with values in the range [0,1].

In Equation (5), the responsibility for DPDCs is entirely attributed to the country. The responsibility for DPFCs that are produced domestically and exported is represented by the coefficient $\alpha$. Meanwhile, the responsibility for FPDCs that are processed domestically and used for final domestic consumption, as well as for FPFCs directly consumed domestically, is represented by $1-\alpha$.

In addition, the value-added of each sector reflects its actual contribution during the production process, while the net output, which excludes intra-sector transactions, more accurately represents the sector’s true contribution to the external economy. Following the “polluter pays” principle, sectors that create greater economic value in production should bear a higher share of environmental responsibility. Therefore, this study adopts the ratio of value-added to net output as the responsibility allocation coefficient. This approach effectively measures the real contributions of each sector, and ensures that high-value-added sectors (typically characterized by advanced technology and higher economic efficiency) assume a greater share of the emission reduction burden. In doing so, it incentivizes technological innovation and environmental improvement, while mitigating the competitive disadvantage imposed by cost pressures on low-value-added sectors, thereby providing a fair and rational basis for the allocation of trade-embedded carbon responsibilities. Drawing on Lenzen et al.’s [21] value-added contribution method, $\alpha$ is defined as the ratio of sectoral value-added to net output:

$$\alpha =V_i/\left(X_i-X_{ii}\right)$$

(6)

where $V_i$ is the value-added of sector $i$; $X_i$ is the total output; $X_{ii}$ represents intra-sectoral transactions; and $X_i-X_{ii}$ is the net output of sector ii. This ensures that high-value-added sectors bear greater emission reduction responsibilities, aligning with the “polluter pays” principle.

2.3. Data Sources

This study utilizes the 2021 version of the OECD’s Inter-country Input–Output Tables to calculate embodied carbon emissions from three responsibility perspectives for the maritime transport sector (water transport sector in D50) in BRICS countries from 1995 to 2018.

The CO₂ direct emission coefficients are sourced from the OECD’s Trade in Embodied CO₂ Database. The CO₂ direct emission coefficient represents the carbon intensity of a specific sector in a country, calculated as the total CO₂ emissions (in tonnes) divided by the sector’s output at basic prices in million USD (in nominal value), with units of tonnes per million USD.

3. Results

3.1. EEPs in the Maritime Transport Industry

(1) EEPs in Brazil’s Maritime Transport Industry

From the producer responsibility perspective, Brazil’s maritime transport industry generated cumulative EEPs of 140.24 million tonnes over 24 years, with an annual average of 5.84 million tonnes. DPDCs accounted for 33.72% of cumulative emissions, while DPFCs contributed 66.28% (see Figure 1).

Temporally, DPDC-related emissions exhibited fluctuating growth, rising from 0.77 million tonnes in 1995 to 3.19 million tonnes in 2017, before sharply declining to 1.00 million tonnes in 2018, likely due to domestic policy adjustments or economic fluctuations. In contrast, DPFC-related emissions peaked at 6.44 million tonnes in 2008, followed by fluctuations, ultimately decreasing to 2.54 million tonnes by 2018.

In terms of composition, DPFC-dominated emissions consistently exceeded 50%, indicating that Brazil’s maritime transport emissions under producer responsibility are primarily export-driven.

Overall, Brazil’s total EEPs peaked at 7.86 million tonnes in 2008, followed by a “rise-then-decline” trend, dropping to 3.54 million tonnes by 2018.

(2) EEPs in Russia’s Maritime Transport Industry

Russia’s maritime transport industry recorded cumulative EEPs of 135.36 million tonnes, averaging 5.64 million tonnes annually. DPDCs accounted for 32.22% of emissions, while DPFCs constituted 67.78% (see Figure 2).

DPFC-related emissions grew from 2.88 million tonnes in 1995 to 6.27 million tonnes in 2018, with a notable surge to 5.80 million tonnes in 2014. Meanwhile, DPDC-related emissions remained stable between 1.0 and 3.7 million tonnes, peaking at 3.77 million tonnes in 2014 (linked to domestic infrastructure investments) before declining to 1.67 million tonnes in 2018.

The total EEPs under producer responsibility exhibited significant volatility, peaking at 9.57 million tonnes in 2014 and falling to 7.94 million tonnes by 2018.

