Environmental policy and green export competitiveness: The enhancing effect of economic complexity

Grazini, Chiara; Guarini, Giulio Article Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review Provided in Cooperation with: Associazione Economia civile, Rome Suggested Citation: Grazini, Chiara; Guarini, Giulio (2025) : Environmental policy and green export competitiveness: The enhancing effect of economic complexity, PSL Quarterly Review, ISSN 2037-3643, Associazione Economia civile, Rome, Vol. 78, Iss. 312, pp. 27-50, https://doi.org/10.13133/2037-3643/18917 This Version is available at: https://hdl.handle.net/10419/324127 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ PSL Quarterly Review This work is licensed under a Creative Commons Attribution – Non-Commercial – No Derivatives 4.0 International License. To view a copy of this license visit http://creativecommons.org/licenses/by-nc-nd/4.0/ vol. 78 n. 312 (March 2025) Environmental policy and green export competitiveness: The enhancing effect of economic complexity CHIARA GRAZINI, GIULIO GUARINI Abstract: Development depends on a nation’s ability to produce sophisticated goods, making economic complexity crucial. Considered as an ecological sophistication of technological, social, and cultural factors, ecological structural change, the core of Green New Developmentalism, can address environmental and socio-economic challenges, particularly in developing countries. As green policies can act as drivers of structural changes, eco-innovations, and international green competitiveness, this paper examines the impact of green policies on green export competitiveness, testing the strictly strong version of the Porter hypothesis and evaluating the moderating effect of economic complexity and pollution intensity. This paper used a panel dataset covering 40 OECD countries from 1990 to 2016, and the results indicate that stringent environmental regulations positively impact green exports only in the medium term. When the moderating factors are introduced, stringent green policies become effective in the short term, and their positive impact increases with the country’s economic complexity and pollution intensity. Combining stringent environmental policies and green economic sophistication could allow for integrating economic growth, sustainable production, and international green competitiveness. Grazini: University of Tuscia, Viterbo (Italy), email: [email protected] Guarini: University of Tuscia, Viterbo (Italy), email: [email protected] How to cite this article: Grazini C., Guarini G. (2024), “Environmental policy and green export competitiveness: The enhancing effect of economic complexity”, PSL Quarterly Review, 78 (312), pp. 27-50. DOI: https://doi.org/10.13133/2037-3643/18917 JEL codes: O11, O14, Q56, Q57, F14 Keywords: international green competitiveness, Environmental Policy Stringency index, moderating factors, economic complexity, ecological structural change Journal homepage: https://rosa.uniroma1.it/rosa04/psl_quarterly_review Although leading global institutions such as the European Commission, the International Energy Agency, the United Nations, the Environmental Protection Agency, and the Organization for Economic Cooperation and Development (OECD) have been intensively working to address the multi-faceted challenges posed by climate change, both at the local and the global levels (Bashir et al., 2020), the international community continues to grapple with sustainable development challenges, so that it becomes critical to comprehend the complex interplay between economic development and the ecological transition. The potential benefits of economic development, such as creating employment opportunities, increasing incomes, and reducing poverty, must not compromise the quality of life for future generations or the health of our planet. Experts thus contend that heightened technology adoption, significant economic expansion and a shift from pollution-intensive industrial activities to service-oriented economies will substantially reduce Special issue on premature deindustrialization and climate change: global North and South perspectives 28 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review environmental pollution (Agozie et al., 2022). Therefore, promoting a more sustainable and equitable growth path, where economic advancement aligns with environmental preservation and societal well-being, is of the utmost importance (Wang et al., 2023). A country’s productive structure embodies its technological and productive capacities, shaping its pathways for diversification and delineating its potential for economic development (Hidalgo et al., 2007). Economic development theorists like Rosenstein-Rodan (1943), Singer (1950), and Hirschman (1958) highlighted the relationship between a nation’s productive structure and its income distribution capabilities, emphasising “structural transformations” as crucial for economic development. According to these authors, development and growth can be understood as a process of structural change of the productive structure, wherein resources are reallocated from lower productivity activities to higher productivity ones. In particular, the New Developmentalism theory emphasises the patterns of structural transformation from the primary sector, which relies on the exploitation of natural resources, to the modern sector, typically represented by the manufacturing industry. Industrialization is fundamental to economic development, proposing a causal relationship between the growth of manufacturing output and the enhancement of both GDP and labour productivity. In contrast to the diminishing returns of the agricultural sector, manufacturing sectors are characterized by significant increasing returns and a high rate of technological change and innovation, as well as strong synergies and linkages driven by labour division, which play a pivotal role in fostering economic development (AngelesCastro et al., 2023). Technological change is acknowledged as cumulative and path-dependent, balancing disruptive innovations that reshape developmental trajectories (path-disrupting) with cumulative innovations that consolidate existing strengths (path-reinforcing), frequently creating significant entry barriers for new competitors (Arthur, 1983; Schot and Steinmueller, 2018). Path dependence manifests through the persistence and entrenchment of specific technological choices, driven by increasing returns associated with the production or adoption of technologies and products, as well as by positive feedback mechanisms and network externalities (Castaldi et al., 2011). Moreover, this modern sector is noted for its substantial potential to enhance labour productivity through increasing productive sophistication and economic complexity (BresserPereira, 2015, 2019). The structural transformation toward more complex industries requires specialized inputs like knowledge, infrastructure, and labour training, creating significant barriers for less developed nations. This highlights the need for active state intervention to overcome these challenges and promote economic diversification (Carson, 2010). Less developed countries face constraints in diversifying toward sophisticated goods due to their poorly integrated economic structure (Botta et al., 2018); structural change is path-dependent, and achieving competitiveness in complex goods demands time and learning new capabilities. The need for public intervention is even more obvious when we consider environmental sustainability, since economic development should be grounded in structural transformations that simultaneously modernize the productive system and address climate change and its associated risks (Romero and Gramkow, 2021). Ecological macroeconomics explores how to achieve environmental sustainability and economic stability. While a strand of literature highlights how green growth may foster cleaner production, job creation, income growth, and poverty alleviation in developing countries by reducing resource use and fossil fuels (Barbier, 2019) and critiques its feasibility, both sides agree on the necessity of effective government