{"paper_id":"3cc10794-1bcf-481f-bc15-ac55bb6dcef0","body_text":"Macroeconomic Influencing Factors on Co2 Emissions in Rwanda: Short-Run Dynamics and Long-Run Equilibrium | 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 Macroeconomic Influencing Factors on Co2 Emissions in Rwanda: Short-Run Dynamics and Long-Run Equilibrium Dekkiche Djamal, Laila Oulad Brahim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4602302/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 aims to study the determinants of the emission of carbon dioxide in Rwanda during the period 1990–2022, considering foreign direct investment, gross domestic product, the industrial sector, and the consumption of renewable energies as explanatory variables. The ARDL model was used to test the short- and long-term relationship between variables, The results of the study concluded that all independent variables have a negative impact on the emission of carbon dioxide in the long term, while in the short term, the results found a positive impact of both foreign investment, domestic output and composite industries on the emission of carbon dioxide in Rwanda, while the industrial sector and the consumption of renewable energies have a negative impact. The results also concluded that GDP is the largest contributor to the emission of carbon dioxide in Rwanda compared to the impact of other variables. This indicates that the rapid growth rates recorded by Rwanda have negatively affected the emission of CO2, as the increase in GDP in Rwanda requires the use of energy, and some energies eventually generate carbon dioxide emissions. The study recommended the need to promote the use of renewable energy and reduce dependence on fossil fuels, in addition to improving energy efficiency in all economic sectors such as the use of bicycles and electric vehicles. The study encourages foreign and domestic investments in clean and environmentally friendly technologies and expands investment in research and development to discover alternative energy sources that maintain high productivity and low levels of CO2 emissions. It also proposes carbon taxes to incentivize companies to reduce their footprint. CO2 emission foreign investment domestic product renewable energies industrial sector Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction There in the Great Lakes region of Central Africa in a small state ravaged by war and massacres called Rwanda, the compass leads you to a success story and a paradigm of an economic miracle. Rwanda, drowned for years in the quagmire of a war that had wrecked everything, soon dusted and rose from the ashes, and inspired the way of adults to achieve in a matter of years. The country's economy has become the fastest-growing in Africa in recent years. Between 2000 and 2015, its economy grew at a rate of 9% per year, becoming one of the world's most important destinations for investors and tourists. Environmental degradation is a challenge in developing countries. Increasing GDP and energy use in economic sectors are among the most important contributors to increasing energy consumption that causes harm to the environment, yet the consequences of environmental degradation cannot be ignored. The main objective of this research is to study the determinants of CO2 emissions using annual data from 1990 to 2022 in Rwanda. Rwanda is one of the African countries with the fastest economic growth rate in Africa. However, like other countries in the world, this growth is usually accompanied by a significant increase in energy consumption and environmental problems, such as CO2 emissions. Rwanda's per capita emission level has remained around 0.12 m CO2 (less than 1%), ranking 153 rd in the world, and is expected to rise rapidly in the near future. Foreign investment, economic growth, and industrialization have a significant impact on Rwanda's environment. According to the Third National Report (2021), Rwanda is likely to become a source of carbon emissions in 2024 if the recommended mitigation option is not implemented. In fact, between 2014 and 2022, estimated CO2 emissions from the energy sector increased at a rate of 7.47 percent per year. Greenhouse gas emissions have increased in tandem with rising fuel consumption and GDP, Rwanda's CO2 emissions are expected to double from 2015 to 2030, growing from 5.3 to 12.1 Mt CO2 eq due to increased energy use from fossil fuels in business and road transport. This climate change-related problem is related to the energy demand from fossil fuels in Rwanda. The added value of this work compared to previous studies lies in addressing one of the African developing countries, as most of the studies that dealt with the issue of carbon dioxide emissions have only touched the developed countries, and the studies that included developing and African countries on this subject are few if not non-existent. In addition, through this research, we will try to search for ways to enhance the economic growth that Rwanda has experienced in the recent period with the reduction of carbon dioxide emissions, and to increase all economic growth, foreign direct investment, the industrial sector, and the consumption of renewable energies (study variables) in Rwanda in parallel with reducing carbon dioxide emissions. Thus, Rwanda will be able to enjoy a high income with a clean environment. In order to study the determinants of increasing the emission of carbon dioxide in Rwanda, this work will be divided into three main axes, starting from presenting previous studies that dealt with the determinants or variables that contribute to the emission of carbon dioxide through to the theoretical framework of the study, which is an analytical study of the development of these variables during the study period. Finally, present the econometric study and discuss the results, recommendations, and proposals based on the results reached. 1. Literature review Empirical research has investigated the impact of several factors on carbon dioxide emissions, such as industry, GDP, Manufacturing, foreign direct investment net, renewable energy consumption, Energy Consumption, and Renewable energy. The interrelationships among them will be examined in the following section. Various studies have examined the factors that influence CO2 emissions in a particular country or set of countries, employing various econometric techniques. Several of these studies comprise: The research conducted by (Chin et al., 2018) examined the factors that influence the release of CO2 in Malaysia by employing the Environmental Kuznets Curve and ARDL methodology. The variables considered in the analysis were CO2 emissions, real GDP, China's FDI outflow, and the value of vertical intra-industry trade in the manufacturing sector. The data covered the years 1997 to 2014. The findings demonstrated that economic expansion is the primary factor responsible for the release of CO2 into the atmosphere. Consequently, it was recommended that the Malaysian government closely oversee the execution of environmentally conscious growth strategies in order to promote sustainability while safeguarding the quality of the environment. (Pao & Tsai, 2010) examined carbon dioxide (CO2) emissions in the BRICs countries (Brazil, Russia, India, and China) by employing a panel vector error correction model over the period spanning from 1978 to 2005. Their research uncovered a substantial two-way causal relationship between emissions and foreign direct investment (FDI), as well as between emissions and energy consumption, and energy consumption and FDI. The report advised BRIC countries to allocate resources towards developing infrastructure for energy efficiency and enhancing energy conservation programs in order to mitigate emissions without compromising economic growth. The study conducted by (Jošić & Žmuk, 2022) employed a dynamic panel and GMM model to examine the primary factors influencing worldwide CO2 emissions between 1995 and 2015 in 115 nations. The variables encompassed in the analysis are GDP per capita, trade, foreign direct investment (FDI), energy consumption, urban population, total population, population density, gross capital formation, poverty headcount ratio, industrial value added, international tourism, renewable power generation, and electricity production. The research yielded practical implications for governments and businesses to enhance their comprehension of the economic ramifications of human activities on the environment. (Wahyudi, 2024) investigated the correlation between renewable energy and CO2 emissions in Indonesia by employing VAR and VECM estimation methods. The study revealed that CO2 emissions exert a substantial and favorable influence in both the immediate and extended periods, but non-renewable energy plays a detrimental and noteworthy function in both the long and short term. (Tan et al., 2024) investigated the interconnectedness of carbon emissions, energy consumption, financial development, and economic growth in SAARC nations using a panel methodology. The study found that financial development, energy consumption, exports of products and services, and economic expansion had a positive impact on CO2 emissions, indicating that these factors contribute to the increase in carbon emissions. (Elmonshid et al., 2024) conducted a study to examine how financial efficiency and renewable energy use affect the decrease of CO2 emissions in economies of the Gulf Cooperation Council (GCC). They used a panel data quantile regression approach to evaluate data from 2001 to 2021. The results highlighted the significance of cultivating effectiveness in financial institutions, encouraging environmentally friendly innovation, and extending the use of renewable energy sources in order to decrease emissions. (Abbas et al., 2023) investigated the impact of population aging, urbanization, and institutional quality on carbon dioxide (CO2) emissions in South Asia from 1996 to 2019 using the Coefficient-Stationary Autoregressive Distributed Lag (CS-ARDL) method. The study discovered that the process of population aging and urbanization led to an increase in carbon emissions, whereas the quality of institutions had a mitigating effect on emissions throughout the region. (Tokpah et al., 2023) performed a comprehensive analysis of the relationship between economic growth and carbon emissions in 15 developing and developed countries from 1991 to 2019. The study utilized the PMG-ARDL methodology. The findings indicate that both foreign direct investment (FDI) and quadratic gross domestic product (GDP) have a significant and negative impact on carbon emissions in developed countries. Moreover, an increase in FDI leads to a reduction in emissions in these nations. (Raihan, 2023) conducted a systematic analysis to examine the impact of economic growth, energy consumption, and agricultural value added on carbon dioxide (CO2) emissions in Vietnam. The study utilized advanced econometric techniques such as Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) to analyze data spanning from 1984 to 2020. The results revealed that economic expansion and energy consumption contribute to environmental degradation, but agricultural value-added enhances environmental quality by reducing CO2 emissions. (Adebayo & Beton Kalmaz, 2021) conducted a study on the factors that influence carbon dioxide (CO2) emissions in Egypt. They employed the autoregressive distributed lag (ARDL) model to analyze data from 1971 to 2014. The analysis revealed a strong and meaningful correlation between energy consumption and CO2 emissions, while there was no significant association observed between urbanization or gross capital formation and CO2 emissions. The study revealed a strong correlation between GDP growth and CO2 emissions, emphasizing the need for policymakers to develop environmental strategies that encourage sustainable urbanization and the use of clean energy. (Adebayo et al., 2020) conducted a study on the factors that influence CO2 emissions in MINT economies from 1980 to 2018, employing panel co-integration analysis. The study revealed a positive correlation between CO2 emissions and energy consumption, with urbanization exerting a positive influence on CO2 levels and trade showing a negative association with CO2. (Appiah et al., 2018) examined the cause-and-effect connection between agricultural productivity and CO2 emissions in certain developing countries. They employed FMOLS and DOLS techniques to evaluate data from 1971 to 2013. The empirical findings suggest that higher levels of economic growth, agricultural production index, and livestock production index are positively associated with increased CO2 emissions. Conversely, higher levels of energy consumption and population are associated with environmental improvements. (Islam et al., 2021) conducted a study to analyze the influence of globalization, foreign direct investment (FDI), and energy consumption on carbon dioxide (CO2) emissions in Bangladesh. The study included the period from 1972 to 2016 and used the autoregressive distributed lag (ARDL) model. The study revealed that globalization, foreign direct investment (FDI), and innovation have an adverse impact on CO2 emissions. Conversely, economic growth, trade, energy consumption, and urbanization have a favorable influence on CO2 emissions, hence contributing to environmental degradation. The report suggests promoting globalization, foreign direct investment (FDI), and innovation, while also ensuring the efficient use of income growth, trade opportunities, energy consumption, urbanization, and institutional quality to enhance environmental quality in Bangladesh. By employing an ARDL strategy, (Hatmanu et al., 2022) investigated the factors influencing CO2 emissions in Bulgaria and Romania. From 1980 to 2019, the study looked at four key variables: carbon dioxide emissions, GDP, energy consumption, and urbanization rate. In both nations, the data showed that the determining factors had a lasting impact on CO2 emissions per capita. According to the results, energy consumption per capita is the main driver in the short term, but in the long run, changes in GDP per capita and energy consumption per capita both have a substantial influence on CO2 emissions per capita in Romania. Similarly, throughout the long and medium term, Bulgaria showed a positive link between CO₂ emissions per capita and energy use per capita. There is a positive long-term consequence for Romania from the fast urbanization in both nations, which has a major influence on CO2 emissions. The place of this research with other studies The majority of research on climate change has mostly concentrated on industrialized countries, with limited emphasis on developing countries. This overlooks the reality that African countries will face the greatest susceptibility to the impacts of climate change as a result of their economy's sensitivity to climate and their limited capacity for adaptation and mitigation technology. Our research will focus on Rwanda, an African country experiencing significant growth, with a growth rate of 7.5% in 2022, based on a review of past studies. There is a lack of available studies on the factors that influence CO2 emissions in this country like industry (including construction), value added (constant 2015 US $ ), GDP (constant 2015 US $ ), Manufacturing, value added (% of GDP), foreign direct investment net inflows (BoP, current US $ ), renewable energy consumption (% of total final energy consumption), world - Energy Consumption per capita - Million Btu per Person, Renewable energy consumption (% of total final energy consumption). In addition, we will endeavor to incorporate some explanatory factors that could elucidate the causes for the rise in CO2 emissions in Rwanda, which amounted to 0.12 kg per 2015 US $ of GDP. Given this information, officials in this country can explore strategies to decrease CO2 emissions and promote the growth of clean industries. This study utilizes the ARDL model. The structure of this study is as follows: Section 2 examines the correlation between CO2 emissions and many other parameters. Section 3 provides an overview of the process used to acquire data and the methods employed. The empirical findings are presented in Section 4. The study conclusion is in Section 5. 