{"paper_id":"0a35ce87-4e02-4cdc-8001-982e8d89ea19","body_text":"Impact of Gasoline and Renewable Energy Consumption on Manufacturing Sector Output in Nigeria: New Evidence From ARDL Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Gasoline and Renewable Energy Consumption on Manufacturing Sector Output in Nigeria: New Evidence From ARDL Model UCHECHUKWU EZE, MBA COLLINS CHIDUMEBI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4205989/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract This research examined how gasoline and renewable energy use affects output in Nigeria’s manufacturing sector from 1990 to 2022, using data from the Central Bank of Nigeria’s Statistical Bulletin and the World Development Indicators. It employed the Auto-Regressive Distributed Lag (ARDL) model for its analysis. The Bounds co-integration test revealed the presence of a long-term relationship among the variables selected for the study. The findings reveal that in the short term, gasoline consumption marginally boosts manufacturing output, but it significantly hampers it over the long term. On the other hand, renewable energy’s influence on manufacturing output is negligible yet positive in both the short and long term. The study suggests that by shifting focus from gasoline to renewable energy sources, Nigeria can enhance the resilience, sustainability, and innovation of its manufacturing sector, thus aligning with international environmental objectives and boosting economic growth. Gasoline Consumption. Renewable Energy ARDL Model Manufacturing Sector Output 1. Introduction The manufacturing sector is a pivotal driver of economic development in Nigeria (Oni et al., 2023). However, it faces significant challenges, including an overreliance on traditional fossil fuel sources like gasoline and an urgent need to transition towards more sustainable energy alternatives (Asaleye et al., 2021 ; Daniel, 2015; Elias et al., 2017 ). Despite being one of Africa’s top oil-producing nations, Nigeria’s continued dependence on fossil fuels exposes the economy to risks stemming from volatile global oil prices and growing environmental concerns over emissions and climate change (Skordoulis et al., 2018 ). Nigeria possesses substantial untapped potential for renewable energy sources such as solar, wind, biomass, and hydropower (Marinaş et al., 2018 ; Neitzel, 2017 ). Harnessing these clean energy resources could help address the manufacturing sector’s extensive energy requirements in a more environmentally sustainable manner. In recognition of this, the Nigerian government has implemented policies like the 2015 Nigerian Renewable Energy and Energy Efficiency Policy (NREEEP) which sets ambitious targets to increase the renewable energy mix, aiming for 20% of the nation’s electricity supply to come from renewables by 2030. In 2016, Nigeria took a significant step towards realizing its renewable energy goals through the development of the National Renewable Energy Action Plans (NREAP) by various ministries, departments, agencies, and representatives from all 36 states. The NREAP outlines specific commitments to incentivize large-scale adoption of renewable energies across key sectors, including manufacturing. Notably, the plan mandates that 16% of the manufacturing sector’s total energy consumption should be derived from renewable sources by 2030, with contributions from small hydropower (7.07%), solar (5.9%), biomass (2.78%), and wind (0.25%) (Global Legal Insights, 2018 ). Nigeria’s abundant sunlight and strong wind resources provide a fertile ground for the development of solar and wind energy projects to meet the manufacturing sector’s growing energy demands (Zahra, 2017 ). Embracing renewable energy sources in manufacturing offers a dual benefit – mitigating the environmental impact of fossil fuels while simultaneously enhancing the nation’s energy security by diversifying its energy mix away from an over-reliance on a single source. While a growing body of literature exists on energy consumption and its impacts on economic sectors globally, the specific nuances of the Nigerian manufacturing landscape necessitate focused attention. Existing studies tend to emphasize either the broader energy landscape or concentrate on specific industries, with limited comprehensive analyses examining the intricate interplay between gasoline consumption, renewable energy adoption, and the overall performance of Nigeria’s manufacturing sector. Despite the introduction of renewable energy policies, Nigeria still lags in effectively utilizing its abundant renewable resources. Increasing the uptake of renewable energy can catalyze the nation’s economic growth while drastically reducing the consumption of finite fossil fuels like gasoline, thereby improving the quality of life (Zahra, 2017 ). With projections indicating Nigeria’s oil reserves may be depleted in approximately 52 years at the current rate of extraction (Chineme, 2018 ), the imperative to transition the manufacturing sector towards renewable energy sources is crucial for achieving sustainable industrial growth aligned with global climate goals (Jacob, 2017). Understanding the impacts of gasoline consumption versus renewable energy adoption on the performance of Nigeria’s manufacturing sector can provide invaluable insights to guide strategies aimed at boosting productivity, enhancing competitiveness, and promoting environmental responsibility (van Vliet, 2012 ; Shaaban & Petinrin, 2014; NNPC, 2018; RECP, 2018; ECN, 2003). As Nigeria navigates this energy transition, research illuminating these dynamics can inform policymaking and industrial practices, ensuring the nation’s manufacturing prowess continues to thrive while prioritizing sustainability. The subsequent sections of this research work are structured as follows: The next part offers a concise review of relevant existing literature related to the topic under investigation. Following that, the third section delineates the methodological approaches and techniques employed to carry out the study. The fourth section then presents a comprehensive analysis and interpretation of the key findings obtained through the research. Finally, the fifth and concluding section summarizes the overarching conclusions drawn from the study, while also providing recommendations based on the results and analysis. 2. Literature Review 2.1 Theoretical Literature This section considers some of the most recent macroeconomic models that support gasoline consumption, renewable energy consumption, and the manufacturing sector, Gasoline Consumption Theories The theory of environmental psychology is a multidisciplinary approach that brings together insights from psychology, sociology, and environmental sciences to understand the psychological factors that influence individual behaviors and attitudes toward gasoline consumption (Green & Black, 2018). Scholars argue that consumer perceptions, awareness of environmental issues like climate change, and concerns about the ecological impact of fossil fuels can significantly shape choices related to transportation modes and fuel consumption patterns. The theory posits that psychological variables such as attitudes towards eco-friendly vehicles, environmental consciousness, and personal values play a pivotal role in decisions to adopt fuel-efficient technologies or alternative modes of transportation that reduce gasoline usage (Jones et al, 2020). By examining the intricate interplay between individuals and their physical surroundings, environmental psychology offers a comprehensive framework for analyzing the cognitive and emotional drivers behind gasoline consumption behaviors (Gifford, 2014; Steg & Vlek, 2009). The technological innovation and adaptation theoretical perspective focuses on the role of technological advancements and their diffusion in influencing gasoline consumption patterns. It considers how the adoption of innovative fuel-efficient technologies, hybrid vehicles, and electric cars can lead to a reduction in overall gasoline demand (Williams, 2016). The theory explores factors such as the rate at which new transportation technologies are adopted by consumers, the impact of government policies and incentives on the uptake of fuel-efficient alternatives, and the barriers or facilitators that affect consumer acceptance of these innovations (Department of Energy, 2019). Drawing from the seminal work of Everett Rogers on the diffusion of innovations, this theory provides a framework for understanding how new ideas and technologies spread through social systems, thereby shaping gasoline consumption behaviors over time (Rogers, 1962). Renewable Energy Consumption Theories The growth models with natural resources and technical change integrate the consideration of natural resources and technological progress into economic growth frameworks. They explore how advancements in technology and technical change can potentially offset the limitations imposed by finite natural resources, including non-renewable energy sources like fossil fuels (Aghion & Howitt, 1998). By incorporating technological progress as a driving force, these models suggest that even when the elasticity of substitution between capital and finite resources is less than one, sustained economic growth can still be achieved through improvements in total factor productivity. However, specific conditions and assumptions need to be met for technological change to render sustainability technically feasible in the long run. These models provide insights into the interplay between resource constraints, technological advancements, and economic growth trajectories. This socioeconomic theory of renewable energy adoption explores the socioeconomic factors that influence the acceptance and utilization of renewable energy technologies across different communities and societies. It examines variables such as income levels, educational attainment, and societal attitudes towards the environment and sustainable practices. The theory posits that higher income levels and better access to education can facilitate the adoption of renewable energy solutions by increasing affordability and fostering greater awareness and technical understanding (Brown & Sovacool, 2011). Additionally, it explores how societal norms, cultural values, and collective attitudes