OIT - Advancing Green Trade and Employment under Global Uncertainty

X Advancing Green Trade and Employment under Global Uncertainty Authors / Christoph Ernst, Gabriel Michelena

October / 2026 ILO Working Paper 181© International Labour Organization 2026 Attribution 4.0 International (CC BY 4.0) This work is licensed under the Creative Commons Attribution 4.0 International. See: https:// creativecommons.org/licenses/by/4.0/. The user is allowed to reuse, share (copy and redistribute), adapt (remix, transform and build upon the original work) as detailed in the licence. The user must clearly credit the ILO as the source of the material and indicate if changes were made to the original content. Use of the emblem, name and logo of the ILO is not permitted in connection with translations, adaptations or other derivative works. Attribution – The user must indicate if changes were made and must cite the work as follows: Ernst, C., Michelena, G. Advancing Green Trade and Employment under Global Uncertainty. ILO Working Paper 181. Geneva: International Labour Office, 2026.© ILO. Translations – In case of a translation of this work, the following disclaimer must be added along with the attribution: This is a translation of a copyrighted work of the International Labour Organization (ILO). This translation has not been prepared, reviewed or endorsed by the ILO and should not be considered an official ILO translation. The ILO disclaims all responsibility for its content and accuracy. Responsibility rests solely with the author(s) of the translation. Adaptations – In case of an adaptation of this work, the following disclaimer must be added along with the attribution: This is an adaptation of a copyrighted work of the International Labour Organization (ILO). This adaptation has not been prepared, reviewed or endorsed by the ILO and should not be considered an official ILO adaptation. The ILO disclaims all responsibility for its content and accuracy. Responsibility rests solely with the author(s) of the adaptation. Third-party materials – This Creative Commons licence does not apply to non-ILO copyright materials included in this publication. If the material is attributed to a third party, the user of such material is solely responsible for clearing the rights with the rights holder and for any claims of infringement. Any dispute arising under this licence that cannot be settled amicably shall be referred to arbitration in accordance with the Arbitration Rules of the United Nations Commission on International Trade Law (UNCITRAL). The parties shall be bound by any arbitration award rendered as a result of such arbitration as the final adjudication of such a dispute. For details on rights and licensing, contact: rights@ilo.org. For details on ILO publications and digital products, visit: www.ilo.org/publns.

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Authorization for publication: Mito Tsukamoto ILO Working Papers can be found at: www.ilo.org/research-and-publications/working-papers Suggested citation: Ernst, C., Michelena, G. 2026. Advancing Green Trade and Employment under Global Uncertainty, ILO Working Paper 181 (Geneva, ILO). https://doi.org/10.54394/0003464201 ILO Working Paper 181

Abstract This paper examines global trade patterns in green goods towards 2030. This is done in the context of current global trade tensions. The paper uses international trade flow data from WITS/ COMTRADE, in conjunction with the IMF’s classification of environmental goods. The paper introduces a structural gravity model of trade to estimate the potential trade in green goods towards 2030. The paper also undertakes a counterfactual analysis to evaluate the potential impact of rising trade tensions on trade flows and employment outcomes. In the model’s simulated scenarios, the United States increases tariffs on its major trading partners, prompting retaliatory measures. The model captures transformations in global trade patterns and highlights the risks that protectionist policies pose to sustainable and inclusive growth. Furthermore, the projected export dynamic is translated into employment outcomes: under the baseline, the

simulated scenarios, the United States increases tariffs on its major trading partners, prompting retaliatory measures. The model captures transformations in global trade patterns and highlights the risks that protectionist policies pose to sustainable and inclusive growth. Furthermore, the projected export dynamic is translated into employment outcomes: under the baseline, the growth of green exports to 2030 is associated with some 9.5 million additional jobs worldwide, of which around 530,000 are forgone once tariffs escalate and partners retaliate. The study uses a multi-regional input-output model to estimate how green exports affect employment. This model combines the Organisation for Economic Co-operation and Development (OECD) Inter-Country Input-Output Tables with labour data from the ILOSTAT database. This approach enables the estimation of both direct and indirect employment effects across countries and sectors. Overall, this study makes a significant contribution to existing literature by jointly analysing green trade flows and employment generation dynamics using a unified quantitative framework. The results emphasize the significance of open trade and investment regimes in promoting the global dissemination of green technologies and generating quality employment opportunities across various regions.

