metadata: authors: 0: author: Bastos,Paulo S. R.
1: author: Castro,Lucio
count: World
docna: 0: docna: The Emergence and Diffusion of Green
            Technologies : Firm-level Evidence from Textual Analysis of
            Patents and Earnings Calls
docty: Policy Research Working Paper
abstracts: cdata!: This paper uncovers new stylized facts
            on the emergence and diffusion of green technologies across
            countries, sectors, and firms. It draws on the textual
            analysis of patents and corporate earnings calls matched
            with multi-country, firm-level panel data for
            2012&#x2013;2021. The paper documents the growing
            importance of green technologies after 2019, as revealed by
            a rapid increase in the share of earnings calls transcripts
            mentioning keywords associated with them. Among initially
            high-emissions firms, those that mentioned green
            technologies in earnings calls tended to observe a decline
            in carbon emissions in subsequent years. Buyer-supplier
            relationships and innovation partnerships with these firms,
            especially when they had high-emissions intensity, are
            systematically linked with the diffusion of green technologies.
display_title: The Emergence and Diffusion of Green
            Technologies : Firm-level Evidence from Textual Analysis of
            Patents and Earnings Calls
listing_relative_url: /research/2025/01/34445815/emergence-diffusion-green-technologies-firm-level-evidence-textual-analysis-patents-earnings-calls
topicv3: Economic Growth m1326241 471,Climate Change m1326829 567,Trade m1326924 698
publishtoextweb_dt: 2025-01-14T00:00:00Z
dois: 10.1596/1813-9450-11036
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 45
author_keys: 0: author_key: 1210378
1: author_key: 607731
model_extractions: Policy Research Working Paper 11036 The Emergence and Diffusion of Green Technologies Firm-level Evidence from Textual Analysis of Patents and Earnings Calls _Paulo Bastos Lucio Castro_ Development Research Group & Prosperity Vertical January 2025
Policy Research Working Paper 11036

# **Abstract**

This paper uncovers new stylized facts on the emergence and diffusion of green technologies across countries, sectors, and firms. It draws on the textual analysis of patents and corporate earnings calls matched with multi-country, firm-level panel data for 2012–2021. The paper documents the growing importance of green technologies after 2019, as revealed by a rapid increase in the share of earnings calls transcripts mentioning keywords associated with them. Among initially high-emissions firms, those that mentioned green technologies in earnings calls tended to observe a decline in carbon emissions in subsequent years. Buyer-supplier relationships and innovation partnerships with these firms, especially when they had high-emissions intensity, are systematically linked with the diffusion of green technologies.

This paper is a product of the Development Research Group, Development Economics and the Prosperity Vertical. 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 and lcastro@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
**The Emergence and Diffusion of Green Technologies: Firm-level Evidence from Textual Analysis of Patents and Earnings Calls**<sup>∗</sup>

Paulo Bastos<sup>†</sup> Lucio Castro<sup>‡</sup>

**Keywords** : Green technologies, earnings calls, patents, carbon emissions, firm-to-firm linkages. **JEL Classification** : F6, O3, Q5.

> ∗We are grateful to Juan Tapia Montenegro, Samuel Asuquo Edet and Matias Garibotti for excellent research assistance. We are also grateful to Juan Pablo Rud and seminar participants for very useful comments. The authors gratefully acknowledge financial support from 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.

> †Development Research Group, World Bank; CEPR; and REM/UECE. E-mail: pbastos@worldbank.org.

> ‡World Bank. Email: lcastro@worldbank.org
# **1 Introduction**

There is now wide agreement that progress towards the decarbonization of the global economy will require a significant increase in the utilization of technologies that contribute to reducing CO2 emissions. While these green technologies are becoming more ubiquitous, there is still little systematic evidence on their emergence and diffusion across firms, sectors, and countries, and the economic mechanisms underlying their diffusion. In this paper, we draw on the textual analysis of patents and earnings conference calls matched with multi-country, firm-level panel data for 2012-2021 to provide systematic evidence on these issues.

We begin by establishing new stylized facts about the emergence and dissemination of green technologies over the last two decades using textual analysis of patents. We identify a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact on climate change mentioned in the World Intellectual Property Organization (WIPO) green patents database. This approach allows us to identify in a systematic way green technologies. The analysis of patent data for the period 2000-2021 reveals that green innovation was primarily driven by new technologies from the construction, energy, and transportation sectors, including technologies such as biofuels, geothermal energy, LED technology, drones, and hybrid/electric vehicles. We then provide evidence on patterns of adoption and diffusion of these green technologies across firms and countries, drawing on the textual analysis of earnings conference calls transcripts (ECTs). We follow a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying business-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in conference calls that publicly listed firms host with financial analysts. This approach has been increasingly used by researchers to measure firm uncertainty (Hassan, Hollander, van Lent, and Tahoun, 2019), firm sentiment (Hassan, Hollander, van Lent, and Tahoun, 2020), and the diffusion of disruptive technologies (Bloom, Hassan, Kalyani, Lerner, and Tahoun, 2021). The rationale behind it is that earnings calls are a venue in which senior management responds directly to questions from market participants about the firm’s prospects. They consist of a management presentation followed by a Q&A session, which requires management to comment on subjects they might not otherwise have voluntarily proffered. Thus, the extent to which the adoption of green technologies is discussed in earnings conference calls serves as a proxy of their importance for both the firm’s management and market participants.

Using unique firm identifiers, we were able to merge these firm-level textual-based indicators 1
with information on inter-firm relationships and firm-level information for the period 2012 to 2021. These multi-country firm-level data cover buyer-supplier relationships, thereby allowing us to inspect for evidence of dissemination of green technologies across global value chains. Furthermore, they contain information on innovation partnerships between firms.

The textual analysis of earnings conference calls transcripts points to a rapidly growing importance of green technologies after 2019, as revealed by a rapid increase in the share of ECTs mentioning keywords associated with specific green technologies, as well as in the set of firms that have ECTs mentioning these green technologies. This rise is primarily driven by a few green technologies, including construction material, carbon capture technology, hybrid/electric vehicles, and wind turbines, which tend to be mentioned together in the same ECT. It is concentrated in firms operating in a few industries—notably manufacturing and utilities—and among larger firms within sectors. We also find evidence of an increased geographical spread in the location of these green firms, with a growing presence in large emerging countries in Asia, Latin America, and the Middle East and North Africa, in addition to North America and Europe. Importantly, we find that high-emissions firms that mention green technologies in their ECTs are significantly more likely to observe a subsequent reduction in their carbon emissions, which supports the hypothesis that ECTs capture firm investments in green technologies among these firms.

We then examine the extent to which the diffusion of green technologies between firms is shaped by firm-to-firm linkages, notably buyer-supplier relationships and innovation partnerships. We find robust evidence that suppliers and buyers of firms that mention green technology keywords in their earnings calls are significantly more likely to mention them as well in their own ECTs. These effects are especially relevant when firms are linked to high-emissions firms that mention green technologies in their own ECTs. Our data consists mainly of multinational enterprises (MNEs), but we also find some evidence of diffusion of green technologies through firms-to-firm linkages to (and from) firms that are not MNEs.

