Analyzing the Impact of Economic Growth, FDI and Energy Use on CO2 Emission in Kenya: An ARDL Approach

Analyzing the Impact of Economic Growth, FDI and Energy Use on CO2 Emission in Kenya: An ARDL Approach | 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 Analyzing the Impact of Economic Growth, FDI and Energy Use on CO2 Emission in Kenya: An ARDL Approach Ayodele Oluwaseun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6058314/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study estimates the effects of Gross Domestic Product (GDP), population, renewable energy consumption, fossil fuels, and foreign direct investment (FDI) on Kenya's carbon emissions between 1972 and 2021. This investigation makes use of the “Autoregressive Distributed Lag (ARDL)” method, which is grounded in the theoretical framework as the “Stochastic Impacts by Regression on Population, Affluence, and Technology” model known as (STIRPAT) model. The ARDL bound test and structural break test were also used in the study. According to our preliminary results, the data exhibits long-run cointegration; as a result, the uses of ARDL, which is adept at handling both short- and long-term effects, is essential. This study lends credence to earlier research by demonstrating that a rise in Kenya's GDP and population can result in an increase in that country's CO 2 emissions. Kenya may reduce its damaging carbon dioxide emissions by transitioning to renewable energy sources. All estimates place the impacts of GDP growth and population growth at parity. Achieving Kenya's sustainable development goals will require substantial investment in the country's energy infrastructure, making this analysis potentially useful in planning and establishing strategies for future financial funding in the energy sector. For ARDL, the effects of fossil fuels are negative but insignificant. FDI has an insignificant but positive effect on the environment. Based on these findings, policymakers can make informed decisions to sustainable use of renewable energy. Environmental Policy CO2 emission Renewable energy Fossil fuels FDI Kenya Figures Figure 1 Figure 2 1. Introduction Carbon emissions cause ozone depletion, which has a negative effect on the environment. Higher global temperatures may result from an unavoidable rise in carbon emissions.CO 2 is the main driver of global climate change, accounting for 65% of all greenhouse gas emissions, compared to other gas emissions including CH4 and N2O [ 1 ]. The potential for natural catastrophes like hurricanes, massive fires, and severe droughts raises concerns about whether climate change is occurring. A rise in global temperatures has an impact on natural animal habitats and agricultural production. In Kenya, the temperature ranged from a record high of 25.56 Celsius in 2009 to a record low of 22.53 Celsius in 1917, with an average of 24.39 Celsius between 1901 and 2021 [ 2 ]. The tourist industry and rain-fed agriculture, both of which are reliant on the environment and subject to harsh weather, are the two main drivers of Kenya's economy. As a result of catastrophic agricultural and animal losses caused by rising temperatures and ongoing droughts, people are at risk of undernourishment, and other hazards to their health and welfare. The majority of Kenya's low-lying seashore and the nearby islands are in danger from sea level rise, which will have a severe impact on the fishing industry and storm surge defense [ 3 ]. Thus, it is important to detect the potential cause of CO 2 emissions and implement proper policies to mitigate the effects of climate change. There is an involvement between emission and economic growth where emission has negative impact on environment and human health. At the same time, economic growth is a positive indicator to explain the health assessment of the people. In Kenya, recently the socio economic development has been raising compare to the previous decade. Economic growth leads by the energy consumption, and other macro economic factors such as labor, capital, savings and investment but there is a tradeoff between environment and development. World Bank was shows that Kenya's GDP grew by an average of 5.5% per year between 2010 and 2020 [ 4 ]. However, this growth has come at a cost, with CO 2 emissions increasing by an average of 4.5% per year between 2010 and 2017, reaching a total of 16.1 million metric tons in 2017 [ 5 ]. The main drivers of CO 2 emissions in Kenya are the energy sector and transportation, which are both closely linked to economic growth. The energy sector, which primarily relies on traditional fossil fuels, is responsible for over 60% of the country's total CO 2 emission. The transportation sector, on the other hand, is responsible for approximately 20% of CO 2 emissions, as a result of increased vehicular traffic on the roads [ 6 ]. The impact of growth on pollution in Kenya is far-reaching, with the increased energy consumption and transportation causative to air pollution in Kenya which is drastically raises the global emission and environmental susceptibility. In Kenya, the use of energy has increased significantly over the past few decades, resulting in a corresponding increase in CO 2 emissions. The most recent statistics from the World Bank indicate that Kenya's CO 2 emissions grew in 2019, hitting 0.4 metric tons per capita [ 3 ]. The main sources of energy in Kenya are traditional fossil fuels, including petroleum, coal, and biomass. These energy sources are responsible for over 80% of the country's energy spending, resulting in high levels of CO 2 giving out [ 7 ]. In addition, the limited entrance to energy in rural areas, the lack of investment in renewable energy sources, and the inefficiency of energy systems have resulted in a dependence on fossil fuels, exacerbating the problem. The collision of energy on emissions in Kenya is far-reaching, with the country's energy sector being responsible for over 60% of its total CO 2 emissions [ 7 ]. This has led to an swell in air pollution and the contribution of Kenya to global climate change, with temperatures increasing by an average of 0.3°C per decade between 1960 and 2018 [ 2 ]. The impacts of this increase in temperatures include changes in weather patterns, which have resulted in decreased agricultural productivity and increased water stress, affecting the livelihoods of millions of people [ 5 ]. To mitigate emissions in Kenya, it is essential to promote the access of clean and renewable energy sources. Thus, compel of energy on CO 2 in Kenya is a critical issue that requires immediate attention. The FDI has been a crucial factor in driving the growth and development of many countries, including Kenya. Over the past decade, Kenya has attracted significant FDI flows, mainly due to its favorable business climate and rapidly growing economy [ 8 ]. However, this growth has also led to increased carbon dioxide (CO 2 ) emissions, which have significant negative impacts on the environment and human health [ 9 ]. In 2021, Kenya's total CO 2 emissions were estimated to be approximately 36.3 million metric tons [ 10 ]. The influx of foreign investment has led to the development of new energy basis and infrastructure, such as coal-fired power plants, which are key cause of CO 2 emissions [ 11 ]. In addition, the growth of the manufacturing sector, driven by FDI, has also put in to the increase in emissions. Despite this, FDI also has the potential to play a constructive role in reducing CO 2 emissions in Kenya [ 12 ]. For example, foreign investors can bring in new technologies and best practices that are more environmentally friendly, which can help to reduce emissions over time. One example of such a policy is the government's requirement for companies operating in Kenya to obtain a Clean Development Mechanism (CDM) certification, which demonstrates that they are operating in a sustainable and environmentally responsible manner [ 13 ]. This has encouraged companies to adopt more environmentally friendly technologies and practices, which has helped to reduce CO 2 emissions. However, there is still much work to be done to fully realize the potential of FDI in reducing CO 2 emissions in Kenya. While the influx of foreign investment has driven economic growth and led to increased emissions, it also has the potential to play an activist function in reducing emissions through the adoption of more environmentally sustainable practices and technologies. Thus, it is important to scrutinize the interplay between FDI and CO 2 emission in the context of Kenya. This study focused on Kenya's rapport between economic affluence and environmental quality. Here, CO 2 emissions serve as proxies for environmental deterioration, while GDP stands in for economic affluence. The main objectives of this study include 1) to ascertain the impact on the environment inside the Kenyan “Stochastic Impacts by Regression on Population, Affluence, and Technology” model known as (STIRPAT) model. 2) To assess the association between Kenya's GDP and CO 2 emissions over the long and short terms. 3) To find out if the use of renewable energy and CO 2 emissions in Kenya are related. 4) To determine how Kenya's use of non-renewable energy and alternative nuclear energy influences CO 2 emissions. The remaining sections' structure is listed below: Section 2 provides a detailed analysis of the literature review. In Section 3 , we outline the study's data and econometric methodology. The “Results and discussion” part of section 4 includes a summary of the econometric studies' results and necessary sources. In section 5, provide the conclusion and policy implications. Section 6 contains references. 2. Literature Review Numerous researches on the interaction between the progress of the economy and CO 2 emissions have been performed over the years. There is a dearth of literature pertinent to Kenya's GDP- CO 2 emission nexus. Al Mulali et al. [ 14 ] examined the EKC premise for the first time in Kenya using data from 1980 to 2012 and discovered evidence of EKC characteristics in Kenya. Likewise, Sarkodie and Ozturk [ 15 ] the EKC hypothesis in Kenya and supported the EKC theory. Most studies discovered a significant relationship between GDP and CO 2 that was positive, but only a few studies discovered insignificant or adverse relationships between Emissions of CO 2 and economic expansion. Using Thailand as an example, Adebayo and Akinsola [ 16 ] investigated the GDP- CO 2 relationship. In order to examine this association, the researchers used wavelet tools. Their empirical findings demonstrated a positive correlation between CO 2 and economic progress, and they also identified a one-way causation between GDP and CO 2 emissions. However, He et al. [ 17 ] and Tufail et al. [ 18 ] observed a favorable interaction involving CO 2 and GDP in their respective studies. This indicates that a rise in GDP reduces environmental sustainability. Furthermore, Adebayo and Kirikkaleli [ 19 ] evaluated the GDP- CO 2 linkage for Japan between 1990 and 2015 using a novel wavelet coherence test. According to their findings, progress in GDP is connected with a rise in emissions of CO 2 . Zhang et al. [ 20 ] analyzed the influence of the growth of the economy on CO 2 between 1970 and 2018 employing data from Malaysia. Their empirical results demonstrate a favorable correlation between CO 2 and GDP. The positive CO 2 -GDP link was verified by the research conducted by Usman et al. [ 21 ] for the United States and Adebayo and Rjoub [ 22 ] for MINT economies. Adebayo and Odugbesan's [ 23 ] research examined how economic development affected releases of CO 2 in South Africa utilizing data throughout the years 1971 to 2017 and contemporary econometric methodologies. The study's conclusions revealed that raising GDP emitted higher CO 2 . Baloch et al. [ 24 ] evaluated the association between GDP and environmental deterioration, and their outcomes proved that GDP had a beneficial influence on CO 2 emissions. Similarly, Joshua and Bekun's [ 25 ] study supports the hypothesis that economic expansion significantly triggered CO 2 . Several economic analyses observed that increasing the usage of sustainable sources would lead to mitigating CO 2 emissions. Sarkodie and Ozturk [ 15 ] concluded that Kenya's renewable energy use massively diminished CO 2 emissions. Azam et al. [ 26 ], using a sophisticated panel quantile regression model, found a optimistic relation between growth and pollution in the top 5 emitter nations for the years 1995–2017 and an inverse correlation between clean energy and CO 2 in the same set of economies. There is an existence of cause GDP growth and emissions [ 27 , 28 ]. Liu et al. [ 29 ] used DOLS technique on temporal data from 1992–2013 to find an inverse relationship between the BRIC nations' utilization of renewable energy sources and their CO 2 emissions. In addition, Liu et al. [ 30 ] also found renewable source mitigate emission in developing countries. Using data from 1990–2019, Ali et al. [ 31 ] explored the relationship between China's use of non-renewable and renewable energies and the country's carbon emission intensity (CEI). The research used the dynamic “Autoregressive Distributed Lag (ARDL)” method to see how the variables were connected through time. The research shows a favorable correlation between CEI and both renewable and non-renewable energy sources. Employing data from 1965 to 2019, Adebayo et al. [ 32 ] addressed the idea that using renewable sources reduces carbon dioxide emissions in Sweden. Research by Dong et al. [ 33 ] looked at whether or not BRICS nations may cut their CO 2 emissions more effectively by increasing their use of nuclear power. According to the results of the research, renewable energy sources contribute significantly to cutting down on carbon dioxide emissions. There are two main ideas on the association between FDI and environmental quality. While some research supports the pollution heaven theory and finds that FDI harms the environment, other research demonstrates that FDI actually enhances air quality via the spread of green technology. Marcellus [ 34 ] conducted a study in the context of Kenya to find out the influence of FDI on the level of CO 2 in Kenya. The findings of the study showed that FDI has a mitigating role in CO 2 emission and increasing FDI lowers the level of CO 2 emission. Evidence from a wide range of research has shown that FDI helps mitigate environmental damage by funding innovative approaches to green technology [ 35 – 38 ]. Eskeland and Harrison [ 39 ] found that U.S. manufacturing facilities in emerging economies use green energy and ecologically sound management techniques. The effects of FDI on ecosystems were investigated in a study of the nations of the Gulf Cooperation Council. Using a multivariate approach, the research found that FDI had no negative effects on ecosystems [ 40 ]. To assess the link stuck between FDI and environment, Demena and Afesorgbor [ 41 ] found that the influences of FDI on CO 2 emissions are negligible. While FDI has been shown to reduce CO 2 emissions by varying degrees depending on the study, the evidence is still mixed. The results held up after accounting for variations in development and pollution levels among nations. Du and Li [ 42 ] looked at how carbon emissions increased across 71 nations from 1992 to 2012. They used a Malmquist index approach with parameters. According to the research's findings, carbon stock production increased across the board over the study period. Additionally, increasing the productivity of all factors that contribute to carbon depends on technological innovation. Between 2006 and 2015, Zhou et al. studied how China's OFDI spillover affected the sustainable technologies of 30 regions [ 43 ]. The research concluded that although Chinese OFDI does not lead to sustainable technologies, there are substantial regional differences since there aren't the necessary enabling circumstances in certain areas. Udemba et al. [ 44 ] used ARDL bound test to investigate the affinity involving FDI and emissions and discovered that FDI affect environment. Solarin et al. [45] also found that FDI degrades the environment as well. According to our literature assessment, no prior study has been performed on the linkage between FDI, energy use, GDP expansion, and the environment in Kenya. Existing studies have shown conflicting evidence about the FDI- CO 2 link. As FDI stimulates the growth of host economies by funding the development of Greenfield projects and expanding existing enterprises, the production units engaged in these processes generate carbon emissions. However, a number of studies have shown that FDI has little or no effect on carbon dioxide emissions. Kenya likewise suffers from a dearth of studies in the energy sector. It is, therefore, important to investigate the connection between Kenya's rising CO 2 emissions, foreign direct investment, and economic expansion. 