The Nexus Between Energy Consumption, Population, and Economic Growth: A Comparative study OECD Countries and Tunisia

The Nexus Between Energy Consumption, Population, and Economic Growth: A Comparative study OECD Countries and Tunisia | 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 Nexus Between Energy Consumption, Population, and Economic Growth: A Comparative study OECD Countries and Tunisia Olfa Ifaoui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8569758/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This paper investigates the relationship between energy consumption, population, and economic growth in 18 OECD countries and Tunisia over the period 1990–2020. Using panel data and applying Swamy’s Random Coefficients Model and Seemingly Unrelated Regression models, the study finds that economic growth positively influences energy consumption, and energy consumption contributes positively to growth in both groups. However, population does not significantly affect energy consumption in these countries. The findings highlight important insights into the energy-growth dynamics for OECD countries and Tunisia, providing useful evidence for energy policy and sustainable development planning. Energy Consumption Economic Growth Population 1. Introduction Energy is a crucial component of the economy and a significant determinant of future social, geographical, and economic dynamics (del Río, and al., 2025). Since the dawn of humanity, energy has been one of our most essential needs. Initially utilized in various forms for necessities, energy took on a new significance with the advent of the industrial revolution and the shift to mass production. This surge in energy demand, driven by industrialization, led to urbanization and rapid population growth (Vo and al., 2024). The relationship between energy consumption, population, and economic growth is complex and multifaceted, particularly when comparing countries with different levels of development, such as Tunisia and those in the OECD. In Tunisia, energy consumption has been a critical factor in supporting economic growth, with an increasing demand driven by industrialization and urbanization. However, the pace of growth and energy use is constrained by limited resources and infrastructure challenges. In contrast, OECD countries generally have more advanced energy infrastructures and greater access to a variety of energy sources, allowing for more efficient energy use and a stronger correlation between energy consumption and economic growth. Additionally, the population dynamics in Tunisia, characterized by a younger and rapidly growing population, differ significantly from those in many OECD countries, where populations are aging and growing at a slower rate. These demographic factors further influence the energy consumption patterns and economic growth trajectories in both contexts (Ma and al., 2024). Therefore, understanding the specificities of each region is essential for developing tailored energy policies that support sustainable economic growth while considering population trends and resource availability. However, empirical studies on the relationship between Energy Consumption, Population, and Economic Growth remain limited. Our study aims to contribute to the development of a better understanding of the Relationship between Energy Consumption, Population, and Economic Growth. To our knowledge, none of the studies have addressed the relationship between the Energy Consumption, Population, and Economic Growth in OECD countries and Tunisia. This is why we decided to research this area. Our study focuses on two objectives: - It suggests that there is a significant relationship between the Energy Consumption, Population, and Economic Growth ; - It identifies and understands the nature of the relationship between the Energy Consumption, Population, and Economic Growth ; Based on the postulates of recent studies, we try to answer the following question: Is there a relationship between the Energy Consumption, Population, and Economic Growth? The main objective of this study is to measure the direction and degree of the relationship between electricity consumption, economic growth, and population in 18 OECD countries and Tunisia. . In this context, population, economic growth, and electricity consumption data of 19 countries (the United States, the United Kingdom, Ireland, Canada, Austria, Belgium, France, Luxembourg, Germany, Netherlands, Denmark, Finland, Norway, Sweden, Italy, Portugal, Spain, Greece, Tunisia) were used. The remainder of the paper is presented as follows: “Literature Review” presents the review of the literature; the data and the empirical approach are presented in “Data” and “Empirical Approach,” respectively. “Results” reports the study results. “Conclusion and Implications” concludes and gives some implications. 2. Literature review Over the past decade, Tunisia and 18 OECD Countries have increased funding due to the need for energy is increasing due to population growth and industrialization. The scarcity of energy resources on Earth has pushed countries to research alternative energy sources and take new measures regarding energy. In this context, the relationship between energy Consumption, population, and economic Growth has become quite interesting. For this reason, this relationship has been the subject of many empirical studies and has been examined as a research topic by many economists. The study of Intisar and al., (2020) examined the relationship between trade openness and economic growth for 19 Asian countries based on the period 1985–2017. Empirical findings have shown that trade openness and economic growth variables have bidirectional causality in West Asia and unidirectional causality in South Asia. Lawal and al., (2020) examined the relationship between economic growth and electricity consumption variables in Sub-Saharan African countries between 1971 and 2017. In the study conducted using the Generalized Method of Moments (GMM), it was determined that there is a two-way relationship between electricity consumption and economic growth variables in the relevant countries. Magazzino and al., (2021) examined the relationship between Information and Communication Technologies, electricity consumption, economic growth, and environmental pollution variables in 16 European countries between 1990 and 2017 with panel data analysis. As a result of the study, it was stated that economic growth is also a driving force behind electricity consumption. Additionally, it was emphasized that a 1% economic growth causes a 0.13% increase in per capita electricity consumption. Qi and al., (2022), in their study examining the relationship between energy consumption, economic growth, and trade openness in West Africa, concluded that the effect of trade openness on economic growth is much more remarkable in countries with low economic development levels in West Africa. In their study, Shaari and al., (2023) examined the relationship between population, energy consumption and economic growth for Malaysia. According to the results of the cointegration model, they showed that there is a cointegration equation that reveals the long-term relationship between population, energy consumption and economic growth in Malaysia. It also showed that population has an impact on energy consumption in Malaysia, and energy consumption contributes to economic growth. Mombekova and al., (2024). In the study, the relationship between variables was investigated using population, economic growth, and energy consumption data of 7 countries in the developing countries category (China, India, South Africa, Indonesia, Turkey, Mexico, Thailand). The direction and magnitude of the impact of economic growth and population growth on energy consumption were examined using 1990–2022 data for 7 countries. The relationship between the variables was examined with Swamy’s Random Coefficients Model and Seemingly Unrelated Regression (SUR) models, and the positive effect of economic growth on energy consumption was observed. However, it was concluded that the population variable did not affect energy consumption in the 2 countries included in the analysis. 3. Methodology and Data In this study, our main objective is to identify which relationship between variables was investigated using population, economic growth, and energy consumption. From the prior literature review, we can retain three kinds of variables: population, economic growth and energy consumption. The data set consists of a panel of 18 OECD countries and Tunisia between 1990 and 2020 (see the Appendix for more details). Dependent Variable : Energy consumption: Renewable energy consumption is the share of renewable energy in total final energy consumption. Independent Variables: Economic growth: GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 prices, expressed in U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2015 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used. Population: Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates. Method : The estimating equation of the econometric model can be stated as follows: econ it = βo i + β1 i lgrowth it + β2 it l population + ε it (1) The model, the econ variable represents energy consumption, the growth variable represents economic growth, and the population variable represents the population. The data used in the study were obtained from the World Bank database. 