JEL codes: F13, F14, F16, C67

Keywords: tariffs; trade tensions; multiregional input–output; labour market; global value chains Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of the institutions related to the authors.

About the authors Christoph Ernst – Lead of the Investment, Sectoral Strategies and Transitions Team of the EMPINVEST Branch, of the International Labour Organization (ILO), Geneva, Switzerland. Gabriel Michelena – Modelos Económicos de Simulación (MESi)-IIEP-UBA-CONICET, Buenos Aires, Argentina.02 ILO Working Paper 181 Abstract 01 About the authors 01 X Introduction 04 X 1 The international trade in green goods 06 X 2 Estimating the dynamic of green trade 09 Methodology 09 The scenarios under analysis 11 Results 12 X 3 The employment related to green exports 15

About the authors 01 X Introduction 04 X 1 The international trade in green goods 06 X 2 Estimating the dynamic of green trade 09 Methodology 09 The scenarios under analysis 11 Results 12 X 3 The employment related to green exports 15 Methodology 15 Results 17 X Concluding remarks 23 References 24 Table of contents03 ILO Working Paper 181 List of Tables Table 1. Green and non-green trade, in USD trillion and growth rate (%), 2012–23 06 Table 2. Green goods: Share in world trade by sector, values (USD billion) and shares (%), 2012 and 2023 07 Table 3. Main green products traded, values (USD billion) and shares (%) in 2012 and 2023 08 Table 4. Gravity model estimation results (2012-23) 12 Table 5. Simulation results: Green exports in 2030 13 Table 6. Change in employment demand (thousands of jobs) in the baseline scenario (with respect to the base year), by region and income group 17 Table 7. Change in employment demand (as per cent of total employment) in baseline scenario (with respect to base year), by region and Income group 18 Table 8. Change in direct employment demand in baseline scenario (with respect to base year), sectoral decomposition (%) 19 Table 9. Change in indirect employment demand in baseline scenario (with respect to base year), sectoral decomposition (%) 20 Table 10. Change in employment (thousand jobs) demand in policy scenario (with respect to baseline scenario) 21 Table 11. Bias by gender, age, skill and formal status employment, direct and indirect employment 2204 ILO Working Paper 181 X Introduction The world faces an urgent environmental challenge that requires coordinated collective action (Calvin et al. 2023). According to the Sixth Assessment Report of the Intergovernmental Panel on

ployment 2204 ILO Working Paper 181 X Introduction The world faces an urgent environmental challenge that requires coordinated collective action (Calvin et al. 2023). According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (Calvin et al. 2023), greenhouse gas emissions must fall by 30–50 per cent over the next two decades to avoid a global temperature increase above 1.5 °C. Globalization has been one of the main drivers of rising emissions, as it has fragmented production processes and increased demand for goods and services (Hummels et al. 2001). Since 1948, the value of global merchandise exports has expanded substantially; by 2016, exports represented an average of 29 per cent of national GDP (Wiedmann & Lenzen, 2018). Yet the globalization of trade often shifts the environmental and social impacts of production away from the place of consumption and towards other countries. As a result, the energy use, pollution and employment effects associated with a certain good may be distributed across several economies. In sustainable trade debates, “green trade” generally refers to trade practices and products that provide environmental benefits. However, the concept remains imprecisely defined, particularly regarding which goods should be included (Balineau & De Melo, 2013). Governments differ in their classifications, covering products such as renewable energy technologies, energy-efficient equipment, organically produced agricultural goods and certified biodiversity-based products. The Asia-Pacific Economic Cooperation (APEC) list of environmental goods, for example, was developed to improve access to environmental technologies and support green growth (Kuriyama, 2021). Given the absence of a unified definition, this study relies on the IMF’s environmental goods data (IMF, 2023). The IMF list was initially based on the EUROSTAT (2009) classification mapped to