(3) EEPs in India’s Maritime Transport Industry

India’s cumulative EEPs totaled 69.80 million tonnes, with an annual average of 2.91 million tonnes. DPDCs contributed 37.00%, while DPFCs accounted for 63.00% (see Figure 3).

DPFC-related emissions grew in phases, rising from 2.17 million tonnes in 2007 to 3.55 million tonnes in 2011, before declining to 2.74 million tonnes in 2012 due to global trade slowdowns. By 2018, emissions rebounded to 2.87 million tonnes. Conversely, DPDC-related emissions shrank persistently, dropping from 1.69 million tonnes in 1995 to 0.33 million tonnes in 2018, reflecting the gradual replacement of domestic maritime transport demand by alternatives (e.g., road and rail).

Overall, India’s total EEP growth remained moderate, peaking at 3.65 million tonnes in 2017, with limited momentum.

(4) EEP in China’s Maritime Transport Industry

China’s maritime transport industry recorded exceptionally high EEPs of 1,910.35 million tonnes, averaging 79.60 million tonnes annually. DPFCs dominated emissions, contributing 77.08%, while DPDCs accounted for 22.92% (see Figure 4).

DPFC-related emissions consistently exceeded 70% of total emissions. In 2006, China’s DPFC emissions surpassed 100 million tonnes for the first time (reaching 101.05 million tonnes), stabilizing between 70 and 80 million tonnes post 2015. Meanwhile, DPDC-related emissions surged from 7.84 million tonnes in 1995 to 47.97 million tonnes in 2018, with a growth rate of 34.3% during 2016–2018.

The total EEPs under producer responsibility grew rapidly, rising from 29.81 million tonnes in 1995 to 113.49 million tonnes in 2017, before slightly declining to 108.71 million tonnes in 2018.

(5) EEPs in South Africa’s Maritime Transport Industry

South Africa’s cumulative EEPs totaled 30.74 million tonnes, averaging 1.28 million tonnes annually. DPFCs dominated, at 86.98%, while DPDCs contributed only 13.02% (see Figure 5).

DPFC-related emissions consistently exceeded 80%, peaking at 91.94% in 2007. Despite low absolute values, DPDC-related emissions grew notably, increasing from 0.21 million tonnes in 1995 to 0.40 million tonnes in 2018, with an 81.8% surge during 2016–2018.

The total EEPs peaked at 1.49 million tonnes in 2014, rising slightly to 1.58 million tonnes by 2018.

3.2. EECs in the Maritime Transport Industry

(1) EECs in Brazil’s Maritime Transport Industry

From 1995 to 2018, Brazil’s maritime transport industry generated cumulative EECs of 126.63 million tonnes, with an annual average of 5.28 million tonnes. DPDCs accounted for 37.34% of cumulative emissions, while FPDCs contributed 62.66% (see Figure 6).

In terms of composition, DPDC-related emissions began to grow against the trend after 2010, reaching 3.19 million tonnes in 2017, but plummeted to 1.00 million tonnes in 2018, likely due to economic recession. Meanwhile, FPDC-related emissions peaked at 5.58 million tonnes in 2005, followed by a gradual decline to 1.75 million tonnes by 2018, driven by falling international commodity prices and inefficiencies in domestic port operations.

The total EECs under consumer responsibility exhibited significant fluctuations, declining from 6.51 million tonnes in 1995 to 2.75 million tonnes in 2018, with a peak of 7.11 million tonnes in 2009. This indicates that Brazil’s maritime transport emissions are heavily reliant on imported goods, yet the contraction of global demand and domestic economic volatility jointly drove the downward trend in total emissions.

(2) EECs in Russia’s Maritime Transport Industry

Russia’s maritime transport industry recorded cumulative EECs of 137.10 million tonnes, averaging 5.71 million tonnes annually. DPDCs accounted for 31.81%, while FPDCs constituted 68.19% (see Figure 7).

DPDC-related emissions remained at low levels, briefly peaking at 3.77 million tonnes in 2014 before rapidly declining. In contrast, FPDC-related emissions grew steadily from 2.77 million tonnes in 1995 to a peak of 10.44 million tonnes in 2017, reflecting phased expansions in Russia’s import demand. By 2018, FPDC emissions slightly decreased to 4.74 million tonnes.

