Do climate policy uncertainty and economic policy uncertainty promote firms’ green activities? Evidence from an emerging market

Hong, Nguyen Thi Hoa et al. Article Do climate policy uncertainty and economic policy uncertainty promote firms’ green activities? Evidence from an emerging market Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Hong, Nguyen Thi Hoa et al. (2024) : Do climate policy uncertainty and economic policy uncertainty promote firms’ green activities? Evidence from an emerging market, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-19, https://doi.org/10.1080/23322039.2024.2307460 This Version is available at: https://hdl.handle.net/10419/321418 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. 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Evidence from an emerging market Nguyen Thi Hoa Hong, Pham Tuan Kien, Ha Gia Linh, Nguyen Vu Ha Thanh, Nguyen Le Tuan & Phung Duc Anh To cite this article: Nguyen Thi Hoa Hong, Pham Tuan Kien, Ha Gia Linh, Nguyen Vu Ha Thanh, Nguyen Le Tuan & Phung Duc Anh (2024) Do climate policy uncertainty and economic policy uncertainty promote firms’ green activities? Evidence from an emerging market, Cogent Economics & Finance, 12:1, 2307460, DOI: 10.1080/23322039.2024.2307460 To link to this article: https://doi.org/10.1080/23322039.2024.2307460 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Published online: 31 Jan 2024. Submit your article to this journal Article views: 3988 View related articles View Crossmark data Citing articles: 11 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 RESEARCH ARTICLE Do climate policy uncertainty and economic policy uncertainty promote firms’green activities? Evidence from an emerging market Nguyen Thi Hoa Hong a , Pham Tuan Kien b , Ha Gia Linh b , Nguyen Vu Ha Thanh b , Nguyen Le Tuan b and Phung Duc Anh b a Financial Management–Statistics Analysis Department, Faculty of Business Administration, Foreign Trade University, Ha Noi, Vietnam; b Faculty of Business Administration, Foreign Trade University, Ha Noi, Vietnam ABSTRACT This study examines the joint effects of climate policy uncertainty (CPU) and economic policy uncertainty (EPU) on the green activities (GAs) of Vietnamese listed companies from 2010 to 2022. CPU and EPU are measured by standardizing the search volume index of relevant keywords using data from Google Trends and Glimpse. Meanwhile, GAs are assessed through variables related to green finance (GF) and green innovation (GI). The findings from multivariate regression models show a positive relationship between CPU, EPU, and either GF or GI in Vietnam during the period of 2010–2022. Furthermore, the interaction between CPU and EPU positively influences both GF and GI among listed firms throughout the research period. This study suggests that governments promoting policies to enhance economic activity or address climate change can facilitate firms in sustaining their green economic activities. ARTICLE HISTORY Received 24 October 2023 Revised 6 December 2023 Accepted 16 January 2024 KEYWORDS Climate policy uncertainty; economic policy uncertainty; green activities; green finance; green innovation REVIEWING EDITOR Akcay Ediz, Bournemouth University, Talbot Campus, UK eakcay@bournemouth. ac.uk SUBJECTS Finance; Environmental Economics; Business; Management and Accounting; Economics JEL CODE G32; G38; Q01 1. Introduction The world has witnessed an unprecedented array of upheavals in recent years, ranging from the global COVID-19 pandemic and climate change to trade wars and armed conflicts (Drucker, 2022). Regardless of their size and political standing, nations, blessed with resources and resilience, are not immune to these consequences (Schofer & Hironaka, 2019; Frank et al., 2020). Among these global disruptions, climate change directly affects socioeconomic development activities across the globe, particularly in developing countries (Ali et al., 2016; Khan & Farooqui, 2021; Subroto & Datta, 2023). Beyond the evident impacts, such as economic losses and human casualties, it has also triggered stigmatization and social disruption, educational interruptions, psychological trauma and long-term developmental setbacks. These burdens further impede the recovery of developing nations in the aftermath of climate-related crises with cross-sectoral ramifications (Knowlton et al., 2011). Given this intricate landscape, countries must continually update and adapt their policies to effectively respond to unforeseen disruptions and their far-reaching consequences. One foundational strategy introduced by governments to address the contemporary complex and interconnected socio-environmental challenges is economic policy–a widely studied subject. Atkinson CONTACT Nguyen Thi Hoa Hong [email protected] Financial Management–Statistics Analysis Department, Faculty of Business Administration, Foreign Trade University, Ha Noi, Vietnam ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2307460 https://doi.org/10.1080/23322039.2024.2307460 and Milward (1996) define economic policy as the government’s use of tools like taxes, spending, subsidies and interest rates to pursue development goals, while Viegi (2006) argues that economic policy