2. Study Methodology 2.1. Study data This study aims to test the determinants of CO2 emission in Rwanda through the use of some explanatory variables represented in GDP, foreign direct investment, the industrial sector and the consumption of renewable energies. Using the linear regression model for the distributed gaps ARDL, annual data were used during the period 1990–2022 selected from the World Bank and the International Monetary Fund, based on the theoretical framework and previous studies. In this part, we will try to project the theoretical study on the practical side by addressing three main parts: Definition of the study variables Test data stationarity and static through ADF and PP testing application ARDL Model Boundary Test Study the effect of dependent variables on the emission of carbon dioxide in the short and long term through the ARDL technique. 2.2. Study Model and Variables This research employs the distributed time gaps model (ARDL) along, with co-integration testing to investigate if there is a lasting interconnected relationship, among variables. The error correction coefficient (ECM) of the ARDL model was derived to examine short-term dynamics. In this research, the ARDL model was employed to examine how independent variables influence the variable, over the study period well as the effects of variables, with a time lag. The ARDL model combines aspects of both a model. Distributed lag models. The ARDL model has an advantage in that it is considered to be an efficient model. This is because it can be applied to data samples. Moreover, the ARDL model allows us to simultaneously determine both term and long-term relationships. In order to study the emission of carbon dioxide in the State of Rwanda during the period 1990–2022, an econometric model was used that was formulated according to(Chin et al., 2018), (Pao & Tsai, 2010) in (Wahyudi, 2024) which the researchers relied on an econometric model consisting mainly of the emission of carbon dioxide CO2 as a dependent variable and both domestic product, foreign direct investment and the consumption of renewable energies as independent variables (Table 1 ) Thus, the model that we will adopt in this study is the same model on which the aforementioned studies were based, with the addition of some other explanatory variables that have a relationship or impact on the increase in carbon dioxide emission, such as the industrial sector ( Eq. 1 ). In light of the information provided the approach taken in this research will be as described below: $$\\varvec{C}\\varvec{O}2 \\varvec{e}\\varvec{m}\\varvec{i}\\varvec{s}\\varvec{s}\\varvec{i}\\varvec{o}\\varvec{n}\\varvec{s} =\\varvec{f}\\left(\\begin{array}{c}Foreign direct investment , GDP, industry value added, manufacturing\\\\ value added, renewable energy consuption\\end{array}\\right)$$ Eq. 1 After converting the variables into logarithms in order to give more homogeneity to the data because of the difference in the unit of measurement, ( Eq. 1 ) becomes of the form : $${\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}_{\\varvec{t}}= {\\varvec{a}}_{0}+ {\\varvec{a}}_{1}{\\varvec{l}\\varvec{n}\\varvec{F}\\varvec{D}\\varvec{I}}_{\\varvec{t}}+{\\varvec{a}}_{2}{\\varvec{l}\\varvec{n}\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{t}}+{\\varvec{a}}_{3}{\\varvec{l}\\varvec{n}\\varvec{I}\\varvec{V}\\varvec{A}}_{\\varvec{t}}+{\\varvec{a}}_{4}{{\\varvec{l}\\varvec{n}\\varvec{M}\\varvec{V}\\varvec{A}}_{\\varvec{t}}+{\\varvec{a}}_{5}{\\varvec{l}\\varvec{n}\\varvec{R}\\varvec{E}\\varvec{C}}_{\\varvec{t}}+\\varvec{u}}_{\\varvec{t}}$$ Eq. 2 where \\({\\varvec{u}}_{\\varvec{t}}\\) is the error, \\({\\varvec{a}}_{0}\\) is the constant, and is both \\({ \\varvec{a}}_{1}\\) , \\({\\varvec{a}}_{2}\\) and \\({\\varvec{a}}_{3}\\) is the coefficients of the variables. The ARDL model ( Pesaran et al., 2001) used in this study is written as: $$\\varDelta {\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}_{\\varvec{t}}={\\varvec{a}}_{0}+\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\beta }}_{\\varvec{i}} \\varDelta {\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}_{\\varvec{t}-1}+\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\delta }}_{\\varvec{i}} \\varDelta {\\varvec{l}\\varvec{n}\\varvec{F}\\varvec{D}\\varvec{I}}_{\\varvec{t}-1}$$ + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\gamma }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{t}-1}+\\) \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\phi }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{I}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\pi }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{M}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\rho }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{I}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{H}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{F}\\varvec{D}\\varvec{I}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{K}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{L}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{I}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{L}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{M}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{L}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{R}\\varvec{E}\\varvec{C}}_{\\varvec{t}-1}\\) + \\({\\varvec{\\epsilon }}_{\\varvec{t}}\\) Eq. 3 where: \\({\\varvec{\\beta }}_{\\varvec{i}}\\) \\({\\varvec{\\delta }}_{\\varvec{i}}\\) , \\({\\varvec{\\gamma }}_{\\varvec{i}}\\) , \\({\\varvec{\\phi }}_{\\varvec{i}}\\) , \\(\\varvec{\\pi }\\) , \\(\\varvec{\\rho }\\) represent constants coefficients and \\({\\mathsf{ }\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{I}\\varvec{V}\\varvec{A}}\\) \\({\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{G}\\varvec{D}\\varvec{P}}\\) , \\({\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{F}\\varvec{D}\\varvec{I}}\\) , \\({\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}\\) \\({\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{M}\\varvec{V}\\varvec{A}}\\) \\({\\mathsf{\\lambda }}_{\\varvec{l}\\varvec{n}\\varvec{R}\\varvec{E}\\varvec{C}}\\) represent long-term coefficients. In the short term, the equation can be estimated from the figure: $$\\varDelta {\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}_{\\varvec{t}}={\\varvec{a}}_{0}+\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\beta }}_{\\varvec{i}} \\varDelta {\\varvec{l}\\varvec{n}\\varvec{C}\\varvec{O}2}_{\\varvec{t}-1}+\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\delta }}_{\\varvec{i}} \\varDelta {\\varvec{l}\\varvec{n}\\varvec{F}\\varvec{D}\\varvec{I}}_{\\varvec{t}-1}$$ + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\gamma }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{t}-1}+\\) \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\phi }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{I}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\pi }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{M}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\(\\sum _{\\varvec{i}=1}^{\\varvec{p}}{\\varvec{\\rho }}_{\\varvec{i}}\\) \\(\\varDelta {\\varvec{l}\\varvec{n}\\varvec{I}\\varvec{V}\\varvec{A}}_{\\varvec{t}-1}\\) + \\({\\mathsf{\\lambda }}_{\\varvec{E}\\varvec{C}\\varvec{M}}{\\varvec{E}\\varvec{C}\\varvec{M}}_{\\varvec{t}-1}\\) + \\({\\varvec{\\epsilon }}_{\\varvec{t}}\\) Eq. 4 where ECM is the error correction coefficient Variables in ( Eq. 1 ) are defined in Table 1 Table 1 Definition of the study variables Variable Definition Source Text CO2 CO2 emissions (kg per 2015 US $ of GDP) World Bank, World Development Indicators (WDI) FDI Foreign direct investment net inflows (BoP, current US $ ) World Bank, World Development Indicators (WDI) GDP GDP (constant 2015 US $ ) World Bank, World Development Indicators (WDI) IVA Industry (including construction), value added (constant 2015 US $ ) World Bank, World Development Indicators (WDI) MVA Manufacturing Value Added World Bank, World Development Indicators (WDI) REC Renewable energy consumption (% of total final energy consumption) World Bank, World Development Indicators (WDI) Source: Prepared by Researcher 2.3. Descriptive Analysis of Study Variables The descriptive analysis of the study aims to give an overview of the trends of the variables under study from a statistical point of view in addition to testing the normal distribution of them through the kurtosis coefficient and the probability of Jarque-Bera The results obtained are represented in Table 2 . below Table 2 Descriptive Statistics CO2 FDI GDP IVA MVA REC Median 0.174997 13300000 3.87E + 09 5.95E + 08 8.874426 75000 Mean 0.175199 97083217 4.88E + 09 7.93E + 08 9.810395 86.89267 Maximum 0.360281 3.66E + 08 1.12E + 10 2.05E + 09 18.30498 12000 Minimum 0.113265 1000 1.28E + 09 1.27E + 08 6.703950 54000 Std. Dev. 0.057166 1.20E + 08 2.81E + 09 5.34E + 08 2.962074 3.685403 Skewness 1.168957 0.868080 0.726984 0.750765 1.551555 -0.753542 Kurtosis 4.637207 2.214431 2.322327 2.386408 4.809051 2.359140 Jarque-Bera 10.18286 4.539214 3.216583 3.288859 16.12745 3.352504 Probability 0.006149 0.103353 0.200229 0.193123 000315 0.187074 sum 5.255959 2.91E + 09 1.46E + 11 2.38E + 10 3119 2606 Sum Sq. Dev. 0.094771 4.18E + 17 2.28E + 20 8.28E + 18 .254 393 Observations 30 30 30 30 30 30 Source: Prepared by the researcher using E-views 10 Table 2 shows an overview of both the factors that rely on each other and those that stand alone. In this research, the actual GDP is computed in US currency for the year 2015. The proportion of energy used is calculated against the total energy utilized. Additionally, carbon dioxide emissions are assessed in tons of carbon dioxide equivalent. Foreign direct investment is measured in current values in US dollars and the value added of the industrial sector in real terms in US dollars for 2015. Carbon dioxide (CO₂) is a natural greenhouse gas and is harmless in small amounts, but if it increases, it can affect productivity, carbon dioxide is released into the atmosphere from various sources; natural sources include animal respiration. Other sources include fossil fuels, burning crude oil, firewood, and other sources that have not been mentioned. The stages of development of the emission of carbon dioxide in Rwanda can be divided into three stages (Fig. 1 ), the first stage was from 1990 to 1993, which witnessed a decline, and then the emission rate rose until 1994 (the period of national devastation in Rwanda), and the next period until 2022 witnessed a continuous decline in the emission of CO2 by breeders. The average CO2 emission rate value is 0.17 t/m CO2 eq. ranges of 0,11 and 0,36. The degree of tolerance also shows a standard deviation of 0,05. During the study period in Rwanda, the average value of real GDP was US $ 4.88 billion. Real GDP ranges between US $ 0.12 billion and US $ 1.12 billion. The degree of inequality also shows a standard deviation of USD 0,281 billion, indicating that the data is not scattered away from the mean value. Rwanda witnessed a rise in the value of GDP during the length of the study period except for 1994 (Fig. 2 ), the year that witnessed the national war in Rwanda and the collapse of the Rwandan economy during which it recorded a value of $ 1.28 billion, which is the lowest value during the entire study period, after which it witnessed increasing growth rates that witnessed a qualitative leap, as the growth rate in 2021 reached a value of 10.87%, which is higher compared to some developed and emerging countries. For FDI variables, the added value of the industrial sector and renewable energies averaged $ 97083217 billion, $ 7.93E + 08 billion at constant prices 86.89267% of total energy consumption respectively, the degree of inequality also shows the standard deviation of $ 1.2 billion, $ 5.3 billion and $ 3.68 Rwanda, which has in the past suffered from problems of government financial corruption, has invested heavily in fighting corruption and political stationarity, attracting more foreign investors (Fig. 3 ). The government has developed clear and business-friendly economic policies, encouraging investment in various sectors such as agriculture, tourism, services, and manufacturing, which led to a rise in the value of investments starting in 2006. (Fig. 4 ) shows per capita energy consumption in Rwanda from 1990 to 1992, where there was a decrease in energy consumption followed by a rise in energy consumption between 1992 and the beginning of 1994. From mid-1994 until around 2008 there was a significant reduction in energy consumption. More precisely, the energy used for transport and cement production was the main objective of the study, and this was due to the genocide against the Tutsis in 1994 which was followed by the rebuilding of the state in various spheres of development. This was followed by a sharp rise in energy consumption used in transport and cement production from 2008 to date. This significant rise in energy consumption is the result of the growth of the transport and cement production sectors as one of the development indicators. 