metadata: authors: 0: author: Rosenow,Samuel Kaspar
1: author: Mealy,Penelope Ann
count: World
docna: 0: docna: Turning Risks into Rewards : Diversifying
            the Global Value Chains of Decarbonization Technologies
docty: Policy Research Working Paper
abstracts: cdata!: Reaching net-zero emissions by 2050
            requires unprecedented scaling up in the global deployment
            of critical decarbonization technologies, such as solar
            photovoltaics, wind turbines, and electric vehicles (EVs).
            This challenge is currently rife with risks and rewards.
            With global production perceived to be concentrated in a
            small number of countries, mitigating against possible
            supply-side risks has become an urgent policy priority for
            many countries. At the same time, these
            technologies&#x2019; high-growth potential offer
            lucrative rewards for countries able to strategically
            position themselves to produce requisite materials,
            components or assemble final products. As green industrial
            policies have become an increasingly popular tool for
            shoring up supply chains and stimulating production in key
            green sectors, this paper presents a data-driven framework
            for identifying which countries could have key strengths and
            latent comparative advantages in the production of solar PV,
            wind turbines and EVs. It constructs a new dataset of traded
            products, components, and materials associated with
            decarbonization technologies and develops new indices
            capturing countries&#x2019; current export strengths and
            future diversification potential in the global value chains
            of these technologies. It also highlights products with
            supply risks due to high market concentration levels and
            those with development rewards in terms of their potential
            for growth, knowledge spillovers, and technological
            upgrading. Our analysis suggests that there is plenty of
            opportunity to diversify these value chains across a larger
            number of countries and reduce risks associated with
            reliance on only a few countries.
display_title: Turning Risks into Rewards :
            Diversifying the Global Value Chains of Decarbonization Technologies
listing_relative_url: /research/2024/02/34254084/turning-risks-rewards-diversifying-global-value-chains-decarbonization-technologies
topicv3: Trade m1326924 698,Technology and Innovation m1327233 389
publishtoextweb_dt: 2024-02-07T00:00:00Z
dois: 10.1596/1813-9450-10696
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 32
author_keys: 0: author_key: 1318546
1: author_key: 2025999
model_extractions: Policy Research Working Paper 10696 Turning Risks into Reward Diversifying the Global Value Chains of Decarbonization Technologies _Samuel Rosenow Penny Mealy_ International Finance Corporation February 2024
Policy Research Working Paper 10696

# **Abstract**

Reaching net-zero emissions by 2050 requires unprecedented scaling up in the global deployment of critical decarbonization technologies, such as solar photovoltaics, wind turbines, and electric vehicles (EVs). This challenge is currently rife with risks and rewards. With global production perceived to be concentrated in a small number of countries, mitigating against possible supply-side risks has become an urgent policy priority for many countries. At the same time, these technologies’ high-growth potential offer lucrative rewards for countries able to strategically position themselves to produce requisite materials, components or assemble final products. As green industrial policies have become an increasingly popular tool for shoring up supply chains and stimulating production in key green sectors, this paper presents a data-driven framework for identifying which countries could have key strengths and latent comparative advantages in the production of solar PV, wind turbines and EVs. It constructs a new dataset of traded products, components, and materials associated with decarbonization technologies and develops new indices capturing countries’ current export strengths and future diversification potential in the global value chains of these technologies. It also highlights products with supply risks due to high market concentration levels and those with development rewards in terms of their potential for growth, knowledge spillovers, and technological upgrading. Our analysis suggests that there is plenty of opportunity to diversify these value chains across a larger number of countries and reduce risks associated with reliance on only a few countries.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at srosenow@ifc.org and pmealy@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Turning Risks into Rewards: Diversifying the Global Value Chains of Decarbonization Technologies<sup>1</sup>

Samuel Rosenow<sup>a</sup> and Penny Mealy<sup>b</sup> Originally published in the Policy Research Working Paper Series on _February 2024._ This version is updated on _June 2024._ To obtain the originally published version, please email prwp@worldbank.org.

JEL classification: F14, F18, Q55 Keywords: Empirical Studies of Trade; Trade and Environment; Technological Innovation.