aims for nominal and real stability through rules-based policymaking. Kowalski (2011) evaluates economic policy before and after the 2008 crisis, highlighting the need to rethink policy targets and tools in an increasingly globalized world. However, not all nations effectively improve and implement economic policies, resulting in unstable and inconsistent policy measures, known as ‘Economic Policy Uncertainty’(EPU) (Rodrik, 1991). EPU refers to uncertainty surrounding government policies like fiscal, monetary, tax and regulatory policies that can significantly impact economic activity (Al-Thaqeb & Algharabali, 2019). EPU has gained increasing importance as globalization has made economies more interconnected and sensitive to policy changes, negatively affecting the economy (Trung, 2019; Cao, 2023), particularly in countries with weak institutions (Liu & Dong, 2020; Shabir et al., 2021). EPU also restricts access to finance (Farooq et al., 2023) and decreases industrial production and inflation in developing countries (Nyawo & Van Wyk, 2018), laying the foundation for further research on this relationship in the context of developing countries, including Vietnam. Under global economic volatility, climate change has emerged as a significant factor impacting the global economy. Most analyses indicate that the long-term net impacts of climate change on the global economy will likely be negative (Bosello et al., 2006; Tol, 2009; Singh et al., 2022). Hence, governments across the globe must implement climate policies to address this imminent threat, which is fraught with uncertainty. One method to measure this uncertainty is through the ‘Climate Policy Uncertainty’(CPU) index, which measures changes in government policies on environmental issues (Shang et al., 2022). CPU can negatively affect firms by delaying or postponing investments and reducing economic output (Schneider & Kuntz-Duriseti, 2002; Shang et al., 2022). However, some studies also show that CPU can have a positive effect on the economic performance of countries in the short term, depending on the institutional background (Alogoskoufis et al., 2021; Shang et al., 2022). Therefore, it is crucial to understand the influence of CPU and how to mitigate them. While governments formulate economic and climate policies to address uncertainties, businesses can proactively tackle these challenges by embracing sustainable development. This can be achieved by integrating green activities (GAs) into their business operations. Saxena and Khandelwal (2012) found that most industries have a positive view of green practices and believe they provide a competitive advantage. Green finance (GF), one branch of GAs, involves utilizing private and public funds to support sustainable development addressing climate change, aiming to promote environmentally friendly investments and business practices (Lindenberg, 2014). Green innovation (GI), another branch of GAs, refers to the implementation of technologies and business models to promote sustainable development and reduce environmental impact (Liu et al., 2023). However, GAs face challenges, including policy gaps and regulatory barriers (Khan & Farooqui, 2021; Ozili, 2022). Policy uncertainty is one of the primary obstacles hindering the implementation of GAs in a business’s operations, even though they are essential for developing countries aspiring to achieve sustainable development. The term ‘green activities’first emerged in the early 2000s when environmental concerns and sustainability considerations began to exert a more significant influence on the financial sector (Berrou et al., 2019). However, only in recent years have GAs gained significant attention. Current research mainly focuses on sustainable energy development and its connection with GF or economic development, as well as its implications for GAs (Liu et al., 2023). The majority of existing studies have already centered on the impacts of climate risks on GAs (Dutta et al., 2023; Clapp, 2014). The escalation of climate risk has prompted increased investments in alternative energy sources, fostering a heightened demand for green energy solutions (Ma et al., 2023). Similarly, the research conducted by Bouri et al. (2023) concentrates on climate uncertainties and their correlation with investment activities and green energy assets. However, a notable gap exists in the current body of research regarding the examination of the impact of policy uncertainties, particularly EPU and CPU, on GAs. Furthermore, the limited research available primarily delves into developed markets, such as the United States and China (Liu et al., 2023; Cui et al., 2023; Wang et al., 2023), leaving a gap in our understanding of these dynamics within emerging markets. By expanding the current discussion beyond the traditional focus on climate uncertainties, we aim to contribute a nuanced understanding of how policy uncertainties, specifically EPU and CPU, may deter businesses from adopting green practices in emerging economies. This approach not only broadens the 2 N. T. H. HONG ET AL. scope of existing research but also facilitates a more comprehensive assessment of the multifaceted factors influencing GAs in dynamic