Revealing Renewable Energy Barrier in LMICs: A Machine Learning and Econometric Approach for Policymaking

Revealing Renewable Energy Barrier in LMICs: A Machine Learning and Econometric Approach for Policymaking | 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 Revealing Renewable Energy Barrier in LMICs: A Machine Learning and Econometric Approach for Policymaking Abbas Khan, Li Shuangjie, Ai Xiaoqing, Muhammad Farrukh Shahzad, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7286782/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Mar, 2026 Read the published version in Letters in Spatial and Resource Sciences → Version 1 posted 9 You are reading this latest preprint version Abstract This study analyzes factors influencing renewable energy development (RE) in low- and middle-income countries (LMICs) using machine learning-econometric approach combing Random Forest (RFM) and Fixed Effects Modeling (FEM). Findings revealed financial access (DCPS) facilitates RE adoption, while high interest rates (INR_Log) hinder it. Corruption control (CC) and government effectiveness (GE) are crucial. Foreign investment (FDI_Log) has significant asymmetric impacts on clean energy goals. Energy imports (EIMP) vary in importance by context. This research links energy transitions, economic growth, and institutional quality key aspects of SDGs 7 and 8. It recommends financial reforms, anti-corruption measures, and interest rate adjustments to develop inclusive, resilient energy systems. The machine learning–econometric approach offers policymakers a strong framework for evidence-based strategies. Future research should integrate sector-specific variables, industrial RE adaptation, and use XGBoost or SHAP with a larger dataset, particularly from Asia and the Middle East. JEL: C01, C23, O13, O44, Q01, Q42, Q54 Renewable Energy Financial Barriers Governance-Issues LMICs Integrating Modelling Figures Figure 1 Figure 2 Figure 3 1. Introduction Low- and middle-income countries (LMICs) face persistent financial, governance, and policy barriers that hinder the Low and middle-income countries (LMICs) face governance issues, financial constraints, and weak policies that hinder the adoption of renewable energy. These barriers obstruct the transition to sustainable energy systems, crucial for environment, energy security, and economic growth. Poor governance leads to corruption and instability, deterring investment and misallocating resources. However, the stronger governance attracts foreign direct investment (FDI), mitigates market inefficiencies and supports the renewable energy policies (Mahmood et al. 2021 ). Governance quality is crucial for the renewable energy transition in LMICs. Effective governance enables better implementation of sustainable energy policies. Corruption creates inefficiencies and discrimination, making it difficult to develop proper infrastructure on a cost-efficient and trust-building basis. This hinders investment and delays the adoption of necessary innovative technologies for energy transformation (Amoah et al. 2022 ). Renewable energy investors tend to flow into a high governance country because strong governance promotes credible and sustainable energy environment (Toh et al. 2023 ; Acheampong et al. 2024 ). Strong financial systems are needed to transition to renewable energy. For many LMICs, lack of capital, high interest rates, and poorly developed infrastructure make financing for renewable investors difficult. Therefore, projects in an underdeveloped market are postponed (Suhrab et al. 2023 ). Renewable energy projects depend on FDI as a source of capital, advanced technologies, expertise, and global market access (Kor and Qamruzzaman 2023 ). FDI drives renewable energy growth, promoting innovation and infrastructure in underfunded areas (Qamruzzaman 2024 ). Renewable energy projects are more attractive to investors with the help of domestic credit and effective financial markets that significantly reduce financial risks (Suhrab et al. 2023 ). Financing is therefore critical for the success of a project as it accelerates renewable energy infrastructure deployment and makes it an affordable alternative. The adoption of renewable energy is motivated by energy security in countries that depend on imports. Vulnerabilities of geopolitical risks, such as supply disruption and price volatility, need investments in renewables to enhance independence (Matallah et al. 2023 ). Importing capital goods, especially from the EU, accelerates renewable energy adoption by granting access to advanced technologies and expertise (Liu et al. 2023 ). The dynamics of renewable energy transitions in high and middle-income countries have been greatly researched. Less work has been done on the uniqueness of LMICs. They must be examined in detail on governance, finance, and energy import dependence. Current studies often focus on generalized policies while neglecting the specific challenges of LMIC (Chu et al. 2023 ; Cheikh and Zaied 2024). Renewable energy adoption in LMICs has a gap that should be addressed by looking at links between energy imports, production of domestic renewable energy, and the financial institutions. This will help to explain how trade patterns, policy frameworks, and government inefficiency influence renewable energy strategies (Dossou et al. 2023 ). An innovative machine learning approach, including random forests, is carried out on governance quality, foreign investment, financial access, and energy security interrelationships. These models address the link of critical variables often overlooked in conventional analysis (Fezai et al. 2020 ). Capital access reforms and barriers' reduction can increase the efficiency of renewable energy investment. However, they fall short until governance and financial challenges are overcome (Ansari et al. 2024 ; Wei et al. 2024 ). By integrating these methods, the framework provides a comprehensive analysis of the challenges to RE in LMICs, surpassing the capabilities of individual methods. Additionally, a novel cross-regional analysis provides comprehensive insights into RE across LMIC regions: Central America, the Caribbean, and Sub-Saharan Africa. While focusing on scope, the selection captures both shared barriers and region-specific dynamics in RE policy and financing, establishing a foundation for future research to explore regional dynamics in more depth, addressing unique challenges to RE. Consequently, this study contributes a methodological innovation and paves the way for more detailed, region-specific studies. The study include data and methodology in Section 2, results and discussion in Section 3, robustness checks in Section 4, and conclusion and policy in Section 5. 