3. Results and discussion 3.1. Stationarity of Variables Through this test, we will try to study whether the variables are stationary or not to avoid the emergence of the problem of false regression, as it leads to good results with regard to the Student and Fisher tests as well as the correlation coefficient R 2 To test the stationarity of the variables, we perform a single root test for Dickie Fuller (Amassoma et al., 2018) using EViews 10 Table 3 Test for Stationarity in Time Series LNCO2 LNFDI LNGDP LNIVA LNMVA LNREC ADF test Level T-Stat -2.5403 -5.0862 -5.3034 -3.4342 -2.8075 0.4344 Prob 0.3081 0.0016** 0.0009** 0.0651 0.2050 0.9984 First Diff T-Stat -5.9140 -5.8199 -4.3954 -3.8562 -8.7183 -7.8838 Prob 0.0002** 0.0003 0.0079 0.0300** 0.0000** 0.0000** PP test Level T-Stat -2.4960 -5.4422 -6.1587 -4.5407 -2.7606 -1.4367 Prob 0.3278 0.0007** 0.0001** 0.0052 0.2211 0.8294 First Diff T-Stat -6.0421 -13.0714 -6.2023 -6.2355 -10.8545 -7.2611 Prob 0.0001** 0.0000** 0.0001** 0.0001** 0.0000** 0.0000** Order of integration 1 0 0 1 1 1 Optimal Lag lenght 1 1 2 1 1 2 1 (* *) Significant at the 5% 1 Indicates lag order selected by the criterion Source: Prepared by the researcher using E-views 10 From the results of Table 3 ., the following can be observed: The carbon dioxide emission variable LNCO2 shows non-stationarity at the given level due, to the tabulated value of 2.5403 being lower than the calculated value of 2.53 at a 5% significance level (ADF test). Additionally, the calculated value of 2.4960 is less than the value of 2.53, at a 5% significance level (PP test) leading to the acceptance of the null hypothesis H0 indicating the presence of a unit root. However, stationarity is achieved after taking the difference. (the calculated value is equal to -5.9140 by conducting the ADF test and − 6,0421 by conducting the PP test, which is less than the critical value of 2,55). Both LNFDI and LNGDP exhibit stationarity in their variables since the computed value of 3.02 is lower than the values (5.0862, 5.3034) for each variable. Consequently, we reject the hypothesis H0, which suggests the presence of a unit root and instead accept the alternative hypothesis H1 indicating that these variables are stationary, at the difference level. Other independent variables such as LNIVA, LNMVA, and LNREC are not stationary at the level because the tabular value of 0.4344 is less than the calculated value of 2,53 but stabilizes after making the first difference where the probability of acceptance becomes 0,000 > 0,05. Through the results in Table 4 , it is found that the lowest value of the optimal slowness score corresponds to the ARDL model ( 1 , 1 , 1 , 2 , 2 , 1 ) and therefore it is the model that we will adopt in this study. 3.2. Testing boundaries Boundary test results are shown in Table 4 Table 4 Bounds test for linear cointegration Model Specification F-Statistic Lower Bound Upper Bound Conclusion Nonlinear 6.572483 2.39 3.38 Cointegration Source : Authors' computation using EViews 10 software Through the results in Table 4, it is found that the value of an F statistic of 6.572483 exceeds the upper limit value of 3.38, F-Statistic = 3.38 < I(1) = 6.57 at the significance level of 5%, which indicates the existence of integration between variables as well as a long-term relationship (Mohd Nasir et al., 2021) 3.3. Short-Term and Long-Term Equilibrium Relationship This research utilizes the Distributed Time Lag (ARDL) model, along with co-integration testing to investigate whether there is a lasting interconnected relationship between factors. By doing this the ARDL models error correction coefficient (ECM) is calculated for term evaluation. The selection of the ARDL model, for this study was based on its ability to analyze how independent variables impact the variable throughout the study period as well as how variables influence one another over time. Moreover, the ARDL model merges elements of both the Autonomous Regression Model (AR). The Time Lag Model (DL). The ARDL approach is deemed impartial and effective since it performs well with datasets and can identify long-term relationships concurrently. The results of the estimation of the equilibrium relationship in the short and long term are shown in Table 5 Table 5 Short run and long equilibrium ardl lnco2 lniva lngdp lnmva lnfdi lnrec, lags (1 1 1 2 2 1) ec ARDL ( 1 , 1 , 1 , 2 , 2 , 1 ) regression Sample: 1992 thru 2019 Adj R-squared = 0.8638 Number of obs = 28 Root MSE = 0.0598 R-squared = 0.9294 Log likelihood = 48.855019 D.lnco2 Coefficient Std. err. t P>|t| ADJ C ( 1 ) LNCO2(-1)* -0.630554 0.184646 -3.41 0.004*** Long-Run LNFDI -0.077766 0.048176 -1.61 0.129 LNGDP -0.44221 0.5945802 -0.74 0.469 LNIVA -0.1212334 0.4783864 0.25 0.804 LNMVA -0.9232345 0.3436536 -2.69 0.018*** LNREC -3.838356 1.105964 -3.47 0.004*** Short-Run C 19.51835 7.915921 2.47 0.027*** D.LNFDI 0.0085932 0.0247088 0.35 0.733 D.LNFDI (-1) 0.0072703 0.0159785 0.46 0.656 LNGDP (-1) 0.6089215 0.4169097 1.46 0.166 D.LNIVA -0.1563153 0.2938253 -0.53 0.603 D.LNMVA 0.4488168 0.255202 1.76 0.100 D.LNMVA (-1) 0.3281899 0.2069601 1.59 0.135 D.LNREC -1.897024 0.8750575 -2.17 0.048*** Tests of the robustness of the model Normality Pr (Skewness) Pr(Kurtosis) Adj chi2( 2 ) Prob > chi2 0.9256 0.9536 0.01 0.9940 > 0,05 Heteroskedasticity chi2 ( 1 ) Prob > chi2 0.25 0.6184 > 0,05 Serial Correlation 1.570 0.2102 > 0,05 Source : Prepared by the researcher using Stata 15 Through the results of estimating the ARDL model, it is clear that: Through the results of the short-term and long-term relationship between the dependent variable represented in the emission of carbon dioxide Lnco2 and other independent variables, it was found that the error correction coefficient is negative, in both the signal and moral domains (probability = 0.004 < 0.05) suggesting a long-term balance relationship exists. and the existence of a mechanism to correct the error from the short term to the long term by a coefficient of 0.6305, which means that the chain cannot deviate away from each other and that convergence is achieved in the long term. We have an error correction coefficient value of 0.6305. This indicates that the adjustment speed is about 63,05% at a significant level of 1%. This means that the speed of the imbalance adjustment process is above average; it is about 63% in one year (annual data). Also, the independent variables explain the CO2 emission changes in Rwanda with a total value of 92% at a 1% confidence level (R-squared = 0.9294; in Table 5 In the short term, all the coefficients of the explained variables are negative in reference and significant in the case of both industrial production and consumption of renewable energies, where it was found that the increase in foreign investment in one unit leads to a decrease in carbon dioxide emissions by 0,077 units, which is consistent with the findings of both(Tokpah et al., 2023),(Islam et al., 2021),(Ahmad et al., 2019),(Rafique et al., 2020),(Abid et al., 2022), (Abid et al., 2022) in the case of developed countries, as they tend to divert their resources from increasing production to investing in renewable or clean energy. In the case (Tsaurai, 2021) of African countries, his study recommended the need to implement policies that attract foreign investors who follow environmentally friendly practices in manufacturing and composite industries. While in the long term, the relationship is direct with a coefficient of 0,088 units, which is the same results reached by each of (Pao & Tsai, 2010) (Doa & Dinhb, 2020),,(Naz et al., 2019),(Gule, 2021), (Panigrahi et al., 2020) and (Farooq et al., 2021) where the results of the Table 5 indicated.... The rise of foreign investment by one unit in the long term in Rwanda leads to an increase in CO2 emission by 0,0085 units. As for the gross domestic product in the long term, the results of the study indicated a negative relationship with the emission of carbon dioxide by a coefficient of 0.44 and intensity, which corresponds to what it has found (Tokpah et al., 2023). (Hatmanu et al., 2022) (Raihan, 2023), 2023 (Mikayilov et al., 2018),(Abid et al., 2022), (Doa & Dinhb, 2020) while in the short term, the results indicated a positive relationship with a coefficient of 0.60. This is because increasing GDP in Rwanda requires the use of energy, and some energies eventually generate carbon dioxide emissions. This is proportional to the findings of(Chin et al., 2018),(Naz et al., 2019),(Wen et al., 2022),(Adebayo & Beton Kalmaz, 2021), (Appiah et al., 2018) (Gule, 2021) (Islam et al., 2021), as well as through the results of Table 5 , it is shown that the GDP has a greater impact on the emission of carbon dioxide compared to the rest of the other variables (0,60 GDP coefficient,0.44 manufacturing value added coefficient) in Rwanda, which means that the increase in GDP and heavy industries in Rwanda leads to an increase in carbon emissions. The added value of the industrial sector has a negative relationship with the emission of carbon dioxide in the short term by a coefficient of 0.12 units, as the increase in the added value of the industrial sector by one unit leads to a decrease in the emission of carbon dioxide CO2 by 0.12 units, which is the same as what it reached… The added value of composite industries would negatively affect the emission of carbon dioxide in the short term according to the results of Table 5 , Its increase by one unit leads to a decrease in carbon dioxide emissions by 0.92 units, which is the same result, (Zhang et al., 2022) indicating that there is a possibility to reduce energy consumption and carbon dioxide emissions (Anwar et al., 2022) (Lin et al., 2014), while in the long term, a positive relationship has been recorded and it corresponds to its results, (Tian et al., 2014) as changing the production structure, especially in the construction and services sectors, is an important source of growth of carbon dioxide emissions. With regard to the consumption of renewable energies, the short and long-term results indicate that there is a negative and moral impact on the emission of CO2 by coefficients 3,83 and 1.89, respectively, which are the same results(Wahyudi, 2024), (Li & Haneklaus, 2022) (Elmonshid et al., 2024) (Hatmanu et al., 2022) in the study he conducted on Romania and reflected his findings in the State of Bulgaria as well as Panigrahi, who found a direct relationship. In Rwanda, policies related to the use of green and clean energy in transport such as the use of electric bicycles and electric vehicles were promoted and implemented by the Government of Rwanda. The reasons behind these policies are to reduce greenhouse gas emissions, including carbon emissions. In fact, greenhouse gas emissions lead to climate change and increased CO2 emissions, but nevertheless, Rwanda has various policies that are used to mitigate CO2 emissions such as focusing on the use of bicycles and green-powered cars that use electricity (battery) as a fuel. In addition, the improvement of renewable energy faces a big high-cost problem in Rwanda. Renewable energy sources can be produced because of the support of the Rwandan government due to its high costs. Furthermore, it should encourage the attraction of foreign investors in the energy sector while reducing tax exemptions. As for the study of structural changes, it was done by testing the cumulative total of the boxes of the restored remnants CUSUM OF squares, which are represented in Fig. 5 . Cusum test, This test is conducted to identify any alterations, in the data structure and assess the consistency of long-term goals, against short-term variables. According to Abbasi et al., 2021) maintaining stationarity involves ensuring that both test results fall within a 5% margin. Conclusion This research paper examined the impact of foreign direct investment, gross domestic product, the industrial sector, and the consumption of renewable energies on the emission of carbon dioxide in Rwanda during the period 1990–2022 using the ARDL model. Initially, the stationarity of the variables was tested to find out whether the variables were stationary in the first level or different and that they were not stationary in the second difference, which means the validity of using the ARDL model in this study. After that, the co-integration was tested to find out the existence of a balanced relationship between the dependent variable represented in the emission of carbon dioxide and the rest of the other explanatory variables, after conducting a stationarity test and a boundary test for co-integration, to then test the short- and long-term equilibrium relationship between carbon dioxide emission and independent variables. The results of the econometric study indicated that all independent variables have a negative impact on the emission of carbon dioxide in the long term, while in the short term, the results of the study concluded that there is a positive impact of both foreign investment, domestic output and composite industries on the emission of carbon dioxide in Rwanda, while the industrial sector and the consumption of renewable energies have a negative impact. The results also found that GDP is the largest contributor to the emission of carbon dioxide in Rwanda compared to the impact of other variables. This indicates that the rapid growth rates recorded by Rwanda have negatively affected the emission of CO2, as the increase in GDP in Rwanda requires the use of energy, and some energies eventually generate carbon dioxide emissions. Based on the experimental results, the study recommends the need to promote the use of renewable energy and reduce dependence on fossil fuels in addition to improving energy efficiency in all economic sectors such as the use of electric bicycles and electric vehicles. Encouraging foreign and domestic investments in clean and environmentally friendly technologies while intensifying efforts to expand investment in research and development (R&D) in order to discover alternative energy sources that lead to high productivity while maintaining low levels of carbon dioxide emissions. Not to mention imposing taxes on carbon emissions to motivate companies to reduce their carbon footprint and supporting research projects aimed at developing innovative solutions to address climate change while raising public awareness of the importance of preserving the environment and reducing greenhouse gas emissions. The government of Rwanda should continue to spend on green infrastructure and encourage investment in renewable energy through subsidies and incentives so that the economy can grow while reducing the threat of climate change by reducing emissions. In addition to the use of the policy tool related to environmental taxes to a large extent. In this case, taxing polluters due to pollution will contribute to mitigating the negative effects of pollution (emissions from manufacturing). Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The data that have been used are from a publicly available dataset. Ethical statement There is no animal study involved in the present study. CRediT authorship contribution statement We did not rely on anyone for data analysis or obtaining the results of the econometric study. Acknowledgments The authors did not receive any assistance as the research involves quantitative analysis of data obtained from the World Bank. Appendix A. Supplementary data There is no Supplementary data to this article References Abbasi, K. R., Adedoyin, F. F., Abbas, J., & Hussain, K. (2021). The impact of energy depletion and renewable energy on CO2 emissions in Thailand: Fresh evidence from the novel dynamic ARDL simulation. 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An Appreciated Response of Disaggregated Energies Consumption towards the Sustainable Growth: A debate on G-10 Economies. Energy , 254 , 124377. https://doi.org/10.1016/J.ENERGY.2022.124377 . 3rd Rwanda National Human Development Report Overview Policy Innovations and Human Development Rwanda’s Home-Grown Solutions (2021). Http://www.hdr.undp.org/ . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 05 Jul, 2024 Submission checks completed at journal 26 Jun, 2024 First submitted to journal 18 Jun, 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. 