The Role of Renewable Energy in Combating Environmental Degradation in Somalia

The Role of Renewable Energy in Combating Environmental Degradation in Somalia | 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 The Role of Renewable Energy in Combating Environmental Degradation in Somalia Bashir Mohamed Osman, Said Ali Shire, Farhan Habib Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5193133/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Mar, 2025 Read the published version in Discover Sustainability → Version 1 posted 11 You are reading this latest preprint version Abstract Environmental degradation is a pressing global issue with far-reaching consequences for the health of our planet and the well-being of its inhabitants. It is characterized by the deterioration of Earth's natural systems due to factors such as pollution, deforestation, and climate change, leading to biodiversity loss and the depletion of natural resources. Addressing these challenges is essential for maintaining ecological balance and ensuring sustainable practices that mitigate environmental impacts and preserve the planet for future generations. This paper explores the key drivers and impacts of environmental degradation in Somalia, with a focus on economic growth, agricultural expansion, and population growth. Methods : The study employs the Autoregressive Distributed Lag (ARDL) Model to examine both long- and short-run relationships between environmental degradation and variables such as economic growth, domestic investment, agricultural land, and population growth. The ARDL model, chosen for its robustness with small sample sizes and flexibility with variable integration, utilizes annual time series data from 1990 to 2020. The model was selected using log-likelihood and the Akaike Information Criterion (AIC). Results and Recommendations : The findings reveal that both long- and short-run estimates show agricultural land expansion as a significant contributor to environmental degradation in Somalia. Economic and population growth further exacerbate the issue, while domestic investment helps mitigate degradation. The study highlights the role of deforestation in biodiversity loss, soil degradation, and climate change. It recommends promoting sustainable agricultural practices such as conservation agriculture and agroforestry to curb deforestation and promote environmental sustainability. Environmental degradation Economic growth ARDL Somalia Deforestation Sustainable agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Environmental degradation is considered one of the world's most severe environmental problems in recent years and is thought to be a possible driver of global warming, making it a global concern due to the significance of environmental protection and sustainability (Grossman & Krueger, 1995). The environmental Kuznets curve (EKC) hypothesis (i.e., an inverted U-shaped environmental pollution and economic growth relation) suggests that growth in the early stages degrades environmental quality, but the quality of the environment enhances after a certain level of income is reached (Sharma, 2011 ). According to the EKC, pollution emissions and other environmental issues disproportionately influence developing nations. When deciding which development initiative to approve, it also implies that these nations must choose between economic development and environmental protection. In the hopes that a higher standard of living will undo any environmental damage, environmental preservation may have to take a backseat to economic development (Abdi, 2023 ). Since the end of the last ice age 10,000 years ago, one-third of the world's forests have disappeared. Only the past century has seen half of this loss. Agriculture is the primary cause of deforestation because people clear forests to make room for crops and grazing areas. The rate of deforestation worldwide is still very high. The combined impact of economic expansion and environmental deterioration may result in a rise in environmental concerns as levels of economic output rise (Abman et al., 2020 ). Deforestation in Africa has emerged as a critical environmental concern, posing significant challenges to the continent's ecosystems and biodiversity. The loss of forest cover in Africa is primarily attributed to various human activities, including agricultural expansion, logging, fuelwood extraction, infrastructure development, and population growth. These factors have led to the destruction of vital forest habitats, disturbance of ecosystems, and adverse effects on climate patterns, soil fertility, and water resources. To address this issue, numerous conservation efforts have been implemented, focusing on sustainable agriculture practices, protected areas, forest management, reforestation, and community engagement. Nigeria has the greatest primary forest destruction rate in the world. In the last five years, it has lost more than half of its main forest, according to the FAO (2005). Logging, subsistence farming, and fuelwood gathering are listed as the causes. Nearly 90% of the rainforest in West Africa has been lost. The Food and Agricultural Organization of the United Nations reports that as of 2005, Nigeria had the highest rate of deforestation in the world, at 12.2%, or 11,089,000 hectares. Between 2000 and 2005, 55.7% of our primary forest was lost, and the rate of forest change increased by 31.2–3.12% annually, or roughly 350,000 to 400,000 hectares annually. Nigeria lost 409,700 hectares of forest annually on average, translating to a 2.38% annual deforestation rate (Mba, 2018 ). Somalia is currently facing significant environmental hardships, including land degradation, flooding, and droughts. The primary driver behind the environmental degradation in the country is deforestation, mainly due to the extensive cutting down of trees for charcoal production, both for export and domestic use (Warsame & Abdi, 2023 ). Over the years, there has been a noticeable decline in the proportion of forested land in Somalia, which has decreased from 13% in 1990 to approximately 9.5% in 2020. This loss of forest cover amounts to around 2.2 million hectares between 1990 and 2020(Warsame & Abdi, 2023 ). The export of charcoal is a significant factor contributing to extensive deforestation in Somalia. This destructive practice leads to soil erosion, desertification, and makes the region more vulnerable to natural disasters such as devastating floods and droughts. These environmental consequences have a detrimental impact on the overall quality of the environment. Deforestation also plays a role in releasing carbon dioxide into the atmosphere, contributing to climate change and raising temperatures. This has adverse effects on various species and ecosystems. The loss of forests poses a threat to agricultural productivity, livelihoods, and food security in Somalia. It disrupts the delicate balance of ecosystems and diminishes habitat availability for many plant and animal species. These impacts further exacerbate the environmental degradation in the region. To address these issues, it is crucial to understand the factors that drive environmental degradation and develop appropriate policies and strategies aimed at reducing deforestation and promoting sustainable land management practices. By doing so, it becomes possible to mitigate the negative effects on the environment, preserve biodiversity, and safeguard the livelihoods and well-being of the local communities (Warsame & Sarkodie, 2022a ). Literature review There is a rapid expansion in empirical research examining the primary drivers of environmental degradation. Numerous dimensions, including population increase, gross capital creation, agricultural land usage, and economic growth, have been examined in relation to this scope. However, the only pertinent material our study gives is that which is pertinent to our goals and relates to economic growth, population growth and agricultural expansion. (Beşe & Kalayci, 2021 )examined the Environmental Kuznets Curve (EKC) hypothesis, exploring the empirical relationship between economic growth, energy consumption, and CO2 emissions in three developed countries: Denmark, the United Kingdom, and Spain. The study used time series data from 1960 to 2014 and applied various econometric tests, including the ARDL bounds test, Johansen cointegration test, and Granger causality test. The results did not confirm the EKC hypothesis for any of the three countries. Specifically, unidirectional causality was found running from energy consumption to CO2 emissions for Denmark and from CO2 emissions to energy consumption for the United Kingdom. The study concluded that these countries could achieve further economic growth without causing environmental degradation, suggesting that policies targeting energy efficiency and green energy usage should continue to be a priority. (Mohamud & Mohamud, 2023 ) examined the impact of renewable energy consumption and economic growth on environmental degradation in Somalia. The purpose of the study was to investigate how renewable energy usage and economic growth affect environmental degradation between 1990 and 2020. The study used econometric tools, specifically the ARDL and Pairwise Granger Causality Test, and employed data from the World Development Indicators and SESRIC. Key variables included renewable energy consumption, economic growth, and foreign direct investment (FDI), population, and oil prices. The findings indicated that in the short term, there is a positive, significant relationship between renewable energy usage and environmental deterioration, while economic growth and renewable energy use have a negative, significant impact in the long term. The study also revealed a unidirectional causality from economic growth to environmental degradation and from renewable energy consumption to environmental quality, and policy recommendations were made to invest in renewable energy sources to mitigate environmental degradation in Somalia. (Warsame & Sarkodie, 2022b ) explore the asymmetric impacts of energy consumption and economic growth on environmental degradation in Somalia, using the nonlinear autoregressive distributed lag model (NARDL) and data from 1985 to 2017. Their findings reveal an asymmetric long-term cointegration among the variables, with energy consumption and economic growth having differential impacts on environmental degradation. The study identifies a unidirectional causality from environmental pollution to increased energy consumption and from negative economic shocks to positive economic changes. Additionally, bidirectional causality is found between population growth and negative economic growth changes. The authors suggest implementing clean energy investment policies, improved farming methods, and better grazing land policies to enhance environmental quality and sustain economic development. (Sekrafi & Sghaier, 2018 ) examine the relationships among corruption, economic growth, environmental degradation, and energy consumption in 13 Middle East and North African (MENA) countries from 1984 to 2012. Utilizing both static (POLS, FE, RE) and dynamic (Diff-GMM, Sys-GMM) panel data approaches, they find that corruption directly affects economic growth, environmental quality, and energy consumption. Indirectly, corruption influences economic growth through energy consumption and environmental quality, and environmental quality through economic growth. The study highlights the negative impact of corruption on economic growth, which, in turn, affects environmental quality and energy consumption. The findings emphasize the need for policymakers to implement sound economic policies that consider these interlinked factors to sustain economic development in the MENA region. (Walker, 1993 )examined the dynamics of deforestation and economic development in tropical countries. The purpose of the study was to explore the complex relationship between economic development and environmental degradation, specifically deforestation. The study utilized various data sources and econometric models to analyze trends and policy impacts, focusing on factors such as land use changes, economic incentives, and infrastructure development. The findings indicated that deforestation rates are significantly influenced by economic factors such as tax exemptions and credit subsidies for agricultural expansion and cattle ranching. The study highlighted the role of government policies in accelerating deforestation through infrastructure projects like road building. Additionally, it was observed that deforestation activities such as logging and mining also contributed to environmental degradation. (Shaw, 1989 )the relationship between rapid population growth and environmental degradation, focusing on distinguishing ultimate versus proximate factors. The study aimed to understand how rapid population growth contributes to environmental degradation, identifying deeper underlying causes. Using data from international sources, Shaw applied a comprehensive analytical framework to explore the impacts of technological advancements, affluence, and population growth on the environment. Findings indicate that while rapid population growth in less developed countries exacerbates deforestation and land overuse, the primary contributors to global environmental degradation are polluting technologies and high levels of affluence in developed countries. Shaw concluded that addressing population growth alone is insufficient for mitigating environmental degradation. Instead, integrated policies tackling both proximate and ultimate causes, such as promoting sustainable practices and improving land