metadata: authors: 0: author: Bastos,Paulo S. R.
1: author: Greenspon,Jacob Neil
2: author: Stapleton,Katherine Anne
3: author: Taglioni,Daria
count: Europe,Ukraine,Russian Federation
docna: 0: docna: Did the 2022 global energy crisis accelerate
            the diffusion of low-carbon technologies?
docty: Policy Research Working Paper
abstracts: cdata!: This paper develops measures of the
            diffusion of a comprehensive range of low-carbon
            technologies in 35 countries from 2019 to 2022 using text
            analysis of job postings and earnings calls transcripts. It
            documents a rapid acceleration in the diffusion of
            low-carbon technologies in 2022, driven by technologies
            related to renewable energy, vehicles, thermal performance,
            and electrical generation and storage. Rapid growth occurred
            in three quarters of the countries studied and 228 of 300
            subnational regions, although was fastest in Europe. Hiring
            for roles related to low-carbon technologies in these 35
            countries doubled between 2019 and the end of 2022, for
            example. It studies the role of the global energy crisis in
            triggering this accelerated technology diffusion, focusing
            on 16 mainly advanced economies. It finds that
            establishments in countries that had a higher pre-crisis
            dependence on imports of natural gas, and were thus more
            exposed to the price shock, differentially increased hiring
            for low-carbon technology related roles from March 2022
            onwards. Within more exposed countries, establishments with
            a higher pre-crisis energy intensity also saw a differential
            increase in hiring relative to less energy intensive ones.
display_title: Did the 2022 global energy crisis
            accelerate the diffusion of low-carbon technologies?
listing_relative_url: /research/2024/05/34321358/2022-global-energy-crisis-accelerate-diffusion-low-carbon-technologies
topicv3: Low Carbon Development m1327545 580,Jobs m1503931 2670
publishtoextweb_dt: 2024-05-20T00:00:00Z
dois: 10.1596/1813-9450-10777
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 75
author_keys: 0: author_key: 1210378
1: author_key: 2188493
2: author_key: 1947953
3: author_key: 1155075
model_extractions: Policy Research Working Paper 10777 Crisis Breeds Innovation The 2022 Energy Crisis and Green Technology Adoption _Paulo Bastos Jacob Greenspon Katherine Stapleton Daria Taglioni_ Development Economics Development Research Group May 2024
Policy Research Working Paper 10777

# **Abstract**

This paper studies how the 2022 energy crisis, which triggered the largest energy price shock since the 1970s, affected the diffusion of green technologies. It uses a dataset of online job postings in 16 countries to construct measures of green technology-related job openings, using these as a proxy for green technology adoption. It documents the significant growth in green-technology-related openings in 2022. It shows that establishments that had a higher exposure to the price shock, due to their pre-crisis energy intensity and location in countries with a greater dependence on natural gas imports, posted more green technology-related openings from March 2022.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank 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 pbastos@worldbank.org, Jacob.greenspon@economics.ox.ac.uk, kstapleton@worldbank.org, and dtaglioni@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
# Crisis breeds innovation: The 2022 crisis and energy green technology adoption

Paulo Bastos, Jacob Greenspon, Katherine Stapleton and Daria Taglioni<sup>*</sup> Originally published in the Policy Research Working Paper Series in _May 2024_ . This version is updated in _April 2026_ .

To obtain the originally published version, please email prwp@worldbank.org.

**Key words** : technology diffusion, low-carbon transition, clean energy, global energy crisis, job postings, energy prices, Russia-Ukraine war

**JEL codes** : F64, O33, O44, Q55, Q56

> *Bastos: World Bank (email: pbastos@worldbank.org); Greenspon: University of Oxford (email: jacob. greenspon@economics.ox.ac.uk); Stapleton: World Bank (email: kstapleton@worldbank.org); Taglioni, World Bank (email: dtaglioni@worldbank.org). Corresponding author: Jacob Greenspon. We are grateful to Juan Jose Tapia and Juan Porras for excellent research assistance and to Hannah Wei, Aur ́elien Saussay, Robert Stavins, and workshop participants at the World Bank, George Washington University, Center for Inclusive Trade Policy, Canadian Economics Association Annual Meeting, the Canadian Resource and Environmental Economics Association Annual Meeting, Harvard Growth Lab, and the University of Oxford for insightful comments and feedback on earlier drafts. The authors gratefully acknowledge support by the “Whole of Economy” Program, a Climate Support facility administered by the World Bank, and by the Umbrella Facility for Trade, which is funded by the governments of The Netherlands, Norway, Switzerland, Sweden and the United Kingdom, and the Research Support Budget of the World Bank.
2