intervention to guide economies toward decarbonization and long-term stability (Oberholzer, 2023). In particular, Green New Developmentalism aims to identify an ecologically sustainable growth path by integrating economic development with C. Grazini, G. Guarini 29 PSL Quarterly Review environmental sustainability and emphasizing net-zero greenhouse emissions, balance of payments stability, and the advancement of productive structural sophistication (Grazini et al., 2024). Focusing on climate change and the current ecological crises, ecological structural change is posited by Guarini and Oreiro (2023) as a viable solution to environmental and social challenges. Promoting the relocation of labour and resources from traditional (“brown”) sectors to modern (“green”) ones, ecological structural change is imperative for enhancing the environmental aspects of production output. Within this perspective, ecological structural change aligns with environmental objectives and serves as a strategic manoeuvre for firms to navigate the evolving economic landscape; this ensures their long-term viability and international competitiveness through strategies based on green innovations because prevailing technological approaches to climate change mitigation depend on the unsustainable depleting nonrenewable resources (Grazini et al., 2024). In particular, the escalating challenges of climate change necessitate a more proactive role from institutions and a set of public policies to achieve environmental objectives while fostering social and technological development (Fontana and Sawyer, 2016). This need is even more substantial for several developing and emerging countries where the ecological structural transformation is more difficult; and it is particularly the case for Latin American countries, which, due to a premature de-industrialisation 1 process, have seen an increased reliance on the production and export of primary goods and low-tech manufactured products and an inversion of the sophistication process of the productive structure (Johnson and Papageorgiou, 2020; Oreiro et al., 2020a) and where ecological crisis exacerbates and intensifies the structural disparities that define their socio-economic development. Therefore, ECLAC (2020) highlights the need for a “big push for sustainability” that is based on coordinating policies to mobilise and expedite sustainable investments. These investments aim to create a virtuous cycle of economic growth, increased income, job creation, and reduced inequalities and structural disparities while preserving and regenerating the natural resource base essential for development. The goal of this strategy is to enhance technological capabilities and promote resilient, low-carbon solutions while supporting a more diversified, complex, and competitive integration into the global economy. As empirically observed by Romero and Gramkow (2021), a well-established productive system and a broad range of productive capabilities will create favourable conditions for green innovations and structural change towards more complex high-tech goods, leading to a reduction in the level of greenhouse emissions. So, promoting economic complexity through green industrial policies is crucial for sustainable development, addressing social, economic, and environmental dimensions while fostering innovation and diversification. Within this framework, Rennings (2000) argues that technology-push and market-pull factors do not provide enough incentives to develop complex green innovations. Ecological policies must transcend compensatory measures for negative externalities, acting instead as drivers of structural changes and innovations to facilitate the environmental transformation of production systems (Guarini, 2020). The Porter hypothesis postulates this crucial role, particularly the strong version, which posits that regulatory policies can foster environmental innovations and facilitate the ecological transition by creating new economic opportunities supporting international competitiveness (Porter and Van der Linde, 1995). However, Petroni et al. (2019) assert that the 1 In the Latin American countries, the Dutch disease is considered one of the main causes of premature deindustralization, that is, “a reduction of the share of manufacturing industry in output and employment before the “Lewis’s point” is reached” (Guarini and Oreiro, 2023, p. 2). In particular, the overvaluation of the exchange rate, driven by the production and export of natural-resource-intensive goods, undermines the price competitiveness of the manufacturing sector, thereby compromising investment profitability and widening the technological gap. 30 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review validity of the Porter hypothesis cannot be universally established, as the profitability construct may be significantly influenced by environmental regulation, both positively and negatively; moreover, numerous other factors, such as pollution intensity, substantially impact this construct, which can either validate or refute the hypothesis. International competitiveness is shaped by both price and nonprice factors, which are enhanced by green activities (Blecker, 2016; Guarini and Porcile, 2016). Green initiatives reduce unit production costs and create new business opportunities, bolstering price competitiveness in manufacturing industries (Gramkow, 2020). Simultaneously, improved environmental efficiency strengthens nonprice competitiveness, driving export growth by enabling the production of secondary raw materials, increasing employment rates, and accelerating output growth (DávilaFernández et al., 2023). Indeed, several traditional sectors currently possessing substantial comparative advantages are lagging in green conversion, indicating that they may risk losing their competitive edge (Fankhauser et al., 2013). Conversely, countries that export green products are well-positioned to gain a competitive advantage (Mealy and Teytelboym, 2022). Even examining the energy transition alone, Oberholzer (2023) suggests that green policies can enhance the resilience of the balance of payments by improving the current account; such policies also have the potential to stimulate international competitiveness, offering a promising development strategy for emerging economies (Oberholzer, 2023). In particular, Costantini and Mazzanti (2012) observe that environmental policies, especially those focusing on innovation, play a crucial role in enhancing green exports, an effect that the two authors define as the strictly strong version. However, to the authors’ knowledge, the drivers of green international competitiveness are still little studied in the literature, and few works have evaluated the possible moderating factors. To fill this gap, this paper wants to verify the strictly strong version of the Porter hypotheses using a panel dataset extracted from the OECD database for 40 countries covering 1990-2016. Our interest is to examine whether environmental regulation could directly support green international competitiveness and the indirect impact of possible moderating factors, such as polluting intensity. According to Hidalgo and Hausmann (2009), a nation’s developmental trajectory depends on its capacity to acquire the essential capabilities for producing a diverse array of highly sophisticated goods. Hence, the complexity of the nation’s productive structure is a crucial driver of development (Felipe et al., 2012). Therefore, higher economic complexity also signifies enhanced green production capabilities, which may facilitate technological advancements towards a greener economy (Doğan et al., 2022). Within this framework, the notion of ecological structural change can be interpreted as an ecological sophistication encompassing social, cultural, and, in particular, technological dimensions. Leading complex economies, characterised by a green and sophisticated array of export products, can be pivotal in promoting sustainable development and environmental sustainability (Rafique et al., 2022). The Economic Complexity Index by Hidalgo and Hausmann (2009) offers a complementary perspective on economically significant