(3) EECs in India’s Maritime Transport Industry

India’s cumulative EECs totaled 397.40 million tonnes, with an annual average of 16.56 million tonnes. DPDCs contributed 6.50%, while FPDCs dominated at 93.50% (see Figure 8).

FPDC-related emissions surged from 2.13 million tonnes in 1995 to 34.87 million tonnes in 2018, with an annual growth rate of 12.3%. Post 2005, growth accelerated, rising from 16.84 million tonnes in 2005 to 35.24 million tonnes in 2017. Meanwhile, DPDC-related emissions declined persistently from 1.69 million tonnes in 1995 to 0.33 million tonnes in 2018, indicating a shift in domestic transport demand toward alternatives (e.g., road and rail).

In summary, India’s EECs under consumer responsibility are almost entirely import-driven, with growth rates far exceeding those of other BRICS nations, exposing significant external dependencies of supply chains.

(4) EECs in China’s Maritime Transport Industry

China’s maritime transport industry recorded cumulative EECs of 803.52 million tonnes, averaging 33.48 million tonnes annually. DPDCs accounted for 54.49%, while FPDCs contributed 45.51% (see Figure 9).

DPDC-related emissions grew from 7.84 million tonnes in 1995 to 47.97 million tonnes in 2018, with a 34.3% surge during 2016–2018, directly linked to the “dual circulation” strategy’s emphasis on domestic demand. FPDC-related emissions also expanded significantly, rising from 2.02 million tonnes in 1995 to 25.59 million tonnes in 2018. Post 2006, FPDC growth accelerated, reaching 28.32 million tonnes in 2017, reflecting China’s sustained demand for imported commodities and high-tech equipment.

The total EECs under consumer responsibility increased by over sevenfold, from 9.85 million tonnes in 1995 to 73.56 million tonnes in 2018, confirming China’s dual role as a global supply chain hub and a major driver of consumption-driven emissions.

(5) EECs in South Africa’s Maritime Transport Industry

South Africa’s cumulative EECs totaled 172.71 million tonnes, averaging 7.20 million tonnes annually. DPDCs accounted for 2.32%, while FPDCs contributed 7.03% (see Figure 10).

DPDC-related emissions remained low, reaching 0.40 million tonnes in 2018. In contrast, FPDC-related emissions fluctuated, declining from 10.00 million tonnes in 1995 to 3.07 million tonnes in 2018, with a temporary rebound to 10.10 million tonnes in 2011, before contraction due to reduced European demand post 2015.

Overall, South Africa’s EECs under consumer responsibility are almost entirely dependent on imported goods. However, its total emissions decreased by 66% from 1995 to 2018, reflecting the combined effects of limited economic scale and global market volatility.

3.3. Analysis of Embodied Carbon Emissions Under Shared Responsibility

The results in this section, obtained using the shared responsibility approach, fall between those derived from consumer responsibility and producer responsibility. Specifically, for export-oriented countries, the shared responsibility result is lower than that of producer responsibility, but higher than that of consumer responsibility; conversely, for import-oriented countries, the shared responsibility result is lower than the consumer responsibility value, yet higher than the value produced under producer responsibility. Consequently, this method not only bridges the gap between the two conventional approaches, but also offers a more balanced, scientifically robust, and equitable framework for assessing global carbon emissions.

(1) Analysis of Shared Responsibility Coefficients

According to

Table 1. Shared responsibility coefficients for carbon emissions in the maritime transport industry of BRICS countries.

Year	Brazil	Russia	India	China	South Africa
1995	0.48	0.57	0.49	0.41	0.50
1996	0.48	0.58	0.48	0.40	0.49
1997	0.50	0.58	0.48	0.38	0.49
1998	0.50	0.58	0.45	0.40	0.47
1999	0.50	0.59	0.42	0.41	0.46
2000	0.44	0.60	0.47	0.46	0.44
2001	0.43	0.59	0.44	0.47	0.43
2002	0.43	0.59	0.39	0.44	0.42
2003	0.40	0.59	0.50	0.43	0.43
2004	0.42	0.59	0.53	0.43	0.44
2005	0.40	0.60	0.51	0.43	0.45
2006	0.40	0.58	0.47	0.47	0.46
2007	0.42	0.56	0.46	0.47	0.46
2008	0.40	0.53	0.47	0.45	0.45
2009	0.42	0.51	0.43	0.47	0.45
2010	0.42	0.52	0.44	0.44	0.44
2011	0.42	0.51	0.40	0.43	0.48
2012	0.43	0.51	0.39	0.41	0.54
2013	0.46	0.51	0.40	0.45	0.55
2014	0.49	0.52	0.40	0.42	0.54
2015	0.50	0.49	0.36	0.39	0.52
2016	0.53	0.49	0.43	0.37	0.51
2017	0.50	0.50	0.40	0.37	0.51
2018	0.53	0.47	0.39	0.33	0.61
Average	0.45	0.55	0.44	0.42	0.48