2. Data and Methodology Annual Data from 1996 to 2023 for the Dominican Republic, Honduras, Jamaica, Kenya, and Nicaragua were collected from the World Bank Open Data Portal, providing limited yet valuable data, especially in niche sectors like RE. The dependent variable EPRE is the electricity from renewable sources (excluding hydroelectric). Independent variables, Control of Corruption (CC), Government Effectiveness (GE), Foreign Direct Investment (FDI), Energy Imports (EIMP), Interest Rate Spread (INR), and Domestic Credit to Private Sector (DCPS), represent key RE drivers. This study combines RFM for predictive performance and FEM for interpretability, enhancing policymakers understanding. RFM captures non-linear relationship and variable importance but lacks interpretable coefficients (Schonlau and Zou 2020 ). In contrast FEM provides coefficient significance for clearer insights (Mizumoto 2023 ). The hybrid approach balance prediction and interpretability, improving the analysis of renewable energy adaptation. The Random Effect Model (RFM) is unsuitable, as variance estimation can be highly imprecise for small datasets and groups (Cornell et al. 2014 ). As confirmed by recent study RFM is robust to small datasets over XGBoost and SHAP (Ding et al. 2024 ). FEM can control for unobserved, time-variant differences and is suitable for panel data with small groups and datasets (Muris et al. 2025 ). The general specification of RFM to each country dynamics is in Eq. 1: \(\:{\widehat{f}}_{c}\left(x\right)=1/B{\sum\:}_{b=1}^{B}{T}_{b}\left(x\right).\:for\:each\:country\:c\) E.q 1 \(\:{\widehat{f}}_{c}\) (x) represents the prediction of EPRE for each country c, \(\:{T}_{b}\) is the decision tree b from the ensemble, and B is the total number of trees (set to 500). RFM builds trees by bootstrapping samples and selecting random predictors at each node, preventing overfitting and promoting diversity. Model performance is assessed through Mean Squared Residuals (MSR): Measures prediction error in Eq. 2 and Percentage of Variance Explained (%Var Explained): computed in Eq. 3. \(\:MSR=1/n{\sum\:}_{i=1}^{n}({y}_{i}-{\widehat{y}}_{i}{)}^{2}\) E.q 2 \(\:{R}^{2}=1-\sum\:({y}_{i}-{\widehat{y}}_{i}{)}^{2}/\sum\:({y}_{i}-{\stackrel{-}{y}}_{i}{)}^{2}\) E.q 3 It reflect model accuracy and explanatory power in each country. FEM is applied in Eq. 4, to control heterogeneity that is time invariant across countries. \(\:{Y}_{it}={\alpha\:}_{i}+{\beta\:}_{1}{X}_{1,it}+{\beta\:}_{2}{X}_{2,it}+\dots\:+{\beta\:}_{k}{X}_{k,it}+{\epsilon\:}_{it}\) E.4 \(\:{Y}_{it}\) is renewable energy production, \(\:{\alpha\:}_{i}\) country specific fixed effect, \(\:{X}_{1,it}\) is time varying explanatory variables, and \(\:{\epsilon\:}_{it}\) is error term. 3. Results and Discussions Table 1 indicates Skewness in FDI and INR, requiring log transformation. EPRE, CC, GE, EIMP, and DCPS show mild Skewness and manageable kurtosis suggest the data is suitable for both. While RFM is robust due to non-parametric nature and FEM can handle mild skewness and kurtosis without compromising accuracy and estimating group specific effects. Table 1 Descriptive statistics Variable Skewness Kurtosis EPRE -0.6370 1.0620 CC 0.8530 0.1860 GE 1.0180 0.0960 FDI -1.5110 2.1370 EIMP 0.1710 -0.7870 INR -1.2560 4.5130 DCPS 0.1700 0.0320 Table 2 demonstrates the FEM and RFM robustness. The VIF values below 10 confirm no multicollinearity, Breisch-Pagan test (p-value = 0.357), Durbin-Watson statistic (p = 0.9), and Pesaran’s CD test (p = 0.915) indicate no issues with heteroscedasticity, autocorrelation, or cross-sectional dependence. These results affirm that both models meet key assumptions, ensuring statistical reliability with a small dataset (Savalei 2019 ). Table 2 Diagnostic Tests Variable VIF Value Multicollinearity CC 2.972436 NA GE 2.637671 NA FDI_Log 1.087734 NA EIMP 1.330414 NA INR_Log 1.062164 NA DCPS 1.102751 NA Breusch-Pagan Test/White's Test Result BP Statistic: 6.6238 (df): 6 p-value: 0.357 P > 0.05 NA Durbin-Watson Test DW 2.324 DW statistic close to 2: No autocorrelation p-value 0.9 p > 0.05, NA Pesaran's CD Test for panels Test Stat -0.1066219 p-value 0.915089 p > 0.05, NA Summary of data per Country: Number of Observations 140 Sufficiently large Number of Groups 5 Balanced across countries Note: VIF 0.05 (no heteroscedasticity), Durbin-Watson p-value > 0.05 (no autocorrelation), and Pesaran CD p-value > 0.05 (no cross-sectional dependence). Table 3 , RFM identified key RE drivers (IncNodePurity) in LMICs. In Honduras, domestic credit access (DCPS, 0.0247) and foreign investment (FDI_Log, 0.0148) are most influential. Jamaica highlights corruption control (CC, 0.0060) and financing costs (INR_Log, 0.0050) as main drivers. Kenya emphasizes financial drivers (DCPS, 0.0029; INR_Log, 0.0029). Nicaragua prioritizes credit access (DCPS, 0.0031) alongside governance (CC, 0.0021). In the Dominican Republic, high interest rate (INR_Log, 0.0076) dominates, with governance effectiveness (GE, 0.0048) and energy imports (EIMP, 0.0037) also relevant. In Table 4 shows that FEM confirms the RFM findings on the importance of credit access (DCPS), corruption control (CC), and interest rates (INR_Log) in RE development in LMICs. While RFM identifies non-linear relationships and ranks DCPS and CC as top predictors, FEM highlights CC significance at 5% and DCPS at 10%. The findings are consistent with a recent study, FDI_Log may be directed toward nonrenewable sectors rather than supporting renewable energy goals (Esmaeili Korani 2025 ). Table 3 Feature Importance for Each Country Country Feature X.IncMSE IncNodePurity DOM CC 2.4329e-05 0.0022675531 GE 0.0001584228 0.0048290761 FDI_Log 2.067565e-06 0.0011464068 EIMP 0.0001868554 0.0036938254 INR_Log 0.0003742153 0.0076308305 DCPS 0.0001490207 0.0028540542 HND CC 0.0003196609 0.0109843731 GE 7.82624e-05 0.0082914283 FDI_Log 0.0011569280 0.0147567273 EIMP 0.0003138518 0.0076546538 INR_Log 0.0001079198 0.0087148106 DCPS 0.0015955560 0.0246904997 JAM CC 0.0005046796 0.0059948482 GE 7.409085e-05 0.0039650566 FDI_Log 2.650215e-05 0.0018740748 EIMP 2.71108e-05 0.0016141208 INR_Log 0.0001360179 0.0049529228 DCPS 0.0001365114 0.0044443679 KEN CC 3.53954e-05 0.0013477176 GE 1.113831e-05 0.0023399879 FDI_Log -0.0000097040 0.0009929496 EIMP 4.576846e-05 0.0015808394 INR_Log 9.97646e-05 0.0028967652 DCPS 0.0001326160 0.0029450068 NIC CC 9.331678e-05 0.0021435691 GE 3.630622e-05 0.0012008509 FDI_Log 6.441703e-05 0.0016713591 EIMP 1.308306e-05 0.0006826238 INR_Log 6.987151e-05 0.0017205272 DCPS 0.0001973813 0.0031307067 Note: %IncMSE and IncNodePurity measure feature importance in RFM, identifying key variables. Table 4 Fixed Effects Model Results: Coefficients, Significance, and Model Fit Evaluation Variable Coefficient p-value FDI_Log -0.025 ** 0.01 DCPS 0.350 * 0.098 CC 0.038 ** 0.049 GE 0.086 * 0.06 INR_Log -0.034 * 0.041 EIMP -0.082 0.153 Total Sum of Squares 0.15682 Residual Sum of Squares 0.0433 R-Squared 0.72389 Adjusted R-Squared 0.46001 F-statistic 7.60491 Model p-value 2.82E-08 Note: p-value < 0.05 = significant; higher R-Squared/Adjusted R-Squared = better fit; high F-statistic and low p-value = significant model. 