toward energy use and environmental concerns shape the willingness of communities to embrace renewable energy alternatives. By acknowledging and addressing these socioeconomic complexities, policymakers and advocates can design more inclusive and effective strategies to promote the widespread adoption of renewable energy technologies. Manufacturing Sector Theories The lean manufacturing theory is a comprehensive theoretical framework that emerged from the Toyota Production System, emphasizing the systematic elimination of waste and the relentless pursuit of continuous improvement within the production process (Womack & Jones, 1996). At its core, the theory advocates for identifying and eliminating any activity or resource that does not add value to the final product, thereby streamlining operations and enhancing efficiency (Womack & Jones, 1996). It also emphasizes a culture of perpetual learning and adaptation, where organizations consistently strive to enhance their processes, products, and overall operations. Additionally, lean manufacturing emphasizes a customer-centric approach, prioritizing the delivery of products with the highest possible quality, in the shortest possible lead times, and at the lowest possible cost. This customer-focused mindset drives the optimization of resources, reduction of lead times, and streamlining of inventory levels, all geared towards maximizing value for the customer. Total Quality Management (TQM) is a holistic quality management theory that advocates for a comprehensive approach to elevating quality standards across all facets of a manufacturing organization (Deming, 1986). Championed by W. Edwards Deming, TQM embodies a set of principles that transcend traditional quality control methods, fostering a cultural shift towards excellence. At its core, TQM emphasizes customer focus, recognizing that understanding and meeting customer needs is paramount to achieving quality excellence (Deming, 1986). It also advocates for continuous improvement through an ongoing cycle of planning, implementing, measuring, and refining processes, products, and services. TQM encourages employee involvement at all levels, fostering a culture of collaboration and shared responsibility for quality improvement. Additionally, the theory promotes a systemic view of quality that extends beyond mere product inspection, embedding quality considerations into every stage of the production process to prevent defects proactively. 2.2 Empirical Literature Oni et al. (2023) found renewable biofuels and fuel oil had a positive effect on manufacturing output and GDP in Nigeria from 1990–2019, highlighting the importance of these energy sources. However, other studies like Adewuyi et al. (2016) identified economic factors like income, exchange rates, and domestic energy production as key determinants of petroleum product imports for manufacturing. Opeyemi et al. (2021) revealed significant substitution possibilities between renewable and non-renewable energy in Nigeria from 1987 to 2016 using the SUR approach. This contrasts with Dong et al. (2020), who found renewable energy negatively impacted emissions across 120 countries from 1995–2015, but the effect was not statistically significant. For low-income countries from 1980–2021, Ehigiamusoe et al. (2022) found renewable energy mitigated carbon emissions using a fixed-effects model, aligning with Saidi et al. (2021) who showed renewables reduced emissions while boosting economic growth across 15 major renewable-consuming nations from 1990–2014. Uzair Ali et al. (2022) supported the Environmental Kuznets Curve hypothesis for India from 1971–2014, with economic development initially increasing and then decreasing emissions. Contrastingly, Begum et al. (2015) found energy consumption and GDP had a positive long-run effect on Malaysian emissions from 1980–2009. While Saidi et al. (2021) found no long-run causality between renewables and emissions from 1990–2014, studies by Neitzel ( 2017 ) on OECD countries, Khobai & Pierre ( 2017 ) on South Africa, and Gherghina et al. ( 2017 ) on EU nations revealed evidence of long-run causality relationships, though the directions differed. Acheampong et al. (2021) showed renewable energy and FDI decreased emissions, while population growth and financial development increased emissions across Sub-Saharan Africa from 1980–2015. This aligns with Dong et al. (2018) who found population size and economic growth boosted emissions globally from 1990–2014. Egila & Diugwu ( 2017 ) highlighted the heavy business reliance on alternative energy in Nigeria, complementing Oni et al.’s (2023) findings on the importance of renewables for manufacturing output. Katsuya’s (2017) panel study from 2002–2011 revealed non-renewable energy hurt economic growth across 42 developing nations, contrasting with Neitzel ( 2017 ) who found a negative relationship between renewables and GDP for OECD countries from 1995–2012. While Riti et al. (2016) found a renewable energy-environmental quality nexus in Nigeria from 1981–2013, Maji ( 2015 ) showed mixed impacts of clean energy types on economic growth from 1971–2011. Some studies focused specifically on the manufacturing sector, with Opaluwa et al. (2010) and Simon-Oke & Aribisala (2010) finding negative effects of exchange rate fluctuations on manufacturing GDP in Nigeria. However, Usman & Adejare (2012) and Enekwe et al. (2013) contradicted these findings. Mansoor et al. (2018) validated the IPAT hypothesis linking population, affluence, and technology to environmental impact in Pakistan from 1975–2016, while Gherghina et al. ( 2017 ) showed biomass had the highest influence on EU economic growth among renewable types from 2003–2014. Contrasting long-run causality evidence emerged, with Khobai & Pierre ( 2017 ) finding unidirectional causality from renewables to growth in South Africa, while Gherghina et al. ( 2017 ) showed causality from growth to renewable production across the EU from 2003 to 2014. Šimelytė & Dudzevičiūtė (2017) found renewable energy boosted economies in 12 of 28 EU nations studied from 1990–2012. 2.3 Value Addition Analyzing the effects of gasoline consumption and renewable energy usage on Nigeria’s manufacturing sector presents a valuable avenue for research. Existing literature shows gaps that future studies could address. A significant gap is the lack of comprehensive research simultaneously considering gasoline consumption, renewable energy, and the manufacturing sector. Current studies often focus solely on renewable energy, neglecting the specific impact of gasoline consumption on manufacturing. Future research should explore how gasoline usage, a crucial aspect of energy consumption, influences manufacturing activities. Moreover, while empirical literature exists, it lacks sufficient evidence regarding the relationship between renewable energy and economic growth in Nigeria. Few studies also consider the impact on manufacturing sector output. These gaps underscore the importance of undertaking this research. 3. Methodology This research work is conducted using econometric analysis. ARDL estimation technique was used in carrying out the analysis. The author included other independent variables in the model because they have be en observed in the literature to have a strong influence on the manufacturing sector. 3.1 Model Specification In an attempt to achieve the objective of this study, this report presents a multiple linear regression model on the impact of gasoline and renewable energy consumption on manufacturing output in Nigeria. In this model, MVA, which is the dependent variable, will serve as a proxy for manufacturing output, while real gasoline consumption, renewable energy consumption, carbon emissions, total government expenditure, real exchange rate, inflation rate, money supply, and lending interest rate are the independent variables. Where; MVA = manufacturing value added (used to proxy manufacturing output) CO2 = carbon emissions REC = Renewable energy consumption GAC = Real gasoline consumption LINTR = lending interest rate MOS = Money Supply REXCH = Real Exchange rate INFL = Inflation rate TGE = total government expenditure 𝛼 0 = Intercept term or constant parameter. 𝜇 𝑡 = The random or error or stochastic term t = The time series property of the respective variables. 𝛽 1 , 𝛽 2 , 𝛽 3 , 𝛽 4 , 𝛽 5 , 𝛽 6 , 𝛽 7 and 𝛽 8 = The Regression parameters and slopes of the respective explanatory variables. The Generalized ARDL (p, q) model is represented as : 𝑌 𝑡 =𝛼 0𝑗 + i = 1p2iYt-i + i = 1p2iXt-i + w t ………………………………………………… 2 Where: provided that p and q do not necessarily suggest symmetry of lag lengths, p = optimum lag length for the predicted parameter. q = optimum lag length for the predictors 4. Presentation and Analysis of Results The estimates from the regression results are presented and analyzed in this section. The results of the ARDL model estimations are presented in the following format: 4.1 Presentation of ARDL Regression Result Table 2 Dependent Variable: LMVA Long Run Regression Result (Dependent Variable: LMVA) Variable Coefficient Std. Error t-Statistic Prob. LCO2 3.555913 0.601872 5.908091 0.0006 LREXCH -0.288030 0.072792 -3.956907 0.0055 LGAC -2.897454 0.547931 -5.287987 0.0011 LINF -0.441078 0.219617 -2.008398 0.0846 LINTER -0.012862 0.004957 -2.594688 0.0357 LMOS 0.499838 0.170857 2.925476 0.0222 LREC 3.051204 1.680854 1.815270 0.1123 LTGE 0.000025 0.000003 9.201347 0.0000 C -10.989464 5.288148 -2.078131 0.0763 R-squared = 0.986117 F-statistic = 41.12307 Durbin-Watson stat = 2.533807 Adjusted R-squared = 0.962137 Prob(F-statistic) = 0.000000 Source: Author’s computation using e-views Table 4.8 Short Run & ECM Regression Result Variable Coefficient Std. Error t-Statistic Prob. D(LC02) 2.206565 0.587837 3.753703 0.0071 D(LEXCH) -0.007649 0.062580 -0.122230 0.9062 D(LGAC) 0.091955 0.702184 0.130955 0.8995 D(LINF) 0.395395 0.344182 1.148794 0.2884 D(LINTR) -0.013933 0.004286 -3.250903 0.0140 D(LMOS) 0.437982 0.181464 2.413600 0.0465 D(LRE) 2.978649 2.242560 1.328236 0.2258 ECM(-1) -1.544372 0.224373 -6.883056 0.0002 Source: Author’s computation using e-views R 2 0.986117 F-statistic 41.12307 R − 2 0.962137 Prob. (F-statistic) 0.00000 Durbin Watson 2.53381 4.2 Discussion of Results Log Carbon Emission (LCO2): The long-run coefficient