# **1. Introduction**

There is wide agreement that achieving net-zero emissions globally requires a rapid increase in the pace and scale of adoption of green technologies. However, to date there has been relatively little empirical evidence on the drivers of green technology _adoption_ , with much of the literature focusing instead on green _innovation_ , measured by patenting (e.g. Popp, 2002, 2019; Aghion et al., 2016; Dechezleprˆetre, Glachant and M ́eni`ere, 2013; Acemoglu et al., 2012).<sup>1</sup> As a result, there remains an open debate over the importance of energy and carbon prices in influencing green technology adoption. Empirical papers on green technology adoption have typically focused on a specific technology in a specific setting (e.g cooling and heating equipment in the US by Newell, Jaffe and Stavins, 1999) and find contrasting results about the role of energy or carbon prices.<sup>2</sup> This paper aims to fill this gap by providing new empirical evidence on the diffusion of a comprehensive range of green technologies across establishments over several countries over time. We follow a growing literature that infers the spread of new technologies through their real-time footprint in the demand for new tasks or skills in the text of job postings (e.g. Acemoglu et al., 2022; Goldfarb, Taska and Teodoridis, 2023). We use a rich and high-frequency dataset of some 580 million online job postings in 16 advanced economies from Lightcast from 2014 to the end of 2022.<sup>3</sup> We create a list of keywords relating to green technologies from the European Patent Office (EPO) Y02 classification of patents related to ‘climate change mitigation technologies’ and classify job postings as green technology-related if they mention these keywords (or close synonyms). We then match the job postings data to firm-level data including balance sheet information and energy consumption from Factset and the Carbon Disclosure Project (CDP).

We first show that growth in openings related to green technologies was modest from 2014 to 2018, before increasing slightly in 2019 and then accelerating rapidly in 2022. The share of all job postings mentioning green technologies more than doubled between 2019 and 2022. Rapid growth in green technology-related hiring was strongest in Europe but occurred in three quarters

> 1See H ́emous and Olsen (2021) for a recent review.

> 2For example, while Newell, Jaffe and Stavins (1999), and Delarue, Ellerman and D’haeseleer (2010) find positive impacts of carbon prices on the adoption of different green technologies, Klemetsen, Rosendahl and Jakobsen (2020) and Calel (2020) do not find any such effects.

> 3Lightcast was formerly Burning Glass Technologies.
3 of the countries studied. Four groups of technologies had particularly large increases from 2021 to 2022: those related to renewable energy, new energy vehicles, improved thermal performance, and electrical generation and storage.

We next focus on the role of the 2022 energy crisis in explaining this accelerated green technology adoption. The unexpected Russian invasion of Ukraine in late February 2022 triggered the largest increase in energy prices since the 1970s oil price shocks, and therefore offers a valuable context to study the effect of energy shocks on the deployment of green technologies. In both Europe and Asia, benchmark natural gas prices sharply increased during the crisis and reached record highs in August 2022, corresponding to large increases in wholesale electricity prices in most countries. In addition to this immediate price impact, the 2022 crisis also raised questions about the future reliability of imported energy supplies (Tertre, 2023).

Adoption of green technologies can reduce a firm’s exposure to higher energy costs, analogous to energy costs incentivizing energy-saving product innovation, which Newell, Jaffe and Stavins (1999) note is a “modern manifestation” of the Hicks (1932) induced innovation hypothesis. This includes technologies that improve energy efficiency as well as those that use clean energy sources. The benefits of such technologies to firms should have increased after February 2022 with increased energy prices and uncertainty about future energy supplies.