1 We thank Stephane Hallegatte, Ralf Martin, Zeinab Partow, Maryla Maliszewska, Nadia Rocha, Ana Fernandes, Michael Ferrantino, Emmanuel Pouliquen, Esther Naikal and Camilla Knudsen for comments. Aicha Lompo and Camille Da Piedade provided excellent research assistance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank or its affiliated organizations, or those of the Executive Directors of the World Bank, their Managements, or the governments they represent.

a b Economist, International Finance Corporation, srosenow@ifc.org. Senior Economist, World Bank, <u>pmealy@worldbank.org</u>
# 1. Introduction

For the world to reach net-zero emissions by 2050, the global deployment of low-carbon technologies such as solar photovoltaics (PV), wind turbines and electric vehicles (EVs) needs to dramatically increase. Current projections suggest growth in installed capacity in solar and wind will need to increase by around 3-5-fold between now and 2030, while 18-fold increases are projected for the global scale-up of EVs (IEA, 2021). Unlike technologies such as nuclear and carbon capture, usage and storage (CCUS), persistent cost declines in solar PV, wind turbines and EVs paint a promising and predictable future for their deployment: the more we produce globally of these technologies, the cheaper they become (Way et al., 2022; Lam and Mercure, 2022).

A key risk however, is that production is not particularly ‘global’, but instead concentrated in a small number of countries. Recent analysis indicated that one out of every solar panels produced worldwide is produced by a single facility (IEA, 2022b) and over half of the cobalt, lithium and graphite processing and refining capacity for EV batteries are located in a single country (IEA, 2022c). With global value chains inherently vulnerable to disruptions such as natural disasters, pandemics, conflict and geopolitical events, such high concentration levels could create bottlenecks and supply-side shortages that could slow the roll out of these technologies around the world.

Despite calls for more diversified value chains in decarbonization technologies (IEA, 2023; IMF, 2022), there has been limited work to identify the countries that are best placed to increase their participation in the production of these technologies or to highlight what the growth opportunities could look like for individual countries. With industrial policy becoming a more popular policy tool – and with many recent industrial policies aiming to target key products or components in the value chains of these green technologies, it is helpful to have a sense of where latent comparative advantages are more likely to exist.

To address this gap, this paper makes several contributions. First, we construct a new dataset of key traded products, components and materials associated with solar PV, wind turbines and EVs and map this to country trade data. This dataset enables the exploration of historical and current trade patterns for over 200 countries between 1995-2021 in these global value chains. We focus these specific value chains for three reasons. First, worldwide consensus exists that these technologies are critical in the green transition, irrespective of countries’ economic conditions and political alignment. This contrasts with green and environmental goods whose classification can sometimes be controversial and subject to countries’ political sensitivities. Second, participating in the trade of these value chains offers important economic advantages for countries. As global demand is beginning to shift away from fossil-fuel based production and towards these technologies, developing the capabilities to competitively produce products and associated components can help countries achieve greater economic growth and export diversification prospects. This is especially true for technologically sophisticated products and components as they offer advantages for technological upgrading and knowledge spillovers into other industrial areas (Hidalgo & Hausmann, 2009). Third, given the potential supply-side risks facing these value chains, building greater resilience – through diversifying production across a greater number of countries with latent comparative advantage can help mitigate against potential disruptions or shortages in the global roll-out of these technologies. Further, such diversification can also help ensure that the rewards from participating in these high-growth global value chains are shared more broadly.

Second, we develop novel indices that summarize the breadth and depth of countries’ current export strengths in these value chains. While China, Germany and the US are the leaders in export competitiveness across all three technologies, middle-income countries such as Türkiye, Mexico, India, South Africa and Brazil have export strengths in a variety of key value chain products, components and materials and are well positioned to capitalize on the projected future growth in these areas. These insights complement existing work documenting current production trends in supply chains of energy technologies (e.g., IEA, 2022a; IEA, 2022b), but here we go further to develop a similar set of new indices that aim to capture the breadth and depth of countries’ future diversification potential in these value chains. We show that countries scoring higher in potential are significantly more likely to develop greater competitiveness in the subsequent periods. Countries that lead in diversification potential include 2
the Netherlands, France, and Spain, but also upper and lower middle-income countries, such as China, India and Türkiye.

