Towards Sustainable Development: Assessing the Significance of World Uncertainty in Green Technology Innovation

Abstract

This paper uses the two-way fixed effects model to empirically examine the impact of the World Uncertainty Index (WUI) on Green Patents (GPs). Quantitative results indicate that WUI negatively affects GPs, meaning that world uncertainty significantly hinders green technology innovation. This effect is mediated through relaxed environmental regulation and reduced financial support. These findings underline that increased uncertainty decreases both the variables of environmental regulation and financial support, thus delaying the innovation of green technology. Low-income countries’ weak economic and technological bases have a statistically insignificant negative impact on GP. However, a statistically significant negative impact is indicated for high-income countries, reflecting their higher sensitivity to world uncertainties. The WUI’s more negative effect on GPs is experienced in middle-income countries due to continuous economic and technological development and less adaptability.

1. Introduction

This article discusses the implications of worldwide uncertainty on green patents, hence attempting to shed light on the question of whether global uncertainty becomes a barrier or facilitator for innovation in green technologies. Green technology innovation has appeared at the opportune time, as the world is facing hard times with climate change, and increased pressures on natural resources and the environment are fostering sustainable development and global economic transformation [1,2,3,4].

However, the way to green technology is fraught with complexities and challenges. Economic uncertainty from volatile markets and unpredictable policies leads to inadequate funding for green initiatives [5,6]. Low spending by firms and investors keeps funding for green technology R&D low, slowing the pace of innovation and the deployment of sustainable technology correspondingly [7,8]. Geopolitical uncertainty, such as trade wars and sanctions, can disrupt resource flow for green technology, impeding trade, collaboration, and access to critical materials and expertise for innovation [9,10]. Pandemic uncertainty disrupts supply chains, reduces workforce availability, shifts focus to crisis response, and hinders momentum in green technology development [11,12]. Climate uncertainty, with extreme weather and unpredictable shifts, complicates the development of green tech by making it difficult to predict the long-term effectiveness of new technologies, thereby increasing risks for investors and developers [13,14,15].

Conversely, these uncertainties can also spur green tech innovation. In times of economic recessions and highly volatile markets, businesses and investors will likely be forced to spot, support, and adapt sustainable, innovative solutions for long-term stability [16,17,18]. Geopolitical uncertainty can also catalyze the development of green technologies that reduce import dependence and strengthen domestic production, such as better recycling and waste management [19,20]. Furthermore, pandemic uncertainty could expedite the pace of green technological development in response to speedy crisis management [21,22]. Climate uncertainty may also spur innovation for adaptable technologies to adjust to changing conditions [23,24], such as climate modelling, early warning systems, and resilient infrastructure.

Global uncertainties pose significant challenges to innovation in green technology, which is crucial for fostering economic resilience and sustainable growth amidst global challenges. Therefore, further exploration into how these uncertainties affect green technology innovation is necessary. By understanding and grasping these uncertainties, policymakers can establish effective policies to accelerate green tech development and promote sustainability within their jurisdictions.

This paper makes novel contributions that are multi-faceted and groundbreaking. Firstly, the past research mainly focused on the impacts of specific uncertainties, such as economic [5,6,7,8], geopolitical [9,10], pandemic [11,12], and climate [14,15,24] uncertainties, on green technology innovation. This paper uses the World Uncertainty Index (WUI), which captures a broader spectrum of uncertainties, and provides a holistic view. The findings are that WUI has a deleterious impact on GPs and thus accentuates the immense obstacles that global uncertainties pose for innovation in green technology. These revelations carry profound economic implications, arguing that policymakers must actively work to reduce uncertainty to foster a more fertile ground for green innovation.

Secondly, a significant void in existing research is the lack of consideration of mediation effects between world uncertainty and green technology innovation. This paper addresses this gap by closely examining the intervening variables of environmental regulation and financial support in moderating the adverse impacts of WUI on GPs. It explores deeper mechanisms through which uncertainty influences green innovation, thereby proving useful for formulating and enhancing policies.

Thirdly, it further pushes the boundaries of research by performing a heterogeneity analysis that critically examines the differential impact of WUI on GPs across different income levels. Our findings reflect that high-income countries experience a severe negative impact on GPs due to their increased susceptibility to WUI. Middle-income countries are in the economic and technological development stage but with limited adaptability. They suffer from an even more significant negative impact of WUI on GP. Low-income countries with weak economic and technological development foundations have a statistically insignificant negative impact of WUI on GP. This subtle analysis provides regionally specific policy advice for policymakers since it equips them with the ability to surmount the hurdles that uncertainties create in fostering green innovation and technological progress.

The structure of this study is outlined as follows: In Section 2, we conduct an exhaustive review of existing research. Section 3 is the theoretical analysis. Section 4 introduces the collected data and quantitative methods employed in our investigation. Section 5 engages in discussions grounded in quantitative evidence. Finally, Section 6 summarizes the key findings and conclusions, offering valuable recommendations for future consideration.

2. Literature Review

Previous studies have investigated the influence of uncertainties on green technology innovation from diverse viewpoints, leading to varied and sometimes contrasting conclusions. From the perspective of economic uncertainty, Jin et al. [25] underline that the effect of macroeconomic uncertainty on hindering the green innovation of less developed cities is more pronounced. In contrast, its negative impact on developed and coastal cities is relatively insignificant. Hou et al. [26] indicate that economic policy uncertainty has a detrimental impact on corporate-level commitment to green initiatives, subsequently influencing green technology innovation. Chen et al. [27] find that the adverse interaction between economic uncertainty and green innovation tends to be more entrenched in firms that are usually subject to partial government-business ties, high levels of monopoly, lax intellectual property protection, lower pollution, and disequilibrium in the ratio of labor to capital. Proof by Cui et al. [7] shows that economic uncertainty significantly decreases green technologies. This negative relationship is stronger for privately owned enterprises, enterprises with less competition in their industry, and enterprises located in regions with poor intellectual property protection. Geng et al. [8] find that changes in economic policies lead to a rise in uncertainty, hampering the efforts of corporations in green technology innovation. Yu and Chen [5] put forward that a negative relationship exists between economic policy uncertainty and green technology innovation because companies tend to reduce the scale of green innovation when there is high uncertainty. Yu et al. [4] put forward that economic growth pressure inhibits green technology innovation; in this case, a 1% increase in EGP leads to a 3.2% decrease in green patent applications. Zhou et al. [6] indicate that economic policy uncertainty exacerbates corporate financing constraints, hindering green technology innovation. Additionally, this negative impact is more pronounced in nonstate-owned and low-tech enterprises compared to state-owned and high-tech enterprises.