activities, such as trade, technical innovation, and scientific research; this contrasts with traditional aggregate indicators (Caldarola et al., 2024), enhancing our capacity to quantify a nation’s productive structure and the macroeconomic significance of structural transformations (Hartmann et al., 2017). This paper wants to contribute to the literature by joining the structuralist and evolutionary economics literature, emphasizing the role of capabilities and sectoral allocation in green innovation and international competitiveness. In particular, the paper’s innovative contribution is analysing the single moderating effect of economic complexity, as well as the joint moderate effect of economic sophistication and pollution intensity on the effectiveness of the stringency of environmental regulation on international green competitiveness. Therefore, the paper intends to identify possible complementarity between the C. Grazini, G. Guarini 31 PSL Quarterly Review different policy tools and the degree of sophistication of the economic system, expressed through the Economic Complexity Index, to validate the hypothesis that stringent environmental policies, combined with a sophistication of technology and exports, could represent a winning strategy to facilitate ecological structural change. The remainder of the paper is structured as follows: Section 1 introduces the conceptual framework of the paper, section 2 describes the data and the estimation strategy, section 3 discusses the results, and section 4 concludes. 1. The conceptual framework A firm’s environmental and economic success relies on innovations that foster sustainability and competitiveness (Fabrizi et al., 2024a). A “virtuous cycle” starts from innovation, particularly green innovation, which leads to enhanced productivity and increased export market share (Foster et al., 2008). Eco-innovations generate more efficient processes, improve productivity and environmental performance, and provide a competitive advantage for firms (Chistov et al., 2021). Therefore, they represent a crucial driver of industrialisation and international green competitiveness (Guarini and Oreiro, 2023; Fabrizi et al., 2024b). On the one hand, green technological advancements, through the reduction of unit energy and raw material costs, can lead to a depreciation of the real exchange rate, thereby enhancing price competitiveness in the global market (Galindo et al., 2020) On the other hand, implementing an ecological structural change can increase nonprice competitiveness by positively affecting the income elasticity of exports (DávilaFernández and Sordi, 2020). Although favouring environmental sustainability and employment creation, eco-innovations are more complex than standard ones; their implementation requires companies to possess multidisciplinary knowledge and skills concerning the standard technologies, so they necessitate the support of stricter environmental regulations to ensure their successful implementation (Kemp and Pearson, 2007; Horbach et al., 2013). Corrective ecological regulations have been enacted to mitigate environmental damage, thereby fostering the green transformation of the economy through advancements in green technology innovation (Guo et al., 2023). However, two different positions emerge in academic literature. In the neoclassical theory, environmental protection leads to additional costs for firms, eroding their profitability and competitiveness. Ecological regulation forces firms to allocate inputs, such as labour or capital, to reduce pollution, which is unproductive from the common profit-maximisation perspective, identifying environmental policies as a cause of cost increases (Ambec et al., 2013). This perspective gave rise to the Pollution haven hypothesis, which posits that polluting industries may relocate to countries or regions with more lenient environmental regulations, referred to as pollution havens (Dou and Han, 2019). The other position, in heterodox literature, recognises environmental regulation as stimulating the development and dissemination of new, cleaner technologies, which are crucial for reducing the environmental impact of human activities. Additionally, promoting ecoinnovation can create a win-win strategy where the generation and diffusion of technologies that enhance ecological performance lead to knowledge spillovers, thereby improving the international competitiveness of high-tech sectors (Costantini et al., 2017). In particular, Porter and van der Linde contested the pollution haven hypothesis in the early 1990s (Fabrizi et al., 2024a); they found that, while the traditional conflict between ecology and the economy arises from a static view of environmental regulation, where technology, products, processes, and 32 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review customer needs are considered fixed, the paradigm of international competitiveness is a dynamic, innovation-based model (Porter and Van der Linde, 1995). This vision has been formalised in the well-known Porter hypothesis (PH), which has been divided into four versions: weak, narrow, strong and strictly strong. The weak PH states that environmental policies may spur innovation. The narrow PH maintains that flexible environmental regulation, particularly market-based instruments, can improve firms’ performance by stimulating the adoption of innovations. The strong PH affirms that environmental regulation represents an essential driving force of environmental innovations, improving firms’ market competitiveness thanks to the generated cost savings and productivity increases (Kunapatarawong and Martínez-Ros, 2016). Indeed, by spurring environmental innovations, green policies can increase price competitiveness by reducing inputs and production costs. At the same time, they can enhance environmental performance and the quality of products as perceived by the international markets, sustaining nonprice competitiveness (Guarini and Oreiro, 2022). Costantini and Mazzanti (2012) introduced the following strictly strong version. In their testing, they identified two different positive effects. First of all, the overall impact of environmental policies does not appear to be detrimental to the export competitiveness of the manufacturing sector. Conversely, targeted energy tax policies and innovation initiatives positively influence the dynamics of export flows, supporting the mechanism indicated by the PH. In particular, the authors highlight a significant positive effect of environmental policies, and, in particular, of more incisively environmental innovation efforts on international green competitiveness. Indeed, Mahmood et al. (2022) recently found that environmental regulations significantly stimulate eco-innovations and foster green growth in OECD countries. Observing the ambiguity surrounding studies aimed at validating or refuting the strong version of the Porter hypothesis, Petroni et al. (2019) highlight that, while its recruitment and policy implications are incredibly relevant, its validity is controversial and subject to certain conditions, under which environmental regulation can lead to profitable outcomes and/or enhance the competitiveness of firms or industries. Different moderating factors can affect the impact of environmental regulation on international competitiveness. For instance, analysing the Chinese manufacturing sector, He et al. (2020) observe how property rights protection influences the relationship between environmental regulation and corporate performance. In particular, the effectiveness of environmental regulation on international competitiveness can especially depend on the country’s pollution intensity. However, there is no consensus in literature on the moderating effect of this factor. On the one hand, Petroni et al. (2019) maintain that stringent environmental regulations influence firms’ environmental strategies and profitability so that they can favour less-polluting firms; this is because the latter may incur lower compliance costs compared to more heavily polluting industries, which are subject to higher pollution abatement requirements and higher compliance costs, threatening