management, are essential for long-term environmental conservation. (Bilsborrow, 1992 ) examines the relationship between population growth, internal migration, and environmental degradation in rural areas of developing countries. The study highlights that higher rural population growth tends to lead to increased arable land area and associated deforestation. The findings suggest that internal migration often results in land extensification, leading to deforestation, soil erosion, and soil desiccation. Bilsborrow concludes that policies should address population pressures by promoting sustainable land use practices and enhancing environmental awareness to mitigate environmental degradation in rural areas of developing countries. (Pimentel,al.2007) analyze the relationship between population growth, environmental degradation, and the increasing prevalence of human diseases. The study highlights that rapid population growth and pollution of air, water, and soil are major contributors to the rise in diseases. They find that about 40% of global deaths are due to environmental degradation, with six infectious diseases causing approximately 90% of all deaths from infectious diseases worldwide. The study underscores the complex interplay between environmental factors and health, emphasizing that sustainable environmental management and population control policies are essential to mitigate the adverse impacts on human health. The authors call for comprehensive policies to address environmental pollution and to promote health and sustainability, aiming to improve the quality of life and reduce the burden of diseases globally. (Warsame et al., 2022 ) examined the effect of renewable energy and institutional quality on environmental degradation in Somalia. The study aimed to investigate how these factors impact environmental quality, focusing on deforestation and CO2 emissions. Using data from 1990 to 2017, they applied an autoregressive distributed lag (ARDL) model and Granger causality tests. Findings indicate that renewable energy and institutional quality significantly improve environmental quality. A 1% increase in renewable energy reduces environmental degradation by 4.57%, while a 1% improvement in institutional quality reduces it by 0.87%. Economic and population growth were found to exacerbate environmental degradation, while domestic investment mitigates it. The study concludes that enhancing renewable energy usage and improving institutional quality are essential for long-term environmental sustainability in Somalia. Policymakers are encouraged to implement policies that promote good governance and investments in renewable energy to mitigate environmental degradation. Zakarie Abdi Warsame ( 2023 ) examined the significance of FDI inflow and renewable energy consumption in mitigating environmental degradation in Somalia. The study aimed to analyze how these factors impact carbon dioxide (CO2) emissions, using data from 1990 to 2019. An autoregressive distributed lag (ARDL) model was employed to investigate short- and long-run relationships between the variables. Findings indicate that renewable energy consumption significantly reduces environmental degradation, while domestic investment and population growth exacerbate it. FDI did not show a significant impact on the environment in the long run. The study concludes that promoting renewable energy and improving its consumption are essential for enhancing environmental quality in Somalia. Policymakers are encouraged to support renewable energy projects and attract FDI in environmentally friendly industries to mitigate environmental degradation. (Benhin, 2006 )examined the role of agriculture in tropical deforestation. The purpose of the study was to analyze how agricultural activities contribute to forest loss, focusing on the competition between agriculture and forestry. The study utilized a critical theoretical and empirical review approach, incorporating data from selected countries in Africa and South America. Findings indicate that agriculture is a major cause of deforestation in the tropics. The forest biomass is often used as an input in agricultural production, and the competition for land between agriculture and forestry is driven by their relative marginal benefits. The study highlights that market, policy, and institutional failures lead to the undervaluation of forest resources, encouraging their conversion to agricultural land. The study concludes that addressing tropical deforestation requires policies that internalize the social costs of forest conversion and promote the use of alternative inputs in agricultural production. Policymakers are encouraged to adopt sustainable agricultural practices and improve forest management to mitigate deforestation. (Carter et al., 2017 ) examined agriculture-driven deforestation in the tropics from 1990 to 2015, focusing on emissions, trends, and uncertainties. The purpose of the study was to quantify CO2 emissions from deforestation and identify the extent to which agriculture drives these emissions. The study utilized data from 91 tropical countries, employing a method that combines multiple datasets to minimize uncertainty. Findings indicate that agriculture is the primary driver of deforestation, with Latin America having the highest proportion (78%) and Africa the lowest (62%). Emissions peaked in Latin America in 2000–2005 and have been rising continuously in Africa from 1990 to 2015. The study concludes that reducing agricultural expansion into forests is essential for mitigating global emissions. Policymakers are encouraged to implement targeted interventions in agriculture, promote sustainable practices, and enhance forest protection to address agriculture-driven deforestation effectively. (Abman et al., 2020 ) examined the impact of agricultural productivity on deforestation in Uganda. The purpose of the study was to investigate how improvements in agricultural productivity influence forest loss, focusing on an agricultural extension program. The study utilized a regression discontinuity design to estimate the effects, leveraging the eligibility criteria of the program which provided inputs and training to farmers. Findings indicate that the program significantly reduced forest loss in eligible villages by 13% compared to ineligible villages. The study found that the program led to intensification of agricultural practices on existing land, such as increased use of irrigation, manure, crop rotation, and inter-cropping. The study concludes that improvements in agricultural productivity can reduce the pressure to clear new land for agriculture, thereby mitigating deforestation. Policymakers are encouraged to support programs that enhance agricultural productivity while promoting sustainable practices to achieve environmental conservation. (Sekrafi & Sghaier, 2018 )examined the effects of environmental degradation on agriculture in 35 European countries. The purpose of the study was to investigate how biodiversity loss, deforestation, and agricultural emissions impact agricultural, cereal, and vegetable production. The study utilized the Driscoll and Kraay estimator to understand these impacts and included variables such as organic farming, renewable energy, political stability, e-governance, social progress, and women empowerment. Findings indicate that biodiversity loss harms agricultural, cereal, and vegetable production, while an increase in forest area positively affects cereal and vegetable production. Agricultural emissions have a negative effect on cereal production but a positive impact on vegetable production. Additionally, renewable energy use, political stability, and women empowerment have positive and significant impacts on all three dependent variables. E-governance positively affects agricultural and vegetable production, while social progress has a positive but insignificant effect. The study concludes that addressing environmental degradation requires integrated policies promoting renewable energy, organic farming, political stability, and women's empowerment to sustain agricultural productivity in Europe. Methodology The study is based on annual time series data ranging from 1990 to 2020. This means that I will use observations that are available within this data series. In addition, information was collected from reputable sources such as the World Bank and Organization of Islamic Cooperation (OIC e SESRIC). Somalia was selected for a case study because it has a high number of environmental challenges. The research involved taking different variables into account such as deforestation which is used to measure environmental degradation. On the contrary, variables like agricultural land use, economic growth, total population and gross fixed capital formation were used as independent variables. They were all then converted into their natural logarithms. Tables 1 provides variable descriptions and sources. Table 1 Definition of Variables Variables Code Measurement Sources Environmental degradation ED Arable land (Deforestation) as a proxy for environmental degradation World Bank Economic growth GDP GDP (constant 2015) price SESRIC Gross fixed capital formation K Gross Fixed Capital Formation, Constant 2015 Prices, Annual Change SESRIC Agricultural land AL Agricultural land (sq. km) World Bank Population growth POP Population growth (annual %) World Bank Model specification The study employed the ARDL bound test developed by Pesaran et al. (2001) to find out the long-run and short-run effects of economic growth, total population, agricultural land and gross fixed capital formation on environmental degradation (deforestation) in Somalia. It is chosen because it has good estimation properties for variables with mixed order of integration as compared to traditional cointegration techniques like Johansen’s method that assumes I(1) integrated variables at first difference. Conversely, ARDL bound test can be used with stationary at level (I(0)), first difference (I(1)) or both types of variables hence it is suitable for datasets having mixed orders of integration. Another reason why this study chose the ARDL bound test is because it has an autoregressive structure which addresses potential endogeneity and thus obtain consistent and reliable results. Additionally, when conducting analysis with a small sample size, its applicability surpasses that of other cointegration methods such as Warsame et al., ( 2023 ).In addition, the standard log function has been expressed as follows: LnED t = β 0 + β 1 LnGDP t + β 2 LnK t + β 3 LnAL t + β 4 LnPOP t +Ɛ t (1) Where: ED is environmental degradation, GDP is Economic growth, K is Gross Fixed Capital Formation AL is agricultural land POP is Population growth Ɛ is error terms, Ln is natural logarithm, and t denoted as time period. The mathematical model illustrating the ARDL model is as follows: ΔLnED t = β 0 + β 1 LnGDP t−1 + β 2 LnK t−1 + β 3 LnAL t−1 + β 4 LnPOP t−1 + \(\:\sum\:_{i=0}^{n}{\delta\:}\) 1i ΔLnED t−i + \(\:\sum\:_{i=0}^{n}{\delta\:}\) 2i ΔLnGDP t−i + \(\:\sum\:_{i=0}^{n}{\delta\:}\) 3i ΔLnK t−i + \(\:\sum\:_{i=0}^{n}{\delta\:}\) 4i ΔLnAL t−i + \(\:\sum\:_{i=0}^{n}{\delta\:}\) 5i ΔLnPOP t−I + Ɛ t (2) Where: β 0 is constants β 1 – β 4 is short-run coefficients δ 1 – δ 4 is long-run coefficients Δ is difference operator, and n is lag length. To prevent inaccurate results, unit root analysis must be done prior to evaluating cointegration in the model. The order of variable integration in this study was ascertained by applying the Philips-Perron (PP) and Augmented Dickey-Fuller (ADF) tests. Cointegration can be looked at if the variables are found to be integrated at level I(0), order I(1), or both. The limits test is used to compare the alternative hypothesis of cointegration to the null hypothesis of no cointegration in order to determine whether cointegration exists among the variables that have been chosen. If the computed F-test value is greater than the upper bound critical value, showing a long-term association, the null hypothesis is rejected. On the other hand, if the F-test result is less than the lower bound critical value, there is no long-term link and the null hypothesis is not rejected. The outcome is unclear if the F-test value lies between the upper and lower critical levels (Pesaran & Pesaran, 1997; Pesaran et al., 2001). The ARDL limits test does not determine the direction of causality; rather, it merely examines long-run cointegration between the variables. Granger causality tests are used to identify the causal linkages between the variables in order to overcome this constraint. RESULTS AND DISCUSSION Table 2 contains a detailed analysis of the variables. This shows key features such as mean, median, maximum, minimum and standard deviation of the variables in question. For instance, Table 4.2 reflects that the average values of environmental degradation (6.03), GDP (9.49), gross capital formation (8.63), agricultural land (6.57) and population growth (16.14). Moreover, these are headed by GDP with its highest maximum value being 9.82 and population growth having the largest number of 16.62 respectively among all other given figures. All other than environmental degradation and population growth variables are positively skewed while for the latter they are negatively skewed in relation to each other on the plot accordingly. It is inferred that if Pop Growth has a larger std, that means there is more variation in its scores from its mean value - when compared to others in this research. This examination deals with basic ideas regarding data distribution and central tendencies which are important for further econometric analysis as well as interpretation. Table 