, Brazil’s shared responsibility coefficient remained stable between 0.40 and 0.53 from 1995 to 2018. After 2016, the coefficient increased from 0.50 to 0.53, indicating a shift towards greater producer responsibility. This change may be associated with domestic economic fluctuations and the global decline in commodity prices, which led to reduced export demand, thereby increasing the pressure on producer responsibility.

Russia’s shared responsibility coefficient decreased from 0.57 in 1995 to 0.47 in 2018, with a more rapid decline after 2014. This trend reflects the growth in maritime demand following the development of the Arctic shipping route, which increased the pressure on Russia’s producer responsibility.

India’s shared responsibility coefficient fluctuated significantly, decreasing from 0.49 in 1995 to 0.39 in 2018. This change reflects India’s growing dependence on imports, leading to an increase in consumer responsibility.

China’s shared responsibility coefficient decreased from 0.41 in 1995 to 0.33 in 2018, signaling a shift from producer responsibility to consumer responsibility. This change is closely related to China’s “dual circulation” strategy, which emphasizes expanding domestic demand, thus increasing consumer responsibility.

South Africa’s shared responsibility coefficient increased from 0.50 in 1995 to 0.61 in 2018, particularly after 2012. This increase indicates that South Africa strengthened its producer responsibility to cope with fluctuations in global markets, mainly driven by mining exports.

(2) Analysis of Embodied Carbon Emissions under Shared Responsibility

According to

Table 2. Embodied carbon emissions under shared responsibility in the maritime transport industry of BRICS countries (Mt).

Year	Brazil	Russia	India	China	South Africa
1995	6.05	4.82	3.11	18.01	5.71
1996	6.02	4.78	3.17	27.57	5.67
1997	5.01	4.68	3.40	17.00	4.63
1998	5.38	4.15	4.02	22.44	5.39
1999	4.90	3.17	3.97	28.32	5.73
2000	4.65	3.25	5.97	30.07	4.24
2001	4.42	3.95	5.27	30.97	3.79
2002	4.99	4.24	7.07	35.12	3.95
2003	4.86	4.31	3.68	46.10	4.78
2004	5.02	4.60	4.74	57.03	4.61
2005	6.31	4.68	9.42	59.07	5.09
2006	6.27	5.12	12.19	68.18	5.03
2007	6.98	5.23	14.41	67.23	6.05
2008	6.09	5.41	9.91	60.41	5.27
2009	7.32	5.35	17.80	63.94	4.62
2010	6.07	4.91	17.47	66.14	4.82
2011	5.98	6.80	14.84	66.03	5.99
2012	5.14	5.50	9.66	63.35	3.18
2013	5.00	7.37	12.99	62.76	3.89
2014	6.25	10.37	17.63	65.56	3.54
2015	5.64	8.98	20.96	70.96	2.73
2016	5.35	8.11	11.04	77.70	2.27
2017	5.92	9.89	22.73	85.82	2.32
2018	3.16	7.12	22.84	85.22	2.33

, Brazil’s shared responsibility carbon emissions declined from 6.05 million tonnes in 1995 to 3.16 million tonnes in 2018, with a peak of 7.32 million tonnes in 2009. The cumulative emissions were 132.78 million tonnes, accounting for 6.96% of the total emissions from the five countries. This result reflects that Brazil’s responsibility distribution is heavily influenced by international market fluctuations, as it is a resource-exporting country.

Russia’s shared responsibility carbon emissions peaked at 10.37 million tonnes in 2014, but declined to 7.12 million tonnes by 2018, mainly due to Western sanctions that disrupted energy exports. The cumulative emissions were 136.80 million tonnes, with more than 30% of the emissions occurring between 2014 and 2017, reflecting the impact of geopolitical factors on Russia’s emission reduction path. The prolonged domestic consumption slump indicates that Russia’s responsibility distribution leans towards the producer side, but it is heavily constrained by technological blockages.