their profitability and market survival. On the contrary, Fabrizi et al. (2024a) observe that pollution intensity has a positive moderating impact on the effectiveness of green policies on international competitiveness, suggesting that higher pollution levels make ecological conversion more economically advantageous globally. Following their work, this paper wants to verify the moderating role of pollution intensity on international green competitiveness. Another significant factor affecting the impact of environmental regulations on international green competitiveness could be the economic complexity of the productive structure. Technical progress creates increasingly sophisticated and complex products and services, integrating a greater and more diverse array of technical and scientific knowledge as well as structural changes that reallocate productive resources and workers from lower-value to higher-added-value sectors C. Grazini, G. Guarini 33 PSL Quarterly Review (Oreiro et al., 2020b). Technical change involves acquiring increasingly complex capabilities, that is, a learning process focused on enhancing the production and export of highly sophisticated goods (Hidalgo and Hausmann, 2009). Therefore, economic complexity reflects a country’s capabilities, including human and social capital, institutions, and technology, which collectively contribute to the sophistication level of its exported products (Zhu and Fu, 2013). Indeed, countries that excel in high-tech production and diversification rank highly on the Economic Complexity Index and enjoy a competitive advantage in export markets (Erkan and Yildirimci, 2015). A body of literature suggests that economic complexity can potentially safeguard environmental quality. Focusing on the ecological transition, at higher levels of economic complexity, there occur structural shifts towards knowledge-intensive industries; these impact energy demand and resource development and allocation, and they exert significant effects on the environment, economy, and society (Neagu et al., 2022). This increase in economic complexity provides the knowledge and technology for economies to transition towards environmentally sustainable activities, including, for example, clean production technology expertise and knowledge of a country (Swart and Brinkmann, 2020; Safi et al., 2023). Consequently, as a country’s economic complexity increases, its environmental quality will likely improve through the adoption of advanced knowledge and technologies (Hausmann et al., 2014). Boleti et al. (2021) verify that higher levels of economic complexity correlate with superior environmental performance, as economic complexity may facilitate the adoption of green innovations. Indeed, this relationship is based on the premise that higher economic complexity indices correlate with increased research and innovation activities, which drive green innovation and environmentally sustainable production methods (Agozie et al., 2022). Engagement in green activities can pave the way for novel sources of competitiveness and business opportunities, particularly in sectors characterised by innovation and the generation of significant added value. Empirically, as shown by Shahbaz et al. (2019), more sophisticated exports that are generated by learning-by-doing and learning-by-exporting activities allow, for instance, the control of energy demand, and they are associated with lower greenhouse gas emissions per unit of output (Stojkoski et al., 2023). However, there is no evidence about the moderating effect of economic complexity on the impact of environmental regulations on international green competitiveness. The only evidence is the study of Mealy and Teytelboym (2022) that builds an ad hoc Green Complexity Index showing the country’s capability to export green, technologically sophisticated products competitively; in particular, using a linear regression model, they observe that higher green productive capabilities are positively correlated with higher environmental patenting rates, lower CO2 emissions, and more stringent environmental policies. 2. Estimation strategy and econometric model This section presents the dataset, the main variables, and the econometric model implemented to verify the validity of the strictly strong version of the Porter hypothesis. 2.1. The dataset, Environmental Policy Stringency index, and Economic Complexity Index The OECD provides reliable and harmonised data on environmental issues to respond to the growing international concern for sustainable development (Grazini and Guarini, 2023a). To evaluate the impact of environmental regulation on green export competitiveness and the 34 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review moderating effect of economic complexity, we use the list of environmentally related goods proposed by the OECD 2 to create the dependent variable ENV_EXP, which is the environmental goods export in current USD. Following Costantini and Mazzanti (2012), we use this variable as a proxy for green export competitiveness. The environmental goods exports are built by aggregating the following 11 environmental goods categories proposed by the OECD (Steenblik, 2005): 1) air pollution control, 2) environmental monitoring, analysis and assessment equipment, 3) management of solid and hazardous waste and recycling systems, 4) noise and vibration abatement, 5) waste water management and potable water treatment, 6) cleaner or more resource efficient technologies and products, 7) environmentally preferable products based on end use or disposal characteristics, 8) clean up or remediation of soil and water, 9) heat and energy management, 10) natural resources protection, and 11) renewable energy plant. We proxy the environmental regulation by using the Environmental Policy Stringency (EPS) index. To confront climate challenges and, particularly, respect the Paris Agreement, nations are committing to more ambitious environmental targets and policy actions. As countries adopt stricter environmental policies, the demand for tools to measure, compare, and evaluate their impacts is increasing. Measuring policy stringency across countries and time serves three purposes: to monitor progress, to identify and benchmark leaders and laggards, and to assess the impact of policies on pollution, economic, and social outcomes. Effective policy implementation requires understanding which measures work best and ensuring protection for vulnerable groups to avoid regressive effects (Kruse et al., 2022). The OECD EPS index was developed in 2014 to overcome the lack of harmonised data that would allow spatial and temporal comparison; it allowed, for the first time, a comprehensive evaluation of a diverse array of policies across different countries and periods. Even though this index encompasses climate change and air pollution policies, as these topics have the most comprehensive data available, it excludes significant environmental areas such as water, biodiversity, and waste management due to the unavailability of extensive cross-country panel data for these sectors. While it initially focused only on market and nonmarket instruments, this index was updated in 2022, adding the technology support policy so that today, it measures the severity of environmental regulation for 40 countries by examining 13 different policy tools. 3 Once the values of the instrument-specific indicators are aggregated into the three broader categories described in footnote 3, these values are aggregated into a composite indicator ranging from 0 to 6, according to the degree of stringency of environmental regulations. Finally, we use the Economic Complexity Index (ECI) to measure the sophistication of a country’s productive structure because it represents the extent of knowledge a society utilises to construct its economic structure and the know-how of its population (Hidalgo, 2015). Developed by Hidalgo and Hausmann (2009) within Harvard’s Growth Lab, 4 this index evaluates a country’s current production knowledge by analysing trade data and assessing the diversification and 2 Source: OECD Data Explorer (available online). 