From the perspective of geopolitical uncertainty, Sohag et al. [28] reveal that, except for geopolitical acts, all geopolitical risk indicators contribute to positive shifts in green investments (encompassing equities and bonds), driving the market from bearish to bullish conditions. Yang et al. [29] ascertain that the beneficial impact of political risk on advancements in green technology is more pronounced in the group consisting of non-high-income, non-OECD countries. Ahmad et al. [30] reveal that geopolitical risks impede ecological innovation, undermining sustainable development. Cheng et al. [9] discover that a rise in geopolitical risks notably impedes the dissemination of green technologies via foreign direct investment and imports. This finding highlights the paramount significance of maintaining political stability and establishing robust internal systems to facilitate green technological collaborations and exchanges. Hunjra et al. [31] highlight historical events’ lingering influence on environmental indicators (e.g., green technology innovation), further emphasizing the significance of proactive measures to prevent conflicts. However, some scholars disagree with this view. Yuen and Yuen [20] suggest that, amidst ongoing geopolitical tensions and political uncertainties, financially stable governments persist in boosting their investments in R&D for renewable energy over the long term.

From the perspective of pandemic uncertainty, Tu et al. [32] confirm that the emergence of the COVID-19 pandemic has increased the expenses associated with producing renewable energy facilities and has negatively affected the profitability of investments in green technologies. Anwar et al. [11] highlight that Corona Virus Disease 2019 (COVID-19) impacts green innovation by influencing urban public transport adoption. Zameer et al. [33] present that COVID-19 highlights the significance of green innovation in addressing environmental challenges, such as strengthening decarbonization efforts and improving environmental management and performance in the industrial sector. Cheah et al. [12] show that the pandemic has heightened awareness of the susceptibility of traditional business models to global upheavals, thereby intensifying the need for innovative and sustainable solutions such as green technology innovation.

From the perspective of climate uncertainty, some scholars advocate a facilitating view. Holzner and Wagner [34] find that the influence of the degree of green process innovation on profitability is positively influenced by climate uncertainty. Bai et al. [23] indicate that climate uncertainty positively influences green innovation, with non-state enterprises, energy-providing industries, and technology-intensive enterprises experiencing a more pronounced impact. Li et al. [24] reveal a correlation between climate policy uncertainty and green innovation within China’s agribusiness sector. Additionally, it is observed that government environmental subsidies and heightened environmental awareness amplify the positive influence of climate policy uncertainty on green innovation in this sector. Ren et al. [35] prove that the beneficial impact of climate risk on green innovation efficiency leads to an additional boost in enterprise green total factor productivity. However, others hold an inhibitory view. He et al. [36] underline that climate risks (e.g., unusual temperature conditions) significantly dampen local enterprises’ green technology innovation output, with a more significant impact observed among non-state-owned and manufacturing enterprises. Sun et al. [14] confirm that climate uncertainty exerts a notable adverse effect on enterprises’ green innovation. As this uncertainty escalates, it hinders green innovation by decreasing government green subsidies, limiting enterprises’ ability to fulfil their environmental and social responsibilities, and tightening financing constraints. Zhou et al. [15] suggest that environmental uncertainty has a considerable detrimental effect on the green innovation and overall productivity of Chinese enterprises, potentially causing a dilemma in achieving green upgrades.

In summary, previous research has predominantly concentrated on the effects of economic, geopolitical, pandemic, and climate-related uncertainties on green technology innovation. Yet, these studies have fallen short in terms of comprehensiveness. This paper distinguishes itself by employing WUI, which encapsulates a more extensive array of uncertainties. Previous studies have demonstrated a strong link between WUI and innovation activities and the role of WUI in hindering green development, thus verifying the reliability of using WUI. On the one hand, Kafka and Kostis [37] find that to be true in advanced economies. At the same time, uncertainty has a detrimental impact on innovation outcomes; these economies mitigate the effects of uncertainty by boosting their research and development (R&D) expenditures. Conversely, developing economies face a more pronounced negative influence from uncertainty than their advanced counterparts. However, Lu et al. [38] believe that as firms’ perception of global uncertainty increases, they redirect financing and labor resources towards R&D activities, thereby encouraging investments in R&D. On the other hand, Dunyo et al. [39] suggest that uncertainty intensifies the positive relationship between energy consumption and CO₂ emissions. However, uncertainty does not exhibit any statistically noteworthy influence on carbon emissions in less developed nations. Pan and Guo [40] underline that although global uncertainty initially hinders green economic growth in the short to medium term, it ultimately speeds up energy transitions and fosters sustainable economic expansion over the long term.

However, these studies have not yet profoundly discussed the specific field of green technology innovation. This paper will use WUI to analyze further and discuss the related issues of green technology innovation. Another gap in existing research is the lack of exploration into the intermediary variables mediating world uncertainty’s impact on green technology innovation. This paper bridges this gap by analyzing how environmental regulation and financial support function as moderators, affecting the adverse effects of WUI on GP. Furthermore, we innovate by undertaking a heterogeneity analysis, which explores how WUI’s impact on GP differs across various income brackets.