In addition to the papers cited above, this paper contributes to several strands of literature. Prior research on low-carbon technologies has often confined its attention to the diffusion of a few technologies in a specific sector or country (see, e.g., Costantini, Crespi, and Palma (2017)). In addition, the literature on low carbon technologies has focused mostly on their invention and production (Popp, 2002, 2022; Aghion, Dechezleprˆetre, H ́emous, Martin, and Van Reenen, 2016; Dechezleprˆetre, Glachant, and M ́eni`ere, 2013) or on the costs of adopting them (Way, Ives, Mealy, and Farmer, 2022; Grubb, Drummond, Poncia, McDowall, Popp, Samadi, Penasco, Gillingham, Smulders, Glachant, Hassall, Mizuno, Rubin, Dechezleprˆetre, and Pavan, 2021), 2
but has had relatively little to say about whether, how and to whom they have diffused across countries, industries, and firms. An important exception is a recent paper by Bastos, Greenspon, Stapleton, and Taglioni (2024), who draw on textual analysis of online job postings to document the diffusion of low carbon technologies across 35 countries, and examine econometrically the role of the 2022 global energy crisis in accelerating their diffusion across countries and firms. The current paper provides complementary stylized facts on both the emergence and diffusion of green technologies by drawing on the textual analysis of patents and ECTs in a wide range of countries; examining the link between green technology adoption and subsequent changes in emissions at the firm-level; and documenting the role of buyer-supplier links in the diffusion of green technologies. Finally, our paper is also related to the broader literature on technology diffusion, including Comin and Hobijn (2004), Comin and Hobijn (2010), Comin and Mestieri (2018), Bloom, Hassan, Kalyani, Lerner, and Tahoun (2021) and Bastos, Stapleton, Taglioni, and Wei (2024). Our approach for examining the role of firm-to-firm linkages in the diffusion of low carbon technologies is related to that adopted in Bastos, Stapleton, Taglioni, and Wei (2024), who use data on online job postings and information on firm-to-firm linkages to examine the spread of new technologies, but do not focus on green technologies.

The remainder of the paper is organized as follows. Section 2 describes the various sources of data, and our approach to identify green technologies and associated keywords. Section 3 presents stylized facts on innovation and dissemination of green technologies based on patent data from the WIPO green database, which allows us to assess the dissemination of the most novel and influential green technologies. Section 4 presents evidence about the diffusion of green technologies among firms, sectors, and countries using textual analysis of earnings calls transcripts. Section 5 reports evidence of patterns of diffusion of green technologies through firm-to-firm relationships. Section 6 concludes the paper with the main takeaways.

# **2 Data sources and textual analysis methodology**

We identify a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact on climate change mentioned in the World Intellectual Property Organization (WIPO) green patents database. Using these keywords and textual analysis of patents and earnings conference calls, we build a multi-country firm-year panel data set of exposure to green construction technologies spanning the period 2012-2022 and covering 10,000 firms in 79 countries. This section describes the data sources and our approach in more detail.

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# **2.1 WIPO Green Patents**

We use information from the WIPO green patents data set for the period 2000-2021. This data set contains detailed information on green technology patents, inventions, and technologies from the WIPO Patentscope database, covering seven sectors: Energy, Water, Farming and Forestry, Pollution Waste, Transportation, Construction, and Products, Materials and Processes (PMP). Green technologies are defined by WIPO as “climate friendly technologies that protect the environment, are less polluting, use resources in more sustainable manner, recycle their wastes and products and handle residual waste in a more sustainable manner”. These technologies include know-how procedures, goods and services, and equipment as well as organizational and managerial procedures. WIPO systematically gathers patent data from 193 national Intellectual Property (IP) offices worldwide. The database provides information about the year of filling of the patent, the country where the IP office and the inventor are located, and technical description and status of the technology (e.g. in development, commercialization, etc.).

# **2.2 Earnings calls transcripts**

We use data from Refinitiv Eikon (formerly Thompson Reuters) on the transcripts from quarterly shareholder earnings calls meetings of publicly listed firms from 2012 to 2021, covering 10,554 firms in 82 countries. Earnings calls are key corporate events on the investor relations agenda in which senior management responds directly to questions from financial analysts and other market participants about the firm’s financial performance over the past quarter and, more broadly, discuss current developments (Hollander, Pronk, and Roelofsen, 2010; Hassan, Hollander, van Lent, and Tahoun, 2020). They consist of a management presentation and, importantly, a Q&A session which requires management to comment on subjects they might not otherwise have voluntarily proffered. The extent to which the adoption of green technologies is discussed in earnings conference calls serves as a proxy of their importance for both the firm’s management and market participants. Given that green technologies potentially account for a large share of firms’ investment decisions, they would be expected to feature in conference call discussions. In fact, these data have been increasingly used by researchers to measure firm uncertainty (Hassan, Hollander, van Lent, and Tahoun, 2019), firm sentiment (Hassan, Hollander, van Lent, and Tahoun, 2020), and proxies for whether new technologies are “disruptive” as inferred by being widely discussed in boardrooms (Bloom, Hassan, Kalyani, Lerner, and Tahoun, 2021).

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# **2.3 Factset data on firm linkages and carbon emissions**

We use data on firm relationships from FactSet, a private company specializing in market intelligence and firm networks. FactSet collates data from various sources, including annual reports, investor presentations, press releases, and company websites. Among FactSet’s offerings, the FactSet Fundamentals provides consolidated financial statements and firm information for publicly traded companies, while FactSet Relationships covers supply chain relationships, including suppliers, customers, and partnerships, as well as firm-to-affiliate relations.

The FactSet Revere Supply Chain Relationships database is regarded as one of the most comprehensive sources for global firm supply chain information (Huang, Lin, Liu, and Tang, 2023). This dataset integrates information from official firm filings (10-K, 10-Q, and 8-K reports submitted to the SEC), investor presentations, press releases, and company websites (FactSet, 2021).<sup>1</sup> FactSet analysts normalize relationship types by cross-referencing a firm’s records with its partners’ (“reverse” links), providing a comprehensive coverage of supply chain linkages (Huang, Lin, Liu, and Tang, 2023).

As of March 2024, FactSet actively monitored over 54,000 “source” companies, resulting in a dataset of more than 2 million relationship links involving approximately 360,000 entities. Each link includes information on start and end dates, with historical data available from 2003 and global coverage starting in 2014, expanding to Latin America in 2016.<sup>2</sup> The database specifies the nature of each relationship. For our study, we focus on buyer and supplier relationships. At any given time, we observe the parent and ultimate parent firm of a company, and any subsidiaries, if applicable.<sup>3</sup> The dataset also includes firm name, industry affiliation, headquarters country, and entity type, which we use in our analysis. Annual sales data, converted to constant 2020 U.S. dollars, are available but not used in our study.

We combine the two aforementioned FactSet data modules to construct a final dataset of 169,420 entities with bilateral relationship information from 2012 to 2022. These entities are defined as de jure corporate institutions (e.g., firms and affiliates), with 27% (46,000 entities) having available information from FactSet Fundamentals for at least one year within the sample period.

To measure carbon emissions at the firm level, we draw on the ISS ESG Carbon and Climate Impact (ICC) data from FactSet. This data set provides comprehensive coverage of emissions

> 1In financial reporting, a 10-Q is a company’s quarterly report with unaudited financial statements, while the 10-K is an annual report with audited financials. An 8-K updates shareholders on significant unscheduled events. U.S. listed firms must detail any customer accounting for more than 10% of revenue (Gofman and Wu, 2022), with some firms voluntarily disclosing additional customers (Huang, Lin, Liu, and Tang, 2023).

> 2Many Latin American records from 2014 suggest backward updating for these firms.

> 3The parent or ultimate parent is identical to the company itself for self-owned firms.

5
data, accounting for all the emissions of the firm (including subsidiaries), spanning from 2012 to 2021. The ICC includes detailed information on companies’ Scope 1, Scope 2, and Scope 3 emissions and emissions’ intensity, sourced from official reports such as Sustainability and ESG reports, the Carbon Disclosure Project (CDP), as well as estimated emissions from ISS ESG when official data is not available. For the analysis, we focus specifically on Scope 1 emissions since these are direct GHG emissions from sources owned or controlled by the firm.