2 Descriptive statistics Stats LnED LnGDP LnK LnAL LnPG Mean 6.026073 9.48719 8.625128 6.569533 16.13728 Median 6.021189 9.480867 8.576133 6.579873 16.16377 Maximum 6.053078 9.82207 8.987729 6.882544 16.62111 Minimum 6 9.183598 8.402158 6.290373 15.6762 Std. Dev. 0.015733 0.210057 0.180125 0.186629 0.289434 Skewness -0.121458 0.155181 0.657055 0.062922 -0.039587 Jarque-Bera 2.421778 2.54942 2.862015 2.044037 1.91371 Probability 0.297932 0.279512 0.239068 0.359868 0.384099 Correlation Matrix The purpose of the correlation analysis is to make sure the variables do not exhibit perfect multicollinearity. The correlation matrix of the variables that were sampled is shown in Table 3 , and it generally shows that there is not much correlation, indicating that the study is robust. Table 3 shows a positive correlation between environmental deterioration and GDP, gross capital formation, agricultural land, and population expansion. These positive correlations imply that higher levels of environmental degradation in the area are linked to increases in these demographic and economic parameters. Table 3 Correlation Matrix Variables LnED LnGDP LnK LnAL LnPG LnED 1 LnGDP 0.563785 1 LnK 0.462114 0.936023 1 LnAL 0.612525 0.980563 0.881638 1 LnPG 0.617687 0.977697 0.867275 0.997327 1 Unit root test Table 4 shows the results of the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests. The results indicate that LnED is stationary at level [I(0)], whereas the remaining series have unit roots and become stationary at their first differences, indicating they are integrated of order one [I(1)]. None of the variables are stationary at the second difference [I(2)], confirming that the first difference is sufficient to achieve stationarity. Table 4 unit root test Variable ADF PP Level First difference Level First difference LnED 3.313295* -6.709508*** -2.592562* -8.842124*** LNGDP 0.742435 -3.310204*** 0.679506 -4.824564*** LnK 1.262393 -2.47207*** 1.5561 -4.51616138*** LnAL -3.262393 -4.47207*** 0.905561 -4.516138*** LnPG -0.621798 -4.215753*** 1.027023 -4.981795*** ***, **, * denote significance level at 1%, 5% and 10% respectively. Cointegration bounds test In order to determine whether long-run cointegration exists between the variables, the Wald F-test is utilized in this study. Cointegration of the variables is indicated by the estimated F-statistics, which are greater than the crucial upper bound value. This validates that a long-term relationship exists, which supports the use of the ARDL bounds testing approach. Table 5 provides a full summary of the F-Bound cointegration test findings. Table 5 examines the possibility of a long-term correlation between other variables and environmental degradation. At a 10% significance level, the data demonstrate that the Wald F-statistic (5.912239) is greater than the upper critical value (5.84). This indicates a sustained relationship between the variables. Table 5 F-Bound Cointegration Tests. Fstatistic Level of significance Bounds test critical values 1(0) 1(1) 5.912239 1% 2.525 3.56 5% 3.058 4.223 10% 4.28 5.84 ARDL Long-run Results Tables 6 display the long-term findings. All of the explanatory variables were found to be statistically significant over the long term. In Somalia, population expansion and economic progress eventually lead to a major increase in environmental deterioration. On the other side, over time, agricultural land and gross fixed capital formation (domestic investment) greatly reduce environmental degradation. In the long run,, the findings show that for every unit increase in GDP and PG, the degradation of the environment rises by approximately 0.187651% and 0.26196%, respectively. This shows that demand on environmental resources increases along with population and economic growth, leading to increased environmental deterioration. Additionally, the analysis shows that a 1% increase in agricultural land (AL) and gross fixed capital formation (domestic investment) reduces environmental degradation by approximately 0.538906% and 0.005691%, respectively. This highlights the importance of expanding agricultural land and strategic investments in infrastructure and technology, which can enhance ecological balance and sustainability. Table 6 Long Run Results Variables Coefficient C 3.557188 (5.709501) LnGDP 0.187651 (-2.31858) LnK -0.005691 (-0.19019) LnAL -0.538906 (-2.571754) LnPG 0.26196 -2.697117 *, **, *** donate at 10%, 5%, and 1% significance levels. The T statistics are cited in (.) ARDL Short-run Results On the contrary, Table 7 provides an estimate of the short-term dynamic effects. In the short and long term all variables have the same impact. In short term, Economic growth and population growth greatly worsen environmental degradation in Somalia. For every 1% increase in Economic growth and population growth, Environmental degradation rises by about 0.724073% and 0.570945% respectively. Into the bargain, Agricultural land and gross fixed capital formation greatly reduce environmental degradation. For every 1% increase in agricultural land and gross fixed capital formation, environmental degradation is decreased by roughly 0.734459% and 0.734691% respectively. A statistically significant speed of adjustment (ECT) and a negative coefficient are shown in Table 7 . The ECT term (-0.91) attests to the long-term cointegration of the variables. According to this, explanatory variables account for about 91% of the short-term shocks to environmental degradation. Table 7: Short Run ECM Results Variables Coefficient C 10.992214 ΔLnGDP 0.724073 (12.00906) *** ΔLnK -0.734691 (-12.44437) * ΔLnAL -0.734459 (8.773306) * ΔLnPG 0.570945 (5.380953) *** ECTt-1 -0.9134 *** *, **, *** donate at 10%, 5%, and 1% significance levels. The T statistics are cited in (.) Diagnostic Tests The diagnostic tests are presented in Table 8. These tests demonstrate that the model is free from heteroskedasticity and serial correlation, ensuring the reliability of the results. The Jarque-Bera normality test confirms that the residuals of the model follow a normal distribution, which is essential for the validity of the statistical inferences. The Ramsey test indicates that there are no misspecification issues within the model, further validating its robustness. Additionally, the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests, illustrated in Figures 2 and 3, confirm the stability of the regression equation's coefficients over time, reinforcing the consistency and accuracy of the model's estimates. The table shows diagnostic test results for the regression model. The Reset test (p-value: 0.6717) indicates no misspecification issues. The serial correlation test (p-value: 0.0639) suggests no significant serial correlation. The heteroscedasticity test ( p-value: 0.2394) confirms the absence of heteroscedasticity. The normality test ( p-value: 0.833546) shows that the residuals are normally distributed. These results imply that the model is well-specified and meets key statistical assumptions. Table 8: Diagnostic Tests Variables Coefficient Reset test 0.198496 [0.6717] Serial correlation 5.010339 [0.0639] Heteroscedasticity 1.712214 [0.2394] Normality 0.364133 [0.833546] *, **, *** donate at 10%, 5%, and 1% significance levels. P-values are presented in [.] Granger Causality Test One limitation of ARDL long-run cointegration is its inability to assess causality among variables. To address this, the study employed the Granger causality test to determine the direction of causation, as shown in Table 9 . The test reveals several unidirectional relationships: economic growth (LNGDP) significantly influences environmental degradation (LNED), capital (LNK), and population (LNPG), indicating that changes in economic growth directly affect these variables. Additionally, agricultural land (LNAL) significantly impacts environmental degradation and economic growth, but not vice versa. Population (LNPG) also affects environmental degradation and capital, highlighting the significant role of population dynamics in these areas. Table 9 Pairwise Granger Causality Null Hypothesis Obs F-Statistic Prob. LNGDP → LNED 29 4.9396 0.016 LNED → LNGDP 29 3.29149 0.0545 LNK → LNED 29 3.0534 0.0659 LNED → LNK 29 4.67759 0.0193 LNAL → LNED 29 5.94607 0.008 LNED → LNAL 29 0.9033 0.4186 LNPG → LNED 29 5.73115 0.0092 LNED → LNPG 29 0.09944 0.9057 LNK → LNGDP 29 5.91955 0.0081 LNGDP → LNK 29 6.27373 0.0064 LNAL → LNGDP 29 11.5101 0.0003 LNGDP → LNAL 29 0.12707 0.8813 LNPG → LNGDP 29 28.2988 5.00E-07 LNGDP → LNPG 29 0.46231 0.6353 LNAL → LNK 29 10.9483 0.0004 LNK → LNAL 29 0.33604 0.7179 LNPG → LNK 29 11.7769 0.0003 LNK → LNPG 29 0.37216 0.6932 LNPG → LNAL 29 2.4907 0.104 LNAL → LNPG 29 1.65143 0.2128 CONCLUSION AND POLICY IMPLICATIONS The primary aim of the study was to understand the key drivers behind environmental degradation in Somalia. Generally, I want to examine how factors like economic growth, gross capital formation (domestic investment) and population growth impact on environmental degradation in Somalia. Also specifically I want to know the impact of agricultural land on environmental degradation. To achieve the hypothesized relationship of the interested parameters, an ARDL bounds test, and Granger causality was adopted to the study. Econometric view (E-view 12) was the program utilized to run and evaluate the data. Furthermore, descriptive statistics were applied to the data analysis. In the study, graphs were also employed to display the data. The research employed secondary data from the World Bank, covering a 30-year period from 1990 to 2020. Both long-run and short-run estimates show that expansion of agricultural land contributes to environmental degradation in Somalia. and also economic growth, population growth play a crucial role exacerbates environmental degradation. while domestic investment mitigates environmental degradation. This study gives insights into possible solutions that focus on how environmental degradation(deforestation)should be controlled. Here are some of the recommended policies: First, encourage farmers to employ sustainable agricultural practices such as conservation agriculture and agroforestry in order to promote sustainable agriculture. Farmers will be able to apply these techniques with the support of resources and training, which will lessen the need to destroy forests to make way for additional farmland and preserve the health of the ecosystem. Second, start planting trees in deforested and degraded areas as part of reforestation programs. Community organizations, educational institutions, and non-governmental organizations can be involved in these initiatives to help promote long-term environmental sustainability by restoring forest cover, increasing biodiversity, and creating new habitats for species. Thirdly, make law enforcement stronger by giving them more tools to stop illicit land clearing and logging. Establishing forest patrols and putting satellite surveillance into place can assist identify and discourage illicit activity, improving the protection of already-existing forests. Fourth, expand access to renewable energy sources like solar and wind power in order to supply alternative energy sources. Subsidies for solar lights and cookers can help people use less wood, lessening the demand on forest resources and promoting the use of sustainable energy sources. Finally, raise public understanding about the value of forests by launching national awareness programs. By including environmental education in school curricula and organizing community workshops, communities can be empowered to sustainably use and maintain forest resources, thereby fostering a culture of conservation. Declarations Author Contributions Bashir Mohamed Osman, Said Ali Shire and Farhan Habib Ali contributed equally to the conceptualization, data collection, and analysis of this study. Bashir Mohamed Osman led the drafting and critical revision of the manuscript. Said Ali Shire provided expertise on methodological approaches and reviewed the final draft. All authors approved the submitted version of the manuscript. Competing Interests The authors declare no competing interests . Declaration of Funding This research was funded by the SIMAD University, Center for Research and Development Office. Data Availability The data supporting the findings of this study are available from the corresponding author, Bashir Mohamed Osman, upon reasonable request via email [email protected] References Abdi, Z. (2023). The significance of fdi inflow and renewable energy consumption in mitigating environmental degradation in Somalia The Significance of FDI Inflow and Renewable Energy Consumption in Mitigating Environmental Degradation in Somalia . https://doi.org/10.32479/ijeep.13943.This Abman, R., Garg, T., Pan, Y., & Singhal, S. (2020). Agriculture and Deforestation. SSRN Electronic Journal . https://doi.org/10.2139/ssrn.3692682 Andersen, L. E., Granger, C. W. J., Reis, E. J., Weinhold, D., & Wunder, S. (2002). The Dynamics of Deforestation and Economic Growth in the Brazilian Amazon. The Dynamics of Deforestation and Economic Growth in the Brazilian Amazon , December . https://doi.org/10.1017/cbo9780511493454 Awan, A. M., & Azam, M. (2022). Evaluating the impact of GDP per capita on environmental degradation for G-20 economies: Does N-shaped environmental Kuznets curve exist? Environment, Development and Sustainability , 24 (9), 11103–11126. https://doi.org/10.1007/s10668-021-01899-8 Benhin, J. K. A. (2006). Agriculture and deforestation in the tropics: A critical theoretical and empirical review. Ambio , 35 (1), 9–16. https://doi.org/10.1579/0044-7447-35.1.9 Beşe, E., & Kalayci, S. (2021). Environmental kuznets