3 The market instrument focuses on tools attributing a price to pollution, such as CO2 taxes, fuel taxes, NOx tax, SOx tax, CO2 trading schemes, and renewable energy trading schemes. The nonmarket category is related to policies that impose an emissions standard, such as emission limit value (ELV) for NOx, ELV for Sox, ELV for particulate matter, and sulphur content limit for diesel. “Well-designed environmental policies provide incentives that increase innovation in clean technologies. Accelerating innovation in low-carbon technologies may require further technology support policies incentivising innovation in and adoption of low-carbon technologies” (Kruse et al., 2022, p. 36). The third sub-index related to the strength of technology support policies allows for the isolation of upstream and downstream instruments that directly support innovations in clean technologies: the first category aims to encourage and finance innovations, while the purpose of the second one is to promote their adoption. 4 Source: https://atlas.hks.harvard.edu/data-downloads. C. Grazini, G. Guarini 41 PSL Quarterly Review proxy of knowledge and green production capabilities within the economy, for enhancing international green competitiveness; but, above all, they’ve underscored the significant role of economic complexity in moderating the impact of environmental regulations on green exports, which becomes positive and significant even in the short term. Countries with higher economic complexity are more adept at utilizing stringent environmental regulations to stimulate green innovations and enhance their international competitiveness. Conversely, ecological structural change is more difficult for emerging economies, where the low sophistication of the production structure produced by the de-industrialization process can compromise the effectiveness of policies inducing technological change. The joint impact of the ECI and the EPS index highlights the importance of advancing economic diversification and enacting effective environmental policies. As maintained by ECLAC (2020), green conversion needs a “big push for sustainability”, which involves the coordination of policies to mobilize and accelerate sustainable investments to foster a virtuous cycle of economic growth, increased income, job creation, and reduced inequalities. At the same time, policymakers can promote investments in innovative and highvalue-added activities that facilitate the transition of diversified economic structures and sophisticated green goods production. Governments should prioritize sectors with lower environmental impact and encourage firms to invest in environmentally friendly products. This strategy can address ecological challenges while strengthening economic resilience and enhancing international competitiveness by effectively positioning countries to leverage green innovations and sustainable practices. Moreover, countries that export complex industrial products generally exhibit greater inclusivity and experience lower income inequality levels than do those exporting more straightforward products (Hartmann et al., 2017). Therefore, the combination of stringent environmental policies and green economic sophistication could represent ECLAC’s (2012) “structural change for equality”, which integrates economic growth with sustainable production and universal social protection to promote knowledge-intensive sectors, reduce income disparities, and ensure equal rights for all. Finally, findings have also revealed that pollution intensity influences both the direct effectiveness of environmental regulations and the moderating effect of the ECI. Stringent regulations are more effective in promoting international green competitiveness in highly polluting countries characterised by high economic sophistication, as they have more opportunities to reduce inefficiencies and benefit from green conversion. This implies that the previous green competitive strategy should be tailored to different countries’ pollution intensity and green knowledge to maximize their effectiveness. This paper has limitations due to the availability of OECD environmental data that may guide future research directions. Firstly, although the OECD provides harmonised data for cross-country comparisons, the sample is limited to only 40 countries, most of which are high-income OECD countries. Further research could enlarge the sample, including more emerging and developing countries, to test the validity of the strictly strong version of the PH and provide a more comprehensive understanding of the global dynamics of green competitiveness. Secondly, the time series of environmental exports covers only the period from 1990 to 2016; future research should extend the analysis to include more recent data to capture the latest trends and policy impact. Moreover, further research could fruitfully explore and analyse in more depth the effects of the EPS index and the ECI by disaggregating environmental exports. Confirming and extending the findings of Fabrizi et al. (2024a), this paper has identified pollution intensity as another moderating factor of the relationship between environmental regulation and international green competitiveness. To achieve diversification in green technology, it is often essential to access foreign expertise and develop the necessary domestic capabilities, requiring international 42 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review linkages and cross-country cooperation, which are crucial for enhancing international competitiveness. Indeed, the UN (UNCTAD, 2023) advocates international cooperation in the green economy, emphasizing the importance of activities in science, technology, and innovation. At the national level, universities and governments can support firms by providing economic incentives and facilitating the transfer of complex knowledge for developing green innovations. Within this perspective, Fabrizi et al. (2024b) identify a positive relationship between environmental networks and green exports. Therefore, further work will evaluate how international collaboration and trade policies influence the adoption of green technologies and practices and include other moderating factors. The narrow version of the PH sustains the effectiveness of environmental regulation on eco-innovation diffusion, and environmental performance varies according to the specific environmental policy tool, so different types of environmental policies have varying impacts on green exports (Kang and Lee, 2021). This paper has identified the existence of a direct impact of environmental policy’s stringency and the single and joint moderating effects of economic sophistication and pollution intensity of its effectiveness on international green competitiveness. However, further research could test the narrow versions of the PH by examining in more depth the impact of specific policy instruments and regulatory frameworks, using the decomposition of the EPS index, on international green competitiveness, as well as the moderating factors affecting its validity, to provide more actionable insights for policymakers. Appendix A Figure A1 – The last available global classification according to the ECI (2023) Source: https://atlas.cid.harvard.edu/rankings C. Grazini, G. Guarini 43 PSL Quarterly Review Appendix B Table B1 – Summary statistics of dependent and independent variables (expressed in logarithms) Obs. Mean Std. Dev. Min. Max. ENV_EXP 546 22.85623 1.64420 17.09108 26.2739 WGDP 1240 32.02996 0.30085 31.57146 32.50363 0 RER 1'079 4.572144 0.174392 3.763807 5.102513 ECI 914 –0.0998606 0.9288676 –5.6003275 1.0504261 EPS 1'196 0.2938750 0.9491239 –2.8903718 1.586965 GHG_GDP 1'128 0.37166 0.66423697 –4.42284862 1.626 GHG_CAP 1'120 2.260371 0.658944 –1.870803 3.92235 EDU 852 3.2312 0.5086 1.5597 4.0936 FIT 819 0.7400 0.7937 –2.0241 2.3691 GDP_POP 1'209 3.3319 0.6926 0.3396 4.7447 TRADE_OPEN 1'044 –0.7056 0.4981 –2.3161 0.5200 GOV_R&D 724 –6.3015 0.7041 –9.0496 –4.9230 Figure B1 – Trend of the average of the EPS index between 1990 and 2016 according to the development level Source: Authors’ elaboration on the OECD database (https://data-explorer.oecd.org). 