Accelerating the development and diffusion of green technologies (GTs) is essential to achieve long-term climate goals (IPCC, 2023).1 However, guiding investments and shaping effective policy support require a deeper understanding of both the rate and direction of innovation in these technologies (Fischer and Newell, 2008;Nesta et al., 2014;Aghion et al., 2019;Barbieri et al., 2023). Although the central role of GTs is widely recognized, recent empirical evidence based on patent data has shown a slowdown in climate-friendly inventions in the last decade. This has been interpreted as a reason for concern in the face of accelerating climate change. For example, Probst et al. (2021) observed an average annual growth rate of 10% in high-value climate change mitigation inventions from 1995 to 2012, followed by an annual decline of 6% from 2013 to 2017. Similarly, IEA (2021) shows that energy technology development has grown more slowly in the 2010s compared to its growth in the early 2000s. These results have been attributed to falling fossil fuel prices, low CO 2 prices and the technological maturity of some GTs, including renewables (Popp et al., 2020;Aghion et al., 2022;Martin and Verhoeven, 2022;Probst et al., 2021). In contrast, the latest empirical evidence suggests a different trajectory, pointing to an increase in GT development and adding ambiguity to these findings (Verendel, 2023;Wang et al., 2024;Wang and Lobo, 2024;Zhang et al., 2024;Balcilar and Agan, 2023;Koteswara Rao Naik et al., 2022). 2Our study aims at delving into these empirical ambiguities by identifying and analyzing the impact of three key mechanisms that influence patent-based measurement of GTs. First, we examine the dynamically changing definitions of GTs, which leads to a "reclassification effect". Patents initially classified as non-green are later reclassified as green, and vice versa, reflecting changing definitions of GT over time. Second, we analyze the diffusion of classification systems, which influences the measurement of GTs via the "set expansion effect." Although classification systems are designed to facilitate patent searches, their adoption is often uneven across patent offices. The limited global coverage can cause selection biases in GT measurements, where only patents from offices using a particular system are sampled. Third, we examine quality distortions that cause a "filtering effect", i.e., the imposition of patent quality thresholds affects the sampling of patents included in the analysis, potentially biasing the results. The present paper shows that the impact of these mechanisms can be significant, shaping the quantitative (i.e., the number of inventions), qualitative (i.e., the type of GTs) and institutional (i.e., the contribution of different patent offices) perspectives of GT inventions. Indeed, these effects may have relevant consequences on the measurement of GTs, and they have the potential to shape policy decisions and R&D strategies. We provide evidence that, once these effects are accounted for, worries about a global innovation slowdown may not be justified. Moreover, we propose best practices to mitigate the influence of these effects when using patents as a tool to measure green technological efforts. Our first mechanism, the dynamically changing definition of GTs, is linked to the debate on "what" technology is considered "green". Societal, political, and scientific definitions of GT vary across investors, inventors, and citizens, and change over time, influenced by evolving social norms, or political constraints and objectives (Nedopil et al., 2021;Golub et al., 2011;Claro et al., 2007;Cisneros et al., 2023). GT definitions -like conventional ones (Mohlin, 2014;Kovács et al., 2021;Bedau et al., 2019;Lafond and Kim, 2019)-change in response to the underlying technological evolution: breakthroughs and deadlocks affect the relative growth of particular technological pathways, as exemplified by rising renewable energy technologies and stagnating nuclear energy (Hötte et al., 2021). At the same time, emerging technologies reach maturity, while others break out from niches too small to form a meaningful category. For research and policy, the dynamic nature of GT definitions poses an analytical challenge to the evaluation of green technological trends and pathways. This aspect is of theoretical interest, but plays an even greater practical role: changes in the boundaries of GTs affect the assessment of how inventive activity evolves in this field. We operationalize the evolving boundary of GTs by exploiting changes in GT classes as available in the Cooperative Patent Classification (CPC) system, the so-called "Y02 tags" (Veefkind et al., 2012;Angelucci et al., 2018). 3 Y02 tags aim to identify "Technologies or applications for mitigation or adaptation against climate change" and have been widely used to track progress, assess policy effectiveness, and identify GT drivers (Barbieri et al., 2023;Persoon et al., 2020;Probst et al., 2021;Hötte et al., 2021;Hötte and Jee, 2022;Su and Moaniba, 2017). 4 Since their introduction in 2013, the CPC codes and the Y02 tags have undergone several changes involving the integration, modification, and deletion of new, existing, and obsolete codes. These updates lead to "reclassifications" of both new and old patents filed many years ago (Lafond and Kim, 2019;Hötte et al., 2021;Hötte and Jee, 2022). Our second mechanism relates to the incomplete diffusion of the CPC classification system among patent offices. Not all offices use CPC codes, but it is the only classification framework with a dedicated section for up-to-date codes related to GTs (the Y02 tags).5 Thus, using CPC codes to detect GTs omits all patents that are filed or for which family members (duplications) seek protection in patent offices that do not use the CPC system, leading to an underestimation of green patenting worldwide. By comparing GTs sampled at different stages of CPC diffusion, we reveal a quantitative expansion of the boundary of GTs driven by the incremental inclusion of green patents from late CPC adopters. The third mechanism relates to quality thresholds imposed on the data. While patent counts are key indicators for measuring the rate and direction of technological change (Jaffe and De Rassenfosse, 2019), the choice of which patents to count often depends on ex-ante decisions aimed at selecting high-quality inventions that have real-world impact. For example, 3 Technology classification codes are hierarchical structures to ease the search for patents related to specific technologies.