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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-4602302\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":323344406,\"identity\":\"51ff00a2-acfe-4dfa-a30e-e0089e708820\",\"order_by\":0,\"name\":\"Dekkiche Djamal\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Oran graduate school of economics\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Dekkiche\",\"middleName\":\"\",\"lastName\":\"Djamal\",\"suffix\":\"\"},{\"id\":323344407,\"identity\":\"764ff584-73a8-4041-95af-da6cdc072427\",\"order_by\":1,\"name\":\"Laila Oulad Brahim\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Ghardaia\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Laila\",\"middleName\":\"Oulad\",\"lastName\":\"Brahim\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-06-19 00:12:04\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4602302/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4602302/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":61385999,\"identity\":\"59d3742e-3a7c-4459-9db5-5e96d0dfbb5c\",\"added_by\":\"auto\",\"created_at\":\"2024-07-30 07:07:16\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":39483,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCO2 emissions, (kg per 2015 US$ of GDP)\\u003c/p\\u003e\\n\\u003cp\\u003eSource :(world bank, 2022)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4602302/v1/84d0c6251aafe221b4635404.png\"},{\"id\":61386002,\"identity\":\"4e85ec2e-b01f-44b7-9b34-cce390f6c741\",\"added_by\":\"auto\",\"created_at\":\"2024-07-30 07:07:17\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":33826,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eGDP, (constant 2015 US$)\\u003c/p\\u003e\\n\\u003cp\\u003eSource :(world bank, 2022)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4602302/v1/5e6a02e5f19d97df1b98003e.png\"},{\"id\":61385998,\"identity\":\"a320828e-5ae6-4403-9b2e-387af51ac8c2\",\"added_by\":\"auto\",\"created_at\":\"2024-07-30 07:07:16\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":36764,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eForeign direct investment net inflows (BoP, current US$)\\u003c/p\\u003e\\n\\u003cp\\u003eSource :(world bank, 2022)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4602302/v1/cee2a70f66b5e4f3c314cc13.png\"},{\"id\":61386003,\"identity\":\"8d3560d3-59a7-42cb-9355-b16a6763c367\",\"added_by\":\"auto\",\"created_at\":\"2024-07-30 07:07:18\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":35455,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRenewable energy consumption (% of total final energy consumption)\\u003c/p\\u003e\\n\\u003cp\\u003eSource :(world bank, 2022)\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4602302/v1/df491fcda3e65bc9079973f8.png\"},{\"id\":61386000,\"identity\":\"2dad2ee7-3da6-4aa6-a508-c0b07a181d34\",\"added_by\":\"auto\",\"created_at\":\"2024-07-30 07:07:17\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":36001,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCusum test\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSource:\\u003c/strong\\u003e Prepared by the researcher using Stata 15\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4602302/v1/1a3140ad2bb3ddc7463bcab7.png\"},{\"id\":61386649,\"identity\":\"61206779-14b5-4a47-9545-158fd6ba435b\",\"added_by\":\"auto\",\"created_at\":\"2024-07-30 07:15:18\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":904161,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4602302/v1/8f860e39-b9d3-48a2-8cde-d0319a7e3fb1.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Macroeconomic Influencing Factors on Co2 Emissions in Rwanda: Short-Run Dynamics and Long-Run Equilibrium\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThere in the Great Lakes region of Central Africa in a small state ravaged by war and massacres called Rwanda, the compass leads you to a success story and a paradigm of an economic miracle.\\u003c/p\\u003e \\u003cp\\u003eRwanda, drowned for years in the quagmire of a war that had wrecked everything, soon dusted and rose from the ashes, and inspired the way of adults to achieve in a matter of years. The country's economy has become the fastest-growing in Africa in recent years. Between 2000 and 2015, its economy grew at a rate of 9% per year, becoming one of the world's most important destinations for investors and tourists.\\u003c/p\\u003e \\u003cp\\u003eEnvironmental degradation is a challenge in developing countries. Increasing GDP and energy use in economic sectors are among the most important contributors to increasing energy consumption that causes harm to the environment, yet the consequences of environmental degradation cannot be ignored. The main objective of this research is to study the determinants of CO2 emissions using annual data from 1990 to 2022 in Rwanda.\\u003c/p\\u003e \\u003cp\\u003eRwanda is one of the African countries with the fastest economic growth rate in Africa. However, like other countries in the world, this growth is usually accompanied by a significant increase in energy consumption and environmental problems, such as CO2 emissions. Rwanda's per capita emission level has remained around 0.12 m CO2 (less than 1%), ranking 153 rd in the world, and is expected to rise rapidly in the near future. Foreign investment, economic growth, and industrialization have a significant impact on Rwanda's environment.\\u003c/p\\u003e \\u003cp\\u003eAccording to the Third National Report (2021), Rwanda is likely to become a source of carbon emissions in 2024 if the recommended mitigation option is not implemented. In fact, between 2014 and 2022, estimated CO2 emissions from the energy sector increased at a rate of 7.47 percent per year. Greenhouse gas emissions have increased in tandem with rising fuel consumption and GDP, Rwanda's CO2 emissions are expected to double from 2015 to 2030, growing from 5.3 to 12.1 Mt CO2 eq due to increased energy use from fossil fuels in business and road transport. This climate change-related problem is related to the energy demand from fossil fuels in Rwanda.\\u003c/p\\u003e \\u003cp\\u003eThe added value of this work compared to previous studies lies in addressing one of the African developing countries, as most of the studies that dealt with the issue of carbon dioxide emissions have only touched the developed countries, and the studies that included developing and African countries on this subject are few if not non-existent. In addition, through this research, we will try to search for ways to enhance the economic growth that Rwanda has experienced in the recent period with the reduction of carbon dioxide emissions, and to increase all economic growth, foreign direct investment, the industrial sector, and the consumption of renewable energies (study variables) in Rwanda in parallel with reducing carbon dioxide emissions. Thus, Rwanda will be able to enjoy a high income with a clean environment.\\u003c/p\\u003e \\u003cp\\u003eIn order to study the determinants of increasing the emission of carbon dioxide in Rwanda, this work will be divided into three main axes, starting from presenting previous studies that dealt with the determinants or variables that contribute to the emission of carbon dioxide through to the theoretical framework of the study, which is an analytical study of the development of these variables during the study period. Finally, present the econometric study and discuss the results, recommendations, and proposals based on the results reached.\\u003c/p\\u003e\"},{\"header\":\"1. Literature review\",\"content\":\"\\u003cp\\u003eEmpirical research has investigated the impact of several factors on carbon dioxide emissions, such as industry, GDP, Manufacturing, foreign direct investment net, renewable energy consumption, Energy Consumption, and Renewable energy. The interrelationships among them will be examined in the following section.\\u003c/p\\u003e \\u003cp\\u003eVarious studies have examined the factors that influence CO2 emissions in a particular country or set of countries, employing various econometric techniques. Several of these studies comprise:\\u003c/p\\u003e \\u003cp\\u003eThe research conducted by (Chin et al., 2018) examined the factors that influence the release of CO2 in Malaysia by employing the Environmental Kuznets Curve and ARDL methodology. The variables considered in the analysis were CO2 emissions, real GDP, China's FDI outflow, and the value of vertical intra-industry trade in the manufacturing sector. The data covered the years 1997 to 2014. The findings demonstrated that economic expansion is the primary factor responsible for the release of CO2 into the atmosphere. Consequently, it was recommended that the Malaysian government closely oversee the execution of environmentally conscious growth strategies in order to promote sustainability while safeguarding the quality of the environment.\\u003c/p\\u003e \\u003cp\\u003e(Pao \\u0026amp; Tsai, 2010) examined carbon dioxide (CO2) emissions in the BRICs countries (Brazil, Russia, India, and China) by employing a panel vector error correction model over the period spanning from 1978 to 2005. Their research uncovered a substantial two-way causal relationship between emissions and foreign direct investment (FDI), as well as between emissions and energy consumption, and energy consumption and FDI. The report advised BRIC countries to allocate resources towards developing infrastructure for energy efficiency and enhancing energy conservation programs in order to mitigate emissions without compromising economic growth.\\u003c/p\\u003e \\u003cp\\u003eThe study conducted by (Jošić \\u0026amp; Žmuk, 2022) employed a dynamic panel and GMM model to examine the primary factors influencing worldwide CO2 emissions between 1995 and 2015 in 115 nations. The variables encompassed in the analysis are GDP per capita, trade, foreign direct investment (FDI), energy consumption, urban population, total population, population density, gross capital formation, poverty headcount ratio, industrial value added, international tourism, renewable power generation, and electricity production. The research yielded practical implications for governments and businesses to enhance their comprehension of the economic ramifications of human activities on the environment.\\u003c/p\\u003e \\u003cp\\u003e(Wahyudi, 2024) investigated the correlation between renewable energy and CO2 emissions in Indonesia by employing VAR and VECM estimation methods. The study revealed that CO2 emissions exert a substantial and favorable influence in both the immediate and extended periods, but non-renewable energy plays a detrimental and noteworthy function in both the long and short term.\\u003c/p\\u003e \\u003cp\\u003e(Tan et al., 2024) investigated the interconnectedness of carbon emissions, energy consumption, financial development, and economic growth in SAARC nations using a panel methodology. The study found that financial development, energy consumption, exports of products and services, and economic expansion had a positive impact on CO2 emissions, indicating that these factors contribute to the increase in carbon emissions.\\u003c/p\\u003e \\u003cp\\u003e(Elmonshid et al., 2024) conducted a study to examine how financial efficiency and renewable energy use affect the decrease of CO2 emissions in economies of the Gulf Cooperation Council (GCC). They used a panel data quantile regression approach to evaluate data from 2001 to 2021. The results highlighted the significance of cultivating effectiveness in financial institutions, encouraging environmentally friendly innovation, and extending the use of renewable energy sources in order to decrease emissions.\\u003c/p\\u003e \\u003cp\\u003e(Abbas et al., 2023) investigated the impact of population aging, urbanization, and institutional quality on carbon dioxide (CO2) emissions in South Asia from 1996 to 2019 using the Coefficient-Stationary Autoregressive Distributed Lag (CS-ARDL) method. The study discovered that the process of population aging and urbanization led to an increase in carbon emissions, whereas the quality of institutions had a mitigating effect on emissions throughout the region.\\u003c/p\\u003e \\u003cp\\u003e(Tokpah et al., 2023) performed a comprehensive analysis of the relationship between economic growth and carbon emissions in 15 developing and developed countries from 1991 to 2019. The study utilized the PMG-ARDL methodology. The findings indicate that both foreign direct investment (FDI) and quadratic gross domestic product (GDP) have a significant and negative impact on carbon emissions in developed countries. Moreover, an increase in FDI leads to a reduction in emissions in these nations.\\u003c/p\\u003e \\u003cp\\u003e(Raihan, 2023) conducted a systematic analysis to examine the impact of economic growth, energy consumption, and agricultural value added on carbon dioxide (CO2) emissions in Vietnam. The study utilized advanced econometric techniques such as Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) to analyze data spanning from 1984 to 2020. The results revealed that economic expansion and energy consumption contribute to environmental degradation, but agricultural value-added enhances environmental quality by reducing CO2 emissions.\\u003c/p\\u003e \\u003cp\\u003e(Adebayo \\u0026amp; Beton Kalmaz, 2021) conducted a study on the factors that influence carbon dioxide (CO2) emissions in Egypt. They employed the autoregressive distributed lag (ARDL) model to analyze data from 1971 to 2014. The analysis revealed a strong and meaningful correlation between energy consumption and CO2 emissions, while there was no significant association observed between urbanization or gross capital formation and CO2 emissions. The study revealed a strong correlation between GDP growth and CO2 emissions, emphasizing the need for policymakers to develop environmental strategies that encourage sustainable urbanization and the use of clean energy.\\u003c/p\\u003e \\u003cp\\u003e(Adebayo et al., 2020) conducted a study on the factors that influence CO2 emissions in MINT economies from 1980 to 2018, employing panel co-integration analysis. The study revealed a positive correlation between CO2 emissions and energy consumption, with urbanization exerting a positive influence on CO2 levels and trade showing a negative association with CO2.\\u003c/p\\u003e \\u003cp\\u003e(Appiah et al., 2018) examined the cause-and-effect connection between agricultural productivity and CO2 emissions in certain developing countries. They employed FMOLS and DOLS techniques to evaluate data from 1971 to 2013. The empirical findings suggest that higher levels of economic growth, agricultural production index, and livestock production index are positively associated with increased CO2 emissions. Conversely, higher levels of energy consumption and population are associated with environmental improvements.\\u003c/p\\u003e \\u003cp\\u003e(Islam et al., 2021) conducted a study to analyze the influence of globalization, foreign direct investment (FDI), and energy consumption on carbon dioxide (CO2) emissions in Bangladesh. The study included the period from 1972 to 2016 and used the autoregressive distributed lag (ARDL) model. The study revealed that globalization, foreign direct investment (FDI), and innovation have an adverse impact on CO2 emissions. Conversely, economic growth, trade, energy consumption, and urbanization have a favorable influence on CO2 emissions, hence contributing to environmental degradation. The report suggests promoting globalization, foreign direct investment (FDI), and innovation, while also ensuring the efficient use of income growth, trade opportunities, energy consumption, urbanization, and institutional quality to enhance environmental quality in Bangladesh.