curve (Ekc): Empirical relationship between economic growth, energy consumption, and co2 emissions: Evidence from 3 developed countries. Panoeconomicus , 68 (4), 483–506. https://doi.org/10.2298/PAN180503004B Bilsborrow, R. E. (1992). Population growth, internal migration, and environmental degradation in rural areas of developing countries. European Journal of Population , 8 (2), 125–148. https://doi.org/10.1007/BF01797549 Carter, S., Herold, M., Avitabile, V., De Bruin, S., De Sy, V., Kooistra, L., & Rufino, M. C. (2017). Agriculture-driven deforestation in the tropics from 1990-2015: Emissions, trends and uncertainties. Environmental Research Letters , 13 (1). https://doi.org/10.1088/1748-9326/aa9ea4 De Koninck, R., & Hai, P. T. (2016). Population growth and environmental degradation in southeast Asia. Routledge Handbook of the Environment in Southeast Asia , 46–68. https://doi.org/10.4324/9781315474892 Imisi, R. A., & Philip, A. O. (2018). Environmental degradation, energy consumption, population growth and economic growth: Does Environmental Kuznets curve matter for Nigeria? Economic and Policy Review , 16 (2). https://www.ajol.info/index.php/epr/article/view/165274 Kahuthu, A. (2006). Economic growth and environmental degradation in a global context. Environment, Development and Sustainability , 8 (1), 55–68. https://doi.org/10.1007/s10668-005-0785-3 Kalipeni, E. (1992). Population growth and environmental degradation in Malawi. Africa Insight , 22 (4), 273–282. https://doi.org/10.1515/9781685854836-004 López, R., & Galinato, G. I. (2005). Trade policies, economic growth, and the direct causes of deforestation. Land Economics , 81 (2), 145–169. https://doi.org/10.3368/le.81.2.145 Mba, E. H. (2018). Assessment of Environmental Impact of Deforestation in Enugu, Nigeria. Resources and Environment , 8 (4), 207–215. https://doi.org/10.5923/j.re.20180804.03 Mendes, C. M., & Junior, S. P. (2012). Deforestation, economic growth and corruption: A nonparametric analysis on the case of Amazon forest. Applied Economics Letters , 19 (13), 1285–1291. https://doi.org/10.1080/13504851.2011.619487 Mohamud, I. H., & Mohamud, A. A. (2023). The Impact of Renewable Energy Consumption and Economic Growth on Environmental Degradation in Somalia. International Journal of Energy Economics and Policy , 13 (5), 533–543. https://doi.org/10.32479/ijeep.14488 Munasinghe, M. (1999). Is environmental degradation an inevitable consequence of economic growth: Tunneling through the environmental Kuznets curve. Ecological Economics , 29 (1), 89–109. https://doi.org/10.1016/S0921-8009(98)00062-7 Ray, S. (2011). Impact of Population Growth on Environmental Degradation : Case of India. Journal of Economics and Sustainable Development , 2 (8), 72–78. Saboori, B., & Sulaiman, J. (2013). Environmental degradation, economic growth and energy consumption: Evidence of the environmental Kuznets curve in Malaysia. Energy Policy , 60 , 892–905. https://doi.org/10.1016/j.enpol.2013.05.099 Sekrafi, H., & Sghaier, A. (2018). Examining the Relationship Between Corruption, Economic Growth, Environmental Degradation, and Energy Consumption: a Panel Analysis in MENA Region. Journal of the Knowledge Economy , 9 (3), 963–979. https://doi.org/10.1007/s13132-016-0384-6 Sharma, S. S. (2011). Determinants of carbon dioxide emissions: Empirical evidence from 69 countries. Applied Energy , 88 (1), 376–382. https://doi.org/10.1016/j.apenergy.2010.07.022 Shaw, R. P. (1989). Rapid Population Growth and Environmental Degradation : Ultimate versus Proximate Factors Author ( s ): R . Paul Shaw Published by : Cambridge University Press Stable URL : http://www.jstor.com/stable/44518343 Rapid Population Growth and Environmental Deg . 16 (3), 199–208. Stern, D. I., Common, M. S., & Barbier, E. B. (1996). Economic growth and environmental degradation: The environmental Kuznets curve and sustainable development. World Development , 24 (7), 1151–1160. https://doi.org/10.1016/0305-750X(96)00032-0 Walker, R. (1993). Deforestation and Economie Development. Cl Canadian Journal of Regional Science , 3 (3), 481–497. Warsame, A. A., & Abdi, A. H. (2023). Towards sustainable crop production in Somalia : Examining the role of environmental pollution and degradation Towards sustainable crop production in Somalia : Examining the role of environmental pollution and degradation. Cogent Food & Agriculture , 9 (1). https://doi.org/10.1080/23311932.2022.2161776 Warsame, A. A., Mohamed, J., & Mohamed, A. A. (2023). The relationship between environmental degradation, agricultural crops, and livestock production in Somalia. Environmental Science and Pollution Research , 30 (3), 7825–7835. https://doi.org/10.1007/s11356-022-22595-8 Warsame, A. A., & Sarkodie, S. A. (2022a). Asymmetric impact of energy utilization and economic development on environmental degradation in Somalia. Environmental Science and Pollution Research , 29 (16), 23361–23373. https://doi.org/10.1007/s11356-021-17595-z Warsame, A. A., & Sarkodie, S. A. (2022b). Asymmetric impact of energy utilization and economic development on environmental degradation in Somalia. Environmental Science and Pollution Research , 29 (16), 23361–23373. https://doi.org/10.1007/s11356-021-17595-z Warsame, A. A., Sheik-Ali, I. A., Mohamed, J., & Sarkodie, S. A. (2022). Renewables and institutional quality mitigate environmental degradation in Somalia. Renewable Energy , 194 , 1184–1191. https://doi.org/10.1016/j.renene.2022.05.109 Zandi, G., & Haseeb, M. (2019). The importance of green energy consumption and agriculture in reducing environmental degradation: Evidence from sub-Saharan African countries. International Journal of Financial Research , 10 (5), 215–227. https://doi.org/10.5430/ijfr.v10n5p215 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Mar, 2025 Read the published version in Discover Sustainability → Version 1 posted Editorial decision: Revision requested 04 Nov, 2024 Reviews received at journal 29 Oct, 2024 Reviews received at journal 27 Oct, 2024 Reviewers agreed at journal 26 Oct, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers agreed at journal 21 Oct, 2024 Reviewers invited by journal 12 Oct, 2024 Editor assigned by journal 11 Oct, 2024 Submission checks completed at journal 10 Oct, 2024 First submitted to journal 02 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5193133","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373616529,"identity":"8feff0c7-843b-46f7-938d-f8e9229559c0","order_by":0,"name":"Bashir Mohamed Osman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYBACAwYGNoYEKOeARAUDD0yQKC2MByzOEKsFCpgPVLZhCGICc/bjzx483GNjr9t+9sCBm/PqZHTbD29g+FB2mEG+vQGrFsueHHODhGdpidvO5CUcnLntMI/ZmbQCxhnnDjMYnDmA3WEHctgkEg4cTjA7kGNwWHLbAR4Qg5m3DahFIgG7lvPPn4G02Judf2Nw+O+cOh4Qg/kvUIv8DBxabiSYgbQwbruRY3BAsoGZxwzIYGYEamG4gV2L5Yw3IC1Av9x4Y3BA4hjQLzeeFRzsOZfOg8sv5vzpzyR/HLABOizH+INETR2QkbzxwY8yazlcIYYdgIznIUH9KBgFo2AUjAI0AACPaGlH60FhggAAAABJRU5ErkJggg==","orcid":"","institution":"SIMAD University","correspondingAuthor":true,"prefix":"","firstName":"Bashir","middleName":"Mohamed","lastName":"Osman","suffix":""},{"id":373616530,"identity":"ff43816a-6198-475c-a3b8-05638a76175a","order_by":1,"name":"Said Ali Shire","email":"","orcid":"","institution":"SIMAD University","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"Ali","lastName":"Shire","suffix":""},{"id":373616531,"identity":"660d97bc-d0a2-4fdf-b443-ae970233fa0b","order_by":2,"name":"Farhan Habib Ali","email":"","orcid":"","institution":"SIMAD University","correspondingAuthor":false,"prefix":"","firstName":"Farhan","middleName":"Habib","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2024-10-02 13:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5193133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5193133/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-024-00786-2","type":"published","date":"2025-03-03T15:58:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":69845284,"identity":"e195422b-844b-47b9-9a9c-040ce914849d","added_by":"auto","created_at":"2024-11-25 19:37:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58038,"visible":true,"origin":"","legend":"\u003cp\u003eDeforestation(annual% change)\u003c/p\u003e\n\u003cp\u003eSource: (Warsame \u0026amp; Sarkodie, 2022a)\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5193133/v1/516c66bcd3f543c7ee41f675.png"},{"id":69845043,"identity":"2a809582-42e5-4da3-84a1-1af59ad029b8","added_by":"auto","created_at":"2024-11-25 19:29:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":128044,"visible":true,"origin":"","legend":"\u003cp\u003eSomalia Forest Area(% of land area\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5193133/v1/3a2cfec7e41ae6d6c60e7ed6.png"},{"id":69845283,"identity":"ef69e157-36f0-4a79-9526-f328a910d7e8","added_by":"auto","created_at":"2024-11-25 19:37:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13123,"visible":true,"origin":"","legend":"\u003cp\u003eCUSUM Test\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5193133/v1/ae9d6c5d1cd9ca364ac6d16f.png"},{"id":69845045,"identity":"67e0a588-63c4-4245-b6ad-924a3dc47cc8","added_by":"auto","created_at":"2024-11-25 19:29:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14593,"visible":true,"origin":"","legend":"\u003cp\u003eCUSUM Square Test\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5193133/v1/a1fc9faa5707757aa9b4795b.png"},{"id":78191519,"identity":"c1b42abf-f15b-4150-ac6d-9cbfe7a6a619","added_by":"auto","created_at":"2025-03-10 20:05:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1080522,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5193133/v1/3b77be95-2968-4482-bbe1-37a854c46749.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of Renewable Energy in Combating Environmental Degradation in Somalia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEnvironmental degradation is considered one of the world's most severe environmental problems in recent years and is thought to be a possible driver of global warming, making it a global concern due to the significance of environmental protection and sustainability (Grossman \u0026amp; Krueger, 1995). The environmental Kuznets curve (EKC) hypothesis (i.e., an inverted U-shaped environmental pollution and economic growth relation) suggests that growth in the early stages degrades environmental quality, but the quality of the environment enhances after a certain level of income is reached (Sharma, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). According to the EKC, pollution emissions and other environmental issues disproportionately influence developing nations. When deciding which development initiative to approve, it also implies that these nations must choose between economic development and environmental protection. In the hopes that a higher standard of living will undo any environmental damage, environmental preservation may have to take a backseat to economic development (Abdi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the end of the last ice age 10,000 years ago, one-third of the world's forests have disappeared. Only the past century has seen half of this loss. Agriculture is the primary cause of deforestation because people clear forests to make room for crops and grazing areas. The rate of deforestation worldwide is still very high. The combined impact of economic expansion and environmental deterioration may result in a rise in environmental concerns as levels of economic output rise (Abman et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDeforestation in Africa has emerged as a critical environmental concern, posing significant challenges to the continent's ecosystems and biodiversity. The loss of forest cover in Africa is primarily attributed to various human activities, including agricultural expansion, logging, fuelwood extraction, infrastructure development, and population growth. These factors have led to the destruction of vital forest habitats, disturbance of ecosystems, and adverse effects on climate patterns, soil fertility, and water resources. To address this issue, numerous conservation efforts have been implemented, focusing on sustainable agriculture practices, protected areas, forest management, reforestation, and community engagement.\u003c/p\u003e \u003cp\u003eNigeria has the greatest primary forest destruction rate in the world. In the last five years, it has lost more than half of its main forest, according to the FAO (2005). Logging, subsistence farming, and fuelwood gathering are listed as the causes. Nearly 90% of the rainforest in West Africa has been lost.