3. Theoretical Analysis and Research Hypothesis

As a unique form of corporate innovation, green technology innovation not only embodies the general characteristics of high risk, substantial investment, long-term duration, and relatively low success rates but also carries the additional social responsibilities of environmental protection and sustainable development [41]. Firstly, green technology innovation projects typically necessitate more far-reaching planning and larger upfront investments, with the realization of investment returns heavily reliant on a stable policy environment and market expectations. When uncertainty escalates, according to the real options theory, enterprises confront heightened uncertainty regarding future investment returns and cash flows, making it challenging to assess the long-term benefits of green technology projects accurately [42]. Consequently, firms tend to adopt conservative strategies, delaying or reducing investments in green technology innovation. Firms opt to wait for more precise policy signals and market conditions to mitigate potential investment risks, thereby directly hindering the advancement of green technology innovation.

Secondly, information asymmetry is particularly pronounced in green technology innovation. On the one hand, green technologies’ complexity and highly specialized nature significantly increase the information asymmetry investors face in assessing the true value of green technology innovation projects [43]. On the other hand, uncertainty further exacerbates the information asymmetry among firms, investors, and banks, which weakens investor confidence in green technology projects, increases the demand for liquid assets, and pushes up the cost of green innovation financing [44].

Ultimately, green technology innovations often benefit from strong support and promotion by government policies. Uncertainty can lead to market misinterpretation or distorted responses to policy signals [45]. Therefore, it is difficult for enterprises to accurately assess the continuity and strength of policy support under world uncertainties, which may adversely affect investment decisions. For example, suppose enterprises face uncertainties such as a sudden shift in policy or a sudden decrease in support when implementing green technology innovation projects. In that case, they will face substantial switching costs and risks. Based on the above analysis, this paper proposes the following Hypothesis 1.

H1. 

World uncertainty inhibits green technology innovation.

Uncertainty exacerbates the complexity and variability of the market environment in which firms operate, creating additional risk burdens and cost expenditures for firms [46]. Enterprises have to invest more resources to cope with the uncertainty, which greatly affects their investment decisions and operational strategies. At the same time, uncertainties can result in relaxed regulations that cause firms to invest less in green technology innovation. This would be because of the fact that such a situation may be one where the government or regulating authorities compromise with short-term economic stability at the cost of long-term environmental concerns. They could also be pressed to scale down investment in green technologies further, given the precarious financial situation massively increased by the financial pressure of operating costs that the current environmental regulations have brought about, now supposedly softened.

Financial support is commonly viewed as a key driver of green technology innovation, providing financial backing for green technology innovation and effectively reducing the cost of financing and investment risks for enterprises. However, its effectiveness can be jeopardized to a certain extent against the backdrop of uncertainty. Fluctuations in economic policy can raise concerns among firms and investors about the stability and continuity of financial support and shake their confidence and willingness to invest in green technology innovation programs [47]. To complicate matters, in the face of uncertainty, governments need to redeploy financial resources to address other challenges, reducing the opportunities and scale of financial support for green technology innovation projects and exacerbating uncertainty in the sector’s development. Therefore, Hypothesis 2 could be formulated.

H2a. 

World uncertainty dampens green technology innovation through relaxed environmental regulation.

H2b. 

World uncertainty dampens green technology innovation through reduced financial support.

Given the significant differences in economic fundamentals, institutional structures, and firm levels in countries with different income levels, coping strategies and response modes to uncertainty vary, shaping the trajectory of green technology innovation in very different ways.

High-income countries have robust economic systems and sound institutional environments but face a “double-edged sword” effect when faced with the challenge of uncertainty. On the one hand, their stable economic environment provides fertile soil for green technology innovation activities, which is conducive to continuously promoting technological progress. On the other hand, green technology innovation is a long-term investment with high risks, and the strict regulatory framework and high entry barriers to innovation in high-income countries make firms tend to adopt a conservative strategy in an uncertain environment and cut down on investment in green technology R&D to avoid potential risks, which in turn restricts the advancement of the innovation process.

Middle-income countries may exhibit characteristics that lie between high-income and low-income nations. Although their economic systems already show some flexibility in enterprises’ ability to adapt to uncertainty, the institutional framework may still need improvement. The governments in these countries try to strike a balance between economic growth and environmental sustainability. They may, therefore, give policy support and incentives to green technology innovation.

Conversely, low-income countries with relatively less mature economic systems and prone to frequent policy adjustments and market fluctuations transform uncertainty into a challenging environment for green technology innovation. Enterprises in such countries face difficulties adjusting their business strategies swiftly in response to uncertainty, making green technology innovation a risky and uncertain path for business transformation, upgrading, and sustainable development [48]. Second, incomplete support policies, tax preferences, and financial subsidies from low-income country governments may not be in place or be implemented consistently, leading to the high cost of innovation and weak innovation capability among enterprises. Green technology innovation still faces the underdeveloped dual incentive mechanism of government and the market, supported by a weak system in low-income countries. Hence, we propose Hypothesis 3 as follows:

H3. 

World uncertainty impacts green technology innovation in various income countries in significantly different ways.

4. Data and Methodology

4.1. Variables Selection and Measurement

4.1.1. Dependent Variable

The development, implementation, and widespread dissemination of green technology innovations are complexly tied to a country’s robust protection mechanism for its intellectual property framework, specifically the effective utilization of green patents (GP). The number of green patent applications is a potent quantitative indicator of a country’s notable achievements in environmental protection and sustainable development [3]. Given that green patents present a unique role in traditional and green technology innovation, they should be considered a core indicator of the effectiveness of inter-organizational interactions and internal changes driven by green technology innovation [13,49]. Drawing on the valuable explorations of previous scholars [50], this study adopts the natural logarithm of the number of green patent applications across countries as a metric to measure the performance of green technology innovation accurately.