Third we set out an analytical framework to identify countries that could be best placed to help diversify a specific product market. For example, although the global production of glass mirrors, a key component in the solar PV value chain, is highly concentrated in a single country, several countries that could be well positioned to expand their export activity in this product include Lithuania, Poland, India, Türkiye and the Netherlands. These countries already have a reasonably strong track record of exports, high recent growth rates, and production capabilities that are closely aligned with this product. This framework builds on the work of Mealy and Teytelboym (2022) that applied a similar approach to products that exhibit environmental benefits. Moreover, it contributes to the broader literature drawing on data-driven approaches to inform green industrial policy and economic development strategies (Montresor and Quatraro, 2019; Balland et al., 2019).

Our work is not without limitations. First, while our product mapping of value chains associated with decarbonization technologies relies on the 6-digit of the Harmonized System (HS), the most detailed internationally standardized product classification, products may have dual use. This means that a product may have additional applications or purposes beyond those relevant to the value chains of decarbonization technologies. Second, although the product classification of the 6-digit HS is remarkably detailed, a HS 6-digit code is not a single product but an average of differentiated product varieties. As a result, our product definition may be too broad to clearly identify products associated with decarbonization technologies. As it is currently not possible to determine what proportion of trade in each product relates primarily to decarbonization technology usage, the total product export volumes shown in this paper should be considered as an upper bound. The collection of more detailed input, output or supply chain data that is comparable across countries would allow for more accurate depictions of these global value chains. Finally, lags in trade data should also be kept in mind as recent developments and/or interventions are not accounted for.

# 2. Results

## 2.1 Mapping global value chains of key decarbonization technologies

To analyze trade patterns in the global value chains for solar PV, wind turbines and EVs, we collated a new dataset of end products, subcomponents, processed and raw materials classified under the 6-digit HS. The 6-digit HS is a standardized classification of traded products used by customs authorities around the world. It is also the most granular classification that is comparable across almost all countries and over time (see the Methods section for more detail). Figure 1 shows an illustration of the solar PV value chain, with example products listed. Due to the challenges of classifying such products under the 6-digit HS (IISD, 2020), our dataset is not exhaustive but intended to focus on the key identifiable elements of each value chain.<sup>2</sup> As EV production includes products that are also used in internal combustion engine (ICE) vehicles, we construct two sets of value chain products: one more broadly defined and one more narrowly defined. The broader set includes HS products associated with the wider vehicle manufacturing value chain, e.g., products used in either ICE vehicles or EVs. The narrower EV value chain only considers products that relate specifically to EVs, e.g., battery end products and components and the assembled EV end product.

> 2 All products included in our dataset were subject to a series of independent evaluations by selected industry specialists (see Methods section 4.1 for further information and Table SI 6 for a list of the included products).