0 0,5 1 1,5 2 2,5 3 3,5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Developed countries Emerging countries Transitioning countries 44 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review Figure B2 – The dynamic of ENV_EXP between 2003 and 2016 for several countries Source: Authors’ elaboration on the OECD database (https://data-explorer.oecd.org). Table B2 – Correlation matrix Nr. Variables 1 2 3 4 5 6 7 8 9 10 11 12 1 ENV_EXP 1.000 2 WGDP 0.084 1.000 3 RER 0.195 –0.090 1.000 4 EPS 0.437 0.249 0.089 1.000 5 ECI 0.602 –0.205 0.119 0.232 1.000 6 FIT 0.810 –0.118 0.100 0.221 0.552 1.000 7 GHG_GDP –0.070 –0.273 –0.072 –0.341 –0.113 0.024 1.000 8 GHG_CAP 0.080 –0.223 0.034 –0.171 0.031 0.017 0.807 1.000 9 EDU 0.127 0.348 0.116 –0.064 0.031 –0.151 –0.190 0.101 1.000 10 GDP_POP 0.234 0.140 0.170 0.330 0.235 –0.015 –0.528 0.075 0.468 1.000 11 GOV_R&D 0.399 –0.034 0.015 0.155 –0.024 0.200 0.276 0.100 –0.048 –0.321 1.000 12 TRADE_OPEN –0.293 0.125 –0.095 0.086 0.178 –0.330 0.055 –0.052 –0.313 –0.167 –0.162 1.000 21,6 22,1 22,6 23,1 23,6 24,1 24,6 25,1 25,6 26,1 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Brazil China France Germany India Italy Mexico Spain United States C. Grazini, G. Guarini 45 PSL Quarterly Review Appendix C Table C1 – The introduction of one, two, and four lags and of the Fitness Economic Metric Index, and estimations with a fixed effect instrumental variables model (column 5) (1) (2) (3) (4) (5) LAG 1 LAG 2 LAG 4 Fitness Economic Metric, FIT FE instrumental variable model WGDP 0.757*** 0.768*** 0.771*** 0.535 –0.320 (78.26) (74.17) (84.83) (1.52) (–0.25) RER 0.207*** 0.150** 0.141** 0.252*** 0.456*** (3.01) (2.04) (2.20) (3.78) (3.98) ECI 0.166** –0.0148 0.00269 0.112 (2.50) (–0.21) (0.04) (0.40) EPS 0.0548 0.110*** 0.0914*** –0.0395 0.122 (1.21) (2.81) (3.10) (–1.00) (0.99) EPS*ECI 0.280*** 0.218*** 0.196*** 0.524** (4.82) (3.86) (4.29) (2.02) GHG_GDP –0.109** –0.0680 –0.0204 –0.00143 –0.0212 (–2.04) (–1.33) (–0.46) (–0.04) (–0.21) EPS*GHG_GDP 0.0639* 0.115*** 0.0545** 0.0103 0.177** (1.84) (3.77) (2.21) (0.47) (2.46) ECI*GHG_GDP 0.00822 –0.119* –0.0957* –0.173 (0.13) (–1.93) (–1.78) (–0.81) EPS*ECI*GHG_GDP 0.252*** 0.193*** 0.143*** 0.471*** (5.13) (3.98) (3.79) (2.75) FIT 0.237*** (3.45) EPS*FIT 0.110*** (2.58) FIT*GHG_GDP 0.0212 (0.56) EPS*FIT*GHG_GDP 0.0949*** (3.83) Time dummies Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes No No. observations 405 404 404 444 400 Countries 30 30 30 33 30 Years 14 14 14 14 13 Wald test 1.36e+07 1.30e+07 1.51e+07 64502.51 Underidentification test (p-value) 0.0545 Hansen J statistic (p-value) 0.3504 Note: t statistics in parentheses. *, **, and *** indicate 10 %, 5 %, and 1 % significance levels. In column (5), a fixed effect instrumental variables model is estimated: all economic regressors are considered potentially endogenous by using these regressors with lag 2, population, and population density as instruments. 46 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review Table C2 – The introduction of other control variables (1) (2) (3) (4) (5) (6) ETC GHG_CAP GDP_POP EDU TRADE_OPEN GOV_R&D WGDP 0.757*** 0.677*** 0.677*** 0.694*** 0.785*** 0.614*** (78.26) (57.53) (57.19) (57.61) (77.03) (42.27) RER 0.207*** 0.130** 0.133** 0.263*** 0.201*** 0.298*** (3.01) (2.04) (2.08) (3.77) (3.00) (3.62) ECI 0.166** 0.220*** 0.218*** 0.0342 0.226** 0.245** (2.50) (3.43) (3.41) (0.37) (2.53) (2.42) EPS 0.0548 –0.0463 –0.0453 0.0500 0.0305 0.209** (1.21) (–1.13) (–1.11) (1.09) (0.59) (2.54) EPS*ECI 0.280*** 0.210*** 0.207*** 0.294*** 0.346*** 0.284*** (4.82) (3.83) (3.79) (4.08) (4.54) (2.96) GHG_GDP –0.109** –0.753*** 0.00711 –0.176*** –0.155** –0.336*** (–2.04) (–8.89) (0.14) (–3.02) (–2.41) (–3.92) EPS*GHG_GDP 0.0639* –0.0164 –0.0154 0.0952*** 0.0233 0.171*** (1.84) (–0.49) (–0.46) (2.73) (0.56) (2.89) ECI*GHG_GDP 0.00822 0.0589 0.0586 –0.0614 0.0920 0.0625 (0.13) (1.01) (1.00) (–0.84) (1.07) (0.65) EPS*ECI*GHG_GDP 0.252*** 0.199*** 0.196*** 0.254*** 0.296*** 0.235*** (5.13) (4.32) (4.25) (4.79) (4.21) (2.88) ETC –1.468*** (–18.05) GHG_CAP 0.757*** (8.61) GDP_POP 0.759*** (8.63) EDU 0.452*** (6.26) TRADE_OPEN 0.610*** (8.73) GOV_R&D 0.0412 (1.25) Time dummies Yes Yes Yes Yes Yes Yes Country dummies Yes Yes Yes Yes Yes Yes No. observations 405 405 405 367 387 234 Countries 30 29 29 28 21 30 Years 14 14 14 14 14 14 Wald test 1.36e+07 1.58e+07 1.58e+07 1.56e+07 1.68e+07 1.65e+07 Note: t statistics in parentheses. *, **, and *** indicate 10 %, 5 %, and 1 % significance levels. References Agozie D.Q., Gyamfi B.A., Bekun F.V., Ozturk I. and Taha A. (2022), “Environmental Kuznets curve hypothesis from lens of economic complexity index for BRICS: evidence from second generation panel analysis”, Sustainable Energy Technologies and Assessments, 53, art. 102597. Ambec S., Cohen M.A., Elgie S. and Lanoie P. (2013), “The Porter hypothesis at 20: can environmental regulation enhance innovation and competitiveness?”, Review of Environmental Economics and Policy, 7(1), pp. 2-22. Angeles-Castro G., Domínguez-Blancas C.S. and Fraga-Castillo C.A. (2023), “Kaldor-Verdoorn Laws in The Latin American Countries, 1992-2021”, Investigación Económica, 82(326), pp. 156-184. C. Grazini, G. Guarini 47 PSL Quarterly Review Arthur W.B. (1983), “On competing technologies and historical small events: the dynamics of choice under increasing returns”, IIASA Working Paper, no. WP-83-090, Laxenburg, Austria: International Institute for Applied Systems Analysis. Available online. Barbier E.B. (2019), “How to make the next Green New Deal work”, Nature, 565(7737), pp. 6-7. Bashir M.A., Sheng B., Doğan B., Sarwar S. and Shahzad U. (2020), “Export product diversification and energy efficiency: Empirical evidence from OECD countries”, Structural Change and Economic Dynamics, 55, pp. 232-243. Blackwell III J.L. (2005), “Estimation and testing of fixed-effect panel-data systems”, The STATA Journal, 5(2), pp. 202207. Blecker R.A. (2016), “The debate over ‘Thirlwall’s law’: balance-of-payments-constrained growth reconsidered”, European Journal of Economics and Economic Policies: Intervention, 13(3), pp. 275-290. Boleti E., Garas A., Kyriakou A. and Lapatinas A. (2021), “Economic complexity and environmental performance: evidence from a world sample”, Environmental Modeling & Assessment, 26, pp. 251-270. Botta A., Porcile G. and Ribeiro R. (2018), “Economic development, technical change and income distribution: A conversation between Keynesians, Schumpeterians and Structuralists. Introduction to the Special Issue”, PSL Quarterly Review, 71(285), pp. 97-101. Bottega A. and Romero J. P. (2021) “Innovation, export performance and trade elasticities across different sectors”, Structural Change and Economic Dynamics, 58, pp. 174-184. Brécard D., Hlaimi B., Lucas S., Perraudeau Y. and Salladarré F. (2009), “Determinants of demand for green products: An application to eco-label demand for fish in Europe”, Ecological Economics, 69(1), pp. 115-125. Bresser-Pereira L.C. (2015), A construção política do Brasil [The political construction of Brazil], São Paulo: Editora 34. Bresser-Pereira L.C. (2019), “From classical developmentalism and post-Keynesian macroeconomics to new developmentalism”, Brazilian Journal of Political Economy, 39, pp. 187-210. Busato F., Chiarini B., Cisco G. and Ferrara M. (2023), “Green preferences”, Environment, Development and Sustainability, 25(4), pp. 3211-3253. Caldarola B., Mazzilli D., Napolitano L., Patelli A. and Sbardella A. (2024), “Economic complexity and the sustainability transition: A review of data, methods, and literature”, Journal of Physics: Complexity, 5(2), art. 022001. Available online. Carrasco C.A., Del-Toro-Rodriguez B.B., Jimenez-Jaime C., Hernandez-Garcia M. and Obregon-de-la-Garza P.M. (2024), “Economic structure, export specialization and greenhouse gas emissions in Latin America”, Revista Economia e Políticas Públicas, 12(1), pp. 61-71. Carson M. (2010), “Guiding structural change: the role of government in development”, ILO Employment Working Paper, no. 49, Geneva: International Labour Organization. Available online. Castaldi C., Dosi G. and Paraskevopoulou E. (2011), “Path dependence in technologies and organizations: a concise