economic environments. As developing countries, including Vietnam, need to incorporate GAs into research and application, there is a lack of studies evaluating the impact of policy uncertainties on these activities. Moreover, the institutional environment will affect the way businesses operate in emerging markets. In the context of Vietnam, a developing nation grappling with a multitude of policy uncertainties, timely strategies are imperative to mitigate these uncertainties and fortify the nation’s socio-political system and economy. As Vietnam strives toward sustainable development, this study examines the impact of CPU and EPU on the GF and GI activities of Vietnamese listed companies during 2010–2022. CPU and EPU are assessed by standardizing the search volume index of related keywords on popular search engines, while GF and GI variables are measured by manually collecting data published by firms and normalizing the evaluation score based on predetermined sets of criteria. This study makes three significant contributions to the existing literature. First, it provides additional evidence in emerging markets to reinforce the positive impact of CPU on GF, as well as the similar effect of EPU on GI, as previously demonstrated in studies (Lopez et al., 2017; Engle et al., 2020; Bouri et al., 2022; Pham & Cepni, 2022; Shang et al., 2022; Wang et al., 2023; Liu et al., 2023; Peng et al., 2023; Zhang et al., 2023). Second, the study finds new empirical evidence on the interaction between CPU and EPU on firms’GAs. It demonstrates the positive effects when EPU and CPU interact, affecting not only GF but also GI. Finally, the study introduces a measure of CPU and EPU in Vietnam by standardizing the search volume index of relevant keywords on popular search engines, and it offers a supplementary research dataset for evaluating GF and GI activities in Vietnam. The remainder of this article is organized as follows. Section 2 reviews the previous studies and develops the hypotheses. The research design, variables, models, and research methods are presented in Section 3. The empirical results and discussion are reported in Section 4. Finally, Section 5 summarizes major findings and provides some recommendations. 2. Literature review and hypotheses development In the realm of environmental economics and sustainable finance, the influence of CPU on enterprises’ GAs stands as a critical area of investigation. Clapp (2014) underscores how policy risks, including those related to climate change, can have tangible economic impacts on investments, potentially leading to stranded assets that support fossil-fuel infrastructure. Additionally, Lamperti et al. (2021) shed light on the banking sector’s climate sentiments and their role in shaping lending conditions for both green and brown firms. The study implies that CPU influences firm GAs through the credit channel, suggesting a direct link between policy uncertainties and financial decisions. Consequently, the combination of these references supports the hypothesis that CPU affects enterprises’GAs. However, it is crucial to acknowledge that conclusions drawn from various studies may present a mix of positive and negative outcomes, highlighting the complexity of these relationships in the literature. Extensive research has focused on the positive relationship between CPU and corporate behavior, particularly in the context of GAs. CPU, especially during crisis periods, has been found to positively influence a firm’s decision to reduce its carbon footprint (Lopez et al., 2017), make green investment decisions (Engle et al., 2020), and engage in GAs (Bouri et al., 2022). This positive correlation is supported by the study of Shang et al. (2022), which indicates the favorable impact of CPU on long-term renewable energy demand within the United States. In addition to studies that mainly focus on CPU, Dutta et al. (2023) indicate that climate risk positively influences the returns of clean energy companies while negatively impacting the volatility of clean energy assets. This suggests that an increase in climate risk motivates investors to divert their investments toward alternative energy sectors, reducing the risk associated with green energy investments. Conversely, some researchers, particularly those examining CPU in emerging economies, have found evidence of a negative impact on GAs. Hu et al. (2023) suggest that corporate green investments can be significantly hindered, aligning with the findings of Ren et al. (2022), who emphasize the impediment of research and development investments in GAs and technological advancements related to sustainability. The uncertainty in the external environment, driven by policy changes and uncertainties, may lead to increased marginal investment costs for enterprises, reducing their willingness to invest in green COGENT ECONOMICS & FINANCE 3 technologies and sustainable practices (Yang et al., 2022). Moreover, rising policy uncertainty is seen to increase the cost of equity and debt financing for listed enterprises, making it more challenging for them to secure funds needed for GI. Mao and Huang (2022) have shown that this kind of policy uncertainty leads firms to reduce