2017 HS codes and later expanded through research and consultations on updates to the HS nomenclature. This list shares some similarities with the APEC classification of environmental goods. This document provides a comprehensive examination of projected dynamic green goods exports over the period from 2023 to 2030. The main goal is to estimate the potential contribution of these specific groups of products to the overall labour market. This study employs a structural gravity model to assess the trade and employment impacts of green exports, following the theoretical framework developed by Anderson and Van Wincoop (2003) and extended by Anderson and Yotov (2016) and Yotov et al. (2016). The gravity approach models bilateral trade flows as a function of economic size, trade costs and multilateral resistance terms, which capture the relative ease or difficulty of market access in a general equilibrium setting. We implement the model in two steps, building on recent methodological guidance by Larch et al. (2025). First, we estimate trade flows using a Poisson Pseudo-Maximum Likelihood (PPML) estimator. The model includes tariff data to identify the impact of trade policy on international versus intra-national trade. Second, we define a counterfactual policy scenario, in which countries engage in a tariff escalation with the United States, to simulate changes in bilateral trade costs and evaluate their effects on trade flows and employment creation towards 2030. To estimate the employment impacts associated with green goods exports, the study employs a multi-regional input-output (MRIO) model that integrates the Organisation for Economic Co-operation and Development’s (OECD) Inter-Country Input-Output tables (OECD, 2021) with labour statistics from the International Labour Organization’s (ILO) Statistics Database (ILO, 2025). This study contributes to the recent literature of trade in sustainable products. Ernst et al. (2025) estimated the economic dimensions of green trade, considering its regional relevance in terms of exports and its impact on labour demand. Ernst et al. (2025) found that China and high-income05 ILO Working Paper 181 countries play a significant role in the global trade of these goods, although exports from midestimated the economic dimensions of green trade, considering its regional relevance in terms of exports and its impact on labour demand. Ernst et al. (2025) found that China and high-income05 ILO Working Paper 181 countries play a significant role in the global trade of these goods, although exports from middle-income countries are accelerating. Labour demand linked to green goods exports represents less than 1 per cent of total employment in most regions, except in the European Union where it accounts for around 2 per cent. We also contribute to the existing literature of multi-regional input-output models to examine the economic and environmental consequences of trade (Timmer et al. 2015). By explicitly linking trade flows to employment outcomes and providing insights into the quality of jobs associated with green exports, this methodological approach emphasizes the significance of global value chains, extending the work of Hummels et al. (2001) and Timmer et al. (2015) on trade in value-added. This document also contributes to the literature that measures the impacts of the recent trade conflict on trade flows and GDP . Recent applications assessing the impacts of trade tensions, using a gravity model, include Gopinath et al. (2025), Fajgelbaum et al. (2020) and Charandabi et al. (2021). The remainder of this study is organized as follows: Section 1 presents an overview of global trends in green goods trade. Section 2 estimates the future evolution of green trade, including a trade-conflict scenario and projections to 2030. Section 3 analyses employment associated with projected green trade. The concluding remarks summarize the findings and outline potential lines for future research.06 ILO Working Paper 181 X 1 The international trade in green goods

This section analyses the dynamics of international trade in green goods, with a particular focus on trends in exports and imports during the period 2012–23. The study also analyses the fundamental characteristics of green trade, considering both regional and sectoral compositions. The evidence assembled in Ernst et al. (2025) indicates that the export of green goods by China and