of 3.555913 for carbon emission is statistically significant at the 5% level. Holding other variables constant, a percentage increase in carbon emission increases manufacturing value added by 3.56% in the long run. This positive relationship between carbon emission and manufacturing output conforms to the a priori expectation and aligns with Oni and Babatunde’s ( 2023 ) finding that carbon emission positively impacts manufacturing sector output in Nigeria. In the short run, the coefficient of 2.206565 indicates a positive and significant impact of carbon emission on manufacturing value added. Holding other variables constant, a percentage increase in carbon emission increases manufacturing sector output by 2.21% in the short run. This reinforces the need for effective emission mitigation strategies to promote sustainable manufacturing growth. Real Exchange Rate (REXCH): The long-run coefficient of -0.288030 indicates an inverse relationship between the exchange rate and manufacturing sector output. Ceteris paribus, a unit increase in the exchange rate leads to a 0.288030 unit decline in manufacturing value added. This negative effect of exchange rate appreciation on manufacturing output conforms to theoretical expectations and corroborates Elias et al.’s ( 2017 ) findings. The short-run coefficient of -0.007649 suggests the negative impact of the exchange rate on manufacturing value added in Nigeria. Ceteris paribus, a unit increase in the exchange rate decreases manufacturing output by 0.007649 units in the short run. Policymakers should, therefore, implement measures to manage exchange rate fluctuations and mitigate the adverse effects on the manufacturing sector. Log Gasoline Consumption (LGAC): With a coefficient of -2.897454, the results reveal a negative long-run relationship between gasoline consumption and manufacturing sector output in Nigeria. A percentage increase in gasoline consumption is associated with a 2.897454% decrease in manufacturing output, holding other variables constant. With a coefficient of 0.091955, the results indicate a positive short-run relationship between gasoline consumption and manufacturing sector output. A percentage increase in gasoline consumption enhances manufacturing output by 0.091955%, holding other variables constant. However, the long-run impact of gasoline consumption on manufacturing output is significantly negative. Log Inflation Rate (LINF): The coefficient of -0.441078 suggests an inverse long-run relationship between the inflation rate and manufacturing value added. A percentage increase in the inflation rate is expected to decrease manufacturing sector output by 0.441078%, ceteris paribus. This finding does not conform to the a priori expectation. The short-run coefficient of 0.395395 suggests a positive relationship between the inflation rate and manufacturing value added in Nigeria. Ceteris paribus, a percentage increase in the inflation rate increases manufacturing sector output by 0.395395% in the short run. Lending Interest Rate (LINTER): The regression results highlight a negative correlation between the lending interest rate and manufacturing output, with a coefficient of -0.012862. This suggests that an increase in the lending interest rates is likely to deter manufacturing companies from borrowing, hence negatively impacting their output. This outcome aligns with theoretical expectations and is corroborated by Zahra ( 2017 ), who also identified a negative impact of interest rates on the manufacturing sector’s output. The short-run effects, although slightly more pronounced at -0.013933, support the long-run findings, indicating a consistent adverse impact of higher lending rates on manufacturing output. Log of Broad Money Supply (LMOS): Contrastingly, the money supply demonstrates a positive relationship with manufacturing output. A coefficient of 0.499838 in the long run, significant at the 5% level, indicates that an increase in the money supply positively influences manufacturing output. This outcome is expected, suggesting that a larger money supply facilitates easier access to capital, stimulating investment and production in the manufacturing sector. The short-run analysis presents a similar positive impact, albeit with a slightly lower coefficient of 0.437982, reinforcing the notion that an increased money supply is beneficial for manufacturing output in both the short and long term. Log of Renewable Energy Consumption (LREC): The regression results find a positive relationship between renewable energy usage and the value added by manufacturing in Nigeria. Specifically, a 1% increase in renewable energy usage leads to approximately a 3.051304% increase in manufacturing value added in the long term, and about a 2.978649% increase in the short term. These results indicate that boosting renewable energy could enhance the manufacturing industry’s output, offering both economic and environmental benefits. This finding contrasts with previous research by Khobai et al. (2017), which identified a negative impact of renewable energy on manufacturing output, possibly due to different study periods, locations, or methodologies. Log of Total Government Expenditure (LTGE): The coefficient for total government expenditure is reported as 0.000025, indicating a positive relationship with the manufacturing sector’s output in the long run. In simpler terms, for every 1% increase in total government expenditure, there is an average increase in the manufacturing sector output by 0.000025%, assuming other factors remain unchanged. This result aligns with the expected outcome, suggesting that more government spending typically supports or boosts manufacturing activity over time. In the short run, the coefficient is slightly reduced to 0.000014. This means that, with all other variables constant, a 1% increase in government expenditure increases the manufacturing sector’s output by 0.000014% in the short term. Although this effect is smaller compared to the long-run scenario, it still reflects a positive impact of government spending on manufacturing output in the shorter term. Constant ©: The constant term in the regression model is -10.989464, which would theoretically mean the manufacturing value added (MVA) would decrease by this amount when other explanatory variables are zero. However, this interpretation does not hold much economic significance since the constant term is mainly a statistical component of the regression equation, adjusting the model to fit the data rather than implying a real-world economic relationship. Error Correction Model (ECM(-1)): The coefficient here is -1.544372, which indicates a rapid correction mechanism. Since 154.437% of the discrepancy or error from the previous year’s level is adjusted in the current year, it ensures that the model swiftly returns to equilibrium following any short-term shocks. This negative coefficient is crucial for the validity of the model, confirming that it effectively accounts for and corrects deviations from the long-run equilibrium path. 4.3 Coefficient of Determination From the estimation, the Multiple Coefficient of Determination (R²) is 0.986117, indicating that roughly 98% of the variability in the dependent variable is explained by the independent variables utilized in the model. This level of explanation meets the threshold for a good fit, demonstrating the model’s robustness. The Adjusted Multiple Coefficient of Determination (Adjusted R²) is shown as 0.962137. The proximity of this figure to the R² value suggests the model is efficiently estimated, highlighting its parsimonious use of variables. 5. Policy Recommendations and Conclusion 5.1 Policy Recommendations Effective policies are essential for maximizing the benefits of the manufacturing sector in the Nigerian economy. Therefore, in light of the findings from this study, the following recommendations are made: Minimizing Dependence on Gasoline : Considering gasoline consumption’s detrimental effects on Nigeria’s manufacturing sector, policymakers must prioritize actions that minimize gasoline dependence. Strategies could involve encouraging the shift towards alternative, renewable energy sources within manufacturing operations. Encouraging the Shift to Renewable Energy : Despite renewable energy’s modest yet positive influence on the manufacturing sector, there’s room for expansion. Policymakers need to develop policies that promote and ease the transition to renewable energy technologies in the manufacturing sector. Tackling Key Influential Factors : In light of the statistical importance of factors like carbon emissions, the exchange rate, lending rates, inflation, the money supply, and total government spending, it’s imperative for policymakers to introduce specific interventions aimed at controlling and enhancing these variables for the manufacturing sector’s advantage. 5.2 Conclusion This study underscores the significance of embracing sustainable energy methods, effectively controlling crucial economic factors, and enacting adaptable policies to bolster the manufacturing sector’s development and durability in Nigeria. By incorporating these suggestions into their policy-making approaches, stakeholders can aim for a stronger and more sustainable manufacturing environment. Declarations Funding Declaration The Authors Sincerely declares that there is no funding made available for the study Availability of data and materials The datasets used and/or analyzed during the current study are available from the link https://docs.google.com/spreadsheets/d/1CZJZzu2QoFVldJPcxl6BicRYbfkvMTzNZxPIHLe5R5Y/edit?usp=sharing Competing Interest Declaration There is no competing interest Author Contribution U.E and M.C did prepared the manuscript jointly, with U.E being the main author who bought the topic and initiated the study. References Asaleye, A. J., Lawal, A. I., Inegbedion, H. E., Oladipo, A. O., Owolabi, A. O., Samuel, O. M., & Igbolekwu, C. O. (2021). Electricity consumption and manufacturing sector performance: evidence from Nigeria. International Journal of Energy Economics and Policy, 11(4), 195-201. Businessday & Sterling Bank (2019). Disruptors: How Off-Grid Energy Companies are Closing Nigeria’s Energy Access Gap. The Nigerian Energy Report. 