\\u003c/p\\u003e \\u003cp\\u003eBy employing an ARDL strategy, (Hatmanu et al., 2022) investigated the factors influencing CO2 emissions in Bulgaria and Romania. From 1980 to 2019, the study looked at four key variables: carbon dioxide emissions, GDP, energy consumption, and urbanization rate. In both nations, the data showed that the determining factors had a lasting impact on CO2 emissions per capita. According to the results, energy consumption per capita is the main driver in the short term, but in the long run, changes in GDP per capita and energy consumption per capita both have a substantial influence on CO2 emissions per capita in Romania. Similarly, throughout the long and medium term, Bulgaria showed a positive link between CO₂ emissions per capita and energy use per capita. There is a positive long-term consequence for Romania from the fast urbanization in both nations, which has a major influence on CO2 emissions.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eThe place of this research with other studies\\u003c/h2\\u003e \\u003cp\\u003eThe majority of research on climate change has mostly concentrated on industrialized countries, with limited emphasis on developing countries. This overlooks the reality that African countries will face the greatest susceptibility to the impacts of climate change as a result of their economy's sensitivity to climate and their limited capacity for adaptation and mitigation technology. Our research will focus on Rwanda, an African country experiencing significant growth, with a growth rate of 7.5% in 2022, based on a review of past studies. There is a lack of available studies on the factors that influence CO2 emissions in this country like industry (including construction), value added (constant 2015 US\\u003cspan\\u003e$\\u003c/span\\u003e), GDP (constant 2015 US\\u003cspan\\u003e$\\u003c/span\\u003e), Manufacturing, value added (% of GDP), foreign direct investment net inflows (BoP, current US\\u003cspan\\u003e$\\u003c/span\\u003e), renewable energy consumption (% of total final energy consumption), world - Energy Consumption per capita - Million Btu per Person, Renewable energy consumption (% of total final energy consumption).\\u003c/p\\u003e \\u003cp\\u003eIn addition, we will endeavor to incorporate some explanatory factors that could elucidate the causes for the rise in CO2 emissions in Rwanda, which amounted to 0.12 kg per 2015 US\\u003cspan\\u003e$\\u003c/span\\u003e of GDP. Given this information, officials in this country can explore strategies to decrease CO2 emissions and promote the growth of clean industries. This study utilizes the ARDL model.\\u003c/p\\u003e \\u003cp\\u003eThe structure of this study is as follows: Section 2 examines the correlation between CO2 emissions and many other parameters. Section 3 provides an overview of the process used to acquire data and the methods employed. The empirical findings are presented in Section 4. The study conclusion is in Section 5.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"2. Study Methodology\",\"content\":\"\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1. Study data\\u003c/h2\\u003e\\n \\u003cp\\u003eThis study aims to test the determinants of CO2 emission in Rwanda through the use of some explanatory variables represented in GDP, foreign direct investment, the industrial sector and the consumption of renewable energies. Using the linear regression model for the distributed gaps ARDL, annual data were used during the period 1990\\u0026ndash;2022 selected from the World Bank and the International Monetary Fund, based on the theoretical framework and previous studies. In this part, we will try to project the theoretical study on the practical side by addressing three main parts:\\u003c/p\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eDefinition of the study variables\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eTest data stationarity and static through ADF and PP testing application\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eARDL Model Boundary Test\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eStudy the effect of dependent variables on the emission of carbon dioxide in the short and long term through the ARDL technique.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2. Study Model and Variables\\u003c/h2\\u003e\\n \\u003cp\\u003eThis research employs the distributed time gaps model (ARDL) along, with co-integration testing to investigate if there is a lasting interconnected relationship, among variables. The error correction coefficient (ECM) of the ARDL model was derived to examine short-term dynamics.\\u003c/p\\u003e\\n \\u003cp\\u003eIn this research, the ARDL model was employed to examine how independent variables influence the variable, over the study period well as the effects of variables, with a time lag. The ARDL model combines aspects of both a model. Distributed lag models.\\u003c/p\\u003e\\n \\u003cp\\u003eThe ARDL model has an advantage in that it is considered to be an efficient model. This is because it can be applied to data samples. Moreover, the ARDL model allows us to simultaneously determine both term and long-term relationships.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\n \\u003cp\\u003eIn order to study the emission of carbon dioxide in the State of Rwanda during the period 1990\\u0026ndash;2022, an econometric model was used that was formulated according to(Chin et al., 2018), (Pao \\u0026amp; Tsai, 2010) in (Wahyudi, 2024) which the researchers relied on an econometric model consisting mainly of the emission of carbon dioxide CO2 as a dependent variable and both domestic product, foreign direct investment and the consumption of renewable energies as independent variables (Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e) Thus, the model that we will adopt in this study is the same model on which the aforementioned studies were based, with the addition of some other explanatory variables that have a relationship or impact on the increase in carbon dioxide emission, such as the industrial sector (\\u003cstrong\\u003eEq.\\u0026nbsp;1\\u003c/strong\\u003e).\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eIn light of the information provided the approach taken in this research will be as described below:\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\n \\u003cdiv class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e$$\\\\varvec{C}\\\\varvec{O}2 \\\\varvec{e}\\\\varvec{m}\\\\varvec{i}\\\\varvec{s}\\\\varvec{s}\\\\varvec{i}\\\\varvec{o}\\\\varvec{n}\\\\varvec{s} =\\\\varvec{f}\\\\left(\\\\begin{array}{c}Foreign direct investment , GDP, industry value added, manufacturing\\\\\\\\ value added, renewable energy consuption\\\\end{array}\\\\right)$$\\u003c/div\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eEq.\\u0026nbsp;1\\u003c/h2\\u003e\\n \\u003cp\\u003eAfter converting the variables into logarithms in order to give more homogeneity to the data because of the difference in the unit of measurement, (\\u003cstrong\\u003eEq.\\u0026nbsp;1\\u003c/strong\\u003e) \\u003cstrong\\u003ebecomes of the form\\u003c/strong\\u003e:\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Equb\\\" class=\\\"Equation\\\"\\u003e\\n \\u003cdiv class=\\\"mathdisplay\\\" id=\\\"FileID_Equb\\\" name=\\\"EquationSource\\\"\\u003e$${\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}_{\\\\varvec{t}}= {\\\\varvec{a}}_{0}+ {\\\\varvec{a}}_{1}{\\\\varvec{l}\\\\varvec{n}\\\\varvec{F}\\\\varvec{D}\\\\varvec{I}}_{\\\\varvec{t}}+{\\\\varvec{a}}_{2}{\\\\varvec{l}\\\\varvec{n}\\\\varvec{G}\\\\varvec{D}\\\\varvec{P}}_{\\\\varvec{t}}+{\\\\varvec{a}}_{3}{\\\\varvec{l}\\\\varvec{n}\\\\varvec{I}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}}+{\\\\varvec{a}}_{4}{{\\\\varvec{l}\\\\varvec{n}\\\\varvec{M}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}}+{\\\\varvec{a}}_{5}{\\\\varvec{l}\\\\varvec{n}\\\\varvec{R}\\\\varvec{E}\\\\varvec{C}}_{\\\\varvec{t}}+\\\\varvec{u}}_{\\\\varvec{t}}$$\\u003c/div\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eEq.\\u0026nbsp;2\\u003c/h2\\u003e\\n \\u003cp\\u003ewhere \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{u}}_{\\\\varvec{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the error, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{a}}_{0}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the constant, and is both\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({ \\\\varvec{a}}_{1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{a}}_{2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{a}}_{3}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is the coefficients of the variables.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eThe ARDL model\\u003c/strong\\u003e ( Pesaran et al., 2001) used in this study is written as:\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Equc\\\" class=\\\"Equation\\\"\\u003e\\n \\u003cdiv class=\\\"mathdisplay\\\" id=\\\"FileID_Equc\\\" name=\\\"EquationSource\\\"\\u003e$$\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}_{\\\\varvec{t}}={\\\\varvec{a}}_{0}+\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\beta }}_{\\\\varvec{i}} \\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}_{\\\\varvec{t}-1}+\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\delta }}_{\\\\varvec{i}} \\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{F}\\\\varvec{D}\\\\varvec{I}}_{\\\\varvec{t}-1}$$\\u003c/div\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u0026nbsp;\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\gamma }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u0026nbsp;\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{G}\\\\varvec{D}\\\\varvec{P}}_{\\\\varvec{t}-1}+\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\phi }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{I}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\pi }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{M}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\rho }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{I}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e+\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e +\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{H}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{F}\\\\varvec{D}\\\\varvec{I}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/h2\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u0026nbsp;\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{K}}\\\\)\\u003c/span\\u003e\\u0026nbsp;\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{G}\\\\varvec{D}\\\\varvec{P}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{L}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{I}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{L}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{M}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{L}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{R}\\\\varvec{E}\\\\varvec{C}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{\\\\epsilon }}_{\\\\varvec{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e\\n \\u003ch2\\u003eEq.\\u0026nbsp;3\\u003c/h2\\u003e\\n \\u003cp\\u003ewhere: \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{\\\\beta }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{\\\\delta }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e,\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{\\\\gamma }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e,\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{\\\\phi }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e ,\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varvec{\\\\pi }\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varvec{\\\\rho }\\\\)\\u003c/span\\u003e\\u003c/span\\u003erepresent constants coefficients and\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\mathsf{ }\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{I}\\\\varvec{V}\\\\varvec{A}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{G}\\\\varvec{D}\\\\varvec{P}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e,\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{F}\\\\varvec{D}\\\\varvec{I}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e, \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{M}\\\\varvec{V}\\\\varvec{A}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\mathsf{\\\\lambda }}_{\\\\varvec{l}\\\\varvec{n}\\\\varvec{R}\\\\varvec{E}\\\\varvec{C}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003erepresent long-term coefficients.\\u003c/p\\u003e\\n \\u003cp\\u003eIn the short term, the equation can be estimated from the figure:\\u003c/p\\u003e\\n \\u003cdiv id=\\\"Equd\\\" class=\\\"Equation\\\"\\u003e\\n \\u003cdiv class=\\\"mathdisplay\\\" id=\\\"FileID_Equd\\\" name=\\\"EquationSource\\\"\\u003e$$\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}_{\\\\varvec{t}}={\\\\varvec{a}}_{0}+\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\beta }}_{\\\\varvec{i}} \\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{C}\\\\varvec{O}2}_{\\\\varvec{t}-1}+\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\delta }}_{\\\\varvec{i}} \\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{F}\\\\varvec{D}\\\\varvec{I}}_{\\\\varvec{t}-1}$$\\u003c/div\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u0026nbsp;\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\gamma }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u0026nbsp;\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{G}\\\\varvec{D}\\\\varvec{P}}_{\\\\varvec{t}-1}+\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\phi }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{I}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\pi }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{M}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\sum _{\\\\varvec{i}=1}^{\\\\varvec{p}}{\\\\varvec{\\\\rho }}_{\\\\varvec{i}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\varDelta {\\\\varvec{l}\\\\varvec{n}\\\\varvec{I}\\\\varvec{V}\\\\varvec{A}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003cstrong\\u003e+\\u003c/strong\\u003e\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\mathsf{\\\\lambda }}_{\\\\varvec{E}\\\\varvec{C}\\\\varvec{M}}{\\\\varvec{E}\\\\varvec{C}\\\\varvec{M}}_{\\\\varvec{t}-1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e \\u003cstrong\\u003e+\\u003c/strong\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\({\\\\varvec{\\\\epsilon }}_{\\\\varvec{t}}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003eEq.\\u0026nbsp;4\\u003c/h2\\u003e\\n \\u003cp\\u003ewhere ECM is the error correction coefficient\\u003c/p\\u003e\\n \\u003cp\\u003eVariables in (\\u003cstrong\\u003eEq.