Achieving Carbon Neutrality Through Renewable Energy Transition Amidst Economic Policy Uncertainty in the Arctic Region
Abstract
In the rapidly unpredictable economic and environmental era, the Arctic region stands at a pivotal crossroads. Globally, the rise in carbon emission (CEM) has continued to escalate in recent decades. Still, extant analyses have not sufficiently investigated the variables contributing to CEM dissipation, particularly in the Arctic Council. Therefore, this paper closes the knowledge gap by assessing the influence of environmental technology (EVT), economic policy uncertainty (EPU), renewable energy transition, and urban transition on CEM. Moreover, the study investigated the moderation effect of environmental governance on the interconnection between EPU and CEMs, incorporating historical data from 30 years ago (1990–2020). The research applied three econometric techniques, moments quantile regression, fully modified ordinary least square (FMOLS), and Driscoll and Kray standard errors (DSK), to ascertain the interaction among the research indicators. The study’s outcome established that (1) the switch to renewable or cleaner power supply, EVT, and governance improves ecological health. (2) Environmental policy uncertainty and urban transition disrupt ecological stability in the Arctic region. (3) Environmental government moderates the effect of EPU and CEM. The study provides policy-makers, stakeholders, and politicians with essential policy recommendations to help establish resilient and environmental quality in the Arctic region and beyond.
1. Introduction
Carbon emission (CEM) has become a significant issue in the past couple of years, contributing to the worldwide problem of climate alteration [1]. Prior analyses have posited that the discharging of environmental pollution and CEM into the air, primarily as a result of the activities and actions of humans, such as deforestation and burning of fossil fuels, has accelerated the warming of the planet [2]. The detrimental repercussions of global warming are extreme weather conditions, ecological disruption, and rising sea levels. Furthermore, pollutants released into the air can lead to acid and soil contamination, harming agricultural productivity and biodiversity. Hence, to alleviate these concerns, governments and international bodies have united under the landmark of the Conference of the Parties (COP26) with the common goal of dissipating the upward trend in world heating to much less than two degrees exceeding the age of industrialization [3, 4].
Additionally, the COP26 agreement encourages attempts to restrict raising the temperature to 1.5°C, considering the severe consequences that even a modest temperature rise can have on vulnerable ecosystems and communities. Aligning with the sustainable development goals (SDGs 6, 7, 11, and 13), addressing CEM problems is integral to achieving targets connected to sanitation and water, clean energy and clean power supply, sustainable communities and cities, and climate action. Hence, addressing CEM challenges and problems is crucial for attaining the interconnectedness between these SDGs and fostering a more resilient and healthier globe. Sampene et al. [5] asserted that a sustainable solution and Initiatives from various stakeholders and researchers could significantly reduce CEM. Therefore, assessing the drivers of CEM emission in the Arctic region is essential.
Accordingly, this study focused on the Arctic Council, comprised of Sweden, Denmark, Russia, Canada, Norway, the United States, and Iceland. The Arctic nations, known for their vulnerability to climate alteration, have become a focal point for global ecological concerns. Rapid urbanization transition (URT) driven by economic expansion raises questions about how this transformation affects environmental sustainability in this region. Morgunova and Westphal [6] asserted that the Arctic nation is grappling with divergent regulatory frameworks and economic interests, resulting in uncertainty in resource exploitation and investment decisions. For example, the ambiguity surrounding exploiting gas and oil reserves among the Arctic nations has resulted in a lack of clear standards and guidelines for extracting these minerals. This uncertainty has led to hesitancy among enterprises, investors, and firms in committing to long-term projects and initiatives, impeding economic progress and reducing environmental quality. Moreover, the lack of regulations governing the creation of renewable energy and carbon reduction initiatives has impeded the transition to more environmentally friendly energy sources. Member nations might emphasize instantaneous fiscal benefit above prospective sustainability goals without a common strategy. Fossil fuel extraction, for instance, would become more important in some nations due to growing economic instability, increasing CEMs, and worsening global warming. The Arctic Council has to collaborate to create clear and consistent economic policies and prioritize sustainable practices and carbon reduction to lessen the detrimental impact of economic activity on the fragile Arctic ecosystem.
Based on the discussion above, it matters the most to grasp and explore the driving factors of CEM and sustainability issues in the Arctic region. Economic policy uncertainty (EPU) has been discussed by prior studies as one of the drivers that cause an upsurge in CEM [7, 8]. EPU pertains to public policies, specifically monetary, and fiscal initiatives, influencing business and enterprise activities and economic operations. Mitigating and understanding EPU is important for fostering a stable and conducive environment for sustainable economic development. The business landscape and environment are mostly influenced by EPU, which eventually impacts businesses’ actions and decisions. EPU may also influence the ecological system and affect economic progress. For example, Ali et al. [9] asserted that EPU will stimulate manufacturers and businesses to adopt ecologically unfriendly production approaches, resulting in higher CEM.
Moreover, in advancing the research discussion, recent analyses have demonstrated the crucial role of energy in contemporary economic transformation and globalization [2, 10]. This is because providing essential services and manufacturing goods depend on energy and power usage. Despite these benefits, extant research has indicated that excessive dependence on traditional forms of energy negatively impacts the environment’s health. Hence, the relevance of renewable energy transition (RET) has become a mantra championed by SDG7, energy experts, and scholars. Aydin and Erdem [11] highlighted that to reach carbon neutrality, RET offers the best alternative compared with non-RET, including coal, gas, and oil. Also, Wiredu et al. [12] opined that RET tools, including wave energy, geothermal, wind, solar, and hydroelectric, are crucial for mitigating CEM by providing affordable and clean power sources. It is worth mentioning that previous research has paid little interest in the causal association between RET and environmental quality, particularly in the Arctic countries. In addition, global urbanization is growing at a never-before-seen pace [13, 14].
Additionally, the United Nations’ [15] report suggested that, as of 2018, almost 55% of the world’s population now resides in cities, and this trend is expected to increase to 68% by 2050. Extant studies have asserted that URT has been coupled with environmental and resource challenges [16, 17]. URT is a multifaceted phenomenon that describes the shift from predominantly rural to urban living. Ecological dilapidation has become an emerging concern as URT progresses in the Arctic nation. Hence, there is a need to highlight sustainable urban planning and environmentally conscious initiatives within these nations to reduce the influence of URT on the ecosystem.