4. Robustness Figure 1 shows the impact of financial access, governance, and energy import reliance on RE development in LMICs. DCPS and FDI_Log highlighted finance needs due to a capital shortage. High interest rates (INR_Log) and energy import (EIMP) dependency are evident in the Dominican Republic, Honduras, and Kenya. Strong governance (CC, GE) in Jamaica and Nicaragua supports RE development. Overall, results confirm prior findings, indicating that policies should enhance financial systems, strengthen institutions, and decrease energy import reliance to foster sustainable transitions in LMICs. Table 5 assesses RFM robustness across countries using MSR and % Variance Explained. Honduras (HND) and Nicaragua (NIC) show the best accuracy with low MSR values of 0.0001285 and 0.0001207, and high variance explained (78.23% and 77.43%). The Dominican Republic (DOM) and Kenya (KEN) also demonstrate solid model fit, with MSR values of 0.0002809 and 0.0002328, and 72.33% and 75.87% variance explained, respectively. Jamaica (JAM) has higher MSR (0.0005555) and lower variance (60.04%), but reflects a reasonable fit. Overall, the model shows robust accuracy and explanatory power across all countries. Table 5 Country-Level Model Evaluation Metrics Country MSR % Var Explained DOM 0.000280929 0.7232909577 HND 0.000128523 0.782265077 JAM 0.000555505 0.60041602 KEN 0.000232781 0.758739744 NIC 0.000120665 0.774251299 Note: Mean of Squared Residuals: Lower = better accuracy; % Variance Explained: Higher = better fit. Table 6 shows evaluation metrics for the model using bootstrap resampling and holdout validation. The Bootstrap MSE is 0.0000238, indicating low error and strong performance. The Standard Deviation of Bootstrap MSE is 0.0000107, reflecting consistent results. The Test MSE is 0.0007964, slightly higher than Bootstrap MSE but still low, indicating good performance on unseen data. The Test R-squared is 0.6351, meaning the model explains 63.5% of the variance in the test set, demonstrating good explanatory power for complex regional data. Table 6 Model Performance Evaluation: Bootstrap Resampling and Holdout Validation Metric Value Note Mean MSE from Bootstrap 0.0000238 Low MSE across resamples indicates good model performance. Standard Deviation of MSE from Bootstrap 0.0000107 Small SD indicates consistent model performance. Test MSE 0.0007964 Low MSE, indicating good performance on unseen data. Test R-squared 0.6351 The model explains 73.5% of the variance in the test set. Figure 2 shows residual vs. fitted plots with random scatter around the zero line across all countries, indicating no heteroscedasticity and supporting the reliability of the RFM predictions. In Fig. 3 , residuals scatter randomly around the zero line, indicating no significant pattern or heteroscedasticity. A few mild outliers exist but do not distort the overall trend. The FEM confirms that key assumptions are satisfied. 5. Conclusion The study employed a machine learning–econometric approach to identify key RE drivers across LMICs, directly supporting the goals of SDG 7 (Affordable and Clean Energy) and SDG 8 (Decent Work and Economic Growth). Results showed that Financial Access (DCPS) is a key enabler in all countries, ranked highly in Random Forest (RFM) importance and statistically significant in the fixed-effect model (FEM). Financing Costs (INR_Log) represented a major constraint in Kenya and the Dominican Republic, with strong statistical significance and high variable importance. Governance Indicators like Corruption Control (CC) and Government Effectiveness (GE) were crucial, especially in Jamaica and Nicaragua, where both are statistically significant, and CC is highly ranked in the RFM. Foreign Investment (FDI_Log) is crucial for Honduras, indicating its dependence on external capital. Despite statistical significance, the negative coefficient in the FEM suggests these investments often conflict with clean energy goals, necessitating stronger policies to redirect capital toward renewables (Esmaeili Korani 2025 ). Energy Imports (EIMP) showed variable importance in RFM for countries like the Dominican Republic but lacked statistical significance. This implies their influence may be situational; significant in the Dominican Republic, but not universally across all countries. Most energy imports are non-renewable, like coal, oil, and natural gas, which has critical implications for RE development and environmental policy (Adekoya et al. 2022 ). To support renewable energy in LMICs, strengthening governance is vital. This involves transparent procurement, digital permitting, and reducing corruption to boost investor confidence. Interest rate subsidies and training for public officials will enhance investment. Countries should create green credit schemes and interest rate caps for renewable projects, particularly for SMEs. Investing in local grids will reduce reliance on imported energy and promote local RE production. Jamaica must improve governance and streamline approval processes. Kenya should set interest rate caps for RE SMEs and invest in RE microgrids. Nicaragua needs independent regulatory bodies, transparent bidding processes, and enhanced financial inclusion via development banks. Honduras must implement low-interest RE loans with risk-sharing and strengthen legal frameworks for investor trust. The Dominican Republic should rectify interest rate distortion, advance digitalization, and adjust energy import tariffs to favor local renewable production. The study addresses common challenges with tailored country-specific policies. This study examined macroeconomic and governance factors. Future research could explore sector-specific influences for tailored policy insights. Limited to Central America, the Caribbean, and Africa, future studies should include Asia and the Middle East. Lastly, RFM captured nonlinear variable importance on small datasets; future research should apply XGBoost or SHAP on large datasets to enhance AI-driven insights in energy policy. Declarations Funding Declaration "No funding was received for this study." Author Contribution A.K. conceptualized the study, conducted the analysis, wrote the original draft, reviewed, and proofread the manuscript. L.S. supervised the project, contributed to methodology, writing, review, and proofreading. A.X. assisted in data analysis, writing, review, and proofreading. M.F.S. contributed to model validation, writing, review, and proofreading. A.R. supported the literature review, writing, review, and proofreading. All authors have read and approved the final manuscript. 