3
<!-- Start of picture text --> ©) \y<br>ui ~C<br>@) FS ee ‘gg<br>RAW PROECESSED SUB- END<br>MATERIALS MATERIALS COMPONENTS PRODUCT<br>Example Silica Polysilicon Wafer Solar PV Module<br>products: _ Silver Ore Specialty Polymers Solar Cell<br>Silver Paste Frame<br>Glass<br>12 ;<br>' === Solar PV value chain<br>10 ' =—— Wind Turbine value chain<br>' === EV value chain (narrow)<br>H m= EV value chain (broad)<br>8 --- Average HHI<br>a " |<br>aVv 11<br>Ni<br>4<br>‘<br>INN<br>; Tile _—<br>0.0 0.2 0.4 0.6 0.8<br>Herfindahl Hirschman Index (HHI)<br><!-- End of picture text -->
<!-- Start of picture text --> Solar Photovoltaics Wind Turbines Electric Vehicles<br>azn8E , a conteuthatesCopper ores& 100 e Copper anodes 70 © Ironconcent ores ates and ©: Raw'materials .<br>=<br>‘N 80 uS Silver ores & forrefining electrolytic Copper(refined) 60 Manganese ores)4 @@ SubcomponentsProcessed materials<br>£ @ concentrates 80 t ra ‘andiconcentrates: ‘NP @ End product<br>EBx eea Aluminum ores & 3 e 50 2 |®<br>o concentrates @<br>sa= 60 Opticallenses @ 60 esg @e7 grindingIronoe or steelSteel, all 40 e°m e é4<br>B @ e ° balls fared ®° Graphite<br>€2 40 @Cx ) r4Silicon electricInsulatedconductors 49 26@ @ 7) @ | (angles,sections)- shapes & 30 @e° ©Py ‘ x Cobalt oresconcentrates and<br>==) @C224 Ce9 ells,PhotovoltaicOptical Glass mirrors OreRte 0 age Esiale; Magnesium @e@ & Se — Lithium&hydroxide oxide /&Cobalt oxideshydroxides<br>fo)5 ao>° ee devices (framed) . Pedy@®@ transformers (processed) 20 ia eee e P LithiumCa<br>el 20 peo) J jo ’ see? @ © x Rare earths: Electric carbonates<br>E ee e a 20 SA 2_¢ scandium & yttrium accumulators va<br>5 e i Ps ee yan e<br>Zz Glass mirrors(unframed) \ Safety(toughened) glasse tT ) e hd 10 ®<— Chlorides of nickel<br>0.2 0.4 0 0.2 0.4 0.2 0.4 0.6 0.8<br>Herfindahl-Hirschman Index<br>Solar PV Wind Turbines Electric Vehicles<br>oad 0.225<br>Raw materials-CHL PER AUS CHN USA MEX IDN CAN BRA DEU (esIN) RUS DEU USA KOR POL IND ITA CAN JPN BRA ZAF CAN UKR SWE IND USA RUS CHN<br>as 0.200 Om<br>0.175<br>0.30<br>Processed materials-CHN DEU USA JPN KOR CAN ARE FRA ITA BEL -=CHN RUS DEU USA IND ITA JPN KOR TUR ZMB -AUS COD CHN BRA RUS CAN DEU NOR USA NLD 0.3 =<br>eae 0.150 g<br>a<br>a2|0.125 wo<br>| 0.20 a<br>Subcomponents-CHN| JPN DEU USA KOR NLD ITA SGP MEX FRA - 0.15 USA JPN MEX ITA FRA KOR HUN ROU -0,100 ~a1) JPN KOR FRA DEU ESP USA IND NLD MYS F025<br>1 - 0.075<br>- 0.10 - 0.1<br>End products-{egil] mys JPN KOR VNM DEU USA THA NLD SGP DEU USA DNK ESP JPN IND ITA TUR MEX - 0.050 _DEU JPN CHN KOR USA BEL GBR SVK FRA POL<br>- 0.05<br>~ 0.025<br>Top 10 Countries Top 10 Countries Top 10 Countries<br><!-- End of picture text -->
<!-- Start of picture text --> Solar Photovoltaics Wind turbines Electric Vehicles<br>g 50 *lapancermany 50 &Italy eGermany ®south Africa a5<br>g e Romania Sndia “China Japan<br>i‘ 40 China e 15 “Belgium<br>o's Kaui Iczechi® Turkey Py Senn USA<br>af K USA 40 eo &e «; Japan USA F Bazi y<br>2 S Austria® Staly ee Mee, Aystria® @ hain<br>5 = 30 ate ular South Korea Norwaye @<br>es Z spalance 30 isigyh 107 © théia ®australia<br>g = Turkey @ pati en Resi<br>vo 8 20 BP viericoUK ® jalaysiafrailand wee<br>23 > te 20 ‘® Russia eo<br>2 ° Malaysia le? 5 a Income group<br>panea £1= 0 % Indonesia = er= prc @© LowLower income middle income<br>FS ° g © Upper middle income<br>$ 0 0 eee © High income<br>a<br>0 0.05 0.1 0.15 0 0.05 0.1 0.15 0 0.05 0.1 0.15<br>Depth: Country’s average market share across value chain products<br><!-- End of picture text -->
manufacturing sectors. The Democratic Republic of the Congo is the only low-income country in the DTS top 15 index, given its strengths in raw materials (specifically, cobalt) in the EV value chain.

