Is embodied renewable energy transfer greening the global supply chain?

doi:10.21203/rs.3.rs-3325845/v1

Download PDF

Article

Is embodied renewable energy transfer greening the global supply chain?

https://doi.org/10.21203/rs.3.rs-3325845/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Energy underpins international trade, and a greener energy mix can contribute to cleaner global supply chains (GSCs). This paper examines how energy transition has influenced GSC greening through embodied renewable energy transfer. Specifically, we analyze the contribution of countries/regions and sectors to the greening process from 2011 to 2021. We find some evidence of GSC greening over the period as the ratio of renewable energy in total transferred energy increased from 4.2% to 7.6%. Emerging economies that have advanced rapidly in energy transition are gradually overtaking developed countries in their contribution to GSC greening. Sectors with a higher electrification rate are main contributors to GSC greening led by significant inter-sectoral spillovers from the heavy industry and business services. However, transfer of embodied renewable energy by high-income developed countries is concentrated in other high-income countries raising concerns of limited access by low-income developing countries and the amelioration of global energy welfare inequality.

Earth and environmental sciences/Environmental social sciences/Sustainability

Earth and environmental sciences/Environmental social sciences/Environmental economics

renewable energy

fossil fuel

international trade

global supply chain

input-output analysis

structural path analysis

Global energy transition has quickened in pace as more and more countries seek to address climate change and engage in sustainable development. The Paris Agreement aims to limit global warming within 1.5℃^{[1, 2]}. To realize this target, 38–88% of primary energy and 59–97% of electricity must be obtained from renewable resources by 2040–2055 according to Intergovernmental Panel on Climate Change (IPCC) ^{[3, 4]}. Increasing the share of renewables in the global energy mix is also one of the SDG7 targets in the UN 2030 Agenda for Sustainable Development^[5]. Against this backdrop, the total installed wind and PV capacity surged from 292.34 GW in 2011 to 1667.96 GW in 2021, with an average annual increase of 47.06%. This is paralleled by similar increases in renewable energy generation from 908.32 TWh in 2011 to 3657.22 TWh in 2021, with an average annual growth of 14.9%^[6]. The proliferation of renewable energy in the power system has become a key feature of the global energy transition^{[7, 8]}.

As a key driver of global supply chains (GSCs), energy is not only captured through direct commodity trade^{[9, 10]}, but also through energy embodied in products and services in the form of raw materials and fuels^[11]. The latter is estimated to account for about 29–35% of global energy consumption^[12]. As energy transition promotes the penetration of renewables into local electricity grids that in turn alters local energy mix, renewables are also embodied in goods and services in GSCs in the form of electricity^{[13, 14]}. Electricity transforms the global energy mix, exerting an impact on the greening and ‘cleaning’ of GSCs and global sustainable development^{[12, 15]}.

How embodied renewable energy transfer is influencing the greening of GSCs remains understudied. Existing studies have examined embodied energy as a whole or focused on fossil fuels^{[9, 16–18]}. Collectively, they have shed light on the energy footprints of different regions, countries, sectors and income groups^[19–23], as well as the inter-regional^{[9, 16, 24, 25]} and inter-sectoral transfer patterns of embodied energy^[26–29]. They have also focused on energy consumption inequality and carbon leakages related to embodied fossil fuels transfer^{[30, 31]}. But these studies tend to neglect the role of renewable energy in influencing the redistribution of clean energy welfare and GSC greening. Unlike fossil fuels that are embedded into GSCs both in the form of electricity and in the physical form of fuel and power, renewable energy is embedded into GSCs mainly in the form of electricity. Distinct embedding forms are likely to affect energy production and consumption differently between countries at different stages of energy transition, and, by sectors in global supply chains. Yet little is known about renewables’ impact on GSCs greening.

This paper addresses the above lacuna using renewable consumption data from electricity consumption data. Based on the latest energy data from British Petroleum (BP) ^[6], International Energy Agency (IEA) ^{[32, 33]}, and Ember^[34], as well as the latest multi-region input-output table from Eora global supply chain database^{[35, 36]}, we constructed a renewable energy extended multi-region input-output table. We then applied the multi-region input-output model and structural path analysis to analyze GSC greening and cleanness between countries and sector over the 2011–2021 period. The findings can help to inform policy on global climate change and sustainable development.