Moreover, environmental technology (EVT) is essential to environmental quality and is strategic in economic transformation and development. Technological advancements during the industrial revolution have changed economies, improved the processing of already-produced goods, encouraged RET, and expanded energy efficiency, ultimately improving environmental quality [18]. Moreover, prior studies have argued that the fast integration of EVT can help monitor and mitigate CEM [19, 20]. Nevertheless, other extant analyses have demonstrated that EVT increases enterprises’ demand for power consumption to run operations. These studies also argue that advanced EVT is heavy, highly energy intensive, and adversary contributes to lessening CEM [21, 22]. This evidence has demonstrated that the connection between EVT and CEM is still unclear and requires further analysis.
Furthermore, environmental governance (EVG) has been integral in driving the switch to a clean environment. Strong and stringent governance systems improve environmental quality and act as a catalyst in reaching decarbonization targets [9]. Nevertheless, Yu and Guo [18] revealed that environmental taxes are the most widely applied proxy for EVG, as they prohibit the deployment of nonrenewable resources in economic activity and electricity consumption among firms. Theoretically, the connection between EVG and CEM is anchored on two assumptions. The first assumption, the “race-to-top,” suggests that CEMs can be mitigated when environmental policies and regulations are tightened. At the same time, the “race-to-bottom” approach contends that policy-makers should relax ecological laws for firms to generate economic and financial gains [23]. Based on this assumption, this current delves into the dynamic connection, spotlighting the crucial moderating role of EVG. The study argues that EVG acts as a pivotal moderator, shaping the extent to which EPU influences CEM. This study aims to close the literature gap and the ongoing discourse, shedding light on the role of EVG in steering the nexus between EPU and CEM.
Although some prior studies have established the connection between the indicators explored in this research, there are certain research limitations in the existing papers. Accordingly, this research seeks to address the following literature gaps. First, there is the need for more analysis focusing particularly on the Arctic context. Thus, scanty analysis has explored the effect of understudied variables on environmental quality from the perspective of this geographical area. Second, one notable research gap lies in the understanding of the impact of EPU on carbon mitigation efforts. Even though EPU is crucial for maintaining a sustainable ecological system, scanty analyses have explored its influence on environmental contamination in energy–environment related works, especially for the Arctic Council. For instance, available literature on the nexus between EPU-CEM has focused on China [24], BRICS [25], and the United States [26]. These studies concluded that CEM may increase due to the upsurge in EPU from this geographical jurisdiction. Hence, the present study fills a research gap by assessing the impact of EPU on CEM in the Arctic Council. Lastly, the role of EVG as a moderator in the outlined relationships is a critical aspect that demands more comprehensive attention. Thus, the existing analysis often falls short of providing an intricate assessment of the mechanisms through which effective EVG can influence the effect of EPU on the connection between EPU and CEM.
Accordingly, this research addresses these questions: (RQ1) How does EPU affect CEM among the Arctic Council? (RQ2) How do RET, URT, and EVT drive CEM in these regions? (3) What is the moderating role of EVG on the interplay between EPU and CEM abatement in this nation? The originality and novelty of this study dwell in its thorough exploration of multiple factors influencing CEM within the Arctic context. By integrating these indicators into a research framework, the study offers a holistic perspective on the region’s sustainability challenges. Furthermore, the exploration of EVG as a moderating indicator adds depth to the analysis, acknowledging the significance of successful systems for policies in advancing ecological stability. The Arctic Council, as a cooperative intergovernmental forum, provides a suitable platform for addressing environmental concerns collaboratively, making this research particularly relevant for shaping policy recommendations that can be implemented at both regional and global levels. The contributions of this research extend to the field of environmental economics and policy-making by providing empirical insights into the specific dynamics shaping CEM in the Arctic. Utilizing the STIRPAT (stochastic impacts by regression on population, affluence, and technology) model and the Driscoll and Kraay standard errors (DSK), fully modified ordinary least square (FMOLS), and moments quantile regression (MMQR) econometric approaches allow for a robust analysis of the relationships between the essential variables. The findings can inform policy-makers within the Arctic Council about the efficacy of current EVG structures and guide them in implementing targeted strategies to curb CEMs while fostering sustainable development in the region. Furthermore, the study adds to the body of knowledge on environmentally sound development in academia by providing insightful analysis to academics, researchers, and policy-makers who must balance safeguarding the environment and economic expansion in fragile ecosystems.
The next part, Section 2, focuses on the theoretical model and related works based on this study. Section 3 then summarizes and critically examines the methods incorporated in this analysis, Section 4 entails the findings from the empirical insights, and Section 5 presents the resulting conclusion implications and directions for further investigation based on this paper.
2. Related Literature
2.1. EPU and CEM
EPU partly influences the country or region’s adoption of CEM reduction technologies. The association between EPU and CEM has been the subject of limited research. Pirgaip and Dinçergök [27] looked at the connection between EPU and CEM in the G7 region. Jiang and Ma [28] focused on the United States, employing sector-specific data to discover the effect of institutional factors on the EPU and CEM; the study discovered a favorable connection between EPU and CEM growth. Further, Alvarado et al. [29] assessed the impact of EPU on CEM within selected MENA regions using an augmented STIRPAT model covering data from 1970 to 2020. Their results indicated that the EPU exacerbates environmental degradation, with significant effects observed amid considerable changes in Morocco, Turkey, and Iran. In addition, Hussain, Arshad, and Bashir [30] investigated how EPU affects environmental quality, using advanced econometric techniques to account for unit roots and cointegration. They found that EPU contributes positively to CEM. In their study, Assamoi and Wang [31] analyzed how EPU and environmental policy stringency jointly affect CEM, applying an asymmetric (nonlinear) approach. The results show that an increase in EPU causes an uprise in CEM.
2.2. EVT and CEM
The dynamic between EVT and CEM is inherently collaborative, where adopting advanced EVT is essential for enhancing environmental quality. These technologies aim to reduce pollution and emissions, conserve natural resources, and protect biodiversity. Research across various studies reflects this connection. Thus, Cheng et al. [32] analyzed data from 35 OECD countries using panel quantile regression (QR). They discovered that technological innovation not only significantly decreases CEM. Contrastingly, a study by Rahman et al. [33] investigated the impact of EVT on CEM in 22 countries. Their findings indicated that negative shocks in EVT increase CEM in the long term. These studies highlight the complex and unexpected relationships between EVT and environmental quality. Employing the MMQR method, Ali et al. [34] explored the effect of EVT on CEM within the G20 nations. Their findings demonstrate that EVT strengthens ecological health. Similarly, Sampene et al. [5] looked at the impact of EVT on pollution levels in the BRICS economies, using data from 1990 to 2020. A cross-section augmented autoregressive distributive lag model was used to examine both long-term and short-term dynamics. The findings revealed that both improved institutional quality and the use of EVT contribute to reducing environmental impact, implying that they play an important role in mitigating environmental degradation.