\\u0026nbsp;1\\u003c/strong\\u003e) are defined in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDefinition of the study variables\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDefinition\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSource Text\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCO2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCO2 emissions (kg per 2015 US\\u003cspan\\u003e$\\u003c/span\\u003e of GDP)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWorld Bank, World Development Indicators (WDI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFDI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eForeign direct investment net inflows (BoP, current US\\u003cspan\\u003e$\\u003c/span\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWorld Bank, World Development Indicators (WDI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGDP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGDP (constant 2015 US\\u003cspan\\u003e$\\u003c/span\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWorld Bank, World Development Indicators (WDI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIVA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIndustry (including construction), value added (constant 2015 US\\u003cspan\\u003e$\\u003c/span\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWorld Bank, World Development Indicators (WDI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMVA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eManufacturing Value Added\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWorld Bank, World Development Indicators (WDI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eREC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRenewable energy consumption (% of total final energy consumption)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWorld Bank, World Development Indicators (WDI)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"3\\\"\\u003eSource: Prepared by Researcher\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.3. Descriptive Analysis of Study Variables\\u003c/h2\\u003e\\n \\u003cp\\u003eThe descriptive analysis of the study aims to give an overview of the trends of the variables under study from a statistical point of view in addition to testing the normal distribution of them through the kurtosis coefficient and the probability of Jarque-Bera\\u003c/p\\u003e\\n \\u003cp\\u003eThe results obtained are represented in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. below\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDescriptive Statistics\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCO2\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFDI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eGDP\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIVA\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMVA\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eREC\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMedian\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.174997\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e13300000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.87E\\u0026thinsp;+\\u0026thinsp;09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.95E\\u0026thinsp;+\\u0026thinsp;08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.874426\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e75000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMean\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.175199\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e97083217\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.88E\\u0026thinsp;+\\u0026thinsp;09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.93E\\u0026thinsp;+\\u0026thinsp;08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9.810395\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e86.89267\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMaximum\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.360281\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.66E\\u0026thinsp;+\\u0026thinsp;08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.12E\\u0026thinsp;+\\u0026thinsp;10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.05E\\u0026thinsp;+\\u0026thinsp;09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e18.30498\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e12000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMinimum\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.113265\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.28E\\u0026thinsp;+\\u0026thinsp;09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.27E\\u0026thinsp;+\\u0026thinsp;08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.703950\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e54000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStd. Dev.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.057166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.20E\\u0026thinsp;+\\u0026thinsp;08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.81E\\u0026thinsp;+\\u0026thinsp;09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.34E\\u0026thinsp;+\\u0026thinsp;08\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.962074\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.685403\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSkewness\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.168957\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.868080\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.726984\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.750765\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.551555\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.753542\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eKurtosis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.637207\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.214431\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.322327\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.386408\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.809051\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.359140\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eJarque-Bera\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e10.18286\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.539214\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.216583\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.288859\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e16.12745\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3.352504\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProbability\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.006149\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.103353\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.200229\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.193123\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e000315\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.187074\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003esum\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5.255959\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.91E\\u0026thinsp;+\\u0026thinsp;09\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.46E\\u0026thinsp;+\\u0026thinsp;11\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.38E\\u0026thinsp;+\\u0026thinsp;10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3119\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2606\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSum Sq. Dev.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.094771\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4.18E\\u0026thinsp;+\\u0026thinsp;17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.28E\\u0026thinsp;+\\u0026thinsp;20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8.28E\\u0026thinsp;+\\u0026thinsp;18\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e.254\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e393\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObservations\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSource:\\u003c/strong\\u003ePrepared by the researcher using E-views 10\\u003c/p\\u003e\\n \\u003cp\\u003eTable \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows an overview of both the factors that rely on each other and those that stand alone. In this research, the actual GDP is computed in US currency for the year 2015. The proportion of energy used is calculated against the total energy utilized. Additionally, carbon dioxide emissions are assessed in tons of carbon dioxide equivalent. Foreign direct investment is measured in current values in US dollars and the value added of the industrial sector in real terms in US dollars for 2015.\\u003c/p\\u003e\\n \\u003cp\\u003eCarbon dioxide (CO₂) is a natural greenhouse gas and is harmless in small amounts, but if it increases, it can affect productivity, carbon dioxide is released into the atmosphere from various sources; natural sources include animal respiration. Other sources include fossil fuels, burning crude oil, firewood, and other sources that have not been mentioned. The stages of development of the emission of carbon dioxide in Rwanda can be divided into three stages (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e), the first stage was from 1990 to 1993, which witnessed a decline, and then the emission rate rose until 1994 (the period of national devastation in Rwanda), and the next period until 2022 witnessed a continuous decline in the emission of CO2 by breeders.\\u003c/p\\u003e\\n \\u003cp\\u003eThe average CO2 emission rate value is 0.17 t/m CO2 eq. ranges of 0,11 and 0,36. The degree of tolerance also shows a standard deviation of 0,05.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n \\u003cp\\u003eDuring the study period in Rwanda, the average value of real GDP was US \\u003cspan\\u003e$\\u003c/span\\u003e4.88 billion. Real GDP ranges between US \\u003cspan\\u003e$\\u003c/span\\u003e0.12 billion and US \\u003cspan\\u003e$\\u003c/span\\u003e1.12\\u0026nbsp;billion. The degree of inequality also shows a standard deviation of USD 0,281\\u0026nbsp;billion, indicating that the data is not scattered away from the mean value.\\u003c/p\\u003e\\n \\u003cp\\u003eRwanda witnessed a rise in the value of GDP during the length of the study period except for 1994 (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), the year that witnessed the national war in Rwanda and the collapse of the Rwandan economy during which it recorded a value of \\u003cspan\\u003e$\\u003c/span\\u003e1.28 billion, which is the lowest value during the entire study period, after which it witnessed increasing growth rates that witnessed a qualitative leap, as the growth rate in 2021 reached a value of 10.87%, which is higher compared to some developed and emerging countries.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n \\u003cp\\u003eFor FDI variables, the added value of the industrial sector and renewable energies averaged \\u003cspan\\u003e$\\u003c/span\\u003e97083217 billion, \\u003cspan\\u003e$\\u003c/span\\u003e7.93E\\u0026thinsp;+\\u0026thinsp;08 billion at constant prices 86.89267% of total energy consumption respectively, the degree of inequality also shows the standard deviation of \\u003cspan\\u003e$\\u003c/span\\u003e 1.2 billion, \\u003cspan\\u003e$\\u003c/span\\u003e5.3 billion and \\u003cspan\\u003e$\\u003c/span\\u003e3.68\\u003c/p\\u003e\\n \\u003cp\\u003eRwanda, which has in the past suffered from problems of government financial corruption, has invested heavily in fighting corruption and political stationarity, attracting more foreign investors (Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). The government has developed clear and business-friendly economic policies, encouraging investment in various sectors such as agriculture, tourism, services, and manufacturing, which led to a rise in the value of investments starting in 2006.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\n \\u003cp\\u003e(Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) shows per capita energy consumption in Rwanda from 1990 to 1992, where there was a decrease in energy consumption followed by a rise in energy consumption between 1992 and the beginning of 1994. From mid-1994 until around 2008 there was a significant reduction in energy consumption.\\u003c/p\\u003e\\n \\u003cp\\u003eMore precisely, the energy used for transport and cement production was the main objective of the study, and this was due to the genocide against the Tutsis in 1994 which was followed by the rebuilding of the state in various spheres of development. This was followed by a sharp rise in energy consumption used in transport and cement production from 2008 to date. This significant rise in energy consumption is the result of the growth of the transport and cement production sectors as one of the development indicators.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results and discussion\",\"content\":\"\\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.1. Stationarity of Variables\\u003c/h2\\u003e\\n \\u003cp\\u003eThrough this test, we will try to study whether the variables are stationary or not to avoid the emergence of the problem of false regression, as it leads to good results with regard to the Student and Fisher tests as well as the correlation coefficient R\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eTo test the stationarity of the variables, we perform a single root test for Dickie Fuller (Amassoma et al., 2018) using EViews 10\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eTest for Stationarity in Time Series\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNCO2\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNFDI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNGDP\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNIVA\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNMVA\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNREC\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003eADF test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLevel\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT-Stat\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.5403\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.0862\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.3034\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-3.4342\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.8075\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.4344\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProb\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3081\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0016**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0009**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0651\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.2050\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9984\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eFirst