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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-7286782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500868732,"identity":"d4798819-ef50-4483-954f-8bec913a7f8a","order_by":0,"name":"Abbas Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACCR4gceAAmM3MYGADpBgbD5CiJQ2kpYEULQyHwQy8Wvhn9x78+OPMHXlz/jOGnwsKztutbT8MtKXGJhqnJXfOJUvz3HhmuHNGjrH0DIPbydvOJAK1HEvLbcCl50aOgTTDh8OMG27wbpDmAWoxOwDUwthwGKcW+Rs5xj9/fDhsv+H82c2/eQzOJZudf4hfi8GNHDMJnhuHEzccyN0GtOWAndkNArYY3jmXZs1z5nDyhhv536xnGCQnmN0A2pKAxy9yt3sP3/xx7LDthvPHkm8X/LGzNzuf/vDBhxob3N5HB4lglQnEKgcBe1IUj4JRMApGwcgAAHJBb6x2I59oAAAAAElFTkSuQmCC","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Abbas","middleName":"","lastName":"Khan","suffix":""},{"id":500868733,"identity":"0043974d-256e-42c6-b886-ea7751f880e9","order_by":1,"name":"Li Shuangjie","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Shuangjie","suffix":""},{"id":500868734,"identity":"97b71f26-c291-4bfa-9ad6-a707cbc2cc93","order_by":2,"name":"Ai Xiaoqing","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ai","middleName":"","lastName":"Xiaoqing","suffix":""},{"id":500868735,"identity":"5d056243-e093-4a76-959a-65f0c92241c6","order_by":3,"name":"Muhammad Farrukh Shahzad","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Farrukh","lastName":"Shahzad","suffix":""},{"id":500868736,"identity":"64b4019b-1678-4800-99b9-ddf8d26f4e52","order_by":4,"name":"Abdul Razzaq","email":"","orcid":"","institution":"Hubei University","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Razzaq","suffix":""}],"badges":[],"createdAt":"2025-08-04 04:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7286782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7286782/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12076-026-00431-8","type":"published","date":"2026-03-10T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89671708,"identity":"d79eea71-8dd3-491a-aa9a-8551f13c1793","added_by":"auto","created_at":"2025-08-22 13:06:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature Importance for Renewable Energy Production across Countries is HERE\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7286782/v1/a980b141999479f69438abc6.png"},{"id":89671293,"identity":"4b085382-6bdd-4dbf-9c16-18eb52036761","added_by":"auto","created_at":"2025-08-22 12:58:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResiduals vs Fitted Values for Random Forest Model across Countries\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7286782/v1/d6c98531872de5f1f7ac17db.png"},{"id":89673274,"identity":"9ce8ccac-fdaa-418c-b9be-63fc1a7f46fd","added_by":"auto","created_at":"2025-08-22 13:14:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32857,"visible":true,"origin":"","legend":"\u003cp\u003eResiduals vs. Fitted Values for Fixed Effects Model\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7286782/v1/c48b56fddb8a2ae8d76509c8.png"},{"id":104440830,"identity":"33c02768-e02f-4650-ac5f-02681edaf031","added_by":"auto","created_at":"2026-03-11 18:18:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":878499,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7286782/v1/05311a36-895e-424d-83cc-e6d2784a79fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revealing Renewable Energy Barrier in LMICs: A Machine Learning and Econometric Approach for Policymaking","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLow- and middle-income countries (LMICs) face persistent financial, governance, and policy barriers that hinder the Low and middle-income countries (LMICs) face governance issues, financial constraints, and weak policies that hinder the adoption of renewable energy. These barriers obstruct the transition to sustainable energy systems, crucial for environment, energy security, and economic growth. Poor governance leads to corruption and instability, deterring investment and misallocating resources. However, the stronger governance attracts foreign direct investment (FDI), mitigates market inefficiencies and supports the renewable energy policies (Mahmood et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGovernance quality is crucial for the renewable energy transition in LMICs. Effective governance enables better implementation of sustainable energy policies. Corruption creates inefficiencies and discrimination, making it difficult to develop proper infrastructure on a cost-efficient and trust-building basis. This hinders investment and delays the adoption of necessary innovative technologies for energy transformation (Amoah et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Renewable energy investors tend to flow into a high governance country because strong governance promotes credible and sustainable energy environment (Toh et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Acheampong et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eStrong financial systems are needed to transition to renewable energy. For many LMICs, lack of capital, high interest rates, and poorly developed infrastructure make financing for renewable investors difficult. Therefore, projects in an underdeveloped market are postponed (Suhrab et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Renewable energy projects depend on FDI as a source of capital, advanced technologies, expertise, and global market access (Kor and Qamruzzaman \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). FDI drives renewable energy growth, promoting innovation and infrastructure in underfunded areas (Qamruzzaman \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Renewable energy projects are more attractive to investors with the help of domestic credit and effective financial markets that significantly reduce financial risks (Suhrab et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Financing is therefore critical for the success of a project as it accelerates renewable energy infrastructure deployment and makes it an affordable alternative.\u003c/p\u003e\u003cp\u003eThe adoption of renewable energy is motivated by energy security in countries that depend on imports. Vulnerabilities of geopolitical risks, such as supply disruption and price volatility, need investments in renewables to enhance independence (Matallah et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Importing capital goods, especially from the EU, accelerates renewable energy adoption by granting access to advanced technologies and expertise (Liu et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe dynamics of renewable energy transitions in high and middle-income countries have been greatly researched. Less work has been done on the uniqueness of LMICs. They must be examined in detail on governance, finance, and energy import dependence. Current studies often focus on generalized policies while neglecting the specific challenges of LMIC (Chu et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cheikh and Zaied 2024). Renewable energy adoption in LMICs has a gap that should be addressed by looking at links between energy imports, production of domestic renewable energy, and the financial institutions. This will help to explain how trade patterns, policy frameworks, and government