India’s shared responsibility carbon emissions increased from 3.11 million tonnes in 1995 to 22.84 million tonnes in 2018, with an average annual growth rate of over 20% after 2005. The cumulative emissions were 258.30 million tonnes, the highest growth rate among the five countries (634%), highlighting India’s “high growth–high emissions” development model.

China’s shared responsibility carbon emissions dramatically increased from 18.01 million tonnes in 1995 to 85.22 million tonnes in 2018. After joining the World Trade Organization (WTO) in 2006, emissions surged by 15% due to export expansion. Especially after 2016, the growth rate accelerated to an annual average of 9.2%, closely linked to the expansion of domestic demand under the “dual circulation” strategy. The cumulative emissions reached 1274.99 million tonnes, accounting for 66.81% of the total emissions from the five countries, underscoring China’s central role in the global supply chain and its significant impact on consumption-driven emissions.

South Africa’s shared responsibility carbon emissions decreased from 5.71 million tonnes in 1995 to 2.33 million tonnes in 2018. In 2012, emissions dropped sharply by 47%, due to reduced European demand. The cumulative emissions were 105.60 million tonnes, with a temporary increase to 5.99 million tonnes in 2011. However, the long-term decline was due to the country’s limited economic scale and reduced emissions as a result of market volatility.

4. Discussion

4.1. Differences Between Producer and Consumer Responsibility

As Figure 11 shows, from 1995 to 2018, China’s EEPs in the water transport sector surged dramatically, far exceeding the levels of the other four BRICS nations. Brazil, Russia, India, and South Africa showed minimal changes in EEPs, with only minor fluctuations tied to trade volumes, and no clear upward trajectory. During the same period, China and India experienced significant rises in EECs, with China leading in growth speed and India following closely. In contrast, Brazil, Russia, and South Africa exhibited stagnant EEC trends, with Brazil even recording a slight decline. Overall, China and India drove the majority of emission growth, while the remaining three countries contributed minimally.

The differences in the share of carbon emissions under producer responsibility and consumer responsibility reflect the distinct roles that each country plays in the global value chain. Producer responsibility primarily focuses on the carbon emissions generated during production processes, while consumer responsibility emphasizes the impact of domestic consumption demand on global carbon emissions. According to empirical analysis, emissions under producer responsibility are generally higher, as they not only include emissions required for domestic consumption, but also encompass a large portion of emissions related to exports.

In China and South Africa, the share of embodied carbon emissions from DPFC under EEP is significantly higher than that from DPDC, with the cumulative shares from 1995 to 2018 being 77.08% and 86.98%, respectively. This indicates that the maritime transport industries in these two countries are heavily export-oriented. As the “world’s factory”, China’s maritime transport carbon emissions are primarily driven by the demand for the transportation of export goods. In contrast, South Africa’s carbon emissions are dominated by the export of mining resources, resulting in sustained pressure on producer responsibility.

In contrast, the EECs more directly reflect the dependence of domestic consumption behavior on external markets. India and Brazil exhibit distinct characteristics of consumer-driven emissions. In India, the share of embodied carbon emissions from FPDC under EEC is as high as 93.50%. Brazil similarly exhibits an import-driven characteristic, with the cumulative share of emissions from FPDC under EEC at 62.66%, reflecting the significant impact of imported goods transportation on domestic carbon emissions.

In Russia, carbon emissions are more balanced, with minimal changes in the shares contributed by DPFCs and FPDCs for both EEPs and EECs. This is closely linked to its energy export policies and other factors.

4.2. Embodied Carbon Emissions Under Shared Responsibility

In shared responsibility accounting, the proportion of producer responsibility and consumer responsibility is balanced through the shared responsibility coefficient. According to empirical data, there are notable differences in the shared responsibility coefficients of Brazil, Russia, and India. For instance, Brazil’s shared responsibility coefficient remained relatively stable from 1995 to 2018, but increased after 2016, reflecting the increased pressure on producer responsibility. This is linked to Brazil’s dependence on resource exports and fluctuations in international markets. In contrast, India’s shared responsibility coefficient gradually decreased from 0.49 to 0.39, indicating an increasing dependence on imported goods and growing consumer responsibility.