We exploit differential exposure of countries and firms to the overall energy shock based on their pre-crisis fuel mix and energy intensity, respectively. Exposure to the energy crisis varied substantially across countries in our sample. Imported natural gas supplied as much as 38% of total energy use in Italy, 28% in Belgium, and 26% in Singapore in 2019, whereas other countries like the US, Canada and Australia relied on domestic supplies or consumed little gas. This differential exposure aligns with observed price increases. Six months after the Russian invasion of Ukraine, benchmark gas prices had increased by nine times as much in Europe as in the United States, Australia, or Canada. Even within Europe, there was substantial variation in price changes: the increase in wholesale electricity prices from 2019 to 2022 in France, for instance, was nearly double that in Norway. We find little evidence that this import dependence is correlated with country-level baseline characteristics that might have predicted the future adoption of green technologies, including GDP per capita, capital intensity, manufacturing share of GDP, or previous renewable energy deployment.
4 Within countries, firms with more energy-intensive production processes pre-crisis also faced greater exposure to the energy price shocks. We calculate that the 75th percentile firm in a country consumed about 12 times as many megawatt-hours of fuel per dollar of revenue as the 25th percentile firm. We also find little evidence of energy intensity being correlated with firmlevel characteristics that might predict future green technology adoption, including revenues, capital intensity, or productivity.

We show that there was a differential increase in green technology-related job openings after February 2022 in establishments located in countries with a higher pre-crisis dependence on imports of natural gas. In addition, within more gas import-dependent countries, establishments of firms that were more energy-intensive before the energy crisis also saw faster growth in green technology-related postings after the crisis. These results are robust to including establishment and time fixed effects, excluding firms in fossil fuel producing industries that may have benefited from the energy price shock, and controlling for potentially confounding national policy changes (fossil fuel subsidies and green industrial policies).

Next, using an event study design, we show that the differential increase in green technologyrelated hiring for establishments that were more exposed to the price shock began in March 2022, shortly after the Russian invasion of Ukraine, but gradually increased and was most pronounced by December 2022. Much of this increase in demand for green technology skills related to one technology group - energy generation - which includes jobs related to renewable energy, heat pumps, low-carbon fuels, decentralized energy and efficient power generation. The increase was concentrated predominantly in high skilled jobs.

Taken together these results are suggestive of induced green innovation in response to the energy price shock. This research adds new insights to the literature on the impacts of energy price shocks and the 2022 energy crisis in particular. To date, there has been limited empirical evidence on the impact of this crisis on technology adoption or innovation, with the existing research focused mostly on the negative consequences for output and employment (e.g. Ferriani and Gazzani, 2022; Lan, Sher and Zhou, 2022; Di Bella et al., 2022) or being theoretical. Our findings are consistent with input-saving technical change as shown in the macroeconomic theory of Hassler, Krusell and Olovsson (2021), who model how markets economize on scarce natural resources. Such a response would be consistent with the empirical findings of Fontagne, Martin
5 and Orefice (2023) that in response to energy price increases during 1996-2019, French manufacturing firms reduced their energy demand and improved their energy efficiency. In addition, our findings support the hypothesis of Zhunussova (2022) that higher energy prices from the crisis could have positive implications on the clean energy transition.

Our results also contribute to the literature on green jobs. For the US, Curtis and Marinescu (2022) use the same online job postings dataset, but focus only on wind and solar energy jobs from 2010 to 2019. Another closely related paper is that of Saussay et al. (2022), who also use job postings data in the USA from 2010-2019 but with a more expansive definition of ‘green jobs’ based on the skills listed in postings. Our research adds to this literature in three important ways. First, our definition of green jobs is based on a carefully selected set of technologies mentioned in the EPO Y02 class, “the international standard for clean innovation studies” (Calel, 2020). Second, by using the full text of the job postings, we may be able to more comprehensively identify green technology-related jobs. Third, by considering a wide range of countries and extending our analysis to the end of 2022, this paper is able to study global patterns of diffusion and explore the impact of the energy crisis.

# **2. Data and methods**

## **2.1 Measuring green technology diffusion using job openings**

Our main data source is 580,441,273 scraped online job postings from Lightcast for 16 advanced economies during 2014-2022. This group of countries represented 46 percent of global GDP in 2019.<sup>4</sup> More details about this data and its representativeness can be found in Bastos et al. (2024).

To measure green technology-related postings we extract a list of keywords from the titles and descriptions of the European Patent Office’s Y02 classification of patents related to ‘climate change mitigation technologies’. This is a continuously updated and comprehensive list of green technologies used for classifying patents.<sup>5</sup> It covers green technologies related to buildings

> 4These countries are the US, Canada, UK, Australia, New Zealand, Singapore, Denmark, Norway, Italy, Austria, Belgium, Switzerland, Germany, France, Luxembourg, Netherlands.