2.3. RET and CEM
The world’s expanding population and subsequent increase in energy demands mean that continuous reliance on fossil fuels creates significant environmental damage. In contrast, renewable energy is abundant, environmentally friendly, and less vulnerable to geopolitical tensions [35]. Research demonstrates that RET reduces CEM and promotes sustainable development and environmental quality [4, 36, 37]. Anwar et al. [38] revealed that increasing the use of renewables reduces CEM in Japan and ecological footprints in Turkey. However, carbon-intensive energy sources such as coal have been related to increased CO2 emissions in South Africa, lowering environmental quality [39]. Moreover, Chen et al. [40] reported that RET positively influenced reducing CEM in 120 nations. According to Wang et al. [41], employing the panel QR approach in 192 nations, renewable energy plays an important role in regulating CEM. Sun et al. [42] investigated the effects of environmental technologies, RET, urbanization, and economic growth on the environmental footprints of 17 Asian-Pacific Economic Cooperation (APEC) economies from 1990 to 2019. The study investigation used the STIRPAT model and found that technological development and RET promote air quality. Adebayo et al. [43] used the quantile-on-quantile approach to study CEM in Sweden. They discovered that RET reduces CEM across most quantiles.
2.4. URT and CEM
The transition to urbanization presents challenges and opportunities for environmental quality, demanding a delicate balance between development and sustainability. For instance, Khan, Weili, and Khan [20] investigated how URT affected the environmental impact and economic growth of selected OECD nations between 1990 and 2015. Their outcome URT and non-RET use have been shown to negatively impact environmental quality while increasing economic growth in these economies. In their research, Pu, Wang, and Wang [44] explored the driving elements behind CEMs among Chinese cities from 2000 to 2016, discovering deep-seated connections among these factors over extended periods. They determined through the vector autoregression method that the study findings revealed that urban built-up settings and roads significantly and positively correlate with CEM. However, limited evidence was discovered between demographic URT and CEM. Similarly, Batool et al. [45] study highlighted the intricate relationships between URT and CEM in five ASEAN regions from 1980 to 2018. Using Granger causality analysis, the findings identified energy usage and urban population increase as key contributors to rising carbon dioxide (CO2) emissions and environmental quality in these geographic areas.
2.5. Moderating Role of EVG
The concept of EVG is crucial as a balancing force in the pursuit of higher environmental standards. It offers an organized structure for conceptualizing and implementing sustainable methods. According to research from multiple disciplines, EVG is critical in guiding the path toward improving environmental conditions. Policies and procedures targeted at sustainability can be efficiently designed and performed in this organized context, gradually altering practices to be more environmentally sound and sustainable. For instance, Ijaz and Chughtai [46] investigated the impact of financial, economic, and environmental concerns on energy efficiency and reliance, considering the function of EVG. By reviewing global data sources, the findings imply that the interaction of governance and market infrastructures strengthens the link between environmental efforts and the goal of sustainability. Borgi et al. [47] researched the linkage between environmental changes and inclusive financial systems in Africa from 1996 to 2020, focusing on governance quality. Their research found a robust correlation between governance quality and environmental change, which was substantially more obvious when combining political, institutional, and EVG. Concurrently, Liu et al. [48] investigated how environmental taxes, governance, and energy costs affected environmental standards in OECD countries between 1996 and 2019. They used sophisticated econometric approaches to establish the inverse influence of EVG measures on environmental conditions. Table 1 summarizes the literature review’s findings according to the relationships explored between EPU, EVT, RET, URT, and CEMs.
| Study | Relationship explored | Key findings |
|---|---|---|
| [27] | EPU and CEM in G7 region | Positive influence of EPU on CEM |
| [28] | EPU and CEM in the United States | Positive connection between EPU and CEM growth |
| [29] | EPU and CEM in MENA regions | EPU exacerbates environmental degradation |
| [30] | EPU and CEM | EPU contributes positively to CEM |
| [31] | EPU and CEM with environmental policy stringency | Increase in EPU causes an uprise in CEM |
| [32] | EVT and CEM in OECD countries | Technological innovation significantly decreases CEM |
| [33] | EVT and CEM | Negative shocks in EVT increase CEM long-term |
| [49] | EVT and CEM in G20 nations | EVT strengthens ecological health |
| [5] | EVT and CEM in BRICS economies | Improved institutional quality and EVT reduce environmental impact |
| [35] | RET and CEM | RET reduces CEM and promotes sustainable development |
| [36] | RET and CEM | RET reduces CEM |
| [4] | RET and CEM | RET reduces CEM |
| [37] | RET and CEM | Technological development and RET promote air quality |
| [50] | RET and CEM in Japan and Turkey | Increasing the use of renewables reduces CEM |
| [39] | Carbon-intensive energy sources and CEM in South Africa | Carbon-intensive energy sources increase CO2 emissions |
| [40] | RET and CEM in 120 nations | RET positively influences reducing CEM |
| [41] | RET and CEM in 192 nations | RET plays an important role in regulating CEM |
| [42] | RET, urbanization, and economic growth in APEC economies | Technological development and RET promote air quality |
| [51] | RET and CEM in Sweden | RET reduces CEM across most quantiles |
| [20] | URT and non-RET use impact on CEM and economic growth in OECD nations | URT and non-RET use negatively impact environmental quality |
| [44] | Urban settings and CEM in Chinese cities | Urban built-up settings and roads correlate with CEM |
| [45] | URT and CEM in ASEAN regions | Energy usage and urban population increase contribute to CEM |
| [46] | Financial, economic, and environmental concerns on energy efficiency and reliance | Governance strengthens the link between environmental efforts and sustainability |
| [47] | Governance quality and environmental change in Africa | Robust correlation between governance quality and environmental change |
| [52] | Environmental taxes, governance, and energy costs on environmental standards in OECD countries | Inverse influence of EVG measures on environmental conditions |
- Abbreviations: APEC, Asian-Pacific Economic Cooperation; CEM, carbon emission; EPU, economic policy uncertainty; EVG, environmental governance; EVT, environmental technology; OECD, Organisation for Economic Co-operation and Development; RET, renewable energy transition.