\u003c/p\u003e \u003cp\u003eThe Food and Agricultural Organization of the United Nations reports that as of 2005, Nigeria had the highest rate of deforestation in the world, at 12.2%, or 11,089,000 hectares. Between 2000 and 2005, 55.7% of our primary forest was lost, and the rate of forest change increased by 31.2\u0026ndash;3.12% annually, or roughly 350,000 to 400,000 hectares annually. Nigeria lost 409,700 hectares of forest annually on average, translating to a 2.38% annual deforestation rate (Mba, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSomalia is currently facing significant environmental hardships, including land degradation, flooding, and droughts. The primary driver behind the environmental degradation in the country is deforestation, mainly due to the extensive cutting down of trees for charcoal production, both for export and domestic use (Warsame \u0026amp; Abdi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Over the years, there has been a noticeable decline in the proportion of forested land in Somalia, which has decreased from 13% in 1990 to approximately 9.5% in 2020. This loss of forest cover amounts to around 2.2\u0026nbsp;million hectares between 1990 and 2020(Warsame \u0026amp; Abdi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe export of charcoal is a significant factor contributing to extensive deforestation in Somalia. This destructive practice leads to soil erosion, desertification, and makes the region more vulnerable to natural disasters such as devastating floods and droughts. These environmental consequences have a detrimental impact on the overall quality of the environment. Deforestation also plays a role in releasing carbon dioxide into the atmosphere, contributing to climate change and raising temperatures. This has adverse effects on various species and ecosystems. The loss of forests poses a threat to agricultural productivity, livelihoods, and food security in Somalia. It disrupts the delicate balance of ecosystems and diminishes habitat availability for many plant and animal species. These impacts further exacerbate the environmental degradation in the region. To address these issues, it is crucial to understand the factors that drive environmental degradation and develop appropriate policies and strategies aimed at reducing deforestation and promoting sustainable land management practices. By doing so, it becomes possible to mitigate the negative effects on the environment, preserve biodiversity, and safeguard the livelihoods and well-being of the local communities (Warsame \u0026amp; Sarkodie, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e "},{"header":"Literature review","content":"\u003cp\u003eThere is a rapid expansion in empirical research examining the primary drivers of environmental degradation. Numerous dimensions, including population increase, gross capital creation, agricultural land usage, and economic growth, have been examined in relation to this scope. However, the only pertinent material our study gives is that which is pertinent to our goals and relates to economic growth, population growth and agricultural expansion.\u003c/p\u003e \u003cp\u003e(Beşe \u0026amp; Kalayci, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)examined the Environmental Kuznets Curve (EKC) hypothesis, exploring the empirical relationship between economic growth, energy consumption, and CO2 emissions in three developed countries: Denmark, the United Kingdom, and Spain. The study used time series data from 1960 to 2014 and applied various econometric tests, including the ARDL bounds test, Johansen cointegration test, and Granger causality test. The results did not confirm the EKC hypothesis for any of the three countries. Specifically, unidirectional causality was found running from energy consumption to CO2 emissions for Denmark and from CO2 emissions to energy consumption for the United Kingdom. The study concluded that these countries could achieve further economic growth without causing environmental degradation, suggesting that policies targeting energy efficiency and green energy usage should continue to be a priority.\u003c/p\u003e \u003cp\u003e(Mohamud \u0026amp; Mohamud, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examined the impact of renewable energy consumption and economic growth on environmental degradation in Somalia. The purpose of the study was to investigate how renewable energy usage and economic growth affect environmental degradation between 1990 and 2020. The study used econometric tools, specifically the ARDL and Pairwise Granger Causality Test, and employed data from the World Development Indicators and SESRIC. Key variables included renewable energy consumption, economic growth, and foreign direct investment (FDI), population, and oil prices. The findings indicated that in the short term, there is a positive, significant relationship between renewable energy usage and environmental deterioration, while economic growth and renewable energy use have a negative, significant impact in the long term. The study also revealed a unidirectional causality from economic growth to environmental degradation and from renewable energy consumption to environmental quality, and policy recommendations were made to invest in renewable energy sources to mitigate environmental degradation in Somalia.\u003c/p\u003e \u003cp\u003e(Warsame \u0026amp; Sarkodie, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e) explore the asymmetric impacts of energy consumption and economic growth on environmental degradation in Somalia, using the nonlinear autoregressive distributed lag model (NARDL) and data from 1985 to 2017. Their findings reveal an asymmetric long-term cointegration among the variables, with energy consumption and economic growth having differential impacts on environmental degradation. The study identifies a unidirectional causality from environmental pollution to increased energy consumption and from negative economic shocks to positive economic changes. Additionally, bidirectional causality is found between population growth and negative economic growth changes. The authors suggest implementing clean energy investment policies, improved farming methods, and better grazing land policies to enhance environmental quality and sustain economic development.\u003c/p\u003e \u003cp\u003e(Sekrafi \u0026amp; Sghaier, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examine the relationships among corruption, economic growth, environmental degradation, and energy consumption in 13 Middle East and North African (MENA) countries from 1984 to 2012. Utilizing both static (POLS, FE, RE) and dynamic (Diff-GMM, Sys-GMM) panel data approaches, they find that corruption directly affects economic growth, environmental quality, and energy consumption. Indirectly, corruption influences economic growth through energy consumption and environmental quality, and environmental quality through economic growth. The study highlights the negative impact of corruption on economic growth, which, in turn, affects environmental quality and energy consumption. The findings emphasize the need for policymakers to implement sound economic policies that consider these interlinked factors to sustain economic development in the MENA region.\u003c/p\u003e \u003cp\u003e(Walker, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1993\u003c/span\u003e)examined the dynamics of deforestation and economic development in tropical countries. The purpose of the study was to explore the complex relationship between economic development and environmental degradation, specifically deforestation. The study utilized various data sources and econometric models to analyze trends and policy impacts, focusing on factors such as land use changes, economic incentives, and infrastructure development. The findings indicated that deforestation rates are significantly influenced by economic factors such as tax exemptions and credit subsidies for agricultural expansion and cattle ranching. The study highlighted the role of government policies in accelerating deforestation through infrastructure projects like road building. Additionally, it was observed that deforestation activities such as logging and mining also contributed to environmental degradation.\u003c/p\u003e \u003cp\u003e(Shaw, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1989\u003c/span\u003e)the relationship between rapid population growth and environmental degradation, focusing on distinguishing ultimate versus proximate factors. The study aimed to understand how rapid population growth contributes to environmental degradation, identifying deeper underlying causes. Using data from international sources, Shaw applied a comprehensive analytical framework to explore the impacts of technological advancements, affluence, and population growth on the environment.\u003c/p\u003e \u003cp\u003eFindings indicate that while rapid population growth in less developed countries exacerbates deforestation and land overuse, the primary contributors to global environmental degradation are polluting technologies and high levels of affluence in developed countries. Shaw concluded that addressing population growth alone is insufficient for mitigating environmental degradation. Instead, integrated policies tackling both proximate and ultimate causes, such as promoting sustainable practices and improving land management, are essential for long-term environmental conservation.\u003c/p\u003e \u003cp\u003e(Bilsborrow, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) examines the relationship between population growth, internal migration, and environmental degradation in rural areas of developing countries. The study highlights that higher rural population growth tends to lead to increased arable land area and associated deforestation. The findings suggest that internal migration often results in land extensification, leading to deforestation, soil erosion, and soil desiccation. Bilsborrow concludes that policies should address population pressures by promoting sustainable land use practices and enhancing environmental awareness to mitigate environmental degradation in rural areas of developing countries.\u003c/p\u003e \u003cp\u003e(Pimentel,al.2007) analyze the relationship between population growth, environmental degradation, and the increasing prevalence of human diseases. The study highlights that rapid population growth and pollution of air, water, and soil are major contributors to the rise in diseases. They find that about 40% of global deaths are due to environmental degradation, with six infectious diseases causing approximately 90% of all deaths from infectious diseases worldwide. The study underscores the complex interplay between environmental factors and health, emphasizing that sustainable environmental management and population control policies are essential to mitigate the adverse impacts on human health. The authors call for comprehensive policies to address environmental pollution and to promote health and sustainability, aiming to improve the quality of life and reduce the burden of diseases globally.\u003c/p\u003e \u003cp\u003e(Warsame et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined the effect of renewable energy and institutional quality on environmental degradation in Somalia. The study aimed to investigate how these factors impact environmental quality, focusing on deforestation and CO2 emissions. Using data from 1990 to 2017, they applied an autoregressive distributed lag (ARDL) model and Granger causality tests. Findings indicate that renewable energy and institutional quality significantly improve environmental quality. A 1% increase in renewable energy reduces environmental degradation by 4.57%, while a 1% improvement in institutional quality reduces it by 0.87%. Economic and population growth were found to exacerbate environmental degradation, while domestic investment mitigates it. The study concludes that enhancing renewable energy usage and improving institutional quality are essential for long-term environmental sustainability in Somalia. Policymakers are encouraged to implement policies that promote good governance and investments in renewable energy to mitigate environmental degradation.\u003c/p\u003e \u003cp\u003eZakarie Abdi Warsame (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) examined the significance of FDI inflow and renewable energy consumption in mitigating environmental degradation in Somalia. The study aimed to analyze how these factors impact carbon dioxide (CO2) emissions, using data from 1990 to 2019. An autoregressive distributed lag (ARDL) model was employed to investigate short- and long-run relationships between the variables. Findings indicate that renewable energy consumption significantly reduces environmental degradation, while domestic investment and population growth exacerbate it. FDI did not show a significant impact on the environment in the long run. The study concludes that promoting renewable energy and improving its consumption are essential for enhancing environmental quality in Somalia. Policymakers are encouraged to support renewable energy projects and attract FDI in environmentally friendly industries to mitigate environmental degradation.\u003c/p\u003e \u003cp\u003e(Benhin, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)examined the role of agriculture in tropical deforestation. The purpose of the study was to analyze how agricultural activities contribute to forest loss, focusing on the competition between agriculture and forestry. The study utilized a critical theoretical and empirical review approach, incorporating data from selected countries in Africa and South America.\u003c/p\u003e \u003cp\u003eFindings indicate that agriculture is a major cause of deforestation in the tropics. The forest biomass is often used as an input in agricultural production, and the competition for land between agriculture and forestry is driven by their relative marginal benefits. The study highlights that market, policy, and institutional failures lead to the undervaluation of forest resources, encouraging their conversion to agricultural land. The study concludes that addressing tropical deforestation requires policies that internalize the social costs of forest conversion and promote the use of alternative inputs in agricultural production. Policymakers are encouraged to adopt sustainable agricultural practices and improve forest management to mitigate deforestation.