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Thousand Oaks, CA: SAGE Publications. Zahra F. (2017). Renewable Energy Consumption and Economic Growth: A Case Study for Developing Countries. International Journal of Energy Economics and Policy, 2017, 7(2), 61-64. Additional Declarations No competing interests reported. Supplementary Files APPENDICES.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 05 Apr, 2024 Submission checks completed at journal 04 Apr, 2024 First submitted to journal 02 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4205989\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":287842931,\"identity\":\"6f0cdf8b-6bc2-4f7f-8a72-540f9504c3e3\",\"order_by\":0,\"name\":\"UCHECHUKWU EZE\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"University of Nigeria\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"UCHECHUKWU\",\"middleName\":\"\",\"lastName\":\"EZE\",\"suffix\":\"\"},{\"id\":287842933,\"identity\":\"86f42f67-6fc0-4b80-86d2-4896ac9a555c\",\"order_by\":1,\"name\":\"MBA COLLINS CHIDUMEBI\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Nigeria\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"MBA\",\"middleName\":\"COLLINS\",\"lastName\":\"CHIDUMEBI\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-04-02 10:38:18\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4205989/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4205989/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":54385829,\"identity\":\"ab434f84-ebd0-4138-a506-808edb529300\",\"added_by\":\"auto\",\"created_at\":\"2024-04-09 17:32:20\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":350304,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4205989/v1/2ef73eef-3a6f-441b-be70-b0ae197f00e8.pdf\"},{\"id\":54385589,\"identity\":\"686dc140-9ae3-4cc0-9fec-07bcd07d815d\",\"added_by\":\"auto\",\"created_at\":\"2024-04-09 17:24:20\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":88452,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"APPENDICES.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4205989/v1/880c273257bdb268bccb9165.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"\\u003cp\\u003eImpact of Gasoline and Renewable Energy Consumption on Manufacturing Sector Output in Nigeria: New Evidence From ARDL Model\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eThe manufacturing sector is a pivotal driver of economic development in Nigeria (Oni et al., 2023). However, it faces significant challenges, including an overreliance on traditional fossil fuel sources like gasoline and an urgent need to transition towards more sustainable energy alternatives (Asaleye et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Daniel, 2015; Elias et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Despite being one of Africa\\u0026rsquo;s top oil-producing nations, Nigeria\\u0026rsquo;s continued dependence on fossil fuels exposes the economy to risks stemming from volatile global oil prices and growing environmental concerns over emissions and climate change (Skordoulis et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNigeria possesses substantial untapped potential for renewable energy sources such as solar, wind, biomass, and hydropower (Marinaş et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Neitzel, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Harnessing these clean energy resources could help address the manufacturing sector\\u0026rsquo;s extensive energy requirements in a more environmentally sustainable manner. In recognition of this, the Nigerian government has implemented policies like the 2015 Nigerian Renewable Energy and Energy Efficiency Policy (NREEEP) which sets ambitious targets to increase the renewable energy mix, aiming for 20% of the nation\\u0026rsquo;s electricity supply to come from renewables by 2030.\\u003c/p\\u003e \\u003cp\\u003eIn 2016, Nigeria took a significant step towards realizing its renewable energy goals through the development of the National Renewable Energy Action Plans (NREAP) by various ministries, departments, agencies, and representatives from all 36 states. The NREAP outlines specific commitments to incentivize large-scale adoption of renewable energies across key sectors, including manufacturing. Notably, the plan mandates that 16% of the manufacturing sector\\u0026rsquo;s total energy consumption should be derived from renewable sources by 2030, with contributions from small hydropower (7.07%), solar (5.9%), biomass (2.78%), and wind (0.25%) (Global Legal Insights, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNigeria\\u0026rsquo;s abundant sunlight and strong wind resources provide a fertile ground for the development of solar and wind energy projects to meet the manufacturing sector\\u0026rsquo;s growing energy demands (Zahra, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Embracing renewable energy sources in manufacturing offers a dual benefit \\u0026ndash; mitigating the environmental impact of fossil fuels while simultaneously enhancing the nation\\u0026rsquo;s energy security by diversifying its energy mix away from an over-reliance on a single source.\\u003c/p\\u003e \\u003cp\\u003eWhile a growing body of literature exists on energy consumption and its impacts on economic sectors globally, the specific nuances of the Nigerian manufacturing landscape necessitate focused attention. Existing studies tend to emphasize either the broader energy landscape or concentrate on specific industries, with limited comprehensive analyses examining the intricate interplay between gasoline consumption, renewable energy adoption, and the overall performance of Nigeria\\u0026rsquo;s manufacturing sector.\\u003c/p\\u003e \\u003cp\\u003eDespite the introduction of renewable energy policies, Nigeria still lags in effectively utilizing its abundant renewable resources. Increasing the uptake of renewable energy can catalyze the nation\\u0026rsquo;s economic growth while drastically reducing the consumption of finite fossil fuels like gasoline, thereby improving the quality of life (Zahra, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). With projections indicating Nigeria\\u0026rsquo;s oil reserves may be depleted in approximately 52 years at the current rate of extraction (Chineme, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), the imperative to transition the manufacturing sector towards renewable energy sources is crucial for achieving sustainable industrial growth aligned with global climate goals (Jacob, 2017).\\u003c/p\\u003e \\u003cp\\u003eUnderstanding the impacts of gasoline consumption versus renewable energy adoption on the performance of Nigeria\\u0026rsquo;s manufacturing sector can provide invaluable insights to guide strategies aimed at boosting productivity, enhancing competitiveness, and promoting environmental responsibility (van Vliet, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Shaaban \\u0026amp; Petinrin, 2014; NNPC, 2018; RECP, 2018; ECN, 2003). As Nigeria navigates this energy transition, research illuminating these dynamics can inform policymaking and industrial practices, ensuring the nation\\u0026rsquo;s manufacturing prowess continues to thrive while prioritizing sustainability.\\u003c/p\\u003e \\u003cp\\u003eThe subsequent sections of this research work are structured as follows: The next part offers a concise review of relevant existing literature related to the topic under investigation. Following that, the third section delineates the methodological approaches and techniques employed to carry out the study. The fourth section then presents a comprehensive analysis and interpretation of the key findings obtained through the research. Finally, the fifth and concluding section summarizes the overarching conclusions drawn from the study, while also providing recommendations based on the results and analysis.\\u003c/p\\u003e\"},{\"header\":\"2. Literature Review\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 \\u003cem\\u003eTheoretical Literature\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eThis section considers some of the most recent macroeconomic models that support gasoline consumption, renewable energy consumption, and the manufacturing sector,\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eGasoline Consumption Theories\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe theory of environmental psychology is a multidisciplinary approach that brings together insights from psychology, sociology, and environmental sciences to understand the psychological factors that influence individual behaviors and attitudes toward gasoline consumption (Green \\u0026amp; Black, 2018). Scholars argue that consumer perceptions, awareness of environmental issues like climate change, and concerns about the ecological impact of fossil fuels can significantly shape choices related to transportation modes and fuel consumption patterns. The theory posits that psychological variables such as attitudes towards eco-friendly vehicles, environmental consciousness, and personal values play a pivotal role in decisions to adopt fuel-efficient technologies or alternative modes of transportation that reduce gasoline usage (Jones et al, 2020). By examining the intricate interplay between individuals and their physical surroundings, environmental psychology offers a comprehensive framework for analyzing the cognitive and emotional drivers behind gasoline consumption behaviors (Gifford, 2014; Steg \\u0026amp; Vlek, 2009).\\u003c/p\\u003e \\u003cp\\u003eThe technological innovation and adaptation theoretical perspective focuses on the role of technological advancements and their diffusion in influencing gasoline consumption patterns. It considers how the adoption of innovative fuel-efficient technologies, hybrid vehicles, and electric cars can lead to a reduction in overall gasoline demand (Williams, 2016). The theory explores factors such as the rate at which new transportation technologies are adopted by consumers, the impact of government policies and incentives on the uptake of fuel-efficient alternatives, and the barriers or facilitators that affect consumer acceptance of these innovations (Department of Energy, 2019). Drawing from the seminal work of Everett Rogers on the diffusion of innovations, this theory provides a framework for understanding how new ideas and technologies spread through social systems, thereby shaping gasoline consumption behaviors over time (Rogers, 1962).