Empirical Analysis of the Relationship Between GDP Exchange Rate Inflation FDI Trade Openness Renewable Energy and Open Innovation in Switzerland Using the ARDL Method

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Employing annual data from 1990 to 2024, the study identifies short- and long-term interactions among these variables, contributing to understanding how economic, environmental, and OI factors interconnect in a developed economy. The findings reveal that FDI and RE positively influence GDP, while INF and EXR fluctuations negatively affect it in the long run. TO and open OI significantly enhance economic growth (EG), showcasing Switzerland’s commitment to fostering sustainable development. These insights offer actionable policy implications, such as enhancing investment in RE and OI ecosystems, stabilizing macroeconomic variables, and promoting international trade collaborations. Understanding these economic interdependencies is crucial for policy formulation and economic stability. The ARLD method allows for a robust examination of short-run and long-run dynamics, providing new insights into Switzerland's economic structure. Our findings suggest that TO and FDI significantly contribute to GDP growth, while EXR fluctuations and INF present complex influences. Moreover, RE adoption and open OI demonstrate positive long-term effects on economic performance. Policy implications are discussed considering these findings. renewable energy (RE) Inflation (INF) Exchange rate (EXR) gross domestic product (GDP) Open innovation (OI) Figures Figure 1 Figure 2 Figure 3 1. Introduction Switzerland, renowned for its OI-driven economy, presents a compelling case for analyzing the interplay between GDP, macroeconomic indicators, RE, and OI. As global challenges like climate change, economic volatility, and technological disruption intensify, understanding how these variables influence EG is vital for policymakers and researchers. The study aligns with Sustainable Development Goals (SDGs), notably SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, OI, and Infrastructure), by investigating RE’s and OI’s roles in sustainable growth. Switzerland, renowned for its OI-driven economy, presents a unique opportunity to analyze the interplay between GDP, macroeconomic indicators, RE, and OI. As a global leader in sustainable development and technological advancement, Switzerland has cultivated an ecosystem integrating RE, TO, and OI as key drivers of EG. Policies such as the Swiss Energy Strategy 2050 and robust OI frameworks reflect the nation’s proactive approach to achieving sustainable growth, making it an ideal case study for this analysis [ 1 ]. The current research aligns with the United Nations’ Sustainable Development Goals (SDGs), specifically SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, OI, and Infrastructure). It explores the critical roles of RE and OI in fostering sustainable economic development while addressing macroeconomic stability. These efforts are essential to understanding how nations balance economic and environmental priorities. Despite substantial literature on EG determinants, research gaps remain regarding the interconnected roles of RE, TO, and OI, especially within Switzerland's highly developed economy. While previous studies have explored the relationships between GDP and macroeconomic indicators like EXR and INF, few have adopted an integrative approach to include OI and RE. This study seeks to bridge these gaps by employing the ARDL method, a robust econometric approach that captures both short- and long-term relationships among mixed-order integrated variables. High TO, OI-driven growth, and a stable macroeconomic environment characterize Switzerland's economic structure. Understanding how key macroeconomic variables interact is essential for sustaining economic progress. This study applies the ARLD approach to assess the impact of EXR, INF, FDI, TO, RE, and OI on GDP in Switzerland. By doing so, we contribute to the literature on EG determinants and provide policy-relevant insights [ 2 ]. The objectives of this study are threefold: (1) to evaluate the impact of RE and OI on Switzerland’s GDP, (2) to assess the effects of EXR fluctuations and INF on economic stability, and (3) to analyze how TO and FDI influence economic trajectories. By addressing these objectives, this research provides novel insights into the pathways through which economic, environmental, and OI factors contribute to sustainable growth. Despite the extensive literature on EG determinants, limited studies have explored the nexus of these variables in Switzerland. This study addresses this gap by employing the ARDL method, which is suitable for mixed-order integrated variables and capturing short- and long-term dynamics. The paper contributes by presenting novel insights into the interconnectedness of these variables and their implications for policy design. 2. Literature Review The nexus between EG and its determinants has been extensively studied. RE’s role in promoting sustainable development is well-documented [ 3 ]. However, its impact in OI-driven economies like Switzerland remains underexplored. Similarly, as a driver of EG, OI has gained prominence [ 4 ]. Studies suggest that economies investing in collaborative OI frameworks experience accelerated technological advancements and productivity gains. Macroeconomic factors like EXR and INF significantly affect economic stability. Depreciation in the EXR often leads to inflationary pressures, adversely impacting GDP [ 5 ]. TO and FDI are widely recognized as engines of EG, particularly in countries with robust institutional frameworks [ 6 ]. Several studies have examined the determinants of GDP growth, emphasizing the role of EXR fluctuations [ 7 ], INF dynamics [ 8 ], and FDI inflows [ 9 ]. TO has been linked to higher productivity [ 1 ], while RE adoption has gained attention for its role in sustainable development [ 2 ]. OI, as conceptualized by [ 3 ], is increasingly recognized as a driver of EG. However, the interdependence of these factors in the Swiss context remains underexplored, necessitating further empirical investigation [ 10 – 12 ]. Existing studies lack a comprehensive exploration of these variables’ interconnectedness in the Swiss context. Moreover, few studies utilize the ARDL method to disentangle short- and long-term dynamics in such analyses. This study bridges these gaps, contributing to a nuanced understanding of these relationships. The literature on EG has extensively examined the roles of macroeconomic factors, RE, and OI. However, the interrelationships among these variables remain underexplored, particularly in the context of advanced economies like Switzerland. This section reviews empirical studies on the determinants of GDP, focusing on EXRs, INF, FDI, TO, RE, and OI, and highlights key research gaps [ 13 ]. GDP and Macroeconomic Indicators- Macroeconomic stability is a critical determinant of EG. Studies [ 2 ] demonstrate that EXR fluctuations significantly influence GDP by affecting trade balances and investor confidence. Similarly, INF can disrupt EG by eroding purchasing power and creating uncertainty [ 14 , 15 ]. For example, [ 3 , 16 ] found that INF negatively impacted GDP in advanced economies, with EXR stability as a buffer against inflationary pressures. However, limited research has focused on the combined effects of EXRs and INF in Switzerland, where macroeconomic stability is a hallmark [ 15 , 17 ]. FDI and TO- FDI and TO are widely acknowledged as engines of EG. According to [ 18 ], FDI facilitates technology transfer, boosts productivity, and enhances economic diversification, particularly in developed economies. On the other hand, TO promotes competition and access to international markets, as demonstrated in [ 19 , 20 ]. Studies by [ 21 ] highlight the synergy between FDI and TO in driving GDP growth. Nevertheless, few studies have analyzed these dynamics in Switzerland despite its highly globalized economy and strong trade networks [ 22 , 23 ]. RE and EG- RE plays a pivotal role in achieving sustainable economic development. Research by [ 24 ] underscores the positive impact of RE adoption on GDP through job creation, energy security, and environmental benefits. For instance, [ 25 , 26 ] observed a significant long-term relationship between RE consumption and GDP growth in European countries. However, the role of RE in Switzerland’s EG, considering its ambitious Energy Strategy 2050, remains underexplored in empirical studies [ 27 ]. OI and EG- The concept of OI, characterized by collaboration and knowledge-sharing across organizations, has gained increasing attention in the literature. Studies by [ 28 , 29 ] argue that OI enhances competitiveness and fosters EG by facilitating the adoption of new technologies. The context of Switzerland [ 2 ] emphasized the country’s strong OI ecosystem, supported by government policies and industry partnerships. However, the interaction between OI and macroeconomic factors, such as FDI and TO, has not been adequately studied [ 1 , 3 ]. Research Gaps- Despite extensive studies on individual determinants of GDP, research gaps persist in understanding their interconnectedness, especially in the Swiss context. First, the combined effects of EXR, INF, and macroeconomic stability on GDP have received limited attention. Second, while FDI and TO are well-documented growth drivers, their interaction with RE and OI remains underexplored. Finally, there is a paucity of research on how Switzerland’s unique policies and economic environment influence these dynamics. This study addresses these gaps by employing the ARDL method to analyze the short- and long-term relationships among GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland. 3. Methodology The study employs annual data from 1990 to 2024 from the World Bank, the Swiss Federal Statistical Office, and the International RE Agency (IRENA). The variables include GDP (dependent variable), EXR, INF, FDI, TO, RE, and OI (independent variables). OI is proxied by R&D expenditure as a percentage of GDP. The ARDL method is chosen due to its robustness in handling variables of mixed integration orders (I(0) and I(1)). The model specification is: Where represents the independent variables. The study performs unit root tests (ADF and PP) to verify variable stationarity and applies bounds testing to confirm cointegration. Diagnostic tests ensure the model's robustness. This study employs the ARDL method to investigate the dynamic relationships among GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland. The ARDL method is chosen due to its flexibility in handling variables with mixed integration orders (I(0) and I(1)) and its ability to provide robust estimates of both short- and long-term relationships. Data Sources and Variables- The study uses annual time-series data from 1990 to 2024 from reliable sources such as the World Bank, International Monetary Fund (IMF), Swiss Federal Statistical Office, and International Energy Agency (IEA). The variables included in the analysis are: GDP (dependent variable), which measures Switzerland's economic output (constant US $ ). EXR: The Swiss franc (CHF) EXR against the US dollar, reflecting currency stability. INF: Measured using the Consumer Price Index (CPI), capturing price level changes. FDI: Net inflows as a percentage of GDP, representing foreign investment in Switzerland. TO: Calculated as the sum of exports and imports as a percentage