4.1.2. Explanatory Variable

The WUI is a unique and accurate measure based on country reports from The Economist Intelligence Unit. The reports’ depth, breadth, and authority, with an average of about 10,000 words per report, ensure that the dataset is complete and representative for analysis [51]. Thus, the share of mentions of “uncertainty” (and its various derivatives) within a report’s text is linearly related to the level of uncertainty felt within a country or economy that the report has covered. By incorporating a set of text mining techniques [52,53], this paper focuses on the levels of attention towards the theme of uncertainty in those reports. A higher percentage value means a high level of uncertainty for the reporting country or region, while a lower percentage increase reflects a relatively low level of uncertainty. The WUI provides harmonized and comparable metrics across 143 countries. The indices in various countries have all been determined by tallying the occurrences of the term “uncertainty” (or its different forms) in the country reports published by the EIU. This index is derived, however, from rich report content, and a simple frequency count of textual occurrences of the term “uncertainty” and its derivatives may fail to comprehensively capture all relevant economic, political, or social factors when measuring uncertainty. Thereupon, the index will be improved in the future with more advanced text analysis techniques that will better identify and understand the textual content relevant to uncertainty.

4.1.3. Control Variables

GDP per capita (GDP) serves as a metric for assessing a country’s economic development level, closely tied to its capacity to foster green technology innovation and facilitate the consumption of eco-friendly products [54]. A higher GDP per capita indicates that the nation has greater economic resources for research, development, and implementation of green technologies [55]. Moreover, the high living standards of its people naturally raise their consciousness in environmental care and prudent use of natural resources, thus creating a market demand for green technology innovations and products.

Population density (Pop) signifies the level of geospatial concentration of the inhabitants in a country or region. As Jung et al. [56] and Kumar and Sen [57] expressed, population density is another factor with dual effects on green technology innovation. For instance, governments in areas with heavy populations typically enact proactive policies to effectively adopt green technology innovations in efforts to mitigate severe environmental burdens and strained natural resources. In contrast, high population density creates extensive opportunities for the widespread dissemination and practical application of green technology innovations at the social level.

Human capital (HCI) refers to a country’s degree in possessing a reservoir of talented human resources endowed with innovative thinking and specialized skills crucially needed for the research, development, and application of green technologies [3]. Furthermore, the human capital nurtured through continuous education and professional skills training fosters deep accumulation and widespread dissemination of knowledge [58]. This, in turn, continuously enhances the environmental awareness of the populace and supplies the intellectual resources and driving force necessary for green technology innovation activities [59].

The exchange rate (Xr) serves as the fundamental mechanism for converting currency values in international trade and cross-border capital flows [60]. Fluctuations in the exchange rate directly affect the acquisition cost of foreign advanced green technology and the international competitiveness of domestic outward direct investment [61]. Consequently, these dynamics influence the financing challenges associated with domestic green technology innovation projects. Hence, the exchange rate movement is a pivotal control variable when examining the role of WUI in driving green technology innovation.

Household consumption (Hc) constitutes the linchpin of national economic activities [62], directly stimulating the market demand for green technology innovation products. As residents’ income levels rise and consumption patterns evolve, households increasingly prioritize purchasing environmentally friendly and sustainable products [63]. Consequently, shifts in consumption structure mirror variations in green technology innovation across nations, making it a significant control variable in this context.

Capital formation (Cf) is an essential driving force for economic growth and green technology innovation [64]. The fixed assets and hard and soft infrastructures formed by investment activities can not only optimize product production conditions and improve production efficiency [65] but also lay the necessary material foundation for green technology innovation. Capital formation’s magnitude and growth rate offer deep reflections on the merits and demerits of a nation’s investment climate and the level of financial backing for green tech innovation endeavors.

Capital stock (Cs) is an important manifestation of national economic strength and an indispensable support for green technology innovation [66]. The scale and structure of capital stock reflect the rationality of the national economic structure and the potential for industrial upgrading [67] as the carriers of capital stock accumulated in a country, fixed assets, and inventories contain a large amount of technology and knowledge accumulation, which is crucial for promoting green technology innovation to a higher level of development.

Table 1 presents the descriptive statistics. The mean values for GPs, WUI, GDP, Pop, HCI, Xr, Hc, Cf, and Cs indicate the concentrations around these specific values. Notably, there is a substantial variation in the maximum and minimum values across each of these sequences, revealing that these variables display notable differences in their ranges. The skewness of GPs, WUI, GDP, Pop, Xr, Hc, Cf, and Cs is positive, indicating that these eight sequences follow a right-skewed distribution. Conversely, the negative skewness of HCI suggests a left-skewed distribution. Regarding kurtosis, WUI, GDP, Pop, Xr, Hc, Cf, and Cs exhibit leptokurtic distributions, which have fatter tails and sharper peaks compared to the normal distribution. On the other hand, GP and HCI show platykurtic distributions, characterized by thinner tails and a lower peak.

4.2. Data Sources

This study explores the intrinsic influence of world uncertainty on green technology innovation, relying on unbalanced panel data for 143 countries worldwide from 2009 to 2022. The selection of the time frame from 2009 to 2022 encompasses the post-global financial crisis recovery, the emergence of green technologies, and significant policy shifts towards sustainability, ensuring the study’s relevance and timeliness in examining recent dynamics in this field. We quantify WUI using text-mining techniques based on the country reports of the Economist Intelligence Unit. The number of green patents as a key proxy for green technology innovation is derived from the Environment Statistics Database of the Organization for Economic Co-operation and Development (OECD). Other control variables are obtained from the China Stock Market & Accounting Research (CSMAR), Wind, and iFinD Database.

4.3. Empirical Model

Drawing on Baylie and Fogarassy [68], this study analyses the intricate association between WUI and GP using the two-way fixed effects model. The two-way fixed effects model not only improves the precision of the econometric estimation but also effectively reduces the potential interference of the multicollinearity problem on the analytical result [69], thus ensuring the robustness and reliability of the research conclusions. We construct the specific model as Equation (1).