**Table 1: DTS Index: Top 15 countries for each value chain and all value chains overall, by income group in 2021**

|DTS Index|All value chainproducts|Solar PV|Wind Turbines|Electric Vehicles|
|---|---|---|---|---|
|1|**China**|**China**|**China**|**China**|
|2|Germany|Germany|Germany|United States|
|3|United States|Japan|United States|Germany|
|4|Japan|United States|Italy|Japan|
|5|Italy|Korea, Rep.|Japan|**South Africa**|
|6|Korea, Rep.|Italy|**India**|Australia|
|7|France|Austria|Korea, Rep.|**Congo, Dem. Rep.**|
|8|**India**|France|France|France|
|9|Austria|Spain|**Türkiye**|**Brazil**|
|10|Spain|Hong Kong SAR, China|Romania|Belgium|
|11|**Türkiye**|United Kingdom|Spain|Finland|
|12|United Kingdom|**Mexico**|Austria|Korea, Rep.|
|13|Czechia|Czechia|Czechia|Spain|
|14|Sweden|Denmark|Sweden|Canada|
|15|Romania|Belgium|United Kingdom|Netherlands|

2.3 Diversification and development opportunities in decarbonization technologies Having considered countries’ export strengths in the value chains of key decarbonization technologies, we now look to identify countries that are likely to be best placed to help further diversify these value chains. Developing new competitive areas in high-growth value chains can boost market participation, enhance supply chain resilience, and lead to significant economic growth and development.<sup>3</sup> Similar to our approach for identifying countries’ export strengths, we consider two dimensions relating to the breadth and depth of a country’s future diversification opportunity in each value chain. We also summarize the depth and breadth of opportunity dimensions into a single Decarbonization Technology Opportunity (DTO) index that can be compared across countries and over time. As for the DTS index, we convert both depth and breadth opportunity dimensions into z-scores to account for their differential scales and distributions. We then take the simple average of these z-scores to define the DTO index.

The breadth dimension considers the number of products in each value chain for which a country’s RCA falls between 0.1 and 1. This metric aims to identify how many products a country shows some existing export capabilities, but at a level that is still not greater than the global average. In the Appendix section A2, we present results for different RCA thresholds, but results do not differ qualitatively. We refer to these set of products as

> 3 We acknowledge that the exploration of export diversification opportunities for natural resource products differs from that of knowledge-based products. While raw material availability determines the former, the latter hinges on a country’s productive capabilities, such as a skilled labor force, among other factors. Our geographic-based relatedness measure is agnostic about the economic forces driving how countries diversify their export baskets into new products.

7
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# 2.4 Analysis of decarbonization technology indices

We now turn to the question of whether the quantification of countries’ opportunities in key value chains is associated with greater competitive strengths in that value chain in future periods. Here we estimate the relationship between the DTO index and future changes in the DTS index. Our estimation approach takes the following form: ΔDTSc,t−(t−1) = α + βDTOc,t−1 + Xc,t−1′ γ + θt + εc,t (2)

> where ΔDTSc,t−(t−1) represents the 1-year change of country c’s decarbonization technology strength (DTS) index

> in each value chain VC; DTOc,t−1 is country c’s decarbonization technology opportunity (DTO) index in each value chain 1-year earlier. Xc,t−1′ represents a vector of lagged control variables: country c’s baseline DTS index, GDP per

> capita, exports of goods and services to GDP ratio, C02 emissions and population. θt represents year fixed effect. Because we observe limited within-country variation in the DTS and DTS indices for the relatively short period covered by our data, we do not use country fixed effects in our baseline specification. εct captures the random error term. We estimate equation (2) with Ordinary Least Squares (OLS), using robust standard errors.

Table 3 shows the resulting analysis. For each specific value chain, higher DTO scores (representing greater decarbonization opportunities) are positively associated with greater decarbonization strengths (see columns 2-4). We note a small effect size. For example, a one standard deviation increase in a country's EV decarbonization opportunities is associated with a 1.3% increase in its decarbonization strengths (column 2). This result is robust to the inclusion of relevant control variables. Interestingly, aggregating opportunities across value chains remains significantly associated with overall changes in the aggregate DTS index, though this association is weaker (column 1).