Evolution of global supply chain greening

From 2011 to 2021, energy embodied in global trade increased from 1525.7 to 1588.5 Mtoe (Fig. 1). This is attributed to embodied renewable energy which rose from 64 to 120.4 Mtoe, and experienced the largest growth rate (88.1%) among the three forms of embodied energy. Accordingly, the ratio of embodied renewable energy in total transferred energy rose from 4.2–7.6%. In contrast, fossil fuel that is largely transferred in the physical form, dropped by 2.5% from 1243.5 to 1212.7 Mtoe, and its ratio in total transferred energy declined from 81.5–76.34%. At the same time, fossil fuel that is transferred in the form of electricity increased by 17.1%. This implies that electricity is increasingly replacing the physical form of fossil fuel, and the substitution of fossil fuel by renewable energy in the electricity grid should be encouraged.

Contribution to global supply chain greening by countries and regions

Over the period, countries with greater renewable energy endowment and an early start in energy transition, such as Canada, Germany, Brazil, Norway, Sweden, Italy and Austria, have been main contributors to GSC greening and progress in “cleaning” international trade. But emerging economies also made significant progress in energy transition, gradually surpassing the former group of developed countries (Fig. 2). In 2011, China was one of the largest countries in the world in pollution contribution rate (PCR=-0.6%). By 2021, with a boom in the installed renewable capacity, China overtook Canada as the top country in green contribution rate (GCR = 0.86%). It exported 35.2 Mtoe of embodied renewable energy, accounting for 29% of global share as the world's largest exporter, exceeding second-ranking Canada’s rate (7.4 Mtoe, 6.1%) by a wide margin (Fig. 2b&d). Vietnam is another example. Renewable energy embodied in its export showed a large increase from 0.8 Mtoe to 3.9 Mtoe. Correspondingly, Vietnam’s GCR also rose from 0.007 to 0.03%. On the other hand, African countries like Zambia and Ethiopia may be abundant in hydropower resources, but their contribution to GSC greening is limited (GCR between 0-0.001%) because they are relatively small traders and insignificant participants in global supply chains and international trade.

Among newly industrialized countries, Russia was the world's ninth largest contributor to GSC greening in 2011 (GCR = 0.02%) but reversed its position by 2021 to become the third largest contributor to pollution (PCR=-0.1%). India’s pollution contribution worsened over the period (from − 0.02 to -0.1%). India’s case is common among developing countries in Africa and Asia where there has been little progress in GSC greening. But developed countries like the United States and Japan also contributed to GSC pollution (Fig. 2a&c) although the rate appears to be falling slowly. In spite of the ramp up of global energy transition, the pace of greening varies not only between developed and developing countries, but also within the two groups.

Among the top ten countries in 2021, embodied renewable energy export was less concentrated in a single region for the three emerging economies of China, Brazil and Vietnam (Fig. 3a). China exported 21.7, 21.6, 18.0, and 14.3% of its total embodied renewable energy to North America, Latin America, Europe and Southeast Asia, respectively. Similarly, the results did not show high regional concentration for Brazil and Vietnam. On the other hand, the transfer pattern was highly concentrated in a region in the seven developed countries. For example, Canada exported 59% of its total to North America (the US and Bermuda) alone. Likewise, Austria’s export of 54% was concentrated in Europe (Fig. 3a). In other words, developed countries tended to export to other developed countries. Overall, embodied renewable energy benefits areas that have actively participated in international trade. Developing countries that are poorly integrated in global supply chains especially in Africa and Central Asia are unlikely to access embodied renewable energy through trade.

Not surprising, embodied renewable energy exported by developed countries in Fig. 3b was also concentrated in high-income countries compared to emerging economies, echoing Fig. 3a. Over 60% of renewable energy transfer targeted other high-income countries compared to slightly over 50% by China. Among the ten countries, China exported the highest share to lower middle income countries. The figure also shows negligible renewable energy transfer to low-income countries, that is less than 1% from each country. Renewable energy welfare for low-income developing countries is an issue of concern because it contributes to global energy inequality.

Sectoral contribution to global supply chain greening

Figure 4 shows that the share of embodied renewable energy in total exported embodied energy grew by over 39% (Fig. 4a). But considerable differences exist between sectors due to heterogeneity in energy consumption volume and energy transition pace of different sectors.

Metal products (GCR = 1.9%, 2021), electrical and machinery (0.5%), wood and paper (0.3%), financial intermediation and business activities (0.2%) and mining and quarrying (0.1%) were major sectors that contributed to GSC greening. The sectors were all characterized by high electrification rates (Fig. 4b) that exceeded 31% in 2021. Among them, electrical and machinery had the highest electrification rate at 65%. A high electrification rate enables renewable energy to be embedded into the electricity grid increasing the potential for fossil fuel substitution. Hence embodied renewable energy dominated in terms of relative shares in total exported embodied energy, that is over 10% (Fig. 4a). In service sectors like public administration and hotels and restaurants, the ratio of embodied renewable energy in total exported embodied energy and their electrification rate might be high, but their contribution to GSC greening was limited because trade level was lower.