(1)$G{P}_{i,t}={\alpha }_0+{\alpha }_{1}WU{I}_{i,t}+{\alpha }_2{Z}_{i,t}+{\mu }_i+{\gamma }_t+{\epsilon }_{i,t}$

where $G{P}_{i,t}$ is the dependent variable that reflects green technology innovation. $WU{I}_{i,t}$ is the explanatory variable for world uncertainty. $Z_{i,t}$ is a vector of control variables affecting green technology innovation, including GDP, Pop, HCI, Xr, Hc, Cf, and Cs. ${\mu }_i$ and ${\gamma }_t$ denote time fixed effects and country fixed effects, respectively. ${\epsilon }_{i,t}$ is the error term.

5. Empirical Analysis

5.1. Benchmark Regression Results

The results of the benchmark regressions on the impact of WUI on GP are presented in Table 2. In Columns (1) to (3), all the regressions account for time-fixed and individual-specific effects. In Column (1), where no control variables are included, the regression coefficient of WUI on GP is significantly negative at the 1% level. In Columns (2)–(3), the coefficients of WUI on GP are significant and negative at the 1% and 10% levels, respectively, when the control variables are added gradually. Using Column (3) as an illustration, when all other control variables are held constant, a 1% increase in WUI results in an 18.4% decrease in GP. This result aligns with H1, indicating that world uncertainty has a negative effect on green technology innovation, with the underlying reasons being as follows: First, policy instability and variability, combined with policy rent-seeking behavior, will probably bring huge risks and uncertainties to the enterprise of green technology innovation. The imperfect and inadequate implementation of the government in environmental policies can lead to business uncertainty. Frequent changes or discontinuation in policy directives will make it hard for the enterprise to predict the future regulatory environment, which dampens the enthusiasm for investment in green technology innovation. More seriously, under high policy uncertainty, there may be so-called “policy rent-seeking behavior” among enterprises to strive for the favor and resources of local government departments rather than the real development of green technology.

The second is the uncertainty and volatility of market demand, which combines with fierce competition in the same industry to enhance the challenges faced by enterprises in green technology innovation. Usually, green technologies depend on consumers’ demands, which highly rely on awareness of the environment, as well as government policy directives. These are normally unpredictable; hence, there is a problem of accurately determining market trends, especially the increase of market risks. Besides, companies may favor short-term, less risky, faster-to-market technological innovations under highly competitive markets over long-term, capital-intensive, and high-risk green technology projects.

Third, financing constraints are one of the most important factors impeding enterprises’ green technology innovation. During a period of global uncertainty, financial institutions and investors become more conservative, raising financing costs. Such an increased financial burden may prevent the enterprise from investing in green technology innovation. In addition, such uncertainty would lead potential investors to wait and see or withdraw their investments, reducing the financing channels for green technology innovation.

Fourth, technological uncertainties and managerial myopia further hinder green technology innovation. Most green technologies are at the forefront of technological development in different fields, which naturally carries more significant risks of technical bottlenecks and failures during the R&D process. Moreover, with the accelerating process of technological progress, green technologies are facing increasing risks of obsolescence, leaving enterprises with the risk of obsolete investment. Managerial myopia, in seeking short-term benefits and symbolic innovative activity, has also annihilated long-term sustainability development. To meet the pressures from environmental regulation and investor expectations of superficial and non-substantive innovations, an enterprise may reduce the investment in the actual green technology innovation.

It is noteworthy to observe that when incorporating more control variables into the fixed effects model, these additional factors may absorb or explain the variability originally accounted for by the variables of interest, such as WUI. Despite this, at a significance level of 10%, WUI still exhibits statistical significance and holds substantial economic importance. In addition, in Column (2), the coefficient of GDP is statistically insignificant for the following reasons: On the one hand, an inadequate model that excludes key control variables may render certain parameters statistically insignificant. On the other hand, despite lacking statistical significance, GDP retains economic importance; its coefficient aligns with the sign in Column (3), suggesting its potential influence on green technology innovation.

5.2. Robustness Tests

To verify the robustness of the results above, this paper employs several robustness tests, including replacing the explained variable, adding new control variables, conducting a systematic Generalized Method of Moments (GMM) test, and applying Driscoll and Kraay standard errors. To begin with, apart from the quantity of green patent applications, the number of patent applications related to climate change also serves as a metric for green technology innovation, and this variable is represented by CP [70]. The results after replacing the explanatory variables are shown in Table 3. Columns (1)–(2) using CP indicate that the regression coefficients are significantly negative at the 1% and 10% levels for the unincluded and added control variables, respectively. This suggests that WUI significantly negatively affects CP; hence, the benchmark results are reliable.

Then, to reinforce the reliability of the benchmark findings, we incorporate additional control variables: the ratio of industrial value added to GDP (Industry), the degree of economic openness (Openness), and employment levels (Emp). Table 4 presents the results. Upon adding these control variables, the effect of WUI on GP remains significantly negative at the 1% level. This indicates that the adverse influence of WUI on GP remains unchanged, mainly with the inclusion of new control variables, thereby further validating the robustness of the benchmark results.

Next, although two-way fixed-effects models can mitigate the effects of omitted variables, they have difficulty capturing dynamic effects. Consequently, we employ a two-step systematic GMM test to tackle the issue of endogeneity. The results indicate that a Sargan and Hansen test p-value exceeding 0.1 suggests over-identification, whereas a p-value for the auto-regression (1) (AR(1)) test below 0.01 confirms the presence of only first-order autocorrelation, thereby validating the model’s appropriateness. Table 5 presents the key findings: WUI has a negative impact on GP, aligning with previous results and reinforcing the robustness of the study.

Finally, we use Driscoll and Kraay’s standard errors to address heteroskedasticity in fixed-effects models and account for the smooth influence exerted by WUI and GP, as suggested by Vogelsang [71]. The regression outcomes, presented in Table 6, reveal that after incorporating the control variables, the coefficient of WUI remains significantly negative, with a high or marginally significant level. This finding aligns with the benchmark result.