2.6. Knowledge Gap
Despite the well-documented impact of EPU on environmental standards, coupled with the known interactions between policy uncertainties, EVT, renewable energy initiatives, URTs, and overall environmental health, scholarly examination and discussion of these factors, particularly their effect on environmental quality in industrially advanced nations such as those in the Arctic region, remains significantly limited. Previous research has investigated the dynamic interactions and cause-and-effect links among critical environmental elements, yielding varied results on environmental economics. Previous research has set the framework for understanding how economic policy uncertainties, urban transitions, the use of renewable resources, and EVT affect overall environmental quality. However, this body of literature rarely explores how these factors interact within Arctic economies. This work aims to rectify this oversight by empirically examining these indicators’ influence on the Arctic Council’s ecology. This study highlights the significant role that EPU plays in impacting CEM by incorporating it into the DKS, FMOLS, and MMQR models. Incorporating EPU into these established models offers new perspectives on how changes in economic policies may be linked to environmental conditions. Consequently, the advanced models provide a deeper and more nuanced analysis, broadening their applicability to address environmental concerns within different economic and policy contexts.
3. Research Methodology
3.1. Study Setting and Data
Considering the availability of data, this paper used the annual data from 1990 to 2020 from the Arctic Council, which is comprised of these countries (Canada, Denmark, Finland, Norway, Russia, Sweden, and the United States)—a graphical representation of the Arctic nations has been depicted in Figure 1.
The study selected this sample size because of the data availability, specification, and suitability of the research model, which can help to provide key policy strategies for improving environmental quality among the selected nations. The Arctic nations are recently undergoing rapid transformation due to increased economic activities and climate change, making it a crucial area for such analysis. Table 2 depicts the source and measurement of the parameters explored in the current analysis. The study retrieved CEM and URT from the World Development Indicator (WDI) [53]. The data for EPU was gathered from FRED’s [54] study, while EVG, RET, and EVT were retrieved from OECD’s [55] study. Figure 2 provides the evolution and trend analysis factors in the present inquiry. The figure indicate a higher and increased trend for all the variables incorporated in this paper.
| Determinants | Source | Acronyms | Nature of variable | Measurement unit |
|---|---|---|---|---|
| Carbon emission | [53] | CEM | Outcome indicator | Kilotons (kt) |
| Economic policy uncertainty | [54] | EPU | Explanatory indicator | Evaluate possible issues with publication trustworthiness and precision, consistency, and biases (EPU index) |
| Environmental technology | [55] | EVT | Explanatory indicator | Patent application (nonresident + resident) |
| Renewable energy transition | [55] | RET | Explanatory indicator | Percentage of total final energy use |
| Urbanization transition | [53] | URT | Control variable | Urban populace’s share of the entire population |
| Environmental governance | [55] | EVG | Moderating indicator | Environmental taxes levied by government entities (as a percentage of GDP) |
- Abbreviations: FRED, Federal Reserve Economic Data; OECD, Organisation for Economic Co-operation and Development; WDI, World Development Indicator.
3.2. Justification of Variables
3.2.1. Dependent Variable (CEM)
CEM, which demonstrates how human behavior impacts the natural environment, particularly concerning climate change, is a crucial indicator for evaluating environmental quality. The release of carbon molecules into the atmosphere, such as CO2, is commonly called CEMs [5]. The primary source of these emissions is burning fossil fuels, such as coal, oil, and natural gas, for power, industrial processes, and transportation.
3.2.2. Independent Variables
The main objective of the current analysis is to explore the effect of EPU on CEM. To achieve this aim, the study applied in the definition and methodology [56] suggested to measure EPU. In the framework proposed by Baker, Bloom, and Davis [56], EPU is defined as the extent of instability and unpredictability surrounding government economic strategies and their potential influence on investment, business, and economic activities. This concept is operationalized through three main concepts: (1) the frequency of news articles referencing uncertainties, (2) the uncertainty expressed in tax code provision, and (3) the dispersion of disagreement among economic forecasters [8]. This research anticipates a positive connection between EPU and CEM. Hence, the coefficient of the indicator α1 is hypothesized to be favorable (i.e., ).
The second independent indicator of this research is RET, which represents the share of cleaner or renewable power in the Arctic countries’ total energy or power usage levels. From this perspective, prior analyses have recommended that the upsurge in the consumption of this power source can be used as a proxy for measuring energy transition policies, which mitigates the extent of dependency and use of conventional power sources [12]. Accordingly, this analysis predicts an adverse association between RET α2 and proposed to be negative (i.e., ).
The third explanatory variable is EVT, which refers to advancement and innovation that promote sustainability and ecological challenges. The study measured EVT by analyzing patents granted to nonresidents and residents. Patents serve as tangible parameters of technological progress, reflecting the investments and efforts made by companies, research institutions, and individuals [57, 58]. Accordingly, the indicator for EVT α3 is expected to indicate an inverse sign (i.e., ).
The study enhanced the research model by including URT as the control variable. URT in this study was estimated in terms of the urban populace’s share of the entire population of the Arctic nations. Global urbanization is growing quickly [14]. Therefore, considering the high dependence of the Arctic nations on fossil power, the URT rate can increase their CEM. The indicator URT α4 is proposed to be positive (i.e., ).
3.2.3. Moderating Variable
The study used EVG as a moderating variable on the connection between EPU and CEM. EVG relates to the decision-making, implementation, and enforcement of policies that promote environmental quality. Yu and Guo [18] noted that one significant measure for EVG is government intervention through imposing environmental taxes. Hence, this research proposes that EVG can help improve the connection between EPU and CEM, which can help promote responsible management of natural resources and environmental sustainability. Hence, the coefficient of the indicator EVG α5 is predicted to indicate a negative sign (i.e., ). Figure 3 provides the theoretical framework for this research.
3.3. Theoretical Foundation and Estimation Model
According to economic modeling theory, the variables used for modeling need to be logarithmic to rule out the possibility of heterogeneity occurrences. Hence, Equation (3) captures the logarithm form for the model.
Here, the indicator a0 represents the constant parameter (i.e., intercept), while the indicators a1, …, a5 accounts for the effect of the regressors (EPU, RET, EVT, URT, and EVG) on the explained variable (CEM). In addition, t denotes 1990–2020, i depicts the countries, and ε represents the error term of the research framework.
To estimate the moderating effect of EVG, the study augmented Model 1 in Equation (4).