In contrast, the electrification rates were low for transport and petroleum, chemical and non-metallic mineral products, and they were the top two sectors contributing to GSC pollution. While the contribution to GSC pollution increased for petroleum, chemical and non-metallic mineral products, it slightly fell for transport, with PCR down from − 15% to -10%. To a certain degree, the push for electrification of the transport sector worldwide has alleviated its contribution to pollution. Transport not only led in growth of electrification rate (57%), its ratio of embodied renewable energy in total exported embodied energy also experienced a high growth rate of 122%, and the fossil fuel embodied in its export reduced by 100 Mtoe (Fig. 4b, c&d). But the transition of transport remains a long way to go given its electrification rate is still the lowest among all sectors.

Contribution to GSC greening by different sectors not only differs in quantity (Fig. 4), but also in scope (Fig. 5). As some sectors improved their degree of greening (Fig. 4a), they also transferred renewable energy to other sectors through inter-sectoral linkages and spillovers. Figure 5 visualizes renewable energy flows by decomposing the entire supply chain. We present the chain segments in terms of layers by dividing them into upstream, midstream and downstream production layers. The figure shows that heavy industry and business services were the top two sectors that impacted greening. Heavy industry acts as a transit node of renewable energy, exerting influence on downstream sectors. Moreover, its impact on greening covered all layers across the global supply chain (i.e., when heavy industry acts as a producer of raw materials in the upstream layer, a manufacturer of intermediate goods in the midstream layer, or as a provider of final products in the downstream layer, it always has an impact on the greening of other sectors). Take year 2021 as an example. At upstream (PL³), mid-upstream (PL²) and mid-downstream (PL¹) layers, heavy industry transferred 53.27%, 51.42% and 50.57% of embodied renewable energy to other sectors through inter-sectoral linkages (e.g., electrical and machinery→retail trade, metal products→construction (Table 1)), that in turn improved the greening of sectors like business services and construction. On the other hand, while the service sector also serves as a transit node of renewable energy, its influence is on upstream sectors, particularly both light and heavy industries. The sector’s impact on greening is concentrated at midstream; more specifically at mid-upstream (PL²) and mid-downstream (PL¹) layers, (i.e., when the service sector acts as a buyer of raw materials or intermediate goods in the midstream layer, it has an impact on the greening of upstream sectors), a total of 47.56% and 45.92% embodied renewable energy were transferred into business services (e.g., metal products→maintenance and repair, metal products→wholesale trade (Table 1)).

Table 1

Top five flows of renewable energy between sectors at different layers of supply chain
Layer	Outflow sector	Inflow sector	Flow/Mtoe	Share/%
PL³→PL²	Metal Products	Maintenance and Repair	11.6	16.2
	Electrical and Machinery	Retail Trade	4.0	5.6
	Metal Products	Wholesale Trade	3.5	4.9
	Financial Intermediation and Business Activities	Maintenance and Repair	2.3	3.3
	Mining and Quarrying	Electrical and Machinery	2.3	3.2
PL²→PL¹	Metal Products	Maintenance and Repair	15.5	13.0
	Electrical and Machinery	Retail Trade	7.0	5.9
	Metal Products	Wholesale Trade	6.3	5.3
	Financial Intermediation and Business Activities	Maintenance and Repair	4.3	3.6
	Mining and Quarrying	Electrical and Machinery	3.6	3.0
PL¹→PL⁰	Metal Products	Construction	17.0	7.8
	Petroleum, Chemical and Non-Metallic Mineral Products	Construction	11.2	5.2
	Electrical and Machinery	Electrical and Machinery	11.1	5.1
	Metal Products	Electrical and Machinery	11.0	5.1
	Agriculture	Food & Beverages	6.6	3.0

From 2011 to 2021, the share of embodied renewable energy in total transferred embodied energy increased from 4.2% to 7.6% pointing to some greening of global supply chains. Greening contribution varies by country/region and sectors. Heterogeneity exists for the contribution to GSC greening not only between developed and developing countries, but also within the two groups. This is partly owed to their difference in the pace of energy transition and their trade volume in the GSC. Four groups of countries should be highlighted.

The first group consists of green contributors from developing countries, represented by China and Vietnam, which are undergoing rapid energy transition. The relatively high share of renewables in the electricity mix and great global trade volume enable them to make an increasing contribution to GSC greening. As renewable energy and fossil fuel are embodied in the export of relatively symmetrical sectors, predominantly the manufacturing sectors (Fig. SI1), the substitution of fossil fuel by renewable energy in the manufacturing sectors should be encouraged to further enhance these countries’ contribution to GSC greening.