3. Methodology 3.1 Theoretical Framework The IPAT model maintains that “impacts on ecosystems (I) are the product of the population size (P), affluence (A), and technology (T)” provided the foundation for “Stochastic Impacts by Regression on Population, Affluence, and Technology” defined as STIRPAT model. York et al. [46] argue that the IPAT model has certain limitations since it does not account for non-monotonic, unevenly scaled changes in the influential elements. Using the York STIRPAT model, this issue is resolved. $$\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:I=\alpha\:{P}_{i}^{\beta\:}{A}_{i}^{\gamma\:}{T}_{i}^{Ɵ}{e}_{i}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ $$\:Ln{I}_{i}=Ln\alpha\:+\beta\:Ln\left({P}_{i}\right)+\gamma\:Ln\left({A}_{i}\right)+ƟLn\left({T}_{i}\right)+{e}_{i}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ Where the anticipated parameters of the model are β, γ, and Ɵ, and e i represents the disturbance term. The aforesaid equation is often simplified in a logarithmic form in the application. Incredibly, the STIRPAT model's structure allows for the dissection of P, A, and T into a number of different factors in the environment; Therefore, researchers have shown a greater extent of interest in this model [47]. The corresponding logarithmic expression is given in Eq. (2). Using the STIRPAT model greatly enhances the forms of significant effect factors that were taken into account in this investigation, which is the model's primary advantage. To further evaluate the factors that contribute to CO 2 emission in Kenya, we updated a STIRPAT model by including indices of demographic, economic, and technical factors. The population was used as a surrogate for demographic change, GDP (per capita) and FDI for affluence, and fossil fuel and renewable energy use for technological factors in this study. Now, substituting the corresponding variable in Eq. (2), we can write Eq. (3) as follows: $$\:LnC{O}_{{2}_{it}}={\alpha\:}_{it}+{\beta\:}_{1}L{GDP}_{it}+{\beta\:}_{2}L{POP}_{it}+{\beta\:}_{3}L{FOS}_{it}+{\beta\:}_{4}L{REN}_{it}+{\beta\:}_{5}L{FDI}_{it}+{€}_{it}\:\:\:\:\:\:\:\left(3\right)\:\:\:\:\:\:$$ Where β 1 to β 2 are coefficients used in Eq. (3) 3.2 Data The ARDL tactic of cointegration suggested by Pesaran et al. [48] was adopted in this empirical investigation to identify the major causes of CO 2 emission in Kenya. The ARDL model was used owing to its capacity to describe a capricious, ever-changing response as the result of one or more forecasting factors. Moreover, it may be used for the study of economics, ecology, and experimental data, as well as for the analysis and forecasting of the actions of dynamic systems [49]. Time series data for Kenya have been gathered from the World Development Indicator (WDI) database and cover the years 1972 to 2021. The explained variable in this analysis is CO 2 emission, whereas the explanatory variables are GDP, population, renewable energy, and fossil fuel energy usage. The variables have been log-transformed to assure normally distributed data. The variables, their logarithms, and the sources of data employed are listed in Table 1 . Table 1 Variable’s Description, Source, and Signifier Variable Signifier Description Source CO2 emissions LCO2 CO2 emissions (kt) World Bank Development Indicator Gross Domestic Product Per Capita LGDP GDP per capita (constant 2015 US $ ) Population LPOP Population, total Renewable energy consumption LREN Renewable energy consumption (% of total energy consumption) Fossil fuels LFOS Fossil fuel energy consumption (% of total) Alternative and nuclear energy LFDI Alternative and nuclear energy (% of total energy use) The variables considered in this inquiry are summarized (minimum, maximum, mean, median, and standard deviation) in Table 2 . Table 2 General Statistics of the Variables VARIABLES Mean Sd Min Max LCO2 9.097 0.331 8.676 10.01 LGDP 7.133 0.105 6.994 7.405 LPOP 17.14 0.450 16.31 17.82 LREN 4.353 0.0371 4.221 4.422 LFOS 2.875 0.118 2.565 3.078 LFDI 17.98 1.721 12.89 21.10 3.3 Empirical Framework and Estimation Method Several inferential estimation methods were adopted to estimate the results more precisely. Figure 1 showed the steps of the estimation technique employed in this study. 3.3.1 Unit Root Test Before proceeding to further in-depth investigation, it is fundamental to look into the integration series. In this way, we apply unit root tests to assess the series' integration properties. First, the study used conventional “augmented Kapetanios, Shin & Snell (KSSUR) [50], Kwiatkowski–Phillips–Schmidt–Shin (KPSS) [51], and Augmented Dickey-Fuller [52]” unit root tests. Secondly, conventional unit root tests may provide misleading findings if there is a structural break(s) in the series being tested. So, we adopted the Zivot and Andrews [53] (ZA) unit root test, which may capture both the stationary aspects of the series and a single structural break (s). 3.3.2 ARDL model To measure the series' co-integration, we used the ARDL bounds test. The following are the reasons why Pesaran et al. [48] limits test is favored over other co-integration tests. The first advantage is that it may be adopted when series are incorporated in mixed order; the second is that it is much more trustworthy, notably for a limited sample; and the third is that it provides accurate estimates of the long-term model. Eq. 4 illustrates the ARDL limits test: No cointegration (the null hypothesis) is contrasted with evidence of cointegration (the alternative hypothesis). If the F-statistic exceeds the threshold values for the upper and lower limits, we cannot accept the null hypothesis. Null and alternative hypotheses are shown in Equations 5 and 6: $$\:{H}_{0}={ⱴ}_{1}={ⱴ}_{2}={ⱴ}_{3}={ⱴ}_{4}={ⱴ}_{5}={ⱴ}_{6}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(5\right)$$ $$\:{H}_{1}={ⱴ}_{1}\ne\:{ⱴ}_{2}\ne\:{ⱴ}_{3}\ne\:{ⱴ}_{4}\ne\:{ⱴ}_{5}\ne\:{ⱴ}_{6\:\:\:\:\:\:}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)$$ H 1 stands for the alternative hypothesis and H 1 for the null hypothesis. We used the ARDL method after establishing that the parameters are co-integrated. Engle and Granger's [54] error correction model (ECM) is applied to evaluate short-term correlations and the “Error Correction Term” after that the long-term associations have been established. Eq. 7 is employed for the long-run ARDL estimation. Where speed of adjustment is denoted by ℓ We have employed the fully modified (FMOLS) [55] and dynamic OLS (DOLS) [56] and canonical correlation regression estimator (CCR) estimation approach to visualize the long-run effect of GDP, POP, REN, FDI, and FOS on CO 2 as a robustness check to the ARDL long-run guesstimate. Using these techniques, it is possible to establish asymptotic coherence while taking serial correlation into account. FMOLS and DOLS should only be used when there is corroboration of cointegration between the series. As a result, this research calculates long-term elasticity using FMOLS and DOLS estimators. As follows The FMOLS equation is shown by Eq. 8; Where t illustrates the timing trend and SIC is used to indicate the lag order. The advantage of FMOLS and DOLS is that they address the issues of endogeneity, auto-regression, and bias resulting from sample bias. 3.3.3 Robustness Check This study employed the FMOLS, DOLS, and CCR to compare how time-varying factors affected the environment, which allowed us to evaluate the model's robustness. There were two primary factors that necessitated the employment of such methods. The cointegration criterion for parameters must be satisfied before the FMOLS, DOLS, or CCR may be used. Moreover, these methods deal with endogeneity and serial correlation biases brought on by the cointegration interaction. Consequently, it yields outcomes with asymptotic efficiency. 3.3.4 Pairwise Granger Causality Test As there is a possibility that theoretical correlations won't work in real life due to certain components that might not be well stated in theory, the concept of a causality test would determine whether past changes in a factor are to cause of the current observation or not. It is claimed that causation extends from X to Y if the sum of X's past and current values deviates considerably from zero. Similar rules apply to Y and X causality; if the results vary from zero, then causation is present on both sides. To determine if the factors had a short-term causal connection, the investigation used the paired Granger causality [57] test. The following Eq. (6) demonstrates the causal connection between Xt and Yt: $$\:E\left({Y}_{t+h}|{J}_{t,}{X}_{t}\right)=E\left({Y}_{t+h}|{J}_{t}\right)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(9\right)$$ Here, Jt denotes the data sets derived from the preceding observations acquired until that point in time (t). 3.3.5 Diagnostic test The investigation used a variety of different diagnostic techniques to confirm the precision of the findings. In this study, heteroscedasticity was determined using the ARCH test [58], specification error was assessed using the Ramsey Reset test [59], autocorrelation was ascertained using the Durbin Watson test [60], normality was determined using the Jarque-Bera test [61], and predicted model stability was identified using the CUSUM & CUSUMsq test [62]. Table (9) provides an overview of the findings of the diagnostic approaches. 4. Empirical Findings 4.1 Unit Root Test Result First, we check the parameters' stationarity characteristics to determine sure they are suitable for use in this empirical study. Based on this, we used unit root tests (KSSUR, KPSS & ADF) to agree on whether or not the series was stationary. The findings of the “unit root tests” are portrayed in Table (3). The results of the stationarity test demonstrated that the variables used in this study had a non-uniform order of integration, which favors the ARDL method over the traditional cointegration-based methods. Table (3) showed that, while all variables [LCO 2 , LGDP, LPOP, LFDI, and LFOS] exhibits I(1), only LREN showed Integrated to Zero or I(0). Thus, the variables employed in this study have mixed order of integration. Table 3: Unit Root Tests Variable KSUR Test ADF Test KPSS Test Remark Level 1 st Dif. Level 1 st Dif. Level 1 st Dif. Stationary at LCO2 -0.12 -3.388*** -0.05 -5.52*** -1.43 -6.99*** I (1) LGDP 2.19 -4.38*** 2.45 -3.53*** 0.94 -7.73*** I (1) LPOP 2.015 -3.53*** 2.19 -3.38*** 1.81 -7.31*** I (1) LREN -5.52*** -5.52*** -4.49** I (0) LFOS -0.12 -6.05*** -0.05 -6.51*** -0.712 -6.92*** I (1) LFDI -4.14** -4.05*** -4.58** I (0) (a) The asterisk symbols (***& **) are utilized for 1% &5% significance levels. (b) Optimal lag selected by AIC & SIC criterion. 4.2. Structural break analysis Table 4: Structural Break Analysis Zivot-Andrews test Variables ZA stat. Break 1% 5% 10% Decision LCO 2 -3.075*** 2005 -5.34 -4.93 -4.58 Break Exist LGDP -2.649*** 1991 -5.34 -4.93 -4.58 LPOP -3.702** 2012 -5.34 -4.93 -4.58 LREN -5.568 1991 -5.34 -4.93 -4.58 LFOS -3.839*** 2005 -5.34 -4.93 -4.58 LFDI -4.299*** 2013 -5.34 -4.93 -4.58 Assuming that the mean, variance, and trend will not change over time is the stationarity assumption, which forms the foundation for applied time series prediction and assessment. A structural break is believed to have happened if any of the aforementioned conditions altered, or if the break period fell within the sample period. In econometrics, a structural break is a sudden shift in the time series data. Large discrepancies in forecasts and inconsistencies in theoretical frameworks may come from it. Zivot-Andrews unit root testing was used in this research to spot the abrupt change in trend. Figure 4 depicts the test results, which indicate that the statistical sample has a substantial structural breakdown. The outcomes depicted in Table 4 also present that LCO 2 , LGDP, LPOP, LFOS, and LFDI observed significant structural breaks in 2005, 1991, 2012, 2005, and 2013, accordingly. 4.3. ARDL Bound Test Table 5: ARDL Bound Test Test Statistics Value K F statistics 0.936 5 Significance level Critical Bounds 10% 5% 2.50% 1% I(0) 2.26 2.62 2.96 3.41 I(1) 3.35 3.79 4.18 4.68 F-statistics are estimated and compared to the critical values evaluated by Pesaran et al. [48] to determine whether or not the null hypothesis should be rejected. If the intended F stat. goes over the tabulated F value, we may reject the null hypothesis such as no cointegration exists. If the calculated F stat has a lower value than the tabulated value, it fails to reject the developed hypothesis. No inference can be made from the data, however, if the F-statistics value falls inside the bounds. A close inspection of Table 5 reveals that the F-statistic is statistically significant at the 1% level. Thus, significant long-run linkage exists between explanatory and dependent variables. Also, F-value is much higher than the formula's upper limit. In light of new information on Kenya's history, we can assess the impact of factors like GDP, population, FDI, and renewable and fossil fuel energy usage on CO 2 emissions in Kenya. 4.4. ARDL Long and Short-Run Results ARDL long-run (LR) and short-run (SR) assessments are depicted in the Table (6) and showed how various factors are connected with CO 2 emission in Kenya. Long-run (LR) estimation results presented that coefficients of LGDP are negative and highly significant at a 5% level of significance. The coefficient value of LGDP is -0.0461 and implies that a 1% increase in LGDP would result in reducing CO 2 emission by 0.0461% in the long run and vice versa. Similarly, the marginal effect of LPOP has significant to boost emission where more population contacts more pollution. The result entail that a 1% enlarges in populace will cause higher emissions in the long run by 0.199% and vice versa. Additionally, the value of LREN is -12.26 and which is significant at a 5% significance level. Thus, a 1% increase in LREN will reduce the LCO 2 by 12.26% in the long run. Finally, the estimation result of ARDL also showed that the value of LFOS and LFDI are 2.398 and -0.139. The value of LFOS and LFDI does not affect Kenya’s long-term CO 2 emissions. Table 6: ARDL Long-Run and Short-Run Results VARIABLES LR SR LGDP -1.0461*(0.63) LPOP 0.199**(0.089) LFOS 2.398(4.91) LREN -12.26***(1.509) LFDI -0.139(0.818) D.LGDP -0.371**(0.145) D.LPOP -1.330(8.293) D.LFOS 0.361(0.265) D.LREN -3.727***(1.287) D.LFDI -0.00253(0.0074) ECT (Speed Adjustment) -0.450***(0.125) Constant -5.264(5.868) R-square 0.654 (a) Asterisk symbol (***, **,*) utilized for 1% ,5%& 10% significance level. (b) S E in brackets. The findings of Short-run (SR) ARDL estimation also showed in the Table (6). The result showed that the coefficient value of LGDP is -0.371 which is tended GDP has no cause to enlarge emission in the SR. Thus, a 1% increase in LGDP will lower emissions in the short run. Moreover, the results depicted in Table (6) showed that the value of LREN is -3.727 and which is highly significant at a 1% significance level. Therefore, a 1% extend in LREN will lower the CO 2 emission by 3.727% in the short run and similar sign of this coefficient was found by Rahman and Majumder [63]. Furthermore, the value of LPOP and LFOS are -1.330 and 0.361. The values of LPOP and LFOS have an insignificant impact on CO 2 emissions in the short run. Additionally, the L.LCO 2 coefficient is positive for the chosen variables, and there is a yearly divergence of 0.0267% between the SR and LR equilibrium. The speed of adjustment is -0.45% means 45% to move forward the factors in an equilibrium situation. 