4. Results and Discussion In panel data models, predictions typically assume a constant slope parameter. However, this assumption is not always valid. When it fails, heterogeneous models are employed. Estimating heterogeneous models under the assumption of homogeneity can lead to significant deviations in parameter estimates. The random coefficients model, a heterogeneous static regression model developed by Hildreth and Houck based on Swamy’s model, addresses this issue. In the random coefficient models proposed by Hildreth and Houck (1968) and Swamy (1970), the random intercept and slope parameters vary around cross-sectional units, or general averages. This model comprises the sum of random parameters, the general mean, and an error term. It does not assume heteroskedasticity or autocorrelation in constructing the panel covariance matrix. The necessity of using the random coefficients model, or the homogeneity of the parameters, is tested with either an F test or a Hausman-type test. The estimation results of the random coefficients linear regression model are presented in Table 1. According to the results, the Wald statistic, which assesses the combined significance of the independent variables, economic growth, and population on the dependent variable energy consumption, is significant. However, while the economic growth variable is statistically significant, the population variable is not significant in explaining energy consumption. An increase in economic growth leads to an average increase in energy consumption. The Hausman test, conducted to determine whether the parameters vary across units, rejects the null hypothesis (H0), indicating that the parameters are not constant. → ((Table 1 here)) Table 1: Random coefficients model results The dependent variable Energy consumption Independent variables Coeffcients/Probability values growth 0.7371*** (0.0000) population 1.5102 (0.4102) C −0.1973 (0.1360) Wald test 77.80 (0.0000) Hausman test 89.41 (0.0000) Independent variables Coefficients/Probability values lGrowth 5.091806 *** (0.0200) lpopulation -11.5549 (0.0000) c 40.25037 (0.0020) Wald test 46.72 (0.0000) Hausman test 35.94 (1.0000) Note: *** indicates significance at the 1% level. Source: Author When examining the units individually (Table 2), it is evident that the parameters differ. Lgdp (Log GDP): The varying coefficients for Lgdp suggest that GDP's effect on the dependent variable is inconsistent across different groups. The lack of statistical significance (p-value > 0.05) implies that GDP may not have a strong or reliable impact on the dependent variable in most contexts and Lpo (Log Population): The consistently negative and significant coefficients for Lpo indicate that an increase in population is associated with a decrease in the dependent variable. This relationship is robust across all groups, suggesting a strong and reliable inverse effect. The results show that the economic growth variable is statistically significant in explaining energy consumption for all countries considered. A 1% increase in economic growth results in an increase in energy consumption, with the parameter for economic growth varying between 4.0 and 8.1 across different countries. In the seemingly unrelated regression (SUR) method, there is no relationship between the equations, meaning that the error terms of the regression models in the system are uncorrelated. Introduced by Zellner in 1962, SUR models are composed of classical linear regression models where no variable in one equation appears in another equation, making the system of equations non-simultaneous. If there is a correlation between units in panel data models, the units cannot be treated as independent. → ((Table 2 here)) Table 2: Random coefficients model results of unit-specific models Countries/ Variables Year /Variables Coefficient Std. err. z P>|z| [95% conf. interval] Group 1 Lgdp Lpo C Group 2 Lgdp Lpo C Group 3 Lgdp Lpo C Group 4 Lgdp Lpo C Group 5 Lgdp Lpo C Group 6 Lgdp Lpo C Group 7 Lgdp Lpo C Group 8 Lgdp Lpo C Group 9 Lgdp Lpo C Group 10 Lgdp Lpo C Group 11 Lgdp Lpo C Group 12 Lgdp Lpo C Group 13 Lgdp Lpo C Group 14 Lgdp Lpo C Group 15 Lgdp Lpo C Group 16 Lgdp Lpo C Group 17 Lgdp Lpo C Group 18 Lgdp Lpo C Group 19 Lgdp Lpo C Group 20 Lgdp Lpo C Group 21 Lgdp Lpo C Group 22 Lgdp Lpo C Group 23 Lgdp Lpo C Group 24 Lgdp Lpo C Group 25 Lgdp Lpo C Group 26 Lgdp Lpo C Group 27 Lgdp Lpo C Group 28 Lgdp Lpo C Group 29 Lgdp Lpo C Group 30 Lgdp Lpo C Group 31 Lgdp Lpo C 4.119367 2.451799 1.68 0.093 -.6860705 8.924804 -10.23672 2.758131 -3.71 0.000 -15.64256 -4.830884 40.62347 13.19963 3.08 0.002 14.75267 66.49427 4.095474 2.452092 1.67 0.095 -.7105375 8.901486 -10.203 2.758623 -3.70 0.000 -15.6098 -4.796201 40.62869 13.19963 3.08 0.002 14.75789 66.49948 4.138634 2.452175 1.69 0.091 -.6675409 8.94481 -10.26483 2.75868 -3.72 0.000 -15.67174 -4.857915 40.61842 13.19964 3.08 0.002 14.7476 66.48924 4.203276 2.452369 1.71 0.087 -.6032785 9.009831 -10.35291 2.758927 -3.75 0.000 -15.76031 -4.945515 40.59999 13.19962 3.08 0.002 14.72921 66.47076 4.095332 2.452264 1.67 0.095 -.7110179 8.901681 -10.20609 2.758953 -3.70 0.000 -15.61354 -4.798644 40.64204 13.19976 3.08 0.002 14.77098 66.5131 4.182208 2.452684 1.71 0.088 -.6249656 8.989381 -10.32768 2.759652 -3.74 0.000 -15.7365 -4.918859 40.62519 13.1998 3.08 0.002 14.75407 66.49632 4.018221 2.451421 1.64 0.101 -.7864757 8.822918 -10.10033 2.757783 -3.66 0.000 -15.50548 -4.695173 40.66976 13.19991 3.08 0.002 14.79841 66.54111 4.194232 2.452908 1.71 0.087 -.6133798 9.001845 -10.345 2.760138 -3.75 0.000 -15.75477 -4.935225 40.62906 13.19989 3.08 0.002 14.75776 66.50036 4.259941 2.452663 1.74 0.082 -.5471895 9.067072 -10.44072 2.759842 -3.78 0.000 -15.84991 -5.03153 40.63192 13.19994 3.08 0.002 14.76051 66.50334 4.190716 2.452487 1.71 0.087 -.6160706 8.997503 -10.34369 2.759647 -3.75 0.000 -15.75249 -4.934877 40.64286 13.19998 3.08 0.002 14.77136 66.51435 4.370021 2.45424 1.78 0.075 -.4402018 9.180243 -10.59156 2.762397 -3.83 0.000 -16.00575 -5.177357 40.59772 13.19996 3.08 0.002 14.72627 66.46916 4.141936 2.453456 1.69 0.091 -.6667483 8.950621 -10.27459 2.761176 -3.72 0.000 -15.6864 -4.862787 40.65555 13.20001 3.08 0.002 14.784 66.5271 4.014788 2.453182 1.64 0.102 -.7933609 8.822937 -10.09892 2.760755 -3.66 0.000 -15.5099 -4.687942 40.69053 13.20004 3.08 0.002 14.81894 66.56213 3.904544 2.448904 1.59 0.111 -.8952187 8.704307 -9.942596 2.75404 -3.61 0.000 -15.34041 -4.544777 40.70867 13.20014 3.08 0.002 14.83686 66.58047 4.050385 2.450017 1.65 0.098 -.7515593 8.852329 -10.14317 2.755783 -3.68 0.000 -15.54441 -4.741935 40.65984 13.20014 3.08 0.002 14.78803 66.53164 4.315816 2.452511 1.76 0.078 -.491018 9.12265 -10.50519 2.759648 -3.81 0.000 -15.914 -5.096378 40.56221 13.20012 3.07 0.002 14.69045 66.43397 4.430229 2.451306 1.81 0.071 -.3742435 9.234701 -10.65508 2.757754 -3.86 0.000 -16.06018 -5.249981 40.50234 13.20016 3.07 0.002 14.6305 66.37418 4.704048 2.451529 1.92 0.055 -.1008601 9.508956 -11.02718 2.758107 -4.00 0.000 -16.43297 -5.621394 40.39938 13.20017 3.06 0.002 14.52753 66.27123 4.971297 2.452923 2.03 0.043 .163657 9.778937 -11.3892 2.760173 -4.13 0.000 -16.79904 -5.979357 40.2909 13.20016 3.05 0.002 14.41906 66.16274 5.191429 2.452196 2.12 0.034 .3852128 9.997645 -11.67857 2.758893 -4.23 0.000 -17.0859 -6.271243 40.17336 13.20017 3.04 0.002 14.30151 66.04521 5.327734 2.450819 2.17 0.030 .524218 10.13125 -11.86152 2.756697 -4.30 0.000 -17.26454 -6.458489 40.10392 13.20022 3.04 0.002 14.23197 65.97586 5.521344 2.450391 2.25 0.024 .7186659 10.32402 -12.12603 2.755953 -4.40 0.000 -17.5276 -6.724458 40.0358 13.20025 3.03 0.002 14.16378 65.90781 5.916338 2.45312 2.41 0.016 1.108312 10.72436 -12.66095 2.760125 -4.59 0.000 -18.0707 -7.251207 39.87787 13.20021 3.02 0.003 14.00593 65.74981 6.158853 2.451458 2.51 0.012 1.354083 10.96362 -12.98539 2.757486 -4.71 0.000 -18.38996 -7.580816 39.77458 13.20026 3.01 0.003 13.90254 65.64661 6.416737 2.451315 2.62 0.009 1.612248 11.22123 -13.33775 2.757217 -4.84 0.000 -18.7418 -7.933709 39.6857 13.20028 3.01 0.003 13.81362 65.55778 6.59862 2.451782 2.69 0.007 1.793216 11.40402 -13.5918 2.757956 -4.93 0.000 -18.99729 -8.186305 39.64314 13.20029 3.00 0.003 13.77106 65.51523 6.728124 2.449645 2.75 0.006 1.926909 11.52934 -13.77464 2.754574 -5.00 0.000 -19.1735 -8.37577 39.62221 13.20033 3.00 0.003 13.75003 65.49438 6.938566 2.448849 2.83 0.005 2.13891 11.73822 -14.0666 2.753271 -5.11 0.000 -19.46292 -8.670293 39.55479 13.20036 3.00 0.003 13.68256 65.42702 7.160962 2.446113 2.93 0.003 2.366668 11.95526 -14.36376 2.748911 -5.23 0.000 -19.75152 -8.975991 39.45394 13.2004 2.99 0.003 13.58164 65.32623 7.378184 2.447123 3.02 0.003 2.581912 12.17446 -14.65894 2.750458 -5.33 0.000 -20.04974 -9.268144 39.37092 13.2004 2.98 0.003 13.49861 65.24323 8.108621 2.448928 3.31 0.001 3.30881 12.90843 -15.64753 2.753359 -5.68 0.000 -21.04401 -10.2510 39.08667 13.20037 2.96 0.003 13.21442 64.95892 Note: *** indicates significance at the 1% level. Source: Author The system can be analyzed using a seemingly unrelated regression (SUR) model when units cannot be considered independently of each other. SUR is used because the constant and slope parameters vary across units, but there is believed to be a connection between the error terms of the units. Therefore, it serves as an alternative to estimators that account for the correlation between units. The results from estimating the model using the SUR method are presented in Table 3, which includes the mean square error, R², and Wald test statistics for all submodels of the units. The significance of the Wald test statistics for all models indicates the overall relevance of the models. When we look at R2, the lowest value is in Portugal with 3.61% and the highest value is in Belgium with 99.05%. → ((Table 3 here)) Table 3: Overall results of the seemingly unrelated regression model Units Equation Obs Params RMSE "R-squared" chi2 P>chi2 United States Econ 31 2 .7101514 0.8866 242.30 0.0000 United Kingdom Econ 31 2 .9764903 0.9260 387.87 0.0000 Ireland Econ 31 2 1.675201 0.7751 106.82 0.0000 Canada Econ 31 2 .5815723 0.5915 44.89 0.0000 Austria Econ 31 2 2.12555 0.7607 98.56 0.0000 Belgium Econ 31 2 .3567175 0.9905 3231.90 0.0000 France Econ 31 2 1.014371 0.7836 112.24 0.0000 Luxembourg Econ 31 2 2.251331 0.8052 128.14 0.0000 Germany Econ 31 2 1.32553 0.9402 487.51 0.0000 Netherlands Econ 31 2 .7990335 0.8927 257.83 0.0000 Denmark Econ 31 2 1.351214 0.9834 1832.19 0.0000 Finland Econ 31 2 1.646441 0.9412 495.90 0.0000 Norway Econ 31 2 1.347432 0.3538 16.97 0.0002 Sweden Econ 31 2 1.860673 0.9388 475.51 0.0000 Italy Econ 31 2 1.249761 0.9408 492.40 0.0000 Portugal Econ 31 2 3.44096 0.0361 1.16 0.5596 Spain Econ 31 2 1.690772 0.7971 121.75 0.0000 Greece Econ 31 2 3.823401 0.1649 6.12 0.0469 Tunisia Econ 31 2 .6524016 0.5564 38.88 0.0000 Note: RMSE = Root Mean Square Error. χ² and P>χ² indicate the overall significance of the model. Source: Author The results for the units are presented in Table 4. The economic growth variable is statistically significant in explaining energy consumption for each country. A 1% increase in economic growth leads to an increase in energy consumption, with all coefficients having positive signs. The population variable is insignificant for Portugal and Tunisia, but is significant in explaining energy consumption for all other countries. For example, in Tunisia , Population significantly reduces energy consumption (-46.62408, p=0.002) and Economic growth significantly increases energy consumption (9.914189, p=0.031). Economic Growth, For