The second group is pollution contributors in developing countries, represented by India and Russia. They progress slowly in energy transition and their exports are dominated by primary products or energy-intensive products^[37]. Accelerating energy transition, such as replacing coal with renewable energy in India’s electricity mix^[15], along with adjusting the export structure, will help these countries shift from being pollution contributors to green contributors. This group also includes many low-income countries in Asia and Africa. They experience low accessibility to renewables, exacerbated by an imbalanced global trading system which is difficult to change, at least in the short run. Renewable energy infrastructure remains underdeveloped in these countries^[38] contributing to global energy welfare inequality^[21]. Addressing energy transition heterogeneity that focuses on developing countries’ low electrification rate is an urgent task. This may involve leveraging the power of institutions such as multilateral development banks that prioritizes renewable energy generation projects in these countries^[39].

In the third group, green contributors from developed countries are captured in countries like Canada and Norway. Despite a relatively small trade volume, these countries lead the world in energy transition, contributing significantly to GSC greening. As renewable energy is mainly embodied in their export of manufacturing sectors (Fig. SI1), they could potentially consider manufacturing reshoring to developed countries^[40], leverage the competitive advantage of the greener nature of their products^[41], so as to expand their global trade volume and enhance their contribution to GSC greening.

The final group is pollution contributors from developed countries such as the US and Japan. The pace of energy transition in these countries lags behind the previous group. Their export structure is relatively asymmetric. Renewable energy is embodied in the export of the tertiary sector, which has a smaller global trade volume, whereas fossil fuel is embodied in the export of the secondary sector, which has a much larger global trade volume (Fig. SI1). Consequently, their pollution contribution exceeds their green contribution. To shift from being pollution contributors to green contributors, they should further leverage the competitive advantage of the tertiary sector and export more renewable energy through it. As for the secondary sector, since they already have an advantage in producing products with high energy efficiency and low carbon intensity compared to developing countries^[42], they should give attention to the sector to further reduce fossil fuel consumption while increasing the use of renewable energy in the sector.

The results show that sectors with a higher electrification rate are the main contributors to GSC greening and both the heavy industry and business services act as renewable energy transit nodes through inter-sectoral spillovers. By comparing the flow path of fossil fuel (Fig. SI2) and carbon^[43] in the GSC, we found that transport is a critical transit node in the flow path of both fossil fuel and carbon but not as important for renewable energy. This contrasts with the heavy industry and business services which are vital transit nodes in all three flow paths of renewable energy, fossil fuel and carbon. Hence, improving the energy mix of heavy industry and business services will help green and reduce carbon emission of other sectors through inter-sectoral spillovers in three flow paths. Moreover, given the importance of transport in the flow path of both fossil fuel and carbon, advancing the electrification transition or using cleaner new fuel will also contribute to GSC greening. In addition, the adoption of SSP (sustainable outsourcing practices)^[44] by multinational firms should be promoted to better integrate upstream and downstream supply chain greening.

Overall, while our study suggests that the embodied energy mix of GSC is greening, as climate mitigation is a global concern, we should further discuss whether a greener embodied energy mix will help carbon reduction. On the one hand, as the emission factor of renewable energy is lower than fossil fuel, a greener embodied energy mix will help reduce GHG emission^[45]. On the other hand, as international trade leads to a separation between production and consumption, and the pace of energy transition varies across countries, countries with a greener energy mix tend not to be major participants in GSC (i.e., they are smaller players in global production and trade). But they are more likely to transfer energy-intensive and carbon-intensive production activities to countries lagging behind energy transition. This hinders the progress of global carbon reduction^[46,47]. In short, the heterogeneity of energy efficiency and carbon intensity between countries impact global carbon reduction^[42]. It suggests that the nexus of energy-carbon-climate change merits further study. In sum, future work could examine the trade-off between the benefits of GSC greening, and, environmental and social burdens related to energy transition such as material footprints^[15,48], and issues of population displacement^[49], so as to better understand the realization of SDG from multidimensional perspectives.

Finally, some caveats should be noted, specifically the collation of renewable energy data. In the paper, renewable energy data consists of processed electricity consumption data of each sector in a country or region. We do not map the energy data to the electricity supply sector in the input-output table in order to avoid unnecessary double counting arising from energy conversion from primary to secondary energy. More detailed renewable energy database is needed to better understand GSC greening.