The codes can be exploited at different digit levels: a low number of digits captures very broad technological domains, which become increasingly narrow as the number of digits increases. Different patent classification systems are used internationally: the International Patent Classification (IPC) and the CPC, in addition to regional codes such as the United States Classification (USPC) and the European Classification (ECLA) (both substituted by the CPC). 4 There are different versions and data sources of green classification codes (Favot et al., 2023). In addition to the CPC Y02 tags, the most widely used are the Env-Tech (Haščič and Migotto, 2015) and Green Inventory classifications, which report IPC and CPC codes related to GTs. The CPC Y02 tag covers a wide range of GTs, such as technologies related to adaptation (CPC Y02A), buildings (CPC Y02B), carbon capture and storage (CPC Y02C), energy use reduction (CPC Y02D), energy production (CPC Y02E), goods production (CPC Y02P), transportation (CPC Y02T), and waste management (CPC Y02W). scholars have used triadic patent families (Criscuolo, 2006), patents cited by subsequent inventions (Aghion et al., 2019), or filed in a particular patent office such as the US Patent and Trademark Office (USPTO) (Acemoglu et al., 2023), families with applications filed in at least two different offices (Probst et al., 2021), etc. Because definitions of patent quality vary according to perspective and objectives (Squicciarini et al., 2013;Van Pottelsberghe, 2011;Hall and Harhoff, 2004),6 it is critical to understand how quality thresholds affect statistical results, as they can significantly alter the size and composition of the data (Higham et al., 2021). Our analysis assesses the effects of these mechanisms on three different dimensions of green invention trends. First, we examine the quantitative perspective to estimate the overall magnitude of their impact. Second, we examine the qualitative impact to determine whether these mechanisms affect certain GTs more than others. Third, we analyze the institutional dimension to assess whether some patent offices are disproportionately affected. Tracking changes in the patent classification system is complex because each release of patent databases comes with the classification system in force at that time. Given that once a change in the technological classification codes is introduced, all previous patents are reclassified according to the newest version of the classification, it becomes impossible to obtain information on the previous codes assigned to the patent if we exploit recent databases. Moreover, when using the latest versions of patent databases, we cannot determine the year in which patents classified under the new classification system (by newly adopting patent offices) were first introduced into the database, making the quantification of this mechanism unfeasible. We address these challenges by constructing an original dataset that integrates multiple releases of Patstat, a global patent repository maintained by the European Patent Office (EPO). This approach allows us to identify which/when patents are affected by changes in the classification system. The resulting dataset provides a unique opportunity to systematically analyze the evolution of classification systems at the patent level. Our analysis shows that the global trends in green technologies are contingent on the version of the patent database used in the study and on the three effects described in the present paper. Our results show that all mechanisms affect the measurement of GTs, with the quality threshold imposition (i.e., filtering effect) having the largest impact. Specifically, we observe that the effects of reclassification and set expansion lead to an increase of 9.2% and 10.6% in the total number of green inventions over the period 1980-2016, respectively. 7 Even though the reclassification of patents is a general phenomenon as all technological classes are continuously reformulated, we find that for green technology, the reclassification effect is particularly strong. For the filtering effect, we find that imposing a quality threshold based on the number of jurisdictions in which patent protection is sought (patent family size) results in the omission of a substantial number of green patent families, particularly those filed at the Chinese, Japanese, and South Korean offices. Using as a reference all green patent families retrieved in the 2023 version of Patstat without imposition of quality thresholds, the filtering effect reduces the number of green patent families by 42% to 93%, depending on the quality indicator used. Citations result in the lowest reduction, while triadic patent families lead to the most substantial drop. Trends in the number of green patent families based on alternative patent quality indicators (e.g., citations) suggest that GT inventions have not been declining in recent years but are instead increasing rapidly. What has changed over time is the relative contribution of different patent offices (affected by the filtering and set expansion effects) with the epicenter of green innovation shifting towards Asian patent offices. These observations suggest the need for a nuanced reconsideration of concerns about the decline of green technology efforts and the discussions surrounding their underlying drivers, particularly when adopting a global perspective in measuring technological progress to address climate change. The remainder of this article is structured as follows: Section 2 reviews existing research on GT definitions, classifications, and patent quality. Section 3 explains the data construction and methods. Section 4 outlines the results, and Section 5 discusses them providing implications for analysts and policy makers. A timely global transition to a climate-friendly economy requires an acceleration in the development and diffusion of GTs. The years 1990-2010 were characterized by a rapid growth in these technologies (Probst et al., 2021;Acemoglu et al., 2023). However, some studies point out that GT development has decelerated significantly after 2010 (IEA, 2021;Acemoglu et al., 2023;Probst et al., 2021;Aghion et al., 2019;Martin and Verhoeven, 2022;Cantone et al., 2023). Probst et al. (2021) provide the most detailed documentation of the decline, showing that the number of green patents grew annually with about 10% between 1995 and 2010, but then declined with an annual rate of -5.5% after 2010. Several explanations have been proposed. For example, Acemoglu et al.
(2023) links the decline to the US shale gas revolution, other studies highlight a link between the number of GTs and oil prices (Probst et al., 2021), CO 2 prices from the Emissions Trading Scheme (Cantone et al., 2023), and the financial crisis (Koteswara Rao Naik et al., 2022). The decline seems to be stronger in the US, Europe, and Japan, and less pronounced in China (Popp et al., 2020). Other recent papers, while not directly aimed at measuring GT activity, suggest that the recent decline in GT growth rates may be less pronounced (Zhang et al., 2024;Balcilar and Agan, 2023;Koteswara Rao Naik et al., 2022) or even absent (Verendel, 2023;Wang et al., 2024;Wang and Lobo, 2024). Comparing these studies is challenging because of differences in data sampling.8 For example, Verendel (2023) and Wang et al. (2024) restrict the sample to US patents, and Probst et al. (2021) only consider patents filed in at least two different jurisdictions. However, where a direct comparison is possible (for example, between Acemoglu et al. (2023), Wang et al. (2024), andVerendel (2023), all of which use US patents), there seems to be a discrepancy between the observed patterns. Resolving this discrepancy is not only relevant for scientific and methodological reasons, but also has policy implications. In addition to variations in the way patents are collected (e.g., selection of patent office, version and type of patent database, etc. ), there are differences in the methods used to identify GTs. First, GT-related patents can be identified by specific keywords in the text corpus of the patent documents (e.g., title, abstract, technical description, etc.). Second, researchers can use lists of patent classification codes that define the boundaries of the focal technology and collect the patents assigned to these codes. Finally, the third option is a combination of the previous ones. Depending on the type of technology being analyzed, one choice may be more effective than another. As noted above, using the Y02 tag of the CPC system has emerged as a key empirical strategy for empirical studies of GT in the last decade (Haščič and Migotto, 2015;Probst et al., 2021;Hötte et al., 2021;Hötte and Jee, 2022;Su and Moaniba, 2017). However, measuring GT efforts requires a specific focus on the mechanisms that may influence the composition of the final dataset. The first effect analyzed in this paper is related to patent reclassification. Patent classification systems are designed to efficiently find prior art. As a side effect, they create reasonable theoretical boundaries between technologies (Jaffe and De Rassenfosse, 2019). A classification system can be understood analytically as the boundaries between different classes in a multidimensional space of technological properties. According to Mohlin (2014), and analogous to the principle of a k-means clustering algorithm (MacQueen, 1967), an optimal classification system is driven by a bias-variance tradeoff, whereby the difference between the properties of a patent and the average patent in a given class is minimized for all patents belonging to that class. As more inventions are