on trends in exports and imports during the period 2012–23. The study also analyses the fundamental characteristics of green trade, considering both regional and sectoral compositions. The evidence assembled in Ernst et al. (2025) indicates that the export of green goods by China and middle-income countries increased significantly over the past decade, while exports by highand low-income countries demonstrated less substantial growth. Regarding the trade balance, that study finds that China, the European Union and the East Asia and Pacific region record the largest surpluses globally. Conversely, North America, Europe and Central Asia and Latin America exhibit the most substantial deficit, suggesting a potential imbalance resulting from a mismatch between a structural change in demand and the lack of technological capabilities to supply it. Table 1 illustrates the growth of green trade between 2012 and 2023. A remarkable expansion of trade in green products at current values occurred, with a clear upward trajectory from 2021 onwards, reaching around US$ 1.92 trillion in 2023. Because these figures are expressed at current values, the share of green goods in world trade is a more informative indicator of the underlying trend, since common price movements affect the numerator and the denominator alike. That share increased between 2013 and 2016 and remained relatively stable during the period 2016–19. The proportion of green goods as a percentage of world trade reached 8.5 per cent in 2020 and declined in the two following years. Between 2021 and 2022, the share of green trade remained higher than in the pre-pandemic period, and in 2023 it marked a new record of 9.2 per cent. X Table 1. Green and non-green trade, in USD trillion and growth rate (%), 2012–23 Year Green (USD tn) Growth rate (%) Non-green (USD tn) Growth rate (%) Share (%)

2012 1.06 14.26 6.9 2013 1.13 6.2 15.29 7.3 6.9 2014 1.18 4.2 15.56 1.8 7.0 2015 1.11 -5.4 13.72 -11.9 7.5 2016 1.12 0.5 13.31 -3.0 7.8 2017 1.25 11.4 15.06 13.2 7.6 2018 1.35 8.1 16.54 9.9 7.5 2019 1.36 0.9 15.84 -4.3 7.9 2020 1.35 -0.7 14.60 -7.8 8.5 2021 1.67 23.8 18.38 25.9 8.3 2022 1.82 8.7 20.28 10.4 8.2 2023 1.92 5.8 18.90 -6.8 9.2

Source: Author’s calculations based on World Integrated Trade System (WITS)/UN COMTRADE.

Table 2 shows the composition of world trade in green goods by sector. In 2023, the three sectors with the largest shares are machinery (24.7 per cent), motor vehicles (20.7 per cent) and electrical equipment (18.2 per cent), which together account for 63.6 per cent of green exports.07 ILO Working Paper 181 Adding computers and electronics (15.2 per cent) brings the four leading sectors to just under four-fifths of the total. Chemicals represent 7.2 per cent and rubber and plastics 6.3 per cent, while the remaining sectors each account for less than 2 per cent. The concentration of green trade in these technologyand capital-intensive manufacturing branches highlights the vital role they play in the transition to a low-carbon global economy (Kirchherr & Urban, 2018).

while the remaining sectors each account for less than 2 per cent. The concentration of green trade in these technologyand capital-intensive manufacturing branches highlights the vital role they play in the transition to a low-carbon global economy (Kirchherr & Urban, 2018). X Table 2. Green goods: Share in world trade by sector, values (USD billion) and shares (%), 2012 and 2023 ICIO Sector Description X2012 Share (%) X2023 Share (%) C28 Machinery n.e.c. 339.84 31.97 473.83 24.65 C29 Motor vehicles 83.93 7.90 397.25 20.67 C27 Electrical equipment 155.19 14.60 350.52 18.24 C26 Computers & electronics 231.76 21.80 292.75 15.23 C20 Chemicals 71.82 6.76 138.29 7.20 C22 Rubber & plastics 78.51 7.39 120.69 6.28 C25 Fabricated metal products 30.05 2.83 35.59 1.85 C19 Coke & refined petroleum 21.01 1.98 16.89 0.88 C17_18 Paper & printing 13.44 1.26 14.69 0.76 C23 Non-metallic minerals 8.34 0.78 15.42 0.80 C10T12 Food, beverages & tobacco 10.43 0.98 16.70 0.87 C30 Other transport equipment 7.62 0.72 17.18 0.89 C13T15 Textiles & apparel 4.99 0.47 7.53 0.39 C31T33 Other manufacturing 2.82 0.27 4.51 0.23

C16 Wood products 1.76 0.17 2.09 0.11 B07_08 Mining (ores) 1.51 0.14 17.94 0.93

Source: Author’s calculations based on WITS/COMTRADE.