their green patent applications only for green invention patent applications, which makes listed firms miss out on a wide range of benefits, from intellectual property protection to funding access. Meanwhile, the impact of CPU is also reported to vary across industries, with the mining industry experiencing significant negative impacts, while the production and supply of electricity, heat, gas and water sectors witness a notably positive influence (Ren et al., 2022). Vietnam faces the substantial challenge of climate change, impacting various sectors and communities across the country. In this dynamic context, climate change poses a substantial threat, compelling the government and enterprises to respond through various strategies and policies. The Vietnamese government has been actively addressing climate change, evident in initiatives such as the National Strategy on Climate Change by 2050, aimed at proactively and effectively adapting to climate change, reducing greenhouse gas emissions to net zero, and dealing with vulnerabilities and risks caused by climate change. Furthermore, Vietnam’s Action Plan on Methane Emissions Reduction by 2030 targets methane emissions in cultivation, animal husbandry, solid waste management, wastewater treatment, oil and gas exploitation, coal mining and fossil fuel consumption. These policies are aimed at encouraging businesses to innovate without unduly restricting investment or patents. Therefore, it is hypothesized that CPU still has a positive association with firms’GAs. Given the above arguments, the first research hypothesis is as follows: H1: CPU has a positive impact on the GAs of listed enterprises. Similarly, research on the effect of EPU on green business activities has produced conflicting results, with varying viewpoints. Pham and Cepni (2022) and Wang et al. (2023), have demonstrated how the relationship between the benefit of GAs and investor focus is significantly affected by EPU fluctuations, stock market volatility, oil prices and bond markets. Some studies suggest a positive association between EPU and GAs, including positive links with green bonds (Liu et al., 2023), firm green commitment (Zhang et al., 2023), and GI (Peng et al., 2023). Furthermore, research by Peng et al. (2023) indicates that the influence of EPU on GI varies significantly among provinces with varying degrees of marketization and trade openness. Specifically, provinces with higher levels of marketization and trade openness experience more pronounced positive effects of EPU on GI and vice versa. Similarly, Yang et al. (2022) corroborate that green patent applications increase with rising EPU, suggesting a positive effect of EPU on GAs in general. On the contrary, other research has revealed a potential adverse connection between EPU and GAs. EPU has been found to have a significantly negative impact on green financial development efficiency (Sarpong et al., 2023), GI (Li et al., 2023; Fakher et al., 2023; Luo et al., 2023; Cui et al., 2023), GF (Wang et al., 2023) and overall firms’green behaviors (Hou et al., 2022). Additionally, research by Li et al. (2023) indicates that monetary policy uncertainty (MPU) significantly inhibits inclusive green growth (IGG) in the region. MPU inhibits IGG by reducing GF, ecological innovation, media attention and employment levels, with all four transmission mechanisms, demonstrating a masking effect. Moreover, a more nuanced perspective on the relationship between EPU and GAs emerges from studies highlighting its time-dependent nature. Pham and Nguyen (2022); Boutabba and Rannou (2022); Wei et al. (2022) demonstrate a time-varying relationship between green bonds and uncertainty. During times of severe uncertainty, such as at the beginning of the COVID-19 epidemic or the conflict between Russia and Ukraine, green bonds are heavily impacted by financial and economic policy uncertainties. In addition, Zhou and Du (2021) identify an inverted U-shaped relationship between EPU and firms’GI capability, with national macro EPU promoting GI but frequent changes in regional economic policies inhibiting it. Vietnam, as an open economy dependent on international trade, is in a modern interconnected world where the environment, structures and economic policies through significant changes, it is highly vulnerable to fluctuations in the global economic landscape. However, Vietnam’s economy has demonstrated remarkable resilience and dynamism in the face of external shocks and domestic challenges since 2008. This resilience suggests that, although Vietnam is influenced by EPU, the impacts may not be immediate, and they are controllable. For instance, the effects of the real estate crisis in China at the end of 2021 on Vietnamese real estate companies, like Novaland, only became apparent in the following year. Similarly, the collapse of major banks in the United States and key financial institutions in Switzerland did not 4 N. T. H. HONG ET AL. immediately disrupt Vietnam’s banking sector, demonstrating the ability to predict and government adapt to upcoming changes. Furthermore, evidence supporting the manageable nature of EPU in Vietnam can be found in the country’s trade openness and political