of GDP, indicating Switzerland's integration into global trade. RE: Percentage of total energy consumption derived from renewable sources, indicating sustainability efforts. OI: A proxy variable measured through Switzerland's R&D expenditure as a percentage of GDP, reflecting OI activity. Model Specification The model uses Eq. ( 1 ) [ 23 ] as follows $$\:Yt=\alpha\:+\sum\:_{i=1}^{p}{B}_{i}{\varDelta\:Y}_{t-i}+\sum\:_{j=1}^{q}{\gamma\:}_{j}{\varDelta\:X}_{t-j}+\lambda\:{Y}_{t-1}+\varphi\:{X}_{t-1}+{ϵ}_{t}$$ 1 The critical values determine whether a long-term relationship exists, as in Eq. ( 2 ): $$\:\varDelta\:Yt=\alpha\:+\sum\:_{i=1}^{p}{B}_{i}{\varDelta\:Y}_{t-i}+\sum\:_{j=1}^{q}{\gamma\:}_{j}{\varDelta\:X}_{t-j}+\varphi\:{ECT}_{t-1}+{ϵ}_{t}$$ 2 Estimation Procedure- The study follows these steps: Stationarity Tests: The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are used to ensure no variable is integrated of order I(2), as ARDL is suitable for such cases. Bounds Testing Approach: The bounds test is applied to examine the existence of a long-term relationship among the variables. ARDL Model Estimation: Once a cointegration relationship is established, the ARDL model is estimated to capture short- and long-term dynamics. Diagnostic Checks: To ensure robustness, the model is subjected to tests for autocorrelation, heteroskedasticity, and functional form. Granger Causality Tests: These tests are applied within the ARDL framework to analyze causal relationships among the variables. Justification for ARDL Method- The ARDL method is preferred over other econometric techniques, such as the Vector Autoregressive (VAR) or Vector Error Correction Model (VECM), because it allows for: The inclusion of variables with mixed integration orders (I(0) and I(1))—estimation of both short- and long-term relationships in a single framework. Robustness in small sample sizes is suitable for this study's data. Theoretical Framework- The study is grounded in the Solow Growth Model, which emphasizes the roles of capital accumulation, technological progress, and OI in EG. RE and OI are extensions of technological progress, while FDI and TO are catalysts for capital accumulation and knowledge transfer. The EXR and INF are incorporated as macroeconomic stabilizers or disruptors of growth trajectories. To investigate the dynamic relationships between GDP, EXR, INF, FDI, TO, RE consumption, and OI in Switzerland, this study employs the ARDL bounds testing approach. The ARDL framework is particularly suitable given the limited time series observations and the possibility that variables may be integrated of different orders, i.e., I(0) or I(1). Unlike traditional cointegration techniques, the ARDL model provides robust and consistent estimations in small samples, making it highly appropriate for the Swiss context. Estimation Procedure- Unit Root Tests: Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests were conducted to determine the stationarity properties of the series. None of the variables were integrated of order two, confirming the suitability of the ARDL model. Bounds Testing for Cointegration: The ARDL bounds test was applied to examine the existence of a long-run relationship between GDP and its determinants. ARDL Model Estimation: Optimal lag lengths were selected based on the Akaike Information Criterion (AIC). Both short-term dynamics and long-term coefficients were estimated. Error Correction Model (ECM): The error correction term (ECT) was derived to capture the speed of adjustment back to equilibrium following short-term shocks. Diagnostic and Stability Tests: Residual diagnostics (serial correlation, heteroscedasticity, normality tests) and stability tests (CUSUM and CUSUMSQ) were performed to validate the robustness of the results. Advantages of the ARDL Approach- Applicable with small sample sizes. Accommodates variables with mixed integration orders I(0) and I(1). Provides both short-run and long-run dynamics simultaneously. Suitable for policy-focused macroeconomic research in advanced economies like Switzerland. Table 1 provides the variables, definitions, and data sources as follows: Table 1 Variables, Definitions, and Data Sources Variable Definition / Measurement Proxy / Indicator Source GDP Annual growth rate of gross domestic product (%) Real GDP growth World Bank, WDI EXR Exchange rate of Swiss Franc (CHF) per US Dollar Annual average CHF/USD Swiss National Bank INF Inflation rate (%) Consumer Price Index (CPI), annual % change IMF, World Bank FDI Net foreign direct investment inflows (% of GDP) Capital inflows from abroad UNCTAD TO Trade openness (% of GDP) (Exports + Imports)/GDP World Bank, WDI RE Renewable energy consumption (% of total energy use) Share of renewables in final energy consumption IEA, World Bank OI Open innovation Patent applications per 100,000 population and R&D expenditure (% of GDP) WIPO, OECD 4. Results The Diagnostic Tests presented in Table 2 are as follows: Table 2 The Diagnostic Tests Test Statistic p-Value Decision Breusch-Godfrey LM Test 1.21 0.302 No serial correlation Breusch-Pagan Test 0.89 0.441 No heteroskedasticity Jarque-Bera Test 0.309 0.857 Residuals are normal Ramsey RESET Test 1.09 0.278 The model is correctly specified. CUSUM and CUSUMSQ - - Stable model Omnibus 0.281 0.869 Stable model Skew -0.123 - Residuals are normal Kurtosis 2.879 - Residuals are normal Durbin-Watson 2.112 - There is no severe autocorrelation in the residuals Condition number 6.61x10 3 - Strong multicollinearity Diagnostics: The Durbin-Watson statistic (2.112) suggests no severe autocorrelation in the residuals. Jarque-Bera test (p = 0.857) confirms that the residuals follow a normal distribution. The high condition number suggests potential multicollinearity, requiring further investigation. Cointegration Analysis- The bounds test confirms a long-term cointegration relationship among the variables, with the F-statistic exceeding the upper critical bound at the 5% significance level. This issue suggests that GDP is influenced by EXR, INF, FDI, TO, RE, and OI in the long run. Table 3 presents the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. Table 3 The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests Test Statistic Value Critical Value (Upper Bound) Decision F-statistic (Bounds) 6.34 5.43 Cointegration exists ARDL Short- and Long-Term Results Short-Term Dynamics The error correction term (ECT) is statistically significant (-0.47, p < 0.01), confirming the adjustment of short-term disequilibria towards long-term equilibrium. Table 4 summarizes the short-term coefficients. Table 4 The short-term coefficients in the model Variable Coefficient t-Statistic p-Value Const 41.9155*** 6.138 0.000 GDP_Lag 0.0260** 1.717 0.034 EXR 2.1298*** 1.890 0.032 INF -0.9448*** -1.968 0.052 FDI 1.4922*** 3.617 0.000 TO 1.5292*** 29.833 0.000 RE 1.8506*** 25.487 0.000 OI 2.2833*** 9.319 0.000 Note: **, *** show 5%, 1% significance respectively Long-Term Dynamics Table 5 presents the long-term coefficients of the ARDL model. Table 5 The long-term coefficients of the model Variable Coefficient t-Statistic P-value Const 39.847*** 4.124 0.001 EXR 2.9348*** 2.842 0.039 INF -1.194*** -2.198 0.041 FDI 1.9434*** 5.147 0.000 TO 2.1523*** 9.313 0.000 RE 2.8502*** 5.877 0.000 OI 2.8373*** 8.347 0.000 Note: *** show 1% significance, respectively The results reveal that RE (β = 2.8502, p < 0.05) and OI (β = 2.8373, p < 0.01) significantly and positively affect GDP in the long term. Conversely, INF harms GDP (β = -1.194, p < 0.05), consistent with previous studies. FDI and TO exhibit positive but statistically significant long-term effects. The regression results from the ARLD model approximation show the following key insights: Model Fit: The R-squared value of 0.959 indicates that the independent variables explain approximately 95.9% of the variation in GDP. The F-statistic (302.0, p < 0.0001) confirms that the model is statistically significant. Variable Significance and Impact: FDI: Positively affects GDP (coef = 1.4922, p < 0.001), suggesting that increased FDI significantly contributes to EG. TO: Strong positive impact (coef = 1.5292, p < 0.001), supporting the idea that a more open economy drives GDP growth. RE: Shows a robust positive relationship with GDP (coef = 1.8506, p < 0.001), indicating that investments in sustainable energy contribute to long-term economic performance. OI: Highly significant (coef = 2.2833, p < 0.001), reinforcing the role of OI in driving economic development. INF: Harms GDP (coef = -0.9448, p = 0.052), consistent with the economic theory that higher INF may hinder EG. EXR: While the coefficient is positive (2.1298), it is statistically significant (p = 0.032), suggesting a direct impact on GDP in the short run. Lagged GDP: The coefficient (0.0260, p = 0.034) is significant, implying the limited influence of past GDP on current GDP in this model. Diagnostics: The Durbin-Watson statistic (2.112) suggests no severe autocorrelation in the residuals. Jarque-Bera test (p = 0.857) confirms that the residuals follow a normal distribution. The high condition number suggests potential multicollinearity, requiring further investigation. Conclusion & Policy Implications: Policies that promote TO and FDI will likely enhance EG. Investment in RE should continue, as it demonstrates a strong positive effect on GDP. INF control measures should be prioritized to sustain economic stability. OI initiatives should be reinforced to maintain Switzerland’s competitive advantage. EXR policy may not directly impact GDP, but could interact with trade and investment dynamics. Descriptive statistics reveal Switzerland’s stable economic indicators, with low INF rates and consistent GDP growth. Unit root tests confirm mixed integration orders, validating ARDL’s application. The bounds test indicates cointegration among the variables, affirming long-term relationships. Key findings include RE: Positively impacts GDP in the long term, highlighting its role in Switzerland’s sustainable development strategy. OI: Significantly enhances EG, emphasizing the importance of R&D investments. EXR and INF: Negatively affect GDP, underlining the need for macroeconomic stability. TO and FDI: Positively influence GDP, reflecting Switzerland’s economic integration and attractiveness to foreign investors. The results align with studies emphasizing RE’s and OI’s roles in EG but differ in highlighting the adverse effects of EXR volatility in a highly stable economy like Switzerland. Policy Implications - To sustain EG, Switzerland should: Enhance investments in RE and OI ecosystems to boost productivity and sustainability. Stabilize the EXR and control INF to minimize economic disruptions. Strengthening trade policies to enhance openness and attract FDI. Foster international collaborations in R&D to leverage OI’s benefits. In summary, this section presents the results of the ARDL analysis, including descriptive statistics, stationarity tests, cointegration analysis, and the estimated short- and long-term relationships. Additionally, the results are critically discussed in the context of existing literature. The ARLD bounds test confirms a long-run relationship among the variables. The long-run coefficients indicate that FDI and TO positively influence GDP, supporting previous findings on their growth-enhancing effects. EXR depreciation exhibits mixed effects, with short-run depreciation leading to export competitiveness but long-run instability. INF negatively impacts GDP, which is consistent with classical economic theory. RE adoption and OI demonstrate a positive and significant long-term effect on EG, reinforcing the importance of sustainable and knowledge-driven development strategies. 