Diff\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT-Stat\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.9140\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.8199\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-4.3954\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-3.8562\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-8.7183\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-7.8838\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProb\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0002**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0079\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0300**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0000**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0000**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"4\\\"\\u003e\\n \\u003cp\\u003ePP test\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eLevel\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT-Stat\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.4960\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-5.4422\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-6.1587\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-4.5407\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.7606\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.4367\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProb\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3278\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0007**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0001**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0052\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.2211\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.8294\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eFirst Diff\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT-Stat\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-6.0421\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-13.0714\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-6.2023\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-6.2355\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-10.8545\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-7.2611\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProb\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0001**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0000**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0001**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0001**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0000**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0000**\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eOrder of integration\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eOptimal Lag lenght\\u003csup\\u003e1\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"9\\\"\\u003e(* *) Significant at the 5%\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"9\\\"\\u003e\\u003csup\\u003e1\\u003c/sup\\u003e Indicates lag order selected by the criterion\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSource:\\u003c/strong\\u003ePrepared by the researcher using E-views 10\\u003c/p\\u003e\\n \\u003cp\\u003eFrom the results of Table \\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e., the following can be observed:\\u003c/p\\u003e\\n \\u003cp\\u003eThe carbon dioxide emission variable LNCO2 shows non-stationarity at the given level due, to the tabulated value of 2.5403 being lower than the calculated value of 2.53 at a 5% significance level (ADF test). Additionally, the calculated value of 2.4960 is less than the value of 2.53, at a 5% significance level (PP test) leading to the acceptance of the null hypothesis H0 indicating the presence of a unit root. However, stationarity is achieved after taking the difference. (the calculated value is equal to -5.9140 by conducting the ADF test and \\u0026minus;\\u0026thinsp;6,0421 by conducting the PP test, which is less than the critical value of 2,55).\\u003c/p\\u003e\\n \\u003cul\\u003e\\n \\u003cli\\u003e\\n \\u003cp\\u003eBoth LNFDI and LNGDP exhibit stationarity in their variables since the computed value of 3.02 is lower than the values (5.0862, 5.3034) for each variable. Consequently, we reject the hypothesis H0, which suggests the presence of a unit root and instead accept the alternative hypothesis H1 indicating that these variables are stationary, at the difference level.\\u003c/p\\u003e\\n \\u003c/li\\u003e\\n \\u003c/ul\\u003e\\n \\u003cp\\u003eOther independent variables such as LNIVA, LNMVA, and LNREC are not stationary at the level because the tabular value of 0.4344 is less than the calculated value of 2,53 but stabilizes after making the first difference where the probability of acceptance becomes 0,000\\u0026thinsp;\\u0026gt;\\u0026thinsp;0,05.\\u003c/p\\u003e\\n \\u003cp\\u003eThrough the results in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, it is found that the lowest value of the optimal slowness score corresponds to the ARDL model (\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) and therefore it is the model that we will adopt in this study.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.2. Testing boundaries\\u003c/h2\\u003e\\n \\u003cp\\u003eBoundary test results are shown in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eBounds test for linear cointegration\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" style=\\\"width: 13.8034%;\\\"\\u003e\\n \\u003cp\\u003eModel Specification\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eF-Statistic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLower Bound\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" style=\\\"width: 32.5214%;\\\"\\u003e\\n \\u003cp\\u003eUpper Bound\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eConclusion\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" style=\\\"width: 13.8034%;\\\"\\u003e\\n \\u003cp\\u003eNonlinear\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6.572483\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" style=\\\"width: 32.5214%;\\\"\\u003e\\n \\u003cp\\u003e3.38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCointegration\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\u003cstrong\\u003eSource\\u003c/strong\\u003e: Authors\\u0026apos; computation using EViews 10 software\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThrough the results in Table 4, it is found that the value of an F statistic of 6.572483 exceeds the upper limit value of 3.38, F-Statistic\\u0026thinsp;=\\u0026thinsp;3.38\\u0026thinsp;\\u0026lt;\\u0026thinsp;I(1)\\u0026thinsp;=\\u0026thinsp;6.57 at the significance level of 5%, which indicates the existence of integration between variables as well as a long-term relationship (Mohd Nasir et al., 2021)\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.3. Short-Term and Long-Term Equilibrium Relationship\\u003c/h2\\u003e\\n \\u003cp\\u003eThis research utilizes the Distributed Time Lag (ARDL) model, along with co-integration testing to investigate whether there is a lasting interconnected relationship between factors. By doing this the ARDL models error correction coefficient (ECM) is calculated for term evaluation. The selection of the ARDL model, for this study was based on its ability to analyze how independent variables impact the variable throughout the study period as well as how variables influence one another over time. Moreover, the ARDL model merges elements of both the Autonomous Regression Model (AR). The Time Lag Model (DL). The ARDL approach is deemed impartial and effective since it performs well with datasets and can identify long-term relationships concurrently. The results of the estimation of the equilibrium relationship in the short and long term are shown in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eShort run and long equilibrium\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003eardl lnco2 lniva lngdp lnmva lnfdi lnrec, lags (1 1 1 2 2 1) ec\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003eARDL (\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) regression\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSample: 1992 thru 2019\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eAdj R-squared\\u0026thinsp;=\\u0026thinsp;0.8638\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eNumber of obs = 28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eRoot MSE = 0.0598\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eR-squared = 0.9294\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eLog likelihood\\u0026thinsp;=\\u0026thinsp;48.855019\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD.lnco2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCoefficient\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStd. err.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003et\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eP\\u0026gt;|t|\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eADJ C\\u003c/strong\\u003e (\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNCO2(-1)*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.630554\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.184646\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-3.41\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.004***\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003eLong-Run\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNFDI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.077766\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.048176\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.61\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.129\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNGDP\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.44221\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.5945802\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.74\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.469\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNIVA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.1212334\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.4783864\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.804\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNMVA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.9232345\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3436536\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.69\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.018***\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNREC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-3.838356\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.105964\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-3.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.004***\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"6\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eShort-Run\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19.51835\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7.915921\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.027***\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD.LNFDI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0085932\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0247088\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.733\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD.LNFDI (-1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0072703\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0159785\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.656\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLNGDP (-1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.6089215\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.4169097\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.46\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD.LNIVA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.1563153\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.2938253\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.603\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD.LNMVA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.4488168\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.255202\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.76\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.100\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD.LNMVA (-1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3281899\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.2069601\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.59\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.135\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eD.LNREC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-1.897024\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.8750575\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-2.17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.048***\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"5\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTests of the robustness of the model\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eNormality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePr (Skewness)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePr(Kurtosis)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAdj chi2(\\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProb\\u0026thinsp;\\u0026gt;\\u0026thinsp;chi2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9256\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9536\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.01\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.9940\\u0026thinsp;\\u0026gt;\\u0026thinsp;0,05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eHeteroskedasticity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003echi2 (\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eProb\\u0026thinsp;\\u0026gt;\\u0026thinsp;chi2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.25\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.6184\\u0026thinsp;\\u0026gt;\\u0026thinsp;0,05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSerial Correlation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1.570\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.2102\\u0026thinsp;\\u0026gt;\\u0026thinsp;0,05\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\" colspan=\\\"1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003cp\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSource :\\u003c/strong\\u003ePrepared by the researcher using Stata 15\\u003c/p\\u003e\\n \\u003cp\\u003eThrough the results of estimating the ARDL model, it is clear that:\\u003c/p\\u003e\\n \\u003cp\\u003eThrough the results of the short-term and long-term relationship between the dependent variable represented in the emission of carbon dioxide Lnco2 and other independent variables, it was found that the error correction coefficient is negative, in both the signal and moral domains (probability\\u0026thinsp;=\\u0026thinsp;0.004\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) suggesting a long-term balance relationship exists. and the existence of a mechanism to correct the error from the short term to the long term by a coefficient of 0.6305, which means that the chain cannot deviate away from each other and that convergence is achieved in the long term. We have an error correction coefficient value of 0.6305. This indicates that the adjustment speed is about 63,05% at a significant level of 1%. This means that the speed of the imbalance adjustment process is above average; it is about 63% in one year (annual data). Also, the independent variables explain the CO2 emission changes in Rwanda with a total value of 92% at a 1% confidence level (R-squared\\u0026thinsp;=\\u0026thinsp;0.9294; in Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eIn the short term, all the coefficients of the explained variables are negative in reference and significant in the case of both industrial production and consumption of renewable energies, where it was found that the increase in foreign investment in one unit leads to a decrease in carbon dioxide emissions by 0,077 units, which is consistent with the findings of both(Tokpah et al., 2023),(Islam et al., 2021),(Ahmad et al., 2019),(Rafique et al., 2020),(Abid et al., 2022), (Abid et al., 2022) in the case of developed countries, as they tend to divert their resources from increasing production to investing in renewable or clean energy. In the case (Tsaurai, 2021) of African countries, his study recommended the need to implement policies that attract foreign investors who follow environmentally friendly practices in manufacturing and composite industries. While in the long term, the relationship is direct with a coefficient of 0,088 units, which is the same results reached by each of (Pao \\u0026amp; Tsai, 2010) (Doa \\u0026amp; Dinhb, 2020),,(Naz et al., 2019),(Gule, 2021), (Panigrahi et al., 2020) and (Farooq et al., 2021) where the results of the Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e indicated.... The rise of foreign investment by one unit in the long term in Rwanda leads to an increase in CO2 emission by 0,0085 units.