Data sources

The multi-region input-output table is obtained from the Eora Global Supply Chain Database^{[35, 36]}(https://worldmrio.com/). The latest electricity mix data of each country or region of 2021 is from bp Statistical Review of World Energy 2022^[6], World Energy Balances Highlights 2022^[33] and Ember^[34]. Specifically, the generation of renewable energy is associated with hydro, geothermal, wind, solar, biomass and tidal sources. The latest final electricity consumption and final traditional fossil fuels consumption data are from World Energy Balances^[32] and data of 2011 and 2020 are used in our study (data of 2021 is not yet available).

Build energy extension for Eora input-output table

We assume that all renewable and traditional fossil fuel sources are connected to the grid and there are no losses from the generation to consumption of electricity. Then the level of renewable energy and traditional fossil fuels consumed in the form of electricity (uniformly converted into energy units Mtoe) in each country or region can be obtained via the multiplication of energy mix and final electricity consumption. As electricity is a secondary energy source that undergoes a process of conversion, double counting may arise while mapping energy data to electricity supply sector and other sectors in the input-output table. Additionally, primary, secondary and tertiary sectors are major carriers of embodied energy in global trade and energy consumed in electricity supply sector tends to be more domestic. Considering all these, we focus on the final energy consumption of sectors other than the electricity, water and gas supply sector of each country or region.

To map final consumption of renewable energy and traditional fossil fuels in IEA to 26 sectors in the Eora input-output table, a 0–1 mapping matrix is constructed based on sectors defined by the IEA and Eora database^[50] (sectors of the electricity, water and gas supply, private households and re-export and re-import are excluded, see Supplementary Table 1 for details). Three mapping rules are imposed. First, if a sector in IEA and a sector in Eora correspond to one another, the final energy consumption of the sector in IEA is mapped to the corresponding sector in Eora directly. Second, if a sector in IEA covers multiple sectors in Eora, then each sector in Eora is mapped with final energy consumption data from IEA according to their economic output values (under the assumption that the final energy consumption of each sector is positively related to their economic output value). Third, if multiple sectors in IEA correspond to one sector in Eora, the final energy consumption of these sectors in IEA will be aggregated, and then mapped to the sector in Eora.

Multi-regional Input and Output Analysis, MRIO

Multi-region input-output analysis provides a comprehensive and systematic picture of direct and indirect economic linkages among regions and industries and is widely used to calculate economic output and resource consumption in global trade networks^[19]. We integrate energy data to construct an environmentally extended multi-region input-output table that accounts for embodied renewable energy flows in 189 countries (regions) in 2021. The equations are expressed as follows^{[19, 22]}.

\({x}_{i}^{r}\) denotes the monetary value of total outputs of sector i in region r. \({z}_{ij}^{rs}\) represents the intermediate inputs from sector i in region r to sector j in region s. \({y}_{i}^{rs}\) indicates the outputs of sector i in region r to satisfy the final demand of sector j in region s. m denotes the number of regions and n denotes the number of sectors.

\({x}_{i}^{r}={\sum }_{s=1}^{m}{\sum }_{j=1}^{n}{z}_{ij}^{rs}+{\sum }_{s=1}^{m}{y}_{i}^{rs}\)

(1)

We define matrix \(\widehat{X}\) as total outputs, matrix Z as intermediate inputs and matrix Y as final demand. Then Eq. (1) can be transformed to Eq. (2).

\(\widehat{X}=Z+\widehat{Y}\)

(2)

\({d}_{i}^{r}+{\sum }_{s=1}^{m}{\sum }_{j=1}^{n}{e}_{j}^{s}{z}_{ji}^{sr}={e}_{i}^{r}{x}_{i}^{r}\)

(3)

We define matrix D as direct energy consumption and matrix E as embodied energy intensity. Then Eq. (3) can be transformed to Eq. (4).

\(D+EZ=E\widehat{X}\)

(4)

According to Eq. (2) and Eq. (4), embodied energy intensity can be expressed as Eq. (5).

\(E=D(\widehat{X}-Z{)}^{-1}\)

(5)

Then energy embodied in imports (EEI) and exports (EEX) of region r can be expressed as follows.

\({EEI}^{r}={\sum }_{i=1}^{n}E{EI}_{i}^{r}={\sum }_{i=1}^{n}{\sum }_{s=1(s\ne r)}^{m}\left({e}_{i}^{s}{y}_{i}^{sr}\right)\)	(6)
\(EE{X}^{r}={\sum }_{i=1}^{n}EE{X}_{i}^{r}={\sum }_{i=1}^{n}{\sum }_{s=1(s\ne r)}^{m}\left({e}_{i}^{r}{y}_{i}^{rs}\right)\)	(7)

Structural Path Analysis, SPA

SPA is used to decompose the supply chain into a series of production chain layers, track energy, carbon, water and other resources embodied in purchases from downstream products by final demand to the input of upstream intermediate products, and identify the key flow paths of resources embodied in the supply chain^{[29, 51, 52]}. Here, we apply SPA to calculate the direct and indirect renewable energy consumption by sectors at different production layers in the global supply chain and explore the flow pattern of embodied renewable energy between different sectors. To simplify the analysis, we aggregate results based on 26 sectors into 8 sectors (See more details in supplementary table 2). The equations are explained below^[52].