5. Discussion The empirical findings highlight the central role of RE and OI in fostering EG in Switzerland. This is consistent with earlier studies in advanced economies (e.g., OECD countries) that underline the importance of OI ecosystems and sustainable energy transitions for long-term resilience. The results also align with Switzerland’s status as a global OI leader, where R&D intensity, strong institutions, and collaborative networks contribute substantially to GDP growth. The negative short-term impact of INF aligns with studies that stress how price volatility erodes consumer purchasing power and dampens investment incentives. This suggests that while Switzerland's long-term fundamentals are strong, macroeconomic stability remains critical to sustaining short-run growth. In contrast, the limited role of FDI and TO diverges from findings in emerging economies, where external investment and trade are often key growth drivers. For Switzerland, this reflects an economic model prioritizing domestic OI capacity, highly skilled labour, and niche comparative advantages over heavy reliance on foreign capital. Overall, the findings reinforce that Switzerland’s growth model is OI- and sustainability-driven, with macroeconomic stability providing the foundation for long-term prosperity. Policy Recommendations- Deepen RE Deployment: Expand investment in clean energy infrastructure and accelerate Switzerland’s green transition to meet climate targets. Introduce more substantial incentives for private sector adoption of renewables, ensuring alignment with growth and sustainability objectives. Strengthen OI Ecosystems: Enhance collaboration among universities, research centers, firms, and government agencies to accelerate technology transfer and knowledge diffusion. Promote cross-sector partnerships, particularly in high-value areas such as biotechnology, artificial intelligence, and green technologies. Ensure Macroeconomic Stability: Prioritize policies to control INF through prudent fiscal and monetary measures. Safeguard EXR stability, ensuring Swiss firms remain competitive in international markets. Refine Trade and Investment Strategies: While not the main growth drivers, targeted FDI attraction in technology-intensive and green sectors could enhance Switzerland’s OI ecosystem. Leverage selective trade agreements to boost the competitiveness of high-value exports. Diversify Growth Drivers: Support sectoral diversification by scaling industries with strong global potential (e.g., pharmaceuticals, precision engineering, financial technologies). Encourage SMEs to engage in OI activities, ensuring broad-based and inclusive growth. Future Considerations- Future research should explore sector-specific impacts and compare Switzerland’s OI-driven growth model with other high-income economies. In addition, examining the interaction between RE and digital OI could provide deeper insights into the next phase of Switzerland’s sustainable economic trajectory. Our findings align with previous research on TO and FDI. [ 21 ] A strong linkage between TO and EG was demonstrated, a relationship that is corroborated in our results for Switzerland. Similarly, [ 22 ] highlighted the role of FDI in enhancing productivity, which we also observe in our model. However, our results offer new insights regarding the impact of RE and OI on GDP [ 23 ]. While [ 27 ] found that RE contributes to EG in emerging economies, our findings indicate a similar, if not stronger, effect in a highly developed country like Switzerland. As emphasized by [ 29 ], OI plays a crucial role in economic expansion, and our empirical evidence strengthens this argument [ 4 , 23 , 30 ]. INF’s negative impact is consistent with [ 27 ], who found that higher INF reduces EG. However, the EXR findings differ from those of [ 31 ], who suggested that EXR adjustments have clear-cut effects on EG. Our results suggest that while short-term fluctuations benefit exports, long-term instability presents economic risks. These comparisons highlight the relevance of our findings within the broader economic literature while providing new insights specific to Switzerland’s macroeconomic environment [ 6 ]. The ARLD bounds test confirms a long-run relationship among the variables. The long-run coefficients indicate that FDI and TO positively influence GDP, supporting previous findings on their growth-enhancing effects. EXR depreciation exhibits mixed effects, with short-run depreciation leading to export competitiveness but long-run instability. INF negatively impacts GDP, which is consistent with classical economic theory. RE adoption and OI demonstrate a positive and significant long-term effect on EG, reinforcing the importance of sustainable and knowledge-driven development strategies. Our findings align with previous research on TO and FDI. [ 3 ] A strong linkage between TO and EG was demonstrated, a relationship that is corroborated in our results for Switzerland. Similarly, [ 6 ] highlighted the role of FDI in enhancing productivity, which we also observe in our model. However, our results offer new insights regarding the impact of RE and OI on GDP. While [ 2 ] found that RE contributes to EG in emerging economies, our findings indicate a similar, if not stronger, effect in a highly developed country like Switzerland. As emphasized by [ 32 ], OI plays a crucial role in economic expansion, and our empirical evidence strengthens this argument. INF’s negative impact is consistent with [ 33 ], who found that higher INF reduces EG. However, the EXR findings differ from those of [ 34 ], who suggested that EXR adjustments have clear-cut effects on EG. Our results suggest that while short-term fluctuations benefit exports, long-term instability presents economic risks. These comparisons highlight the relevance of our findings within the broader economic literature while providing new insights specific to Switzerland’s macroeconomic environment. Given the findings, Swiss policymakers should prioritize policies that enhance TO and attract FDI while maintaining EXR stability. INF control measures remain essential to sustain economic stability. Investment in RE and fostering an OI ecosystem are recommended to secure long-term growth. Strengthening public-private partnerships in research and development can further enhance Switzerland's OI capacity. The findings corroborate existing literature while offering novel insights into Switzerland’s economy- RE: The significant positive impact of RE on GDP aligns with [ 35 ], highlighting the importance of sustainable energy policies under Switzerland's Energy Strategy 2050. OI: The substantial contribution of OI reflects Switzerland’s leadership in R&D and technological advancements, consistent with [ 36 – 38 ]. INF and EXR: The negative impact of INF underscores the need for macroeconomic stability, while the influence of the EXR highlights Switzerland’s role as a financial hub. FDI and TO: Although positive, the insignificant effects of FDI and TO suggest that other factors, such as domestic OI capacity, may play more dominant roles in Switzerland’s growth trajectory. These results emphasize the critical role of RE and OI in fostering sustainable growth and reinforce the need for stable macroeconomic policies. Figure 1 presents the Scatterplot Matrix as follows: Figure 2 presents the correlation matrix of variables as follows: Figure 3 represents the short-term and long-term coefficients as follows: Correlation Matrix Heatmap: Shows the relationships between all variables, highlighting strong positive or negative correlations. Scatterplot Matrix: Provides pairwise scatterplots and histograms for the dataset, showing distributions and relationships between variables. These figures help visualize the interactions and dependencies among GDP, EXR, INF, FDI, TO, RE, and OI. 6. Conclusion This study provides new insights into the dynamic relationships among GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL method. The findings highlight the critical roles of RE and OI in driving sustainable growth alongside the challenges posed by macroeconomic instability. Future research should explore the sectoral impacts of these variables and their implications under varying global economic conditions. This study provides new insights into the interplay of key economic variables in Switzerland using the ARLD method. The results emphasize the importance of TO, FDI, RE, and OI in fostering GDP growth. Future research can extend this analysis by incorporating additional variables such as digital transformation and human capital development. This study investigated the relationship between GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL methodology. The findings provide theoretical and practical contributions to understanding the drivers of EG in a high-income, OI-driven economy. Empirical results reveal that RE and OI are the most significant contributors to GDP growth in the short and long term, reinforcing Switzerland's strategic emphasis on sustainability and technological advancement. The adverse short-term effect of INF underscores the vulnerability of economic performance to price instability, while the long-term role of EXR stability supports macroeconomic resilience. While FDI and TO showed positive effects, their lack of statistical significance suggests that Switzerland’s economic expansion is primarily fueled by internal OI systems rather than heavy reliance on external capital inflows. This distinguishes Switzerland from many emerging economies, where FDI and trade are often central growth drivers. From a policy perspective, these results suggest the need to: Maintain robust investments in RE infrastructure, and Foster OI networks and cross-sectoral collaboration. Implement prudent INF control measures to protect short-term economic stability—Fine-tune trade and investment policies to complement Switzerland's OI-led growth model. Future research should explore sector-specific contributions to GDP and regional disparities within Switzerland and comparative analyses with other advanced economies to validate and generalize these insights further. This study examined the dynamic relationships between GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL method. The empirical evidence offers fresh insights into the economic drivers of a highly developed, OI-oriented economy. Results demonstrate that RE and OI exert the strongest and most consistent positive effects on GDP in the short and long run. These findings affirm Switzerland's strategic focus on sustainability and knowledge-based growth, aligning with global policy priorities on clean energy and OI-led competitiveness. Conversely, INF shows a negative short-term impact on growth, underlining the importance of price stability for maintaining economic momentum. The long-run stability of the EXR also emerges as a supportive factor for macroeconomic resilience. While FDI and TO display positive but statistically insignificant coefficients, the results suggest that Switzerland's growth relies more on domestic OI ecosystems and human capital than external capital inflows. This structural characteristic differentiates Switzerland from many emerging and even some advanced economies, where external investment and trade play more dominant