\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eRenewable Energy Consumption Theories\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe growth models with natural resources and technical change integrate the consideration of natural resources and technological progress into economic growth frameworks. They explore how advancements in technology and technical change can potentially offset the limitations imposed by finite natural resources, including non-renewable energy sources like fossil fuels (Aghion \\u0026amp; Howitt, 1998). By incorporating technological progress as a driving force, these models suggest that even when the elasticity of substitution between capital and finite resources is less than one, sustained economic growth can still be achieved through improvements in total factor productivity. However, specific conditions and assumptions need to be met for technological change to render sustainability technically feasible in the long run. These models provide insights into the interplay between resource constraints, technological advancements, and economic growth trajectories.\\u003c/p\\u003e \\u003cp\\u003eThis socioeconomic theory of renewable energy adoption explores the socioeconomic factors that influence the acceptance and utilization of renewable energy technologies across different communities and societies. It examines variables such as income levels, educational attainment, and societal attitudes towards the environment and sustainable practices. The theory posits that higher income levels and better access to education can facilitate the adoption of renewable energy solutions by increasing affordability and fostering greater awareness and technical understanding (Brown \\u0026amp; Sovacool, 2011). Additionally, it explores how societal norms, cultural values, and collective attitudes toward energy use and environmental concerns shape the willingness of communities to embrace renewable energy alternatives. By acknowledging and addressing these socioeconomic complexities, policymakers and advocates can design more inclusive and effective strategies to promote the widespread adoption of renewable energy technologies.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eManufacturing Sector Theories\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe lean manufacturing theory is a comprehensive theoretical framework that emerged from the Toyota Production System, emphasizing the systematic elimination of waste and the relentless pursuit of continuous improvement within the production process (Womack \\u0026amp; Jones, 1996). At its core, the theory advocates for identifying and eliminating any activity or resource that does not add value to the final product, thereby streamlining operations and enhancing efficiency (Womack \\u0026amp; Jones, 1996). It also emphasizes a culture of perpetual learning and adaptation, where organizations consistently strive to enhance their processes, products, and overall operations. Additionally, lean manufacturing emphasizes a customer-centric approach, prioritizing the delivery of products with the highest possible quality, in the shortest possible lead times, and at the lowest possible cost. This customer-focused mindset drives the optimization of resources, reduction of lead times, and streamlining of inventory levels, all geared towards maximizing value for the customer.\\u003c/p\\u003e \\u003cp\\u003eTotal Quality Management (TQM) is a holistic quality management theory that advocates for a comprehensive approach to elevating quality standards across all facets of a manufacturing organization (Deming, 1986). Championed by W. Edwards Deming, TQM embodies a set of principles that transcend traditional quality control methods, fostering a cultural shift towards excellence. At its core, TQM emphasizes customer focus, recognizing that understanding and meeting customer needs is paramount to achieving quality excellence (Deming, 1986). It also advocates for continuous improvement through an ongoing cycle of planning, implementing, measuring, and refining processes, products, and services. TQM encourages employee involvement at all levels, fostering a culture of collaboration and shared responsibility for quality improvement. Additionally, the theory promotes a systemic view of quality that extends beyond mere product inspection, embedding quality considerations into every stage of the production process to prevent defects proactively.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 \\u003cem\\u003eEmpirical Literature\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eOni et al. (2023) found renewable biofuels and fuel oil had a positive effect on manufacturing output and GDP in Nigeria from 1990\\u0026ndash;2019, highlighting the importance of these energy sources. However, other studies like Adewuyi et al. (2016) identified economic factors like income, exchange rates, and domestic energy production as key determinants of petroleum product imports for manufacturing.\\u003c/p\\u003e \\u003cp\\u003eOpeyemi et al. (2021) revealed significant substitution possibilities between renewable and non-renewable energy in Nigeria from 1987 to 2016 using the SUR approach. This contrasts with Dong et al. (2020), who found renewable energy negatively impacted emissions across 120 countries from 1995\\u0026ndash;2015, but the effect was not statistically significant.\\u003c/p\\u003e \\u003cp\\u003eFor low-income countries from 1980\\u0026ndash;2021, Ehigiamusoe et al. (2022) found renewable energy mitigated carbon emissions using a fixed-effects model, aligning with Saidi et al. (2021) who showed renewables reduced emissions while boosting economic growth across 15 major renewable-consuming nations from 1990\\u0026ndash;2014.\\u003c/p\\u003e \\u003cp\\u003eUzair Ali et al. (2022) supported the Environmental Kuznets Curve hypothesis for India from 1971\\u0026ndash;2014, with economic development initially increasing and then decreasing emissions. Contrastingly, Begum et al. (2015) found energy consumption and GDP had a positive long-run effect on Malaysian emissions from 1980\\u0026ndash;2009.\\u003c/p\\u003e \\u003cp\\u003eWhile Saidi et al. (2021) found no long-run causality between renewables and emissions from 1990\\u0026ndash;2014, studies by Neitzel (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) on OECD countries, Khobai \\u0026amp; Pierre (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) on South Africa, and Gherghina et al. (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) on EU nations revealed evidence of long-run causality relationships, though the directions differed.\\u003c/p\\u003e \\u003cp\\u003eAcheampong et al. (2021) showed renewable energy and FDI decreased emissions, while population growth and financial development increased emissions across Sub-Saharan Africa from 1980\\u0026ndash;2015. This aligns with Dong et al. (2018) who found population size and economic growth boosted emissions globally from 1990\\u0026ndash;2014.\\u003c/p\\u003e \\u003cp\\u003eEgila \\u0026amp; Diugwu (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) highlighted the heavy business reliance on alternative energy in Nigeria, complementing Oni et al.\\u0026rsquo;s (2023) findings on the importance of renewables for manufacturing output.\\u003c/p\\u003e \\u003cp\\u003eKatsuya\\u0026rsquo;s (2017) panel study from 2002\\u0026ndash;2011 revealed non-renewable energy hurt economic growth across 42 developing nations, contrasting with Neitzel (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) who found a negative relationship between renewables and GDP for OECD countries from 1995\\u0026ndash;2012.\\u003c/p\\u003e \\u003cp\\u003eWhile Riti et al. (2016) found a renewable energy-environmental quality nexus in Nigeria from 1981\\u0026ndash;2013, Maji (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) showed mixed impacts of clean energy types on economic growth from 1971\\u0026ndash;2011.\\u003c/p\\u003e \\u003cp\\u003eSome studies focused specifically on the manufacturing sector, with Opaluwa et al. (2010) and Simon-Oke \\u0026amp; Aribisala (2010) finding negative effects of exchange rate fluctuations on manufacturing GDP in Nigeria. However, Usman \\u0026amp; Adejare (2012) and Enekwe et al. (2013) contradicted these findings.\\u003c/p\\u003e \\u003cp\\u003eMansoor et al. (2018) validated the IPAT hypothesis linking population, affluence, and technology to environmental impact in Pakistan from 1975\\u0026ndash;2016, while Gherghina et al. (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) showed biomass had the highest influence on EU economic growth among renewable types from 2003\\u0026ndash;2014.\\u003c/p\\u003e \\u003cp\\u003eContrasting long-run causality evidence emerged, with Khobai \\u0026amp; Pierre (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) finding unidirectional causality from renewables to growth in South Africa, while Gherghina et al. (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) showed causality from growth to renewable production across the EU from 2003 to 2014. Šimelytė \\u0026amp; Dudzevičiūtė (2017) found renewable energy boosted economies in 12 of 28 EU nations studied from 1990\\u0026ndash;2012.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 \\u003cem\\u003eValue Addition\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eAnalyzing the effects of gasoline consumption and renewable energy usage on Nigeria\\u0026rsquo;s manufacturing sector presents a valuable avenue for research. Existing literature shows gaps that future studies could address. A significant gap is the lack of comprehensive research simultaneously considering gasoline consumption, renewable energy, and the manufacturing sector. Current studies often focus solely on renewable energy, neglecting the specific impact of gasoline consumption on manufacturing. Future research should explore how gasoline usage, a crucial aspect of energy consumption, influences manufacturing activities. Moreover, while empirical literature exists, it lacks sufficient evidence regarding the relationship between renewable energy and economic growth in Nigeria. Few studies also consider the impact on manufacturing sector output. These gaps underscore the importance of undertaking this research.