inefficiency influence renewable energy strategies (Dossou et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn innovative machine learning approach, including random forests, is carried out on governance quality, foreign investment, financial access, and energy security interrelationships. These models address the link of critical variables often overlooked in conventional analysis (Fezai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Capital access reforms and barriers' reduction can increase the efficiency of renewable energy investment. However, they fall short until governance and financial challenges are overcome (Ansari et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wei et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy integrating these methods, the framework provides a comprehensive analysis of the challenges to RE in LMICs, surpassing the capabilities of individual methods. Additionally, a novel cross-regional analysis provides comprehensive insights into RE across LMIC regions: Central America, the Caribbean, and Sub-Saharan Africa. While focusing on scope, the selection captures both shared barriers and region-specific dynamics in RE policy and financing, establishing a foundation for future research to explore regional dynamics in more depth, addressing unique challenges to RE. Consequently, this study contributes a methodological innovation and paves the way for more detailed, region-specific studies.\u003c/p\u003e\u003cp\u003eThe study include data and methodology in Section 2, results and discussion in Section 3, robustness checks in Section 4, and conclusion and policy in Section 5.\u003c/p\u003e"},{"header":"2. Data and Methodology","content":"\u003cp\u003eAnnual Data from 1996 to 2023 for the Dominican Republic, Honduras, Jamaica, Kenya, and Nicaragua were collected from the World Bank Open Data Portal, providing limited yet valuable data, especially in niche sectors like RE. The dependent variable EPRE is the electricity from renewable sources (excluding hydroelectric). Independent variables, Control of Corruption (CC), Government Effectiveness (GE), Foreign Direct Investment (FDI), Energy Imports (EIMP), Interest Rate Spread (INR), and Domestic Credit to Private Sector (DCPS), represent key RE drivers. This study combines RFM for predictive performance and FEM for interpretability, enhancing policymakers understanding. RFM captures non-linear relationship and variable importance but lacks interpretable coefficients (Schonlau and Zou \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast FEM provides coefficient significance for clearer insights (Mizumoto \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The hybrid approach balance prediction and interpretability, improving the analysis of renewable energy adaptation. The Random Effect Model (RFM) is unsuitable, as variance estimation can be highly imprecise for small datasets and groups (Cornell et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As confirmed by recent study RFM is robust to small datasets over XGBoost and SHAP (Ding et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). FEM can control for unobserved, time-variant differences and is suitable for panel data with small groups and datasets (Muris et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The general specification of RFM to each country dynamics is in Eq.\u0026nbsp;1:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{f}}_{c}\\left(x\\right)=1/B{\\sum\\:}_{b=1}^{B}{T}_{b}\\left(x\\right).\\:for\\:each\\:country\\:c\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE.q 1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{f}}_{c}\\)\u003c/span\u003e\u003c/span\u003e(x) represents the prediction of EPRE for each country c, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{b}\\)\u003c/span\u003e\u003c/span\u003e is the decision tree b from the ensemble, and B is the total number of trees (set to 500). RFM builds trees by bootstrapping samples and selecting random predictors at each node, preventing overfitting and promoting diversity.\u003c/p\u003e\u003cp\u003eModel performance is assessed through Mean Squared Residuals (MSR): Measures prediction error in Eq.\u0026nbsp;2 and Percentage of Variance Explained (%Var Explained): computed in Eq.\u0026nbsp;3.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MSR=1/n{\\sum\\:}_{i=1}^{n}({y}_{i}-{\\widehat{y}}_{i}{)}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE.q 2\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{R}^{2}=1-\\sum\\:({y}_{i}-{\\widehat{y}}_{i}{)}^{2}/\\sum\\:({y}_{i}-{\\stackrel{-}{y}}_{i}{)}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE.q 3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIt reflect model accuracy and explanatory power in each country.\u003c/p\u003e\u003cp\u003eFEM is applied in Eq.\u0026nbsp;4, to control heterogeneity that is time invariant across countries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e\u003ccolgroup cols=\"2\"\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\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}={\\alpha\\:}_{i}+{\\beta\\:}_{1}{X}_{1,it}+{\\beta\\:}_{2}{X}_{2,it}+\\dots\\:+{\\beta\\:}_{k}{X}_{k,it}+{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}\\)\u003c/span\u003e\u003c/span\u003e is renewable energy production, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e country specific fixed effect, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{1,it}\\)\u003c/span\u003e\u003c/span\u003e is time varying explanatory variables, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e is error term.\u003c/p\u003e"},{"header":"3. Results and Discussions","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates Skewness in FDI and INR, requiring log transformation. EPRE, CC, GE, EIMP, and DCPS show mild Skewness and manageable kurtosis suggest the data is suitable for both. While RFM is robust due to non-parametric nature and FEM can handle mild skewness and kurtosis without compromising accuracy and estimating group specific effects.