As depicted in Model 2, the parameter (EPU × EVG) indicates the nexus regarding the indicators of EPU and CEM, whereby the coefficient indicates how EVG can enhance the connection between EPU and CEM in Arctic nations.
3.4. Econometric Procedure
To estimate the effect of EPU, RET, EVT, URT, and EVG on environmental quality, the research employed several econometric approaches, which have been displayed in Figure 4. The explanation for the econometric procedure is presented in the figure.
3.4.1. Cross-Sectional Dependence (CD)
3.4.2. Heterogenous Slope (HS)
3.4.3. Inspection of Unit Root Test
In Equation (10), the CIPS values are estimated and compared with the critical values table outlined by Pesaran’s [63] simulation, which measures the stationarity propositions. The assumption is that if the estimated CIPS estimates are smaller than the critical estimates, the null hypothesis, which suggests the presence of stationarity, should be accepted and vice versa.
3.4.4. Panel Cointegration Analysis
Here, averages for the group statistics are indicated by (Gτ − Ga) whereas the panel statistics are shown by (Pτ − Pa).
3.4.5. Panel Data Estimation Approaches
The paper applied three approaches to estimate the coefficients among the research indicators. Thus, the paper utilized the FMOLS and standard-error model suggested by [66] (DSK) for comparability of the findings. While the FMOLS model features a single intercept in its estimation process, it is important to note that various groups within the panel set may exhibit distinct serial correlations. Nonetheless, the DSK enhanced the FMOLS approach by employing robust standard errors, rendering the model dependable for addressing CD, simultaneity issues, and autocorrections across different lag lengths. In addition to these two models, prior analyses have demonstrated that the QR approach can help recognize the heterogenous impact and distribution across various quantiles to alleviate the issues regarding panel data assessment [67]. Hence, the study employed a recent and novel MMQR approach propounded by [68]. The MMQR helps to examine the conditional quantile of the explained indicator, making it particularly helpful for evaluating the effect of covariates on different parts of the distribution.
3.4.6. Panel Causality Test
4. Empirical Results and Discussion
4.1. Descriptive Analysis
The result of the correlation matrix, variance inflation factor (VIF), and descriptive assessment of the parameters are captured in Table 2. The finding delineated that the median and mean score across all the indicators is positive, with EVT showing the lowest score for mean and RET emerging as the highest mean score. Regarding the skewness of CEM, the result found a positive coefficient, implying a consistent rise in emission levels among the Arctic Council over the research period. In addition, the minimum, maximum, median, and mean for the understudied variables demonstrated a sharp upsurge in the economies understudied during this analysis. The Jarque–Bera (J–B) test findings highlighted that all the series are significant at 1%. The results confirm the existence of normal distribution among the research series. In addition, Table 3 provides the outcome of the pairwise connection among the indicators. The results revealed that EVG, RET, and EVT inversely influence CEM, while EPU and URT positively affect CEM. The last section of Table 3 depicts the VIF score among the understudied series. Following Segbefia et al. [71], it is proposed that the VIF score for the panel series should be below 10. Consequently, the results of this study indicate the absence of multicollinearity issues within the dataset. This finding paves the way for additional in-depth analysis, allowing the paper to derive insightful and conclusive outcomes.
| Variables | Mean | Median | Maximum | Minimum | Standard deviation | Kurtosis | J–B | Obs. |
|---|---|---|---|---|---|---|---|---|
| Descriptive analysis | ||||||||
| InCEM | 12.378 | 11.012 | 15.589 | 10.215 | 1.920 | 1.562 | 4.070 | 201 |
| InEPU | 1.428 | −0.751 | 6.081 | −3.527 | 3.339 | 1.172 | 5.963 | 201 |
| InRET | 2.627 | 2.865 | 3.910 | 0.900 | 0.967 | 1.182 | 8.786 | 201 |
| InEVT | 2.377 | 2.327 | 3.274 | 1.720 | 0.322 | 3.365 | 6.154 | 201 |
| InEVG | 0.725 | 0.832 | 1.678 | −0.478 | 0.517 | 2.333 | 3.046 | 201 |
| InURT | 16.741 | 15.925 | 19.428 | 1.686 | 1.752 | 2.333 | 4.161 | 201 |
| Pairwise correlation matrix | ||||||||
| InCEM | InEPU | InRET | InEVT | InEVG | InURT | |||
| InCEM | 1.000 | — | — | — | — | — | ||
| InEPU | 0.527 | 1.000 | — | — | — | — | ||
| InRET | 0.705 | 0.504 | 1.000 | — | — | — | ||
| InEVT | 0.703 | 0.306 | 0.703 | 1.000 | — | — | ||
| InEVG | 0.115 | 0.501 | 0.115 | 0.677 | 1.000 | — | ||
| InURT | 0.823 | 0.445 | 0.701 | 0.104 | 0.208 | 1.000 | ||
| VIF assessment | ||||||||
| VIF score | 1/VIF score | Mean VIF | ||||||
| InCEM | 1.964 | 0.509 | 0.435 | |||||
| InEPU | 2.306 | 0.433 | — | |||||
| InRET | 3.048 | 0.328 | — | |||||
| InEVT | 1.637 | 0.610 | — | |||||
| InEVG | 2.822 | 0.354 | — | |||||
| InURT | 2.657 | 0.376 | — | |||||
- Abbreviations: CEM, carbon emission; EPU, economic policy uncertainty; EVG, environmental governance; EVT, environmental technology; RET, renewable energy transition.
4.2. Outcome of HS and CD Test
This paper evaluates the HS for which the outcome has been captured in the second aspect of Table 4. The proposition states the nonprevalence of slope heterogeneity, while the alternative proposition reflects heterogeneity in the HS coefficients. The finding from the study revealed that the estimates for and adjusted outcomes are substantial at 1%, implying the acceptance of the null hypothesis. Furthermore, the analysis assessed CD, and the output is captured in the first section of Table 4. The outcome from all three CD tests revealed the prevalence of CD in the research data at a 1% significant level.
| Variables | Breusch–PaganLM | Bias-corrected scaledLM | PesaranLM |
|---|---|---|---|
| InCEM | 215.082∗∗∗ | 29.830∗∗∗ | 29.947∗∗∗ |
| InEPU | 144.615∗∗∗ | 16.527∗∗∗ | 16.643∗∗∗ |
| InRET | 234.314∗∗∗ | 32.798∗∗∗ | 32.915∗∗∗ |
| InEVT | 307.231∗∗∗ | 44.049∗∗∗ | 44.166∗∗∗ |
| InEVG | 157.527∗∗∗ | 20.186∗∗∗ | 20.302∗∗∗ |
| InURT | 493.238∗∗∗ | 72.751∗∗∗ | 72.867∗∗∗ |
| Slope homogeneity | Test statistics | ||
| 9.553∗∗∗ | |||
| adjusted | 12.190∗∗∗ | ||
- Abbreviations: CD, cross-sectional dependence; CEM, carbon emission; EPU, economic policy uncertainty; EVG, environmental governance; EVT, environmental technology; HS, heterogenous slope; RET, renewable energy transition.