> 5We found alternative lists of green technologies, including the International Energy Agency (2021)’s Clean Energy Technology Guide or the classifications of Dussaux, Dechezleprˆetre and Glachant (2022), to be more limited
6 (subclass Y02B); greenhouse gas capture (Y02C); information and communication technologies (Y02D); energy (Y02E); industry and agriculture (Y02P); transport (Y02T); waste and wastewater management (Y02W); and smart grids (Y04S). This resulted in a list of 392 English-language keywords, which was professionally translated into the other official languages of the countries in our data. These keywords map to 46 different green technologies. Supplementary Appendix Table A1 lists all keywords, their corresponding green technology, and the number of related job postings.

We classify a job posting as related to each green technology if it includes at least one associated keyword in its text. The keywords found in the highest number of job postings are “insulation”, “renewable energy”, “renewable energies”, and “electric car”. Aggregated up to technologies, the most prevalent green technologies mentioned in job postings are renewable energy (as an umbrella term), solar energy, insulation, electric vehicles and wind energy.

Figure 1 displays the growth in green technology-related postings over time. From 2014 until 2019, growth in hiring was modest across 16 advanced economies. This trend is consistent with earlier findings on green technology diffusion, which pointed to slow initial deployment despite significant advances in green technology invention and innovation (Popp, 2019; IEA, 2020) and with those on green and clean energy jobs in the US wind and solar industries during the 2010s (Curtis and Marinescu, 2022). However, starting in 2019, green technology diffusion began to accelerate. The share of online job postings mentioning green technologies grew steadily between 2019 and 2021, as demand for skills related to renewable energy, electric vehicles, and energy-efficient solutions increased. By the end of 2022, the share of job postings mentioning green technologies had more than doubled from 2021 levels, reaching 1.7% across advanced economies.

Growth was particularly pronounced in Europe.<sup>6</sup> The largest increases occurred in job postings related to renewable energy, electric vehicles, thermal performance improvements, and electrical generation and storage technologies.<sup>7</sup> For example, postings for electric vehicle jobs were 3.3 times higher in 2022 compared to 2021, and solar energy and electric vehicle charging jobs also experienced rapid growth.

in scope, less recently updated, or more challenging to turn into a list of technology-related keywords. 6See Supplementary Appendix Figure A1 for the share of total job postings related to green technologies for each
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8 electricity. Countries with more imported natural gas in their energy mix before the crisis were more exposed to the natural gas price shocks that followed the Russian invasion of Ukraine because of supply disruptions to global gas markets. In comparison, countries with little gas consumption were less affected by supply decreases, as were countries able to rely on domestic gas production. This exposure measure is supported by the data in Figure 2 demonstrating the positive relationship between country-level gas import dependence in 2019 and the rise in electricity prices between 2019 and 2022.

Establishment-level pre-crisis energy intensity is constructed using data on firm-level energy consumption from the Carbon Disclosure Project (CDP) divided by revenue data from Factset in 2020.<sup>9</sup> Energy consumption is measured as fuel and purchased steam, heat, electricity, and cooling consumption in megawatt-hour (MWh) equivalents and revenue is measured in millions of 2020 US dollars. Establishments that belong to the same firm, but are located in different countries, have the same energy intensity in our data.

# **2.3 Firm-level data and matching of datasets**

We match establishment-level job vacancies data to firm-level data on energy consumption from CDP and balance sheet information from Factset (more details are provided in Supplementary Appendix Section A.3). Our final dataset is at the level of establishments defined as firm-countrysubnational region triplets, following Acemoglu et al. (2020).<sup>10</sup> CDP includes information on energy consumption during 2020 for 3,598 firms operating in the 16 countries in our sample. We use information available from CDP on each firm’s ISIN or CUSIP identifier code to match these firms to Factset. Factset is a private company that collates data on ‘firm-level entities’ from financial statements and other sources for publicly traded companies as well as data on entities’ supply chain relationships.<sup>11</sup> We use fuzzy matching methods to identify Factset entities in the Lightcast data on job open-

> 9Annual energy consumption is available only for 2020. While this value may have been impacted by the economic effects of the COVID-19 pandemic in 2020, we focus on energy intensity, i.e. energy consumption relative to sales, and since both are measured as of 2020 we believe this ratio is likely relatively constant across years.

> 10Acemoglu et al. (2020) define establishments of a firm in Lightcast (previously called Burning Glass) data as the collection of firm job vacancies in each US commuting zone. We define subnational regions as the highest-level administrative subdivision in each country, Territorial Level 2 (TL2) in OECD (2021).