# **2.4 List of green technologies and associated keywords**

In line with the approach of Bloom, Hassan, Kalyani, Lerner, and Tahoun (2021), we use the WIPO green patent data and the ECTs to identify biagrams or a set of two words that represent each green technology. We follow several steps. First, we extract the summary of the green technologies included in each patent in the WIPO database. Second, we transform the summary’s text into biagrams by applying a text recognition algorithm, removing numbers, special characters, and stop words (e.g. of, the, from, etc.) and converting each plural word into its singular form (eg. batteries, battery). Third, we drop bigrams that are not included in at least 20 patents and prior to the year 2000. The rationale behind this choice is to include not only the most influential but also the most novel technologies in our analysis. Fourth, we perform a human audit of the resulting biagrams. Fifth, we train a word embedding algorithm (word2ovec) with the patent’s summary to identify similar biagrams to each biagram and dropped nontechnical bigrams using Google Bigrams. Sixth, we perform a human audit of the new set of biagrams. Seventh, we train the word embedding algorithm with ECTs to capture business-like language referring to technical bigrams. Finally, we restrict the focus to technologies mentioned in more than 50 ECTs during the 2012-2021 period. Table A1 in the Appendix contains the final set of green technologies considered and the corresponding bigrams searched in the textual analysis of patents and earnings calls. Table A2 reports the number of ECTs that mention each technology in the period 2012-2021. As can be observed in this Table, the technologies that appear more frequently in ECTs include construction material, air conditioning, wind turbines, LED technology, hybrid/electric vehicles, Li-ion batteries, carbon capture technologies, transgenic crops, biofuels, and solar energy.

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# **3 Evidence on the emergence and dissemination of green technologies identified in patents**

In this section, we present the main findings of our analysis of innovation and diffusion of green technologies based on patent data from the WIPO green database. This analysis allows us to assess the dissemination of the most novel and influential green technologies. As discussed above, we follow a recent literature seeking to infer innovation and dissemination of technologies using general-text recognition methods for identifying patents associated with specific technologies.

Figure 1 reports the share of each WIPO green sector in the total mentions of green technologies in patents. The evidence in this figure reveals that green technologies belonging to the WIPO green sectors energy and construction account for the vast majority of mentions in patents over the period 2000-2021, accounting for about 80 percent of total mentions in most years. Transportation is the third most important sector, and its relative importance experienced significant growth in the mid-2000s, remaining relatively stable thereafter. Technologies from the PMP and Farming and Forestry sectors are the least important, accounting together for less than 10 percent of total patent mentions in most years.

Figure 2 reports the percentage of patents mentions by technology within each WIPO green sector over the same period. Among green technologies from the construction sector, we observe a clear downward trend for the relative importance of air conditioning, and a growing importance of LED technology, sensors, and construction material. Since 2005, LED technology accounted for about 40 percent of total mentions in patents within the construction sector, while the other technologies stabilized at around 20 percent each. In the energy sector, we observe a growing importance of biofuels, geothermal energy, and inverter technology. In transportation, drones accounted for a progressively higher share of total mentions, reaching about 80 percent in the early 2000s and stabilizing around 70 percent between 2015 and 2020. Among the PMP sector, carbon capture became the most important technology since 2005, reaching about 60 percent of total mentions at the end of the sample period. In Farming and Forestry, transgenic crops became the most important technology (as measured by mentions in patents) since 2012, reaching about 70 percent of total mentions in this sector at the end of the sample period.

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# **4 Evidence on the diffusion of green technologies as revealed by ECTs**

We now turn to the evidence on patterns of adoption and diffusion of these green technologies across firms and countries drawing on the textual analysis of earnings conference calls transcripts. As discussed above, we follow a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying technical-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in the quarterly earnings conference calls that publicly listed firms host with financial analysts. The rationale behind this approach is that earnings calls are a venue in which senior management responds directly to questions from market participants about the firm’s prospects. They consist of a management presentation followed by a Q&A session, which requires management to comment on subjects they might not otherwise have voluntarily proffered. Thus, the extent to which the adoption of green technologies feature in ECTs serves as a proxy of their importance for both the firm’s management and market participants.

The evidence in Figure 3 reveals that green technologies have featured more prominently in ECTs and firms since 2015. In 2015, green technologies were mentioned in about 5 percent of ECTs and 8 percent of firms. By 2021, the last year of our sample period, green technologies were mentioned in 10 percent of ECTs and by 18 percent of firms. This increase in the relevance of green technologies in quarterly earnings conference calls was especially marked after 2019, rising from about 6 percent to 10 percent of ECTs and from 12 to 18 percent of firms. Panel A in Figure A1 in the Appendix reveals that a similar pattern is observed when looking at the economic relevance of firms mentioning green technologies in their ECTs, which increased from around 20 percent of sales in 2019 to almost 35 percent of sales in 2021. This suggests that firms mentioning green technologies in ECTs tend to be relatively large, a pattern to which we will return below.

Looking across WIPO green sectors in Figure 4, we observe that this marked increase observed after 2019 was accompanied by a rise in the relative importance of technologies from the PMP sector, whose relative importance became similar to those from the energy sector. Mentions of technologies from the construction sector accounted for the majority of mentions in 2021, but their relative importance observed a downward trend since 2014. Figure 5 reveals that a few technologies drove these trends. In the PMP sector, carbon capture experienced a marked increase after 2019. In the energy sector, we observe a significant increase in the relative importance of Wind Turbines and Li-ion Batteries after 2019. In the construction sector, we ob8
serve a significant rise in mentions of green construction material. Finally, in the transportation sector, we observe a marked increase in mentions of hydro-electric vehicles after 2019.

Interestingly, Figure 6 reveals that green technologies are often not mentioned in isolation in ECTs. The figure depicts a network representation of the relationship between the green technologies identified in ECTs over the period 2012-2021. The position of the nodes indicates how connected each technology is in the entire network, with technologies in the center being the most frequently mentioned in boardroom discussions. The closeness between technologies indicates how related they are between them, i.e. how often they are mentioned together in the same ECT. The results reveal that Construction Material, LED technologies, Air conditioning, hybrid-electric vehicles wind turbines, and Li-ion batteries are the most central technologies, and show a high degree of closeness among themselves.

Figure A2 in the Appendix depicts the distribution of ECTs mentioning green technologies across the NAICS sectors to which the corresponding firm belongs. Panel A in this figure reveals that manufacturing and utilities are the main sectors in which these firms operate. In 2021, manufacturing firms accounted for about 60 percent of ECTs mentioning green technologies, while firms in the utilities sector accounted for more than 25 percent of total mentions. Panel B reveals that, in these two sectors, the proportion of ECTs mentioning green technologies was clearly the largest among all sectors, and observed a significant rise after 2019.

As discussed above, by the end of the sample period firms mentioning green technologies in their ECTs account for a relatively large share of total sales. This is in part because they tend to be relatively large firms. The evidence in column (1) of Table 1 reveals for technologies within each WIPO Green sector, the probability of mentioning green technologies is positively and significantly associated with firm sales. These regressions include year, sector and country fixed effects and hence these positive correlations are observed within these cells. The coefficients tend to be stronger for technologies from construction and energy, but are also positive and significant for PMP and Transportation. Only for Farming and Forestry we do not observe a significant correlation with firm size. Th estimates in column (2) reveal that a broadly similar pattern can be observed for firms’ operating income, which also displays a positive correlation with the probability of mentioning green technologies in construction, energy and transportation. Figure A4 in the Appendix reveals that a positive association between firm sales and the likelihood of mentioning green technologies in ECTs is also observed within each NAICS sector. The sole exceptions are for firms in accommodation and food services and from finance and insurance.

Figure A6 in the Appendix shows the geographic distribution of green firms per capita in the 2012-2017 period and the 2018-2021 period. The evidence in this figure can be compared 9
with the distribution of firms from the full sample in the same period (Figure A5). Taken together, the analysis of these figures reveals that green technologies are mainly used in highincome countries and large developing countries such as China, India, the Russian Federation and Brazil. It also reveals evidence of some diffusion to these and other emerging economies in the period 2018-2021, as revealed by a growing presence of firms with ECTs mentioning green technologies in these countries.