stability. Vietnam’s trade openness is associated with better results in GI (Peng et al., 2023). Additionally, the country’s one-party system and 5-year economic and social development plans, decided during the National Party Congress, contribute to consistent and predictable policy direction, leading to lower EPU. Therefore, in an environment of evolving economic policies, along with increasing focus on GAs of businesses, it is expected that EPU will have a positive impact on firms’GAs in Viet Nam. This is further emphasized when Vietnam’s status as a country with an open economy to the world, and domestic policies well-controlled by the Party. In light of these arguments, the second hypothesis is proposed: H2: EPU has a positive impact on the GAs of listed enterprises. Despite a substantial body of literature on EPU and CPU separately, there remains a notable gap in our understanding of how these uncertainties interact and jointly influence a firm’s GAs. Lamperti et al. (2021) suggest that when banks adhere to eco-friendly rules, akin to green Basel-type requirements, it enhances productivity and contributes to economy grow. On the other hand, incorporating adjustments for carbon risk and offering guarantees for green (public) credits not only help reduces emissions but also creates a safety net for the economy, rendering it less susceptible to negative impacts. This hints at the possibility that the joint effects of CPU and EPU can positively influence companies’GAs through financial regulation mechanisms. Drawing from the arguments above, both EPU and CPU are expected to exert a positive influence on firms’GAs. Consequently, the interaction between EPU and CPU is also anticipated to have a positive impact on the GAs of businesses in Vietnam. In conjunction with the limited existing literature and the affirmative nature of H1 and H2, we propose the following third hypothesis: H3: The joint effects of CPU and EPU have a positive influence on the GAs of listed enterprises. 3. Research design 3.1. Data and sampling The initial sample consists of a total of 774 enterprises listed on the two most prominent stock exchanges in Vietnam, the Ho Chi Minh Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX), from 2010 to 2022. However, a subset of approximately one-third of the initially collected data was excluded from the research sample for various justifiable reasons. Firms operating within the banking and finance sectors were removed due to their distinct operational scale and management policies, which markedly differ from those of other businesses. This exclusionary measure was taken to preserve the objectivity and homogeneity of the study. Furthermore, any firms that did not disclose sufficient information on their financial statements or annual reports against the criteria established for this study were also removed to ensure a consistent standard of data quality and completeness. The secondary data, including firms’financial statements and annual reports used in the study, were provided by the FiinPro database. Additionally, to mitigate the influence of outliers, the research winsorizes the variables at the 1st and 99th percentiles. After this process, the final sample comprises 515 companies, corresponding to 6695 observations spanning the period from 2010 to 2022 (Table 1). 3.2. Empirical models and variables Definitions To assess the impact of EPU and CPU on the GAs, we run the regression models of Ordinary Least Square (OLS), Fixed Effect Model (FEM), Random Effect Model (REM), System Generalized Method of Table 1. Sample selection. Total number of listed companies 774 (1) Eliminate companies in the banking and finance industry 148 (2) Eliminate companies that do not have enough information on annual reports and financial statements 111 Total number of remaining companies 515 The ratio of the sample to the total number of original samples 66.54% COGENT ECONOMICS & FINANCE 5 Moments (SGMM) and relevant statistical tests for Equations (1–3) as follows. Model (1) and (2) are used to test the influence of CPU and EPU on firms’GAs, respectively, while model (3) is applied to examine the joint effects between CPU and EPU on firms’GAs. GAit ¼a0þa1GAit−1þa2CPUtþa3SOit þa4REVit þa5LEVit þa6LIQit þa7AGEit þa8ROAit þa9RMit þeit (1) GAit ¼a0þa1GAit−1þa2EPUtþa3SOit þa4REVit þa5LEVit þa6LIQit þa7AGEit þa8ROAit þa9RMit þeit (2) GAit ¼a0þa1GAit−1þa2CPUtþa3EPUtþa4CEtþa5SOit þa6REVit þa7LEVit þa8LIQit þa9AGEit þa10ROAit þa11RMit þeit (3) where the subscript iand trepresent firm iand year t. The dependent variable GAs is measured by two indexes: GF (GF) and GI. The two indexes are measured by manually collecting data published by firms and normalizing the evaluation score based on predetermined sets of criteria. The criteria will be scored on a scale of 0 or 1. Enterprises that do not declare information will be assigned a score of 0, while those providing qualitative information regarding the criteria will receive a score of 1. The criteria used in the GF index are based on established and recognized indicators of green bonds, carbon pricing, renewable energy capacity, green loans, and sustainable finance commitment. The criteria used in the GI