> 11These Factset firm-level entities are defined as _de jure_ corporate institutions (e.g., firms and affiliates).
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10 for the months in between these observed postings for which we assume the establishment was operational, but not hiring. In total, 23.5% of all green technology-related postings in our sample countries in this period are matched to a firm with both CDP energy consumption and Factset sales data.

# **3. Differential impact of the energy crisis**

## **3.1 Background on the energy crisis**

The 2022 global energy crisis began in the aftermath of the COVID-19 pandemic, when natural gas prices in the EU and Asia started to increase in response to a supply-demand gap and a tight liquefied natural gas (LNG) market. Rising prices subsequently escalated into a widespread global energy crisis following the Russian invasion of Ukraine in February 2022. The invasion, and the international sanctions that followed, resulted in a reduction in natural gas imports from Russia, the suspension of the Nord Stream 2 pipeline’s certification and the halting of gas deliveries through the Nord Stream 1 pipeline. In the first half of 2022, Russia’s gas exports via pipeline to the European Union and the United Kingdom fell by nearly 50 percent compared with the previous five-year average (EIA, 2022). These supply shortages were exacerbated by uncertainty and concerns of further reductions as well as reduced hydroelectric and nuclear electricity output that pushed gas consumption for electricity (Tertre, 2023).

This led to soaring natural gas prices in parts of the world dependent on Russian imports and in other countries that competed in the global LNG market. In Europe the benchmark natural gas price peaked in August 2022 at a record high of $69.98 USD per MMBtu, a $42.09 increase from the January 2022 price, while the benchmark LNG price in Asia increased by $27.33 over the same period. Natural gas prices were comparatively stable in countries not reliant on imports, with the Australian benchmark price increasing by $4.79, the US benchmark by $4.55, and the Canadian benchmark falling by $0.99 over this period (see Supplementary Appendix Figure A3(a)).

In the EU, the increase in natural gas prices translated into an increase in wholesale electricity prices in all countries, although there was also sizable variation in the extent to which countries
11 were affected, as indicated on the Y-axis of Figure 2.<sup>13</sup> The 2022 energy crisis therefore provides a novel context in which to study the relationship between energy price shocks and the diffusion of green technologies, as it was large in magnitude, its immediate trigger (the Russian invasion of Ukraine) was unexpected, and there was substantial international variation in price increases.

# **3.2 Empirical specification**

Our empirical specification aims to estimate the effect of the energy shock on establishmentlevel diffusion of green technologies. As energy prices are unlikely to be exogenous to green technology hiring, we use measures of pre-crisis exposure to the price shock, interacted with the timing of the energy crisis, as our explanatory variables. We discuss the validity of this approach further below.

We use a difference-in-differences framework, which allows for comparison between establishments that were more or less exposed _ex-ante_ to the crisis. We estimate the following regression on a panel of establishments in each month from January 2021 to December 2022: where the dependent variable IHS( _green j,i,c,t_ ) is the inverse hyperbolic sine (IHS) transformed number of green technology-related job postings by establishment _j_ (belonging to firm _i_ and located in country _c_ ) in year-month _t_ .<sup>14</sup> The explanatory variables of interest are **1** ( _YearMontht ≥ Feb_ 2022) an indicator equal to 1 if _YearMontht_ is February 2022 or later, when the Russian invasion of Ukraine started, _Exposurec_ the 2019 ex-ante country exposure to the energy price shock

> 13See also Supplementary Appendix Figure A3(b), which shows that the European country with the 10th percentile electricity price shock saw a rise in electricity prices only a third as large as the EU country with the 90th percentile electricity price shock in 2022.

> 14We take the IHS to balance the panel such that firms that do not post green technology-related jobs in a given month are not dropped from the sample. See Section 4.1 for robustness checks and interpretation.
12 (imported natural gas dependence), and _EnergyIntensityi_ an estimate of ex-ante _firm_ -level energy intensity in 2020.<sup>15</sup> Each regression is run at the level of establishments _j_ , defined as the firm-country-region triplet of firm _i_ in a certain subnational region of a country _c_ , in a month _t_ . We include year-month fixed effects as well as establishment fixed effects to account for time-invariant characteristics, ensuring that the differential effects observed are driven by exposure to the energy crisis rather than other factors. We additionally control for the interaction between **1** ( _YearMontht ≥ Feb_ 2022) and _Exposurec_ as well as the interaction between **1** ( _YearMontht ≥ Feb_ 2022) and _EnergyIntensityi_ .