made and assigned to existing patent classes, the class average changes, leading to possible reclassification of class members. The influx of inventions may also cause existing classes to break up or merge if the relative number of patents per class in a given class becomes too large or too small, undermining the purpose of the class as a tool for efficient patent searching (Kovács et al., 2021). Patent classification systems are subject to ongoing change, leading to patent reclassifications even if patents have been filed long ago (Lafond and Kim, 2019;Veefkind et al., 2012;Angelucci et al., 2018). In an ongoing process of refining and optimizing the system, classes are added, deleted, and transformed (e.g., changes to the title or indentation),9 with patents migrating from class to class. While updates to the classification system are based on expert opinion, much of the classification process is automated, using algorithms to process millions of patents worldwide (Kamateri et al., 2024). Lafond and Kim (2019) argue that the impact of past reclassifications on current classifications is substantial and should be taken into account when studying the evolution of technology. Therefore, we should carefully distinguish two ways in which patents can be added to a technological class: (1) if a patent is added because it is new, it reflects the growth of an existing class over time; (2) if it is added as a result of reclassification, it reflects a changing perspective on a given technological class or the content of the patent. In this research, we use patent reclassification to examine how the boundaries of GTs (and thus their societal definitions) have changed over time. 10 Table 1: The evolution of the Y02 tagging system for GT

CPC subclass Deleted codes New codes Titles changes Indentation changes 2020

The table provides an overview of changes in the Y02 tagging system since its introduction in 2013. Column 2 indicates the CPC subclass affected by a change; columns 3 and 4 indicate whether groups have been deleted or newly introduced to these subclasses; column 5 indicates whether the description of the technology subclass has been amended; and column 6 indicates changes in the indentation, i.e., the hierarchical level of the code. Table 1 summarizes the evolution of the Y02 tag since its inception. When the CPC system was introduced in 2013, it included four main domains of GTs related to buildings (Y02B), carbon capture and storage (Y02C), energy (Y02E), and transportation (Y02T). Over time, the Y02 tagging system has been subject to continuous change, with new technology domains added in 2015 and 2018, such as waste (Y02W), products (Y02P), and adaptation technologies (Y02A). The overall structure of the Y02 tags has also undergone adjustments that did not involve the creation of new 4-digit technological domains, but instead affected the substructures and nesting of the existing codes. These include the addition of new lower-level codes, the reclassification of existing ones due to the expiration of older codes, and the modification of titles and indentations of these technological domains.
The described changes in the Y02 tagging system have two implications for empirical research based on green patents. First, the boundaries of the GT domain have been substantially redefined, as reflected in structural changes in the Y02 tag for newly filed patents. Second, these changes induce the reclassification of all previously filed patents according to the updated structure. This retrospective aspect of reclassification is empirically significant because, as shown in Section 3, detecting trends in green technological efforts requires understanding their classification both before and after the changes in the Y02 structure. Technological classification codes provide a way to search for relevant patents without having to know the appropriate keywords for the query (Simmons, 2014). To facilitate the search process, a classification system should meet certain criteria. First, it should categorize patents based on their "functions" to reflect the use of an invention in industries. Second, it should be flexible and allow for updates to accommodate technological advances and the increasing size of rapidly growing classes. Third, to facilitate the operation of internationally harmonized intellectual property right systems and prior art searches on a global scale, classifications should be internationally compatible with existing local systems to allow examiners to access codes across patent offices. From an international search perspective, an ideal classification system would be globally uniform, creating a community of users who share the same codes (Simmons, 2014). The CPC system was designed to have these features. It was developed jointly by the EPO and USPTO to harmonize their respective classification systems, the European classification (ECLA) and the USPC. The CPC improves global patent classification by integrating the best practices of both systems and ensuring compliance with international standards such as the International Patent Classification (IPC). This collaboration aims to streamline the patent search process, improve transparency and support the ongoing process of international harmonization. While the CPC is intended to be a universal system, not all patent offices around the world currently use it, and some are only beginning to adopt it. This is important because the degree of adoption by patent offices has a significant impact on the analysis of specific technology areas. In the context of GTs, this issue is critical because the detection of GTs often depends on the Y02 tag, which is exclusively available in the CPC system. In particular, between 2012 and 2018, for some of the larger patent offices, only a fraction of patents were classified using the CPC system (CPC, 2018). By 2024, the number of patent offices adopting the CPC system (or starting to adopt it) was 38,11 which indicates the CPC still has some ground to cover. The limited diffusion of the CPC system automatically leads to a selection of patents that are filed or extended (family propagation) in these offices. Patent counts remain a key measure of technological progress, but many patents are never used and/or are filed for defensive purposes only, undermining their suitability as an indicator of innovation. Most researchers therefore filter patents by requiring them to meet a certain quality threshold. Patent quality can refer to the importance, impact, value, or significance of a patent. These concepts are subjective and vary depending on whether the viewpoint is that of the inventor, policymaker, or the general public (Higham et al., 2021). A variety of quality metrics have been proposed (Squicciarini et al., 2013;Higham et al., 2021) and applied to analyze the characteristics of GTs (Dechezleprêtre et al., 2013;Popp and Newell, 2012;Barbieri et al., 2020). One can distinguish between ex-ante and ex-post indicators of quality, where ex-ante characteristics capture the intrinsic technological properties of a patent and ex-post characteristics capture a patent's impact on future technological development and its commercial value. Ex-ante measures include, for example, the diversity of a patent's knowledge inputs and its reliance on science as captured by backward citations, its scope as indicated by the number of claims, its family size, etc. Ex-post measures are revealed over time. For example, the technological impact of a patent is supposed to be reflected in the number of forward citations, and its economic value is supposed to be visible in stock market reactions after patent grant or the number of years of patent renewal. While widely used, a systematic analysis by Higham et al. (2021) reveals weak and technology-dependent patent-level overlap of different quality indicators: thus, patents with high commercial value do not necessarily have high technological or scientific impact, and patents with high ex-ante scientific or technological quality may not be necessarily widespread application in the economy. Ultimately, the choice of the appropriate patent quality filter depends on the research objective and perspective. For example, some studies may be interested in assessing the impact of specific national policies, others may focus on the global perspective to assess the economic impact of technology, etc. Some of the public good characteristics of inventions or the scientific contribution of a technological breakthrough may not be well reflected in the economic value, which may, however, better capture aspects of commercialization, technology diffusion and market uptake. In the context of GT, this difference can be crucial, as both the promotion of technological breakthroughs (technology push) and mass diffusion (demand pull) are key areas of GT policy. These policy areas often overlap, but to assess the effectiveness of push or pull policies, the choice of an appropriate quality filter should be conceptually aligned with the policy objectives (Nemet, 2009;Costantini et al., 2015;Hötte, 2023;Choi, 2024;Yuan et al., 2023). Quantifying the impact of the mechanisms that may systematically influence the measurement of GT inventions, i.e., changing GT definitions, classification diffusion, and quality distortions, is complex. Each release of patent databases comes with the information of the classification system in force at that time. Given that once a change in the classification codes is introduced all previous patents are reclassified according to the newest version of the classification, it is not possible to obtain information on the previous codes assigned to the patent.