\u003c/p\u003e \u003cp\u003e(Carter et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) examined agriculture-driven deforestation in the tropics from 1990 to 2015, focusing on emissions, trends, and uncertainties. The purpose of the study was to quantify CO2 emissions from deforestation and identify the extent to which agriculture drives these emissions. The study utilized data from 91 tropical countries, employing a method that combines multiple datasets to minimize uncertainty. Findings indicate that agriculture is the primary driver of deforestation, with Latin America having the highest proportion (78%) and Africa the lowest (62%). Emissions peaked in Latin America in 2000\u0026ndash;2005 and have been rising continuously in Africa from 1990 to 2015.\u003c/p\u003e \u003cp\u003eThe study concludes that reducing agricultural expansion into forests is essential for mitigating global emissions. Policymakers are encouraged to implement targeted interventions in agriculture, promote sustainable practices, and enhance forest protection to address agriculture-driven deforestation effectively.\u003c/p\u003e \u003cp\u003e(Abman et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) examined the impact of agricultural productivity on deforestation in Uganda. The purpose of the study was to investigate how improvements in agricultural productivity influence forest loss, focusing on an agricultural extension program. The study utilized a regression discontinuity design to estimate the effects, leveraging the eligibility criteria of the program which provided inputs and training to farmers. Findings indicate that the program significantly reduced forest loss in eligible villages by 13% compared to ineligible villages. The study found that the program led to intensification of agricultural practices on existing land, such as increased use of irrigation, manure, crop rotation, and inter-cropping.\u003c/p\u003e \u003cp\u003eThe study concludes that improvements in agricultural productivity can reduce the pressure to clear new land for agriculture, thereby mitigating deforestation. Policymakers are encouraged to support programs that enhance agricultural productivity while promoting sustainable practices to achieve environmental conservation.\u003c/p\u003e \u003cp\u003e(Sekrafi \u0026amp; Sghaier, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)examined the effects of environmental degradation on agriculture in 35 European countries. The purpose of the study was to investigate how biodiversity loss, deforestation, and agricultural emissions impact agricultural, cereal, and vegetable production. The study utilized the Driscoll and Kraay estimator to understand these impacts and included variables such as organic farming, renewable energy, political stability, e-governance, social progress, and women empowerment.\u003c/p\u003e \u003cp\u003eFindings indicate that biodiversity loss harms agricultural, cereal, and vegetable production, while an increase in forest area positively affects cereal and vegetable production. Agricultural emissions have a negative effect on cereal production but a positive impact on vegetable production. Additionally, renewable energy use, political stability, and women empowerment have positive and significant impacts on all three dependent variables. E-governance positively affects agricultural and vegetable production, while social progress has a positive but insignificant effect. The study concludes that addressing environmental degradation requires integrated policies promoting renewable energy, organic farming, political stability, and women's empowerment to sustain agricultural productivity in Europe.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe study is based on annual time series data ranging from 1990 to 2020. This means that I will use observations that are available within this data series. In addition, information was collected from reputable sources such as the World Bank and Organization of Islamic Cooperation (OIC e SESRIC). Somalia was selected for a case study because it has a high number of environmental challenges.\u003c/p\u003e \u003cp\u003eThe research involved taking different variables into account such as deforestation which is used to measure environmental degradation. On the contrary, variables like agricultural land use, economic growth, total population and gross fixed capital formation were used as independent variables. They were all then converted into their natural logarithms. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides variable descriptions and sources.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eDefinition of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\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\u003eCode\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSources\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental degradation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArable land (Deforestation) as a proxy for environmental degradation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic growth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDP (constant 2015) price\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSESRIC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross fixed capital formation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGross Fixed Capital Formation, Constant 2015 Prices, Annual Change\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSESRIC\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural land\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgricultural land (sq. km)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation growth\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation growth (annual %)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel specification\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe study employed the ARDL bound test developed by Pesaran et al. (2001) to find out the long-run and short-run effects of economic growth, total population, agricultural land and gross fixed capital formation on environmental degradation (deforestation) in Somalia. It is chosen because it has good estimation properties for variables with mixed order of integration as compared to traditional cointegration techniques like Johansen’s method that assumes I(1) integrated variables at first difference. Conversely, ARDL bound test can be used with stationary at level (I(0)), first difference (I(1)) or both types of variables hence it is suitable for datasets having mixed orders of integration.\u003c/p\u003e \u003cp\u003eAnother reason why this study chose the ARDL bound test is because it has an autoregressive structure which addresses potential endogeneity and thus obtain consistent and reliable results. Additionally, when conducting analysis with a small sample size, its applicability surpasses that of other cointegration methods such as Warsame et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).In addition, the standard log function has been expressed as follows:\u003c/p\u003e \u003cp\u003eLnED\u003csub\u003et\u003c/sub\u003e = β\u003csub\u003e0\u003c/sub\u003e + β\u003csub\u003e1\u003c/sub\u003eLnGDP\u003csub\u003et\u003c/sub\u003e + β\u003csub\u003e2\u003c/sub\u003eLnK\u003csub\u003et\u003c/sub\u003e + β\u003csub\u003e3\u003c/sub\u003eLnAL\u003csub\u003et\u003c/sub\u003e + β\u003csub\u003e4\u003c/sub\u003eLnPOP\u003csub\u003et\u003c/sub\u003e +Ɛ\u003csub\u003et\u003c/sub\u003e (1)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eED is environmental degradation,\u003c/p\u003e \u003cp\u003eGDP is Economic growth,\u003c/p\u003e \u003cp\u003eK is Gross Fixed Capital Formation\u003c/p\u003e \u003cp\u003eAL is agricultural land\u003c/p\u003e \u003cp\u003ePOP is Population growth\u003c/p\u003e \u003cp\u003eƐ is error terms,\u003c/p\u003e \u003cp\u003eLn is natural logarithm, and t denoted as time period.\u003c/p\u003e \u003cp\u003eThe mathematical model illustrating the ARDL model is as follows:\u003c/p\u003e \u003cp\u003eΔLnED\u003csub\u003et\u003c/sub\u003e = β\u003csub\u003e0\u003c/sub\u003e + β\u003csub\u003e1\u003c/sub\u003eLnGDP\u003csub\u003et−1\u003c/sub\u003e + β\u003csub\u003e2\u003c/sub\u003eLnK\u003csub\u003et−1\u003c/sub\u003e + β\u003csub\u003e3\u003c/sub\u003eLnAL\u003csub\u003et−1\u003c/sub\u003e + β\u003csub\u003e4\u003c/sub\u003eLnPOP\u003csub\u003et−1\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=0}^{n}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e1i\u003c/sub\u003eΔLnED\u003csub\u003et−i\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=0}^{n}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e2i\u003c/sub\u003eΔLnGDP\u003csub\u003et−i\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=0}^{n}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e3i\u003c/sub\u003eΔLnK\u003csub\u003et−i\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=0}^{n}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e4i\u003c/sub\u003eΔLnAL\u003csub\u003et−i\u003c/sub\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=0}^{n}{\\delta\\:}\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e5i\u003c/sub\u003eΔLnPOP\u003csub\u003et−I\u003c/sub\u003e + Ɛ\u003csub\u003et\u003c/sub\u003e (2)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eβ\u003csub\u003e0\u003c/sub\u003e is constants\u003c/p\u003e \u003cp\u003eβ\u003csub\u003e1\u003c/sub\u003e – β\u003csub\u003e4\u003c/sub\u003e is short-run coefficients\u003c/p\u003e \u003cp\u003eδ\u003csub\u003e1\u003c/sub\u003e – δ\u003csub\u003e4\u003c/sub\u003e is long-run coefficients\u003c/p\u003e \u003cp\u003eΔ is difference operator, and n is lag length.\u003c/p\u003e \u003cp\u003eTo prevent inaccurate results, unit root analysis must be done prior to evaluating cointegration in the model. The order of variable integration in this study was ascertained by applying the Philips-Perron (PP) and Augmented Dickey-Fuller (ADF) tests. Cointegration can be looked at if the variables are found to be integrated at level I(0), order I(1), or both. The limits test is used to compare the alternative hypothesis of cointegration to the null hypothesis of no cointegration in order to determine whether cointegration exists among the variables that have been chosen.\u003c/p\u003e \u003cp\u003eIf the computed F-test value is greater than the upper bound critical value, showing a long-term association, the null hypothesis is rejected. On the other hand, if the F-test result is less than the lower bound critical value, there is no long-term link and the null hypothesis is not rejected. The outcome is unclear if the F-test value lies between the upper and lower critical levels (Pesaran \u0026amp; Pesaran, 1997; Pesaran et al., 2001). The ARDL limits test does not determine the direction of causality; rather, it merely examines long-run cointegration between the variables. Granger causality tests are used to identify the causal linkages between the variables in order to overcome this constraint.\u003c/p\u003e "},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e contains a detailed analysis of the variables. This shows key features such as mean, median, maximum, minimum and standard deviation of the variables in question. For instance, Table\u0026nbsp;4.2 reflects that the average values of environmental degradation (6.03), GDP (9.49), gross capital formation (8.63), agricultural land (6.57) and population growth (16.14). Moreover, these are headed by GDP with its highest maximum value being 9.82 and population growth having the largest number of 16.62 respectively among all other given figures. All other than environmental degradation and population growth variables are positively skewed while for the latter they are negatively skewed in relation to each other on the plot accordingly. It is inferred that if Pop Growth has a larger std, that means there is more variation in its scores from its mean value - when compared to others in this research. This examination deals with basic ideas regarding data distribution and central tendencies which are important for further econometric analysis as well as interpretation.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eStats\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnED\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnAL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnPG\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.026073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.48719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.625128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.569533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.13728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.021189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.480867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.576133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.579873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.16377\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.053078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.82207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.987729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.882544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.62111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.183598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.402158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.290373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.6762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.210057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.186629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.289434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.121458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.657055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.039587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJarque-Bera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.421778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.54942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.862015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.044037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.91371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProbability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.297932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.279512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.239068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.359868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.384099\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\u003cstrong\u003eCorrelation Matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe purpose of the correlation analysis is to make sure the variables do not exhibit perfect multicollinearity. The correlation matrix of the variables that were sampled is shown in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e, and it generally shows that there is not much correlation, indicating that the study is robust. Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e shows a positive correlation between environmental deterioration and GDP, gross capital formation, agricultural land, and population expansion. These positive correlations imply that higher levels of environmental degradation in the area are linked to increases in these demographic and economic parameters.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCorrelation Matrix\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnED\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnAL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLnPG\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.563785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.462114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.936023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.612525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.980563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.881638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.617687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.867275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\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\u003cstrong\u003eUnit root test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e shows the results of the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests. The results indicate that LnED is stationary at level [I(0)], whereas the remaining series have unit roots and become stationary at their first differences, indicating they are integrated of order one [I(1)]. None of the variables are stationary at the second difference [I(2)], confirming that the first difference is sufficient to achieve stationarity.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eunit root test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFirst difference\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFirst difference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.313295*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.709508***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.592562*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.842124***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.742435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.310204***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.679506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.824564***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.262393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.47207***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.51616138***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.262393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.47207***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.905561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.516138***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLnPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.621798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.215753***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.027023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.981795***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e***, **, * denote significance level at 1%, 5% and 10% respectively.\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\u003cstrong\u003eCointegration bounds test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to determine whether long-run cointegration exists between the variables, the Wald F-test is utilized in this study. Cointegration of the variables is indicated by the estimated F-statistics, which are greater than the crucial upper bound value. This validates that a long-term relationship exists, which supports the use of the ARDL bounds testing approach. Table \u003cspan\u003e5\u003c/span\u003e provides a full summary of the F-Bound cointegration test findings.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e examines the possibility of a long-term correlation between other variables and environmental degradation. At a 10% significance level, the data demonstrate that the Wald F-statistic (5.912239) is greater than the upper critical value (5.84). This indicates a sustained relationship between the variables.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eF-Bound Cointegration Tests.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFstatistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLevel of significance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBounds test critical values\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1(0)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.912239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.84\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\u003cstrong\u003eARDL Long-run Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTables\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e display the long-term findings. All of the explanatory variables were found to be statistically significant over the long term. In Somalia, population expansion and economic progress eventually lead to a major increase in environmental deterioration. On the other side, over time, agricultural land and gross fixed capital formation (domestic investment) greatly reduce environmental degradation. In the long run,, the findings show that for every unit increase in GDP and PG, the degradation of the environment rises by approximately 0.187651% and 0.26196%, respectively. This shows that demand on environmental resources increases along with population and economic growth, leading to increased environmental deterioration.\u003c/p\u003e\n\u003cp\u003eAdditionally, the analysis shows that a 1% increase in agricultural land (AL) and gross fixed capital formation (domestic investment) reduces environmental degradation by approximately 0.538906% and 0.005691%, respectively. This highlights the importance of expanding agricultural land and strategic investments in infrastructure and technology, which can enhance ecological balance and sustainability.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eLong Run Results\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.557188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.709501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLnGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.187651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.31858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLnK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.19019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLnAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.538906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.571754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eLnPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.697117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e*, **, *** donate at 10%, 5%, and 1% significance levels. The T statistics are cited in (.)\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\u003cstrong\u003eARDL Short-run Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn the contrary, Table\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e provides an estimate of the short-term dynamic effects. In the short and long term all variables have the same impact. In short term, Economic growth and population growth greatly worsen environmental degradation in Somalia. For every 1% increase in Economic growth and population growth, Environmental degradation rises by about 0.724073% and 0.570945% respectively. Into the bargain, Agricultural land and gross fixed capital formation greatly reduce environmental degradation. For every 1% increase in agricultural land and gross fixed capital formation, environmental degradation is decreased by roughly 0.734459% and 0.734691% respectively.\u003c/p\u003e\n\u003cp\u003eA statistically significant speed of adjustment (ECT) and a negative coefficient are shown in Table\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e. The ECT term (-0.91) attests to the long-term cointegration of the variables. According to this, explanatory variables account for about 91% of the short-term shocks to environmental degradation.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;7: Short Run ECM Results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e10.992214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u0026Delta;LnGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.724073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(12.00906) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u0026Delta;LnK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-0.734691\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(-12.44437) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u0026Delta;LnAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-0.734459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(8.773306) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 223px;\"\u003e\n \u003cp\u003e\u0026Delta;LnPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e0.570945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e(5.380953) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 223px;\"\u003e\n \u003cp\u003eECTt-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 148px;\"\u003e\n \u003cp\u003e-0.9134 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*, **, *** donate at 10%, 5%, and 1% significance levels. The T statistics are cited in (.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Tests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diagnostic tests are presented in Table 8. These tests demonstrate that the model is free from heteroskedasticity and serial correlation, ensuring the reliability of the results. The Jarque-Bera normality test confirms that the residuals of the model follow a normal distribution, which is essential for the validity of the statistical inferences. The Ramsey test indicates that there are no misspecification issues within the model, further validating its robustness. Additionally, the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests, illustrated in Figures 2 and 3, confirm the stability of the regression equation\u0026apos;s coefficients over time, reinforcing the consistency and accuracy of the model\u0026apos;s estimates.\u003c/p\u003e\n\u003cp\u003eThe table shows diagnostic test results for the regression model. The Reset test (p-value: 0.6717) indicates no misspecification issues. The serial correlation test (p-value: 0.0639) suggests no significant serial correlation. The heteroscedasticity test ( p-value: 0.2394) confirms the absence of heteroscedasticity. The normality test ( p-value: 0.833546) shows that the residuals are normally distributed. These results imply that the model is well-specified and meets key statistical assumptions.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;8: Diagnostic Tests\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.75%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.4236%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.8264%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.75%;\"\u003e\n \u003cp\u003eReset test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.4236%;\"\u003e\n \u003cp\u003e0.198496 [0.6717]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.8264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.75%;\"\u003e\n \u003cp\u003eSerial correlation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.4236%;\"\u003e\n \u003cp\u003e5.010339 [0.0639]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.8264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.75%;\"\u003e\n \u003cp\u003eHeteroscedasticity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.4236%;\"\u003e\n \u003cp\u003e1.712214 [0.2394]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.8264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.75%;\"\u003e\n \u003cp\u003eNormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.4236%;\"\u003e\n \u003cp\u003e0.364133 [0.833546]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24.8264%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*, **, *** donate at 10%, 5%, and 1% significance levels. P-values are presented in [.]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGranger Causality Test\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne limitation of ARDL long-run cointegration is its inability to assess causality among variables. To address this, the study employed the Granger causality test to determine the direction of causation, as shown in Table\u0026nbsp;\u003cspan\u003e9\u003c/span\u003e. The test reveals several unidirectional relationships: economic growth (LNGDP) significantly influences environmental degradation (LNED), capital (LNK), and population (LNPG), indicating that changes in economic growth directly affect these variables. Additionally, agricultural land (LNAL) significantly impacts environmental degradation and economic growth, but not vice versa. Population (LNPG) also affects environmental degradation and capital, highlighting the significant role of population dynamics in these areas.