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Methodology\",\"content\":\"\\u003cp\\u003eThis research work is conducted using econometric analysis. ARDL estimation technique was used in carrying out the analysis. The author included other independent variables in the model because they have \\u003cb\\u003ebe\\u003c/b\\u003een observed in the literature to have a strong influence on the manufacturing sector.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 \\u003cem\\u003eModel Specification\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eIn an attempt to achieve the objective of this study, this report presents a multiple linear regression model on the impact of gasoline and renewable energy consumption on manufacturing output in Nigeria. In this model, MVA, which is the dependent variable, will serve as a proxy for manufacturing output, while real gasoline consumption, renewable energy consumption, carbon emissions, total government expenditure, real exchange rate, inflation rate, money supply, and lending interest rate are the independent variables.\\u003c/p\\u003e \\u003cp\\u003eWhere;\\u003c/p\\u003e \\u003cp\\u003eMVA\\u0026thinsp;=\\u0026thinsp;manufacturing value added (used to proxy manufacturing output)\\u003c/p\\u003e \\u003cp\\u003eCO2\\u0026thinsp;=\\u0026thinsp;carbon emissions\\u003c/p\\u003e \\u003cp\\u003eREC\\u0026thinsp;=\\u0026thinsp;Renewable energy consumption\\u003c/p\\u003e \\u003cp\\u003eGAC\\u0026thinsp;=\\u0026thinsp;Real gasoline consumption\\u003c/p\\u003e \\u003cp\\u003eLINTR\\u0026thinsp;=\\u0026thinsp;lending interest rate\\u003c/p\\u003e \\u003cp\\u003eMOS\\u0026thinsp;=\\u0026thinsp;Money Supply\\u003c/p\\u003e \\u003cp\\u003eREXCH\\u0026thinsp;=\\u0026thinsp;Real Exchange rate\\u003c/p\\u003e \\u003cp\\u003eINFL\\u0026thinsp;=\\u0026thinsp;Inflation rate\\u003c/p\\u003e \\u003cp\\u003eTGE\\u0026thinsp;=\\u0026thinsp;total government expenditure\\u003c/p\\u003e \\u003cp\\u003e\\u0026#120572;\\u003csub\\u003e0\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;Intercept term or constant parameter.\\u003c/p\\u003e \\u003cp\\u003e\\u0026#120583;\\u003csub\\u003e\\u0026#119905;\\u003c/sub\\u003e = The random or error or stochastic term\\u003c/p\\u003e \\u003cp\\u003et\\u0026thinsp;=\\u0026thinsp;The time series property of the respective variables.\\u003c/p\\u003e \\u003cp\\u003e\\u0026#120573;\\u003csub\\u003e1\\u003c/sub\\u003e, \\u0026#120573;\\u003csub\\u003e2\\u003c/sub\\u003e, \\u0026#120573;\\u003csub\\u003e3\\u003c/sub\\u003e, \\u0026#120573;\\u003csub\\u003e4\\u003c/sub\\u003e, \\u0026#120573;\\u003csub\\u003e5\\u003c/sub\\u003e, \\u0026#120573;\\u003csub\\u003e6\\u003c/sub\\u003e, \\u0026#120573;\\u003csub\\u003e7 and\\u003c/sub\\u003e \\u0026#120573;\\u003csub\\u003e8\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;The Regression parameters and slopes of the respective explanatory variables.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eThe Generalized ARDL (p, q) model is represented as\\u003c/b\\u003e:\\u003c/p\\u003e \\u003cp\\u003e\\u0026#119884;\\u003csub\\u003e\\u0026#119905;\\u003c/sub\\u003e=\\u0026#120572;\\u003csub\\u003e0\\u0026#119895;\\u003c/sub\\u003e+ i\\u0026thinsp;=\\u0026thinsp;1p2iYt-i\\u0026thinsp;+\\u0026thinsp;i\\u0026thinsp;=\\u0026thinsp;1p2iXt-i\\u0026thinsp;+\\u0026thinsp;\\u003cem\\u003ew\\u003c/em\\u003e\\u003csub\\u003et\\u003c/sub\\u003e\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip;\\u0026hellip; 2\\u003c/p\\u003e \\u003cp\\u003eWhere: provided that p and q do not necessarily suggest symmetry of lag lengths,\\u003c/p\\u003e \\u003cp\\u003ep\\u0026thinsp;=\\u0026thinsp;optimum lag length for the predicted parameter.\\u003c/p\\u003e \\u003cp\\u003eq\\u0026thinsp;=\\u0026thinsp;optimum lag length for the predictors\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Presentation and Analysis of Results\",\"content\":\" \\u003cp\\u003eThe estimates from the regression results are presented and analyzed in this section. The results of the\\u003c/p\\u003e \\u003cp\\u003eARDL model estimations are presented in the following format:\\u003c/p\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 \\u003cem\\u003ePresentation of ARDL Regression Result\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDependent Variable: LMVA Long Run Regression Result (Dependent Variable: LMVA)\\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\\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\\u003eStd. Error\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003et-Statistic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eProb.\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLCO2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.555913\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.601872\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.908091\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLREXCH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.288030\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.072792\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.956907\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0055\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLGAC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-2.897454\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.547931\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-5.287987\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0011\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLINF\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.441078\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.219617\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.008398\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0846\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLINTER\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.012862\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.004957\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.594688\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0357\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLMOS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.499838\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.170857\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.925476\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0222\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLREC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.051204\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.680854\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.815270\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.1123\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLTGE\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.000025\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.000003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.201347\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-10.989464\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.288148\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-2.078131\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0763\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eR-squared\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;0.986117\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eF-statistic\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;41.12307\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDurbin-Watson stat\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;2.533807\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAdjusted R-squared\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;0.962137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eProb(F-statistic)\\u003c/b\\u003e\\u0026thinsp;=\\u0026thinsp;0.000000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eSource: Author\\u0026rsquo;s computation using e-views\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\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 4.8\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eShort Run \\u0026amp; ECM Regression Result\\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\\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\\u003eStd. Error\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003et-Statistic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eProb.\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD(LC02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.206565\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.587837\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.753703\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0071\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD(LEXCH)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.007649\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.062580\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.122230\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9062\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD(LGAC)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.091955\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.702184\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.130955\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.8995\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD(LINF)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.395395\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.344182\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.148794\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.2884\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD(LINTR)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.013933\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.004286\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.250903\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0140\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD(LMOS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.437982\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.181464\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.413600\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0465\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eD(LRE)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.978649\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.242560\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.328236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.2258\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eECM(-1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-1.544372\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.224373\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-6.883056\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.0002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003cb\\u003eSource: Author\\u0026rsquo;s computation using e-views\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003e\\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e\\u003cem\\u003e2\\u003c/em\\u003e\\u003c/sup\\u003e \\u003cem\\u003e0.986117 F-statistic 41.12307\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eR\\u003c/em\\u003e \\u003csup\\u003e \\u003cem\\u003e\\u0026minus;\\u0026thinsp;2\\u003c/em\\u003e \\u003c/sup\\u003e \\u003cem\\u003e0.962137 Prob. (F-statistic) 0.00000 Durbin Watson 2.53381\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 \\u003cem\\u003eDiscussion of Results\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eLog Carbon Emission (LCO2): The long-run coefficient of 3.555913 for carbon emission is statistically significant at the 5% level. Holding other variables constant, a percentage increase in carbon emission increases manufacturing value added by 3.56% in the long run. This positive relationship between carbon emission and manufacturing output conforms to the a priori expectation and aligns with Oni and Babatunde\\u0026rsquo;s (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) finding that carbon emission positively impacts manufacturing sector output in Nigeria. In the short run, the coefficient of 2.206565 indicates a positive and significant impact of carbon emission on manufacturing value added. Holding other variables constant, a percentage increase in carbon emission increases manufacturing sector output by 2.21% in the short run. This reinforces the need for effective emission mitigation strategies to promote sustainable manufacturing growth.\\u003c/p\\u003e \\u003cp\\u003eReal Exchange Rate (REXCH): The long-run coefficient of -0.288030 indicates an inverse relationship between the exchange rate and manufacturing sector output. Ceteris paribus, a unit increase in the exchange rate leads to a 0.288030 unit decline in manufacturing value added. This negative effect of exchange rate appreciation on manufacturing output conforms to theoretical expectations and corroborates Elias et al.\\u0026rsquo;s (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) findings. The short-run coefficient of -0.007649 suggests the negative impact of the exchange rate on manufacturing value added in Nigeria. Ceteris paribus, a unit increase in the exchange rate decreases manufacturing output by 0.007649 units in the short run. Policymakers should, therefore, implement measures to manage exchange rate fluctuations and mitigate the adverse effects on the manufacturing sector.\\u003c/p\\u003e \\u003cp\\u003eLog Gasoline Consumption (LGAC): With a coefficient of -2.897454, the results reveal a negative long-run relationship between gasoline consumption and manufacturing sector output in Nigeria. A percentage increase in gasoline consumption is associated with a 2.897454% decrease in manufacturing output, holding other variables constant. With a coefficient of 0.091955, the results indicate a positive short-run relationship between gasoline consumption and manufacturing sector output. A percentage increase in gasoline consumption enhances manufacturing output by 0.091955%, holding other variables constant. However, the long-run impact of gasoline consumption on manufacturing output is significantly negative.\\u003c/p\\u003e \\u003cp\\u003eLog Inflation Rate (LINF): The coefficient of -0.441078 suggests an inverse long-run relationship between the inflation rate and manufacturing value added. A percentage increase in the inflation rate is expected to decrease manufacturing sector output by 0.441078%, ceteris paribus. This finding does not conform to the a priori expectation. The short-run coefficient of 0.395395 suggests a positive relationship between the inflation rate and manufacturing value added in Nigeria. Ceteris paribus, a percentage increase in the inflation rate increases manufacturing sector output by 0.395395% in the short run.\\u003c/p\\u003e \\u003cp\\u003eLending Interest Rate (LINTER): The regression results highlight a negative correlation between the lending interest rate and manufacturing output, with a coefficient of -0.012862. This suggests that an increase in the lending interest rates is likely to deter manufacturing companies from borrowing, hence negatively impacting their output. This outcome aligns with theoretical expectations and is corroborated by Zahra (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), who also identified a negative impact of interest rates on the manufacturing sector\\u0026rsquo;s output. The short-run effects, although slightly more pronounced at -0.013933, support the long-run findings, indicating a consistent adverse impact of higher lending rates on manufacturing output.\\u003c/p\\u003e \\u003cp\\u003eLog of Broad Money Supply (LMOS): Contrastingly, the money supply demonstrates a positive relationship with manufacturing output. A coefficient of 0.499838 in the long run, significant at the 5% level, indicates that an increase in the money supply positively influences manufacturing output. This outcome is expected, suggesting that a larger money supply facilitates easier access to capital, stimulating investment and production in the manufacturing sector. The short-run analysis presents a similar positive impact, albeit with a slightly lower coefficient of 0.437982, reinforcing the notion that an increased money supply is beneficial for manufacturing output in both the short and long term.\\u003c/p\\u003e \\u003cp\\u003eLog of Renewable Energy Consumption (LREC): The regression results find a positive relationship between renewable energy usage and the value added by manufacturing in Nigeria. Specifically, a 1% increase in renewable energy usage leads to approximately a 3.051304% increase in manufacturing value added in the long term, and about a 2.978649% increase in the short term. These results indicate that boosting renewable energy could enhance the manufacturing industry\\u0026rsquo;s output, offering both economic and environmental benefits. This finding contrasts with previous research by Khobai et al. (2017), which identified a negative impact of renewable energy on manufacturing output, possibly due to different study periods, locations, or methodologies.\\u003c/p\\u003e \\u003cp\\u003eLog of Total Government Expenditure (LTGE): The coefficient for total government expenditure is reported as 0.000025, indicating a positive relationship with the manufacturing sector\\u0026rsquo;s output in the long run. In simpler terms, for every 1% increase in total government expenditure, there is an average increase in the manufacturing sector output by 0.000025%, assuming other factors remain unchanged. This result aligns with the expected outcome, suggesting that more government spending typically supports or boosts manufacturing activity over time. In the short run, the coefficient is slightly reduced to 0.000014. This means that, with all other variables constant, a 1% increase in government expenditure increases the manufacturing sector\\u0026rsquo;s output by 0.000014% in the short term. Although this effect is smaller compared to the long-run scenario, it still reflects a positive impact of government spending on manufacturing output in the shorter term.\\u003c/p\\u003e \\u003cp\\u003eConstant \\u0026copy;: The constant term in the regression model is -10.989464, which would theoretically mean the manufacturing value added (MVA) would decrease by this amount when other explanatory variables are zero. However, this interpretation does not hold much economic significance since the constant term is mainly a statistical component of the regression equation, adjusting the model to fit the data rather than implying a real-world economic relationship.\\u003c/p\\u003e \\u003cp\\u003eError Correction Model (ECM(-1)): The coefficient here is -1.544372, which indicates a rapid correction mechanism. Since 154.437% of the discrepancy or error from the previous year\\u0026rsquo;s level is adjusted in the current year, it ensures that the model swiftly returns to equilibrium following any short-term shocks. This negative coefficient is crucial for the validity of the model, confirming that it effectively accounts for and corrects deviations from the long-run equilibrium path.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 \\u003cem\\u003eCoefficient of Determination\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eFrom the estimation, the Multiple Coefficient of Determination (R\\u0026sup2;) is 0.986117, indicating that roughly 98% of the variability in the dependent variable is explained by the independent variables utilized in the model. This level of explanation meets the threshold for a good fit, demonstrating the model\\u0026rsquo;s robustness. The Adjusted Multiple Coefficient of Determination (Adjusted R\\u0026sup2;) is shown as 0.962137. The proximity of this figure to the R\\u0026sup2; value suggests the model is efficiently estimated, highlighting its parsimonious use of variables.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Policy Recommendations and Conclusion\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 \\u003cem\\u003ePolicy Recommendations\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eEffective policies are essential for maximizing the benefits of the manufacturing sector in the Nigerian economy. Therefore, in light of the findings from this study, the following recommendations are made:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cem\\u003eMinimizing Dependence on Gasoline\\u003c/em\\u003e: Considering gasoline consumption\\u0026rsquo;s detrimental effects on Nigeria\\u0026rsquo;s manufacturing sector, policymakers must prioritize actions that minimize gasoline dependence. Strategies could involve encouraging the shift towards alternative, renewable energy sources within manufacturing operations.