We deal with this issue by building an original dataset that integrates two different releases of Patstat, a widely used patent database in innovation research (e.g. Probst et al., 2021;Barbieri et al., 2023;Hötte and Jee, 2022;Persoon et al., 2020;Su and Moaniba, 2017). We exploit the 2019 and 2023 Spring versions through which we can capture the changes in the structure of the patent classification system. According to Table 1, these two versions enable us to capture the deep change that the Y02 class underwent in August 2020, when all subclasses have been impacted. We use DOCDB patent families with patents filed between 1980 and 2016. We limit the period to 2016 to mitigate truncation effects that characterize more recent years. Patent trends are often characterized by the so-called "flat tail" shape, for which in more recent years only a portion of patents is included in Patstat. This is due to different mechanisms. First, given the gap between priority and publication (e.g. EPO patents are published 18 months after filing), data for recent months may be incomplete. 12 Second, there may be communication and synchronization delays affecting data from non-European patent offices. Third, some patents exploit the international route (i.e., patents filed under the Patent Cooperation Treaty) that extends the time for choosing the jurisdictions where duplicating the patent. These factors contribute to the decline in patent statistics in recent years, which is due to procedural and administrative delays and may not reflect a real decline in inventions. Therefore, a prudent approach would be to exclude the last two to three years from the analysis to avoid misleading conclusions. In our case, closing the time window in 2016 helps us to address this issue in Patstat 2019 and 2023. However, as we will show, the flat-tail effect interacts with the quality threshold, and three years of a cut-off may be insufficient when imposing quality indicators based on family size or citations. 13 It is important to note that our paper accounts for two temporal dimensions: the year of the patent family, defined by its earliest priority year, and the year of the Patstat versions used in the analysis. Patstat 2023 spring version, such a patent would not yet be included. However, in this Patstat release we would find patents filed in March 2021, leading to an apparent decrease in patent filings for 2021. 13 In particular, we observe that new patents appearing in the 2023 version are dated in years that should have been covered by the earlier Patstat version. This can happen when patent families expand into additional jurisdictions in more recent years. These expansions are captured by the newer dataset releases, allowing the patent families to meet the quality threshold defined by a patent family size greater than one. An example illustrates this process. Consider a patent family A, consisting of only one application filed in 2016, with a family size of one. Since it does not meet the quality threshold, it is filtered out. However, in 2018, this family expands to another jurisdiction (as captured by subsequent Patstat releases), increasing its family size to two. As a result, the family now meets the quality threshold and is included in the statistics, even though, based on its priority date, it should have already been present in the earlier Patstat version. From the integration of Patstat 2019 and 2023 releases we derive three different datasets that allow us to isolate the reclassification, set expansion, and filtering effects and quantify their impact on the measurement of green inventions. First, to analyze the reclassification effect, we merge patent applications and their classification codes from the Patstat 2019 and 2023 versions (larger diagrams in Figure 1a) and collect patents that were included in both. Therein, we detect green patent families from each Patstat version (gray diagrams) if the family includes at least one application assigned to (at least) one GT classification code ("Y02" tag). 14 Three potential lists of green patents emerge from the Venn diagram reported in Figure 1a: (A) patents that were green in 2019 and not in the 2023 Patstat version; (B) patents that were green in both version; and (C) patents that were non-green in the 2019 version but become green due to reclassification, i.e., our reclassification dataset. A reclassification from non-green to green occurs when none of the patent family members were classified as green (Y02 tag) in the 2019 Patstat release, but at least one of its members was reclassified as green in the 2023 release. 15  Second, to study the set expansion effect, we build a dataset that collects green patent families that were added to the sample in Patstat 2023 (Figure 1b). These patent families should have been included in the Patstat 2019 release because they had an earliest priority year from 1980 to 2016, but were not available due to the fact that the classification into CPC codes was not yet implemented in some of the patent offices or not yet completed with only a fraction of patents being classified (point D of Figure 1b). Third, we complement the analysis with a dataset that measures the impact of the filtering effect resulting from the imposition of quality thresholds on the data. For this part of the analysis, we calculate for each patent family a set of patent indicators that are commonly used to measure the quality of inventions (Squicciarini et al., 2013;Barbieri et al., 2020). In particular, we calculate the number of forward citations received in the following 5 years 16 and the size of the patent family (the number of patent offices in which patent protection is sought). In this section, we isolate and quantify the impact of the three effects (described above) on GT inventions. We define a baseline scenario that serves as a reference point for analyzing the three effects and the extent to which they affect the global trends in GT inventions.