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEPRE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.6370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.0620\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1860\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.0180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.5110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1370\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.7870\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.2560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.5130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0320\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates the FEM and RFM robustness. The VIF values below 10 confirm no multicollinearity, Breisch-Pagan test (p-value\u0026thinsp;=\u0026thinsp;0.357), Durbin-Watson statistic (p\u0026thinsp;=\u0026thinsp;0.9), and Pesaran\u0026rsquo;s CD test (p\u0026thinsp;=\u0026thinsp;0.915) indicate no issues with heteroscedasticity, autocorrelation, or cross-sectional dependence. These results affirm that both models meet key assumptions, ensuring statistical reliability with a small dataset (Savalei \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic Tests\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVIF Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMulticollinearity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.972436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.637671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.087734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.330414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.062164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.102751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eBreusch-Pagan Test/White's Test Result\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBP Statistic:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.6238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(df):\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05 NA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eDurbin-Watson Test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDW statistic close to 2: No autocorrelation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05, NA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003ePesaran's CD Test for panels\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest Stat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1066219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.915089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05, NA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eSummary of data per Country:\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Observations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSufficiently large\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Groups\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBalanced across countries\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNote: VIF\u0026thinsp;\u0026lt;\u0026thinsp;10, Breusch-Pagan p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (no heteroscedasticity), Durbin-Watson p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (no autocorrelation), and Pesaran CD p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (no cross-sectional dependence).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, RFM identified key RE drivers (IncNodePurity) in LMICs. In Honduras, domestic credit access (DCPS, 0.0247) and foreign investment (FDI_Log, 0.0148) are most influential. Jamaica highlights corruption control (CC, 0.0060) and financing costs (INR_Log, 0.0050) as main drivers. Kenya emphasizes financial drivers (DCPS, 0.0029; INR_Log, 0.0029). Nicaragua prioritizes credit access (DCPS, 0.0031) alongside governance (CC, 0.0021). In the Dominican Republic, high interest rate (INR_Log, 0.0076) dominates, with governance effectiveness (GE, 0.0048) and energy imports (EIMP, 0.0037) also relevant. In Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows that FEM confirms the RFM findings on the importance of credit access (DCPS), corruption control (CC), and interest rates (INR_Log) in RE development in LMICs. While RFM identifies non-linear relationships and ranks DCPS and CC as top predictors, FEM highlights CC significance at 5% and DCPS at 10%. The findings are consistent with a recent study, FDI_Log may be directed toward nonrenewable sectors rather than supporting renewable energy goals (Esmaeili Korani \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFeature Importance for Each Country\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFeature\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX.IncMSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIncNodePurity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4329e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0022675531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001584228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0048290761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFDI_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.067565e-06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0011464068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001868554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0036938254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003742153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0076308305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001490207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0028540542\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003196609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0109843731\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.82624e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0082914283\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFDI_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0011569280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0147567273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0003138518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0076546538\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001079198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0087148106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0015955560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0246904997\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0005046796\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0059948482\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.409085e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0039650566\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFDI_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.650215e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0018740748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71108e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0016141208\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001360179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0049529228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001365114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0044443679\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.53954e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0013477176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.113831e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0023399879\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFDI_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.0000097040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0009929496\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.576846e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0015808394\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.97646