Russia’s shared responsibility coefficient decreased, and there was a temporary increase in producer responsibility. South Africa’s coefficient increased significantly, reflecting strengthened producer responsibility to cope with international market volatility. China’s coefficient decreased from 0.41 to 0.33, showing a shift towards consumer responsibility, aligned with China’s domestic demand expansion under its dual circulation strategy.

Looking at changes in carbon emissions, the shared responsibility carbon emissions are usually the sum of producer responsibility and consumer responsibility emissions. Brazil’s shared responsibility carbon emissions were relatively low, accounting for only 6.96% of the total emissions from the five countries, indicating that its emission responsibility is more influenced by export markets. In contrast, China’s shared responsibility emissions far exceeded those of other countries, reaching 85.22 million tonnes in 2018, which accounted for 44.7% of the total, highlighting its core role in the global supply chain.

Based on the unique emission profiles and responsibility allocations of the BRICS countries, we propose the following concise policy recommendations: (1) Brazil should enhance the low-carbon transition of its export industries and develop sustainable resource extraction strategies, while diversifying its markets to mitigate the risks of international fluctuations. (2) Russia ought to accelerate innovations in energy technology and the greening of its maritime transport, as well as promote economic diversification to reduce its dependence on fossil fuel exports. (3) India needs to establish strict import carbon emission standards and foster green production, alongside a circular economy, to alleviate the challenges of high emissions amid rapid growth. (4) China should expedite the shift toward low-carbon consumption and industrial upgrading by refining its carbon border adjustment mechanisms and strengthening green supply chain development. (5) South Africa is encouraged to drive the low-carbon transformation of its mining and heavy industries, thereby building a resilient low-carbon development model to counteract international market volatility.

In addition, significant technological advancements in the maritime sector—such as improvements in fuel efficiency, the adoption of alternative fuels, and optimized vessel designs—played a crucial role in shaping emission trends over the study period. These innovations not only reduced the carbon intensity of shipping operations, but also helped to offset the potential increase in emissions driven by higher trade volumes and export activities.

4.3. Analysis of Cumulative Embodied Carbon Emissions Under Different Responsibility Perspectives

From 1995 to 2018, the carbon emission trajectories of the BRICS countries under different responsibility perspectives showed significant differences (Figure 12). Brazil, as a resource-exporting economy, had cumulative EEPs of 140.24 million tonnes, while its cumulative EECs were 126.63 million tonnes. The relatively small difference between these values reflects Brazil’s dual characteristics of being export-oriented and having import demands. In Russia, the cumulative values of EEPs and EECs were 135.36 million tonnes and 137.10 million tonnes, respectively, almost equal, indicating a dynamic balance between energy exports and import demands. In contrast, India’s carbon emission structure is quite unique: although its cumulative EEPs were only 69.80 million tonnes, its cumulative EECs reached as high as 397.40 million tonnes. China, with its vast scale, dominated with cumulative EEPs of 1910.35 million tonnes, far surpassing its cumulative EECs of 803.52 million tonnes, a difference of 1107 million tonnes, reinforcing its status as the “world’s factory”. South Africa showed the typical characteristics of a small-scale economy, with a notable difference of 142 million tonnes between its cumulative EEPs (30.74 million tonnes) and EECs (172.71 million tonnes), highlighting its heavy reliance on the transportation of imported goods.

The impact of different accounting methods on the interest structure of each country is distinct. Under producer responsibility accounting, China’s carbon emission pressure is significantly amplified, as its export-driven production network contributes nearly half of the embodied carbon transferred in the global supply chain. In contrast, India and South Africa would face an additional 328 million tonnes and 142 million tonnes of carbon emissions, respectively, if measured under consumer responsibility, due to their economic structures. The cases of Brazil and Russia show that when the difference between EEPs and EECs is small, the choice of responsibility accounting method has less sensitivity. This difference in sensitivity essentially reflects the asymmetric position of countries in the global value chain: export-oriented countries tend to minimize their producer responsibility, while import-driven countries try to avoid consumer responsibility.