As baseline scenario, we take all patent families assigned to at least one Y02 code (as described 14 Our analysis imposes a lower bound effect because we measure reclassifications at the patent family level, while there may be additional reclassifications at the level of individual family members. The reclassification process occurs at the patent application level, but it does not necessarily mean that the entire patent family has changed its label from non-green to green. This lower bound at the family level applies to all effects quantified in this study. Finally, to better interpret the magnitude of green reclassification, we make a comparison with the magnitude of general reclassification, i.e. the reclassification of any patent. 15 We exclude from the analysis group A (i.e., patents that transit from green to non-green) because this effect is quantitatively small. This group of patent families could also be informative about the dynamics of the GT transition, but is left for future study. 16 To calculate the number of forward citation we follow the approach proposed in De Rassenfosse et al. ( 2014) in Section 3) and do not impose any quality filter. This allows us to separate the three effects and quantify the magnitude of their impact. Figure 2 shows the number of green patent families calculated using Patstat 2019 (gray dashed line) and Patstat 2023 (black solid line). The figure provides four main insights. First, the initial period, spanning the 1980s and 1990s, is characterized by relatively modest growth, reflecting the early stages of climate awareness and policy development. A turning point emerges in the early 2000s, coinciding with intensified global efforts to address climate change and promote sustainable development. Second, the two charts follow a similar pattern throughout the period. This suggests that the core dynamics of green patenting activity are consistent between the two versions of Patstat. Third, the two graphs diverge especially in the most recent years. This effect is due to the fact that only a part of the patents from the most recent years is actually included in the database at the time of its publication (the so-called "flat tail" effect). Fourth, the differences between the two trends are present over the entire period, highlighting that the reclassification and the set expansion affect the quantitative assessment of GT over the entire period. Our analysis first examines the impact of changing GT definitions by examining the number of patent families that undergo the reclassification process. To isolate the impact of reclas-sification, we focus on patents that appear in both versions of Patstat (2019 and 2023), but are only classified as green in 2023 release (see Figure 1a). Figure 3 shows the number of reclassified patents by earliest priority year. Initially, between 1980 and the early 2000s, this number was relatively stable and low, with only small fluctuations. Since the 2000s, the number began to increase, with a steep rise after 2010. By 2016, the number exceeded 20,000, which is more than 16% of all green patent families. This indicates that the evolving definitions of GTs have the strongest impact on recent trends in GT. Overall, the reclassification effect impacts almost 152.000 patent families, or 9.2% of green patent families in the Patstat 2023 version (excluding the patent families affected by the set expansion effect). For comparison, the general reclassification percentage (of any patent) on CPC class level is 2.2% percent, almost 4 times smaller than that of green reclassification. The impact of green reclassification can therefore be considered relatively strong. Moreover, green reclassification is substantial even compared to reclassification in comparable, other CPC classes, as shown in Appendix A.1. The percentage of reclassification is higher in only for three other such classes (out of 129 total), and in absolute numbers of patents reclassified, GT is not matched by any other class, despite not being the largest CPC class. Next, we examine the qualitative nature of changes in the boundaries of GTs. Figure 4 illustrates the distribution of patent reclassifications across Y02 8-digit CPC groups. Since the number of reclassifications in a group is approximately proportional to the number of families in that group (see Appendix A.1), we adopt two related perspectives: in the left panel we present the absolute number of green patent families, while in the right panel we show their share relative to the total number of green patent families within each Y02 group. The latter allows us to account for differences in the size of each GT subcategory. The most significant changes occur in CPC group Y02E60, which encompasses enabling technologies for energy storage. This group exhibits a disproportionately high number of green patent families added through reclassification. It includes technologies such as batteries, capacitors, and fuel cells, which play a crucial role in facilitating a deep energy transition at the system level, extending beyond electricity generation to the integration of renewable energy into the broader energy system. Notably, even when controlling for the size of each subgroup (right panel), these fields remain among those experiencing the highest levels of reclassification activity. Technologies related to production processes for final industrial or consumer products (Y02P70) rank second with about 18,000 reclassified patents, indicating that these fields are similarly dynamic and subject to boundary shifts. We also observe large impacts of reclassifcations in renewable energy generation technologies (Y01E10) and nuclear energy (Y02E30), which rank 3rd and 4th.17 This is indicative for the energy sector as a whole to be very dynamic and experiencing a quantitative and qualitative expansion through reclassifications. Compared to renewables, the number of reclassifications of nuclear energy is only about a third as high, yet it may underline the societal dimension of reclassifications as the greenness of nuclear energy is controversial. Reclassification intensities for the other groups are much lower, with road transport technology and green buildings scoring similarly to nuclear energy around 3-5 thousands. The low numbers for road transport suggest relative stability in its definition as a GT, as this is a relatively large patent group.