e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0028967652\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001326160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0029450068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.331678e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0021435691\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.630622e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0012008509\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFDI_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.441703e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0016713591\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.308306e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0006826238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eINR_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.987151e-05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0017205272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0001973813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0031307067\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNote: %IncMSE and IncNodePurity measure feature importance in RFM, identifying key variables.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFixed Effects Model Results: Coefficients, Significance, and Model Fit Evaluation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFDI_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.025 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.350 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.038 **\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.086 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINR_Log\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.034 *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIMP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.153\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Sum of Squares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.15682\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual Sum of Squares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.0433\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-Squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.72389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted R-Squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.46001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eF-statistic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e7.60491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel p-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2.82E-08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNote: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026thinsp;=\u0026thinsp;significant; higher R-Squared/Adjusted R-Squared\u0026thinsp;=\u0026thinsp;better fit; high F-statistic and low p-value\u0026thinsp;=\u0026thinsp;significant model.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Robustness","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the impact of financial access, governance, and energy import reliance on RE development in LMICs. DCPS and FDI_Log highlighted finance needs due to a capital shortage. High interest rates (INR_Log) and energy import (EIMP) dependency are evident in the Dominican Republic, Honduras, and Kenya. Strong governance (CC, GE) in Jamaica and Nicaragua supports RE development. Overall, results confirm prior findings, indicating that policies should enhance financial systems, strengthen institutions, and decrease energy import reliance to foster sustainable transitions in LMICs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e assesses RFM robustness across countries using MSR and % Variance Explained. Honduras (HND) and Nicaragua (NIC) show the best accuracy with low MSR values of 0.0001285 and 0.0001207, and high variance explained (78.23% and 77.43%). The Dominican Republic (DOM) and Kenya (KEN) also demonstrate solid model fit, with MSR values of 0.0002809 and 0.0002328, and 72.33% and 75.87% variance explained, respectively. Jamaica (JAM) has higher MSR (0.0005555) and lower variance (60.04%), but reflects a reasonable fit. Overall, the model shows robust accuracy and explanatory power across all countries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCountry-Level Model Evaluation Metrics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMSR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e% Var Explained\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDOM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000280929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7232909577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000128523\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.782265077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJAM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000555505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60041602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKEN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000232781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.758739744\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000120665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.774251299\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNote: Mean of Squared Residuals: Lower\u0026thinsp;=\u0026thinsp;better accuracy; % Variance Explained: Higher\u0026thinsp;=\u0026thinsp;better fit.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows evaluation metrics for the model using bootstrap resampling and holdout validation. The Bootstrap MSE is 0.0000238, indicating low error and strong performance. The Standard Deviation of Bootstrap MSE is 0.0000107, reflecting consistent results. The Test MSE is 0.0007964, slightly higher than Bootstrap MSE but still low, indicating good performance on unseen data. The Test R-squared is 0.6351, meaning the model explains 63.5% of the variance in the test set, demonstrating good explanatory power for complex regional data.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel Performance Evaluation: Bootstrap Resampling and Holdout Validation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetric\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNote\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean MSE from Bootstrap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0000238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow MSE across resamples indicates good model performance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard Deviation of MSE from Bootstrap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0000107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmall SD indicates consistent model performance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest MSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0007964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow MSE, indicating good performance on unseen data.