4.5. Robustness Check and Causality Test We also employed several estimation approaches such as FMOLS, DOLS, and CCR to observe the robustness of ARDL estimation findings. The results of FMOLS, DOLS, and CCR are recorded in Table (7). The upshots of the DOLS showed that the estimated value of LGDP is -1.811 and which is highly significant at a 1% level of significance. Thus increase in LGDP will significantly lower the CO 2 emission and this ruling is reliable with the outcomes of ARDL results. Similarly, the coefficient value of LPOP is positive and highly significant at a “1% level of significance under the FMOLS, DOLS, and CCR estimation” approach. The result implies that an increase in LPOP also triggers the emission of CO 2 and these results are also reliable with the findings of the ARDL estimation approach. Moreover, the coefficient value of LREN is negative and significant under FMOLS and DOLS approaches. Rahman and Majumder [63] found LREN was negative coefficient by using FMOLS model in N-11 countries. The negative association between LREN and LCO2 also corroborated the results of the ARDL estimation approach. The findings of FMOLS, DOLS, and CCR assessment showed that the coefficient value of LFDI is insignificant and this results in line with the ARDL estimation technique. Thus, the ARDL estimation results are robust and this result is consistent with the findings of FMOLS, DOLS, and CCR approaches. The results of the paired Ganger causality test are shown in Table 8. The null hypothesis of no causality is rejected if F-statistics are significant. Table (8) demonstrates a one-way causation presence between LCO2 and LGDP, and LFOS and LCO2. In addition, there are also bidirectional causal relationships exist between LREN and LCO2, and LFDI and LCO2. Table 7: Robustness Check Variables FMOLS DOLS CCR LnCO2 dependent LGDP -0.642 (0.814) -1.811*** (0.406) 0.627* (0.346) LPOP 0.478***(0.145) 0.767***(0.131) 0.463***(0.166) LREN -3.558** (1.573) -10.849*** (1.214) -2.980 (3.003) LFOS 1.254*** (0.438) 0.174 (0.213) 1.289** (0.478) LFDI 0.028 (0.022) 0.009 (0.014) -0.031 (0.030) C 16.843 55.318 14.335 R-squared 0.733 0.982 0.725 (a) Asterisk symbol (***, **,*) utilized for 1% ,5%& 10% significance level; (b) SE in brackets. Table 8: Granger Causality Test Outcomes Null Hypothesis: F-Statistic Prob. LGDP ≠LCO 2 0.85918 0.4307 LCO 2 ≠ LGDP 5.40911 0.008 LPOP ≠ LCO 2 0.55552 0.5778 LCO 2 ≠LPOP 2.10907 0.1337 LREN ≠LCO 2 8.40584 0.0008 LCO 2 ≠LREN 3.23988 0.0489 LFOS ≠LCO2 4.14323 0.024 LCO 2 ≠LFOS 0.15757 0.8548 LFDI ≠LCO 2 3.51271 0.0388 LCO 2 ≠LFDI 5.0728 0.0106 LPOP ≠LGDP 1.21214 0.3075 (a) Asterisk symbol (***, **,*) utilized for 1%, 5%& 10% significance level. (b) Optimal lag selected by AIC & SIC criterion. 4.7 Outcomes of Diagnostic Tests Finally, we think it's important to address how well the ARDL error correction model fits the data. Multiple diagnostic and stability analyses were performed with this goal in mind. Table 9: Diagnostic tests for Model adequacy Test Null Hypothesis Test Statistic P-Value AECH Heteroskedasticity test Ho: Homoskedasticity 0.425 (F- statistic) 0.254 Normality/Jarque Bera Ho: residuals have a normal distribution. 0.7854 0.3785 B-G LM test Ho: No serial correlation up to 2 lags 2.142 (F- statistic) 0.190 R 2 .784 Adjusted R 2 .841 DW value 1.854 Ram. RESET (F) Ho: The model's functional form is valid. 3.192 (F- statistic) 0.086 Homoscedasticity, heteroscedasticity, Serial correlation, normalcy, and model specification are all examined by the diagnostic tests. According to the findings in Table 9, the model is not challenged by measurement errors, heteroscedasticity, autocorrelation, or normalcy. This makes it clear that the findings of this inquiry can be used to reliably draw conclusions. Figure (2) portrayed the outcomes of the CUSUM and CUSUM square test and indicates that the blue line lies within the red lines at a 5% level of significance and makes the parameters of the estimated model stable. 5. Conclusion This research examined the influence of economic growth, energy usage, and FDI on Kenya's CO 2 emission using data from 1972 to 2021. This study used the KSSUR, ADF, and KPSS unit root tests to determine the stationary characteristic within the dataset. The results of those tests indicated that variables displayed mixed-order integration. The Zivot-Andrewes unit root test was also used in this research to identify the structural break within the sample period, and the findings of this study demonstrated the existence of a substantial structural break within the sample period. To guarantee the validity of the results, the inquiry utilized the FMOLS, DOLS, and CCR long-run estimators in addition to the ARDL model. According to ARDL long-term estimates, economic development increases CO 2 emissions, but the use of renewable energy reduces CO 2 emissions over time. These findings were also supported by FMOLS, DOLS, and CCR estimate outputs. The usages of fossil fuels for energy, population growth, and FDI have minimal impact on Kenya's carbon emissions. The results indicate that Kenya will need more renewable energy sources in the future. Kenya's CO 2 emissions are pushed up by the growing GDP, expanding FDI size, and substantial increase in population. Kenya needs to identify the factors that contribute most to the nation's CO 2 emissions at this time. The major objective is to identify the principal contributors to CO 2 emission. Then consider using more sustainable energy sources and using less fossil fuel energy. Several diagnostic tests, such as the Breush pagan Godfrey test, Jarque Bera test, Breush Godfrey LM test, and CUSUM & CUSUMSQ test to check model adequacy and certify that the model is devoid of all forms of problematic conditions. 6. Policy Implication Carbon dioxide (CO 2 ) emissions can be reduced by encouraging sustainable economic growth and decreasing reliance on fossil fuels, both of which can be measured using GDP. To lessen reliance on fossil fuels and GHG, GDP can incentivize the research, development, and deployment of renewable sources including solar, wind, and hydro power. Companies that put money into renewable energy sources should be rewarded monetarily for their efforts. Corporations that put money into renewable energy should be rewarded monetarily for their efforts. In order to encourage businesses and individuals to decrease their carbon footprint, a carbon tax should be imposed on the production and consumption of fossil fuels. Revenue from the carbon tax can be used to fund initiatives to expand access to renewable energy sources and strengthen regulatory safeguards for the planet. Investment in infrastructure and monetary incentives for users are two ways GDP can promote eco-friendly means of transportation including public transit, biking, and walking. Global economic growth can encourage nations to work together to solve climate change by facilitating the sharing of innovative solutions, the transfer of cutting-edge technologies, and concerted action on environmental concerns. By offering fiscal incentives like tax credits and subsidies, GDP may promote the development of low-carbon businesses like electric vehicles, energy storage, and clean energy. Emissions of carbon dioxide (CO 2 ) can be heavily influenced by population numbers and habits. The promotion of family planning and reproductive health can be aided by lowering financial, institutional, and societal barriers to these issues. As a result, population growth will be slowed and energy consumption will decrease. Encourage people to adopt sustainable lifestyles that lessen their reliance on fossil fuels and their contribution to global warming by spreading information on the effects of individual actions. By encouraging investment in low-carbon businesses and technology, FDI can help reduce emissions. Offering tax breaks, subsidies, and other financial incentives to foreign direct investment in low-carbon businesses like renewable energy, energy efficiency, and sustainable transportation is a good start. To ensure FDI projects have a negligible effect on the environment and aid in the reduction of CO 2 emissions, it is important to implement environmental guidelines for them. Keep an eye on foreign direct investment projects to make sure they're helping the planet and cutting down on carbon emissions. Declarations Conflict of interest statement: No competing interests declared by authors. Data availability statement: On request, corresponding author will provide data. References Ahmad S, Raihan A, Ridwan M (2024) Pakistan's trade relations with BRICS countries: trends, export-import intensity, and comparative advantage. Front Finance, 2(2) Ahmad S, Raihan A, Ridwan M (2024) Role of economy, technology, and renewable energy toward carbon neutrality in China. Journal of Economy and Technology Akhter A, Al Shiam SA, Ridwan M, Abir SI, Shoha S, Nayeem MB, Bibi R (2024) Assessing the Impact of Private Investment in AI and Financial Globalization on Load Capacity Factor: Evidence from United States. J Environ Sci Econ 3(3):99–127 Chowdhury AAA, Rafi AH, Sultana A, Noman AA (2024) Enhancing green economy with artificial intelligence: Role of energy use and FDI in the United States. arXiv preprint arXiv:2501.14747 Chowdhury AAA, Sultana A, Rafi AH, Tariq M (2024) AI-driven predictive analytics in orthopedic surgery outcomes. Revista Esp de Documentacion Cient 19(2):104–124 Hossain T (2022) A Comparative Analysis of Adversarial Capabilities, Attacks, and Defenses Across the Machine Learning Pipeline in White-Box and Black-Box Settings. Appl Res Artif Intell Cloud Comput 5(1):195–212 Hossain T (2024) Q J Emerg Technol Innovations 9(2):33–46A Novel Integrated Privacy Preserving Framework for Secure Data-Driven Artificial Intelligence Systems Islam S, Raihan A, Ridwan M, Rahman MS, Paul A, Karmakar S, Al Jubayed A (2023) The influences of financial development, economic growth, energy price, and foreign direct investment on renewable energy consumption in the BRICS. J Environ Energy Econ 2(2):17–28 Onwe JC, Ridzuan AR, Uche E, Ray S, Ridwan M, Razi U (2024) Greening Japan: Harnessing energy efficiency and waste reduction for environmental progress. Sustainable Futures 8:100302 Pattak DC, Tahrim F, Salehi M, Voumik LC, Akter S, Ridwan M, Zimon G (2023) The driving factors of Italy’s CO2 emissions based on the STIRPAT model: ARDL, FMOLS, DOLS, and CCR approaches. Energies 16(15):5845 Polcyn J, Voumik LC, Ridwan M, Ray S, Vovk V (2023) Evaluating the influences of health expenditure, energy consumption, and environmental pollution on life expectancy in Asia. Int J Environ Res Public Health 20(5):4000 Rafi AH, Chowdhury AAA, Sultana A, Noman AA (2024) Unveiling the role of artificial intelligence and stock market growth in achieving carbon neutrality in the United States: An ARDL model analysis. arXiv preprint arXiv :241216166 Rafi AH, Chowdhury AA, Sultana A, Tariq M (2024) Artificial intelligence for early diagnosis and personalized treatment in gynecology. Int J Adv Eng Technol Innovations 2(1):286–306 Rahman J, Foisal MZU, Mohajan B, Islam S, Rafi AH, Paul A & Ahmad10, S. Role of Renewable Energy, Economic Growth, Agricultural Productivity, and Urbanization Toward Achieving China’s Goal of Net-zero Emissions Rahman J, Foisal MZU, Mohajan B, Rafi AH, Islam S, Paul A Nexus Between Agriculture, Industrialization, Imports, and Carbon Emissions in Bangladesh Rahman J, Raihan A, Tanchangya T, Ridwan M (2024) Optimizing the digital marketing landscape: A comprehensive exploration of artificial intelligence (AI) technologies, applications, advantages, and challenges. Front Finance, 2(2) Raihan A, Atasoy FG, Atasoy M, Ridwan M, Paul A (2022) The role of green energy, globalization, urbanization, and economic growth toward environmental sustainability in the United States. J Environ Energy Econ 1(2):8–17 Raihan A, Atasoy FG, Coskun MB, Tanchangya T, Rahman J, Ridwan M, Yer H (2024) Fintech adoption and sustainable deployment of natural resources: Evidence from mineral management in Brazil. Resour Policy 99:105411 Raihan A, Bala S, Akther A, Ridwan M, Eleais M, Chakma P (2024) Advancing environmental sustainability in the G-7: The impact of the digital economy, technological innovation, and financial accessibility using panel ARDL approach. Journal of Economy and Technology Raihan A, Hasan MA, Voumik LC, Pattak DC, Akter S, Ridwan M (2024) Sustainability in Vietnam: Examining Economic Growth, Energy, Innovation, Agriculture, and Forests' Impact on CO2 Emissions. World Development Sustainability, p 100164 Raihan A, Rahman J, Tanchangtya T, Ridwan M, Islam S (2024) An overview of the recent development and prospects of renewable energy in Italy. Renew Sustainable Energy 2(2):0008 Raihan A, Rahman J, Tanchangya T, Ridwan M, Bari ABM (2024) Influences of economy, energy, finance, and natural resources on carbon emissions in Bangladesh. Carbon Res 3(1):1–16 Raihan A, Rahman J, Tanchangya T, Ridwan M, Rahman MS, Islam S (2024) A review of the current situation and challenges facing Egyptian renewable energy technology. J Technol Innovations Energy 3(3):29–52 Raihan A, Ridwan M, Rahman MS (2024) An exploration of the latest developments, obstacles, and potential future pathways for climate-smart agriculture. Clim Smart Agric, 100020 Raihan A, Ridwan M, Tanchangya T, Rahman J, Ahmad S (2023) Environmental Effects of China's Nuclear Energy within the Framework of Environmental Kuznets Curve and Pollution Haven Hypothesis. J Environ Energy Econ 2(1):1–12 Raihan A, Tanchangya T, Rahman J, Ridwan M (2024) The Influence of Agriculture, Renewable Energy, International Trade, and Economic Growth on India's Environmental Sustainability. J Environ Energy Econ, 37–53 Raihan A, Tanchangya T, Rahman J, Ridwan M, Ahmad S (2022) The influence of Information and Communication Technologies, Renewable Energies and Urbanization toward Environmental Sustainability in China. J Environ Energy Econ 1(1):11–23 Raihan A, Voumik LC, Ridwan M, Ridzuan AR, Jaaffar AH, Yusoff NYM (2023) From growth to green: navigating the complexities of economic development, energy sources, health spending, and carbon emissions in Malaysia. Energy Rep 10:4318–4331 Ridwan M (2023) Unveiling the powerhouse: Exploring the dynamic relationship between globalization, urbanization, and economic growth in Bangladesh through an innovative ARDL approach Ridwan M, Raihan A, Ahmad S, Karmakar S, Paul P (2023) Environmental sustainability in France: The role of alternative and nuclear energy, natural resources, and government spending. J Environ Energy Econ 2(2):1–16 Ridwan M, Urbee AJ, Voumik LC, Das