Although the existing literature suggests that the mention of green technologies in ECTs is likely to be a suitable proxy for the adoption of green technologies, this approach may be subject to some measurement error, for example, if firms mention green technologies in ECTs but do not invest in them, or if these investments are relatively small to have a meaningful impact on carbon emissions. In Table 2, we provide more direct evidence on these concerns. We examine the extent to which the presence of keywords associated with green technologies in ECTs is followed by a decline in carbon emissions in the corresponding firms. The dependent variable is the log of Scope 1 emissions of the firm, while the independent variable is a dummy variable that equals 1 in the year the firm has an ECT mentioning at least one green technology and in all subsequent years. We also include interactions between this variable and a dummy variable for firms that in 2012 had emissions above the sample mean. Our prior is that these firms would have greater incentives to make significant investments in green technologies that reduce their carbon emissions. The regressions include year-fixed effects, firm-fixed effects, and time-varying controls (notably firm sales).

The estimates in columns (1) and (2) of Table 2 reveal that having a green ECT leads to a systematic reduction in carbon emissions among firms that had high-emissions to begin with – and hence had a greater incentive to make significant investments in green technologies. These results are observed in the full sample, as well as in sub-samples referring to ECTs mentioning technologies from the energy sector, farming and forestry, and transportation. For construction and PMP, we also observe a negative coefficient on the interaction term of interest, but this coefficient is not statistically significant.

In Table 3, we examine the robustness of this finding, considering an alternative definition of high-emissions firm. In particular, we adopt a dummy variable for firms that in 2012 had emissions above the 90th percentile of the sample. Once again, the estimates in columns (1) and (2) of Table 3 reveal that having a green ECT leads to a systematic reduction in carbon emissions among firms that had high-emissions to begin with. These results are observed in the full sample, as well as in sub-samples referring to ECTs mentioning technologies from most sectors. The coefficients of interest are larger than in Table 2, consistent with a larger reduction 10
in emissions for firms that had higher emissions to begin with.

# **5 Evidence on the diffusion of green technologies through firmto-firm linkages**

The evidence in the previous section revealed that green technologies featured more prominently in ECTs after 2019, especially among large firms from the manufacturing and energy sectors, and across a wide range of high-income and large emerging economies. We have also shown that the mentioning of green technologies in ECTs tends to be followed by a reduction in scope 1 emissions of the corresponding firms that had high emissions to begin with.

In this section, we examine the extent to which the diffusion of green technologies across firms and countries was systematically linked with firm-to-firm relationships. We consider four types of firm-to-firm linkages: innovation partnerships with a green firm, customer of a green firm, supplier of a green firm, and customer and supplier of a green firm.

Table 3 reports the estimates on the relationship between having a linkage with a green firm in year _t_ (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . We also consider differential effects if the linkages were with a high-emission firm, i.e. with a firm that had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The data set for these regressions is at the related firm pair-year level, where each related firm pair must have had at least one active relationship (customer, supplier or tech partner) at any point between 2012-2021. The yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year.

The estimates in column (1) of this table reveal that firms that had an innovation partnership with a green firm in year _t_ were significantly more likely to also have an ECT mentioning a green technology in year _t_ . Column (2) shows that this relationship is considerably stronger if the linkage was with a firm that had high emissions in 2012 (and for which the evidence above shows a significant decline in carbon emissions after mentioning a green technology in their ECTs). Columns (3)-(4), (5)-(6) and (7)-(8) show a similar association for the other types of linkages considered: respectively, customer of a green firm, supplier of a green firm, and customer and supplier of a green firm. Taken together this evidence suggests that these firm-to-firm linkages are systematically associated with the diffusion of green technologies across firms and countries, especially if the green firm with which the linkage exists was a firm with high-emissions.

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Table 4 reports analogous estimates, but allowing for a 1-year lag in the transmission of green technologies (as measured by mentions in the corresponding ECTs). The evidence shows a broadly similar pattern with positive and significant coefficients for the variables of interest. The sole exception for the coefficient on the interaction term in column (8), which suggests that when firms are simultaneously buyers and suppliers of a green firm, they are significantly likely to become green irrespective of the level of emissions of the green firm to which they were linked.

Since firms may have various types of linkages at the same time, it may be difficult to isolate the role of each type of linkage when considering the full sample. For robustness, in Table 5 we examine if the baseline results are sensitive to the exclusion of firms with multiple linkages. Once again, we observe that the results remain qualitatively very similar.

The majority of firms in our sample have affiliates in other countries and hence are multinational enterprises. In Table 6 we restrict the sample to include only linkages with MNEs. Once again we observe qualitatively similar results. In Table 7, we consider only linkages with non-MNEs. In this case, we are left with a relatively small number of observations, but we still observe positive and significant coefficients for most linkages considered.

In Table 8 we consider instead a sub-sample in which the firm that is linked to the green firm is a MNE, while the green firm could be or not an MNE. Once again, the baseline results remain valid. Finally, in Table 9 we consider a sub-sample in which the linked firm is not an MNE, while the green firm may or not be an MNE. In this case, we are again left with a relatively small number of observations, but we observe once again a positive and significant coefficient for most linkages considered.

Taken together, these estimates suggest that firm-to-firm linkages play an important role in the transmission of green technologies across multinational enterprises, especially when the green firm had high emissions to begin with. We also observe some evidence that these linkages matter when one of the firms involved is not an MNE, but in this case the relationships of interest are estimated from a small sample and are less precise.

# **6 Concluding remarks**

While there is a growing recognition that green technologies are key for reducing the carbon footprint of economic activity at the global level, there is still little systematic evidence on their emergence and diffusion across firms, sectors, and countries, and on the economic mechanisms underlying this process. In this paper, we have used textual analysis of patents and earnings conference calls matched with multi-country, firm-level panel data for 2012-2021 to provide 12
systematic evidence on these issues.

We first uncovered new stylized facts on the emergence and dissemination of green technologies over the last two decades using textual analysis of patents. We identified a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact to climate change mentioned in WIPO green patents data set over the period 2000-2021. The textual analysis of these data suggests that green innovation was primarily driven by new technologies from the construction, energy and transportation sectors, including technologies such as biofuels, geothermal energy, LED technology, drones and hybrid/electric vehicles.

We have then provided evidence on patterns of adoption and diffusion of these green technologies across firms and countries drawing on the textual analysis of ECTs. We followed a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying business-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in conference calls that public listed firms host with financial analysts. Using unique firm identifiers, we were able to merge these firm-level textual based indicators with information on inter-firm relationships and firm-level information for the period 2012-2021. These multi-country firm-level data cover buyer-supplier relationships, thereby allowing us to inspect for evidence of dissemination of green technologies across global value chains. They also contain information on innovation partnerships between firms.

The textual analysis of earnings conference calls transcripts reveals a growing importance of green technologies since 2019, as revealed by a rapid increase in the share of ECTs mentioning keywords associated with specific green technologies, as well as in the set of firms that have ECTs mentioning these green technologies. This increase is primarily driven by a few green technologies, including construction material, carbon capture technology, hybrid/electric vehicles, and wind turbines, which tend to be mentioned together in the same ECT. It is concentrated in firms operating in a few industries— especially manufacturing and utilities and among larger firms within industries. We also find evidence of an increase in the spatial spread of these green firms, with a growing presence in large emerging countries in Asia, Latin America and the Middle East and North Africa, in addition to North America and Europe. Importantly, we find that firms that initially had a high degree of emissions intensity that mention green technologies in their ECTs are significantly more likely to observe a subsequent reduction in carbon emissions. This evidence supports the hypothesis that ECTs capture firm investments in green technologies among these firms.