- ∗∗∗Reflects the rejection of the null hypothesis at a 1% level of significance, p < 0.001.
4.3. Stationarity Analysis
The utilization of second-generational stationarity assessments such as CIPS and CADF has been demonstrated in the existing body of literature to produce reliable estimates and accounts for potential HS and CD among the data set [12, 60]. The outcome obtained from these tests indicates that, after the first difference in the data, the study indicators exhibited an integrated order of I(1), establishing stationarity among the under-studied indicators. These findings substantiate the rationale for investigating the interrelationships among the variables while analyzing the study’s coefficients (Table 5).
| Series | CIPS | First difference | CADF | First difference |
|---|---|---|---|---|
| Level | Level | |||
| InCEM | −0.090 | −8.084∗∗∗ | 1.107 | −9.843∗∗∗ |
| InEPU | −1.709 | −12.195∗∗∗ | 0.292 | −12.255∗∗∗ |
| InRET | −1.679 | −14.632∗∗∗ | 1.496 | −14.900∗∗∗ |
| InEVT | −0.993 | −9.036∗∗∗ | −0.411 | −9.769∗∗∗ |
| InEVG | 0.496 | −13.526∗∗∗ | 1.678 | −17.631∗∗∗ |
| InURT | 1.145 | −11.186∗∗∗ | −1.486 | −8.772∗∗∗ |
- Abbreviations: CEM, carbon emission; EPU, economic policy uncertainty; EVG, environmental governance; EVT, environmental technology; RET, renewable energy transition.
- ∗∗∗Reflects a 1% level of significance, p < 0.001.
4.4. Analysis of Cointegration
The outcome of this cointegration test is provided in Table 6, and all the statistics, including (Gτ, Ga, Pτ, Pa) are statistically significant, thereby necessitating the rejection of the null hypothesis at the substantial 1% significance level. Consequently, the study endorses the alternative hypothesis that cointegration exists within the models employed in this research. With this understanding, the analysis estimates the requisite regression model that aligns with the research objectives.
| Test | Statistics | p-Value |
|---|---|---|
| Gτ | −24.122∗∗∗ | ≤0.001 |
| Ga | −59.002∗∗∗ | ≤0.001 |
| Pτ | −87.347∗∗∗ | ≤0.001 |
| Pa | −53.179∗∗∗ | ≤0.001 |
| Variance ratio | 152.661∗∗∗ | ≤0.001 |
- ∗∗∗Reflects a 1% level of significance, p ≤ 0.001.
4.5. Result of Long-Term Estimators
This research then proceeds to explore the influence of EPU, RET, EVT, URT, and EVG on environmental quality by applying three econometric approaches: DSK, FMOLS, and MMRQ. The outcome for the FMOLS and DSK has been presented in Table 7.
| Indicators | Model 1 | Model 2 | ||
|---|---|---|---|---|
| Lineup | DSK | FMOLS | DSK | FMOLS |
| InEPU |
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| InRET |
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| InEVT |
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| InEVG |
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| InURT |
|
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| InEPU × EVG | — | — |
|
|
- Abbreviations: EPU, economic policy uncertainty; EVG, environmental governance; EVT, environmental technology; RET, renewable energy transition.
- ∗∗∗Reflects a 1% level of significance p < 0.001; carbon emission is the dependent indicator.
The findings show that EPU substantially and positively influences CEM. Thus, the outcome confirmed that a 1% upward shift in EPU will cause a decline in environmental quality by 0.208% for DSK and 0.372% for the FMOLS, respectively. The positive connection between EPU and CEM in the Arctic Council can be accounted for by factual evidence that rising EPU may hinder long-term investments in sustainable development projects, affecting crucial sectors such as conservation efforts and energy efficiency. Moreover, prior studies have revealed that EPU may discourage businesses from committing resources to environmentally responsible actions and practices, leading to an undesirable upsurge in CEM and ecological degradation [72]. Prior studies have demonstrated that EPU can have adverse effects on environmental outcomes. For instance [73, 74], some extant analysis contradicts these findings suggesting that in certain situations, EPU may lead to improvement in environmental quality due to heightened awareness and adaptability [7, 75].
Moreover, the study’s findings highlighted that the connection between RET and CEM may improve environmental sustainability. More precisely, the data analysis’s conclusions indicated a 1% increase in RET that will cause CEM to decrease by 0.255% for the DSK and 0.475% for the FMOLS. This result indicates that the Arctic region’s commitment to RET, such as solar power and wind, aligns with the region’s sustainable goals to achieve ecological stability. Contemporary situations in Arctic nations, such as the United States, Denmark, and Sweden, have demonstrated how a shift toward renewables can reduce CEM. Thus, the urgent demand for energy storage solutions and the varying degree of energy infrastructure development across the Arctic economies can influence the effectiveness of RET in universal help in reducing CEM. Prior studies have demonstrated an inverse connection between RET and CEM, placing the potential of clean energy sources to displace carbon-intensive alternatives [10, 12, 37].
In addition, this analysis proceeds by exploring the influence of EVT on CEM. The study’s outcome suggested that an EVT could help improve the environment in the Arctic nations. Thus, the study suggested a 1% upward shift in EVT to help reduce ecological dilapidation by 0.310% by FMOLS and 0.771% by DSK approaches. The explanation for this outcome is that the advancement and adoption of environmental technologies among the Arctic nations can help them reduce CEM. In recent times, the Arctic nations have increasingly recognized the importance of investing in EVT to address environmental concerns. For example, nations such as the United States, Sweden, and Norway have outlined initiatives that promote innovative waste management and sustainable transportation systems that contribute to reducing CEM in this region. This inverse connection between EVT and CEM is constant with extant studies that recognize the use of environmentally friendly technologies as critical tactics in meeting carbon reduction objectives [76–78]. On the contrary, Jahanger et al. [67] concluded that EVT deteriorates environmental health by contributing to CEM.