most countries, economic growth significantly affects energy consumption, with some countries showing a positive impact (Germany, Sweden, Tunisia) and others showing a negative impact (most other countries). Population, For the majority of the countries, the population variable significantly increases energy consumption, except for Germany and Tunisia, where it decreases energy consumption, and Portugal and Norway, where it is not significant. The table you provided seems to be a correlation matrix, which shows the relationships between three variables: econ, LGDP, and lpo. Correlation coefficients range from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation. → ((Table 4 here)) Table 4: Seemingly unrelated regression model coefficient result Units economic growth population United States -36.76057 (0.002) 149.0804 (0.000) United Kingdom -31.31297 (0.000) 250.199 (0.000) Ireland -3.24422 5 (0.504) 72.6954 (0.000) Canada -35.19141(0.000) 84.62573 (0.000) Austria -16.91807 (0.275) 273.9707 (0.000) Belgium -28.05545 (0.000) 265.5064 (0.000) France -95.34281 (0.000) 337.6574 (0.000) Luxembourg -56.76589 (0.000) 169.8319 (0.000) Germany 109.3589 (0.000) -223.8779 (0.000) Netherlands -42.16864 (0.000) 288.3713 (0.000) Denmark -90.81058 (0.000) 956.3603 (0.000) Finland -22.61798 (0.005) 609.0426 (0.000) Norway -21.42762 (0.003) 29.67535 (0.116) Sweden 21.07997 (0.024) 230.556 (0.000) Italy -16.4281 (0.059) 481.5195 (0.000) Portugal 24.61002 (0.293) -146.3995 (0.303) Spain -58.12868 (0.000) 232.3378 (0.000) Greece -52.38517 (0.019) 371.4479 (0.015) Tunisia 9.914189 (0.031) -46.62408 (0.002) Note : Values in parentheses represent p-values. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.The results indicate that economic growth has a negative and statistically significant effect on energy consumption in most OECD countries, while population growth generally exerts a positive and significant impact. Tunisia shows an opposite trend, where economic growth increases energy consumption less strongly, and population has a negative influence. Source: Author Here's a clearer version of the table for better readability: → ((Table 5 here)) Table 5: Seemingly unrelated regression model correlation matrix : econ LGDP lpo econ LGDP Lpo 1.0000 -0.1849 1.0000 0.0000 -0.2704 0.8855 1.0000 0.0000 0.0000 Note: The table reports the correlation coefficients between the variables Econ (energy consumption), LGDP (economic growth), and Lpo (population). All correlations are significant at the 1% level (p < 0.01). Source: Author Looking at the inter-unit correlation matrix of the residues (Table 5), it is seen that there is a correlation of over a positive linear relationship between the economic growth and population variables. For these pairs, the Pearson correlation coefficients are: economic growth and population, 0.8855. These values indicate a moderate positive relationship between the variables. A negative linear relationship exists for the following pairs, with negative Pearson correlation coefficients. The other coefficients are negative, which indicates that as econ increases, lGDP and lpo decrease. → ((Table 6 here)) Table 6: List of countries included in the sample list of countries United States United Kingdom Ireland Canada Austria Belgium France Luxembourg Germany Netherlands Denmark Finland Norway Sweden Italy Portugal Spain Greece Tunisia 5. Conclusion Over the past half-century, many developing countries have seen a significant rise in exports, imports, and energy consumption to support economic growth. As economies expand, the demand for energy, a crucial resource throughout human history, continues to rise. Additionally, urbanization and industrialization processes accelerate energy needs, with intensive energy use positively impacting both total production and the welfare levels of countries (Vo et al., 2024). The increasing global population, advancements in the industrial sector, and rising living standards further drive up energy consumption and demand. This study analyzed the relationship between energy consumption, population, and economic growth in Tunisia and 18 OECD countries. The findings reveal that economic growth significantly influences energy consumption across all countries, with a positive correlation observed in every instance. While the population variable was found to be insignificant in the cases of Portugal and Tunisia, it significantly explained energy consumption in the other countries. These results underscore the importance of considering both economic growth and population dynamics when formulating energy policies. The distinct differences between Tunisia and the OECD countries highlight the need for tailored energy strategies that address specific national contexts Declarations Funding: This research received no external funding. Author Contribution Author Contributions: O.I. conceived and designed the study, collected and analyzed the data, prepared all tables and figures, and wrote the main manuscript text. O.I. reviewed and approved the final manuscript. Acknowledgement The author would like to thank Professor hadil hanana for valuable guidance and the University of Manouba for supporting this research. References Amna Intisar R, Yaseen MR, Kousar R, Usman M, Makhdum MSA (2020) Impact of trade openness and human capital on economic growth: a comparative investigation of Asian countries. Sustainability 12(7):2930 Del Río P, Burguillo M, Kiefer CP (2025) Which are the main determinants of energy poverty? A systematic review of the literature. 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Sustainability 14(6):3630 Shaari MS, Majekodunmi TB, Zainal NF, Harun NH, Ridzuan AR (2023) The linkage between natural gas consumption and industrial output: New evidence based on time series analysis. Energy 284:129395 Vo DH, Ho CM (2024) Urbanization and renewable energy consumption in the emerging ASEAN markets: A comparison between short and long-run effects. Heliyon, 10 (9) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviewers invited by journal 20 Feb, 2026 Editor invited by journal 11 Feb, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 12 Jan, 2026 First submitted to journal 10 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8569758","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573609409,"identity":"0572ed33-9b1b-4b0b-a72f-ca14c2b3243a","order_by":0,"name":"Olfa Ifaoui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIie3PMWrDMBTGcYkHzmJHq73kDAqGJkPJRbq8EOimoXQxNE0MgXTpATqE9Aru4tnmgb0EMpcuPoJHjZU6Fuy4WyH6D+Ib9AOJMZfrPzbiDcNbM0A00zaxA9J+AiAZ3tvhs8fwZAcfQJglzGdtsLfjAhEAvG1wMZF1UGbR8flOvBiik7yTRDuAEHEVSxqv5ENeqzfiKX89fXUSSaIIlxqWGbEbGeWVSg0Bvu8jABpxa8lMB4dKvQ8gnnkYGeJLGaRrlV0i5i/eHLGOI/IwDqtCfRhS9v1FjHbwqfFpMj5TMW3XG3U8U9nopJv8jn7OYvB90+Yvl10ul+tK+gbiG1kdyRCzzQAAAABJRU5ErkJggg==","orcid":"","institution":"École Supérieure de Commerce de Tunis, Université de la Manouba","correspondingAuthor":true,"prefix":"","firstName":"Olfa","middleName":"","lastName":"Ifaoui","suffix":""}],"badges":[],"createdAt":"2026-01-10 17:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8569758/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8569758/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100442166,"identity":"057d5d30-d19b-4fd2-83ba-27c1866ea524","added_by":"auto","created_at":"2026-01-16 16:59:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36034,"visible":true,"origin":"","legend":"","description":"","filename":"A5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8569758/v1/956a08cdc11945ac9ff5c297.docx"},{"id":100442170,"identity":"caa7bb65-c105-498f-a0c4-6d5bed0d66db","added_by":"auto","created_at":"2026-01-16 16:59:07","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2781,"visible":true,"origin":"","legend":"","description":"","filename":"fc6a002d46d94914a86ff7fec160760e.json","url":"https://assets-eu.researchsquare.com/files/rs-8569758/v1/f8318031924573d8b196fc7a.json"},{"id":100546805,"identity":"a6bd6f35-b018-4f1c-b345-c148c931513e","added_by":"auto","created_at":"2026-01-19 08:12:39","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":70050,"visible":true,"origin":"","legend":"","description":"","filename":"fc6a002d46d94914a86ff7fec160760e1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8569758/v1/5bbfcec41b2adc93d059b853.xml"},{"id":100546912,"identity":"ef408b6d-3c8a-4f34-b9cb-e6544a4d9a57","added_by":"auto","created_at":"2026-01-19 08:13:08","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68217,"visible":true,"origin":"","legend":"","description":"","filename":"fc6a002d46d94914a86ff7fec160760e1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8569758/v1/0195f98ff43248102f32707d.xml"},{"id":100442168,"identity":"2e28e707-0453-48cc-8747-dddb7a34860f","added_by":"auto","created_at":"2026-01-16 16:59:07","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":75667,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8569758/v1/32b7915df9b4f6ec628606de.html"},{"id":100554252,"identity":"23952f66-af48-406f-9a8f-6204132ac0ed","added_by":"auto","created_at":"2026-01-19 08:38:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":945484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8569758/v1/3d02ebe9-b602-47c4-ad36-27cec1284bbc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Nexus Between Energy Consumption, Population, and Economic Growth: A Comparative study OECD Countries and Tunisia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEnergy is a crucial component of the economy and a significant determinant of future social, geographical, and economic dynamics (del R\u0026iacute;o, and al., 2025). Since the dawn of humanity, energy has been one of our most essential needs. Initially utilized in various forms for necessities, energy took on a new significance with the advent of the industrial revolution and the shift to mass production. This surge in energy demand, driven by industrialization, led to urbanization and rapid population growth (Vo and al., 2024). The relationship between energy consumption, population, and economic growth is complex and multifaceted, particularly when comparing countries with different levels of development, such as Tunisia and those in the OECD. In Tunisia, energy consumption has been a critical factor in supporting economic growth, with an increasing demand driven by industrialization and urbanization. However, the pace of growth and energy use is constrained by limited resources and infrastructure challenges. In contrast, OECD countries generally have more advanced energy infrastructures and greater access to a variety of energy sources, allowing for more efficient energy use and a stronger correlation between energy consumption and economic growth. Additionally, the population dynamics in Tunisia, characterized by a younger and rapidly growing population, differ significantly from those in many OECD countries, where populations are aging and growing at a slower rate. These demographic factors further influence the energy consumption patterns and economic growth trajectories in both contexts (Ma and al., 2024). Therefore, understanding the specificities of each region is essential for developing tailored energy policies that support sustainable economic growth while considering population trends and resource availability.