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We then studied if the diffusion of green technologies between firms is mediated by firmto-firm linkages, notably buyer-supplier relationships and innovation partnerships. We report robust evidence that suppliers and buyers of firms that mention green technologies in their earnings calls are significantly more likely to mention them as well in their own ECTs. These effects are especially relevant when firms are linked to green high-emissions firms. Although our data consists mainly of multinational enterprises (MNEs), we also find some evidence of green technology diffusion through firms-to-firm linkages to (and from) firms that are not MNEs.

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Figure 1: Percentage of patents mentions by WIPO green sector, 2000-2021 <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Construction Energy Farming and Forestry PMP Transportation<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of patents mentions<br><!-- End of picture text --> Notes: The figure depicts the share of patents that belong to each of the 5 WIPO Green sectors in the period 2000-2021.

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Figure 2: Percentage of patents mentions by technology within each WIPO green sector, 20002021 <!-- Start of picture text --> Construction Energy Farming and Forestry<br>80 A i r Cond i t i oning 80 B i ofue l s 80 Soiless Ag r icu l ture<br>LED Technology Geothermal Energy Transgenic Crops<br>Sensors Inverter Technology Water Saving Irrigation<br>60 Construction Material 60 Solar Energy 60<br>Li-ion Battery<br>40 40 40<br>20 20 20<br>0 0 0<br>PMP Transportation<br>80 Ca r bon Capture 80 Dr o nes<br>Insulating Glass Hybrid/Electric Vehicles<br>60 60<br>40 40<br>20 20<br>0 0<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>2000 2005 2010 2015 2020 2000 2005 2010 2015 2020<br>% of patents mentions<br><!-- End of picture text --> Notes: The figure depicts the share of patents that belong to each of the selected technologies within each WIPO Green sector in the period 2000-2021.

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Figure 3: Percentage of green ECTs and firms, 2012-2021 <!-- Start of picture text --> A. ECTs<br>10<br>8<br>6<br>4<br>B. Firms<br>18<br>16<br>14<br>12<br>10<br>8<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of ECs mentioning green tech<br>% of firms with at least one EC mentioning green tech<br><!-- End of picture text --> Notes: Panel A depicts the share of ECTs mentioning at least one green technology over the period 2012-2021. Panel B depicts the share of firms that mentioned at least one green technology in the corresponding ECTs.

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Figure 4: Percentage of green ECTs and firms by WIPO sector, 2012-2021 <!-- Start of picture text --> A. ECTs<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Construction Energy Farming and Forestry PMP Transportation<br>B. Firms<br>100<br>80<br>60<br>40<br>20<br>0<br>Construction Energy Farming and Forestry PMP Transportation<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of green ECs by technology sector<br>% of green firms by technology sector<br><!-- End of picture text --> Notes: Panel A depicts the proportion of ECTs that mention green technologies by WIPO green sector over the period 2012-2021. Panel B depicts the percentage by WIPO green sector over the period 2012-2021.

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Figure 5: Percentage of ECTs mentioning technologies from WIPO green patents, 2012-2021 <!-- Start of picture text --> Construction Energy Farming and Forestry<br>2.5 Con s truc t ion Materia l 2.5 Wind Turbine s 2.5 T r ansgeni c Crops<br>Air Conditioning Li Ion Battery Soilless Agriculture<br>Led Technology Biofuels Water Saving Irrigation<br>2 Sensors 2 Solar Energy 2<br>Inverter Technology<br>1.5 1.5 1.5<br>1 1 1<br>.5 .5 .5<br>0 0 0<br>PMP Transportation<br>2.5 Ca r bon Capture Techno l ogy 2.5 Hybridelectr i c Vehic l e<br>Insulating Glass Drones<br>2 2<br>1.5 1.5<br>1 1<br>.5 .5<br>0 0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of ECs mentioning green patents<br><!-- End of picture text --> Notes: The figure depicts the share of ECTs that belong to each of the selected technologies within each of WIPO Green sector in the period 2021-2021.

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Figure 6: Relationship between green technologies in ECTs, 2012-2021 <!-- Start of picture text --> Soilless Agriculture<br>Geothermal Energy<br>Transgenic Crops<br>Water Saving Irrigation<br>CCUS<br>Construction Material<br>LED Technology<br>HEV Insulating Glass<br>Air Conditioning<br>Biofuels<br>Wind Turbines<br>Sensors<br>Li-ion Battery<br>Drones Solar Energy<br>Inverter Technology<br><!-- End of picture text --> Notes: The network depicts the relationship between the green technologies identified in the ECTs over the period 2012-2021. The position of the nodes indicates how connected each technology is in the entire network, with technologies in the center being the most mentioned in boardroom discussions. Size of the nodes is proportional to the number of times each technology is mentioned in the ECTs. The closeness between technologies indicates how related they are between them (i.e., mentioned together in the same ECTs).

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Table 1: Relationship between mentioning green technologies in ECTs and firm performance, 2012-2021

|Dep. var.: green frm = 1|(1)|(2)|
|---|---|---|
|**Construction**|||
|log sales|0.021***<br>(0.005)||
|log operating income||0.018***<br>(0.004)|
|**Energy**|||
|log sales|0.013***<br>(0.004)||
|log operating income||0.007**<br>(0.003)|
|**Farming and Forestry**|||
|log sales|0.001<br>(0.001)||
|log operating income||0.002<br>(0.001)|
|**PMP**|||
|log sales|0.005***<br>(0.002)||
|log operating income||0.002<br>(0.001)|
|**Transportation**|||
|log sales|0.010***<br>(0.003)||
|log operating income||0.006***<br>(0.002)|
|Year FE|Yes|Yes|
|Sector FE|Yes|Yes|
|Country FE|Yes|Yes|
|Observations|21,490|19,388|

Notes: Table reports OLS estimates on the relationship between firm attributes and the probability of having an ECT mentioning keywords related to green technology adoption by WIPO Green sector. The dependent variable takes the value of 1 in the year the firm mentioned keywords related to a green technology of the WIPO green sector, and in all subsequent years. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively. All models include controls for the country’s GDP per capita (USD).

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Table 2: Relationship between mentioning <u>green</u> technologies in ECTs and CO2 emissions, 2012-2021

|Dep. var.: log Scope 1 emissions|All s|ectors|Constr|uction|En|ergy|Farming a|nd Forestry|PM|P|Transp|ortation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Green tech frm|0.052|0.090**|0.083**|0.095**|0.121**|0.218***|0.026|-0.072|0.002|-0.004|-0.053|-0.014|
||(0.035)|(0.040)|(0.041)|(0.048)|(0.053)|(0.069)|(0.134)|(0.116)|(0.078)|(0.117)|(0.066)|(0.077)|
|Green tech frm _×_|||||||||||||
|High-emission frm||-0.210**||-0.123||-0.352***||-0.244**||-0.087||-0.249*|
|||(0.084)||(0.085)||(0.103)||(0.115)||(0.158)||(0.151)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time varying controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|

Notes: Table reports OLS estimates on the relationship between having an ECT mentioning keywords related to green technology and CO2 emissions. The variable ”green tech firm” takes the value of 1 in the year the firm mentions keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively. Log sales included as time varying control.
Table 3: Relationship between mentioning green technologies in ECTs and CO2 emissions, alternative measure of emissions intensity, <u>2012-2021</u>

|Dep. var.: log Scope 1 emissions|All s|ectors|Const|ruction|En|ergy|Farming a|nd Forestry|PM|P|Transp|ortation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|Green tech frm|0.052|0.095**|0.083**|0.107**|0.121**|0.204***|0.026|-0.072|0.002|0.015|-0.053|-0.012|
||(0.035)|(0.038)|(0.041)|(0.045)|(0.053)|(0.062)|(0.134)|(0.116)|(0.078)|(0.105)|(0.066)|(0.075)|
|Green tech frm _×_|||||||||||||
|High intensive frms<br>2||-0.375***||-0.278***||-0.446***||-0.244**||-0.182||-0.352**|
|4||(0.096)||(0.101)||(0.113)||(0.115)||(0.153)||(0.147)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time varying controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|20,523|19,339|