index are based on established and recognized indicators of green products, green processes, green marketing (GMKT) and investment in green research and development (Table 2). For the independent variables, CPU and EPU for each research year are measured by standardizing the search volume index of related keywords using tools Google Trends and Glimpse. The selected keywords are random variations, grouped according to the synonym formula of ‘(1) climate/economic þ(2) policy þ(3) uncertainty’. In this study, the CPU-relevant keywords refer to climate policy, environmental Table 2. Green innovation indicator. Category No Criteria Description Green Product 1 DSP1 Enterprises prioritize the use of less polluting materials 2 DSP2 Enterprises prioritize the use of materials that consume fewer resources and energy 3 DSP3 Enterprises use the least number of raw materials to create products 4 DSP4 Enterprises will consider the product’s recycling before proceeding with production 5 DSP5 Enterprises will consider the reuse of products before proceeding with production 6 DSP6 Enterprises will consider the decomposition of products before proceeding with production 7 DSP7 Enterprises are often the first to bring green products to the market 8 DSP8 Enterprises improve environmentally friendly packaging for products 9 DSP9 Enterprises use recyclable/reusable packaging 10 DSP 10 Enterprises have a vision to develop sustainably Green Process 11 DQT1 Enterprise’s production process reduces harmful substances 12 DQT2 wastewater 13 DQT3 Enterprise’s production process reduces emissions 14 DQT4 Enterprise’s production process reduces noise 15 DQT5 Wastewater from the production process of enterprises is treated to meet standards 16 DQT6 Wastewater from the production process after being treated is reused by enterprises 17 DQT7 Your production process reduces the consumption of raw materials (water, electricity, coal, or oil) 18 DQT8 Enterprises apply energy conservation technologies 19 DQT9 Enterprises apply renewable resource technologies 20 DQT10 Enterprises apply industrial waste recycling technologies 21 DQT11 Enterprises apply technologies in the process of preventing pollution Green Marketing 22 GM1 The use of environmentally friendly logos, slogans, and packaging, as well as the use of eco-certifications and labels. 23 GM2 Advertising budget allocated to green advertising, the number of green advertising campaigns, and the reach of green advertising campaigns. 24 GM3 Which a firm is sponsoring environmentally sustainable events and initiatives Green Investment 25 GRD1 Firms allocate investment in research and development of environmentally sustainable technologies 6 N. T. H. HONG ET AL. regulations, and government actions concerning climate change. The EPU-relevant keywords refer to economic policies, fiscal regulations, trade agreements and major economic events. The interaction variable CE is constructed based on the product between the two variables CPU and EPU. CEt¼CPUtEPUt(i) Along with dependent and independent variables, all the controlling variables are presented in Table 3. 4. Results and discussion 4.1. Descriptive Statistics Table 4 presents descriptive statistics for twelve distinct variables, denoted as GF, GI, CPU, EPU, CE, LEV, SO, AGE, REV, LIQ, ROA and RM. The total sample size for all variables comprises 6695 observations. The variable GF spans values from 0 to 0.714, with a calculated mean of 0.033 and a corresponding standard deviation of 0.087. These statistics indicate a modest presence of GF activities within the research sample, signifying that only a fraction of firms are involved in such practices. The variable of GI, representing GI, exhibits values ranging from 0 to 1, with a mean value of 0.033 and a standard deviation of 0.131. These figures suggest that the prevalence of GI activity among the firms in our sample is relatively low, with only a minority of firms actively engaging in GI endeavors. Regarding policy uncertainty variables, CPU exhibits values ranging from 0 to 1, with a computed mean of 0.197 and a standard deviation of 0.267. This data pattern highlights the relatively low attention directed toward CPU during the period from 2010 to 2022. This is evidenced by the substantial number of Google searches related to this topic, indicating a lower level of concern among the firms. In contrast, the EPU variable demonstrates positive values ranging from 0.021 to 1, with a mean of 0.273 and a standard deviation of Table 4. Descriptive statistics. Variables Obs Mean Std. Dev Min Max GF 6695 0.033 0.087 0 0.714 GI 6695 0.033 0.130 0 1 CPU 6695 0.196 0.267 0 1 EPU 6695 0.272 0.278 0.021 1 CE 6695 0.122 0.282 0 1 LEV 6695 1.502 1.679 0 13.751 SO 6695 0.250 0.257 0 1 AGE 6695 3.131 0.692 0 4.356 REV 6695 27.140 1.690 17.752 33.348 LIQ 6695 2.149 2.008 0 15.811 ROA 6695 0.068 0.082 −0.624 0.8122 RM 6695 0.183 0.386 0 1 Table 4 presents descriptive statistics of the variables used in the study. The definitions of these variables are provided in Table 3. Table 3. Definitions of the variables in regression models. No Variables Description Dependent variable 1 GF Green finance 2 GI Green innovation Independent and Interaction