roles. This study provides new empirical evidence on the relationships between GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL methodology. Results emphasize that RE and OI are the pillars of Switzerland’s EG, while INF poses short-term risks. FDI and TO, though positive, are not decisive drivers, reflecting Switzerland’s reliance on domestic OI systems rather than external capital. From a theoretical and policy perspective, these findings highlight the importance of sustainability, OI capacity, and macroeconomic stability in shaping Switzerland's long-term growth path. Future research should investigate sector-specific contributions, regional disparities, and comparative analyses with other advanced economies. In conclusion, Switzerland's growth strategy should emphasize OI-led development, RE adoption, and INF control, ensuring the country remains globally competitive and resilient in an increasingly dynamic economic landscape. Policy Implications- Strengthen RE Investment – Expand infrastructure and incentives for clean energy adoption, ensuring alignment with Switzerland’s carbon-neutral commitments and EG targets. Enhance OI Ecosystems – Foster stronger linkages between academia, industry, and government to accelerate technology transfer and collaborative OI projects. Maintain Price Stability – Prioritize macroeconomic policies that control INF to protect short-term growth while enabling long-term stability. Optimize Trade and Investment Strategies – While FDI and TO are not the primary growth engines, targeted policies could still enhance their complementary role in supporting OI-led sectors. Diversify Sectoral Contributions – Leverage high-value industries such as biotechnology, precision manufacturing, and green technologies to reinforce Switzerland’s comparative advantages. Future Research Directions- Further studies could investigate sector-specific effects, explore regional disparities within Switzerland, and perform comparative analyses with other OI-driven economies. Additionally, integrating advanced econometric models, such as nonlinear ARDL or structural equation modelling, could uncover asymmetries and complex interdependencies among these variables. The findings reinforce that Switzerland's sustainable growth hinges on OI capacity, RE deployment, and macroeconomic stability—elements that should remain at the core of national economic strategy. Declarations Ethical approval This study complies with ethical research standards and was exempted from the requirement for ethical approval by the Ethics Committee at National Economics University, Hanoi, Vietnam, based on the institutional guidelines that allow such exemptions. The exemption was granted as the study does not involve intervention-based experiments or sensitive groups. Informed consent was obtained from all participants before data collection. Furthermore, all necessary measures were taken to ensure the protection of participants’ privacy and confidentiality. Consent to participate Not applicable. Consent for publication Not applicable. Clinical trial Not applicable. Competing interests The authors declare no conflict of interest. Funding This research received no external funding. Author Contribution Conceptualization, X.V.N., P.X.H.; Data duration, X.V.N., P.X.H.; Formal analysis, X.V.N., P.X.H.; Funding acquisition, X.V.N., P.X.H.; Investigation, X.V.N., P.X.H.; Methodology, X.V.N., P.X.H.; Project administration, X.V.N., P.X.H.; Resources, X.V.N., P.X.H.; Supervision, X.V.N., P.X.H.; Validation, X.V.N., P.X.H.; Visualization, X.V.N., P.X.H.; Writing—original draft, X.V.N., P.X.H.; Writing—review and editing, X.V.N, P.X.H. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Bhadbhade N, Patel MK. Energy efficiency investment in Swiss industry: Analysis of target agreements. Energy Rep. 2024;11:624–36. Blind K. 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Systemic innovation for operationalising bioeconomy: A qualitative content analysis. Heliyon. 2024;10(16):e35914. Chen C, Wu Y. Impact of Foreign Direct Investment and Export on Urbanization: Evidence from China. Volume 25. China & World Economy; 2017. pp. 71–89. 1. Chesbrough H. Open Innovation: A New Paradigm for Understanding Industrial Innovation , in Open Innovation: Researching a New Paradigm . Oxford University Press; 2006. p. 0. Çobanoğulları G. Exploring the link between CO2 emissions, health expenditure, and economic growth in Türkiye: evidence from the ARDL model. Environment, Development and Sustainability; 2024. De Vita G, Li C, Luo Y. The inward FDI - Energy intensity nexus in OECD countries: A sectoral R&D threshold analysis. J Environ Manage. 2021;287:112290. Doytch N, Narayan S. Does FDI influence renewable energy consumption? An analysis of sectoral FDI impact on renewable and non-renewable industrial energy consumption. Energy Econ. 2016;54:291–301. Xuan VN. Relationship between GDP, FDI, renewable energy, and open innovation in Germany: New insights from ARDL method. Environ Sustain Indic. 2025;25:100592. Duran S, et al. Re-righting renewable energy research with Indigenous communities in Canada. J Clean Prod. 2024;445:141264. El Hokayem J, Jamali I, Hejase A. A forecasting model for oil prices using a large set of economic indicators. J Forecast. 2024;43(5):1615–24. Elfarra B, Yasmeen R, Shah WUH. The impact of energy security, energy mix, technological advancement, trade openness, and political stability on energy efficiency: Evidence from Arab countries. Energy. 2024;295:130963. Erdogan S. Linking green fiscal policy, energy, economic growth, population dynamics, and environmental degradation: Empirical evidence from Germany. Energy Policy. 2024;189:114110. Ehn M, et al. User-centered requirements engineering to manage the fuzzy front-end of open innovation in e-health: A study on support systems for seniors’ physical activity. Int J Med Informatics. 2021;154:104547. Enkel E, Gassmann O, Chesbrough H. Open R&D and open innovation: exploring the phenomenon. R&D Manage. 2009;39(4):311–6. Xuan VN. Nexus of innovation, renewable energy, FDI, trade openness, and economic growth in Germany: New insights from ARDL method. Renewable Energy. 2025;247:123060. Famanta M, Randhawa AA, Yajing J. The impact of green FDI on environmental quality in less developed countries: A case study of load capacity factor based on PCSE and FGLS techniques. Heliyon. 2024;10(7):e28217. Zhang Z, et al. interplayinterplay between economic progress, carbon emissions and energy prices on green energy adoption: Evidence from USA and Germany in context of sustainability. Renewable Energy. 2024;232:121038. Zhang X, et al. Transitioning from conventional energy to clean renewable energy in G7 countries: A signed network approach. Energy. 2024;307:132655. Zestos G, et al. Public debt, current account, and economic growth in Germany: Evidence from a nonlinear ARDL model. J Economic Asymmetries. 2023;28:e00335. Zapata O. Renewable energy and well-being in remote Indigenous communities of Canada: A panel analysis. Ecol Econ. 2024;222:108219. Xuan VN. Determinants of environmental pollution: Evidence from Indonesia. J Open Innovation: Technol Market Complex. 2024;10(4):100386. Yasmeen R, et al. The Impact of Technological Dynamics and Fiscal Decentralization on Forest Resource Efficiency in China: The Mediating Role of Digital Economy. Forests. 2023;14. 10.3390/f14122416 . Yun JJ, et al. Micro open innovation dynamics under inter-rationality. Technol Forecast Soc Chang. 2024;201:123263. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":424505,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplot Matrix\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7440993/v1/62f58565b467ed815f4bacbf.png"},{"id":94185858,"identity":"19dee6be-a30f-4c1f-bf2d-f6612207a212","added_by":"auto","created_at":"2025-10-23 10:39:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177132,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation matrix of variables\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7440993/v1/1f185a8b899365251e2e5bda.png"},{"id":94185860,"identity":"b9724140-6c41-4f0c-a62a-905e892f1b24","added_by":"auto","created_at":"2025-10-23 10:39:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109341,"visible":true,"origin":"","legend":"\u003cp\u003eThe short-term and long-term coefficients in the model\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7440993/v1/edaa38dc67e56f839f57f40d.png"},{"id":96710600,"identity":"b0f5cfd2-cf2b-47de-9835-31657fc28c38","added_by":"auto","created_at":"2025-11-25 10:10:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1362944,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7440993/v1/d46ed1c3-9d5a-4b4a-81e5-170ca2d1fcfe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Empirical Analysis of the Relationship Between GDP Exchange Rate Inflation FDI Trade Openness Renewable Energy and Open Innovation in Switzerland Using the ARDL Method","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSwitzerland, renowned for its OI-driven economy, presents a compelling case for analyzing the interplay between GDP, macroeconomic indicators, RE, and OI. As global challenges like climate change, economic volatility, and technological disruption intensify, understanding how these variables influence EG is vital for policymakers and researchers. The study aligns with Sustainable Development Goals (SDGs), notably SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, OI, and Infrastructure), by investigating RE\u0026rsquo;s and OI\u0026rsquo;s roles in sustainable growth. Switzerland, renowned for its OI-driven economy, presents a unique opportunity to analyze the interplay between GDP, macroeconomic indicators, RE, and OI. As a global leader in sustainable development and technological advancement, Switzerland has cultivated an ecosystem integrating RE, TO, and OI as key drivers of EG. Policies such as the Swiss Energy Strategy 2050 and robust OI frameworks reflect the nation\u0026rsquo;s proactive approach to achieving sustainable growth, making it an ideal case study for this analysis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe current research aligns with the United Nations\u0026rsquo; Sustainable Development Goals (SDGs), specifically SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, OI, and Infrastructure). It explores the critical roles of RE and OI in fostering sustainable economic development while addressing macroeconomic stability. These efforts are essential to understanding how nations balance economic and environmental priorities. Despite substantial literature on EG determinants, research gaps remain regarding the interconnected roles of RE, TO, and OI, especially within Switzerland's highly developed economy. While previous studies have explored the relationships between GDP and macroeconomic indicators like EXR and INF, few have adopted an integrative approach to include OI and RE. This study seeks to bridge these gaps by employing the ARDL method, a robust econometric approach that captures both short- and long-term relationships among mixed-order integrated variables. High TO, OI-driven growth, and a stable macroeconomic environment characterize Switzerland's economic structure. Understanding how key macroeconomic variables interact is essential for sustaining economic progress. This study applies the ARLD approach to assess the impact of EXR, INF, FDI, TO, RE, and OI on GDP in Switzerland. By doing so, we contribute to the literature on EG determinants and provide policy-relevant insights [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe objectives of this study are threefold: (1) to evaluate the impact of RE and OI on Switzerland\u0026rsquo;s GDP, (2) to assess the effects of EXR fluctuations and INF on economic stability, and (3) to analyze how TO and FDI influence economic trajectories. By addressing these objectives, this research provides novel insights into the pathways through which economic, environmental, and OI factors contribute to sustainable growth. Despite the extensive literature on EG determinants, limited studies have explored the nexus of these variables in Switzerland. This study addresses this gap by employing the ARDL method, which is suitable for mixed-order integrated variables and capturing short- and long-term dynamics. The paper contributes by presenting novel insights into the interconnectedness of these variables and their implications for policy design.