Notes: Table reports OLS estimates on the relationship between having an ECT mentioning keywords related to green technology and CO2 emissions. The variable ”green tech firm” takes the value of 1 in the year the firm mentions keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the 90th percentile. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively. Log sales included as time varying control.
Table 4: Firm linkages and the <u>probability</u> of becoming <u>green,</u> 2012-2021

|Dep. var: green frm = 1|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Green tech partner|0.047***<br>(0.003)|0.022***<br>(0.004)|||||||
|Green tech partner _×_|||||||||
|High-emission frm||0.096***<br>(0.009)|||||||
|Green customer|||0.031***<br>(0.002)|0.026***<br>(0.003)|||||
|Green customer _×_|||||||||
|High-emission frm||||0.031***<br>(0.006)|||||
|Green supplier|||||0.029***<br>(0.002)|0.018***<br>(0.003)|||
|Green supplier _×_|||||||||
|High-emission frm||||||0.041***<br>(0.005)|||
|Green customer and supplier|||||||0.038***<br>(0.007)|0.031***<br>(0.008)|
|Green customer and supplier _×_|||||||||
|High-emission frm||||||||0.034*<br>(0.020)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep.var.|0.195|0.196|0.216|0.216|0.199|0.199|0.179|0.180|
|Observations|155,216|151,287|311,344|297,047|311,344|300,090|34,168|32,677|

Notes: Table reports OLS estimates on the relationship between having a linkage with a green firm in year _t_ (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . The dummy variables for green firm take the value of 1 in the year the firm mentioned keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The dataset for these regressions is at the related firm pair-year level. Each related firm pair must have had at least one active relationship (customer, supplier or tech partner) at any point between 2012-2021. Yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively.

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Table 5: Firm linkages and the <u>probability</u> of becoming <u>green,</u> lagged one <u>year,</u> 2012-2021

|Dep. var: green frm = 1|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Green tech partner (t-1)|0.037***<br>(0.004)|0.022***<br>(0.004)|||||||
|Green tech partner (t-1) _×_|||||||||
|High-emission frm||0.064***<br>(0.009)|||||||
|Green customer (t-1)|||0.016***<br>(0.002)|0.014***<br>(0.003)|||||
|Green customer (t-1) _×_|||||||||
|High-emission frm||||0.018***<br>(0.007)|||||
|Green supplier (t-1)|||||0.023***<br>(0.002)|0.014***<br>(0.003)|||
|Green supplier (t-1) _×_|||||||||
|High-emission frm||||||0.034***<br>(0.006)|||
|Green customer and supplier (t-1)|||||||0.025***<br>(0.008)|0.017**<br>(0.008)|
|Green customer and supplier (t-1) _×_|||||||||
|High-emission frm||||||||0.032<br>(0.021)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.197|0.198|0.219|0.219|0.199|0.199|0.180|0.181|
|Observations|139,654|136,135|280,138|267,298|280,138|270,047|30,736|29,402|

Notes: Table reports OLS estimates on the relationship between having a linkage with a green firm in year _t −_ 1 (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . The dummy variables for green firm take the value of 1 in the year the firm mentioned keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The dataset for these regressions is at the related firm pair-year level. Each related firm pair must have had at least one active relationship (customer, supplier, or tech partner) at any point between 2012-2021. Yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels denoted by ***, **, and *, respectively.

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Table 6: Firm linkages and the <u>probability</u> of becoming <u>green,</u> unique linkages, 2012-2021

|Dep. var: green frm = 1|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Green tech partner|0.049***<br>(0.005)|0.014**<br>(0.006)|||||||
|Green tech partner _×_|||||||||
|High-emission frm||0.110***<br>(0.012)|||||||
|Green customer|||0.029***<br>(0.003)|0.025***<br>(0.003)|||||
|Green customer _×_|||||||||
|High-emission frm||||0.024***<br>(0.007)|||||
|Green supplier|||||0.027***<br>(0.002)|0.015***<br>(0.003)|||
|Green supplier _×_|||||||||
|High-emission frm||||||0.042***<br>(0.006)|||
|Green customer and supplier|||||||0.024**<br>(0.011)|0.036***<br>(0.013)|
|Green customer and supplier _×_|||||||||
|High-emission frm||||||||-0.034<br>(0.026)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.206|0.207|0.225|0.224|0.204|0.204|0.184|0.185|
|Observations|63,044|61,522|240,242|228,234|240,242|231,588|15,864|14,873|

Notes: Table reports OLS estimates on the relationship between having a linkage with a green firm in year _t_ (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . The dummy variables for green firm take the value of 1 in the year the firm mentioned keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The dataset for these regressions is at the related firm pair-year level. Each related firm pair must have had at least one active relationship (customer, supplier or tech partner) at any point between 2012-2021. Only firm pairs with a single type of relationship are considered. Yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively.

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Table 7: Firm linkages and the <u>probability</u> of becoming <u>green,</u> linkages with MNEs, 2012-2021

|Dep. var: green frm = 1|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Green tech partner|0.046***<br>(0.003)|0.023***<br>(0.004)|||||||
|Green tech partner _×_|||||||||
|High-emission frm||0.094***<br>(0.009)|||||||
|Green customer|||0.031***<br>(0.002)|0.026***<br>(0.003)|||||
|Green customer _×_|||||||||
|High-emission frm||||0.028***<br>(0.007)|||||
|Green supplier|||||0.029***<br>(0.002)|0.018***<br>(0.003)|||
|Green supplier _×_|||||||||
|High-emission frm||||||0.041***<br>(0.005)|||
|Green customer and supplier|||||||0.036***<br>(0.008)|0.031***<br>(0.008)|
|Green customer and supplier _×_|||||||||
|High-emission frm||||||||0.025<br>(0.021)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.194|0.195|0.217|0.217|0.199|0.199|0.176|0.177|
|Observations|151,606|148,057|298,102|285,259|298,292|287,828|33,098|31,697|

Notes: Table reports OLS estimates on the relationship between having a linkage with a green firm in year _t_ (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . The dummy variables for green firm take the value of 1 in the year the firm mentioned keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The dataset for these regressions is at the related firm pair-year level. Each related firm pair must have had at least one active relationship (customer, supplier or tech partner) at any point between 2012-2021. Only firm pairs in which the green firm is an MNE are considered. Yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively.

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Table 8: Firm linkages and the <u>probability</u> of becoming <u>green,</u> from non-MNEs, 2012-2021

|Dep. var: green frm = 1|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Green tech partner|0.071***<br>(0.024)|0.027<br>(0.032)|||||||
|Green tech partner _×_|||||||||
|High-emission frm||0.112**<br>(0.051)|||||||
|Green customer|||0.033***<br>(0.010)|0.009<br>(0.013)|||||
|Green customer _×_|||||||||
|High-emission frm||||0.071***<br>(0.024)|||||
|Green supplier|||||0.047***<br>(0.011)|0.030**<br>(0.014)|||
|Green supplier _×_|||||||||
|High-emission frm||||||0.031<br>(0.023)|||
|Green customer and supplier|||||||0.100**<br>(0.042)|0.077<br>(0.094)|
|Green customer and supplier _×_|||||||||
|High-emission frm||||||||0.027<br>(0.107)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.230|0.229|0.205|0.210|0.208|0.205|0.250|0.258|
|Observations|3,610|3,230|13,242|11,788|13,052|12,262|1,070|980|

Notes: Table reports OLS estimates on the relationship between having a linkage with a green firm in year _t_ (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . The dummy variables for green firm take the value of 1 in the year the firm mentioned keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The dataset for these regressions is at the related firm pair-year level. Each related firm pair must have had at least one active relationship (customer, supplier or tech partner) at any point between 2012-2021. Only firm pairs in which the green firm is not an MNE are considered. Yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively.