URT was demonstrated to have a favorable and substantial influence on CEM by 0.782% for the DSK method and 0.488 for FMOLS in the Arctic countries. This finding is in tandem with the theoretical proposition of this research by revealing that as URT accelerates in the Arctic communities, driven by indicators such as population growth and economic expansion, infrastructure development, and energy use have increased in tandem, raising CEM. The shift from a traditional lifestyle to a more urbanized setting often involves greater dependence on transportation, fossil energy, and heating, which contribute substantially to CEM. Among the Arctic nations, issues such as land use, expanding industrial activities, and the demand for modern amenities have intensified the carbon footprint in these areas. Previous extant analysis aligns with a positive nexus between URT and CEM. These studies argue that while URT brings an improved standard of living, it often accompanies a surge in carbon-intensive activities [5, 79]. Nevertheless, an analysis presents conflicting evidence, suggesting that the URT can reduce CEM [80, 81].
The study results highlighted that EVG reflects a substantial and inverse influence on CEM. Thus, a 1% upsurge in EVG will cause CEM to decline by 0.332% in DSK and 0.427% in FMOLS. This outcome can be explained by the assumption that the Arctic nations have implemented robust EVG strategies, including imposing environmental taxes on CEM. For instance, Norway’s carbon tax on hydrocarbon fueled has been instrumental in curbing CEM. This outcome aligns with this prior research [52, 82]. For example, the outcomes by Yu and Guo [18] highlighted that EVG contributes significantly to transforming an economy toward cleaner power sources to help achieve sustainable ecological targets. In contrast, Nie et al. [83] argued that among emerging economies, EVG may inhibit economic activities and cause obstacles in adopting cleaner power and higher levels of ecological pollution.
Finally, the iteration effect of EVG on the connection between EPU and CEM was explored. The evidence outlined in this research proved that EVG × EPU has an inverse effect on CEM. More specifically, the link between EPU and CEM can be strengthened by EVG by 0.521% in DSK and 0.548% in FMOLS accordingly. The outcome proves that a robust EVG mechanism can act as a regulatory buffer in times of policy uncertainty in the Arctic nations. Thus, the imposition of environmental tax serves as a financial disincentive for carbo-intense business activities and stabilizes during periods of uncertainty. Hence, in this scenario, a negative moderating influence of EVG can alleviate the potential adverse impact of EPU on CEM, reflecting a commitment to sustainability within the Arctic nations. The findings from this research enrich extant analysis authors that argued that environmental taxes are fiscal instruments designed to internalize the external costs of environmental pollution, discouraging harmful activities while promoting sustainable practices [49, 84]. By levying taxes on activities that produce emissions or lead to resource depletion, the government aims to stimulate individuals and companies to embrace ecologically friendly practices. The graphical outcome of the empirical outcome has been depicted in Figure 5.
4.6. Robustness Check
The study applied the MMQR model outlined by [68] as a robustness test to the FMOLS and DSK estimators. The result from the panel MMQR is depicted in Table 8. The outcome demonstrates that the projected coefficients of EPU and URT are positive and substantial across all the quantiles (0.1–0.9). In contrast, EVG, RET, and EP × EVG showed an inverse and significant association with CEM. Accordingly, the outcome of this alternative model is consistent with the results of the FMOLS and DSK. The visualization for the effect of the various indicators on RET has been depicted in Figure 6 across all the quantiles in this study.
| Variables | OLS | Lower quantile | Middle quantile | Upper quantile | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| q.01 | q.02 | q.03 | q.04 | q.05 | q.06 | q.07 | q.08 | q.09 | ||
| InEPU |
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| InRET |
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| InEVT |
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| InEVG |
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| InURT |
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| InEPU × EVG |
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- Note: Carbon emission is the dependent indicator.
- Abbreviations: EPU, economic policy uncertainty; EVG, environmental governance; EVT, environmental technology; MMQR, moments quantile regression; RET, renewable energy transition.
- ∗∗∗, ∗∗Reflect a 0.05% and 1% level of significance p < 0.001, respectively.
4.7. Panel Causality Analysis
The heterogenous causality analysis is captured in Table 9. According to the findings of the D–H causality, a bidirectional flow of causality exists between RET and CEM. This outcome implies that policies and plans favoring URT in a mutual connection can help enhance environmental sustainability. Literature on URT and CEM confirms this outcome [48, 85]. However, in the case of the other indicators, which comprise EPU, RET, EVG, EVT, and CEM, the D–H causality outcome confirmed a unidirectional nexus among these indicators. The inference is that any changes in one of these indicators will significantly affect the other series. Erstwhile studies have found similar outcomes in these indicators [86, 87]. In addition, these findings comply with the outcome of the regression estimates, and the study can conclude that substantial causality is prevalent among the explanatory and dependent indicators in this analysis.
| Null hypothesis | Zbar-Stats | Prob. | Decision |
|---|---|---|---|
| InEPU ⇎ InCEM | 5.119∗∗∗ | 0.000 | Unidirectional |
| InCEM ⇎ InEPU | 2.165 | 0.304 | — |
| InRET ⇎ InCEM | 9.105∗∗∗ | 0.000 | Unidirectional |
| InCEM ⇎ InEPU | 4.718 | 0.107 | — |
| InEVT ⇎ InCEM | 7.142∗∗∗ | 0.000 | Unidirectional |
| InCEM ⇎ InEVT | 2.464 | 0.754 | — |
| InEVG ⇎ InCEM | 6.964∗∗∗ | 0.001 | Unidirectional |
| InCEM ⇎ InEVG | 4.306 | 0.187 | — |
| InURT ⇎ InCEM | 8.440 | 0.000 | Bidirectional |
| InCEM ⇎ InURT | 7.882 | 0.000 | — |
- Note: ⇎ Does not Granger cause, ⟷ bidirectional, and → unidirectional.
- Abbreviations: CEM, carbon emission; EPU, economic policy uncertainty; EVG, environmental governance; EVT, environmental technology; RET, renewable energy transition.
- ∗∗∗Represents a 1% significance level, p < 0.001.