According to Taylor series approximation, the Leontief inverse matrix L can be unpacked into different production layers. The element L_pq of L represents the direct and indirect output from sector p needed to satisfy unit output from sector q. A denotes the direct requirements matrix, of which the element a_pq indicates the direct output from sector p needed to satisfy unit output from sector q^[52].

\(L={(I-A)}^{-1}=I+A+{A}^{2}+{A}^{3}+{A}^{4}+\dots \dots +{A}^{t}=0\)

(8)

We define f as energy intensity matrix and matrix m can be calculated as Eq. (9), where the element m_q represents the direct and indirect energy consumed in unit output from sector q.

\(m=f{(I-A)}^{-1}=fL\)

(9)

Then, the direct energy consumption by sectors at different production layer PL can be calculated as follows (Table 1), where y denotes the final demand matrix, i, j, k and l represent different sectors, and 0, 1, 2 and 3 represent different production layers from downstream to upstream. And energy flow \({E}_{ij}^{t+1\to t}\) between different sectors at different production layers can be calculated as follows (Table 2).

Table 1

Direct energy consumption equation by sectors at different production layers
Production layer	Calculation equation
PL⁰	\({D}_{i}^{0}={f}_{i}{y}_{i}\)	(10)
PL¹	\({D}_{j}^{1}={f}_{j}{A}_{j:}y\)	(11)
PL²	\({D}_{k}^{2}={f}_{k}{A}_{k:}Ay\)	(12)
PL³	\({D}_{l}^{3}={f}_{l}{A}_{l:}{A}^{2}y\)	(13)

Table 2

Energy flow between different sectors at different production layers
Production layer	Calculation equation
PL¹->PL⁰	\({E}_{ji}^{1\to 0}={m}_{j}{A}_{ji}{y}_{i}\)	(14)
PL²->PL¹	\({E}_{kj}^{2\to 1}={m}_{k}{A}_{kj}{A}_{j:}y\)	(15)
PL³->PL²	\({E}_{lk}^{3\to 2}={m}_{l}{A}_{lk}{A}_{k:}Ay\)	(16)

Green contribution rate and Pollution contribution rate

We construct the indicator of green contribution rate (GCR) and pollution contribution rate (PCR) to evaluate a country/region or sector’s contribution to the greening of global supply chains (GSCs). We assume that the larger the amount of renewable energy embodied in the global trade, the greener or cleaner the GSCs. The renewable energy embodied in the export of each country/region or sector will contribute to GSC greening, while the fossil fuel embodied in the export of each country/region or sector will impede GSC greening. Our indicators are as follows:

\(GCR=\left(\frac{{REX}_{r\left(i\right)}}{REX}-\frac{{FFX}_{r\left(i\right)}}{FFX}\right)*\frac{{REX}_{r\left(i\right)}}{REX} , if \frac{{REX}_{r\left(i\right)}}{REX}-\frac{{FFX}_{r\left(i\right)}}{FFX}>0\)	(17)
\(PCR= \left(\frac{{REX}_{r\left(i\right)}}{REX}-\frac{{FFX}_{r\left(i\right)}}{FFX}\right)*\frac{{FFX}_{r\left(i\right)}}{FFX} , if \frac{{FFX}_{r\left(i\right)}}{FFX}-\frac{{REX}_{r\left(i\right)}}{REX}>0\)	(18)

where \(REX\) represents the total amount of renewable energy transferred in global trade, \({REX}_{r\left(i\right)}\) represents the renewable energy embodied in the export of region r or sector i (this can be calculated according to Eq. (7)), \(FFX\)represents the total amount of fossil fuel transferred in global trade, and \({FFX}_{r\left(i\right)}\)represents the fossil fuel embodied in the export of region r or sector i (this can be calculated according to Eq. (7)).