MK, Rashid M, Esquivias MA (2024) Investigating the environmental Kuznets curve hypothesis with urbanization, industrialization, and service sector for six South Asian Countries: Fresh evidence from Driscoll Kraay standard error. Res Globalization 8:100223 Ridzuan AR, Rahman NHA, Singh KSJ, Borhan H, Ridwan M, Voumik LC, Ali M (2023), May Assessing the Impact of Technology Advancement and Foreign Direct Investment on Energy Utilization in Malaysia: An Empirical Exploration with Boundary Estimation. In International Conference on Business and Technology (pp. 1–12). Cham: Springer Nature Switzerland Sultana A, Rafi AH, Chowdhury AAA, Tariq M (2023) Leveraging artificial intelligence in neuroimaging for enhanced brain health diagnosis. Revista de Inteligencia Artif en Med 14(1):1217–1235 Sultana A, Rafi AH, Chowdhury AAA, Tariq M (2023) AI in neurology: Predictive models for early detection of cognitive decline. Revista Esp de Documentacion Cient 17(2):335–349 Tanchangya T, Raihan A, Rahman J, Ridwan M, Islam N (2024) A bibliometric analysis of the relationship between corporate social responsibility (CSR) and firm performance in Bangladesh. Front Finance, 2(2) Tipon Tanchangya MR, Raihan A, Khayruzzaman MSR, Rahman J, Foisal MZU, Mohajan B, Islam AP S. Nexus Between Financial Development and Renewable Energy Usage in Bangladesh Tipon Tanchangya MR, Raihan A, Khayruzzaman MSR, Rahman J, Foisal MZU, Mohajan B, Islam AP S. Nexus Between Financial Development and Renewable Energy Usage in Bangladesh Urbee AJ, Ridwan M, Raihan A (2024) Exploring Educational Attainment among Individuals with Physical Disabilities: A Case Study in Bangladesh. Journal of Integrated Social Sciences and Humanities Voumik LC, Ridwan M (2023) Impact of FDI, industrialization, and education on the environment in Argentina: ARDL approach. Heliyon, 9(1) Voumik LC, Akter S, Ridwan M, Ridzuan AR, Pujiati A, Handayani BD, Razak MIM (2023) Exploring the factors behind renewable energy consumption in Indonesia: Analyzing the impact of corruption and innovation using ARDL model. Int J Energy Econ Policy 13(5):115–125 Voumik LC, Rahman MH, Rahman MM, Ridwan M, Akter S, Raihan A (2023) Toward a sustainable future: Examining the interconnectedness among Foreign Direct Investment (FDI), urbanization, trade openness, economic growth, and energy usage in Australia. Reg Sustain 4(4):405–415 Voumik LC, Ridwan M, Rahman MH, Raihan A (2023) An investigation into the primary causes of carbon dioxide releases in Kenya: Does renewable energy matter to reduce carbon emission? Renew Energy Focus 47:100491 Waqar M, Zada H, Rafi A, Artas A (2023) Asymmetry in Oil Price Shocks Effect Economic Policy Uncer-tainty? An Empirical Study from Pakistan. Jinnah Business Review, 11(1) Waqar M, Zada H, Rafi A, Artas A (2023) Asymmetry in Oil Price Shocks Effect Economic Policy Uncer-tainty? An Empirical Study from Pakistan. Jinnah Business Review, 11(1) Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6058314","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":417625521,"identity":"da0265fe-897c-41ff-a70a-1b47fe29882d","order_by":0,"name":"Ayodele Oluwaseun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYJACCYYCIMnDwHiAoQLIYGZuIEKLAVgLwwGGMyAtjKRoYWwD8Qlo4Z+RfPDGBwMbBn6eMwaHeefVRvO3A7X8qNiG24YbacmWMwzSGCR7e4Bath3PnXGYsYGx58xt3NbcyDGT5jE4zGBwngek5VhuA1ALM2Mbbi3yIC1/DP5Dtcw5ljufkBYDkBYGgwMMBmdBDmuoyd1ASIvhmWfJlj0GyTySPccKDs45diB3I1DLQXx+kTsODLEfFXZy/DzJGx+8qanLnXf+8MEHPyrweF8gAUzxQLmHweQB3OqBgB9Vug6v4lEwCkbBKBiZAACyE1ugRs2WMgAAAABJRU5ErkJggg==","orcid":"","institution":"Professor (Associate) at Obafemi Awolowo University, Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Ayodele","middleName":"","lastName":"Oluwaseun","suffix":""}],"badges":[],"createdAt":"2025-02-18 17:39:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6058314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6058314/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76745738,"identity":"164f7a1e-fef7-4bbe-bfe2-53da2f0407a9","added_by":"auto","created_at":"2025-02-20 08:58:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFlow Chart of Estimation Method\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6058314/v1/d516ad35f493da7994eccaba.png"},{"id":76744238,"identity":"beb02216-2d04-414a-824f-cf6efb05805a","added_by":"auto","created_at":"2025-02-20 08:50:45","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCUSUM and CUSUM Square Tests\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage21.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6058314/v1/09a858c5c6000e46f17aff54.jpeg"},{"id":76746036,"identity":"a2610c14-5d6b-490b-8326-d6ecf76ace52","added_by":"auto","created_at":"2025-02-20 09:06:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1208598,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6058314/v1/727931ad-a488-4199-b92c-8b693197f557.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAnalyzing the Impact of Economic Growth, FDI and Energy Use on CO2 Emission in Kenya: An ARDL Approach\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCarbon emissions cause ozone depletion, which has a negative effect on the environment. Higher global temperatures may result from an unavoidable rise in carbon emissions.CO\u003csub\u003e2\u003c/sub\u003e is the main driver of global climate change, accounting for 65% of all greenhouse gas emissions, compared to other gas emissions including CH4 and N2O [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The potential for natural catastrophes like hurricanes, massive fires, and severe droughts raises concerns about whether climate change is occurring. A rise in global temperatures has an impact on natural animal habitats and agricultural production. In Kenya, the temperature ranged from a record high of 25.56 Celsius in 2009 to a record low of 22.53 Celsius in 1917, with an average of 24.39 Celsius between 1901 and 2021 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The tourist industry and rain-fed agriculture, both of which are reliant on the environment and subject to harsh weather, are the two main drivers of Kenya's economy. As a result of catastrophic agricultural and animal losses caused by rising temperatures and ongoing droughts, people are at risk of undernourishment, and other hazards to their health and welfare. The majority of Kenya's low-lying seashore and the nearby islands are in danger from sea level rise, which will have a severe impact on the fishing industry and storm surge defense [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Thus, it is important to detect the potential cause of CO\u003csub\u003e2\u003c/sub\u003e emissions and implement proper policies to mitigate the effects of climate change.\u003c/p\u003e \u003cp\u003eThere is an involvement between emission and economic growth where emission has negative impact on environment and human health. At the same time, economic growth is a positive indicator to explain the health assessment of the people. In Kenya, recently the socio economic development has been raising compare to the previous decade. Economic growth leads by the energy consumption, and other macro economic factors such as labor, capital, savings and investment but there is a tradeoff between environment and development. World Bank was shows that Kenya's GDP grew by an average of 5.5% per year between 2010 and 2020 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, this growth has come at a cost, with CO\u003csub\u003e2\u003c/sub\u003e emissions increasing by an average of 4.5% per year between 2010 and 2017, reaching a total of 16.1\u0026nbsp;million metric tons in 2017 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The main drivers of CO\u003csub\u003e2\u003c/sub\u003e emissions in Kenya are the energy sector and transportation, which are both closely linked to economic growth. The energy sector, which primarily relies on traditional fossil fuels, is responsible for over 60% of the country's total CO\u003csub\u003e2\u003c/sub\u003e emission. The transportation sector, on the other hand, is responsible for approximately 20% of CO\u003csub\u003e2\u003c/sub\u003e emissions, as a result of increased vehicular traffic on the roads [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The impact of growth on pollution in Kenya is far-reaching, with the increased energy consumption and transportation causative to air pollution in Kenya which is drastically raises the global emission and environmental susceptibility.\u003c/p\u003e \u003cp\u003eIn Kenya, the use of energy has increased significantly over the past few decades, resulting in a corresponding increase in CO\u003csub\u003e2\u003c/sub\u003e emissions. The most recent statistics from the World Bank indicate that Kenya's CO\u003csub\u003e2\u003c/sub\u003e emissions grew in 2019, hitting 0.4 metric tons per capita [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The main sources of energy in Kenya are traditional fossil fuels, including petroleum, coal, and biomass. These energy sources are responsible for over 80% of the country's energy spending, resulting in high levels of CO\u003csub\u003e2\u003c/sub\u003e giving out [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In addition, the limited entrance to energy in rural areas, the lack of investment in renewable energy sources, and the inefficiency of energy systems have resulted in a dependence on fossil fuels, exacerbating the problem. The collision of energy on emissions in Kenya is far-reaching, with the country's energy sector being responsible for over 60% of its total CO\u003csub\u003e2\u003c/sub\u003e emissions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This has led to an swell in air pollution and the contribution of Kenya to global climate change, with temperatures increasing by an average of 0.3\u0026deg;C per decade between 1960 and 2018 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The impacts of this increase in temperatures include changes in weather patterns, which have resulted in decreased agricultural productivity and increased water stress, affecting the livelihoods of millions of people [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. To mitigate emissions in Kenya, it is essential to promote the access of clean and renewable energy sources. Thus, compel of energy on CO\u003csub\u003e2\u003c/sub\u003e in Kenya is a critical issue that requires immediate attention.\u003c/p\u003e \u003cp\u003eThe FDI has been a crucial factor in driving the growth and development of many countries, including Kenya. Over the past decade, Kenya has attracted significant FDI flows, mainly due to its favorable business climate and rapidly growing economy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, this growth has also led to increased carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions, which have significant negative impacts on the environment and human health [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In 2021, Kenya's total CO\u003csub\u003e2\u003c/sub\u003e emissions were estimated to be approximately 36.3\u0026nbsp;million metric tons [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The influx of foreign investment has led to the development of new energy basis and infrastructure, such as coal-fired power plants, which are key cause of CO\u003csub\u003e2\u003c/sub\u003e emissions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, the growth of the manufacturing sector, driven by FDI, has also put in to the increase in emissions. Despite this, FDI also has the potential to play a constructive role in reducing CO\u003csub\u003e2\u003c/sub\u003e emissions in Kenya [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For example, foreign investors can bring in new technologies and best practices that are more environmentally friendly, which can help to reduce emissions over time. One example of such a policy is the government's requirement for companies operating in Kenya to obtain a Clean Development Mechanism (CDM) certification, which demonstrates that they are operating in a sustainable and environmentally responsible manner [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This has encouraged companies to adopt more environmentally friendly technologies and practices, which has helped to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions. However, there is still much work to be done to fully realize the potential of FDI in reducing CO\u003csub\u003e2\u003c/sub\u003e emissions in Kenya. While the influx of foreign investment has driven economic growth and led to increased emissions, it also has the potential to play an activist function in reducing emissions through the adoption of more environmentally sustainable practices and technologies. Thus, it is important to scrutinize the interplay between FDI and CO\u003csub\u003e2\u003c/sub\u003e emission in the context of Kenya.\u003c/p\u003e \u003cp\u003eThis study focused on Kenya's rapport between economic affluence and environmental quality. Here, CO\u003csub\u003e2\u003c/sub\u003e emissions serve as proxies for environmental deterioration, while GDP stands in for economic affluence. The main objectives of this study include 1) to ascertain the impact on the environment inside the Kenyan \u0026ldquo;Stochastic Impacts by Regression on Population, Affluence, and Technology\u0026rdquo; model known as (STIRPAT) model. 2) To assess the association between Kenya's GDP and CO\u003csub\u003e2\u003c/sub\u003e emissions over the long and short terms. 3) To find out if the use of renewable energy and CO\u003csub\u003e2\u003c/sub\u003e emissions in Kenya are related. 4) To determine how Kenya's use of non-renewable energy and alternative nuclear energy influences CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eThe remaining sections' structure is listed below: Section 2 provides a detailed analysis of the literature review. In Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we outline the study's data and econometric methodology. The \u0026ldquo;Results and discussion\u0026rdquo; part of section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4\u003c/span\u003e includes a summary of the econometric studies' results and necessary sources. In section 5, provide the conclusion and policy implications. Section 6 contains references.