variables 3 CPU Climate policy uncertainty 4 EPU Economic policy uncertainty 5C E The interaction between CPU and EPU Firm-level controlling variables 6 SO State Ownership–Proportion of state ownership or state-controlled organizations in the firm 7 LEV Total debt divided by total equity 8 REV Natural logarithm of revenue 9 AGE Natural logarithm of years since the business was established 10 LIQ Current assets divided by current liabilities 11 ROA Net income divided by total assets 12 RM Risk Management–A dummy variable, taking the value of 1 in the following cases and 0 otherwise: 1. The firm has a Risk Management Board/Risk Committee 2. The firm has the position of Director of Risk Management/Chief Risk Officer 3. The firm has stated a clear Risk Map Table 3 presents the detailed calculations for each variable identified in our models as discussed in the Empirical Models section above. COGENT ECONOMICS & FINANCE 7 intersection of these uncertainties compels companies to invest proactively in green solutions, promoting sustainable practices, resource optimization, and consumer interest in eco-friendly goods and services. In Vietnam, the interplay between CPU and EPU encourages ecologically friendly practices, fostering economic resilience and effective environmental management. 4.6. Robustness check To check the robustness of the models for the three hypotheses, we adjust the calculation methods for the variables CPU, EPU and the interaction variable CE. For the two first hypotheses, two new variables, ‘Change in CPU’(CC) and ‘Change in EPU’(CE) are introduced to replace CPU and EPU, respectively, to utilize data encompassing two consecutive years. This is calculated by subtracting the figures from the later year from those of the earlier year, effectively capturing the annual change. The equations for CC and CE are as follows: CCt¼CPUt−CPUt−1(ii) CEt¼EPUt−EPUt−1(iii) As the index gap between two years can be negative, it is not appropriate for use in Hypothesis 3. Therefore, variables for each year in this hypothesis will be replaced by the corresponding data from the previous year to conduct the robustness test. The same research method discussed above was used to conduct the robustness test. GAit ¼a0þa1GAit−1þa2CCtþa3SOit þa4REVit þa5LEVit þa6LIQit þa7AGEit þa8ROAit þa9RMit þeit (4) GAit ¼a0þa1GAit−1þa2CEtþa3SOit þa4REVit þa5LEVit þa6LIQit þa7AGEit þa8ROAit þa9RMit þeit (5) Table 10. Robustness test results for the effects of climate policy uncertainty and economic policy uncertainty on the green activities of Vietnamese listed enterprises. Variable Model (4) Impact of CPU Model (5) Impact of EPU Model (6) Impact of CPU and EPU GF GI GF GI GF GI GA i(t − 1) 1.521 (9.93) 0.992 (384.45) 1.012 (18.82) 0.996 (522.88) 1.738 (4.62) 0.996 (387.05) CPU 0.028 (2.50) 0.0003 (0.87) 0.092 (2.44) 0.011 (2.16) EPU 0.008 (1.24) 0.021 (2.38) 0.100 (3.82) 0.003 (2.03) CE 0.008 (2.38) 0.008 (2.22) SO 0.011 (1.92) −0.0003 (−0.20) 0.009 (1.84) 0.0001 (0.09) 0.007 (1.04) −0.0004 (−0.31) LEV −0.00008 (−0.05) −0.0001 (−0.35) −0.002 (−1.91) −0.00003 (−0.15) 0.002 (0.98) −0.00003 (−0.15) REV 0.004 (2.59) 0.0002 (1.14) −0.001 (−0.37) −0.0002 (−1.79) 0.003 (1.18) 0.0005 (1.72) AGE 0.005 (2.79) 0.0001 (0.31) 0.002 (0.74) 0.0001 (0.29) 0.006 (1.70) 0.0003 (0.42) ROA −0.117 (−2.00) −0.006 (−0.99) −0.069 (−1.41) −0.001 (−0.82) −0.031 (−0.43) −0.006 (−0.67) LIQ −0.002 (−0.91) 0.0003 (0.65) −0.001 (−0.47) −0.001 (−2.34) −0.002 (−1.08) 0.001 (1.87) RM 0.003 (0.53) 0.001 (1.26) 0.008 (1.81) 0.001 (1.13) 0.003 (0.38) 0.0017 (1.45) Constant −0.102 (−2.40) −0.007 (−0.99) 0.012 (2.33) 0.009 (1.71) −0.052 (−0.77) −0.013 (−1.62) Obs. 6695 6695 6695 6695 6695 6695 Endogeneity Yes Yes Yes Yes Yes Yes AR(2) 0.337 0.746 0.204 0.982 0.300 0.691 Hansen 0.457 0.505 0.235 0.632 0.378 0.489 Table 10 presents robustness test results for the effects of climate policy uncertainty and economic policy uncertainty on the green activities of listed enterprises in Equations (4–6). The symbols ,, indicate statistical significance at 10%, 5% and 1%, respectively. 14 N. T. H. HONG ET AL. GAit ¼a0þa1GAit−1þa2CPUt−1þa3EPUt−1þa4CEt−1þa5SOit þa6REVit þa7LEVit þa8LIQit þa9AGEit þa10ROAit þa11RMit þeit (6) where subscripts iand tdenote the firm and year, respectively. All variables, except CC and CE in Equations (ii) and (iii), are defined in Table 3. The results presented in Table 10 reinforce our findings in the main models, affirming that CPU and EPU positively impact the GAs of Vietnamese listed enterprises from 2010 to 2022. 