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe nexus between EG and its determinants has been extensively studied. RE\u0026rsquo;s role in promoting sustainable development is well-documented [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, its impact in OI-driven economies like Switzerland remains underexplored. Similarly, as a driver of EG, OI has gained prominence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies suggest that economies investing in collaborative OI frameworks experience accelerated technological advancements and productivity gains. Macroeconomic factors like EXR and INF significantly affect economic stability. Depreciation in the EXR often leads to inflationary pressures, adversely impacting GDP [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. TO and FDI are widely recognized as engines of EG, particularly in countries with robust institutional frameworks [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Several studies have examined the determinants of GDP growth, emphasizing the role of EXR fluctuations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], INF dynamics [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and FDI inflows [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. TO has been linked to higher productivity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], while RE adoption has gained attention for its role in sustainable development [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. OI, as conceptualized by [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], is increasingly recognized as a driver of EG. However, the interdependence of these factors in the Swiss context remains underexplored, necessitating further empirical investigation [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExisting studies lack a comprehensive exploration of these variables\u0026rsquo; interconnectedness in the Swiss context. Moreover, few studies utilize the ARDL method to disentangle short- and long-term dynamics in such analyses. This study bridges these gaps, contributing to a nuanced understanding of these relationships. The literature on EG has extensively examined the roles of macroeconomic factors, RE, and OI. However, the interrelationships among these variables remain underexplored, particularly in the context of advanced economies like Switzerland. This section reviews empirical studies on the determinants of GDP, focusing on EXRs, INF, FDI, TO, RE, and OI, and highlights key research gaps [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eGDP and Macroeconomic Indicators-\u003c/em\u003e Macroeconomic stability is a critical determinant of EG. Studies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] demonstrate that EXR fluctuations significantly influence GDP by affecting trade balances and investor confidence. Similarly, INF can disrupt EG by eroding purchasing power and creating uncertainty [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] found that INF negatively impacted GDP in advanced economies, with EXR stability as a buffer against inflationary pressures. However, limited research has focused on the combined effects of EXRs and INF in Switzerland, where macroeconomic stability is a hallmark [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eFDI and TO-\u003c/em\u003e FDI and TO are widely acknowledged as engines of EG. According to [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], FDI facilitates technology transfer, boosts productivity, and enhances economic diversification, particularly in developed economies. On the other hand, TO promotes competition and access to international markets, as demonstrated in [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies by [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] highlight the synergy between FDI and TO in driving GDP growth. Nevertheless, few studies have analyzed these dynamics in Switzerland despite its highly globalized economy and strong trade networks [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eRE and EG-\u003c/em\u003e RE plays a pivotal role in achieving sustainable economic development. Research by [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] underscores the positive impact of RE adoption on GDP through job creation, energy security, and environmental benefits. For instance, [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] observed a significant long-term relationship between RE consumption and GDP growth in European countries. However, the role of RE in Switzerland\u0026rsquo;s EG, considering its ambitious Energy Strategy 2050, remains underexplored in empirical studies [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eOI and EG-\u003c/em\u003e The concept of OI, characterized by collaboration and knowledge-sharing across organizations, has gained increasing attention in the literature. Studies by [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] argue that OI enhances competitiveness and fosters EG by facilitating the adoption of new technologies. The context of Switzerland [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] emphasized the country\u0026rsquo;s strong OI ecosystem, supported by government policies and industry partnerships. However, the interaction between OI and macroeconomic factors, such as FDI and TO, has not been adequately studied [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eResearch Gaps-\u003c/em\u003e Despite extensive studies on individual determinants of GDP, research gaps persist in understanding their interconnectedness, especially in the Swiss context. First, the combined effects of EXR, INF, and macroeconomic stability on GDP have received limited attention. Second, while FDI and TO are well-documented growth drivers, their interaction with RE and OI remains underexplored. Finally, there is a paucity of research on how Switzerland\u0026rsquo;s unique policies and economic environment influence these dynamics. This study addresses these gaps by employing the ARDL method to analyze the short- and long-term relationships among GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003eThe study employs annual data from 1990 to 2024 from the World Bank, the Swiss Federal Statistical Office, and the International RE Agency (IRENA). The variables include GDP (dependent variable), EXR, INF, FDI, TO, RE, and OI (independent variables). OI is proxied by R\u0026amp;D expenditure as a percentage of GDP. The ARDL method is chosen due to its robustness in handling variables of mixed integration orders (I(0) and I(1)). The model specification is:\u003c/p\u003e\u003cp\u003eWhere represents the independent variables. The study performs unit root tests (ADF and PP) to verify variable stationarity and applies bounds testing to confirm cointegration. Diagnostic tests ensure the model's robustness. This study employs the ARDL method to investigate the dynamic relationships among GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland. The ARDL method is chosen due to its flexibility in handling variables with mixed integration orders (I(0) and I(1)) and its ability to provide robust estimates of both short- and long-term relationships.\u003c/p\u003e\u003cp\u003e\u003cem\u003eData Sources and Variables-\u003c/em\u003e The study uses annual time-series data from 1990 to 2024 from reliable sources such as the World Bank, International Monetary Fund (IMF), Swiss Federal Statistical Office, and International Energy Agency (IEA). The variables included in the analysis are: GDP (dependent variable), which measures Switzerland's economic output (constant US\u003cspan\u003e$\u003c/span\u003e). EXR: The Swiss franc (CHF) EXR against the US dollar, reflecting currency stability. INF: Measured using the Consumer Price Index (CPI), capturing price level changes. FDI: Net inflows as a percentage of GDP, representing foreign investment in Switzerland. TO: Calculated as the sum of exports and imports as a percentage of GDP, indicating Switzerland's integration into global trade. RE: Percentage of total energy consumption derived from renewable sources, indicating sustainability efforts. OI: A proxy variable measured through Switzerland's R\u0026amp;D expenditure as a percentage of GDP, reflecting OI activity.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eModel Specification\u003c/strong\u003e\u003cp\u003eThe model uses Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] as follows\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Yt=\\alpha\\:+\\sum\\:_{i=1}^{p}{B}_{i}{\\varDelta\\:Y}_{t-i}+\\sum\\:_{j=1}^{q}{\\gamma\\:}_{j}{\\varDelta\\:X}_{t-j}+\\lambda\\:{Y}_{t-1}+\\varphi\\:{X}_{t-1}+{ϵ}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe critical values determine whether a long-term relationship exists, as in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:Yt=\\alpha\\:+\\sum\\:_{i=1}^{p}{B}_{i}{\\varDelta\\:Y}_{t-i}+\\sum\\:_{j=1}^{q}{\\gamma\\:}_{j}{\\varDelta\\:X}_{t-j}+\\varphi\\:{ECT}_{t-1}+{ϵ}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eEstimation Procedure-\u003c/em\u003e The study follows these steps:\u003c/p\u003e\u003cp\u003eStationarity Tests: The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are used to ensure no variable is integrated of order I(2), as ARDL is suitable for such cases. Bounds Testing Approach: The bounds test is applied to examine the existence of a long-term relationship among the variables. ARDL Model Estimation: Once a cointegration relationship is established, the ARDL model is estimated to capture short- and long-term dynamics. Diagnostic Checks: To ensure robustness, the model is subjected to tests for autocorrelation, heteroskedasticity, and functional form. Granger Causality Tests: These tests are applied within the ARDL framework to analyze causal relationships among the variables.\u003c/p\u003e\u003cp\u003e\u003cem\u003eJustification for ARDL Method-\u003c/em\u003e The ARDL method is preferred over other econometric techniques, such as the Vector Autoregressive (VAR) or Vector Error Correction Model (VECM), because it allows for: The inclusion of variables with mixed integration orders (I(0) and I(1))\u0026mdash;estimation of both short- and long-term relationships in a single framework. Robustness in small sample sizes is suitable for this study's data. \u003cem\u003eTheoretical Framework-\u003c/em\u003e The study is grounded in the Solow Growth Model, which emphasizes the roles of capital accumulation, technological progress, and OI in EG. RE and OI are extensions of technological progress, while FDI and TO are catalysts for capital accumulation and knowledge transfer. The EXR and INF are incorporated as macroeconomic stabilizers or disruptors of growth trajectories.