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eNumerous researches on the interaction between the progress of the economy and CO\u003csub\u003e2\u003c/sub\u003e emissions have been performed over the years. There is a dearth of literature pertinent to Kenya's GDP- CO\u003csub\u003e2\u003c/sub\u003e emission nexus. Al Mulali et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] examined the EKC premise for the first time in Kenya using data from 1980 to 2012 and discovered evidence of EKC characteristics in Kenya. Likewise, Sarkodie and Ozturk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] the EKC hypothesis in Kenya and supported the EKC theory. Most studies discovered a significant relationship between GDP and CO\u003csub\u003e2\u003c/sub\u003e that was positive, but only a few studies discovered insignificant or adverse relationships between Emissions of CO\u003csub\u003e2\u003c/sub\u003e and economic expansion. Using Thailand as an example, Adebayo and Akinsola [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] investigated the GDP- CO\u003csub\u003e2\u003c/sub\u003e relationship. In order to examine this association, the researchers used wavelet tools. Their empirical findings demonstrated a positive correlation between CO\u003csub\u003e2\u003c/sub\u003e and economic progress, and they also identified a one-way causation between GDP and CO\u003csub\u003e2\u003c/sub\u003e emissions. However, He et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Tufail et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] observed a favorable interaction involving CO\u003csub\u003e2\u003c/sub\u003e and GDP in their respective studies. This indicates that a rise in GDP reduces environmental sustainability. Furthermore, Adebayo and Kirikkaleli [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] evaluated the GDP- CO\u003csub\u003e2\u003c/sub\u003e linkage for Japan between 1990 and 2015 using a novel wavelet coherence test. According to their findings, progress in GDP is connected with a rise in emissions of CO\u003csub\u003e2\u003c/sub\u003e. Zhang et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] analyzed the influence of the growth of the economy on CO\u003csub\u003e2\u003c/sub\u003e between 1970 and 2018 employing data from Malaysia. Their empirical results demonstrate a favorable correlation between CO\u003csub\u003e2\u003c/sub\u003e and GDP. The positive CO\u003csub\u003e2\u003c/sub\u003e-GDP link was verified by the research conducted by Usman et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] for the United States and Adebayo and Rjoub [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] for MINT economies. Adebayo and Odugbesan's [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] research examined how economic development affected releases of CO\u003csub\u003e2\u003c/sub\u003e in South Africa utilizing data throughout the years 1971 to 2017 and contemporary econometric methodologies. The study's conclusions revealed that raising GDP emitted higher CO\u003csub\u003e2\u003c/sub\u003e. Baloch et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] evaluated the association between GDP and environmental deterioration, and their outcomes proved that GDP had a beneficial influence on CO\u003csub\u003e2\u003c/sub\u003e emissions. Similarly, Joshua and Bekun's [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] study supports the hypothesis that economic expansion significantly triggered CO\u003csub\u003e2\u003c/sub\u003e. Several economic analyses observed that increasing the usage of sustainable sources would lead to mitigating CO\u003csub\u003e2\u003c/sub\u003e emissions. Sarkodie and Ozturk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] concluded that Kenya's renewable energy use massively diminished CO\u003csub\u003e2\u003c/sub\u003e emissions. Azam et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], using a sophisticated panel quantile regression model, found a optimistic relation between growth and pollution in the top 5 emitter nations for the years 1995\u0026ndash;2017 and an inverse correlation between clean energy and CO\u003csub\u003e2\u003c/sub\u003e in the same set of economies. There is an existence of cause GDP growth and emissions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Liu et al. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] used DOLS technique on temporal data from 1992\u0026ndash;2013 to find an inverse relationship between the BRIC nations' utilization of renewable energy sources and their CO\u003csub\u003e2\u003c/sub\u003e emissions. In addition, Liu et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] also found renewable source mitigate emission in developing countries. Using data from 1990\u0026ndash;2019, Ali et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] explored the relationship between China's use of non-renewable and renewable energies and the country's carbon emission intensity (CEI). The research used the dynamic \u0026ldquo;Autoregressive Distributed Lag (ARDL)\u0026rdquo; method to see how the variables were connected through time. The research shows a favorable correlation between CEI and both renewable and non-renewable energy sources. Employing data from 1965 to 2019, Adebayo et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] addressed the idea that using renewable sources reduces carbon dioxide emissions in Sweden. Research by Dong et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] looked at whether or not BRICS nations may cut their CO\u003csub\u003e2\u003c/sub\u003e emissions more effectively by increasing their use of nuclear power. According to the results of the research, renewable energy sources contribute significantly to cutting down on carbon dioxide emissions.\u003c/p\u003e \u003cp\u003eThere are two main ideas on the association between FDI and environmental quality. While some research supports the pollution heaven theory and finds that FDI harms the environment, other research demonstrates that FDI actually enhances air quality via the spread of green technology. Marcellus [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] conducted a study in the context of Kenya to find out the influence of FDI on the level of CO\u003csub\u003e2\u003c/sub\u003e in Kenya. The findings of the study showed that FDI has a mitigating role in CO\u003csub\u003e2\u003c/sub\u003e emission and increasing FDI lowers the level of CO\u003csub\u003e2\u003c/sub\u003e emission. Evidence from a wide range of research has shown that FDI helps mitigate environmental damage by funding innovative approaches to green technology [\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Eskeland and Harrison [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] found that U.S. manufacturing facilities in emerging economies use green energy and ecologically sound management techniques. The effects of FDI on ecosystems were investigated in a study of the nations of the Gulf Cooperation Council. Using a multivariate approach, the research found that FDI had no negative effects on ecosystems [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To assess the link stuck between FDI and environment, Demena and Afesorgbor [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] found that the influences of FDI on CO\u003csub\u003e2\u003c/sub\u003e emissions are negligible. While FDI has been shown to reduce CO\u003csub\u003e2\u003c/sub\u003e emissions by varying degrees depending on the study, the evidence is still mixed. The results held up after accounting for variations in development and pollution levels among nations. Du and Li [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] looked at how carbon emissions increased across 71 nations from 1992 to 2012. They used a Malmquist index approach with parameters. According to the research's findings, carbon stock production increased across the board over the study period. Additionally, increasing the productivity of all factors that contribute to carbon depends on technological innovation. Between 2006 and 2015, Zhou et al. studied how China's OFDI spillover affected the sustainable technologies of 30 regions [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The research concluded that although Chinese OFDI does not lead to sustainable technologies, there are substantial regional differences since there aren't the necessary enabling circumstances in certain areas. Udemba et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] used ARDL bound test to investigate the affinity involving FDI and emissions and discovered that FDI affect environment. Solarin et al. [45] also found that FDI degrades the environment as well.\u003c/p\u003e \u003cp\u003eAccording to our literature assessment, no prior study has been performed on the linkage between FDI, energy use, GDP expansion, and the environment in Kenya. Existing studies have shown conflicting evidence about the FDI- CO\u003csub\u003e2\u003c/sub\u003e link. As FDI stimulates the growth of host economies by funding the development of Greenfield projects and expanding existing enterprises, the production units engaged in these processes generate carbon emissions. However, a number of studies have shown that FDI has little or no effect on carbon dioxide emissions. Kenya likewise suffers from a dearth of studies in the energy sector. It is, therefore, important to investigate the connection between Kenya's rising CO\u003csub\u003e2\u003c/sub\u003e emissions, foreign direct investment, and economic expansion.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Theoretical Framework\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe IPAT model maintains that \u0026ldquo;impacts on ecosystems (I) are the product of the population size (P), affluence (A), and technology (T)\u0026rdquo; provided the foundation for \u0026ldquo;Stochastic Impacts by Regression on Population, Affluence, and Technology\u0026rdquo; defined as STIRPAT model. York et al. [46] argue that the IPAT model has certain limitations since it does not account for non-monotonic, unevenly scaled changes in the influential elements. Using the York STIRPAT model, this issue is resolved.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:I=\\alpha\\:{P}_{i}^{\\beta\\:}{A}_{i}^{\\gamma\\:}{T}_{i}^{Ɵ}{e}_{i}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Ln{I}_{i}=Ln\\alpha\\:+\\beta\\:Ln\\left({P}_{i}\\right)+\\gamma\\:Ln\\left({A}_{i}\\right)+ƟLn\\left({T}_{i}\\right)+{e}_{i}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere the anticipated parameters of the model are β, γ, and Ɵ, and e\u003csub\u003ei\u003c/sub\u003e represents the disturbance term. The aforesaid equation is often simplified in a logarithmic form in the application. Incredibly, the STIRPAT model's structure allows for the dissection of P, A, and T into a number of different factors in the environment; Therefore, researchers have shown a greater extent of interest in this model [47]. The corresponding logarithmic expression is given in Eq.\u0026nbsp;(2).\u003c/p\u003e \u003cp\u003eUsing the STIRPAT model greatly enhances the forms of significant effect factors that were taken into account in this investigation, which is the model's primary advantage. To further evaluate the factors that contribute to CO\u003csub\u003e2\u003c/sub\u003e emission in Kenya, we updated a STIRPAT model by including indices of demographic, economic, and technical factors. The population was used as a surrogate for demographic change, GDP (per capita) and FDI for affluence, and fossil fuel and renewable energy use for technological factors in this study. Now, substituting the corresponding variable in Eq.\u0026nbsp;(2), we can write Eq.\u0026nbsp;(3) as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:LnC{O}_{{2}_{it}}={\\alpha\\:}_{it}+{\\beta\\:}_{1}L{GDP}_{it}+{\\beta\\:}_{2}L{POP}_{it}+{\\beta\\:}_{3}L{FOS}_{it}+{\\beta\\:}_{4}L{REN}_{it}+{\\beta\\:}_{5}L{FDI}_{it}+{\u0026euro;}_{it}\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere β\u003csub\u003e1\u003c/sub\u003e to β\u003csub\u003e2\u003c/sub\u003e are coefficients used in Eq.\u0026nbsp;(3)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data\u003c/h2\u003e \u003cp\u003eThe ARDL tactic of cointegration suggested by Pesaran et al. [48] was adopted in this empirical investigation to identify the major causes of CO\u003csub\u003e2\u003c/sub\u003e emission in Kenya. The ARDL model was used owing to its capacity to describe a capricious, ever-changing response as the result of one or more forecasting factors. Moreover, it may be used for the study of economics, ecology, and experimental data, as well as for the analysis and forecasting of the actions of dynamic systems [49]. Time series data for Kenya have been gathered from the World Development Indicator (WDI) database and cover the years 1972 to 2021. The explained variable in this analysis is CO\u003csub\u003e2\u003c/sub\u003e emission, whereas the explanatory variables are GDP, population, renewable energy, and fossil fuel energy usage. The variables have been log-transformed to assure normally distributed data. The variables, their logarithms, and the sources of data employed are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable\u0026rsquo;s Description, Source, and Signifier\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignifier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCO2 emissions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCO2 emissions (kt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eWorld Bank Development Indicator\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Domestic Product Per Capita\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP per capita (constant 2015 US\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation, total\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenewable energy consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRenewable energy consumption (% of total energy consumption)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFossil fuels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLFOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFossil fuel energy consumption (% of total)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlternative and nuclear energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlternative and nuclear energy (% of total energy use)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe variables considered in this inquiry are summarized (minimum, maximum, mean, median, and standard deviation) in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral Statistics of the Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLPOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLREN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Empirical Framework and Estimation Method\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eSeveral inferential estimation methods were adopted to estimate the results more precisely. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the steps of the estimation technique employed in this study.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Unit Root Test\u003c/h2\u003e \u003cp\u003eBefore proceeding to further in-depth investigation, it is fundamental to look into the integration series. In this way, we apply unit root tests to assess the series' integration properties. First, the study used conventional \u003cem\u003e\u0026ldquo;augmented Kapetanios, Shin \u0026amp; Snell (KSSUR) [50], Kwiatkowski\u0026ndash;Phillips\u0026ndash;Schmidt\u0026ndash;Shin (KPSS) [51], and Augmented Dickey-Fuller [52]\u0026rdquo;\u003c/em\u003e unit root tests. Secondly, conventional unit root tests may provide misleading findings if there is a structural break(s) in the series being tested. So, we adopted the Zivot and Andrews [53] (ZA) unit root test, which may capture both the stationary aspects of the series and a single structural break (s).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 ARDL model\u003c/h2\u003e \u003cp\u003eTo measure the series' co-integration, we used the ARDL bounds test. The following are the reasons why Pesaran et al. [48] limits test is favored over other co-integration tests. The first advantage is that it may be adopted when series are incorporated in mixed order; the second is that it is much more trustworthy, notably for a limited sample; and the third is that it provides accurate estimates of the long-term model. Eq.\u0026nbsp;4 illustrates the ARDL limits test:\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1740039351.png\"\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eNo cointegration (the null hypothesis) is contrasted with evidence of cointegration (the alternative hypothesis). If the F-statistic exceeds the threshold values for the upper and lower limits, we cannot accept the null hypothesis. Null and alternative hypotheses are shown in Equations 5 and 6:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{H}_{0}={ⱴ}_{1}={ⱴ}_{2}={ⱴ}_{3}={ⱴ}_{4}={ⱴ}_{5}={ⱴ}_{6}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{H}_{1}={ⱴ}_{1}\\ne\\:{ⱴ}_{2}\\ne\\:{ⱴ}_{3}\\ne\\:{ⱴ}_{4}\\ne\\:{ⱴ}_{5}\\ne\\:{ⱴ}_{6\\:\\:\\:\\:\\:\\:}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eH\u003csub\u003e1\u003c/sub\u003e stands for the alternative hypothesis and H\u003csub\u003e1\u003c/sub\u003e for the null hypothesis.