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTest R-squared\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.6351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe model explains 73.5% of the variance in the test set.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows residual vs. fitted plots with random scatter around the zero line across all countries, indicating no heteroscedasticity and supporting the reliability of the RFM predictions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, residuals scatter randomly around the zero line, indicating no significant pattern or heteroscedasticity. A few mild outliers exist but do not distort the overall trend. The FEM confirms that key assumptions are satisfied.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe study employed a machine learning\u0026ndash;econometric approach to identify key RE drivers across LMICs, directly supporting the goals of SDG 7 (Affordable and Clean Energy) and SDG 8 (Decent Work and Economic Growth). Results showed that Financial Access (DCPS) is a key enabler in all countries, ranked highly in Random Forest (RFM) importance and statistically significant in the fixed-effect model (FEM). Financing Costs (INR_Log) represented a major constraint in Kenya and the Dominican Republic, with strong statistical significance and high variable importance. Governance Indicators like Corruption Control (CC) and Government Effectiveness (GE) were crucial, especially in Jamaica and Nicaragua, where both are statistically significant, and CC is highly ranked in the RFM. Foreign Investment (FDI_Log) is crucial for Honduras, indicating its dependence on external capital. Despite statistical significance, the negative coefficient in the FEM suggests these investments often conflict with clean energy goals, necessitating stronger policies to redirect capital toward renewables (Esmaeili Korani \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Energy Imports (EIMP) showed variable importance in RFM for countries like the Dominican Republic but lacked statistical significance. This implies their influence may be situational; significant in the Dominican Republic, but not universally across all countries. Most energy imports are non-renewable, like coal, oil, and natural gas, which has critical implications for RE development and environmental policy (Adekoya et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo support renewable energy in LMICs, strengthening governance is vital. This involves transparent procurement, digital permitting, and reducing corruption to boost investor confidence. Interest rate subsidies and training for public officials will enhance investment. Countries should create green credit schemes and interest rate caps for renewable projects, particularly for SMEs. Investing in local grids will reduce reliance on imported energy and promote local RE production.\u003c/p\u003e\u003cp\u003eJamaica must improve governance and streamline approval processes. Kenya should set interest rate caps for RE SMEs and invest in RE microgrids. Nicaragua needs independent regulatory bodies, transparent bidding processes, and enhanced financial inclusion via development banks. Honduras must implement low-interest RE loans with risk-sharing and strengthen legal frameworks for investor trust. The Dominican Republic should rectify interest rate distortion, advance digitalization, and adjust energy import tariffs to favor local renewable production. The study addresses common challenges with tailored country-specific policies.\u003c/p\u003e\u003cp\u003eThis study examined macroeconomic and governance factors. Future research could explore sector-specific influences for tailored policy insights. Limited to Central America, the Caribbean, and Africa, future studies should include Asia and the Middle East. Lastly, RFM captured nonlinear variable importance on small datasets; future research should apply XGBoost or SHAP on large datasets to enhance AI-driven insights in energy policy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eFunding Declaration\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\"No funding was received for this study.\"\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eA.K. conceptualized the study, conducted the analysis, wrote the original draft, reviewed, and proofread the manuscript. L.S. supervised the project, contributed to methodology, writing, review, and proofreading. A.X. assisted in data analysis, writing, review, and proofreading. M.F.S. contributed to model validation, writing, review, and proofreading. A.R. supported the literature review, writing, review, and proofreading. All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their sincere gratitude to the faculty and staff of the College of Economics and Management, Beijing University of Technology, for their valuable support and guidance throughout this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data and codes can be provided upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcheampong, A.O., Boateng, E., Annor, C.B.: Do corruption, income inequality and redistribution hasten transition towards (non) renewable energy economy? Struct. Change Econ. Dyn. \u003cb\u003e68\u003c/b\u003e, 329\u0026ndash;354 (2024)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdekoya, O.B., Oliyide, J.A., Fasanya, I.O.: Renewable and non-renewable energy consumption\u0026ndash;Ecological footprint nexus in net-oil exporting and net-oil importing countries: Policy implications for a sustainable environment. Renew. Energy. \u003cb\u003e189\u003c/b\u003e, 524\u0026ndash;534 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmoah, A., Asiama, R.K., Korle, K., Kwablah, E.: Corruption: Is it a bane to renewable energy consumption in Africa? Energy Policy. \u003cb\u003e163\u003c/b\u003e, 112854 (2022)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnsari, M.A.A., Sajid, M., Khan, S.N., et al.: Unveiling the effect of renewable energy and financial inclusion towards sustainable environment: Does interaction of digital finance and institutional quality matter? 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Findings revealed financial access (DCPS) facilitates RE adoption, while high interest rates (INR_Log) hinder it. Corruption control (CC) and government effectiveness (GE) are crucial. Foreign investment (FDI_Log) has significant asymmetric impacts on clean energy goals. Energy imports (EIMP) vary in importance by context. This research links energy transitions, economic growth, and institutional quality key aspects of SDGs 7 and 8. It recommends financial reforms, anti-corruption measures, and interest rate adjustments to develop inclusive, resilient energy systems. The machine learning–econometric approach offers policymakers a strong framework for evidence-based strategies. 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