5.3. Mechanism Tests

This paper defines mechanism tests as a process through which a mediator clarifies the intricate mechanism by which an independent variable (WUI) influences a dependent variable (GP). This clarification is achieved by meticulously identifying and examining an intermediary variable, such as environmental regulation or financial support, which operates as a conduit from WUI to GP. By doing so, the mediator provides insights into the fundamental processes or pathways that mediate the effect of WUI on GP. WUI could influence the formulation and enforcement of environmental regulations, affecting the incentives and resources available for green technology innovation [71,72]. Similarly, financial support can be impacted by WUI, thereby influencing the funding and development of green technologies [46]. Thus, our investigation into the influence of WUI on GP entails scrutinizing environmental regulations and financial support as pivotal mechanisms. We choose the environmental regulation index (ERI) and fiscal expenditure (FE) as indicators to quantify environmental regulations and financial support, respectively, and these data are obtained from the OECD Database.

This paper employs the two-step method proposed by Jiang [73] for testing mediation effects, which has also been proven effective by other scholars [74,75]. The outcomes of mechanism analysis are displayed in Table 7; the coefficient of WUI on ERI is significantly negative, indicating that world uncertainty loses environmental regulations and thus inhibits green technology innovation. This result is consistent with H2a, and we can explain this phenomenon as follows: when world uncertainty rises, it often translates into economic pressures and policy changes that favor short-run economic gains at the expense of long-term environmental sustainability. Consequently, governments and businesses may weaken or relax environmental regulations to stimulate economic activity and maintain competitiveness. This would weaken environmental regulations and create an enabling environment where investment in green technologies is not encouraged, as enterprises would have an easy way out by cutting costs and moving on with less environmentally friendly practices. Therefore, green technology innovation is hindered without stringent environmental regulations, and efforts toward more sustainable and eco-friendly solutions are slowed.

As shown in Column (2) of Table 7, the coefficient of WUI on FE is significantly negative, indicating that world uncertainty would reduce financial support and thus inhibit green technology innovation. This result is consistent with H2b, which could be demonstrated as follows: Generally, financial support is considered one of the driving forces for green technology innovation, providing the necessary capital to reduce financing costs and investment risks for enterprises effectively. However, WUI may make enterprises and investors question the stability and sustainability of financial support, shake their confidence, and reduce their desire to invest in implementing green technology innovation programs. In addition, during turmoil, governments are often forced to divert funds to other important economic issues, reducing the availability and scope of financial support for green technology innovation projects.

5.4. Heterogeneity Test

In the heterogeneity analysis, this paper examines the impacts of WUI on GP across countries with varying income levels, utilizing the World Bank’s income classification system, which categorizes nations as high-income (with GNI exceeding USD 14,005), middle-income (with GNI ranging from USD 1145 to USD 14,005), and low-income (with GNI below USD 1145). Based on the estimated results obtained from the Chow test conducted on the interaction term model, the p-value associated with the coefficient difference is 0.035, indicating statistically significant differences among the three groups. The heterogeneity results are shown in Table 8, which are consistent with H3.

Column (1) shows that world uncertainty in high-income countries significantly inhibits green technology innovation, and this negative effect reaches a statistical significance level of 5%. This is because the economic and technological system in high-income countries is well developed and sensitive to world uncertainty. Thus, with the increase in WUI, businesses and investors in high-income countries may be more cautious about investment in green technology innovation. Moreover, the environmental policies and financial markets in high-income countries could also be influenced by world uncertainty, hindering green technology innovation. These factors together result in a high effect of world uncertainty on green technology innovation in high-income countries.

Column (2) indicates that world uncertainty in middle-income countries has a much larger negative effect on green technology innovation at the 1% significance level, and this negative effect exceeds that of high-level income countries in absolute terms. This might be because middle-income countries are in a fast economic and technological development period, and their adaptability to global uncertainty is relatively weak. When world uncertainty increases, middle-income countries may face greater economic pressures and market risks, leading businesses and investors to reduce their investments in green technology innovation. In addition, the policy environments and financial markets in middle-income countries may not be mature or stable enough to resist the shocks brought about by world uncertainty. All these factors amplify the negative impact of world uncertainty on green technology innovation in middle-income countries, making it stronger than in high-income countries.

It is worth noting that the results in Column (3) show a different trend in the impact of world uncertainty on green technology innovation in low-level income countries, where the effect is negative but does not reach the level of statistical significance. This is not surprising, mainly because countries with low income are somewhat weak in their economic and technological bases; hence, other factors obscure or buffer most of the shocks in world uncertainty. The countries would not be able to muster enough resources and capacity required to handle the situation brought forth by global uncertainty, such as inadequacy of funds and technological bottlenecks, to mention a few. In addition, other factors may cause the insignificance of world uncertainty on green technology innovation in low-income countries. For example, the data quality problem may reduce the results’ accuracy and reliability; therefore, the real effect of world uncertainty is hard to detect. The weakness in policy implementation may weaken any policies that try to develop green technology innovation. Additionally, the previously mentioned factors further reduce the influence of world uncertainty on green technology innovation in low-income countries.