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cem\\u003eEncouraging the Shift to Renewable Energy\\u003c/em\\u003e: Despite renewable energy\\u0026rsquo;s modest yet positive influence on the manufacturing sector, there\\u0026rsquo;s room for expansion. Policymakers need to develop policies that promote and ease the transition to renewable energy technologies in the manufacturing sector.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003e \\u003cem\\u003eTackling Key Influential Factors\\u003c/em\\u003e: In light of the statistical importance of factors like carbon emissions, the exchange rate, lending rates, inflation, the money supply, and total government spending, it\\u0026rsquo;s imperative for policymakers to introduce specific interventions aimed at controlling and enhancing these variables for the manufacturing sector\\u0026rsquo;s advantage.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 \\u003cem\\u003eConclusion\\u003c/em\\u003e\\u003c/h2\\u003e \\u003cp\\u003eThis study underscores the significance of embracing sustainable energy methods, effectively controlling crucial economic factors, and enacting adaptable policies to bolster the manufacturing sector\\u0026rsquo;s development and durability in Nigeria. By incorporating these suggestions into their policy-making approaches, stakeholders can aim for a stronger and more sustainable manufacturing environment.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe Authors Sincerely declares that there is no funding made available for the study\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analyzed during the current study are available from the link https://docs.google.com/spreadsheets/d/1CZJZzu2QoFVldJPcxl6BicRYbfkvMTzNZxPIHLe5R5Y/edit?usp=sharing\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interest Declaration\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThere is no competing interest\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cb\\u003eAuthor Contribution\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eU.E and M.C did prepared the manuscript jointly, with U.E being the main author who bought the topic and initiated the study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAsaleye, A. J., Lawal, A. I., Inegbedion, H. E., Oladipo, A. O., Owolabi, A. O., Samuel, O. M., \\u0026amp; Igbolekwu, C. O. (2021). Electricity consumption and manufacturing sector performance: evidence from Nigeria. International Journal of Energy Economics and Policy, 11(4), 195-201.\\u003c/li\\u003e\\n\\u003cli\\u003eBusinessday \\u0026amp; Sterling Bank (2019). Disruptors: How Off-Grid Energy Companies are Closing Nigeria\\u0026rsquo;s Energy Access Gap. The Nigerian Energy Report.\\u003c/li\\u003e\\n\\u003cli\\u003eCharles, A., \\u0026amp; Meisen, P. (2014). How is 100% renewable energy possible for Nigeria? Global Energy Network Institute (GENI), California.\\u003c/li\\u003e\\n\\u003cli\\u003eChineme, O. (2018, Dec. 19). DPR: Nigeria to Run out of Oil in 52 years, This Day. https://www.google.com/amp/s/www.thisdaylive.com/index.php/2018/12/19/dpr- Nigeria-to-run-out-of-oil-in-52-years/%3famp\\u003c/li\\u003e\\n\\u003cli\\u003eEgila A. \\u0026amp; Diugwu I. (2017). An Assessment of Renewable Energy Impact on Economic Development in Nigeria. 2\\u003csup\\u003end\\u003c/sup\\u003e International Engineering Conference, Federal University of Technology, Minna, Nigeria.\\u003c/li\\u003e\\n\\u003cli\\u003eElias et al. (2017) The interconnections between Renewable Energy, Economic Development and Environmental Pollution. A simultaneous equation system approach. CeBER Working Papers No. 10.\\u003c/li\\u003e\\n\\u003cli\\u003eEmodi, N. V., \\u0026amp; Boo, K. J. (2015). Sustainable energy development in Nigeria: Current status and policy options. Renewable and Sustainable Energy Reviews, 51, 356-381.\\u003c/li\\u003e\\n\\u003cli\\u003eEnergy Reports 1: 145\\u0026ndash;150. Elsevier Ltd.\\u003c/li\\u003e\\n\\u003cli\\u003eGherghina et al (2017) Does Renewable Energy Drive Sustainable Economic Growth?\\u003c/li\\u003e\\n\\u003cli\\u003eGherghina et al (2017) Does Renewable Energy Drive Sustainable Economic Growth?\\u003c/li\\u003e\\n\\u003cli\\u003eGigaton Third Report (2017). Renewable Energy and Energy Efficiency in Developing Countries: Contributions to Reducing Global Emissions. United Nations Environmental Programme publication.\\u003c/li\\u003e\\n\\u003cli\\u003eGigaton Third Report (2017). Renewable Energy and Energy Efficiency in Developing Countries: Contributions to Reducing Global Emissions. United Nations Environmental Programme publication.\\u003c/li\\u003e\\n\\u003cli\\u003eGlobal Legal Insights (2018) Energy Laws and Regulations. Global Legal Group Ltd. https://www.globallegalinsights.com/practice-areas/energy-laws-and- regulations/Nigeria\\u003c/li\\u003e\\n\\u003cli\\u003eJournal of Engineering and Applied Sciences 5(2):171-177.\\u003c/li\\u003e\\n\\u003cli\\u003eKhobai H. \\u0026amp; Pierre R. (2017) Does renewable energy consumption drive economic growth: Evidence from Granger causality technique. South Africa: MPRA Paper No. 82464\\u003c/li\\u003e\\n\\u003cli\\u003eMaji I.K. (2015) Does clean energy contribute to economic growth? Evidence from Nigeria.\\u003c/li\\u003e\\n\\u003cli\\u003eMaradin, D., Cerović, L., \\u0026amp; Mjeda, T. (2017). Economic Effects of Renewable Energy Technologies. Na\\u0026scaron;e gospodarstvo/Our Economy, 63(2), 49\\u0026ndash;59. DOI: 10.1515/ngoe- 2017-0012\\u003c/li\\u003e\\n\\u003cli\\u003eMarinaş M. et al (2018) Renewable energy consumption and economic growth. Causality relationship in Central and Eastern European countries. 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ENERGY CONSUMPTION OF MANUFACTURING SECTOR: A SEARCH FOR SUSTAINABLE ENERGY SOURCE IN NIGERIA. ENERGY, 6(1).\\u003c/li\\u003e\\n\\u003cli\\u003ePerrings, C. A. (1987). Economy and Environment: A Theoretical Essay on the Interdependence of Economic and Environmental Systems. Cambridge: Cambridge University Press.\\u003c/li\\u003e\\n\\u003cli\\u003eSkordoulis et al (2018) Renewable Energy and Economic Growth: Evidence from European Countries. Sustainability 2018, 10, 2626. MDPI\\u003c/li\\u003e\\n\\u003cli\\u003eSoava G. et al. (2018). Impact of Renewable Energy Consumption on Economic Growth: Evidence from European Union Countries. Technological and Economic Development of Economy, 2018. 24(3): 914-932.\\u003c/li\\u003e\\n\\u003cli\\u003eSolow. R. M. (1974). Intergenerational Equity and Exhaustible Resources. Review of Economic Studies, Symposium on the Economics of Exhaustible Resources: 29-46.\\u003c/li\\u003e\\n\\u003cli\\u003eVan Vliet, B. (2012). Renewable resources. In D. Southerton (Ed.), Encyclopedia of Consumer Culture (pp. 1212-1214). Thousand Oaks, CA: SAGE Publications.\\u003c/li\\u003e\\n\\u003cli\\u003eZahra F. (2017). Renewable Energy Consumption and Economic Growth: A Case Study for Developing Countries. International Journal of Energy Economics and Policy, 2017, 7(2), 61-64.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"swiss-journal-of-economics-and-statistics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"sjes\",\"sideBox\":\"Learn more about [Swiss Journal of Economics and Statistics](https://sjes.springeropen.com/)\",\"snPcode\":\"41937\",\"submissionUrl\":\"https://submission.nature.com/new-submission/41937/3\",\"title\":\"Swiss Journal of Economics and Statistics\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Open\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gasoline Consumption. Renewable Energy, ARDL Model, Manufacturing Sector Output\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4205989/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4205989/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis research examined how gasoline and renewable energy use affects output in Nigeria\\u0026rsquo;s manufacturing sector from 1990 to 2022, using data from the Central Bank of Nigeria\\u0026rsquo;s Statistical Bulletin and the World Development Indicators. It employed the Auto-Regressive Distributed Lag (ARDL) model for its analysis. The Bounds co-integration test revealed the presence of a long-term relationship among the variables selected for the study. The findings reveal that in the short term, gasoline consumption marginally boosts manufacturing output, but it significantly hampers it over the long term. On the other hand, renewable energy\\u0026rsquo;s influence on manufacturing output is negligible yet positive in both the short and long term. The study suggests that by shifting focus from gasoline to renewable energy sources, Nigeria can enhance the resilience, sustainability, and innovation of its manufacturing sector, thus aligning with international environmental objectives and boosting economic growth.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Impact of Gasoline and Renewable Energy Consumption on Manufacturing Sector Output in Nigeria: New Evidence From ARDL Model\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-04-09 17:24:15\",\"doi\":\"10.21203/rs.3.rs-4205989/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-04-05T09:08:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-04-04T04:30:20+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Swiss Journal of Economics and Statistics\",\"date\":\"2024-04-02T10:37:05+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"swiss-journal-of-economics-and-statistics\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"sjes\",\"sideBox\":\"Learn more about [Swiss Journal of Economics and Statistics](https://sjes.springeropen.com/)\",\"snPcode\":\"41937\",\"submissionUrl\":\"https://submission.nature.com/new-submission/41937/3\",\"title\":\"Swiss Journal of Economics and Statistics\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Open\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"719e1e8c-5a32-406c-ad67-02a3309c9ce8\",\"owner\":[],\"postedDate\":\"April 9th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-04-09T17:24:15+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-04-09 17:24:15\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4205989\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4205989\",\"identity\":\"rs-4205989\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}