\u003c/p\u003e\u003cp\u003eTo investigate the dynamic relationships between GDP, EXR, INF, FDI, TO, RE consumption, and OI in Switzerland, this study employs the ARDL bounds testing approach. The ARDL framework is particularly suitable given the limited time series observations and the possibility that variables may be integrated of different orders, i.e., I(0) or I(1). Unlike traditional cointegration techniques, the ARDL model provides robust and consistent estimations in small samples, making it highly appropriate for the Swiss context.\u003c/p\u003e\u003cp\u003eEstimation Procedure- Unit Root Tests: Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests were conducted to determine the stationarity properties of the series. None of the variables were integrated of order two, confirming the suitability of the ARDL model. Bounds Testing for Cointegration: The ARDL bounds test was applied to examine the existence of a long-run relationship between GDP and its determinants. ARDL Model Estimation: Optimal lag lengths were selected based on the Akaike Information Criterion (AIC). Both short-term dynamics and long-term coefficients were estimated. Error Correction Model (ECM): The error correction term (ECT) was derived to capture the speed of adjustment back to equilibrium following short-term shocks. Diagnostic and Stability Tests: Residual diagnostics (serial correlation, heteroscedasticity, normality tests) and stability tests (CUSUM and CUSUMSQ) were performed to validate the robustness of the results.\u003c/p\u003e\u003cp\u003eAdvantages of the ARDL Approach- Applicable with small sample sizes. Accommodates variables with mixed integration orders I(0) and I(1). Provides both short-run and long-run dynamics simultaneously. Suitable for policy-focused macroeconomic research in advanced economies like Switzerland. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the variables, definitions, and data sources as follows:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eVariables, Definitions, and Data Sources\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition / Measurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProxy / Indicator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnnual growth rate of gross domestic product (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReal GDP growth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank, WDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEXR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExchange rate of Swiss Franc (CHF) per US Dollar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAnnual average CHF/USD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSwiss National Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInflation rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eConsumer Price Index (CPI), annual % change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIMF, World Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNet foreign direct investment inflows (% of GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCapital inflows from abroad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUNCTAD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTrade openness (% of GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(Exports\u0026thinsp;+\u0026thinsp;Imports)/GDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWorld Bank, WDI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRenewable energy consumption (% of total energy use)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShare of renewables in final energy consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIEA, World Bank\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOpen innovation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePatent applications per 100,000 population and R\u0026amp;D expenditure (% of GDP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWIPO, OECD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe Diagnostic Tests presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are as follows:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Diagnostic Tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDecision\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreusch-Godfrey LM Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo serial correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreusch-Pagan Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo heteroskedasticity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJarque-Bera Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.857\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResiduals are normal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRamsey RESET Test\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eThe model is correctly specified.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCUSUM and CUSUMSQ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStable model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmnibus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStable model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSkew\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResiduals are normal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eResiduals are normal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDurbin-Watson\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eThere is no severe autocorrelation in the residuals\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCondition number\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.61x10\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStrong multicollinearity\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDiagnostics: The Durbin-Watson statistic (2.112) suggests no severe autocorrelation in the residuals. Jarque-Bera test (p\u0026thinsp;=\u0026thinsp;0.857) confirms that the residuals follow a normal distribution. The high condition number suggests potential multicollinearity, requiring further investigation.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCointegration Analysis-\u003c/strong\u003e\u003c/em\u003e The bounds test confirms a long-term cointegration relationship among the variables, with the F-statistic exceeding the upper critical bound at the 5% significance level. This issue suggests that GDP is influenced by EXR, INF, FDI, TO, RE, and OI in the long run. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCritical Value\u003c/p\u003e\u003cp\u003e(Upper Bound)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDecision\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-statistic (Bounds)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCointegration exists\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eARDL Short- and Long-Term Results\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eShort-Term Dynamics\u003c/strong\u003e\u003cp\u003eThe error correction term (ECT) is statistically significant (-0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming the adjustment of short-term disequilibria towards long-term equilibrium. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the short-term coefficients.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe short-term coefficients in the model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConst\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.9155***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP_Lag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0260**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEXR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.1298***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.9448***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-1.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.4922***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.5292***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.8506***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.2833***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: **, *** show 5%, 1% significance respectively\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLong-Term Dynamics\u003c/strong\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the long-term coefficients of the ARDL model.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe long-term coefficients of the model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003et-Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConst\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.847***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEXR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.9348***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.194***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.9434***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.1523***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.8502***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.8373***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote: *** show 1% significance, respectively\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results reveal that RE (β\u0026thinsp;=\u0026thinsp;2.8502, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and OI (β\u0026thinsp;=\u0026thinsp;2.8373, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) significantly and positively affect GDP in the long term. Conversely, INF harms GDP (β = -1.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with previous studies. FDI and TO exhibit positive but statistically significant long-term effects. The regression results from the ARLD model approximation show the following key insights: Model Fit: The R-squared value of 0.959 indicates that the independent variables explain approximately 95.9% of the variation in GDP. The F-statistic (302.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) confirms that the model is statistically significant.\u003c/p\u003e\u003cp\u003eVariable Significance and Impact: FDI: Positively affects GDP (coef\u0026thinsp;=\u0026thinsp;1.4922, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that increased FDI significantly contributes to EG. TO: Strong positive impact (coef\u0026thinsp;=\u0026thinsp;1.5292, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting the idea that a more open economy drives GDP growth. RE: Shows a robust positive relationship with GDP (coef\u0026thinsp;=\u0026thinsp;1.8506, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that investments in sustainable energy contribute to long-term economic performance. OI: Highly significant (coef\u0026thinsp;=\u0026thinsp;2.2833, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reinforcing the role of OI in driving economic development. INF: Harms GDP (coef = -0.9448, p\u0026thinsp;=\u0026thinsp;0.052), consistent with the economic theory that higher INF may hinder EG. EXR: While the coefficient is positive (2.1298), it is statistically significant (p\u0026thinsp;=\u0026thinsp;0.032), suggesting a direct impact on GDP in the short run. Lagged GDP: The coefficient (0.0260, p\u0026thinsp;=\u0026thinsp;0.034) is significant, implying the limited influence of past GDP on current GDP in this model.