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Table 9: Firm linkages and the <u>probability</u> of becoming <u>green,</u> to MNEs, 2012-2021

|Dep. var: green frm = 1|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|MNE Green tech partner|0.047***<br>(0.003)|0.021***<br>(0.004)|||||||
|MNE Green tech partner _×_|||||||||
|High-emission frm||0.099***<br>(0.009)|||||||
|MNE Green customer|||0.031***<br>(0.002)|0.025***<br>(0.003)|||||
|MNE Green customer _×_|||||||||
|High-emission frm||||0.030***<br>(0.007)|||||
|MNE Green supplier|||||0.029***<br>(0.002)|0.018***<br>(0.003)|||
|MNE Green supplier _×_|||||||||
|High-emission frm||||||0.041***<br>(0.005)|||
|MNE Green customer and supplier|||||||0.036***<br>(0.008)|0.030***<br>(0.008)|
|MNE Green customer and supplier _×_|||||||||
|High-emission frm||||||||0.029<br>(0.020)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.196|0.197|0.220|0.220|0.201|0.201|0.179|0.181|
|Observations|151,606|147,741|298,292|284,715|298,102|287,366|33,098|31,687|

Notes: Table reports OLS estimates on the relationship between having a linkage with a green firm in year _t_ (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . The dummy variables for green firm take the value of 1 in the year the firm mentioned keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The dataset for these regressions is at the related firm pair-year level. Each related firm pair must have had at least one active relationship (customer, supplier or tech partner) at any point between 2012-2021. Only firm pairs in which the firm linked to a green firm is an MNE are considered. Yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively.

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Table 10: Firm linkages and the <u>probability</u> of becoming <u>green,</u> to non-MNEs, 2012-2021

|Dep. var: green frm = 1|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Non-MNE Green tech partner|0.054***<br>(0.018)|0.060***<br>(0.023)|||||||
|Non-MNE Green tech partner _×_|||||||||
|High-emission frm||0.003<br>(0.038)|||||||
|Non-MNE Green customer|||0.044***<br>(0.011)|0.035***<br>(0.012)|||||
|Non-MNE Green customer _×_|||||||||
|High-emission frm||||0.046*<br>(0.027)|||||
|Non-MNE Green supplier|||||0.033***<br>(0.011)|0.018<br>(0.013)|||
|Non-MNE Green supplier _×_|||||||||
|High-emission frm||||||0.045**<br>(0.023)|||
|Non-MNE Green customer and supplier|||||||0.078**|0.050|
||||||||(0.033)|(0.034)|
|Non-MNE Green customer and supplier _×_|||||||||
|High-emission frm||||||||0.129|
|||||||||(0.086)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm pair FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean dep. var.|0.152|0.153|0.139|0.138|0.154|0.153|0.157|0.146|
|Observations|3,610|3,546|13,052|12,332|13,242|12,724|1,070|990|

Notes: Table reports OLS estimates on the relationship between having a linkage with a green firm in year _t_ (i.e. with a firm that has an ECT mentioning keywords related to green technology) and being a green firm in year _t_ . The dummy variables for green firm take the value of 1 in the year the firm mentioned keywords related to a green technology and in all subsequent years. High-emission firms had emission intensity (GHG emissions per million USD) in 2012 above the sample mean. The dataset for these regressions is at the related firm pair-year level. Each related firm pair must have had at least one active relationship (customer, supplier or tech partner) at any point between 2012-2021. Only firm pairs in which the firm linked to a green firm is not an MNE are considered. Yearly coverage for each related firm pair ranges from 2012 to 2021, conditional on having relationship information of both firms for each year. Standard errors clustered at the firm level in parentheses. Significance levels at the 1, 5, and 10 percent levels are denoted by ***, **, and *, respectively.

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# **A.1 Appendix Figures and Tables**

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Figure A1: Economic relevance of firms mentioning green technologies in ECTs, 2012-2021 <!-- Start of picture text --> A. Overall<br><!-- End of picture text --> <!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>B. By WIPO Green sector<br>15<br>10<br>5<br>0<br>Construction Energy Farming and Forestry PMP Transportation<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of sales by firms with ECTs mentioning green keywords<br>% of sales by firms with ECTs mentioning green keywords<br><!-- End of picture text --> Notes: Panel A depicts the share of total sales of firms mentioning green technologies in ECTs over the period 2012-2021. Panel B depicts the share of total sales of firms mentioning green technologies in ECTs over the period 2012-2021 by WIPO Green sector.

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Figure A2: Percentage of green ECTs and firms by NAICS sector, 2012-2021 A. Distribution of ECTs mentioning at least one green technology across sectors <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Information Manufacturing Professional Services Utilities Other Sectors<br>B. ECTs mentioning at least one green technology within each sector<br>30<br>Manufacturing<br>Information<br>Professional, Scientific, and Technical Services<br>Utilities<br>Other Sectors<br>20<br>10<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of green ECs by NAICS sector<br>% of ECTs mentioning green tech<br><!-- End of picture text --> Notes: Panel A depicts the proportion of ECTs that mention green technologies by NAICS sector over the period 2012-2021. Panel B depicts the percentage of ECTs that are green within each NAICS sector over the period 2012-2021.

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Figure A3: Economic relevance of firms mentioning green technologies in ECTs by sector, 20122021

# A. By WIPO Green sector

<!-- Start of picture text --> 15<br>10<br>5<br>0<br>Construction Energy Farming and Forestry PMP Transportation<br>B. By NAICS sector<br>80<br>Manufacturing<br>Information<br>70 Professional, Scientific, and Technical Services<br>Utilities<br>Other Sectors<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>% of sales by firms with ECTs mentioning green keywords<br>in each sector<br>% of sales by firms with ECTs mentioning green keywords<br><!-- End of picture text --> Notes: Panel A depicts the share of total sales of firms mentioning green technologies in ECTs by WIPO green sector over the period 2012-2021. Panel B depicts the share of total sales of firms mentioning green technologies in ECTs by NAICS sector over the period 2012-2021.

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Figure A4: Average sales of firms mentioning green technologies in ECTs versus other firms by NAICs sector, 2012-2021 <!-- Start of picture text --> Accommodation and Food Services Construction Finance and Insurance Manufacturing<br>20000<br>8000 10000 20000<br>6000 8000 15000 15000<br>4000 3426 . 27 6000 4945.28 (*) 10000 10000 7894.91 (***)<br>2516.98 4000 3177 . 77 ( *) 5447.52 5920.77<br>2000 2000 5000 5000 2473.20 (***)<br>0 0 0 0<br>Green Firms Other Firms Green Firms Other Firms Green Firms Other Firms Green Firms Other Firms<br>Mining, Oil and Gas Professional Services Real Estate Retail Trade<br>30000 8000 3000 40000<br>6000 30000<br>20000 2000<br>14187.96 (***) 4000 2980.41 (**) 1279.69 (**) 20000 18153 . 31 ( *** )<br>10000 2000 1604.81 (**) 1000 807 . 66 ( **) 10000 6139.00 (***)<br>1141.92 (***)<br>0 0 0 0<br>Green Firms Other Firms Green Firms Other Firms Green Firms Other Firms Green Firms Other Firms<br>Transportation and Warehousing Utilities Waste Management Wholesale Trade<br>15000 20000 8000 40000<br>15000 6000 30000<br>10000 4976.86 (***)<br>7488.16 (***) 10000 8147.48 (**) 4000 20000 15351.19 (**)<br>5000 2887.83 (***) 5000 3599.71 (**) 2000 2005.18 (***) 10000 7019.37 (**)<br>0 0 0 0<br>Green Firms Other Firms Green Firms Other Firms Green Firms Other Firms Green Firms Other Firms<br>Mean sales (Millions USD)<br><!-- End of picture text --> Notes: Figure depicts average sales of firms mentioning green technologies in ECTs versus the average sales of other firms by NAICS sector in the period 2012-2021 36
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Table A1: List of bigrams searched by technology