In addition to the baseline regression analysis, we employ an event study design to investigate the timing of the differential effects, evaluating monthly changes in green technology-related hiring from January 2021 to December 2022. The event study approach allows for a more granular understanding of how firms responded month-by-month to the energy crisis, identifying the magnitude of the effects over time. We estimate the following event study regressions over the same establishment by year-month panel: where, as above, the dependent variable IHS( _green j,i,c,t_ ) is the inverse hyperbolic sine transformed number of green technology-related job postings by establishment _j_ (belonging to firm _i_ and located in country _c_ ) in year-month _t_ . We estimate the differential effect of the energy shock on hiring each month via the interaction between the year-month indicator _YearMontht_ and the same explanatory variables of interest above: country ex-ante exposure to the energy price shock _Exposurec_ and _firm_ -level energy intensity _EnergyIntensityi_ . Each regression is again run at the level of subnational establishments _j_ , defined as above.

All event study regressions are run at the monthly level beginning in 2021Q2 (the omitted base period) to 2022Q4.<sup>16</sup> We cluster standard errors at the country level throughout. Further

> 15The data on energy consumption was only available from 2020.

> 16While the Russia-Ukraine invasion began in 2022Q1, there were some signs of anticipation of the invasion, with prices rising in the final half of 2021, and hence we take 2021Q2 as the base period.
13 details on the econometric specification, robustness checks, and results can be found in the Supplementary Appendix.

For a causal interpretation, the ex-ante exposure measures would need to be uncorrelated with the error term (which could include any confounding factors) conditional on establishment fixed effects. To show this, following Goldsmith-Pinkham, Sorkin and Swift (2020), we regress each ex-ante exposure measure on a range of possible confounders. We first regress the countrylevel exposure variable, the share of total energy supply from imported natural gas in 2019, on other country-level baseline (pre-2020) characteristics. Results are provided in Column (1) of Supplementary Appendix Table A3, showing country exposure is not correlated with any ex-ante country characteristics including distance to Russia, income per capita, emissions intensity of GDP, manufacturing share of GDP, renewables share of electricity, or the human capital index.

We similarly regress the firm-level exposure measure, energy intensity, and firm-by-countrylevel exposure on other ex-ante firm characteristics (as of 2019). The results are provided in Columns (2) and (3) of Supplementary Appendix Table A3. There is a weak negative correlation between energy intensity and the firm’s debt-to-assets ratio and a weak positive correlation with gross profit margins, but no correlation between energy intensity and other key variables like firm size, capital intensity or labor productivity. In addition, these correlations no longer hold when exposure is measured as the product of the firm-level energy intensity and country-level import dependence as in our regression analysis. More details on these regressions is provided in the Supplementary Appendix.

# **4. Results**

## **4.1 Baseline regression results**

Results from our baseline difference-in-differences regression specification in Equation 1 are included in Column (1) of Table 1. The estimated coefficient in the first row, on the interaction between the post-crisis time indicator and pre-crisis natural gas import dependence, is positive and significant at the 1% level, demonstrating that establishments in more exposed countries saw a larger post-crisis increase in job postings indicative of green technology adoption.

Additionally, we find that within more-exposed countries, firms with higher pre-crisis energy
14 intensity had greater demand for jobs related to green technologies. The interaction term between firm-level energy intensity and country-level import dependence in the second row also yields a positive coefficient, significant at the 5 percent level. This shows that more energy-intensive businesses, which were more vulnerable to rising energy costs, increased their posting of green technology-related roles after the energy shock relative to their less energy-intensive peers.

||Baseline||Robustness Che|cks||
|---|---|---|---|---|---|
||Dep. var. IHS(# of<br>green tech. postings)<br>(1)|Drop fossil<br>fuel frms<br>(2)|Control for<br>fuel subsidies<br>(3)|Control for<br>industrial policy<br>(4)|Dep. var. **1**(Any<br>green tech. posting)<br>(5)|
|Post-February 2022||||||
|_×_Country exposure|0.153***<br>(0.049)|0.153***<br>(0.050)|0.189***<br>(0.045)|0.105***<br>(0.019)|0.086***<br>(0.027)|
|Post-February 2022||||||
|_×_Country exposure<br>_×_Firm exposure|0.034**|0.038**|0.044**|0.038***|0.024**|
||(0.015)|(0.015)|(0.015)|(0.010)|(0.009)|
|Year-month FE|Yes|Yes|Yes|Yes|Yes|
|Establishment FE<br>Post-Feb ’22|Yes|Yes|Yes|Yes|Yes|
|_×_Firm Exposure|Yes|Yes|Yes|Yes|Yes|
|Observations|1,982,647|1,948,455|1,954,918|1,796,280|1,982,647|
|Adjusted R-squared|0.608|0.608|0.609|0.613|0.444|