5. Conclusion and recommendations This study investigates the impact of CPU and EPU on the GF and GI activities of Vietnamese listed companies between 2010 and 2022. Three different regression methods, namely OLS, FEM, and REM are employed on a sample of 515 enterprises, with 6695 observations during the research period. In our models, three variables are utilized to gauge the uncertainty index: CPU, which stands for CPU; EPU, which represents EPU ; and an interactive variable CE designed to capture the combined impact of CPU and EPU. The GAs of companies are classified into two specific categories, each represented by two measurable variables: GF and GI. The findings highlight the positive influence of CPU on GF, while EPU primarily affects GI among Vietnamese listed companies. When these uncertainty indices are combined, the joint impact of these two indices reveals a positive correlation with GAs, measured through both GF and GI. This trend can be attributed to the environment of innovative policies in Vietnam, coupled with the growing emphasis on green initiatives among businesses. This effect is further accentuated by Vietnam’s status as a country with an open economy, wherein domestic policies are tightly controlled by the Party. This study contributes to the existing literature on emerging markets by providing new evidence regarding the relationship between CPU and EPU and firms’GAs. The research findings have important implications for managers and regulators. On the one hand, balancing strategies for these major uncertainties can be developed through clear regulatory frameworks and targeted incentives. One approach involves encouraging public-private partnerships to foster collaboration in co-creating sustainable projects. Regular consultations and dialogues with industry stakeholders provide a comprehensive understanding of their specific needs, challenges and suggestions regarding climate and economic policies. Moreover, our second hypothesis suggests that during periods of economic uncertainty, firms may consider green investments as a safe option. Hence, during economic instability, the government could employ policy tools like tax incentives, subsidies, insurance, guarantees or credit enhancement to reduce risks for investors and issuers, thus encouraging firms to make green investments. However, the government should also be aware of the potential risks. If firms invest in green initiatives primarily as a hedge against policy uncertainty, they may reduce these investments as the policy environment becomes more certain. Therefore, the government should consider ways to promote sustained green investments, such as long-term policy commitments or stable incentive structures. On the other hand, enterprises should consider managerial implications to harness the potential benefits of this dual uncertainty scenario effectively. If businesses see policy uncertainty as a growth opportunity, they may face substantial setbacks by prioritizing profits and blindly pursuing innovation initiatives. Collaborative endeavors within the green sector can help enterprises collectively address common challenges and advocate for regulatory frameworks that promote sustainability. By incorporating scenario planning into their strategic decision-making processes through scenario analyses, companies can develop contingency plans and strategies adaptable to various economic and climate policy outcomes. Finally, strengthening control over enterprise capital is indisputably important. Adequate cash flow forms the foundation for maintaining daily operations, enabling enterprises to seize opportunities, make flexible decisions and foster sustainable development. While this study has provided valuable insights and addressed some research gaps, it has limitations. First, the research sample includes only listed companies. Second, due to time constraints, macroeconomic factors like GDP growth and inflation are not considered as control variables. These factors could also be explored as interaction variables to better understand their influence on the relationship COGENT ECONOMICS & FINANCE 15 between policy uncertainty and corporate GAs. Third, the study does not analyze the interaction between GF and GI activities, nor does it explore the reverse relationship between policy uncertainty and GAs. To address these limitations, future research could incorporate macroeconomic variables, explore interactions with political factors, and expand the focus beyond GAs to encompass broader aspects of sustainable development and corporate social responsibility. Author contributions All authors have made substantial contributions to the design and implementation of the research, the analysis of the results, and the writing of the manuscript. All authors have read and agreed to the published version of the manuscript. Disclosure statement The authors declare no conflict of interest. Funding No funding was received. About the authors Dr. Nguyen Thi Hoa Hong is a lecturer of financial management in Faculty of Business Administration at Foreign Trade University (FTU), Vietnam. She is interested in Financial Economics, International Finance, Corporate Finance and Corporate Restructuring. Pham Tuan Kien, Ha Gia Linh, Nguyen Vu Ha Thanh, Nguyen Le Tuan and Phung Duc Anh are senior students majoring in International Business Management, Faculty of Business Administration, Foreign Trade University (FTU), Vietnam. Data availability statement The data used for this study includes secondary data on the characteristics of Vietnamese-listed companies from 2010 to 2022, which was obtained from the FiinPro Platform Website as well as information disclosed in annual reports, news, and company websites for calculating green activity variables. We utilized Google Trends and Glimpse for computing CPU and EPU. 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