We also observe negligible amounts of reclassification for other groups of GT, suggesting that the importance of the reclassification effect is very uneven. Some of these groups are generally very small fields of GT, which may explain some of the low counts. For larger fields, low reclassification dynamics are an indication of stability. We now explore the institutional perspective and examine how the reclassification process affects the distribution of green patenting across patent offices by associating a family with an office if at least one family member has been filed there. We then identify the patent offices that experience the most significant increases in GT patent families due to reclassifications. Figure 5 presents the distribution of reclassified green patent families across different patent offices, keeping the two distinct perspectives described before: the left panel illustrates the absolute number of green patent families registered at each office, while the right panel shows the share of reclassified patent families relative to the total number of green patent families in each jurisdiction. From the left panel, it is evident that the USPTO, Chinese (CN), and Japanese (JP) patent offices are the leading jurisdictions in terms of the absolute number of green patent families reclassified as green, followed by international filings under the Patent Cooperation Treaty (WO) and the South Korean (KR) patent office. The EPO and German patent office (DE) also have significant volumes, while the Canadian (CA), Taiwanese (TW), and Australian (AU) patent offices are characterized by relatively smaller number of patents that are reclassified as green. The right panel normalizes this distribution by showing the proportion of reclassified patent families within each jurisdiction. The USPTO exhibits the highest share of reclassified patents, followed closely by the WO and KR filings. Interestingly, while the Chinese patent office ranks second in the absolute count (left panel), its relative share of reclassifications is lower, suggesting that the expansion of green technologies in China might be driven more by new filings rather than reclassification. Other jurisdictions with notable reclassification shares include Taiwan (TW), Germany (DE), and Sweden (SE). The second effect that captures the impact of the changing classification system is the set expansion effect. It concerns GT patent families filed throughout the period 1980-2016 that should have been included in the Patstat 2019 release but were not, likely due to a delayed adoption of the classification system by certain patent offices. Many offices began adopting the CPC system after its introduction in 2013, often taking several years to fully implement and process all existing and incoming patent documents under the updated framework. This sluggish diffusion contributes significantly to the observed expansion in the dataset, as previously unclassified families are gradually integrated into the system. This highlights the importance of taking into account patent office practices when analyzing trends in green patenting over time. Figure 6 shows the number of green patent families added through the set expansion effect in the 2023 version of Patstat by earliest filing year, holding the classification codes constant as in the Patstat 2023 version (Figure 1b). The number of additional green patent families remains relatively stable in the 1980s, followed by a continuous increase in the 1990s until the late 2000s. Around 2010, we observe a sharp increase that peaks around 2013, followed by a significant decline in more recent years. This effect accounts for a total of 175 thousand additional GTs patent families, almost 10.6% of the green patent families available in the Patstat 2023 version (when we exclude reclassified green patents from the denominator). Figure 7 illustrates a key finding of the paper which arises from the study of the distribution of the set expansion effect across patent offices. This effect is highly concentrated in a few key Asian jurisdictions, with the Japanese, Chinese, and Korean patent offices exhibiting the most pronounced increases. This finding suggests that the importance of these offices as contributors to the GT transition could have been underestimated in earlier versions of Patstat. The right panel of Figure 7 shows the share of the top 10 patent offices with respect to the total number of green patent families filed in those offices.18 These missed families reflect both recently filed patents and older patents that were excluded due to delayed or incomplete adoption of the CPC classification system by these offices. To study the filtering effect, we examine how different quality thresholds for identifying highvalue inventions affect the global landscape of GTs as captured by patents. The choice of indicators and thresholds may inadvertently exclude a substantial fraction of inventions, with such exclusions unevenly distributed across technological fields and patent offices. We use five different quality indicators based on family size, forward citations, and filings at the EPO and USPTO, as well as triadic patent families (i.e., families that include filings to the EPO, USPTO and Japanese patent office). First, we filter based on patent family size larger than one (Figure 8a), whereby family size refers to the number of jurisdictions in which patent protection is sought, which is widely used in various GT studies (e.g. IEA, 2021;Probst et al., 2021;Popp et al., 2020). Second, we filter families with at least one forward citation (Figure 8b). Citations aim to capture the extent to which inventions influence subsequent ones, or in other words, how useful the knowledge encoded in the patent has been for follow-on technological advances that refer (via citations) to the focal patent. This indicator has been widely used to identify high quality inventions that have a real-world impact (see, among others, Trajtenberg (1990) and Aghion et al. (2019)). Our third and fourth filters (Figure 8c and8d) select patent families based on the presence of an application at the EPO or USPTO, and Figure 8e includes only families that contain applications at the EPO, USPTO, and Japanese patent office ("triadic patent families"). Each figure in 8 shows the annual number of observed GT families for 2019 (gray dashed line) and 2023 data (black solid line).
We compare the results with the baseline scenario shown in Figure 2 and explore the extent to which the filtering effect affects the quantitative, qualitative, and institutional perspectives in the global trends of GT inventions. Figure 8a, 8c-8e illustrate the dynamics for the jurisdiction-based filters. Using the 2019 version of Patstat, we observe a relatively low number of GTs during 1980-2000, and a sharp increase starting in the early 2000s that peaks around 2012. After the peak, the number of GT families begins to decline. This decline has puzzled scholars, who have proposed various explanations, as discussed above (Section 2). Contrasting this finding with the results based on Patstat 2023 (solid line), the post-2012 decline in GT inventions diminishes: after a slight slowdown, the number of GTs increases again around 2015 for the family size, EPO, and USPTO quality filters. This increase is not observed for triadic families. A different pattern emerges for the citation-based filter (Figure 8b): a steady increase over time, speeding up around the early 2000s. The Patstat 2023 line consistently reports higher counts than the Patstat 2019 version, which may coincide with a rising number of potentially citing patents in the updated database. 19These observations suggest that trends in green patenting may change significantly depending on the filter. Moreover, some patents may belong to smaller families or may not have been filed in jurisdictions often considered representative of the global frontier of GT. However, their technological relevance and impact may still be high, as reflected in the citation counts. In this section, we combine the three effects depicted above (reclassification, set expansion, and filtering). Table 2 shows the number of green patent families according to the Pastat version (2019 and 2023), and how the strength of the reclassification and set expansion effects varies across quality filters. From the table we can derive the following insights: first, whenever we observe in Figure 8 a decline in green patents after the 2010s, the set expansion effect is tiny, with a magnitude of 0.05-0.7% (depending on the iquality indicator adopted). In contrast, the size of the set expansion effect when no quality filters are implemented and citations are concerned are 9.7 and 8.3%, respectively (using the Patstat 2023 green patent families as reference point). Second, the size of the reclassification effect is relatively constant across different filters (ranging from 8-14% depending on the quality filter). Given that the set expansion effect is related to the diffusion of the CPC system, we further investigate how the institutional distribution of GTs interacts with the different quality filters. Figure 9 shows the number of GT families by patent office according to the quality filter implemented. The graph shows that using forward citations as a quality indicator maintains a distribution of patent families across patent offices that is consistent with the general trend observed when using the raw data without any filtering. This suggests that relying on forward citations as a measure of patent quality does not lead to a disproportionate exclusion of GT contributions from Chinese, Japanese, and Korean offices. This insight is further confirmed when focusing on green patent families filed after 2010. Again, Figure 10 highlights how the application of different quality filters significantly affects the count of green patent families across jurisdictions, revealing structural discrepancies in patenting practices and classification systems. Notably, the use of quality indicators other than forward citations leads to a disproportionate exclusion of Chinese patents, suggesting that green patenting activity in China is particularly sensitive to these quality filtering criteria. Similarly, the effect of quality filtering on patents filed at the JPO appears positive but less pronounced with respect to the previous case (Figure 10), implying that a substantial share of green patent families classified under JPO originates from earlier periods. Interestingly, applying quality filters to patents filed at the USPTO and the EPO produces results that remain largely consistent with the unfiltered dataset across most indicators. The exception concerns the application of the triadic patent family filter, which leads to a substantial reduction in patent counts. The paper disentangles and quantifies the impact of reclassification, set expansion, and filtering effects on green patenting statistics. We operationalize the analysis by examining these impacts from quantitative, qualitative, and institutional perspectives, specifically by assessing changes in the size of inventive efforts over time and their distribution across technological domains and patent offices. The reclassification effect significantly alters the quantitative and qualitative landscape of GT but has a limited impact from an institutional perspective. It redistributes patents across GT domains, creating uneven effects across subfields. Between 2019 and 2023, reclassifications expanded the GT boundary, incorporating a broader range of recently developed technologies relevant to climate change adaptation and mitigation. The set expansion effect primarily influences GT statistics from an institutional perspective, with quantitative implications for GT growth. As patent offices gradually implement the CPC system, delays in classification disproportionately affect different offices in GT statistics. This particularly affects time trends, as contributions from major catching-up economies -often late adopters of the CPC system-become visible only with a lag. Finally, the filtering effect influences patent-based GT measures in diverse ways, depending on the quality indicators applied. Quality thresholds incorporating institutional elements can introduce significant distortions in institutional representation, mirroring the quantitative implications of the set expansion effect. In contrast, citation-based thresholds appear (more) institutionally neutral and do not significantly alter GT growth dynamics. Notably, the reclassification effect remains largely independent of filtering biases. Our paper aims to reach two types of audience. On the one hand, it speaks to analysts and scholars who exploit patent data to measure innovative activity. The paper disentangles and evaluates the potential threats associated with the use of these data. On the other hand, our findings have implications for policy makers. Understanding global dynamics of technological progress is crucial for tracking progress, allocating resources, and designing regulations to accelerate the green technological transition.