Note: Regressions of the number of green technology-related job postings by an establishment each month on indicators for a month after February 2022 interacted with measures of the establishment’s ex-ante exposure to the energy price shock. Standard errors clustered at the country level in parentheses. Sample is firm establishments (defined by a firm-country-region triplet) with monthly data spanning from Q2 2021 to Q4 2022 (with singleton observations automatically dropped). Dependent variable in Columns (1) to (4) is inverse hyperbolic sine of total number of green technology-related job postings by establishment in each month. Country exposure measure is the fraction of total energy use from imported natural gas in 2019. Firm exposure is energy intensity, calculated as fuel and SHEC consumption of the firm divided by its sales (both in 2020). Only firms with information in CDP included in the regressions. Coefficients on Post-February 2022 × Country exposure × Firm exposure multiplied by 10,000. Robustness check in Column (2) excludes establishments of fossil fuel firms, defined those with NAICS codes categorized as upstream or midstream oil and gas or upstream coal industries in Greenspon and Raimi (2024); Column (3) includes a control for fossil fuel subsidies (% of GDP) from the OECD Inventory of Support Measures for Fossil Fuels; Column (4) includes a control for green industrial policies from the Global Trade Alert database; and Column (5) repeats the baseline analysis but with a dependent variable equal to one if an establishment had any green technology-related job postings that month. Levels of significance denoted by (*) _<_ 0.10, (**) _<_ 0.05, and (***) _<_ 0.01.

# Table 1: Baseline results and robustness

We show these results are robust to a range of alternative specifications in Columns 2 through 5 of Table 1.<sup>17</sup> In Column (2) we exclude 2,948 establishments of fossil fuel producing-firms,

# which may have been positively impacted by the energy shock, and find virtually unchanged

17In addition, placebo test results provided in Supplementary Appendix Table A4 show that establishments more exposed to the 2022 energy price shock did not differentially increase job openings unrelated to green technologies after February 2022.
15 results. We next add measures of potentially confounding national policy changes.<sup>18</sup> In Column (3) we control for fossil fuel subsidies (as % of GDP), since governments in more exposed countries may have been more likely to respond to the crisis by subsidizing more affected firms. Including this control slightly increases the magnitude of our coefficient estimates, consistent with the targeting of subsidies to dampen the energy price impact lowering our results. In Column (4) we control for national industrial policy support for solar and wind energy and electric vehicles, using policies appearing in the Global Trade Alert database targeting HS codes for these technologies as outlined in Rosenow and Mealy (2024). We find that while the number of these industrial policies is correlated with green technology-related job postings, controlling for the former does not substantially weaken the estimated impact of pre-crisis exposure in differential establishment adoption of green technologies. Finally, to address concerns with the use of the IHS transformation, notably around coefficient interpretation, following Chen and Roth (2023), the specification in Column (5) repeats the baseline analysis but with a dependent variable that equals one if an establishment had any green technology-related job postings in year-month _t_ and zero otherwise. The statistically significant result at the 1 percent level indicates that the impact of the energy shock on demand for green technologies, as evidenced by related job openings, is also strong at the extensive margin. The results at this margin have a more straightforward interpretation:

# **4.2 Event study results: Timing and magnitude of effects**

We next examine how the increase in green technology-related hiring evolved throughout the 2022 energy crisis. Event study results presented in Figure 3 plot estimates of the coefficient on the interaction between the country- and firm-level exposure measures and a year-month indicator, illustrating the effect on establishments’ demand for green technology-related hiring in each month (relative to the 2021Q2 omitted period).