guide”, LEM Working Paper Series, no. 2011/12, Pisa (Italy): Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM). Available online. Chistov V., Aramburu N. and Carrillo-Hermosilla J. (2021), “Open eco-innovation: A bibliometric review of emerging research”, Journal of Cleaner Production, 311, art. 127627. Costantini V. and Mazzanti M. (2012), “On the green and innovative side of trade competitiveness? The impact of environmental policies and innovation on EU exports”, Research Policy, 41(1), pp. 132-153. Costantini V., Crespi F., Marin G. and Paglialunga E. (2017), “Eco-innovation, sustainable supply chains and environmental performance in European industries”, Journal of Cleaner Production, 155, pp. 141-154. Dávila-Fernández M.J. and Sordi S. (2020), “Attitudes towards climate policies in a macrodynamic model of the economy”, Ecological Economics, 169, art. 106319. Dávila-Fernández M.J., Sordi S. and Cafferata A. (2023), “How do you feel about going green? Modelling environmental sentiments in a growing open economy”, Journal of Economic Interaction and Coordination, 19, pp. 649-687. Doğan B., Ghosh S., Hoang D.P. and Chu L.K. (2022), “Are economic complexity and eco-innovation mutually exclusive to control energy demand and environmental quality in E7 and G7 countries?”, Technology in Society, 68, art. 101867. Dou J. and Han X. (2019), “How does the industry mobility affect pollution industry transfer in China: Empirical test on Pollution Haven Hypothesis and Porter Hypothesis”, Journal of Cleaner Production, 217, pp. 105-115. ECLAC – Economic Commission for Latin America and the Caribbean (2012), Structural change for equality: an integrated approach to development. Thirty-four session of ECLAC, Santiago (Chile): ECLAC. Available online. ECLAC – Economic Commission for Latin America and the Caribbean (2020). A big push for sustainability in Brazil’s energy sector. Input and evidence for policy coordination, Santiago (Chile): United Nations – ECLAC. Available online. Erkan B. and Yildirimci E. (2015), “Economic complexity and export competitiveness: The case of Turkey”, ProcediaSocial and Behavioral Sciences, 195, pp. 524-533. 48 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review Fabrizi A., Gentile M., Guarini G. and Meliciani V. (2024a), “The impact of environmental regulation on innovation and international competitiveness”, Journal of Evolutionary Economics, 34(1), pp. 169-204. Fabrizi A., Guarini G. and Meliciani V. (2024b), “The impact of environmental research networks on green exports: an analysis of a sample of European countries”, Structural Change and Economic Dynamics, 69, pp. 143-154. Fankhauser S., Bowen A., Calel R., Dechezleprêtre A., Grover D., Rydge J. and Sato M. (2013), “Who will win the green race? In search of environmental competitiveness and innovation”, Global Environmental Change, 23(5), pp. 902913. Felipe J., Kumar U., Abdon A. and Bacate M. (2012), “Product complexity and economic development”, Structural Change and Economic Dynamics, 23(1), pp. 36-68. Fontana G. and Sawyer M. (2016), “Towards post-Keynesian ecological macroeconomics”, Ecological Economics, 121, pp. 186-195. Foster L., Haltiwanger J. and Syverson C. (2008), “Reallocation, firm turnover, and efficiency: selection on productivity or profitability?”, American Economic Review, 98(1), pp. 394-425. Galindo L., Guarini G. and Porcile G. (2020), “Environmental innovations, income distribution, international competitiveness and environmental policies: a Kaleckian growth model with a balance of payments constraint”, Structural Change and Economic Dynamics, 53, pp. 16-25. Ge T., Li J., Sha R. and Hao X. (2020), “Environmental regulations, financial constraints and export green-sophistication: evidence from China’s enterprises”, Journal of Cleaner Production, 251, art. 119671. Ghisetti C., Marzucchi A. and Montresor S. (2015), “The open eco-innovation mode. An empirical investigation of eleven European countries”, Research Policy, 44(5), pp. 1080-1093. Gramkow C. (2020), “Green fiscal policies: An armoury of instruments to recover growth sustainably”, Studies and Perspectives series‐ECLAC Office in Brasilia, No. 5 (LC/TS.2020/24) (LC/BRS/TS.2019/7), Santiago (Chile), Economic Commission for Latin America and the Caribbean. Available online. Grazini C. and Guarini G. (2023a), “Politiche e competitività internazionale in ambito ambientale: alcune evidenze empiriche”, Working Paper CiMET/Policy Brief, no. 8/2023, Ferrara: Italy’s National University Centre for Applied Economic Studies (CiMET). Available online. Grazini C. and Guarini G. (2023b), “The impact of economic complexity and green policies on environmental efficiency”, Revista Economia e Políticas Públicas, 11(2), pp. 22-34. Grazini C., Guarini G. and Oreiro J.L. (2024), “Harrod-Domar Dilemma, Thirlwall’s Law and Green New Developmentalism”, Brazilian Journal of Political Economy, 44(4), pp. 679-697. Available online. Greene W.H. (2018), Econometric analysis, 8th edition, London: Pearson. Guarini G. (2015), “Complementarity between environmental efficiency and labour productivity in a cumulative growth process”, PSL Quarterly Review, 68(272), pp. 41-56. Available online. Guarini G. (2020), “The macroeconomic impact of the Porter hypothesis: sustainability and environmental policies in a post-Keynesian model”, Review of Political Economy, 32(1), pp. 30-48. Guarini G. and Oreiro J.L. (2022), “The Ecological Structural Change and the Middle-Income Trap”, paper prepared for the 19th STOREP Annual Conference, Tuscia University, Viterbo, Italy, 26-28 May 2022. Available online. Guarini G. and Oreiro J.L. (2023), “Ecological transition and structural change: A new-developmentalist analysis”, SocioEconomic Planning Sciences, 90, art. 101727. Guarini G. and Porcile G. (2016), “Sustainability in a post-Keynesian growth model for an open economy”, Ecological Economics, 126, pp. 14-22. Guo M., Wang H. and Kuai Y. (2023), “Environmental regulation and green innovation: Evidence from heavily polluting firms in China”, Finance Research Letters, 53, art. 103624. Hartmann D., Guevara M. R., Jara-Figueroa C., Aristarán M. and Hidalgo C.A. (2017), “Linking economic complexity, institutions, and income inequality”, World Development, 93, pp. 75-93. Hausmann R., Hwang J. and Rodrik D. (2007), “What you export matters”, Journal of Economic Growth, 12, pp. 1-25. Hausmann R., Hidalgo C.A., Bustos S., Coscia M. and Simoes A. (2014), “The atlas of economic complexity: Mapping paths to prosperity”, Cambridge (MA): Mit Press. He W., Tan L., Liu Z. J. and Zhang H. (2020), “Property rights protection, environmental regulation and corporate financial performance: Revisiting the Porter Hypothesis”, Journal of Cleaner Production, 264, art. 121615. Hidalgo C. (2015), Why information grows: The evolution of order, from atoms to economies”, New York: Basic Books. Hidalgo C.A. and Hausmann R. (2009), “The building blocks of economic complexity”, Proceedings of the National Academy of Sciences, 106(26), pp. 10570-10575. Hidalgo C.A., Klinger B., Barabási A.