6. Conclusions and Implications

6.1. Conclusions

Against the backdrop of global climate change and ever-increasing stress exerted on resources and the environment, innovation within green technologies is an important catalyst that represents both sustainable development and a modification of economic change in general. On the other hand, great uncertainty chimes into the already turbulent global arena that influences green technological innovations, thus demanding immense analysis of such topics. This, therefore, explains why the present study will investigate whether global uncertainty serves as a hindrance to or acts as a catalyst for innovation in green technologies. We achieve the stated objective of the study by utilising an unbalanced panel dataset of 143 countries from 2009 to 2022 and applying a two-way fixed effect model that systematically explores the impact of WUI on GP. These empirical results indicate that WUI significantly discourages green technology innovation. Several robustness checks confirm that our benchmark findings are robust. The mediating effect shows that in the case of loosened environmental regulation and reduction of financial support, WUI significantly hinders green technology innovation. Heterogeneity analyses indicate that the effect of WUI on green technology innovation differs significantly across income levels. High-income countries are greatly negatively affected by world uncertainty in their GP, as they have higher sensitivity. Middle-income countries are developing economically and technologically but suffer an even larger negative effect from world uncertainty on GP due to lower adaptability. On the contrary, low-income countries with relatively weak economic and technological foundations observe negligible adverse impacts of WUI on their GP.

6.2. Theoretical Implications

The theoretical implications of this study can be summarised as follows: First, the results of this study contribute to the existing theoretical framework by providing empirical evidence that WUI significantly discourages GP. This goes against the conventional wisdom that uncertainty can stimulate innovation and suggests that uncertainty may act as a barrier rather than a catalyst in the context of green technologies. This matters for policymakers and researchers wanting to advance green technological progress amidst ever-escalating global uncertainty. A second aspect of this research involves the identification of regulatory and financial frameworks that have been considered pivotal in lessening the negative effects of global uncertainty on green technology innovation. Mediating-effect analysis indicates that slackened environmental regulation and financial support increased the inhibiting effect of WUI on GP. This would mean that supportive policy and financial incentives are crucial in developing green technological advancements, even under uncertain global conditions. Finally, the heterogeneity analyses indicate how different income levels change the impact of global uncertainty on innovation in green technologies. This has theoretical implications for the differential resilience of various economic systems to global uncertainty and implies that tailored strategies may be required to promote green technological development across different income groups.

6.3. Policy Implications

To develop green technology innovation, policymakers should focus on uncertainties first around the world. First, to mitigate the negative impacts of global uncertainties on green technology innovation effectively, policymakers have to strive to create a stable and predictable regulatory environment. This includes maintaining a coherent set of policies and ensuring that any changes or updates to these policies are clearly and transparently communicated to all relevant stakeholders. In addition, international cooperation can go a long way in decreasing global uncertainties and building a more propitious environment for green technological developments. In working together on an international scale, countries and regions would combine resources, knowledge, and expertise, improving their collective ability to rise to the challenges of uncertainty while promoting sustainable innovation in the green sector.

Second, it is only possible for policymakers to compensate for the negative effects of higher uncertainty on green technology innovation by following a two-pronged approach: tightening environmental regulation and increasing financial incentives. By tightening environmental regulations, policymakers will ensure that firms are working within strict environmental standards, which can eventually force them to switch to cleaner and greener alternatives. In tandem, through the provision of heavy financial support, such as grants, tax incentives, and low interest rates, specifically for green initiatives, it will be economically feasible for businesses to invest in green technology R&D. The combination of both will provide a facilitating environment for businesses to emphasise green innovation with particular prominence and hence accelerate the pace of technological development in the eco-friendly sector.

Third, global uncertainties impact green technology innovation differently across groups; thus, policymakers might consider different group strategies. For example, high-income countries, whose economic and technological systems are so sensitive to world uncertainty, require policymakers first to mitigate the negative effects by building resilience through diversified economic structures and strong social safety nets. For middle-income countries, which are still within the threshold of economic and technological development but at the same time face limited adaptability, policymakers should fashion supportive policies to enhance their adaptive capacities by offering them selected financial and technical assistance in shifting towards greener technologies. In the case of low-income countries, whose negative impacts from WUI are negligible due to their weak economic and technological foundations, the emphasis should be on foundational development through investment in education, infrastructure, and basic technological capabilities that would form a strong foundation for future green technological development. This would definitely help policymakers properly address the diverse economic landscapes with appropriate strategies and policies to facilitate green technology innovation.

Author Contributions

Conceptualization, J.D. (Jie Dou) and M.Q.; methodology, J.D. (Junyi Dou); software, J.D. (Junyi Dou); validation, J.D. (Junyi Dou), C.-W.S. and M.Q.; formal analysis, J.D. (Junyi Dou); investigation, J.D.(Jie Dou); resources, J.D. (Jie Dou); data curation, J.D. (Junyi Dou); writing—original draft preparation, J.D. (Jie Dou); writing—review and editing, M.Q.; visualization, C.-W.S.; supervision, C.-W.S. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data could be found here: https://www.policyuncertainty.com/ (accessed on 31 December 2024); https://www.oecd.org/en/data.html (accessed on 31 December 2024); https://data.csmar.com/ (accessed on 31 December 2024); https://www.wind.com.cn/ (accessed on 31 December 2024); http://www.51ifind.net/ (accessed on 31 December 2024).

Acknowledgments

This work was supported by a grant from the Romanian Ministry of Research, Innovation and Digitalization, the project with the title “Economics and Policy Options for Climate Change Risk and Global Environmental Governance” (CF 193/28.11.2022, Funding Contract no. 760078/23.05.2023), within Romania’s National Recovery and Resilience Plan (PNRR)—Pillar III, Component C9, Investment I8 (PNRR/2022/C9/MCID/I8)—Development of a program to attract highly specialized human resources from abroad in research, development and innovation activities.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Tables

Table 1

Descriptive statistics.

	Mean	SD	Min	Max	Skewness	Kurtosis
GP	4.342	2.442	0.223	9.791	0.312	2.258
WUI	0.201	0.160	0.000	1.231	2.236	11.464
GDP	1.57 × 106	3.09 × 106	10,921.800	2.00 × 107	4.379	23.294
Pop	94.734	229.480	1.655	1421.020	4.868	26.575
HCI	2.877	0.547	1.596	4.154	−0.224	2.244
Xr	1983.698	19,947.856	0.544	3.89 × 105	18.587	359.804
Hc	0.715	1.247	0.187	23.123	14.543	232.191
Cf	0.755	2.048	0.304	33.371	15.352	242.977
Cs	0.569	1.250	0.171	20.322	14.773	230.962

Table 2

Benchmark regression of the impact of WUI on GP.