Figure 3 shows that more energy-intensive establishments in countries with higher levels of natural gas dependence saw a relative increase in green technology adoption after the onset of the energy crisis in February 2022, in line with the difference-in-difference results in Table 1. Specifically, we show that the differential increase in job openings began in March 2022, just one

> 18 See full results and discussion in Supplementary Appendix Section A.5.1.
16 <!-- Start of picture text --> Jul21 Aug21 Sep21 Oct21 Nov21 Dec21 Jan22 Feb22 Mar22 Apr22 May22 Jun22 Jul22 Aug22 Sep22 Oct22 Nov22 Dec22<br>.00008<br>.00006<br>.00004<br>Coef of month x exposure<br>.00002<br>0<br><!-- End of picture text --> _Notes:_ This figure displays the estimated coefficients and 95% confidence intervals of the interaction between each year-month and the interaction of ex-ante natural gas import dependence and energy intensity from a regression of the IHS of green technology-related job postings as described in Equation (2). Regressions include year-month and establishment fixed effects. Standard errors are clustered by country.

# Figure 3: Timing of green technology openings as a function of exposure

month after the Russian invasion of Ukraine. This suggests more energy-intensive businesses in countries more dependent on natural gas imports responded rapidly to the energy shock, rather than waiting for long-term energy price trends to fully materialize before taking action. Instead, they began adjusting their labor demand in anticipation of prolonged energy disruptions and rising operational costs. This is underlined by the coefficient estimates generally growing in magnitude over the course of 2022 as the cumulative impact of the crisis fully materialized. The effect was most pronounced in December 2022.
17

## **4.3 Heterogeneity of effects**

This section explores heterogeneity in the adoption of green technologies in response to the 2022 energy crisis. We first explore the results by technology, as shown in Figure 4(a). This repeats the specification in Equation 1 but keeps job postings from specific technology groups only as the outcome variable. The results show that the differential increase in total green technology postings by more-exposed establishments after February 2022 was driven by generation technologies. This includes those related to energy generation, including renewable energy and applications of renewable energy, energy storage, heatpumps, biofuels, and decentralized energy systems.

We next explore the results by country and job skill group among the generation technologies driving our overall results. Figure 4(b) shows results from regressions run separately for each country in our sample, exploiting only the variation in firm energy intensity within countries. The effects with the greatest order of magnitude were for the Netherlands, Switzerland, Italy, Germany and the UK. In contrast, other countries and notably the United States, did not see significant effects for more energy-intensive establishments, likely reflecting their lower reliance on imported fossil fuels and therefore exposure to the energy price shock. Finally, in a similar specification as in Equation 1, we explore results by skill level. Figure 4(c) plots coefficient estimates when the dependent variable is the IHS-transformed number of generation technologyrelated job openings for occupations at each skill level. The rise in labor demand for these technologies after February 2022 was concentrated almost solely in high-skilled jobs, while lowand medium- skilled jobs saw no statistically significant effect.

# **5. Conclusion**

This paper constructs new measures of the diffusion of a comprehensive range of green technologies using online job postings data from 16 advanced economies from 2014 to 2022. It shows that growth in these job openings rapidly accelerated in 2022, and explores the role of the 2022 global energy crisis in driving this accelerated diffusion. It shows that establishments with greater exposure to the crisis, as measured by their pre-crisis energy intensity and location in countries with greater dependence on natural gas imports, saw a relative increase in openings related to green technologies after February 2022. This increased hiring was concentrated in generation
<!-- Start of picture text --> All technologies | 1<br>Vehicles 44|<br>Generation |4<br>|<br>Consumer !<br>Energy-use ——<br>|<br>Waste/materials :<br>+05 0 05 1<br>Coefficient on Post-Feb '22 x Firm exposure x Country exposure<br><!-- End of picture text -->

<!-- Start of picture text --> Netherlands | —-—4<br>Switzerland |><br>GermanyItaly |1 -—e——re<br>UK | hea<br>Canada —<br>Luxembourg 4<br>USA +<br>Australiai 4'<br>Singapore +<br>France ——<br>Belgium ——<br>Austria -————*+——4<br>Norway 3._4-]9)]q0q_]|]_q]*]TMq]qSqaqaqaUt_YSsSH<br>Denmark i<br>-1 -.05 0 05 a<br>Coefficient on Post-Feb '22 x Firm exposure<br><!-- End of picture text -->

<!-- Start of picture text --> High Skill I ——_e—_§_——<br>|<br>|<br>I<br>|<br>|<br>|<br>|<br>|<br>|<br>I<br>|<br>Low Skill 14<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>Medium Skill<br>-.5 0 5 1<br>Coefficient on Post-Feb '22 x Firm exposure x Country exposure<br><!-- End of picture text -->