Understanding the pace and direction of green innovation is also critical for identifying emerging technologies and monitoring the effectiveness of existing policy. Effective measures of innovative activity allow policymakers to make data-driven decisions to target the most promising areas of technological development. Our findings have implications for GT research and policy, as patent-based indicators are important tools for assessing policy effectiveness and identifying the drivers of GT innovation (Popp, 2019;Barbieri et al., 2023;Acemoglu et al., 2023;Martin and Verhoeven, 2022). The evolving definition of GT, as reflected in the reclassification effect, the diffusion of classification systems and the quality indicators have significant implications for empirical research on sustainable innovation drivers. As these effects unfold, the inclusion or exclusion of certain patents can affect the assessment of the impact of green technological efforts on, for instance, firms' performance. In addition, the exclusion of relevant green patents may bias the analysis of the policy inducement effect (Hicks, 1963;Popp, 2002;Lindman and Söderholm, 2016;Dugoua and Gerarden, 2023), particularly for emerging radical GTs, as their contributions may remain unrecognized until reclassification occurs. For example, the growing prominence of battery technologies and ICTs aimed at reducing energy usage in the updated CPC classification system is consistent with the electrification of transportation and energy systems, as well as the broader macro-level trend of digitalization. It also reflects that the energy transition has moved to the next stage: from the mere production of renewable energy to system integration, which only becomes relevant when high levels of renewable energy production are achieved (Verendel, 2023;Liu and Cai, 2024;Zhang et al., 2023). Reclassifications are particularly relevant for evaluating policies that incentivize the generation and adoption of breakthrough inventions, whereas the set expansion effect plays a crucial role in measuring progress in the (international) diffusion of GTs (Dechezleprêtre et al., 2011). The delayed adoption of CPC-based GT classification codes distorts empirical assessments of GT development by under-representing patent applications from certain patent offices. This distortion is further compounded by variations in national "green" innovation systems (Andersen and Foxon, 2009) and technological specialization patterns (Wong et al., 2016), which create potential interactions between the set expansion and reclassification effects. These interactions illustrate the complex interplay between institutional and technological factors in shaping the global distribution of green patents, often leading to the underestimation of the technological contributions of highly specialized countries. For instance, the rise of battery inventions in China exemplifies this dynamic, as changes in classification systems and the delayed adoption of CPC codes have influenced the visibility of China's contributions to green technological progress. Furthermore, we have shown that the choice of quality thresholds significantly influences the measurement of GTs: certain patent quality indicators used to identify high-quality inventions can lead to significant biases in assessing the dynamics of GTs. For example, indicators based on the number of international filings may be unsuitable for capturing highly novel and radical GTs, as such technologies often face diffusion barriers. These barriers arise when adoption requires a high degree of technology-specific absorptive capacity (Burrell et al., 2023). As a result, high-impact GTs may initially remain confined to domestic markets. This is relevant for policy evaluation, as the overall picture of progress in GTs may be significantly underestimated. Our results indicated that citation-based quality thresholds may be the least biased in all analyzed dimensions. Previous studies of GT trends have often reported a decline in green patenting since the 2010s. We have shown that this decline is much less pronounced when using more recent versions of the patent dataset, or even absent if we rely on other quality indicators. Our analysis showed that the methodology through which patents are selected systematically excludes a substantial number of green patent families, especially those originating from Asian patent offices. One might speculate that these excluded patents suffer from lower quality, as has sometimes been claimed for Chinese contributions (Boeing and Mueller, 2016). Using citation indicators as an alternative measure of quality, we have shown that this concern is not justified, in line with the findings provided by Kwon et al. (2017), which emphasizes an increasing quality of Chinese, South Korean and Taiwanese inventions. Indeed, it seems that a shift in the global distribution of innovation activity -such as the rise of Asian countriesis taking place. Recognizing the growing technological leadership of countries such as China, Japan, and South Korea can encourage policies that promote technology transfer and crossborder research partnerships. Given the role of patent data in guiding policy decisions and assessing innovation trends, ensuring methodological clarity is essential to improve the reliability of empirical findings. To this end, we propose a set of best practices that enhance data quality. First, researchers should explicitly state whether their analysis is conducted at the level of individual patent applications or patent families, and if the latter, specify the type of family used. Second, the jurisdictional scope of the study must be clearly defined, as variations in patent office coverage can introduce institutional biases. A detailed specification of the dataset and its version is also necessary, given that patent classifications evolve over time, impacting the identification of green patents. Another key practice is the explicit documentation of quality filters. Studies should provide detailed explanations of the criteria used to select high-value patents, whether based on grant status, family size, forward citations, or filings in multiple jurisdictions. Each of these filters introduces potential biases, and their implications should be carefully considered when drawing conclusions about innovation dynamics. Testing the robustness of these choices would increase the quality of the research. Finally, studies must specify how they handle patents assigned to multiple classifications. The methodological choices made in distributing patents across different technological domains can influence sectoral analyses and policy recommendations. By adhering to these best practices, researchers can enhance the comparability and robustness of patent-based studies, reducing the risk of misinterpretation due to methodological inconsistencies. The results of this study open up several avenues for future research.