The results are robust to alternative lag structures (Appendix Table SI4) and more demanding country fixed effect panel regression (Appendix Table SI5), which increases the explanatory power by an order of magnitude.

|**Table 3: OLS regression re**<br> ΔDD IIIc,t−(t−1)|**sults of equati**<br>(1)<br>All VCs|**on (2)**<br>(2)<br>EV narrow|(3)<br>Solar|(4)<br>Wind|
|---|---|---|---|---|
|<br>DTO Indexc, t-1|0.004*|0.013***|0.008***|0.008***|
||(0.002)|(0.004)|(0.003)|(0.003)|
|DTS Indexc, t-1|-0.003|-0.016***|-0.007*|-0.007**|
||(0.004)|(0.005)|(0.004)|(0.004)|
|GDP per capitac, t-1, log|-0.007***|-0.004|-0.008***|-0.006**|
||(0.002)|(0.004)|(0.003)|(0.003)|
|Exports/GDPc, t-1|0.000**|0.000|0.000|0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|
|CO2 emissionc, t-1, log|0.003**|0.000|0.005***|0.003|
||(0.002)|(0.003)|(0.002)|(0.002)|
|Populationc, t-1, log|-0.001|0.002|-0.003|-0.001|
||(0.002)|(0.004)|(0.002)|(0.002)|
|Observations|3,579|3,425|3,545|3,568|
|R-squared|0.015|0.013|0.016|0.013|
|Year Fixed Effects|YES|YES|YES|YES|

**Table 3: OLS regression results of equation (2)**

Note: Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 9
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technologies. This can also help reduce supply-side risks and contribute to the global public good of greater resilience in these global value chains.

It is important to distinguish between products relating to minerals and raw materials, and those relating to manufacturing. Both are important for solar, wind and EV technologies, but these two product categories entail different types of development strategies and considerations. Developing export competitiveness in manufacturing products – particularly products that are more technologically sophisticated – has been linked to a wide range of economic benefits such as higher economic growth, employment growth, productivity increases and technological upgrading (Hausmann et al., 2006; Anand et al., 2012). Export-oriented manufacturing growth strategies also played an important role in the East Asian ‘growth miracles’ of the 20<sup>th</sup> century (Stiglitz, 2018). Although recent premature de-industrialization trends across many countries have led scholars and policy makers to question whether manufacturing-led growth is still a viable development path (Rodrik, 2016), growth in demand for green technologies and products could unleash sizable new growth opportunities (Hausmann, 2023).

While minerals-oriented development strategies have been successful in certain countries and contexts, they generally create less productivity benefits, fewer growth-enhancing linkages across other economic sectors (Hirschman, 1958) and are also associated with greater resource curse risks (Papyrakis, 2016). However, with the overall demand for critical minerals projected to increase by nearly 500% by 2050 to meet decarbonization goals (Hund et al., 2020), it will be increasingly important to encourage economically and environmentally responsible mineralsoriented development strategies, and to take active strategies to reduce resource-curse risks.

There are plenty of avenues for future research. First, one could extend the product mapping to other tradeable decarbonization technologies and services with pro-development characteristics (OECD, 2017). For example, understanding countries’ productive capabilities to recycle and maintain solar panels could help generate labor demand in small and medium sized enterprises. Second, exploring how countries’ policy space relates to their strengths in decarbonization technologies is another open question. Do countries with competitive exports in decarbonization technologies have lower trade costs, more ambitious nationally determined contributions (NDCs) and decarbonization targets, benefit from domestic subsidies and low regulatory barriers to FDI or other policies? Finally, diffusion of decarbonization technologies beyond production hubs is critical to help countries transition to a more low-carbon economy. This requires a better understanding of the structural and policy drivers behind countries’ and firms’ adoption of decarbonization technologies.

# 4.) Methods

The analysis in this paper involved (i) developing a new mapping of global value chain products for solar PV, wind turbines and EVs, (ii) merging this mapping with export data, and (iii) constructing and calculating several metrics.

## 4.1) Mapping global value chains of key decarbonization technologies

We used the 6-digit product classification of the Harmonized System (HS) to identify tradeable products associated with the value chains of solar PV, wind turbines and EVs. The 6-digit HS classification is the most granular, internationally harmonized classification of products. While many existing classifications of environmental goods have been based on the 6-digit HS coding classification<sup>4</sup> , it has some limitations, notably dual use and product specificity.