\u003c/p\u003e \u003cp\u003eWe used the ARDL method after establishing that the parameters are co-integrated. Engle and Granger's [54] error correction model (ECM) is applied to evaluate short-term correlations and the \u0026ldquo;Error Correction Term\u0026rdquo; after that the long-term associations have been established. Eq.\u0026nbsp;7 is employed for the long-run ARDL estimation.\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1740039474.png\"\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eWhere speed of adjustment is denoted by ℓ\u003c/p\u003e \u003cp\u003eWe have employed the fully modified (FMOLS) [55] and dynamic OLS (DOLS) [56] and canonical correlation regression estimator (CCR) estimation approach to visualize the long-run effect of GDP, POP, REN, FDI, and FOS on CO\u003csub\u003e2\u003c/sub\u003e as a robustness check to the ARDL long-run guesstimate. Using these techniques, it is possible to establish asymptotic coherence while taking serial correlation into account. FMOLS and DOLS should only be used when there is corroboration of cointegration between the series. As a result, this research calculates long-term elasticity using FMOLS and DOLS estimators. As follows The FMOLS equation is shown by Eq.\u0026nbsp;8;\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1740039544.png\"\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003eWhere t illustrates the timing trend and SIC is used to indicate the lag order. The advantage of FMOLS and DOLS is that they address the issues of endogeneity, auto-regression, and bias resulting from sample bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Robustness Check\u003c/h2\u003e \u003cp\u003eThis study employed the FMOLS, DOLS, and CCR to compare how time-varying factors affected the environment, which allowed us to evaluate the model's robustness. There were two primary factors that necessitated the employment of such methods. The cointegration criterion for parameters must be satisfied before the FMOLS, DOLS, or CCR may be used. Moreover, these methods deal with endogeneity and serial correlation biases brought on by the cointegration interaction. Consequently, it yields outcomes with asymptotic efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Pairwise Granger Causality Test\u003c/h2\u003e \u003cp\u003eAs there is a possibility that theoretical correlations won't work in real life due to certain components that might not be well stated in theory, the concept of a causality test would determine whether past changes in a factor are to cause of the current observation or not. It is claimed that causation extends from X to Y if the sum of X's past and current values deviates considerably from zero. Similar rules apply to Y and X causality; if the results vary from zero, then causation is present on both sides. To determine if the factors had a short-term causal connection, the investigation used the paired Granger causality [57] test. The following Eq.\u0026nbsp;(6) demonstrates the causal connection between Xt and Yt:\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:E\\left({Y}_{t+h}|{J}_{t,}{X}_{t}\\right)=E\\left({Y}_{t+h}|{J}_{t}\\right)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(9\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, Jt denotes the data sets derived from the preceding observations acquired until that point in time (t).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.5 Diagnostic test\u003c/h2\u003e \u003cp\u003eThe investigation used a variety of different diagnostic techniques to confirm the precision of the findings. In this study, heteroscedasticity was determined using the ARCH test [58], specification error was assessed using the Ramsey Reset test [59], autocorrelation was ascertained using the Durbin Watson test [60], normality was determined using the Jarque-Bera test [61], and predicted model stability was identified using the CUSUM \u0026amp; CUSUMsq test [62]. Table\u0026nbsp;(9) provides an overview of the findings of the diagnostic approaches.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Empirical Findings","content":"\u003cp\u003e\u003cstrong\u003e4.1 Unit Root Test Result\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we check the parameters\u0026apos; stationarity characteristics to determine sure they are suitable for use in this empirical study. Based on this, we used unit root tests (KSSUR, KPSS \u0026amp; ADF) to agree on whether or not the series was stationary. The findings of the \u0026ldquo;unit root tests\u0026rdquo; are portrayed in Table (3). The results of the stationarity test demonstrated that the variables used in this study had a non-uniform order of integration, which favors the ARDL method over the traditional cointegration-based methods. Table (3) showed that, while all variables [LCO\u003csub\u003e2\u003c/sub\u003e, LGDP, LPOP, LFDI, and LFOS] exhibits I(1), only LREN showed Integrated to Zero or I(0). Thus, the variables employed in this study have mixed order of integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3: Unit Root Tests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cem\u003eKSUR Test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003eADF Test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cem\u003eKPSS Test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eRemark\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003eLevel\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003csup\u003est\u003c/sup\u003e Dif.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003eLevel\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003csup\u003est\u003c/sup\u003e Dif.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003eLevel\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003csup\u003est\u003c/sup\u003e Dif.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eStationary at\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eLCO2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e-0.12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.388***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003e-0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.52***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-1.43\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-6.99***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eI (1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eLGDP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.38***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.45\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.53***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.94\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-7.73***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eI (1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eLPOP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.53***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.38***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.81\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-7.31***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eI (1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eLREN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.52***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.52***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.49**\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eI (0)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eLFOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e-0.12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003e-6.05***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003e-0.05\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e-6.51***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-0.712\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-6.92***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eI (1)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cem\u003eLFDI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.14**\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.05***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.58**\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cem\u003eI (0)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(a) \u0026nbsp;The asterisk symbols (***\u0026amp; **) are utilized for 1% \u0026amp;5% significance levels. (b) Optimal lag selected by AIC \u0026amp; SIC criterion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStructural break analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4: Structural Break Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 623px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eZivot-Andrews test\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003eZA stat.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003eBreak\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e1%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e5%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e10%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eDecision\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eLCO\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.075***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e2005\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eBreak\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eExist\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eLGDP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003e-2.649***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e1991\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eLPOP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.702**\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e2012\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eLREN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.568\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e1991\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eLFOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.839***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e2005\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cem\u003eLFDI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.299***\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cem\u003e2013\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-5.34\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cem\u003e-4.58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAssuming that the mean, variance, and trend will not change over time is the stationarity assumption, which forms the foundation for applied time series prediction and assessment. A structural break is believed to have happened if any of the aforementioned conditions altered, or if the break period fell within the sample period. In econometrics, a structural break is a sudden shift in the time series data. Large discrepancies in forecasts and inconsistencies in theoretical frameworks may come from it. Zivot-Andrews unit root testing was used in this research to spot the abrupt change in trend. Figure 4 depicts the test results, which indicate that the statistical sample has a substantial structural breakdown. The outcomes depicted in Table 4 also present that LCO\u003csub\u003e2\u003c/sub\u003e, LGDP, LPOP, LFOS, and LFDI observed significant structural breaks in 2005, 1991, 2012, 2005, and 2013, accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. ARDL Bound Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5: ARDL Bound Test\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eTest Statistics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eValue\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eK\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eF statistics\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.936\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cem\u003eSignificance level\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eCritical Bounds\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e10%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e5%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.50%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e1%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eI(0)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.26\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.62\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.96\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e3.41\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003eI(1)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e3.35\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e3.79\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e4.18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cem\u003e4.68\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eF-statistics are estimated and compared to the critical values evaluated by Pesaran et al. [48] to determine whether or not the null hypothesis should be rejected. If the intended F stat. goes over the tabulated F value, we may reject the null hypothesis such as no cointegration exists. If the calculated F stat has a lower value than the tabulated value, it fails to reject the developed hypothesis. No inference can be made from the data, however, if the F-statistics value falls inside the bounds. A close inspection of Table 5 reveals that the F-statistic is statistically significant at the 1% level. Thus, significant long-run linkage exists between explanatory and dependent variables. Also, F-value is much higher than the formula\u0026apos;s upper limit. In light of new information on Kenya\u0026apos;s history, we can assess the impact of factors like GDP, population, FDI, and renewable and fossil fuel energy usage on CO\u003csub\u003e2\u003c/sub\u003e emissions in Kenya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. \u0026nbsp; \u0026nbsp; \u0026nbsp; ARDL Long and Short-Run Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eARDL long-run (LR) and short-run (SR) assessments are depicted in the Table (6) and showed how various factors are connected with CO\u003csub\u003e2\u003c/sub\u003e emission in Kenya. Long-run (LR) estimation results presented that coefficients of LGDP are negative and highly significant at a 5% level of significance. The coefficient value of LGDP is -0.0461 and implies that a 1% increase in LGDP would result in reducing CO\u003csub\u003e2\u003c/sub\u003e emission by 0.0461% in the long run and vice versa. Similarly, the marginal effect of LPOP has significant to boost emission where more population contacts more pollution. The result entail that a 1% enlarges in populace will cause higher emissions in the long run by 0.199% and vice versa. Additionally, the value of LREN is -12.26 and which is significant at a 5% significance level. Thus, a 1% increase in LREN will reduce the LCO\u003csub\u003e2\u003c/sub\u003e by 12.26% in the long run. Finally, the estimation result of ARDL also showed that the value of LFOS and LFDI are 2.398 and -0.139. The value of LFOS and LFDI does not affect Kenya\u0026rsquo;s long-term CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 6: ARDL Long-Run and Short-Run Results\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eSR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eLGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-1.0461*(0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eLPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e0.199**(0.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eLFOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e2.398(4.