Table 3 shows a marked transformation in the composition of green goods trade between 2012 and 2023. Although green exports were initially concentrated in intermediate and capital goods, such as photovoltaic cells, electrical equipment, plastics and machinery, the period is characterized by a strong expansion of electric passenger vehicles and related upstream inputs. Electric passenger vehicles (HS 870390) exhibit the most pronounced growth, increasing from US$ 2.6 billion in 2012 (0.25 per cent of green exports) to over US$ 300 billion in 2023, accounting for more than 15 per cent of green trade. Lithium-ion batteries (HS 850760) also experienced a substantial rise, reflecting their central role in the electrification of transport and energy systems. At the same time, traditional, green-related manufacturing inputs, such as photovoltaic cells and LEDs, electrical control panels, valves, plastics and machinery, continued to expand in absolute terms, although their relative shares evolved more moderately. The overall pattern points to a structural shift towards final green products and strategic enabling technologies, consistent with the scaling-up of global value chains associated with electric mobility and renewable energy deployment.08 ILO Working Paper 181 X Table 3. Main green products traded, values (USD billion) and shares (%) in 2012 and 2023 HS6 Description (short) X2012 Share (%) X2023 Share (%) 870390 Passenger vehicles (electric) 2.62 0.25 300.37 15.63 850760 Lithium-ion batteries 9.04 0.85 113.05 5.88

854140 Photovoltaic cells and LEDs 44.51 4.19 87.60 4.56 853710 Electric control panels (≤1000V) 36.69 3.45 78.94 4.11 392690 Other plastic articles n.e.c. 42.98 4.04 72.17 3.75 848180 Valves, taps and similar appliances 43.18 4.06 56.60 2.95 854370 Electrical apparatus n.e.c. 24.57 2.31 48.84 2.54 847989 Machinery n.e.c. 29.30 2.76 45.14 2.35

Source: Author’s calculations based on WITS/COMTRADE, under nomenclature HS 2012.09 ILO Working Paper 181

X 2 Estimating the dynamic of green trade

This section provides an estimate of the dynamics of green exports for the 2023–30 period. This estimation is carried out using the gravity model (GM), which is inspired by Newton’s equation of gravity. The basic idea, originally proposed by Tinbergen (1962) and later developed by Anderson (1979), is that the volume of trade between two countries depends mainly on the relative size of their economies and the distance between them as a proxy of bilateral frictions. It is expected that a pair of countries with larger size and lower frictions will demonstrate larger trade flows than a pair of countries with smaller size and higher frictions. This section introduces the framework of the structural gravity model, following the model developed by Anderson and Yotov (2016) and the practical guidance set out in Yotov et al. (2016). This approach emphasizes how trade is interconnected, responding to barriers or frictions that arise from both natural and policy-driven factors. The model developed in this section also follows the best practices summarized carefully in Larch et al. (2025) and Yotov (2022), considering the potential biases and inconsistencies associated with a naive implementation of the model. To evaluate the potential dynamics of green trade, a business-as-usual scenario is developed for

lows the best practices summarized carefully in Larch et al. (2025) and Yotov (2022), considering the potential biases and inconsistencies associated with a naive implementation of the model. To evaluate the potential dynamics of green trade, a business-as-usual scenario is developed for the period 2023–30. This scenario incorporates exogenous projections for key economic variables, including the annual GDP growth rate, as presented in the World Economic Outlook (IMF, 2025). In addition, a trade conflict scenario is simulated to estimate the potential limitations that could hinder the growth of green goods exports over the next decade. The main goal is to provide an overall estimate of the potential employment created by these new exports, while considering how trade tensions could limit the positive impact of trade on the labour market. Recent applications assessing the impacts of different trade policies, using a gravity model, include the impact assessment of Brexit (Dhingra et al. 2017), the United States–China trade conflict (Fajgelbaum et al. 2020) and the African Continental Free Trade Area (World Bank, 2020). Methodology The analytical framework is based on a structural gravity model of trade (Anderson et al. 2017). This framework captures the complex interrelations between trade and economic outcomes in a general equilibrium setting. The bilateral trade from country originating i and hosted in country j is formally expressed as: (1) X ij
t Y i
tE j
t Y t