Variables	(1)	(2)	(3)
	GP	GP	GP
WUI	−0.309 ***	−0.302 ***	−0.184 *
	(−2.85)	(−2.78)	(−1.72)
GDP		−0.000	−0.000 *
		(−0.93)	(−1.92)
Pop		0.006 **	0.006 **
		(2.50)	(2.53)
HCI		0.390 **	0.303 *
		(2.19)	(1.73)
Xr		−0.000 ***	0.000 ***
		(−2.59)	(2.87)
Hc			0.541 **
			(2.16)
Cf			−1.776 ***
			(−5.62)
Cs			2.221 ***
			(5.52)
Constant	4.096 ***	2.564 ***	2.394 ***
	(90.42)	(4.90)	(4.60)
Fixed effects	Yes	Yes	Yes
Observations	786	786	786
R-squared	0.144	0.166	0.228

Note: t statistics in parentheses; *, **, and *** mean that it is significant at 10%, 5%, and 1% levels.

Table 3

Robustness tests of changing the explained variable.

Variables	(1)	(2)
	CP	CP
WUI	−0.331 ***	−0.202 *
	(−2.97)	(−1.83)
Constant	3.892 ***	2.278 ***
	(83.35)	(4.24)
Control variables	No	Yes
Fixed effects	Yes	Yes
Observations	775	775
R-squared	0.146	0.168

Note: t statistics in parentheses; * and *** mean that it is significant at 10% and 1% levels.

Table 4

Robustness tests of adding new control variables.

Variables	(1)	(2)	(3)
	GP	GP	GP
WUI	−0.431 ***	−0.453 ***	−0.454 ***
	(−2.74)	(−2.87)	(−2.87)
Constant	2.505 ***	2.158 **	2.404 **
	(3.01)	(2.58)	(2.45)
Control variables	YES (add Industry)	YES (add Industry and Openness)	YES (add Industry, Openness and Emp)
Fixed effects	Yes	Yes	Yes
Observations	407	395	395
R-squared	0.236	0.265	0.266

Note: t statistics in parentheses; ** and *** mean that it is significant at 5% and 1% levels.

Table 5

Robustness tests of conducting systematic GMM tests.

Variables	(1)	(2)
	GP	GP
L.ert1	0.699 ***	0.597 ***
	(8.06)	(6.79)
WUI	0.399	−0.997 ***
	(1.24)	(−6.20)
Constant	0.892	−0.185
	(1.52)	(−0.13)
Control variables	YES	YES (add Industry, Openness and Emp)
Fixed effects	Yes	Yes
Observations	597	302

Note: t statistics in parentheses; *** means that it is significant at a 1% level.

Table 6

Robustness tests of applying Driscoll and Kraay standard errors.

Variables	(1)	(2)	(3)
	ert1	ert1	ert1
WUI	−0.309 **	−0.302 **	−0.184
	(−2.78)	(−2.54)	(−1.49)
Constant	4.096 ***	2.564 ***	2.394 ***
	(296.67)	(13.14)	(15.05)
Control variables	No	Yes (delete Hc, Cf and Cs)	Yes
Fixed effects	Yes	Yes	Yes
Observations	786	786	786

Note: t statistics in parentheses; ** and *** mean that it is significant at 5% and 1% levels.

Table 7

Mechanism analysis results.

Variables	(1)	(2)	(3)	(4)
	ERI	ERI	FE	FE
WUI	−1.437 **	−1.190 *	−0.212 ***	−0.139 *
	(−2.13)	(−1.69)	(−2.73)	(−1.84)
GDP		0.000		0.000
		(−0.72)		(0.14)
Pop		0.008		−0.000
		(0.85)		(−0.13)
HCI		−2.044		0.391 ***
		(−1.42)		(3.94)
Xr		−0.000		0.000 **
		(−0.63)		(2.23)
Hc		2.229 *		0.664 ***
		(1.94)		(4.11)
Cf		−2.880		−0.165
		(−1.31)		(−0.510)
Cs		2.141		0.219
		(1.02)		(0.690)
Constant	2.710 ***	6.944	3.938 ***	2.404 ***
	(17.34)	(1.57)	(151.82)	(8.15)
Fixed effects	Yes	Yes	Yes	Yes
Observations	137	137	533	533
R-squared	0.112	0.186	0.061	0.169

Note: t statistics in parentheses; *, **, and *** mean that it is significant at 10%, 5%, and 1% levels.

Table 8

Heterogeneity analysis results.

Variables	(1)	(2)	(3)
	GP	GP	GP
	High	Middle	Low
WUI	−0.323 **	−0.596 ***	−0.136
	(−2.02)	(−3.05)	(−0.39)
GDP	−0.000 ***	−0.000 ***	−0.000
	(−3.62)	(−3.70)	(−0.34)
Pop	0.068 ***	0.050 ***	0.008
	(3.68)	(3.62)	(0.62)
HCI	−0.261	0.776	−0.210
	(−1.39)	(1.58)	(−0.16)
Xr	−0.001	−0.000 ***	0.000
	(−0.74)	(−2.71)	(0.24)
Hc	0.182	0.187	0.513
	(0.53)	(0.24)	(0.35)
Cf	0.917	−1.759	−4.495 **
	(1.21)	(−1.46)	(−2.25)
Cs	−0.017	1.922	6.009 ***
	(−0.02)	(1.65)	(3.02)
Constant	3.638 ***	−4.897 **	1.029
	(4.69)	(−2.57)	(0.25)
Fixed effects	Yes	Yes	Yes
Observations	338	202	149
R-squared	0.274	0.287	0.305

Note: t statistics in parentheses; ** and *** mean that it is significant at 5% and 1% levels.

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