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eLREN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-12.26***(1.509)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eLFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e-0.139(0.818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eD.LGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.371**(0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eD.LPOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-1.330(8.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eD.LFOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e0.361(0.265)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eD.LREN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-3.727***(1.287)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eD.LFDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.00253(0.0074)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eECT (Speed Adjustment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.450***(0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e-5.264(5.868)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 27px;\"\u003e\n \u003cp\u003eR-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e(a) \u0026nbsp;Asterisk symbol (***, **,*) utilized for 1% ,5%\u0026amp; 10% significance level. (b) S E in brackets.\u003c/p\u003e\n\u003cp\u003eThe findings of Short-run (SR) ARDL estimation also showed in the Table (6). The result showed that the coefficient value of LGDP is -0.371 which is tended GDP has no cause to enlarge emission in the SR. Thus, a 1% increase in LGDP will lower emissions in the short run. Moreover, the results depicted in Table (6) showed that the value of LREN is -3.727 and which is highly significant at a 1% significance level. Therefore, a 1% extend in LREN will lower the CO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003eemission by 3.727% in the short run and similar sign of this coefficient was found by Rahman and Majumder [63]. Furthermore, the value of LPOP and LFOS are -1.330 and 0.361. The values of LPOP and LFOS have an insignificant impact on CO\u003csub\u003e2\u003c/sub\u003e emissions in the short run. Additionally, the L.LCO\u003csub\u003e2\u003c/sub\u003e coefficient is positive for the chosen variables, and there is a yearly divergence of 0.0267% between the SR and LR equilibrium. The speed of adjustment is -0.45% means 45% to move forward the factors in an equilibrium situation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.5.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; Robustness Check and Causality Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also employed several estimation approaches such as FMOLS, DOLS, and CCR to observe the robustness of ARDL estimation findings. The results of FMOLS, DOLS, and CCR are recorded in Table (7). The upshots of the DOLS showed that the estimated value of LGDP is -1.811 and which is highly significant at a 1% level of significance. Thus increase in LGDP will significantly lower the CO\u003csub\u003e2\u003c/sub\u003e emission and this ruling is reliable with the outcomes of ARDL results. Similarly, the coefficient value of LPOP is positive and highly significant at a \u0026ldquo;1% level of significance under the FMOLS, DOLS, and CCR estimation\u0026rdquo; approach. The result implies that an increase in LPOP also triggers the emission of CO\u003csub\u003e2\u003c/sub\u003e and these results are also reliable with the findings of the ARDL estimation approach. Moreover, the coefficient value of LREN is negative and significant under FMOLS and DOLS approaches. Rahman and Majumder [63] found LREN was negative coefficient by using FMOLS model in N-11 countries. The negative association between LREN and LCO2 also corroborated the results of the ARDL estimation approach. The findings of FMOLS, DOLS, and CCR assessment showed that the coefficient value of LFDI is insignificant and this results in line with the ARDL estimation technique. Thus, the ARDL estimation results are robust and this result is consistent with the findings of FMOLS, DOLS, and CCR approaches.\u003c/p\u003e\n\u003cp\u003eThe results of the paired Ganger causality test are shown in Table 8. The null hypothesis of no causality is rejected if F-statistics are significant. Table (8) demonstrates a one-way causation presence between LCO2 and LGDP, and LFOS and LCO2. In addition, there are also bidirectional causal relationships exist between LREN and LCO2, and LFDI and LCO2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 7: Robustness Check\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"638\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFMOLS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDOLS\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCCR\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 638px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLnCO2 dependent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eLGDP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e-0.642 (0.814)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e-1.811*** (0.406)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.627* (0.346)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eLPOP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.478***(0.145)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.767***(0.131)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.463***(0.166)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eLREN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e-3.558** (1.573)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e-10.849*** (1.214)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003e-2.980 (3.003)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eLFOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.254*** (0.438)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.174 (0.213)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.289** (0.478)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eLFDI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.028 (0.022)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.009 (0.014)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003e-0.031 (0.030)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e16.843\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e55.318\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003e14.335\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cem\u003eR-squared\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.733\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.982\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.725\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(a) \u0026nbsp;Asterisk symbol (***, **,*) utilized for 1% ,5%\u0026amp; 10% significance level; (b) SE in brackets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 8: Granger Causality Test Outcomes\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Null Hypothesis:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eF-Statistic\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eProb.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LGDP \u0026ne;LCO\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.85918\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.4307\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LCO\u003csub\u003e2\u003c/sub\u003e\u0026ne; LGDP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e5.40911\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.008\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LPOP \u0026ne; LCO\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.55552\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.5778\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LCO\u003csub\u003e2\u003c/sub\u003e \u0026ne;LPOP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.10907\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.1337\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LREN \u0026ne;LCO\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e8.40584\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.0008\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LCO\u003csub\u003e2\u003c/sub\u003e \u0026ne;LREN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e3.23988\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.0489\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LFOS \u0026ne;LCO2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e4.14323\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.024\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LCO\u003csub\u003e2\u003c/sub\u003e \u0026ne;LFOS\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.15757\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.8548\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LFDI \u0026ne;LCO\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e3.51271\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.0388\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LCO\u003csub\u003e2\u003c/sub\u003e \u0026ne;LFDI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e5.0728\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.0106\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 409px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;LPOP \u0026ne;LGDP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.21214\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.3075\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e(a) \u0026nbsp;Asterisk symbol (***, **,*) utilized for 1%, 5%\u0026amp; 10% significance level. (b) Optimal lag selected by AIC \u0026amp; SIC criterion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.7 Outcomes of Diagnostic Tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we think it\u0026apos;s important to address how well the ARDL error correction model fits the data. Multiple diagnostic and stability analyses were performed with this goal in mind.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 9: Diagnostic tests for Model adequacy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNull Hypothesis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest Statistic\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP-Value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cem\u003eAECH Heteroskedasticity test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eHo: Homoskedasticity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.425 (F- statistic)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.254\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cem\u003eNormality/Jarque Bera\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eHo: residuals have a normal distribution.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.7854\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.3785\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cem\u003eB-G LM test\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eHo: No serial correlation up to 2 lags\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e2.142 (F- statistic)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.190\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e.784\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e.841\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cem\u003eDW value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.854\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 215px;\"\u003e\n \u003cp\u003e\u003cem\u003eRam. RESET (F)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cem\u003eHo: The model\u0026apos;s functional form is valid.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e3.192 (F- statistic)\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cem\u003e0.086\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHomoscedasticity, heteroscedasticity, Serial correlation, normalcy, and model specification are all examined by the diagnostic tests. According to the findings in Table 9, the model is not challenged by measurement errors, heteroscedasticity, autocorrelation, or normalcy. This makes it clear that the findings of this inquiry can be used to reliably draw conclusions. Figure (2) portrayed the outcomes of the CUSUM and CUSUM square test and indicates that the blue line lies within the red lines at a 5% level of significance and makes the parameters of the estimated model stable.\u0026nbsp;\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis research examined the influence of economic growth, energy usage, and FDI on Kenya's CO\u003csub\u003e2\u003c/sub\u003e emission using data from 1972 to 2021. This study used the KSSUR, ADF, and KPSS unit root tests to determine the stationary characteristic within the dataset. The results of those tests indicated that variables displayed mixed-order integration. The Zivot-Andrewes unit root test was also used in this research to identify the structural break within the sample period, and the findings of this study demonstrated the existence of a substantial structural break within the sample period. To guarantee the validity of the results, the inquiry utilized the FMOLS, DOLS, and CCR long-run estimators in addition to the ARDL model. According to ARDL long-term estimates, economic development increases CO\u003csub\u003e2\u003c/sub\u003e emissions, but the use of renewable energy reduces CO\u003csub\u003e2\u003c/sub\u003e emissions over time. These findings were also supported by FMOLS, DOLS, and CCR estimate outputs. The usages of fossil fuels for energy, population growth, and FDI have minimal impact on Kenya's carbon emissions. The results indicate that Kenya will need more renewable energy sources in the future. Kenya's CO\u003csub\u003e2\u003c/sub\u003e emissions are pushed up by the growing GDP, expanding FDI size, and substantial increase in population. Kenya needs to identify the factors that contribute most to the nation's CO\u003csub\u003e2\u003c/sub\u003e emissions at this time. The major objective is to identify the principal contributors to CO\u003csub\u003e2\u003c/sub\u003e emission. Then consider using more sustainable energy sources and using less fossil fuel energy. Several diagnostic tests, such as the Breush pagan Godfrey test, Jarque Bera test, Breush Godfrey LM test, and CUSUM \u0026amp; CUSUMSQ test to check model adequacy and certify that the model is devoid of all forms of problematic conditions.