   tij
t /g51i
tP j
t 1 /g86
where X ij denotes the nominal trade flows from exporter i to destination j; E j is the total expenditure on importer j, Y i is the value of total production in exporter i; Y is the value of the world output; tij denotes bilateral trade frictions between partners i and j, and σ >1 is the elasticity of substitution among goods from different countries.10 ILO Working Paper 181 The variablesP jand Πi are structural parameters defined by Anderson and Van Wincoop (2003)

output; tij denotes bilateral trade frictions between partners i and j, and σ >1 is the elasticity of substitution among goods from different countries.10 ILO Working Paper 181 The variablesP jand Πi are structural parameters defined by Anderson and Van Wincoop (2003) as the inward and outward multilateral resistance terms, respectively. They represent the general equilibrium impacts of bilateral trade frictions. (2) P i
    

N  j
1



   tji /g51j
1 /g86 Y j Y 1 1 /g86 (3) /g51j
    

N  i
1



   tij P i
1 /g86 E i Y 1 1 /g86 These multilateral resistance terms aggregate the bilateral trade costs faced by each country, thereby indicating how global trade integration impacts individual economies. Equation (4) is a restatement of the market-clearing condition according to which, at delivered prices, the value of output in country j is equal to the value of total sales. In its current version, equation (4) captures the inverse relationship between the outward multilateral resistance faced by the producers in country j and their factory-gate prices. (4) E j
pjY j Factory-gate prices in equation (5) refer to the cost of goods as they leave the production facility, excluding transportation, distribution and retail markups. In structural gravity models of trade, they play a crucial role in determining a country’s competitiveness. Lower factory-gate prices, driven by lower production costs, wages, or subsidies, make goods more attractive for exports. (5) pj






   Y j N j
1Y j 1 1 /g86 1 yj/g51j As outlined in earlier discussions, Equation (6) delineates the value of production in a country as a function of factor demand. (6) Y j
pjA jL j1 αjK jαj The following generic econometric gravity equation can be estimated to obtain estimates of trade costs and trade elasticities: (7) X jitexp
it
/g70 jt
tji /g72jit11 ILO Working Paper 181 Equation (7) is estimated with the PPML estimator (Silva & Tenreyro, 2006), using a panel of intra-national and international trade data with yearly intervals, and controlling for exporter-time fixed effects (i
t) and importer-time fixed effects (χ j
t). (8)

Equation (7) is estimated with the PPML estimator (Silva & Tenreyro, 2006), using a panel of intra-national and international trade data with yearly intervals, and controlling for exporter-time fixed effects (i
t) and importer-time fixed effects (χ j
t). (8) tji /g69 t/g87ji
/g69 rt
RT A ji
/g69 cntgCN TG ji
/g69 smcSM CT RY ji
/g69 dstlog
DIST ji
/g69 lngLA NG ji
/g69 clyCL NY ji where tj
i are the trade cost estimates that include all the usual bilateral gravity variables, such as regional trade agreements (RTA), common border (CNTG), domestic flows (SMCTRY), distance (DIST), common language (LANG) and historical colony relationships (CLNY). The representative non-uniform trade policy, τj
i
corresponds to the continuous variable for MFN tariff. Finally, εj
i
t is a stochastic error term that is assumed to not carry any systematic information about trade costs. This second step entails defining the counterfactual experiment of interest. The counterfactual scenario involves the introduction or removal of trade barriers, which will result in a change in bilateral trade costs. The definition of trade policy variables for counterfactual trade costs will depend on the policy question under investigation (Larch et al. 2025). Any adjustment to the trade policy variables specified in the structural gravity model will result in a new matrix of counterfactual (CFL) bilateral trade costs. The differences between the baseline trade costs defined in the equation (8) and the counterfactual trade costs reported; this is the initial trade policy shock introduced in the g