\u003c/p\u003e \u003cp\u003eHowever, empirical studies on the relationship between Energy Consumption, Population, and Economic Growth remain limited. Our study aims to contribute to the development of a better understanding of the Relationship between Energy Consumption, Population, and Economic Growth.\u003c/p\u003e \u003cp\u003eTo our knowledge, none of the studies have addressed the relationship between the Energy Consumption, Population, and Economic Growth in OECD countries and Tunisia. This is why we decided to research this area.\u003c/p\u003e \u003cp\u003eOur study focuses on two objectives:\u003c/p\u003e \u003cp\u003e \u003cp\u003e- It suggests that there is a significant relationship between the Energy Consumption, Population, and Economic Growth ;\u003c/p\u003e \u003cp\u003e- It identifies and understands the nature of the relationship between the Energy Consumption, Population, and Economic Growth ;\u003c/p\u003e \u003cp\u003eBased on the postulates of recent studies, we try to answer the following question: Is there a relationship between the Energy Consumption, Population, and Economic Growth?\u003c/p\u003e \u003cp\u003eThe main objective of this study is to measure the direction and degree of the relationship between electricity consumption, economic growth, and population in 18 OECD countries and Tunisia.\u003c/p\u003e \u003cp\u003e. In this context, population, economic growth, and electricity consumption data of 19 countries (the United States, the United Kingdom, Ireland, Canada, Austria, Belgium, France, Luxembourg, Germany, Netherlands, Denmark, Finland, Norway, Sweden, Italy, Portugal, Spain, Greece, Tunisia) were used.\u003c/p\u003e \u003cp\u003eThe remainder of the paper is presented as follows: \u0026ldquo;Literature Review\u0026rdquo; presents the review of the literature; the data and the empirical approach are presented in \u0026ldquo;Data\u0026rdquo; and \u0026ldquo;Empirical Approach,\u0026rdquo; respectively. \u0026ldquo;Results\u0026rdquo; reports the study results. \u0026ldquo;Conclusion and Implications\u0026rdquo; concludes and gives some implications.\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eOver the past decade, Tunisia and 18 OECD Countries have increased funding due to the need for energy is increasing due to population growth and industrialization. The scarcity of energy resources on Earth has pushed countries to research alternative energy sources and take new measures regarding energy. In this context, the relationship between energy Consumption, population, and economic Growth has become quite interesting. For this reason, this relationship has been the subject of many empirical studies and has been examined as a research topic by many economists. The study of Intisar and al., (2020) examined the relationship between trade openness and economic growth for 19 Asian countries based on the period 1985\u0026ndash;2017. Empirical findings have shown that trade openness and economic growth variables have bidirectional causality in West Asia and unidirectional causality in South Asia.\u003c/p\u003e \u003cp\u003eLawal and al., (2020) examined the relationship between economic growth and electricity consumption variables in Sub-Saharan African countries between 1971 and 2017. In the study conducted using the Generalized Method of Moments (GMM), it was determined that there is a two-way relationship between electricity consumption and economic growth variables in the relevant countries.\u003c/p\u003e \u003cp\u003eMagazzino and al., (2021) examined the relationship between Information and Communication Technologies, electricity consumption, economic growth, and environmental pollution variables in 16 European countries between 1990 and 2017 with panel data analysis. As a result of the study, it was stated that economic growth is also a driving force behind electricity consumption. Additionally, it was emphasized that a 1% economic growth causes a 0.13% increase in per capita electricity consumption.\u003c/p\u003e \u003cp\u003eQi and al., (2022), in their study examining the relationship between energy consumption, economic growth, and trade openness in West Africa, concluded that the effect of trade openness on economic growth is much more remarkable in countries with low economic development levels in West Africa.\u003c/p\u003e \u003cp\u003eIn their study, Shaari and al., (2023) examined the relationship between population, energy consumption and economic growth for Malaysia. According to the results of the cointegration model, they showed that there is a cointegration equation that reveals the long-term relationship between population, energy consumption and economic growth in Malaysia. It also showed that population has an impact on energy consumption in Malaysia, and energy consumption contributes to economic growth.\u003c/p\u003e \u003cp\u003eMombekova and al., (2024). In the study, the relationship between variables was investigated using population, economic growth, and energy consumption data of 7 countries in the developing countries category (China, India, South Africa, Indonesia, Turkey, Mexico, Thailand). The direction and magnitude of the impact of economic growth and population growth on energy consumption were examined using 1990\u0026ndash;2022 data for 7 countries. The relationship between the variables was examined with Swamy\u0026rsquo;s Random Coefficients Model and Seemingly Unrelated Regression (SUR) models, and the positive effect of economic growth on energy consumption was observed. However, it was concluded that the population variable did not affect energy consumption in the 2 countries included in the analysis.\u003c/p\u003e"},{"header":"3. Methodology and Data","content":"\u003cp\u003eIn this study, our main objective is to identify which relationship between variables was investigated using population, economic growth, and energy consumption. From the prior literature review, we can retain three kinds of variables: population, economic growth and energy consumption. The data set consists of a panel of 18 OECD countries and Tunisia between 1990 and 2020 (see the \u0026lt;link rid=\"Sec6\"\u0026gt;\u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e\u0026lt;/link\u0026gt; for more details).\u003c/p\u003e \u003cp\u003eDependent Variable :\u003c/p\u003e \u003cp\u003eEnergy consumption: Renewable energy consumption is the share of renewable energy in total final energy consumption.\u003c/p\u003e \u003cp\u003eIndependent Variables:\u003c/p\u003e \u003cp\u003eEconomic growth: GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 prices, expressed in U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2015 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.\u003c/p\u003e \u003cp\u003ePopulation: Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.\u003c/p\u003e \u003cp\u003eMethod :\u003c/p\u003e \u003cp\u003eThe estimating equation of the econometric model can be stated as follows:\u003c/p\u003e \u003cp\u003eecon\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;βo\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β1\u003csub\u003ei\u003c/sub\u003e lgrowth\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β2\u003csub\u003eit l\u003c/sub\u003e population\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit (1)\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eThe model, the econ variable represents energy consumption, the growth variable represents economic growth, and the population variable represents the population. The data used in the study were obtained from the World Bank database.\u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cp\u003eIn panel data models, predictions typically assume a constant slope parameter. However, this assumption is not always valid. When it fails, heterogeneous models are employed. Estimating heterogeneous models under the assumption of homogeneity can lead to significant deviations in parameter estimates. The random coefficients model, a heterogeneous static regression model developed by Hildreth and Houck based on Swamy\u0026rsquo;s model, addresses this issue. In the random coefficient models proposed by Hildreth and Houck (1968) and Swamy (1970), the random intercept and slope parameters vary around cross-sectional units, or general averages. This model comprises the sum of random parameters, the general mean, and an error term. It does not assume heteroskedasticity or autocorrelation in constructing the panel covariance matrix. The necessity of using the random coefficients model, or the homogeneity of the parameters, is tested with either an F test or a Hausman-type test. The estimation results of the random coefficients linear regression model are presented in Table 1.