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\n \\u003cp\\u003eAs for the gross domestic product in the long term, the results of the study indicated a negative relationship with the emission of carbon dioxide by a coefficient of 0.44 and intensity, which corresponds to what it has found (Tokpah et al., 2023). (Hatmanu et al., 2022) (Raihan, 2023), 2023 (Mikayilov et al., 2018),(Abid et al., 2022), (Doa \\u0026amp; Dinhb, 2020) while in the short term, the results indicated a positive relationship with a coefficient of 0.60. This is because increasing GDP in Rwanda requires the use of energy, and some energies eventually generate carbon dioxide emissions. This is proportional to the findings of(Chin et al., 2018),(Naz et al., 2019),(Wen et al., 2022),(Adebayo \\u0026amp; Beton Kalmaz, 2021), (Appiah et al., 2018) (Gule, 2021) (Islam et al., 2021), as well as through the results of Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, it is shown that the GDP has a greater impact on the emission of carbon dioxide compared to the rest of the other variables (0,60 GDP coefficient,0.44 manufacturing value added coefficient) in Rwanda, which means that the increase in GDP and heavy industries in Rwanda leads to an increase in carbon emissions.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eThe added value of the industrial sector has a negative relationship with the emission of carbon dioxide in the short term by a coefficient of 0.12 units, as the increase in the added value of the industrial sector by one unit leads to a decrease in the emission of carbon dioxide CO2 by 0.12 units, which is the same as what it reached\\u0026hellip;\\u003c/p\\u003e\\n \\u003cp\\u003eThe added value of composite industries would negatively affect the emission of carbon dioxide in the short term according to the results of Table \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e, Its increase by one unit leads to a decrease in carbon dioxide emissions by 0.92 units, which is the same result, (Zhang et al., 2022) indicating that there is a possibility to reduce energy consumption and carbon dioxide emissions (Anwar et al., 2022) (Lin et al., 2014), while in the long term, a positive relationship has been recorded and it corresponds to its results, (Tian et al., 2014) as changing the production structure, especially in the construction and services sectors, is an important source of growth of carbon dioxide emissions.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\n \\u003cp\\u003eWith regard to the consumption of renewable energies, the short and long-term results indicate that there is a negative and moral impact on the emission of CO2 by coefficients 3,83 and 1.89, respectively, which are the same results(Wahyudi, 2024), (Li \\u0026amp; Haneklaus, 2022) (Elmonshid et al., 2024) (Hatmanu et al., 2022) in the study he conducted on Romania and reflected his findings in the State of Bulgaria as well as Panigrahi, who found a direct relationship. In Rwanda, policies related to the use of green and clean energy in transport such as the use of electric bicycles and electric vehicles were promoted and implemented by the Government of Rwanda. The reasons behind these policies are to reduce greenhouse gas emissions, including carbon emissions. In fact, greenhouse gas emissions lead to climate change and increased CO2 emissions, but nevertheless, Rwanda has various policies that are used to mitigate CO2 emissions such as focusing on the use of bicycles and green-powered cars that use electricity (battery) as a fuel. In addition, the improvement of renewable energy faces a big high-cost problem in Rwanda. Renewable energy sources can be produced because of the support of the Rwandan government due to its high costs. Furthermore, it should encourage the attraction of foreign investors in the energy sector while reducing tax exemptions.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eAs for the study of structural changes, it was done by testing the cumulative total of the boxes of the restored remnants CUSUM OF squares, which are represented in Fig. \\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e. Cusum test, This test is conducted to identify any alterations, in the data structure and assess the consistency of long-term goals, against short-term variables. According to \\u003cstrong\\u003eAbbasi et al., 2021)\\u003c/strong\\u003e maintaining stationarity involves ensuring that both test results fall within a 5% margin.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThis research paper examined the impact of foreign direct investment, gross domestic product, the industrial sector, and the consumption of renewable energies on the emission of carbon dioxide in Rwanda during the period 1990\\u0026ndash;2022 using the ARDL model. Initially, the stationarity of the variables was tested to find out whether the variables were stationary in the first level or different and that they were not stationary in the second difference, which means the validity of using the ARDL model in this study. After that, the co-integration was tested to find out the existence of a balanced relationship between the dependent variable represented in the emission of carbon dioxide and the rest of the other explanatory variables, after conducting a stationarity test and a boundary test for co-integration, to then test the short- and long-term equilibrium relationship between carbon dioxide emission and independent variables.\\u003c/p\\u003e \\u003cp\\u003eThe results of the econometric study indicated that all independent variables have a negative impact on the emission of carbon dioxide in the long term, while in the short term, the results of the study concluded that there is a positive impact of both foreign investment, domestic output and composite industries on the emission of carbon dioxide in Rwanda, while the industrial sector and the consumption of renewable energies have a negative impact. The results also found that GDP is the largest contributor to the emission of carbon dioxide in Rwanda compared to the impact of other variables. This indicates that the rapid growth rates recorded by Rwanda have negatively affected the emission of CO2, as the increase in GDP in Rwanda requires the use of energy, and some energies eventually generate carbon dioxide emissions.\\u003c/p\\u003e \\u003cp\\u003eBased on the experimental results, the study recommends the need to promote the use of renewable energy and reduce dependence on fossil fuels in addition to improving energy efficiency in all economic sectors such as the use of electric bicycles and electric vehicles.\\u003c/p\\u003e \\u003cp\\u003eEncouraging foreign and domestic investments in clean and environmentally friendly technologies while intensifying efforts to expand investment in research and development (R\\u0026amp;D) in order to discover alternative energy sources that lead to high productivity while maintaining low levels of carbon dioxide emissions. Not to mention imposing taxes on carbon emissions to motivate companies to reduce their carbon footprint and supporting research projects aimed at developing innovative solutions to address climate change while raising public awareness of the importance of preserving the environment and reducing greenhouse gas emissions.\\u003c/p\\u003e \\u003cp\\u003eThe government of Rwanda should continue to spend on green infrastructure and encourage investment in renewable energy through subsidies and incentives so that the economy can grow while reducing the threat of climate change by reducing emissions. In addition to the use of the policy tool related to environmental taxes to a large extent. In this case, taxing polluters due to pollution will contribute to mitigating the negative effects of pollution (emissions from manufacturing).\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDeclaration of competing interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data that have been used are from a publicly available dataset.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical statement\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThere is no animal study involved in the present study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCRediT authorship contribution statement\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWe did not rely on anyone for data analysis or obtaining the results of the econometric study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors did not receive any assistance as the research involves quantitative analysis of data obtained from the World Bank.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAppendix A. Supplementary data\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThere is no Supplementary data to this article\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbbasi, K. R., Adedoyin, F. F., Abbas, J., \\u0026amp; Hussain, K. (2021). 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An Appreciated Response of Disaggregated Energies Consumption towards the Sustainable Growth: A debate on G-10 Economies. \\u003cem\\u003eEnergy\\u003c/em\\u003e, \\u003cem\\u003e254\\u003c/em\\u003e, 124377. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/J.ENERGY.2022.124377\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/J.ENERGY.2022.124377\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003e\\u003cem\\u003e3rd\\u003c/em\\u003e Rwanda National Human Development Report Overview \\u003cem\\u003ePolicy Innovations and Human Development Rwanda\\u0026rsquo;s Home-Grown Solutions\\u003c/em\\u003e (2021). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003eHttp://www.hdr.undp.org/\\u003c/span\\u003e\\u003cspan address=\\\"http://Http://www.hdr.undp.org/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\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\":\"environmental-modeling-and-assessment\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"enmo\",\"sideBox\":\"Learn more about [Environmental Modeling \\u0026 Assessment](https://www.springer.com/journal/10666)\",\"snPcode\":\"10666\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10666/3\",\"title\":\"Environmental Modeling \\u0026 Assessment\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"CO2 emission, foreign investment, domestic product, renewable energies, industrial sector\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4602302/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4602302/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThis research aims to study the determinants of the emission of carbon dioxide in Rwanda during the period 1990\\u0026ndash;2022, considering foreign direct investment, gross domestic product, the industrial sector, and the consumption of renewable energies as explanatory variables. The ARDL model was used to test the short- and long-term relationship between variables, The results of the study concluded that all independent variables have a negative impact on the emission of carbon dioxide in the long term, while in the short term, the results found a positive impact of both foreign investment, domestic output and composite industries on the emission of carbon dioxide in Rwanda, while the industrial sector and the consumption of renewable energies have a negative impact. The results also concluded that GDP is the largest contributor to the emission of carbon dioxide in Rwanda compared to the impact of other variables. This indicates that the rapid growth rates recorded by Rwanda have negatively affected the emission of CO2, as the increase in GDP in Rwanda requires the use of energy, and some energies eventually generate carbon dioxide emissions.\\u003c/p\\u003e \\u003cp\\u003eThe study recommended the need to promote the use of renewable energy and reduce dependence on fossil fuels, in addition to improving energy efficiency in all economic sectors such as the use of bicycles and electric vehicles. The study encourages foreign and domestic investments in clean and environmentally friendly technologies and expands investment in research and development to discover alternative energy sources that maintain high productivity and low levels of CO2 emissions. It also proposes carbon taxes to incentivize companies to reduce their footprint.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Macroeconomic Influencing Factors on Co2 Emissions in Rwanda: Short-Run Dynamics and Long-Run Equilibrium\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-30 07:07:11\",\"doi\":\"10.21203/rs.3.rs-4602302/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-07-06T00:52:30+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-06-26T07:32:11+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Environmental Modeling \\u0026 Assessment\",\"date\":\"2024-06-19T00:10:40+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"environmental-modeling-and-assessment\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"enmo\",\"sideBox\":\"Learn more about [Environmental Modeling \\u0026 Assessment](https://www.springer.com/journal/10666)\",\"snPcode\":\"10666\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10666/3\",\"title\":\"Environmental Modeling \\u0026 Assessment\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"707eda53-aae5-44dc-bd0f-00208f647052\",\"owner\":[],\"postedDate\":\"July 30th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-07-30T07:07:11+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-07-30 07:07:11\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4602302\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4602302\",\"identity\":\"rs-4602302\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}