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

UN. Global warming of 1.5°C[R/OL]. (2015)[2023-02-20]. https://unfccc.int/resource/docs/2015/cop21/eng/l09r01.pdf.
Mercure J F, Salas P, Vercoulen P, et al. Reframing incentives for climate policy action[J]. Nature Energy, 2021, 6(12): 1133-1143.
IPCC. Global Warming of 1.5 oC —[R/OL]. (2019)[2023-02-20]. https://www.ipcc.ch/sr15/.
Wang M, Mao X, Xing Y, et al. Breaking down barriers on PV trade will facilitate global carbon mitigation[J]. Nature Communications, 2021, 12(1): 6820.
Fuso Nerini F, Tomei J, To L S, et al. Mapping synergies and trade-offs between energy and the Sustainable Development Goals[J]. Nature Energy, 2017, 3(1): 10-15.
BP. Statistical Review of World Energy 2022[R/OL]. (2022)[2023-02-28]. https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html.
Mitchell C. Momentum is increasing towards a flexible electricity system based on renewables[J]. Nature Energy, 2016, 1(2): 15030.
Markard J. The next phase of the energy transition and its implications for research and policy[J]. Nature Energy, 2018, 3(8): 628-633.
Song Z, Zhu Q, Han M. Tele-connection of global crude oil network: Comparisons between direct trade and embodied flows[J]. Energy, 2021, 217(10): 119359.
Yang Y, Poon J P H, Liu Y, et al. Small and flat worlds: A complex network analysis of international trade in crude oil[J]. Energy, 2015, 93: 534-543.
Costanza R. Embodied Energy and Economic Valuation[J]. Science, 1980, 210(4475): 1219-1224.
Wiedmann T, Lenzen M. Environmental and social footprints of international trade[J]. Nature Geoscience, 2018, 11(5): 314-321.
Wang Q, Jiang F, Li R. Assessing supply chain greenness from the perspective of embodied renewable energy – A data envelopment analysis using multi-regional input-output analysis[J]. Renewable Energy, 2022, 189: 1292-1305.
He J, Yang Y, Liao Z, et al. Linking SDG 7 to assess the renewable energy footprint of nations by 2030[J]. Applied Energy, 2022, 317: 119167.
Peng K, Feng K, Chen B, et al. The global power sector’s low-carbon transition may enhance sustainable development goal achievement[J]. Nature Communications, 2023, 14(1): 3144.
Chen B, Li J S, Wu X F, et al. Global energy flows embodied in international trade: A combination of environmentally extended input–output analysis and complex network analysis[J]. Applied Energy, 2018, 210: 98-107.
Wu X F, Chen G Q. Coal use embodied in globalized world economy: From source to sink through supply chain[J]. Renewable and Sustainable Energy Reviews, 2018, 81: 978-993.
Kan S Y, Chen B, Wu X F, et al. Natural gas overview for world economy: From primary supply to final demand via global supply chains[J]. Energy Policy, 2019, 124: 215-225.
Chen G Q, Wu X F. Energy overview for globalized world economy: Source, supply chain and sink[J]. Renewable and Sustainable Energy Reviews, 2017, 69: 735-749.
Yang L, Wang Y, Wang R, et al. Environmental-social-economic footprints of consumption and trade in the Asia-Pacific region[J]. Nature Communications, 2020, 11(1): 4490.
Oswald Y, Owen A, Steinberger J K. Large inequality in international and intranational energy footprints between income groups and across consumption categories[J]. Nature Energy, 2020, 5(3): 231-239.
Yang Y. Energy globalization of China: Trade, investment, and embedded energy flows[J]. Journal of Geographical Sciences, 2022, 32(3): 377-400.
Guo S, Zheng S, Hu Y, et al. Embodied energy use in the global construction industry[J]. Applied Energy, 2019, 256: 113838.
Chen Z M, Chen G Q. An overview of energy consumption of the globalized world economy[J]. Energy Policy, 2011, 39(10): 5920-5928.
Jiang L, He S, Tian X, et al. Energy use embodied in international trade of 39 countries: Spatial transfer patterns and driving factors[J]. Energy, 2020, 195: 116988.
Tang M, Hong J, Liu G, et al. Exploring energy flows embodied in China’s economy from the regional and sectoral perspectives via combination of multi-regional input–output analysis and a complex network approach[J]. Energy, 2019, 170: 1191-1201.
Zhang B, Qu X, Meng J, et al. Identifying primary energy requirements in structural path analysis: A case study of China 2012[J]. Applied Energy, 2017, 191: 425-435.
Zhang Y, Chen Q, Chen B, et al. Identifying hotspots of sectors and supply chain paths for electricity conservation in China[J]. Journal of Cleaner Production, 2020, 251: 119653.
Shi J, Li C, Li H. Energy consumption in China’s ICT sectors: From the embodied energy perspective[J]. Renewable and Sustainable Energy Reviews, 2022, 160: 112313.