\u003c/p\u003e\n\u003cp\u003eAccording to the results, the Wald statistic, which assesses the combined significance of the independent variables, economic growth, and population on the dependent variable energy consumption, is significant. However, while the economic growth variable is statistically significant, the population variable is not significant in explaining energy consumption. \u0026nbsp;An increase in economic growth leads to an average increase in energy consumption. The Hausman test, conducted to determine whether the parameters vary across units, rejects the null hypothesis (H0), indicating that the parameters are not constant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026rarr; ((Table 1 \u0026nbsp; here))\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 1: Random coefficients model results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 619px;\"\u003e\n \u003cp\u003eThe dependent variable \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Energy consumption\u003c/p\u003e\n \u003cp\u003eIndependent variables \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Coeffcients/Probability values\u003c/p\u003e\n \u003cp\u003egrowth \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.7371*** (0.0000)\u003c/p\u003e\n \u003cp\u003epopulation \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.5102 (0.4102)\u003c/p\u003e\n \u003cp\u003eC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026minus;0.1973 (0.1360)\u003c/p\u003e\n \u003cp\u003eWald test \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;77.80 (0.0000)\u003c/p\u003e\n \u003cp\u003eHausman test \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 89.41 (0.0000)\u003c/p\u003e\n \u003cp\u003eIndependent variables \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Coefficients/Probability values\u003c/p\u003e\n \u003cp\u003elGrowth \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 5.091806 *** \u0026nbsp;(0.0200)\u003c/p\u003e\n \u003cp\u003elpopulation \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -11.5549 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.0000)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;c \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 40.25037 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(0.0020)\u003c/p\u003e\n \u003cp\u003eWald test \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 46.72 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (0.0000)\u003c/p\u003e\n \u003cp\u003eHausman test \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 35.94 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (1.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: *** indicates significance at the 1% level. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Source: Author\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen examining the units individually (Table 2), it is evident that the parameters differ. Lgdp (Log GDP): The varying coefficients for Lgdp suggest that GDP\u0026apos;s effect on the dependent variable is inconsistent across different groups. The lack of statistical significance (p-value \u0026gt; 0.05) implies that GDP may not have a strong or reliable impact on the dependent variable in most contexts and Lpo (Log Population): The consistently negative and significant coefficients for Lpo indicate that an increase in population is associated with a decrease in the dependent variable. This relationship is robust across all groups, suggesting a strong and reliable inverse effect. The results show that the economic growth variable is statistically significant in explaining energy consumption for all countries considered. A 1% increase in economic growth results in an increase in energy consumption, with the parameter for economic growth varying between 4.0 and 8.1 across different countries. In the seemingly unrelated regression (SUR) method, there is no relationship between the equations, meaning that the error terms of the regression models in the system are uncorrelated. Introduced by Zellner in 1962, SUR models are composed of classical linear regression models where no variable in one equation appears in another equation, making the system of equations non-simultaneous. If there is a correlation between units in panel data models, the units cannot be treated as independent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026rarr; ((Table 2 here))\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Random coefficients model results of unit-specific models\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003eCountries/\u003c/p\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003cp\u003eYear /Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eStd. err.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 52px;\"\u003e\n \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003e[95% conf. interval]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 1\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 2\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 3\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 4\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 5\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 6\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 7\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 8\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 9\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Group 10\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003eGroup 11\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003eGroup 12\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003eGroup 13\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003eGroup 14\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 15\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 16\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 17\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 18\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 19\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 20\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 21\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 22\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 23\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 24\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 25\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 26\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 27\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 28\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003eGroup 29\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 30\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGroup 31\u003c/p\u003e\n \u003cp\u003eLgdp\u003c/p\u003e\n \u003cp\u003eLpo\u003c/p\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 523px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;4.119367 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.451799 \u0026nbsp; \u0026nbsp; \u0026nbsp;1.68 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.093 \u0026nbsp; \u0026nbsp;-.6860705 \u0026nbsp; \u0026nbsp;8.924804\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-10.23672 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.758131 \u0026nbsp; \u0026nbsp;-3.71 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp;-15.64256 \u0026nbsp; -4.830884\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;40.62347 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.19963 \u0026nbsp; \u0026nbsp; 3.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.002 \u0026nbsp; \u0026nbsp; 14.75267 \u0026nbsp; \u0026nbsp;66.49427\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;4.095474 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.452092 \u0026nbsp; \u0026nbsp; 1.67 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.095 \u0026nbsp; \u0026nbsp;-.7105375 \u0026nbsp; \u0026nbsp;8.901486\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-10.203 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.758623 \u0026nbsp; \u0026nbsp;-3.70 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; -15.6098 \u0026nbsp; -4.796201\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;40.62869 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.19963 \u0026nbsp; \u0026nbsp; 3.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.002 \u0026nbsp; \u0026nbsp; 14.75789 \u0026nbsp; \u0026nbsp;66.49948\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4.138634 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.452175 \u0026nbsp; \u0026nbsp; 1.69 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.091 \u0026nbsp; \u0026nbsp; -.6675409 \u0026nbsp; \u0026nbsp; 8.94481\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;-10.26483 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.75868 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-3.72 \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; -15.67174 \u0026nbsp; -4.857915\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;40.61842 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.19964 \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.08 \u0026nbsp; \u0026nbsp; \u0026nbsp;0.002 \u0026nbsp; \u0026nbsp; \u0026nbsp;14.7476 \u0026nbsp; \u0026nbsp;66.48924\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.203276 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.452369 \u0026nbsp; \u0026nbsp; \u0026nbsp;1.71 \u0026nbsp; \u0026nbsp; \u0026nbsp;0.087 \u0026nbsp; \u0026nbsp; \u0026nbsp; -.6032785 \u0026nbsp; \u0026nbsp;9.009831\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-10.35291 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.758927 \u0026nbsp; \u0026nbsp; \u0026nbsp; -3.75 \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-15.76031 \u0026nbsp; -4.945515\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;40.59999 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.19962 \u0026nbsp; \u0026nbsp; \u0026nbsp;3.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.002 \u0026nbsp; \u0026nbsp; 14.72921 \u0026nbsp; \u0026nbsp;66.47076\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;4.095332 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.452264 \u0026nbsp; \u0026nbsp; \u0026nbsp;1.67 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.095 \u0026nbsp; \u0026nbsp; \u0026nbsp;-.7110179 \u0026nbsp; \u0026nbsp;8.901681\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-10.20609 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.758953 \u0026nbsp; \u0026nbsp; -3.70 \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; -15.61354 \u0026nbsp; -4.798644\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;40.64204 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.19976 \u0026nbsp; \u0026nbsp; \u0026nbsp;3.