> 4 <mark>Steenblik, R. (2005). Environmental goods: A comparison of the APEC and OECD lists (No. 2005/4). OECD Publishing; Sugathan, M. (2013). Lists of environmental goods: an overview. International Center for Trade and Sustainable Development.</mark>

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However, the 6-digit HS classification has the advantage that it enables analysis of comparable trade data for almost all countries, and over time.<sup>5</sup> To identify HS 6-digit products associated with the value chains of solar PV, wind turbines and EVs, we followed three steps: First, we undertook a review of the academic and grey literature, finding key papers that have previously identified HS 6-digit products associated with wind turbines, solar PV, and EVs (see Table SI 1 for key sources used). Second, after collating the various HS 6-digit codes for each technology, we drew on further desktop research to classify each product into four value chain segments: raw materials, processed materials, subcomponents, and end products (see Table 5 for a definition of each segment). Third, we validated our product mapping for each green value chain with industry specialists in the supply chains of wind, solar and batteries for EVs. Reviewing the technical specifications of products in these value chains with our product description helped us trim the list of HS 6-digit products for each value chain – and ensured consistency across the three value chains. The final mapping and classification of HS 6-digit codes into value chain segments can be seen in Table 4.

**Table 4: Definitions of value chain segments**

|Value Chain Seg-<br>ment|Definition|
|---|---|
|Raw Materials|Basic materials that are mined, extracted or harvested from the earth. Also referred to as<br>‘unprocessed material’, examples include raw biomass and iron ore. In this link of the sup-<br>ply chain, value added comes from extracting, harvesting, and preparing raw materials for<br>international marketingin substantial volumes.|
|Processed Materi-<br>als|Materials that have been transformed or refined from basic raw materials as an intermedi-<br>ate step in the manufacturing process. Processed materials include steel, glass and cement.<br>In this link of the supply chain, value added comes from processing raw materials into pre-<br>cursors that can be easily transported, stored and used for downstream subcomponent<br>fabrication.|
|Subcomponents|Unique constituent parts or elements that contribute to a finished product. Clean energy<br>technology examples include generation sets for wind turbines and crystalline wafers for<br>crystalline silicon PV modules. Note that what is considered a component by the manufac-<br>turer may be considered the finished product by its supplier. In this link of the supply chain,<br>value added comes from fabricating processed materials into subcomponents that can<br>then be assembled(with other subcomponents)into endproducts|
|End Products|The finished product of the manufacturing process, assembled from subcomponents and<br>readyfor sale to customers as a completed item. Clean energyexamples include|

5 The analysis of value chain relationships, such as determining which input products are used to make other downstream products, generally requires the use of input-output tables or supply chain data. Unfortunately, consistent and comparable input-output or supply chain data is not presently available for products at the 6-digit HS classification. For example, the World Input Output Database (WIOD) currently only covers 43 countries and 56 sectors identified under the International Standard Industrial Classification (ISIC Rev. 4). Only a handful of these countries are developing countries and while a mapping exists from the HS classification to the ISIC classification, too much information is lost when trying to aggregate one classification with around 5,000 products to another with 56 industries. An alternative approach that is sometimes used in the literature is to draw on more detailed input-output data that are only available for certain countries and assume these relationships hold for other countries. For example, a more detailed input-output table (covering 400 industries) is available from the Bureau of Economic Analysis (BEA) for the United States, and this has been mapped to estimate value chain relationships for HS trade data5. However, aggregation issues remain in mapping 400 industries to 5,000 products, and it is questionable whether the input-output relationships of the US are generalizable to other countries.

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photovoltaic modules and lithium-ion battery cells. In this link of the supply chain, value added comes from assembling components into a marketable product that customers value.

**Table 5: Number of HS 6-digit products by value chain and segment**

|VC|Raw Materials|Subcomponents|Processed|End products|Total|
|---|---|---|---|---|---|
|EV|9|43|32|5|89|
|Solar|13|53|22|1|89|
|Wind|5|44|55|3|107|
|Total|27|140|109|9|285|

# 4.2) Description of data sources

Bilateral export flows for 226 countries in 5,000 HS 6-digit products (HS 92 nomenclature) between 1995-2021 come from the BACI international trade dataset, reported by the Centre d’Etudes Prospectives et d’Informations Internationales (CEPII). Gaulier and Soledad (2010) reconcile declarations of the exporter and the importer in the United Nations Commodity Trade Statistics Database (COMTRADE). To smooth out data anomalies such as re-exports and focus on structural patterns, we take 5-year rolling averages of export data. Where 5 years of export data for country-product cells is not available, we take the average over the number of available years.