\u003c/p\u003e"},{"header":"6. Policy Implication","content":"\u003cp\u003eCarbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) emissions can be reduced by encouraging sustainable economic growth and decreasing reliance on fossil fuels, both of which can be measured using GDP. To lessen reliance on fossil fuels and GHG, GDP can incentivize the research, development, and deployment of renewable sources including solar, wind, and hydro power. Companies that put money into renewable energy sources should be rewarded monetarily for their efforts. Corporations that put money into renewable energy should be rewarded monetarily for their efforts. In order to encourage businesses and individuals to decrease their carbon footprint, a carbon tax should be imposed on the production and consumption of fossil fuels. Revenue from the carbon tax can be used to fund initiatives to expand access to renewable energy sources and strengthen regulatory safeguards for the planet. Investment in infrastructure and monetary incentives for users are two ways GDP can promote eco-friendly means of transportation including public transit, biking, and walking. Global economic growth can encourage nations to work together to solve climate change by facilitating the sharing of innovative solutions, the transfer of cutting-edge technologies, and concerted action on environmental concerns. By offering fiscal incentives like tax credits and subsidies, GDP may promote the development of low-carbon businesses like electric vehicles, energy storage, and clean energy. Emissions of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) can be heavily influenced by population numbers and habits. The promotion of family planning and reproductive health can be aided by lowering financial, institutional, and societal barriers to these issues. As a result, population growth will be slowed and energy consumption will decrease. Encourage people to adopt sustainable lifestyles that lessen their reliance on fossil fuels and their contribution to global warming by spreading information on the effects of individual actions. By encouraging investment in low-carbon businesses and technology, FDI can help reduce emissions. Offering tax breaks, subsidies, and other financial incentives to foreign direct investment in low-carbon businesses like renewable energy, energy efficiency, and sustainable transportation is a good start. To ensure FDI projects have a negligible effect on the environment and aid in the reduction of CO\u003csub\u003e2\u003c/sub\u003e emissions, it is important to implement environmental guidelines for them. Keep an eye on foreign direct investment projects to make sure they're helping the planet and cutting down on carbon emissions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest statement:\u003c/h2\u003e \u003cp\u003eNo competing interests declared by authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eData availability statement:\u003c/h2\u003e \u003cp\u003eOn request, corresponding author will provide data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad S, Raihan A, Ridwan M (2024) Pakistan's trade relations with BRICS countries: trends, export-import intensity, and comparative advantage. Front Finance, 2(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad S, Raihan A, Ridwan M (2024) Role of economy, technology, and renewable energy toward carbon neutrality in China. Journal of Economy and Technology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhter A, Al Shiam SA, Ridwan M, Abir SI, Shoha S, Nayeem MB, Bibi R (2024) Assessing the Impact of Private Investment in AI and Financial Globalization on Load Capacity Factor: Evidence from United States. J Environ Sci Econ 3(3):99\u0026ndash;127\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury AAA, Rafi AH, Sultana A, Noman AA (2024) Enhancing green economy with artificial intelligence: Role of energy use and FDI in the United States. arXiv preprint arXiv:2501.14747\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury AAA, Sultana A, Rafi AH, Tariq M (2024) AI-driven predictive analytics in orthopedic surgery outcomes. Revista Esp de Documentacion Cient 19(2):104\u0026ndash;124\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain T (2022) A Comparative Analysis of Adversarial Capabilities, Attacks, and Defenses Across the Machine Learning Pipeline in White-Box and Black-Box Settings. Appl Res Artif Intell Cloud Comput 5(1):195\u0026ndash;212\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain T (2024) Q J Emerg Technol Innovations 9(2):33\u0026ndash;46A Novel Integrated Privacy Preserving Framework for Secure Data-Driven Artificial Intelligence Systems\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam S, Raihan A, Ridwan M, Rahman MS, Paul A, Karmakar S, Al Jubayed A (2023) The influences of financial development, economic growth, energy price, and foreign direct investment on renewable energy consumption in the BRICS. J Environ Energy Econ 2(2):17\u0026ndash;28\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnwe JC, Ridzuan AR, Uche E, Ray S, Ridwan M, Razi U (2024) Greening Japan: Harnessing energy efficiency and waste reduction for environmental progress. Sustainable Futures 8:100302\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePattak DC, Tahrim F, Salehi M, Voumik LC, Akter S, Ridwan M, Zimon G (2023) The driving factors of Italy\u0026rsquo;s CO2 emissions based on the STIRPAT model: ARDL, FMOLS, DOLS, and CCR approaches. Energies 16(15):5845\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePolcyn J, Voumik LC, Ridwan M, Ray S, Vovk V (2023) Evaluating the influences of health expenditure, energy consumption, and environmental pollution on life expectancy in Asia. Int J Environ Res Public Health 20(5):4000\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafi AH, Chowdhury AAA, Sultana A, Noman AA (2024) Unveiling the role of artificial intelligence and stock market growth in achieving carbon neutrality in the United States: An ARDL model analysis. arXiv preprint arXiv :241216166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRafi AH, Chowdhury AA, Sultana A, Tariq M (2024) Artificial intelligence for early diagnosis and personalized treatment in gynecology. Int J Adv Eng Technol Innovations 2(1):286\u0026ndash;306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman J, Foisal MZU, Mohajan B, Islam S, Rafi AH, Paul A \u0026amp; Ahmad10, S. Role of Renewable Energy, Economic Growth, Agricultural Productivity, and Urbanization Toward Achieving China\u0026rsquo;s Goal of Net-zero Emissions\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman J, Foisal MZU, Mohajan B, Rafi AH, Islam S, Paul A Nexus Between Agriculture, Industrialization, Imports, and Carbon Emissions in Bangladesh\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman J, Raihan A, Tanchangya T, Ridwan M (2024) Optimizing the digital marketing landscape: A comprehensive exploration of artificial intelligence (AI) technologies, applications, advantages, and challenges. Front Finance, 2(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Atasoy FG, Atasoy M, Ridwan M, Paul A (2022) The role of green energy, globalization, urbanization, and economic growth toward environmental sustainability in the United States. J Environ Energy Econ 1(2):8\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Atasoy FG, Coskun MB, Tanchangya T, Rahman J, Ridwan M, Yer H (2024) Fintech adoption and sustainable deployment of natural resources: Evidence from mineral management in Brazil. Resour Policy 99:105411\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Bala S, Akther A, Ridwan M, Eleais M, Chakma P (2024) Advancing environmental sustainability in the G-7: The impact of the digital economy, technological innovation, and financial accessibility using panel ARDL approach. Journal of Economy and Technology\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Hasan MA, Voumik LC, Pattak DC, Akter S, Ridwan M (2024) Sustainability in Vietnam: Examining Economic Growth, Energy, Innovation, Agriculture, and Forests' Impact on CO2 Emissions. World Development Sustainability, p 100164\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Rahman J, Tanchangtya T, Ridwan M, Islam S (2024) An overview of the recent development and prospects of renewable energy in Italy. Renew Sustainable Energy 2(2):0008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Rahman J, Tanchangya T, Ridwan M, Bari ABM (2024) Influences of economy, energy, finance, and natural resources on carbon emissions in Bangladesh. Carbon Res 3(1):1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Rahman J, Tanchangya T, Ridwan M, Rahman MS, Islam S (2024) A review of the current situation and challenges facing Egyptian renewable energy technology. J Technol Innovations Energy 3(3):29\u0026ndash;52\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Ridwan M, Rahman MS (2024) An exploration of the latest developments, obstacles, and potential future pathways for climate-smart agriculture. Clim Smart Agric, 100020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Ridwan M, Tanchangya T, Rahman J, Ahmad S (2023) Environmental Effects of China's Nuclear Energy within the Framework of Environmental Kuznets Curve and Pollution Haven Hypothesis. J Environ Energy Econ 2(1):1\u0026ndash;12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Tanchangya T, Rahman J, Ridwan M (2024) The Influence of Agriculture, Renewable Energy, International Trade, and Economic Growth on India's Environmental Sustainability. J Environ Energy Econ, 37\u0026ndash;53\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Tanchangya T, Rahman J, Ridwan M, Ahmad S (2022) The influence of Information and Communication Technologies, Renewable Energies and Urbanization toward Environmental Sustainability in China. J Environ Energy Econ 1(1):11\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaihan A, Voumik LC, Ridwan M, Ridzuan AR, Jaaffar AH, Yusoff NYM (2023) From growth to green: navigating the complexities of economic development, energy sources, health spending, and carbon emissions in Malaysia. Energy Rep 10:4318\u0026ndash;4331\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRidwan M (2023) Unveiling the powerhouse: Exploring the dynamic relationship between globalization, urbanization, and economic growth in Bangladesh through an innovative ARDL approach\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRidwan M, Raihan A, Ahmad S, Karmakar S, Paul P (2023) Environmental sustainability in France: The role of alternative and nuclear energy, natural resources, and government spending. J Environ Energy Econ 2(2):1\u0026ndash;16\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRidwan M, Urbee AJ, Voumik LC, Das MK, Rashid M, Esquivias MA (2024) Investigating the environmental Kuznets curve hypothesis with urbanization, industrialization, and service sector for six South Asian Countries: Fresh evidence from Driscoll Kraay standard error. Res Globalization 8:100223\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRidzuan AR, Rahman NHA, Singh KSJ, Borhan H, Ridwan M, Voumik LC, Ali M (2023), May Assessing the Impact of Technology Advancement and Foreign Direct Investment on Energy Utilization in Malaysia: An Empirical Exploration with Boundary Estimation. In International Conference on Business and Technology (pp. 1\u0026ndash;12). Cham: Springer Nature Switzerland\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultana A, Rafi AH, Chowdhury AAA, Tariq M (2023) Leveraging artificial intelligence in neuroimaging for enhanced brain health diagnosis. Revista de Inteligencia Artif en Med 14(1):1217\u0026ndash;1235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultana A, Rafi AH, Chowdhury AAA, Tariq M (2023) AI in neurology: Predictive models for early detection of cognitive decline. Revista Esp de Documentacion Cient 17(2):335\u0026ndash;349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanchangya T, Raihan A, Rahman J, Ridwan M, Islam N (2024) A bibliometric analysis of the relationship between corporate social responsibility (CSR) and firm performance in Bangladesh. Front Finance, 2(2)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTipon Tanchangya MR, Raihan A, Khayruzzaman MSR, Rahman J, Foisal MZU, Mohajan B, Islam AP S. Nexus Between Financial Development and Renewable Energy Usage in Bangladesh\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTipon Tanchangya MR, Raihan A, Khayruzzaman MSR, Rahman J, Foisal MZU, Mohajan B, Islam AP S. Nexus Between Financial Development and Renewable Energy Usage in Bangladesh\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUrbee AJ, Ridwan M, Raihan A (2024) Exploring Educational Attainment among Individuals with Physical Disabilities: A Case Study in Bangladesh. Journal of Integrated Social Sciences and Humanities\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoumik LC, Ridwan M (2023) Impact of FDI, industrialization, and education on the environment in Argentina: ARDL approach. Heliyon, 9(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoumik LC, Akter S, Ridwan M, Ridzuan AR, Pujiati A, Handayani BD, Razak MIM (2023) Exploring the factors behind renewable energy consumption in Indonesia: Analyzing the impact of corruption and innovation using ARDL model. Int J Energy Econ Policy 13(5):115\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoumik LC, Rahman MH, Rahman MM, Ridwan M, Akter S, Raihan A (2023) Toward a sustainable future: Examining the interconnectedness among Foreign Direct Investment (FDI), urbanization, trade openness, economic growth, and energy usage in Australia. Reg Sustain 4(4):405\u0026ndash;415\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoumik LC, Ridwan M, Rahman MH, Raihan A (2023) An investigation into the primary causes of carbon dioxide releases in Kenya: Does renewable energy matter to reduce carbon emission? Renew Energy Focus 47:100491\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaqar M, Zada H, Rafi A, Artas A (2023) Asymmetry in Oil Price Shocks Effect Economic Policy Uncer-tainty? An Empirical Study from Pakistan. Jinnah Business Review, 11(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaqar M, Zada H, Rafi A, Artas A (2023) Asymmetry in Oil Price Shocks Effect Economic Policy Uncer-tainty? An Empirical Study from Pakistan. Jinnah Business Review, 11(1)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CO2 emission, Renewable energy, Fossil fuels, FDI, Kenya","lastPublishedDoi":"10.21203/rs.3.rs-6058314/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6058314/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study estimates the effects of Gross Domestic Product (GDP), population, renewable energy consumption, fossil fuels, and foreign direct investment (FDI) on Kenya's carbon emissions between 1972 and 2021. This investigation makes use of the \u003cem\u003e\u0026ldquo;Autoregressive Distributed Lag (ARDL)\u0026rdquo;\u003c/em\u003e method, which is grounded in the theoretical framework as the \u003cem\u003e\u0026ldquo;Stochastic Impacts by Regression on Population, Affluence, and Technology\u0026rdquo;\u003c/em\u003e model known as \u003cem\u003e(STIRPAT)\u003c/em\u003e model. The ARDL bound test and structural break test were also used in the study. According to our preliminary results, the data exhibits long-run cointegration; as a result, the uses of ARDL, which is adept at handling both short- and long-term effects, is essential. This study lends credence to earlier research by demonstrating that a rise in Kenya's GDP and population can result in an increase in that country's CO\u003csub\u003e2\u003c/sub\u003e emissions. Kenya may reduce its damaging carbon dioxide emissions by transitioning to renewable energy sources. All estimates place the impacts of GDP growth and population growth at parity. Achieving Kenya's sustainable development goals will require substantial investment in the country's energy infrastructure, making this analysis potentially useful in planning and establishing strategies for future financial funding in the energy sector. For ARDL, the effects of fossil fuels are negative but insignificant. FDI has an insignificant but positive effect on the environment. Based on these findings, policymakers can make informed decisions to sustainable use of renewable energy.\u003c/p\u003e","manuscriptTitle":"Analyzing the Impact of Economic Growth, FDI and Energy Use on CO2 Emission in Kenya: An ARDL Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-20 08:50:41","doi":"10.21203/rs.3.rs-6058314/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"604d3dbc-01ca-4adb-9ea9-9f29326d4737","owner":[],"postedDate":"February 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44527280,"name":"Environmental Policy"}],"tags":[],"updatedAt":"2025-02-20T08:50:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-20 08:50:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6058314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6058314","identity":"rs-6058314","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}