-L. and Hausmann R. (2007), “The product space conditions the development of nations”, Science, 317(5837), pp. 482-487. Hirschman A.O. (1958), The strategy of economic development, New Haven (CT): Yale University Press. C. Grazini, G. Guarini 49 PSL Quarterly Review Horbach J., Oltra V. and Belin J. (2013), “Determinants and specificities of eco-innovations compared to other innovations—an econometric analysis for the French and German industry based on the community innovation survey”, Industry and Innovation, 20(6), pp. 523-543. Johnson P. and Papageorgiou C. (2020), “What remains of cross-country convergence?”, Journal of Economic Literature, 58(1), pp129-175. Kang S.J. and Lee S. (2021), “Impacts of environmental policies on global green trade”, Sustainability, 13(3), art. 1517. Available online. Kemp R. and Pearson P. (2007), Final report MEI project about measuring eco-innovation, Maastricht: UM-MERIT. Available online. Kowalska-Styczeń A., Bublyk M. and Lytvyn V. (2023), “Green innovative economy remodeling based on economic complexity”, Journal of Open Innovation: Technology, Market, and Complexity, 9(3), art. 100091. Kunapatarawong R. and Martínez-Ros E. (2016), “Towards green growth: How does green innovation affect employment?”, Research Policy, 45(6), pp. 1218-1232. Kruse T., Dechezleprêtre A., Saffar R. and Robert L. (2022), “Measuring environmental policy stringency in OECD countries: An update of the OECD composite EPS indicator”, OECD Economics Department Working Papers, No. 1703, Paris: OECD Publishing. Available online. Lanoie P., Patry M. and Lajeunesse R. (2008), “Environmental regulation and productivity: Testing the porter hypothesis”, Journal of Productivity Analysis, 30, pp. 121-128. Lapatinas A., Garas A., Boleti E. and Kyriakou A. (2019), “Economic complexity and environmental performance: Evidence from a world sample”, MPRA Paper, no. 92833, Munich: Munich Personal RePEc Archive. Available online. Mahmood N., Zhao Y., Lou Q. and Geng J. (2022), “Role of environmental regulations and eco-innovation in energy structure transition for green growth: Evidence from OECD”, Technological Forecasting and Social Change, 183, art. 121890. Martínez-Zarzoso I., Bengochea-Morancho A. and Morales-Lage R. (2019), “Does environmental policy stringency foster innovation and productivity in OECD countries?”, Energy Policy, 134, art. 110982. Mazzucato M. and Semieniuk G. (2018), “Financing renewable energy: Who is financing what and why it matters”, Technological Forecasting and Social Change, 127, pp. 8-22. Mealy P. and Teytelboym A. (2022), “Economic complexity and the green economy”, Research Policy, 51(8), art. 103948. Meidute-Kavaliauskiene I., Çiğdem Ş., Vasilis Vasiliauskas A. and Yıldız B. (2021), “Green innovation in environmental complexity: The implication of open innovation”, Journal of Open Innovation: Technology, Market, and Complexity, 7(2), art. 107. Milindi C.B. and Inglesi-Lotz R. (2022), “The role of green technology on carbon emissions: does it differ across countries’ income levels?”, Applied Economics, 54(29), pp. 3309-3339. Nawaz M.A., Hussain M.S., Kamran H.W., Ehsanullah S., Maheen R. and Shair F. (2021), “Trilemma association of energy consumption, carbon emission, and economic growth of BRICS and OECD regions: quantile regression estimation”, Environmental Science and Pollution Research, 28, pp. 16014-16028. Neagu O., Neagu M.-I. and Gavurova B. (2022), “How green is the economic complexity in the central and eastern European union countries?”, Frontiers in Environmental Science, 10, art. 910454. Oberholzer B. (2023), “Green Growth and the Balance‐of‐payments Constraint”, Development and Change, 54(4), pp. 804-840. OECD (2010), “Linkages between environmental policy and competitiveness”, OECD Environment Working Papers, no. 13, Paris: OECD Publishing. Available online. Oreiro J.L., da Silva K.M. and Dávila-Fernández M.J. (2020a), “A New Developmentalist model of structural change, economic growth and middle-income traps”, Structural Change and Economic Dynamics, 55, pp. 26-38. Oreiro J.L., Manarin L.L. and Gala P. (2020b), “Deindustrialization, economic complexity and exchange rate overvaluation: the case of Brazil (1998-2017)”, PSL Quarterly Review, 73(295), pp. 313-341. Petroni G., Bigliardi B. and Galati F. (2019), “Rethinking the Porter hypothesis: The underappreciated importance of value appropriation and pollution intensity”, Review of Policy Research, 36(1), pp. 121-140. Porter M.E. and Van der Linde C. (1995), “Toward a new conception of the environment-competitiveness relationship”, Journal of Economic Perspectives, 9(4), pp. 97-118. Rafique M.Z., Nadeem A.M., Xia W., Ikram M., Shoaib H.M. and Shahzad U. (2022), “Does economic complexity matter for environmental sustainability? Using ecological footprint as an indicator”, Environment, Development and Sustainability, 24(4), pp. 4623-4640. Rennings K. (2000), “Redefining innovation—eco-innovation research and the contribution from ecological economics”, Ecological Economics, 32(2), pp. 319-332. Rennings K. and Rexhäuser S. (2011), “Long-term impacts of environmental policy and eco-innovative activities of firms”, International Journal of Technology, Policy and Management, 11(3-4), pp. 274-290. 50 Environmental policy and green export competitiveness: The enhancing effect of economic complexity PSL Quarterly Review Romero J.P. and Gramkow C. (2021), “Economic complexity and greenhouse gas emissions”, World Development, 139, art. 105317. Rosenstein-Rodan P.N. (1943), “Problems of industrialisation of eastern and south-eastern Europe”, The Economic Journal, 53(210-211), pp. 202-211. Safi A., Wei X., Sansaloni E. M. and Umar M. (2023), “Breaking down the complexity of sustainable development: A focus on resources, economic complexity, and innovation”, Resources Policy, 83, art. 103746. Sakamoto T. and Managi S. (2017), “New evidence of environmental efficiency on the export performance”, Applied Energy, 185, pp. 615-626. Saqib N. and Dincă G. (2023), “Exploring the asymmetric impact of economic complexity, FDI, and green technology on carbon emissions: Policy stringency for clean-energy investing countries”, Geoscience Frontiers, art. 101671. Schot J. and Steinmueller W.E. (2018), “Three frames for innovation policy: R&D, systems of innovation and transformative change”, Research Policy, 47(9), pp. 1554-1567. Shahbaz M., Gozgor G. and Hammoudeh S. (2019), “Human capital and export diversification as new determinants of energy demand in the United States”, Energy Economics, 78, pp. 335-349. Shahzad U., Lv Y., Doğan B. and Xia W. (2021), “Unveiling the heterogeneous impacts of export product diversification on renewable energy consumption: New evidence from G-7 and E-7 countries”, Renewable Energy, 164, pp. 14571470. Singer H.W. (1950), “The Distribution of Gains between Investing and Borrowing Countries”, The American Economic Review, 40(2), pp. 473-485. Steenblik R. (2005), “Environmental goods: A comparison of the APEC and OECD lists, OECD Trade and Environment Working Papers, no. 2005/04, Paris: OECD Publishing. Available online. Stojkoski V., Koch P. and Hidalgo C. A. (2023), “Multidimensional economic complexity and inclusive green growth”, Communications Earth & Environment, 4(1), art. 130. Swart J. and Brinkmann L. (2020), “Economic complexity and the environment: evidence from Brazil”, in Leal Filho W., Tortato U., Frankenberger F. (eds.), Universities and Sustainable Communities: Meeting the Goals of the Agenda 2030 (pp. 3-45), Cham: Springer. Thirlwall A.P. (2011), “The balance of payments constraint as an explanation of international growth rate differences”, PSL Quarterly Review, 64(259), pp. 429-438. UNCTAD – United Nations Conference on Trade and Development (2023), Technology and innovation for cleaner and more productive and competitive production, commission on science and technology for development. Report of the secretary-general, Geneva: UNCTAD. Available online. Usman M., Kousar R., Yaseen M.R. and Makhdum M.S.A. (2020), “An empirical nexus between economic growth, energy utilization, trade policy, and ecological footprint: a continent-wise comparison in upper-middle-income countries”, Environmental Science and Pollution Research, 27, pp. 38995-39018. Wang W., Wei K., Kubatko O., Piven V., Chortok Y. and Derykolenko O. (2023), “Economic Growth and Sustainable Transition: Investigating Classical and Novel Factors in Developed Countries. Sustainability”, 15(16), art. 12346. Wooldridge J.M. (2019), Introductory Econometrics: A Modern Approach, 7th edition, Boston (MA): Cencage. Yang W., Zheng X. and Yang Y. (2024), “Impact of environmental regulation on export technological complexity of hightech industries in Chinese manufacturing”, Economies, 12(2), 50. Zhang D. (2022), “Environmental regulation, green innovation, and export product quality: What is the role of greenwashing?”, International Review of Financial Analysis, 83, art. 102311. Zhang J., Liang G., Feng T., Yuan C. and Jiang W. (2020), “Green innovation to respond to environmental regulation: how external knowledge adoption and green absorptive capacity matter?”, Business Strategy and the Environment, 29(1), pp. 39-53. Zhu S. and Fu X. (2013), “Drivers of export upgrading”, World Development, 51, pp. 221-233.