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.002 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;14.77098 \u0026nbsp; \u0026nbsp; 66.5131\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.182208 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.452684 \u0026nbsp; \u0026nbsp; 1.71 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.088 \u0026nbsp; \u0026nbsp; \u0026nbsp;-.6249656 \u0026nbsp; \u0026nbsp;8.989381\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-10.32768 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.759652 \u0026nbsp; \u0026nbsp;-3.74 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp;-15.7365 \u0026nbsp; -4.918859\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;40.62519 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.1998 \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.002 \u0026nbsp; \u0026nbsp; \u0026nbsp;14.75407 \u0026nbsp; \u0026nbsp;66.49632\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.018221 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.451421 \u0026nbsp; \u0026nbsp; 1.64 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.101 \u0026nbsp; \u0026nbsp; \u0026nbsp; -.7864757 \u0026nbsp; \u0026nbsp;8.822918\u003c/p\u003e\n \u003cp\u003e-10.10033 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.757783 \u0026nbsp; \u0026nbsp;-3.66 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-15.50548 \u0026nbsp; -4.695173\u003c/p\u003e\n \u003cp\u003e40.66976 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.19991 \u0026nbsp; \u0026nbsp; 3.08 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.002 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 14.79841 \u0026nbsp; \u0026nbsp;66.54111\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.194232 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.452908 \u0026nbsp; \u0026nbsp;1.71 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.087 \u0026nbsp; 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\u0026nbsp;-17.26454 \u0026nbsp; -6.458489\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;40.10392 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.20022 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.04 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.002 \u0026nbsp; \u0026nbsp; 14.23197 \u0026nbsp; \u0026nbsp;65.97586\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.521344 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.450391 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.25 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.024 \u0026nbsp; \u0026nbsp; \u0026nbsp;.7186659 \u0026nbsp; \u0026nbsp;10.32402\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-12.12603 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.755953 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -4.40 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; -17.5276 \u0026nbsp; -6.724458\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;40.0358 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.20025 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3.03 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.002 \u0026nbsp; \u0026nbsp; \u0026nbsp; 14.16378 \u0026nbsp; \u0026nbsp;65.90781\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.916338 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.45312 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.41 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.016 \u0026nbsp; \u0026nbsp; 1.108312 \u0026nbsp; \u0026nbsp;10.72436\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-12.66095 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.760125 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -4.59 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000 \u0026nbsp; \u0026nbsp; -18.0707 \u0026nbsp; -7.251207\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.87787 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.20021 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3.02 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.003 \u0026nbsp; \u0026nbsp; 14.00593 \u0026nbsp; \u0026nbsp;65.74981\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.158853 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.451458 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.51 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.012 \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.354083 \u0026nbsp; \u0026nbsp;10.96362\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-12.98539 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.757486 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-4.71 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; -18.38996 \u0026nbsp; -7.580816\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.77458 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.20026 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.01 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.90254 \u0026nbsp; \u0026nbsp;65.64661\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;6.416737 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.451315 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.62 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.009 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.612248 \u0026nbsp; \u0026nbsp;11.22123\u003c/p\u003e\n \u003cp\u003e-13.33775 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.757217 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-4.84 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -18.7418 \u0026nbsp; -7.933709\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.6857 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.20028 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.01 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.81362 \u0026nbsp; \u0026nbsp;65.55778\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.59862 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.451782 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.69 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.007 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.793216 \u0026nbsp; \u0026nbsp;11.40402\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-13.5918 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.757956 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-4.93 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -18.99729 \u0026nbsp; -8.186305\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.64314 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.20029 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.77106 \u0026nbsp; \u0026nbsp;65.51523\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;6.728124 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.449645 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.75 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.006 \u0026nbsp; \u0026nbsp; 1.926909 \u0026nbsp; \u0026nbsp;11.52934\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-13.77464 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.754574 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -5.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; -19.1735 \u0026nbsp; \u0026nbsp;-8.37577\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.62221 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.20033 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.003 \u0026nbsp; \u0026nbsp; 13.75003 \u0026nbsp; \u0026nbsp;65.49438\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.938566 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.448849 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.83 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.005 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.13891 \u0026nbsp; \u0026nbsp;11.73822\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;-14.0666 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.753271 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-5.11 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; -19.46292 \u0026nbsp; -8.670293\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;39.55479 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.20036 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3.00 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.68256 \u0026nbsp; \u0026nbsp;65.42702\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.160962 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.446113 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.93 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.366668 \u0026nbsp; \u0026nbsp;11.95526\u003c/p\u003e\n \u003cp\u003e-14.36376 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.748911 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-5.23 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -19.75152 \u0026nbsp; -8.975991\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.45394 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.2004 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.99 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 13.58164 \u0026nbsp; \u0026nbsp;65.32623\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.378184 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.447123 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.02 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.581912 \u0026nbsp; \u0026nbsp;12.17446\u003c/p\u003e\n \u003cp\u003e-14.65894 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.750458 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -5.33 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -20.04974 \u0026nbsp; -9.268144\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;39.37092 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.2004 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.98 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.49861 \u0026nbsp; \u0026nbsp;65.24323\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.108621 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;2.448928 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 3.31 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;3.30881 \u0026nbsp; \u0026nbsp;12.90843\u003c/p\u003e\n \u003cp\u003e-15.64753 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.753359 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-5.68 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-21.04401 \u0026nbsp; -10.2510\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;39.08667 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.20037 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 2.96 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.003 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;13.21442 \u0026nbsp; \u0026nbsp;64.95892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 523px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 523px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 523px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 523px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 523px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: *** indicates significance at the 1% level. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Source: Author\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe system can be analyzed using a seemingly unrelated regression (SUR) model when units cannot be considered independently of each other. SUR is used because the constant and slope parameters vary across units, but there is believed to be a connection between the error terms of the units. Therefore, it serves as an alternative to estimators that account for the correlation between units. The results from estimating the model using the SUR method are presented in Table 3, which includes the mean square error, R\u0026sup2;, and Wald test statistics for all submodels of the units. The significance of the Wald test statistics for all models indicates the overall relevance of the models. When we look at R2, the lowest value is in Portugal with 3.61% and the highest value is in Belgium with 99.05%.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026rarr; ((Table 3 \u0026nbsp; here))\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 3: Overall results of the seemingly unrelated regression model\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"671\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUnits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEquation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eParams\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026quot;R-squared\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003echi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003eP\u0026gt;chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e.7101514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.8866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e242.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eUnited Kingdom \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e.9764903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.9260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e387.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eIreland \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.675201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.7751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e106.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e.5815723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.5915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e44.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAustria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e2.12555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.7607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e98.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e.3567175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.9905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3231.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.014371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.7836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e112.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eLuxembourg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2.251331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.8052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e128.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.32553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.9402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e487.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e.7990335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.8927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e257.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.351214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.9834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1832.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.646441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.9412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e495.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.347432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.3538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e16.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.860673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.9388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e475.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.249761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.9408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e492.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3.44096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.0361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.5596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e1.690772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.7971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e121.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e3.823401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.1649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0469\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eTunisia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eEcon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e.6524016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003e0.5564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e38.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: RMSE = Root Mean Square Error. \u0026chi;\u0026sup2; and P\u0026gt;\u0026chi;\u0026sup2; indicate the overall significance of the model. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Source: Author\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results for the units are presented in Table 4. The economic growth variable is statistically significant in explaining energy consumption for each country. A 1% increase in economic growth leads to an increase in energy consumption, with all coefficients having positive signs. The population variable is insignificant for Portugal and Tunisia, but is significant in explaining energy consumption for all other countries. For example, in \u003cstrong\u003eTunisia\u003c/strong\u003e, \u0026nbsp;Population significantly reduces energy consumption (-46.62408, p=0.002) and Economic growth significantly increases energy consumption (9.914189, p=0.031).\u003c/p\u003e\n\u003cp\u003eEconomic Growth, For most countries, economic growth significantly affects energy consumption, with some countries showing a positive impact (Germany, Sweden, Tunisia) and others showing a negative impact (most other countries). Population, For the majority of the countries, the population variable significantly increases energy consumption, except for Germany and Tunisia, where it decreases energy consumption, and Portugal and Norway, where it is not significant.\u003c/p\u003e\n\u003cp\u003eThe table you provided seems to be a correlation matrix, which shows the relationships between three variables: econ, LGDP, and lpo. Correlation coefficients range from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no correlation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026rarr; ((Table 4 here))\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4: Seemingly unrelated regression model coefficient result\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"619\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 619px;\"\u003e\n \u003cp\u003e\u0026nbsp; Units \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; economic growth \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 619px;\"\u003e\n \u003cp\u003eUnited States \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-36.76057 (0.002) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 149.0804 (0.000)\u003c/p\u003e\n \u003cp\u003eUnited Kingdom \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-31.31297 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 250.199 (0.000)\u003c/p\u003e\n \u003cp\u003eIreland \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-3.24422 5 (0.504) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;72.6954 (0.000)\u003c/p\u003e\n \u003cp\u003eCanada \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -35.19141(0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 84.62573 (0.000)\u003c/p\u003e\n \u003cp\u003eAustria \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -16.91807 (0.275) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;273.9707 (0.000)\u003c/p\u003e\n \u003cp\u003eBelgium \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-28.05545 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 265.5064 (0.000)\u003c/p\u003e\n \u003cp\u003eFrance \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -95.34281 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 337.6574 (0.000)\u003c/p\u003e\n \u003cp\u003eLuxembourg \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -56.76589 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;169.8319 (0.000)\u003c/p\u003e\n \u003cp\u003eGermany \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;109.3589 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -223.8779 (0.000)\u003c/p\u003e\n \u003cp\u003eNetherlands \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-42.16864 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 288.3713 (0.000)\u003c/p\u003e\n \u003cp\u003eDenmark \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-90.81058 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 956.3603 (0.000)\u003c/p\u003e\n \u003cp\u003eFinland \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-22.61798 (0.005) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;609.0426 (0.000)\u003c/p\u003e\n \u003cp\u003eNorway \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-21.42762 (0.003) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 29.67535 (0.116)\u003c/p\u003e\n \u003cp\u003eSweden \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;21.07997 (0.024) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 230.556 (0.000)\u003c/p\u003e\n \u003cp\u003eItaly \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-16.4281 (0.059) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;481.5195 (0.000)\u003c/p\u003e\n \u003cp\u003ePortugal \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;24.61002 (0.293) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-146.3995 (0.303)\u003c/p\u003e\n \u003cp\u003eSpain \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -58.12868 (0.000) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 232.3378 (0.000)\u003c/p\u003e\n \u003cp\u003eGreece \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; -52.38517 (0.019) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 371.4479 (0.015)\u003c/p\u003e\n \u003cp\u003eTunisia \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;9.914189 (0.031) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;-46.62408 (0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote : Values in parentheses represent p-values. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.The results indicate that economic growth has a negative and statistically significant effect on energy consumption in most OECD countries, while population growth generally exerts a positive and significant impact. Tunisia shows an opposite trend, where economic growth increases energy consumption less strongly, and population has a negative influence. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Source: Author\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHere\u0026apos;s a clearer version of the table for better readability: \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026rarr; ((Table 5 here))\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 5: Seemingly unrelated regression model correlation matrix\u0026nbsp;:\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 369px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;econ \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;LGDP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;lpo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003eecon\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLGDP \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; Lpo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 369px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003cp\u003e-0.1849 \u0026nbsp; \u0026nbsp; \u0026nbsp;1.0000\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003cp\u003e-0.2704 \u0026nbsp; \u0026nbsp; \u0026nbsp; 0.8855 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 1.0000\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;0.0000 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote: The table reports the correlation coefficients between the variables Econ (energy consumption), LGDP (economic growth), and Lpo (population). All correlations are significant at the 1% level (p \u0026lt; 0.01). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Source: Author\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLooking at the inter-unit correlation matrix of the residues (Table 5), it is seen that there is a correlation of over a positive linear relationship between the economic growth and population variables. For these pairs, the Pearson correlation coefficients are: economic growth and population, 0.8855. These values indicate a moderate positive relationship between the variables. A negative linear relationship exists for the following pairs, with negative Pearson correlation coefficients. The other coefficients are negative, which indicates that as econ increases, lGDP and lpo decrease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp; \u0026nbsp; \u0026rarr; ((Table 6 here))\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 6: \u0026nbsp; \u0026nbsp; List of countries included in the sample\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 247px;\"\u003e\n \u003cp\u003elist of \u0026nbsp;countries \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 247px;\"\u003e\n \u003cp\u003eUnited States\u003c/p\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003cp\u003eIreland\u003c/p\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003cp\u003eAustria\u003c/p\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003cp\u003eLuxembourg\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003cp\u003eNetherlands\u003c/p\u003e\n \u003cp\u003eDenmark\u003c/p\u003e\n \u003cp\u003eFinland\u003c/p\u003e\n \u003cp\u003eNorway\u003c/p\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003cp\u003ePortugal\u003c/p\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003cp\u003eGreece\u003c/p\u003e\n \u003cp\u003eTunisia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOver the past half-century, many developing countries have seen a significant rise in exports, imports, and energy consumption to support economic growth. As economies expand, the demand for energy, a crucial resource throughout human history, continues to rise. Additionally, urbanization and industrialization processes accelerate energy needs, with intensive energy use positively impacting both total production and the welfare levels of countries (Vo et al., 2024). The increasing global population, advancements in the industrial sector, and rising living standards further drive up energy consumption and demand. This study analyzed the relationship between energy consumption, population, and economic growth in Tunisia and 18 OECD countries. The findings reveal that economic growth significantly influences energy consumption across all countries, with a positive correlation observed in every instance. While the population variable was found to be insignificant in the cases of Portugal and Tunisia, it significantly explained energy consumption in the other countries. These results underscore the importance of considering both economic growth and population dynamics when formulating energy policies. The distinct differences between Tunisia and the OECD countries highlight the need for tailored energy strategies that address specific national contexts\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: O.I. conceived and designed the study, collected and analyzed the data, prepared all tables and figures, and wrote the main manuscript text. O.I. reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author would like to thank Professor hadil hanana for valuable guidance and the University of Manouba for supporting this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmna Intisar R, Yaseen MR, Kousar R, Usman M, Makhdum MSA (2020) Impact of trade openness and human capital on economic growth: a comparative investigation of Asian countries. Sustainability 12(7):2930\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel R\u0026iacute;o P, Burguillo M, Kiefer CP (2025) Which are the main determinants of energy poverty? A systematic review of the literature. Energ Effi 18(6):58\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawal AI, Ozturk I, Olanipekun IO, Asaleye AJ (2020) Examining the linkages between electricity consumption and economic growth in African economies. Energy 208:118363\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagazzino C, Porrini D, Fusco G, Schneider N (2021) Investigating the link among ICT, electricity consumption, air pollution, and economic growth in EU countries. Energy Sources Part B: Econ Plann Policy 16(11\u0026ndash;12):976\u0026ndash;998\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMombekova G, Nurgabylov M, Baimbetova A, Keneshbayev B, Izatullayeva B (2024) The relationship between energy consumption, population and economic growth in developing countries. Int J Energy Econ Policy 14(3):368\u0026ndash;374\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa S, Li S, Luo Q, Yu Z, Wang Y (2024) Revisiting the relationships between energy consumption, economic development and urban size: A global perspective using remote sensing data. Heliyon, \u003cem\u003e10\u003c/em\u003e(5)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi M, Xu J, Amuji NB, Wang S, Xu F, Zhou H (2022) The nexus among energy consumption, economic growth and trade openness: evidence from West Africa. Sustainability 14(6):3630\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaari MS, Majekodunmi TB, Zainal NF, Harun NH, Ridzuan AR (2023) The linkage between natural gas consumption and industrial output: New evidence based on time series analysis. Energy 284:129395\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVo DH, Ho CM (2024) Urbanization and renewable energy consumption in the emerging ASEAN markets: A comparison between short and long-run effects. Heliyon, \u003cem\u003e10\u003c/em\u003e(9)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"sn-business-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"43546","submissionUrl":"https://submission.nature.com/new-submission/43546/3","title":"SN Business \u0026 Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Energy Consumption, Economic Growth, Population","lastPublishedDoi":"10.21203/rs.3.rs-8569758/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8569758/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper investigates the relationship between energy consumption, population, and economic growth in 18 OECD countries and Tunisia over the period 1990\u0026ndash;2020. Using panel data and applying Swamy\u0026rsquo;s Random Coefficients Model and Seemingly Unrelated Regression models, the study finds that economic growth positively influences energy consumption, and energy consumption contributes positively to growth in both groups. However, population does not significantly affect energy consumption in these countries. The findings highlight important insights into the energy-growth dynamics for OECD countries and Tunisia, providing useful evidence for energy policy and sustainable development planning.\u003c/p\u003e","manuscriptTitle":"The Nexus Between Energy Consumption, Population, and Economic Growth: A Comparative study OECD Countries and Tunisia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 16:59:02","doi":"10.21203/rs.3.rs-8569758/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"104025176727051644645019379609486034004","date":"2026-03-18T07:15:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"337459155329240520394823149824443682901","date":"2026-03-15T22:06:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-20T15:45:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T07:28:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T02:39:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-13T02:38:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"SN Business \u0026 Economics","date":"2026-01-10T17:10:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"sn-business-and-economics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"43546","submissionUrl":"https://submission.nature.com/new-submission/43546/3","title":"SN Business \u0026 Economics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"18ac8e8a-41d1-4292-9f80-0948fbe98f79","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-20T15:53:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 16:59:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8569758","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8569758","identity":"rs-8569758","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}