5. Research Policy Implications and Conclusions
5.1. Conclusions
Reducing CEM has become critical for the Arctic Council as the globe experiences accelerated warm and environmental shifts. Prior analysis has enumerated several socioeconomic parameters that drive the pace and scope of CEM; however, the effect of policy uncertainty and EVG on environmental quality is still unattended, especially in the Arctic region. Accordingly, this research explored the combined influence of EPU, EVT, EVG, RET, and URT on the Arctic nation’s agenda of achieving carbon neutrality by 2030 and beyond. Based on this motivation, the study applied the advanced econometric process such as conducting CADF and CIP stationarity test, cointegration analysis proposed by Westerlund [65], three novel panel estimators, which include FMOLS, DSK, and MMQR, and panel data from 1990 to 2020. The results confirmed that the indicators are stationary cointegrated, and the outcomes were summarized as follows. First, the research espoused that EPU and RET are detrimental to ecological stability, while RET, EVG, and EVT help mitigate CEM in the Arctic region. Also, this analysis found that EPU and CEM can be strengthened through the iteration influence of EVG. Lastly, the study outcome found a bidirectional association between URT and CEM, while there is a unidirectional nexus between RET, EPU, EVG, EVT, and CEM among the Arctic nations.
5.2. Policy Directions
In accordance with the research findings, the enumerated point is suggested to promote environmental quality in the Arctic Council. First, to address the positive influence of EPU on CEM, this research proposes that members in the Arctic countries can establish a coordinated initiative to promote green investments during periods of economic uncertainty. Thus, these countries’ policy-makers and governments can provide subsidies, tax breaks, and incentives for individuals and businesses who invest in sustainable infrastructure and cleaner power supply for their activities. By doing so, EPU can drive increased interest and funding in clean power alternatives and solutions, ultimately contributing to lower CEM. Second, the empirical output captured that EVT helps eradicate environmental pollution. Thus, the result necessitates that increased investment and advocacy of green and ecological innovation should be included in strategic approaches of the Arctic nations. In addition, there is an urgent need to encourage research and innovation in environmental technologies designed to dissipate CEM in the Arctic. Support initiatives focusing on developing cleaner transportation options for rural centers, sustainable agriculture programs, and carbon capture and storage technologies tailored to the Arctic nations’ unique challenges. This innovation can be facilitated by cooperation between academic institutions, industrial partners, and member states of the Arctic Council.
Third, because RET has a beneficial influence on CEM, this research suggests that nations globally and, in the Arctic, economies should provide consistent and clear incentives and policies for renewable energy projects. In addition, stakeholders and governments in these countries should develop a comprehensive and region-specific RET plan for the member council in the Arctic nations. This plan should prioritize using cleaner power sources such as solar, hydroelectric power, and wind, which have lower CEM than conventional ones. In addition, this plan should include measures to reduce the ecological effect of RET projects, such as conducting thorough environmental impact assessments and ensuring responsible infrastructure and development. Fourth, the Arctic Council member states must prioritize sustainable urban planning and design projects encouraging small, energy-efficient cities. Policies like mixed-use zoning, effective public transit, and green building standards can decrease energy consumption and CEMs related to urban growth. Furthermore, encouraging Arctic urban regions to embrace renewable energy sources for transportation, heating, and cooling will substantially decrease (CO2) production.
The research findings highlighted EVG’s negative and substantial iteration influence on the connection between EPU and CEM. Accordingly, this paper recommends that these council member states work together to harmonize EVG policies related to CEM. This paper suggests a “green investment incentive program.” This initiative could provide financial support to enterprises and industries that invest in low-carbon technologies and practices by offering financial support and grants for green, cleaner technologies, and creating economic stability through sustainable growth. This approach can help address environmental concerns and alleviate EPU by providing clear incentives for businesses to transition toward ecologically friendly tools, ultimately reducing CEM levels in the Arctic region.
Lastly, it is crucial to tailor policy recommendations to Arctic nations’ specific characteristics and contexts, including Canada, Denmark, Finland, Norway, Russia, Sweden, and the United States. These countries have unique economic, environmental, and social landscapes influencing their EVG and climate action approach. For instance, Canada and the United States, with vast northern territories and significant indigenous populations, may focus on integrating traditional ecological knowledge and prioritizing community-driven sustainable development initiatives that respect indigenous rights and livelihoods. Denmark, through Greenland, has distinct challenges related to its remote, small communities, and could benefit from localized renewable energy solutions like wind and hydropower, which align with its existing renewable energy strengths. Finland and Sweden, with advanced technology sectors, could lead in the development of cutting-edge environmental technologies and innovations, such as advanced biofuels or smart grids, leveraging their strong research and innovation capacities. With its significant investments in hydroelectric power and a history of leadership in maritime technology, Norway could prioritize enhancing cleaner maritime transport and expanding its existing clean energy infrastructure. Given its vast natural resources and heavy reliance on fossil fuels, Russia may need to focus on diversifying its energy mix and improving energy efficiency, particularly in industrial sectors, while navigating complex political and economic landscapes. Also, with its large and varied Arctic region, the United States could leverage its technological capabilities and economic resources to drive large-scale renewable energy projects and robust EVG frameworks. By considering these varied characteristics, the policy directions can be more relevant and impactful, ensuring that each Arctic nation’s unique strengths and challenges are addressed in the collective effort to improve environmental quality across the Arctic region.
5.3. Research Limitations and Future Study
Although this research offers valuable insight into the interplay of factors influencing sustainability in the Arctic region, the study faces limitations that warrant future analysis. First, a more thorough analysis of certain EVG rules and their influence on financial choices and environmental results would benefit the study. The literature in this field might also benefit from expanding the study to incorporate case studies or comparative evaluations of the policies of other Arctic nations and how successful they are at reducing CEMs and fostering the switch to renewable energy sources. Last but not least, ongoing research is required to give current insights into the sustainability difficulties encountered by Arctic economies due to the dynamic character of global environmental regulation and its changing influence on regional policies. On the other hand, the impact of economic policies and other factors on CEMs exhibits long-term and dynamic characteristics. To address this, dynamic effect analysis will be supplemented in future studies to enhance the understanding of these complex interactions further. Moreover, future studies will explore the spatial spillover effects in the impact process of economic policies and EVT on CEMs, considering how policy learning and imitation across countries may influence these dynamics.
Conflicts of Interest
The authors declare no conflicts of interest.
Author Contributions
Mansoor Khan: formal analysis, validation. Hazrat Hassan: conceptualization, methodology, formal analysis. Cai Li: supervision, review. Agyemang Kwasi Sampene: conceptualization, methodology. Francis Kyere: investigation, visualization.
Funding
This study was supported by the National Social Science Foundation Project “Research on the Paths and Countermeasures for High-Quality Development of Rural Featured Industries Empowered by Digitalization” (project no. 23BJY010).
Open Research
Data Availability Statement
The data supporting these study’s findings will be available upon reasonable request.