Akizu-Gardoki O, Wakiyama T, Wiedmann T, et al. Hidden Energy Flow indicator to reflect the outsourced energy requirements of countries[J]. Journal of Cleaner Production, 2021, 278: 123827.
Zhang Z, Guan D, Wang R, et al. Embodied carbon emissions in the supply chains of multinational enterprises[J]. Nature Climate Change, 2020, 10(12): 1096-1101.
IEA. World Energy Balances[R/OL]. (2019)[2023-02-23]. https://www.iea.org/data-and-statistics/data-product/world-energy-balances.
IEA. World Energy Balances Highlights[R/OL]. (2022)[2023-02-23]. https://www.iea.org/data-and-statistics/data-product/world-energy-balances-highlights.
Ember. Global Electricity Review 2021 Explorer[EB/OL]//Ember. (2021)[2023-04-15]. https://ember-climate.org/data/global-electricity/.
Lenzen M, Moran D, Kanemoto K, et al. Building Eora: A Global Multi-Region Input–Output Database at High Country and Sector Resolution[J]. Economic Systems Research, 2013, 25(1): 20-49.
Lenzen M, Kanemoto K, Moran D, et al. Mapping the Structure of the World Economy[J]. Environmental Science & Technology, 2012, 46(15): 8374-8381.
Ren B, Li H, Shi J, et al. Identifying the key sectors and paths of the embodied energy in BRICS nations: A weighted multilayer network approach[J]. Energy, 2022, 239: 122091.
Mutezo G, Mulopo J. A review of Africa’s transition from fossil fuels to renewable energy using circular economy principles[J]. Renewable and Sustainable Energy Reviews, 2021, 137: 110609.
Steffen B, Schmidt T S. A quantitative analysis of 10 multilateral development banks’ investment in conventional and renewable power-generation technologies from 2006 to 2015[J]. Nature Energy, 2019, 4(1): 75-82.
Liang H, You F. Reshoring silicon photovoltaics manufacturing contributes to decarbonization and climate change mitigation[J]. Nature Communications, 2023, 14(1): 1274.
Li R, Wang Q, Wang X, et al. Germany’s contribution to global carbon reduction might be underestimated – A new assessment based on scenario analysis with and without trade[J]. Technological Forecasting and Social Change, 2022, 176: 121465.
Meng J, Huo J, Zhang Z, et al. The narrowing gap in developed and developing country emission intensities reduces global trade’s carbon leakage[J]. Nature Communications, 2023, 14(1): 3775.
Xu D, Zhang Y, Chen B, et al. Identifying the critical paths and sectors for carbon transfers driven by global consumption in 2015[J]. Applied Energy, 2022, 306: 118137.
Thorlakson T, de Zegher J F, Lambin E F. Companies’ contribution to sustainability through global supply chains[J]. Proceedings of the National Academy of Sciences, 2018, 115(9): 2072-2077.
Zheng S, Zhou X, Tan Z, et al. Assessment of the global energy transition: Based on trade embodied energy analysis[J]. Energy, 2023, 273: 127274.
Meng J, Mi Z, Guan D, et al. The rise of South–South trade and its effect on global CO2 emissions[J]. Nature Communications, 2018, 9(1): 1871.
Ang B W, Su B. Carbon emission intensity in electricity production: A global analysis[J]. Energy Policy, 2016, 94: 56-63.
Fu R, Peng K, Wang P, et al. Tracing metal footprints via global renewable power value chains[J]. Nature Communications, 2023, 14(1): 3703.
Owen J R, Kemp D, Lechner A M, et al. Energy transition minerals and their intersection with land-connected peoples[J]. Nature Sustainability, 2023, 6(2): 203-211.
IEA. World EnergyBalance Database documentation[R/OL]. (2022). https://www.iea.org/subscribe-to-data-services/world-energy-balances and-statistics.
Lenzen M. Structural path analysis of ecosystem networks[J]. Ecological Modelling, 2007, 200(3-4): 334-342.
Skelton A, Guan D, Peters G P, et al. Mapping Flows of Embodied Emissions in the Global Production System[J]. Environmental Science & Technology, 2011, 45(24): 10516-10523.

There is NO Competing Interest.

Supplementaryinformation.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Is embodied renewable energy transfer greening the global supply chain?

Status:

Version 1

Abstract

Figures

Introduction

Evolution of global supply chain greening

Contribution to global supply chain greening by countries and regions

Sectoral contribution to global supply chain greening

Discussion

Methods

Data sources

Build energy extension for Eora input-output table

Multi-regional Input and Output Analysis, MRIO

Structural Path Analysis, SPA

Green contribution rate and Pollution contribution rate

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1