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePairwise Granger Causality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNull Hypothesis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF-Statistic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProb.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP \u0026rarr; LNED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.9396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNED \u0026rarr; LNGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.29149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNK \u0026rarr; LNED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNED \u0026rarr; LNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAL \u0026rarr; LNED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.94607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNED \u0026rarr; LNAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPG \u0026rarr; LNED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.73115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNED \u0026rarr; LNPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNK \u0026rarr; LNGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP \u0026rarr; LNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.27373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAL \u0026rarr; LNGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.5101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP \u0026rarr; LNAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8813\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPG \u0026rarr; LNGDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.2988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNGDP \u0026rarr; LNPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAL \u0026rarr; LNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.9483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNK \u0026rarr; LNAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPG \u0026rarr; LNK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.7769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNK \u0026rarr; LNPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6932\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNPG \u0026rarr; LNAL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLNAL \u0026rarr; LNPG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"CONCLUSION AND POLICY IMPLICATIONS","content":"\u003cp\u003eThe primary aim of the study was to understand the key drivers behind environmental degradation in Somalia. Generally, I want to examine how factors like economic growth, gross capital formation (domestic investment) and population growth impact on environmental degradation in Somalia. Also specifically I want to know the impact of agricultural land on environmental degradation. To achieve the hypothesized relationship of the interested parameters, an ARDL bounds test, and Granger causality was adopted to the study. Econometric view (E-view 12) was the program utilized to run and evaluate the data. Furthermore, descriptive statistics were applied to the data analysis. In the study, graphs were also employed to display the data. The research employed secondary data from the World Bank, covering a 30-year period from 1990 to 2020.\u003c/p\u003e\u003cp\u003eBoth long-run and short-run estimates show that expansion of agricultural land contributes to environmental degradation in Somalia. and also economic growth, population growth play a crucial role exacerbates environmental degradation. while domestic investment mitigates environmental degradation. This study gives insights into possible solutions that focus on how environmental degradation(deforestation)should be controlled. Here are some of the recommended policies:\u003c/p\u003e\u003cp\u003eFirst, encourage farmers to employ sustainable agricultural practices such as conservation agriculture and agroforestry in order to promote sustainable agriculture. Farmers will be able to apply these techniques with the support of resources and training, which will lessen the need to destroy forests to make way for additional farmland and preserve the health of the ecosystem.\u003c/p\u003e\u003cp\u003eSecond, start planting trees in deforested and degraded areas as part of reforestation programs. Community organizations, educational institutions, and non-governmental organizations can be involved in these initiatives to help promote long-term environmental sustainability by restoring forest cover, increasing biodiversity, and creating new habitats for species.\u003c/p\u003e\u003cp\u003eThirdly, make law enforcement stronger by giving them more tools to stop illicit land clearing and logging. Establishing forest patrols and putting satellite surveillance into place can assist identify and discourage illicit activity, improving the protection of already-existing forests.\u003c/p\u003e\u003cp\u003eFourth, expand access to renewable energy sources like solar and wind power in order to supply alternative energy sources. Subsidies for solar lights and cookers can help people use less wood, lessening the demand on forest resources and promoting the use of sustainable energy sources.\u003c/p\u003e\u003cp\u003eFinally, raise public understanding about the value of forests by launching national awareness programs. By including environmental education in school curricula and organizing community workshops, communities can be empowered to sustainably use and maintain forest resources, thereby fostering a culture of conservation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBashir Mohamed Osman, Said Ali Shire and Farhan Habib Ali contributed equally to the conceptualization, data collection, and analysis of this study. Bashir Mohamed Osman led the drafting and critical revision of the manuscript. Said Ali Shire provided expertise on methodological approaches and reviewed the final draft. All authors approved the submitted version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the SIMAD University, Center for Research and Development Office.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author, Bashir Mohamed Osman, upon reasonable request via email [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdi, Z. 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Paul Shaw Published by : Cambridge University Press Stable URL : http://www.jstor.com/stable/44518343 Rapid Population Growth and Environmental Deg\u003c/em\u003e. \u003cem\u003e16\u003c/em\u003e(3), 199\u0026ndash;208.\u003c/li\u003e\n\u003cli\u003eStern, D. I., Common, M. S., \u0026amp; Barbier, E. B. (1996). Economic growth and environmental degradation: The environmental Kuznets curve and sustainable development. \u003cem\u003eWorld Development\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(7), 1151\u0026ndash;1160. https://doi.org/10.1016/0305-750X(96)00032-0\u003c/li\u003e\n\u003cli\u003eWalker, R. (1993). Deforestation and Economie Development. \u003cem\u003eCl Canadian Journal of Regional Science\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 481\u0026ndash;497.\u003c/li\u003e\n\u003cli\u003eWarsame, A. A., \u0026amp; Abdi, A. H. (2023). Towards sustainable crop production in Somalia : Examining the role of environmental pollution and degradation Towards sustainable crop production in Somalia : Examining the role of environmental pollution and degradation. \u003cem\u003eCogent Food \u0026amp; Agriculture\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1). https://doi.org/10.1080/23311932.2022.2161776\u003c/li\u003e\n\u003cli\u003eWarsame, A. A., Mohamed, J., \u0026amp; Mohamed, A. A. (2023). The relationship between environmental degradation, agricultural crops, and livestock production in Somalia. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(3), 7825\u0026ndash;7835. https://doi.org/10.1007/s11356-022-22595-8\u003c/li\u003e\n\u003cli\u003eWarsame, A. A., \u0026amp; Sarkodie, S. A. (2022a). Asymmetric impact of energy utilization and economic development on environmental degradation in Somalia. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(16), 23361\u0026ndash;23373. https://doi.org/10.1007/s11356-021-17595-z\u003c/li\u003e\n\u003cli\u003eWarsame, A. A., \u0026amp; Sarkodie, S. A. (2022b). Asymmetric impact of energy utilization and economic development on environmental degradation in Somalia. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(16), 23361\u0026ndash;23373. https://doi.org/10.1007/s11356-021-17595-z\u003c/li\u003e\n\u003cli\u003eWarsame, A. A., Sheik-Ali, I. A., Mohamed, J., \u0026amp; Sarkodie, S. A. (2022). Renewables and institutional quality mitigate environmental degradation in Somalia. \u003cem\u003eRenewable Energy\u003c/em\u003e, \u003cem\u003e194\u003c/em\u003e, 1184\u0026ndash;1191. https://doi.org/10.1016/j.renene.2022.05.109\u003c/li\u003e\n\u003cli\u003eZandi, G., \u0026amp; Haseeb, M. (2019). The importance of green energy consumption and agriculture in reducing environmental degradation: Evidence from sub-Saharan African countries. \u003cem\u003eInternational Journal of Financial Research\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5), 215\u0026ndash;227. https://doi.org/10.5430/ijfr.v10n5p215\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Environmental degradation, Economic growth, ARDL, Somalia, Deforestation, Sustainable agriculture","lastPublishedDoi":"10.21203/rs.3.rs-5193133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5193133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEnvironmental degradation is a pressing global issue with far-reaching consequences for the health of our planet and the well-being of its inhabitants. It is characterized by the deterioration of Earth's natural systems due to factors such as pollution, deforestation, and climate change, leading to biodiversity loss and the depletion of natural resources. Addressing these challenges is essential for maintaining ecological balance and ensuring sustainable practices that mitigate environmental impacts and preserve the planet for future generations. This paper explores the key drivers and impacts of environmental degradation in Somalia, with a focus on economic growth, agricultural expansion, and population growth.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e: The study employs the Autoregressive Distributed Lag (ARDL) Model to examine both long- and short-run relationships between environmental degradation and variables such as economic growth, domestic investment, agricultural land, and population growth. The ARDL model, chosen for its robustness with small sample sizes and flexibility with variable integration, utilizes annual time series data from 1990 to 2020. The model was selected using log-likelihood and the Akaike Information Criterion (AIC).\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults and Recommendations\u003c/b\u003e: The findings reveal that both long- and short-run estimates show agricultural land expansion as a significant contributor to environmental degradation in Somalia. Economic and population growth further exacerbate the issue, while domestic investment helps mitigate degradation. The study highlights the role of deforestation in biodiversity loss, soil degradation, and climate change. It recommends promoting sustainable agricultural practices such as conservation agriculture and agroforestry to curb deforestation and promote environmental sustainability.\u003c/p\u003e","manuscriptTitle":"The Role of Renewable Energy in Combating Environmental Degradation in Somalia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-25 19:29:53","doi":"10.21203/rs.3.rs-5193133/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-04T05:08:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-29T13:30:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-27T21:52:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292574320882391350909507747418118509842","date":"2024-10-26T07:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256128617194743037406687049467006380320","date":"2024-10-21T10:00:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291545375109820256992439777631671963345","date":"2024-10-21T07:53:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48741518138801902843607309311904605635","date":"2024-10-21T07:49:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-12T10:13:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-11T10:50:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-10T09:41:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2024-10-02T13:13:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"24d22b37-a0e3-4646-92fb-cf9a44beeac8","owner":[],"postedDate":"November 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T20:05:43+00:00","versionOfRecord":{"articleIdentity":"rs-5193133","link":"https://doi.org/10.1007/s43621-024-00786-2","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-03-03 15:58:47","publishedOnDateReadable":"March 3rd, 2025"},"versionCreatedAt":"2024-11-25 19:29:53","video":"","vorDoi":"10.1007/s43621-024-00786-2","vorDoiUrl":"https://doi.org/10.1007/s43621-024-00786-2","workflowStages":[]},"version":"v1","identity":"rs-5193133","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5193133","identity":"rs-5193133","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}