|**Technology**|**Bigrams**|
|---|---|
|Air Conditioning|air condition, air conditioner, air conditioning, air cooler, conditioning compressor,<br>conditioning heating, conditioning hvac, conditioning industrial, conditioning sys-<br>tem, conditioning ventilation, efcient hvac, fridge air, heat system, hvac heating,<br>hvac system, power conditioner, refrigeration system|
|Biofuels|material biodiesel, biodiesel also, biodiesel blend, biodiesel feedstock, biodiesel glyc-<br>erin, biodiesel plant, biodiesel process, biodiesel product, biodiesel using, biofuel<br>bioethanol, biofuels biodiesel, biofuels ethanol, ethanol biofuel, generation biofuel,<br>produce biodiesel, produce biofuel, producing biofuels, use biodiesel, advanced bio-<br>fuels, ddgs distiller, distiller dried, distiller grain, dried distiller, dried grain, dry<br>distiller, grain ddg, solubles ddgs|
|Carbon<br>Capture<br>Technology (CCT)|capture carbon, capture co, capture sequestration, capture storage, captured co,<br>capturing co, carbon capture, carbon sequestration, carbon storage, co capture, co<br>captured, co could, co sequestered, co sequestration, co storage, combustion cap-<br>ture, dioxide sequestration, extracting co, mineral carbonation, recovered co, se-<br>quester carbon, sequester co, sequestered carbon, sequestering carbon, sequestering<br>co, sequestration carbon, sequestration co, storage co|
|Construction<br>Mate-<br>rial|alkali metal, alternative cementitious, arc furnace, ash basin, ash cloud, ash coal, ash<br>concrete, ash disposal, ash fy, ash pond, ash remediation, ash silica, blast furnace,<br>calcium carbonate, carbon dioxide, carbonate talc, cement concrete, cement opc,<br>cement slurry, coal ash, concrete cement, electric arc, fy ash, furnace blast, furnace<br>slag, hydraulic binder, hydraulic cement, magnesium carbonate, micro silica, ordi-<br>nary portland, readymix concrete, recycling coal, sand fy, silica fume, silica sand,<br>slag cement, slag fy, supplementary cementitious, volcanic ash|
|Drones|aerial drone, aerial vehicle, aircraft drone, uav unmanned, uavs drone, unmanned<br>aerial, vehicle drone, vehicle uavs, vehicle unmanned|
|Geothermal Energy|cycle orc, efciency geothermal, geothermal energy, heat geothermal, system egs|
|Hybrid/Electric Ve-<br>hicle|automobile hybrid, autonomous electric, bev hybrid, charger obc, charging ev, cng<br>hybrid, electric hybrid, ev charger, ev charging, ev electric, ev hybrid, ev motor,<br>hev hybrid, hev vehicle, hv hybrid, hybrid car, hybrid electrical, hybrid ev, hybrid<br>vehicle, mild hybrid, motor hybrid, phev electric, phev vehicle, plug hybrid, plugin<br>hybrid, pure ev, suv electric, suv hybrid, transmission hybrid, v hybrid, vehicle bevs,<br>vehicle charger, vehicle ev, vehicle hybrid, vehicle robot Insulating Glass insulated<br>glass, strengthened glass, vacuum insulated|
|Inverter Technology|bidirectional inverter, energy inverter, igbt device, inverter battery, inverter con-<br>verter, micro inverter, module inverter, mosfet igbt, pfc controller, svc static|

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|Led Technology|bulb led, connected led, controlling led, converter led, current leds, diode led, diode<br>semiconductor, emitting diode, led application, led bulb, led chip, led converter, led<br>device, led diode, led display, led electronics, led fxture, led fuorescent, led illumi-<br>nation, led lamp, led light, led lighting, led luminaires, led module, led replacement,<br>led retroft, led semiconductor, led string, led tube, led tv, light led, lighting led,<br>lighting module, organic leds, retroft bulb, retroft lighting, semiconductor led|
|---|---|
|Li Ion Battery|battery lithium, ion cell, li battery, lithium battery, lithium ion|
|Sensors|daylight sensor, sensor gesture, sensor led, sensor lighting, temperature sensor, ther-<br>mal sensor|
|Soilless Agriculture|farming vertical, grow pod, horticulture lighting, hydroponic farming, hydroponic<br>gardening, hydroponic greenhouse, hydroponics system, use hydroponic, vertical<br>farm, vertical farming, vertical garden, vertical grow|
|Solar Energy|cell pv, concentrated photovoltaic, cpv module, cpv system, cpv technology, csp con-<br>centrated, csp plant, csp technology, generation photovoltaic, inverter solar, module<br>photovoltaic, module pv, module solar, photovoltaic laminate, photovoltaic pv, pho-<br>tovoltaic receiver, photovoltaic string, photovoltaics cpv, power photovoltaic, pv<br>array, pv module, pv panel, pv solar, roof photovoltaic, system photovoltaic, tech-<br>nology photovoltaic, thinflm photovoltaic, tower generator|
|Transgenic Crops|biotech trait, codon optimized, crop trait, gene editing, gene modifcation, gene<br>trait, genome dna, genome editing, interfering rna, rna interference, rna sirnas, rnai<br>process, silencing complex, targeted genome, trait crop|
|Water Saving Irriga-<br>tion|agricultural irrigation, irrigation pump, irrigation technology|
|Wind Turbines|blade nacelle, component nacelle, energy blade, generator blade, nacelle blade, na-<br>celle generator, nacelle hub, nacelle tower, system nacelle, turbine nacelle, turbine<br>ofshore, wind blade, wind energy, wind turbine|

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Table A2: Number of Earnings Call Transcripts associated with each green technology belonging to each WIPO Green sector in the <u>period</u> 2012-2021

|WIPO Green sector|Technology|ECTs|
|---|---|---|
|Construction|Construction Material|2,225|
|Construction|Air Conditioning|2,215|
|Energy|Wind Turbines|1,918|
|Construction|LED Technology|1,655|
|Transportation|Hybrid/Electric Vehicle|1,440|
|Energy|Li-ion Battery|1,058|
|PMP|Carbon Capture Technology|1,011|
|Farming and Forestry|Transgenic Crops|404|
|Energy|Biofuels|337|
|Energy|Solar Energy|300|
|Transportation|Drones|183|
|Construction|Sensors|160|
|Energy|Inverter Technology|107|
|Farming and Forestry|Soilless Agriculture|87|
|Energy|Geothermal Energy|61|
|PMP|Insulating Glass|53|
|Farming and Forestry|Water Saving Irrigation|49|

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Table A3: Green firms <u>per</u> NAICS sector, 2012-2021

|NAICS Sector|Number of green frms|
|---|---|
|Accommodation and Food Services|39|
|Administrative and Support and Waste Management and Remediation Services|47|
|Construction|75|
|Finance and Insurance|171|
|Information|142|
|Manufacturing|1271|
|Mining, Quarrying, and Oil and Gas Extraction|202|
|Professional, Scientifc, and Technical Services|122|
|Real Estate and Rental and Leasing|138|
|Retail Trade|124|
|Transportation and Warehousing|135|
|Utilities|201|
|Wholesale Trade|53|

Notes: The table presents the number of green firms (that mentioned at least one green technology in ECTs) for each NAICS sector from 2012 to 2021 and the percentage of green firms (that mentioned at least one technology in ECTs) in each WIPO green sector.

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