Against this backdrop, the shared responsibility (SR) mechanism, through a dynamic allocation coefficient ($\alpha$), offers a practical pathway to reconcile conflicting interests. In 2018, China’s cumulative SR value reached 1274.99 million tonnes, accounting for 66.81% of the total emissions among the five countries. Notably, the dynamic allocation coefficient $\alpha$ gradually decreased after 2009—from 0.47 to 0.33—reflecting the impact of the RMB 4 trillion economic stimulus plan announced in November 2008, which shifted the burden toward the consumption side and underscored the rapid growth in domestic consumption. This shift laid the groundwork for the “dual circulation” strategy in 2020. In contrast, India’s cumulative SR value was 258.30 million tonnes in 2018, representing 13.53% of the total, while Brazil (132.78 million tonnes), Russia (136.80 million tonnes), and South Africa (105.60 million tonnes) exhibited relatively modest cumulative SR values. This shared responsibility framework not only quantifies each country’s actual contribution within the value chain, but also, through its flexible adjustment mechanism, provides a realistic foundation for a global carbon governance system that balances efficiency and fairness.

It is particularly noteworthy that in 2018, China’s cumulative SR emissions accounted for 66.81% of the total among the BRICS countries. This dominant share reflects China’s unique position in global supply chain division and its developmental stage, as well as its dual role in global climate governance. On the one hand, as the “world’s factory”, China’s production-side carbon emissions are enormous; on the other hand, the rapid expansion of domestic consumption is gradually shifting the burden towards consumer responsibility. The decline of the dynamic allocation coefficient—from 0.47 in 2009 to 0.33 in 2018—illustrates China’s transition from passively absorbing carbon emissions to actively guiding low-carbon transformation, thereby offering a new paradigm for global decarbonization. Thus, China’s high share of SR emissions among the BRICS countries not only poses a major challenge for global climate governance, but also serves as a catalyst for innovative mechanisms.

Recognizing the differential impacts of various carbon accounting methods on national interests, it is essential for BRICS countries to adopt tailored negotiation strategies at international climate forums. For instance, nations with high export-related emissions, such as China, could advocate for shared responsibility frameworks that fairly balance producer and consumer obligations. In contrast, countries with predominantly import-driven emissions, like India, might push for mechanisms that emphasize consumer responsibility, thereby ensuring equitable carbon attribution. By aligning negotiation tactics with their unique economic profiles and emission dynamics, the BRICS countries can more effectively secure terms that advance both their national interests and the collective goals for global climate mitigation.

5. Conclusions

This study provides a comprehensive analysis of embodied carbon emissions in the maritime transport industry among BRICS countries from 1995 to 2018, highlighting the distinct characteristics under different responsibility frameworks—producer, consumer, and shared responsibility. The findings reveal that export-oriented economies, such as China and South Africa, experience a pronounced amplification of carbon emissions under the producer responsibility framework, while import-dependent nations, such as India and Brazil, tend to see an increase in emissions under the consumer responsibility approach. The shared responsibility mechanism, with its dynamic allocation coefficient α, emerges as a balanced framework that not only quantifies each country’s contribution to the global value chain, but also reflects evolving national economic environments, as demonstrated by shifts in responsibility in response to market fluctuations and domestic policy changes.

Building on these insights, this study opens up several avenues for future discussion and research. First, the empirical findings provide a solid foundation for setting country-specific mitigation targets that account for both production and consumption dimensions. Such targets could be integrated into international negotiations and climate policy frameworks, where technological innovation and the introduction of low-carbon practices play a pivotal role. Additionally, prioritizing the deployment of green technologies—tailored to the unique economic and trade profiles of each nation—could further enhance the efficiency of carbon governance. Future studies might also expand the scope beyond the maritime transport industry to include other sectors, thereby offering a more holistic view of embodied carbon emissions on a global scale. Moreover, extending the analysis to countries outside of the BRICS grouping would provide comparative insights that could enrich international climate policy discussions.

However, this study is not without its limitations. The analysis is confined to the maritime transport sector and focuses solely on the BRICS countries, which may limit the generalizability of the findings to other industries or regions. Furthermore, the allocation of responsibility based on the dynamic coefficient α is subject to uncertainties related to data quality and model assumptions. These limitations suggest that future research should explore alternative methodologies, incorporate broader datasets, and examine additional sectors to validate and expand upon the current findings.

In conclusion, while this study significantly advances our understanding of embodied carbon emissions through the lens of responsibility allocation, it also highlights the need for continued research and policy innovation. By addressing the outlined limitations and exploring new dimensions in future studies, policymakers and researchers can better navigate the complexities of global carbon governance in an increasingly interconnected world.