First, researchers may be interested in testing whether their results hold using new versions of patent databases that include an updated configuration of the GT landscape. Second, the broad coverage of IPC codes can be used to estimate green patenting activity, thanks to the co-classification of some patents under both the IPC and CPC systems. This figure illustrates the relationship between the number of reclassifications (DOCDB patent families being added or deleted from a given class) and the size of the class, measured by the number of DOCDB patent families per class. We follow the same specifications as the main analysis in the text, i.e, the reclassifications are measured from the changes between the 2019 and 2023 versions of the CPC system, without imposing a quality filter, only selecting families with earliest filing year prior to 2017 and the classifications for each family are the pooled classifications of each of its family members. The slopes of the fits are close to 1, which suggests a direct proportionality between class size and reclassifications. Y02 is found at the top right. We find that larger patent classes (measured by the number of patents in those classes) show larger number of positive and negative fluctuations, indicated by a high number of patents being added or deleted from a given class. In Figure A.1 we plot the number of reclassifications for class size for all CPC classes on a log-log scale. The slopes of the fits are both close to 1, so even though we have a log-log scale, this suggests direct proportionality between class size and number of reclassifications. This relation suggests that, when researching the magnitude of reclassification of a class, it makes sense to consider the number of reclassifications relative to class size (or, the 'reclassification percentage'). Notes: These figures show the top-10 most dynamically changing patent classes and subclasses, identified by the largest number of patents being removed or added to the class. On the left we have a ranking according to absolute changes (in number of reclassifications) on the right we have a ranking according to relative changes (in number of reclassifications per family). For these rankings, we selected only classes larger than a 1000 families. A.1 are found neatly on the proportionality line: if a class is far above (below) the line, this indicates that even accounting for class size, there are relatively many (few) reclassifications in that class. Green technology (Y02) is on the top right, far above the fit. This means that green technology is reclassified relatively strongly not only in an absolute but also in a relative sense. We also looked into those CPC classes and subclasses that are most dynamically changing by counts of patents being added and removed. Figure A.2 shows the top-10 CPC classes ranked by their absolute (left panel) and relative (right panel) number of patents being migrated from and to the class (where we divide by the number of patents in those classes in 2023, we only consider classes larger than a 1000 families). Considering the absolute class rankings first, we first observe that the reclassifications are distributed rather unequally, with only a small number of 'turbulent' classes being reclassified very frequently. Again we observe that green technology is one of these classes. Moreover, it is interesting that where the reclassifications of green technology mostly consist of patents added, the reclassification of Electrical Elements (ranked second) consists of many more patents removed. Learning from the earlier mentioned relation between reclassification and class size, we also consider the class ranking relative to size in Figure A.2. The first two classes can be considered outliers: the first is a miscellaneous class for semiconductors introduced between 2019 and 2023 and the second, 'adapted ICT', was also introduced shortly before 2019. Naturally, newly introduced classes show a disproportionately large reclassification fraction. Again green technology scores high, with only three other classes showing a greater reclassification fraction. Finally, we note that green technology is the only class with a high rank both in an absolute as well as in a relative sense. SeeBarbieri et al. (2016) andPopp (2019)  for a review of studies on green technologies. Throughout this article, we use GT as an abbreviation for technological inventions that help in climate change mitigation and adaptation. These studies do not explicitly aim to measure trends in green patenting, yet they do so implicitly in addressing their research questions. Note that lists of climate-related codes, such as Env-Tech(Haščič and Migotto, 2015), also use CPC codes to define GTs. For example, legal quality can be reflected by the likelihood of enforceability and the ability to withstand litigation(Higham et al., 2021); technical quality relates to the usefulness and depth of knowledge embodied in the patent and its value for follow-on innovation(Trajtenberg, 1990); economic quality can be captured by its commercial use in the economy(Giuri et al., 2007) or its impact on firms' stock market performance(Kogan et al., 2017). We use as the denominator the number of patent families unaffected by both of these effects. This is often exacerbated by the lack of sufficiently detailed information on sample construction. Indentation refers to changes in the hierarchies of the system. For example, an indentation change can be the split of a CPC group into several subgroups that are newly introduced. The transition to climate-friendly technologies is an incremental process that begins with the phase-out of harmful technologies. Technologies, such as clean combustion, that help in the early stages of the transition may not be considered green once combustion, for example, is phased out. Finally, any judgment about climate transition pathways (as reflected in green classifications) always contains a normative dimension, including debates about nuclear energy, genetic and geological engineering, and negative emissions technology(Palmer et al., 2014).
The list of offices is as follows: AT, AR, AU, BE, BG, BR, CA, CH, CL, CN, CZ, DK, EA, EE, ES, FI, GB, GR, HU, IL, IT, KR, LU, LV, MA, MC, MX, NL, NO, PE, PL, PT, RO, RU, SE and TR (CPC, 2024). For instance, a patent filed in September 2021 would typically be published by February 2023. In the

In the right panel, there is a slight discrepancy in rankings as Y02E10 is excluded from the top ten, while Y02E30 ranks fifth. To mitigate the influence of smaller patent offices when analyzing the share of green patents, we restrict our focus to the leading offices in terms of the number of patent families affected by the set expansion effect (left panel). The increase in the number of citations in the more recent Patstat versions can be attributed to the inclusion of more potentially citing patents. As new patent applications are filed and published over time, the pool of potential citing patents grows. More recent versions of Patstat, such as the

version, capture this expanding citation network, as newer patents may cite older GTs that were not previously cited in earlier data releases.