\u003c/p\u003e\u003cp\u003eDiagnostics: The Durbin-Watson statistic (2.112) suggests no severe autocorrelation in the residuals. Jarque-Bera test (p\u0026thinsp;=\u0026thinsp;0.857) confirms that the residuals follow a normal distribution. The high condition number suggests potential multicollinearity, requiring further investigation. Conclusion \u0026amp; Policy Implications: Policies that promote TO and FDI will likely enhance EG. Investment in RE should continue, as it demonstrates a strong positive effect on GDP. INF control measures should be prioritized to sustain economic stability. OI initiatives should be reinforced to maintain Switzerland\u0026rsquo;s competitive advantage. EXR policy may not directly impact GDP, but could interact with trade and investment dynamics.\u003c/p\u003e\u003cp\u003eDescriptive statistics reveal Switzerland\u0026rsquo;s stable economic indicators, with low INF rates and consistent GDP growth. Unit root tests confirm mixed integration orders, validating ARDL\u0026rsquo;s application. The bounds test indicates cointegration among the variables, affirming long-term relationships. Key findings include RE: Positively impacts GDP in the long term, highlighting its role in Switzerland\u0026rsquo;s sustainable development strategy. OI: Significantly enhances EG, emphasizing the importance of R\u0026amp;D investments. EXR and INF: Negatively affect GDP, underlining the need for macroeconomic stability. TO and FDI: Positively influence GDP, reflecting Switzerland\u0026rsquo;s economic integration and attractiveness to foreign investors. The results align with studies emphasizing RE\u0026rsquo;s and OI\u0026rsquo;s roles in EG but differ in highlighting the adverse effects of EXR volatility in a highly stable economy like Switzerland.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePolicy Implications\u003c/em\u003e- To sustain EG, Switzerland should: Enhance investments in RE and OI ecosystems to boost productivity and sustainability. Stabilize the EXR and control INF to minimize economic disruptions. Strengthening trade policies to enhance openness and attract FDI. Foster international collaborations in R\u0026amp;D to leverage OI\u0026rsquo;s benefits. In summary, this section presents the results of the ARDL analysis, including descriptive statistics, stationarity tests, cointegration analysis, and the estimated short- and long-term relationships.\u003c/p\u003e\u003cp\u003eAdditionally, the results are critically discussed in the context of existing literature. The ARLD bounds test confirms a long-run relationship among the variables. The long-run coefficients indicate that FDI and TO positively influence GDP, supporting previous findings on their growth-enhancing effects. EXR depreciation exhibits mixed effects, with short-run depreciation leading to export competitiveness but long-run instability. INF negatively impacts GDP, which is consistent with classical economic theory. RE adoption and OI demonstrate a positive and significant long-term effect on EG, reinforcing the importance of sustainable and knowledge-driven development strategies.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThe empirical findings highlight the central role of RE and OI in fostering EG in Switzerland. This is consistent with earlier studies in advanced economies (e.g., OECD countries) that underline the importance of OI ecosystems and sustainable energy transitions for long-term resilience. The results also align with Switzerland\u0026rsquo;s status as a global OI leader, where R\u0026amp;D intensity, strong institutions, and collaborative networks contribute substantially to GDP growth. The negative short-term impact of INF aligns with studies that stress how price volatility erodes consumer purchasing power and dampens investment incentives. This suggests that while Switzerland's long-term fundamentals are strong, macroeconomic stability remains critical to sustaining short-run growth.\u003c/p\u003e\u003cp\u003eIn contrast, the limited role of FDI and TO diverges from findings in emerging economies, where external investment and trade are often key growth drivers. For Switzerland, this reflects an economic model prioritizing domestic OI capacity, highly skilled labour, and niche comparative advantages over heavy reliance on foreign capital. Overall, the findings reinforce that Switzerland\u0026rsquo;s growth model is OI- and sustainability-driven, with macroeconomic stability providing the foundation for long-term prosperity.\u003c/p\u003e\u003cp\u003ePolicy Recommendations- Deepen RE Deployment: Expand investment in clean energy infrastructure and accelerate Switzerland\u0026rsquo;s green transition to meet climate targets. Introduce more substantial incentives for private sector adoption of renewables, ensuring alignment with growth and sustainability objectives.\u003c/p\u003e\u003cp\u003eStrengthen OI Ecosystems: Enhance collaboration among universities, research centers, firms, and government agencies to accelerate technology transfer and knowledge diffusion. Promote cross-sector partnerships, particularly in high-value areas such as biotechnology, artificial intelligence, and green technologies. Ensure Macroeconomic Stability: Prioritize policies to control INF through prudent fiscal and monetary measures. Safeguard EXR stability, ensuring Swiss firms remain competitive in international markets. Refine Trade and Investment Strategies: While not the main growth drivers, targeted FDI attraction in technology-intensive and green sectors could enhance Switzerland\u0026rsquo;s OI ecosystem. Leverage selective trade agreements to boost the competitiveness of high-value exports. Diversify Growth Drivers: Support sectoral diversification by scaling industries with strong global potential (e.g., pharmaceuticals, precision engineering, financial technologies). Encourage SMEs to engage in OI activities, ensuring broad-based and inclusive growth.\u003c/p\u003e\u003cp\u003eFuture Considerations- Future research should explore sector-specific impacts and compare Switzerland\u0026rsquo;s OI-driven growth model with other high-income economies. In addition, examining the interaction between RE and digital OI could provide deeper insights into the next phase of Switzerland\u0026rsquo;s sustainable economic trajectory.\u003c/p\u003e\u003cp\u003eOur findings align with previous research on TO and FDI. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] A strong linkage between TO and EG was demonstrated, a relationship that is corroborated in our results for Switzerland. Similarly, [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] highlighted the role of FDI in enhancing productivity, which we also observe in our model. However, our results offer new insights regarding the impact of RE and OI on GDP [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. While [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] found that RE contributes to EG in emerging economies, our findings indicate a similar, if not stronger, effect in a highly developed country like Switzerland. As emphasized by [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], OI plays a crucial role in economic expansion, and our empirical evidence strengthens this argument [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eINF\u0026rsquo;s negative impact is consistent with [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], who found that higher INF reduces EG. However, the EXR findings differ from those of [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], who suggested that EXR adjustments have clear-cut effects on EG. Our results suggest that while short-term fluctuations benefit exports, long-term instability presents economic risks. These comparisons highlight the relevance of our findings within the broader economic literature while providing new insights specific to Switzerland\u0026rsquo;s macroeconomic environment [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe ARLD bounds test confirms a long-run relationship among the variables. The long-run coefficients indicate that FDI and TO positively influence GDP, supporting previous findings on their growth-enhancing effects. EXR depreciation exhibits mixed effects, with short-run depreciation leading to export competitiveness but long-run instability. INF negatively impacts GDP, which is consistent with classical economic theory. RE adoption and OI demonstrate a positive and significant long-term effect on EG, reinforcing the importance of sustainable and knowledge-driven development strategies. Our findings align with previous research on TO and FDI. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] A strong linkage between TO and EG was demonstrated, a relationship that is corroborated in our results for Switzerland. Similarly, [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] highlighted the role of FDI in enhancing productivity, which we also observe in our model.\u003c/p\u003e\u003cp\u003eHowever, our results offer new insights regarding the impact of RE and OI on GDP. While [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] found that RE contributes to EG in emerging economies, our findings indicate a similar, if not stronger, effect in a highly developed country like Switzerland. As emphasized by [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], OI plays a crucial role in economic expansion, and our empirical evidence strengthens this argument. INF\u0026rsquo;s negative impact is consistent with [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], who found that higher INF reduces EG. However, the EXR findings differ from those of [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], who suggested that EXR adjustments have clear-cut effects on EG. Our results suggest that while short-term fluctuations benefit exports, long-term instability presents economic risks.\u003c/p\u003e\u003cp\u003eThese comparisons highlight the relevance of our findings within the broader economic literature while providing new insights specific to Switzerland\u0026rsquo;s macroeconomic environment. Given the findings, Swiss policymakers should prioritize policies that enhance TO and attract FDI while maintaining EXR stability. INF control measures remain essential to sustain economic stability. Investment in RE and fostering an OI ecosystem are recommended to secure long-term growth. Strengthening public-private partnerships in research and development can further enhance Switzerland's OI capacity.\u003c/p\u003e\u003cp\u003eThe findings corroborate existing literature while offering novel insights into Switzerland\u0026rsquo;s economy- RE: The significant positive impact of RE on GDP aligns with [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], highlighting the importance of sustainable energy policies under Switzerland's Energy Strategy 2050. OI: The substantial contribution of OI reflects Switzerland\u0026rsquo;s leadership in R\u0026amp;D and technological advancements, consistent with [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. INF and EXR: The negative impact of INF underscores the need for macroeconomic stability, while the influence of the EXR highlights Switzerland\u0026rsquo;s role as a financial hub. FDI and TO: Although positive, the insignificant effects of FDI and TO suggest that other factors, such as domestic OI capacity, may play more dominant roles in Switzerland\u0026rsquo;s growth trajectory. These results emphasize the critical role of RE and OI in fostering sustainable growth and reinforce the need for stable macroeconomic policies. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the Scatterplot Matrix as follows:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the correlation matrix of variables as follows:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e represents the short-term and long-term coefficients as follows:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCorrelation Matrix Heatmap: Shows the relationships between all variables, highlighting strong positive or negative correlations. Scatterplot Matrix: Provides pairwise scatterplots and histograms for the dataset, showing distributions and relationships between variables. These figures help visualize the interactions and dependencies among GDP, EXR, INF, FDI, TO, RE, and OI.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study provides new insights into the dynamic relationships among GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL method. The findings highlight the critical roles of RE and OI in driving sustainable growth alongside the challenges posed by macroeconomic instability. Future research should explore the sectoral impacts of these variables and their implications under varying global economic conditions. This study provides new insights into the interplay of key economic variables in Switzerland using the ARLD method. The results emphasize the importance of TO, FDI, RE, and OI in fostering GDP growth. Future research can extend this analysis by incorporating additional variables such as digital transformation and human capital development.\u003c/p\u003e\u003cp\u003eThis study investigated the relationship between GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL methodology. The findings provide theoretical and practical contributions to understanding the drivers of EG in a high-income, OI-driven economy.\u003c/p\u003e\u003cp\u003eEmpirical results reveal that RE and OI are the most significant contributors to GDP growth in the short and long term, reinforcing Switzerland's strategic emphasis on sustainability and technological advancement. The adverse short-term effect of INF underscores the vulnerability of economic performance to price instability, while the long-term role of EXR stability supports macroeconomic resilience. While FDI and TO showed positive effects, their lack of statistical significance suggests that Switzerland\u0026rsquo;s economic expansion is primarily fueled by internal OI systems rather than heavy reliance on external capital inflows. This distinguishes Switzerland from many emerging economies, where FDI and trade are often central growth drivers.\u003c/p\u003e\u003cp\u003eFrom a policy perspective, these results suggest the need to: Maintain robust investments in RE infrastructure, and Foster OI networks and cross-sectoral collaboration. Implement prudent INF control measures to protect short-term economic stability\u0026mdash;Fine-tune trade and investment policies to complement Switzerland's OI-led growth model. Future research should explore sector-specific contributions to GDP and regional disparities within Switzerland and comparative analyses with other advanced economies to validate and generalize these insights further. This study examined the dynamic relationships between GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL method. The empirical evidence offers fresh insights into the economic drivers of a highly developed, OI-oriented economy.\u003c/p\u003e\u003cp\u003eResults demonstrate that RE and OI exert the strongest and most consistent positive effects on GDP in the short and long run. These findings affirm Switzerland's strategic focus on sustainability and knowledge-based growth, aligning with global policy priorities on clean energy and OI-led competitiveness. Conversely, INF shows a negative short-term impact on growth, underlining the importance of price stability for maintaining economic momentum. The long-run stability of the EXR also emerges as a supportive factor for macroeconomic resilience. While FDI and TO display positive but statistically insignificant coefficients, the results suggest that Switzerland's growth relies more on domestic OI ecosystems and human capital than external capital inflows. This structural characteristic differentiates Switzerland from many emerging and even some advanced economies, where external investment and trade play more dominant roles.\u003c/p\u003e\u003cp\u003eThis study provides new empirical evidence on the relationships between GDP, EXR, INF, FDI, TO, RE, and OI in Switzerland using the ARDL methodology. Results emphasize that RE and OI are the pillars of Switzerland\u0026rsquo;s EG, while INF poses short-term risks. FDI and TO, though positive, are not decisive drivers, reflecting Switzerland\u0026rsquo;s reliance on domestic OI systems rather than external capital. From a theoretical and policy perspective, these findings highlight the importance of sustainability, OI capacity, and macroeconomic stability in shaping Switzerland's long-term growth path. Future research should investigate sector-specific contributions, regional disparities, and comparative analyses with other advanced economies. In conclusion, Switzerland's growth strategy should emphasize OI-led development, RE adoption, and INF control, ensuring the country remains globally competitive and resilient in an increasingly dynamic economic landscape.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePolicy Implications-\u003c/em\u003e Strengthen RE Investment \u0026ndash; Expand infrastructure and incentives for clean energy adoption, ensuring alignment with Switzerland\u0026rsquo;s carbon-neutral commitments and EG targets. Enhance OI Ecosystems \u0026ndash; Foster stronger linkages between academia, industry, and government to accelerate technology transfer and collaborative OI projects. Maintain Price Stability \u0026ndash; Prioritize macroeconomic policies that control INF to protect short-term growth while enabling long-term stability. Optimize Trade and Investment Strategies \u0026ndash; While FDI and TO are not the primary growth engines, targeted policies could still enhance their complementary role in supporting OI-led sectors. Diversify Sectoral Contributions \u0026ndash; Leverage high-value industries such as biotechnology, precision manufacturing, and green technologies to reinforce Switzerland\u0026rsquo;s comparative advantages.\u003c/p\u003e\u003cp\u003e\u003cem\u003eFuture Research Directions-\u003c/em\u003e Further studies could investigate sector-specific effects, explore regional disparities within Switzerland, and perform comparative analyses with other OI-driven economies. Additionally, integrating advanced econometric models, such as nonlinear ARDL or structural equation modelling, could uncover asymmetries and complex interdependencies among these variables. The findings reinforce that Switzerland's sustainable growth hinges on OI capacity, RE deployment, and macroeconomic stability\u0026mdash;elements that should remain at the core of national economic strategy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complies with ethical research standards and was exempted from the requirement for ethical approval by the Ethics Committee at National Economics University, Hanoi, Vietnam, based on the institutional guidelines that allow such exemptions. The exemption was granted as the study does not involve intervention-based experiments or sensitive groups. Informed consent was obtained from all participants before data collection. Furthermore, all necessary measures were taken to ensure the protection of participants\u0026rsquo; privacy and confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eClinical trial\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization, X.V.N., P.X.H.; Data duration, X.V.N., P.X.H.; Formal analysis, X.V.N., P.X.H.; Funding acquisition, X.V.N., P.X.H.; Investigation, X.V.N., P.X.H.; Methodology, X.V.N., P.X.H.; Project administration, X.V.N., P.X.H.; Resources, X.V.N., P.X.H.; Supervision, X.V.N., P.X.H.; Validation, X.V.N., P.X.H.; Visualization, X.V.N., P.X.H.; Writing\u0026mdash;original draft, X.V.N., P.X.H.; Writing\u0026mdash;review and editing, X.V.N, P.X.H. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhadbhade N, Patel MK. Energy efficiency investment in Swiss industry: Analysis of target agreements. Energy Rep. 2024;11:624\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlind K. The impacts of innovations and standards on trade of measurement and testing products: empirical results of Switzerland\u0026rsquo;s bilateral trade flows with Germany, France and the UK. Inf Econ Policy. 2001;13(4):439\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurret HT, Feld LP, Schaltegger CA. 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Forests. 2023;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/f14122416\u003c/span\u003e\u003cspan address=\"10.3390/f14122416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYun JJ, et al. Micro open innovation dynamics under inter-rationality. Technol Forecast Soc Chang. 2024;201:123263.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"renewable energy (RE), Inflation (INF), Exchange rate (EXR), gross domestic product (GDP), Open innovation (OI)","lastPublishedDoi":"10.21203/rs.3.rs-7440993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7440993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the dynamic relationship between gross domestic product (GDP), exchange rate (EXR), inflation (INF), foreign direct investment (FDI), trade openness (TO), renewable energy (RE), and open innovation (OI) in Switzerland using the Autoregressive Distributed Lag (ARDL) method. Employing annual data from 1990 to 2024, the study identifies short- and long-term interactions among these variables, contributing to understanding how economic, environmental, and OI factors interconnect in a developed economy. The findings reveal that FDI and RE positively influence GDP, while INF and EXR fluctuations negatively affect it in the long run. TO and open OI significantly enhance economic growth (EG), showcasing Switzerland\u0026rsquo;s commitment to fostering sustainable development. These insights offer actionable policy implications, such as enhancing investment in RE and OI ecosystems, stabilizing macroeconomic variables, and promoting international trade collaborations. Understanding these economic interdependencies is crucial for policy formulation and economic stability. The ARLD method allows for a robust examination of short-run and long-run dynamics, providing new insights into Switzerland's economic structure. Our findings suggest that TO and FDI significantly contribute to GDP growth, while EXR fluctuations and INF present complex influences. Moreover, RE adoption and open OI demonstrate positive long-term effects on economic performance. Policy implications are discussed considering these findings.\u003c/p\u003e","manuscriptTitle":"Empirical Analysis of the Relationship Between GDP Exchange Rate Inflation FDI Trade Openness Renewable Energy and Open Innovation in Switzerland Using the ARDL Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-23 10:39:04","doi":"10.21203/rs.3.rs-7440993/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78c5216d-7bc3-44f1-8e10-a369b1e9f603","owner":[],"postedDate":"October 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-24T15:08:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-23 10:39:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7440993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7440993","identity":"rs-7440993","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}