For the regression analysis, we define control variables - GDP per capita, exports (of goods and services) to GDP ratio, C02 emissions and population - from the World Bank World Development Indicators (WDI), available from <u>https://databank.worldbank.org/source/world-development-indicators.</u>

# 4.3) Key metrics

## _4.3.1) Hirschman-Herfindahl Index_

To study market concentration in products associated with decarbonization technologies, we compute the Hirschman-Herfindahl Index (HHI) for each HS 6-digit product p.<sup>6</sup> It is defined as follows: c p where c is the market share of country c’s export value in total global exports of product p. A HHI of 1 indicates XX that the market is a perfect monopoly (one country exports 100% of the product), while HHI scores approaching 0 XXcc that the market is a perfect monopoly (one country exports 100% of the product), while HHI scores approaching 0 indicate a much more competitive market.

## _4.3.2) Decarbonization Technology Strength (DTS) index_

To define countries’ Decarbonization Technology Strength (DTS) index, we first quantify their strengths in products, using depth and breadth dimensions. To measure breadth, we count the number of HS 6-digit products in each value chain for which a country’s export share is greater than the global average (RCA > 1), as defined in equation (1). To measure depth in decarbonization technologies, we compute a country c’s average market share in export values of HS 6-digit products across value chains (vc):

> 6 For ease of exposition, we leave out the time subscript in all equations.

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where Np is the number of HS 6-digt products in a given value chain, Xcp relates to the exports of country of product p and Xp relates to the total global exports of product . To make the different scales and distributions of cc depth and breadth dimensions comparable, we normalize both into z-scores with zero mean and unit standard deviation: pp d d where xc,vc is country’s c value in strength dimension d (depth or breath) in value chain vc; μvc represents the d mean of dimension d in value chain vc; σvc captures the standard deviation of dimension d in value chain vc. To define the DTS index, we then take the simple average of depth and breadth’s z-scores.

# _4.3.3) Decarbonization Technology Opportunity’ (DTO) index_

To define countries’ Decarbonization Technology Opportunity’ (DTO) index, we similarly take the simple average of z-scores related to depth and breadth opportunity dimensions. To measure breadth, we count the number of HS 6- digit products that a country exports with 0.1>RCA>1. Thus, breadth captures all products that a country has not gained export competitiveness in, yet that are established. While the choice of an RCA of 0.1 is arbitrary, we choose it to include products that are significantly established (in relative terms). We do so to avoid that decarbonization opportunities result from small exports for a given country-product, which could be explained by idiosyncratic reasons. Tables SI 2 and SI 3 show that the DTO index of countries is robust to RCA thresholds of 0.2 and 0.5, respectively. Moreover, both variants of the DTO index are highly correlated (coefficients of 0.98 and 0.95) with our DTO index that uses an RCA threshold of 0.1.

To define the depth of countries’ decarbonization opportunities, we draw on existing work in the economic geography and economic complexity literature which has shown that countries are more likely to develop future export strengths that involve similar (or related) production capabilities to their existing exports (Hidalgo et al., 2007). Following Hausmann et al. (2014), we follow three steps to define capability alignment. First, we define a country’s productive capabilities embodied in its export structure. To that end, we rely on RCA as our indicator of relative export intensity, as defined in equation 1 We binarize RCAcp to define Mcp, our matrix of export competitiveness of country c in product p, which takes value 1 if RCAcp for country c in product p exceeds 1, and 0 otherwise.

Second, we calculate a measure of relatedness between products based on the conditional probability of co-exporting two given products with RCA > 1 This measure, which is always distributed between 0 and 1, posits that two products are more related to each other the higher the probability that countries co-export them. Specifically, product relatedness φp,p′ between products p and p’ for a particular year is defined as: Third, to define the proximity of a product as it relates to other existing products, we still need a measure that can be expressed at the country, product and year level. To that end, we construct capability alignment around each product which captures the intensity with which the product under consideration p is related to the current export basket of the same country c. Note that we define products at the HS 6-digit level to achieve the most granular distinction, for example to distinguish cars with and without combustion engine. Relatedness refers here to the relatedness measure defined above. More formally, cc,cc′ φφ To define depth, we then take the simple average of countries’ (normalized) capability alignment across opportunity products in each value chain.

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