Rosdiana Sijabat
© 2025 The author. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
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Investigating energy use is critical because it addresses the decreasing energy supply. The majority of global energy use is nonrenewable, with much of it coming from fossil fuels emitting greenhouse gases. As a result, energy consumption research is vital for understanding energy usage trends and developing methods to reduce energy use or employ renewable energy sources. This study investigates the impact of industry, service sectors, urbanization, exports, and inflation on energy consumption in a panel of 38 nations from 2019 to 2023. Based on the static panel approach, the key findings of the Pool model reveal that the industrial and service sectors have a positive and significant impact on energy consumption, emphasizing the vitality of these sectors as major energy users. The Fixed Effects model (FEM) suggests that the industrial and service sectors have a significant and negative impact on energy usage. Furthermore, the FE model reveals that urbanization and export significantly and negatively impact energy consumption. In the Pool model, inflation is associated positively with energy consumption. The dynamic panel approach additionally suggests that the industrial and service sectors significantly impact energy consumption in the investigated countries. Exports have a significant and negative impact on energy consumption. The CPI, a measure of inflation, significantly and positively impacts energy consumption. The findings of this study provide helpful policy recommendations for identifying the significant variables influencing world energy consumption. Policymakers in the examined countries must promote energy consumption efficiency initiatives and shift to renewable energy sources.
energy consumption, industry sector, service sector, urbanization, export, inflation, panel analysis
Energy plays a substantial part in a country's economic development, particularly considering energy is regarded as one of the primary sources of power that supports production and manufacturing sector [1, 2]. Energy significantly contributes to machine growth, which is essential for stimulating economic activity [3]. Insufficient energy hinders optimal economic activity across all sectors, impeding economic progress. In this setting, energy-related laws and regulations become critical for ensuring the availability and efficiency of its use, particularly in understanding the relationship between energy consumption and GDP [4, 5].
According to Sijabat [6] and Ahmed and Shimada [7], a suitable policy associated with energy consumption can influence direct economic growth, with energy consumption serving as a critical measure in evaluating a country's economic success. As a result, energy consumption plays a critical role in determining a country's growth patterns, particularly in growing globalization and urbanization [8, 9]. In today's economy, energy plays a significant role in production. It catalyzes the development of diverse manufacturing, services, and infrastructure sectors. Energy consumption and increased global demand for products and services are also improving significantly. Several studies have shown a substantial association between energy consumption and economic growth, particularly in emerging countries. According to Piccirilli et al. [10], energy consumption is not only a result of economic expansion but also serves as a primary driver of growth. The relationship demonstrates that existence bait has a beneficial effect, as increased energy consumption stimulates economic activity, which increases the need for energy [2, 11, 12].
Many developing and developed countries experience rapid economic growth. According to Solow's classical theory [13], economic growth is determined by capital accumulation, increased power work, and technological improvement. As a power source for the production and consumption of many activities, energy has become essential to achieving growth [14, 15]. As a result, the availability of energy and how efficiently it is used can directly impact a country's economic performance. Xu et al. [15] and Zhang et al. [16] have shown that rising urbanization and industrialization, particularly in countries like China, are producing a surge in demand for significant energy in crowded energy sectors such as transportation and housing. Therefore, this study emphasizes how energy consumption dynamics impact a country's economic growth.
According to Kuznets [17], energy demand rises with economic growth and urbanization. As a result, energy consumption rises dramatically with industrial sector expansion and urban economic activity. According to Shazed et al. [18], large cities in China are experiencing a more substantial spike in energy consumption than small cities, owing to the intensity of energy use in large cities being significantly higher due to more sophisticated infrastructure and economic activity. Thus, urbanization changes energy consumption patterns and introduces new issues to energy resource management.
Furthermore, globalization and international trade enhance the nature of energy consumption. Sadorsky [19] and Fatima et al. [20] contend that trade liberalization and increasing exports directly impact energy consumption, particularly in export-oriented manufacturing sectors. Abboud and Betz's [21] research in BRICS countries discovered that increased economic openness through exports and FDI led to increased energy consumption in energy-intensive industries. This surge happened due to increasing manufacturing activity to meet export demand, which required significant additional energy. At the same time, these countries face the task of improving energy efficiency to remain competitive in the global market [22].
Energy demand is additionally affected by other economic factors, including energy pricing and inflation. Dahl [23] proposed the notion of energy price elasticity, asserting that energy cost variations might influence energy demand. Increased energy prices typically diminish energy usage, particularly in sectors very susceptible to price fluctuations, such as transportation and residential heating. Bekun et al. [24] stated that elevated inflation could diminish consumer purchasing power, thereby reducing energy usage, particularly in OECD nations. In this context, inflation significantly influences energy consumption levels, particularly when price escalations diminish household purchasing power.
In the past few years, a global paradigm shift has increasingly underscored the significance of transitioning to renewable energy sources. Numerous nations, particularly emerging ones, need help in regulating rising energy consumption while striving to mitigate the environmental repercussions of fossil fuel utilization. Kongkuah et al. [22] and Yang et al. [25] emphasize the significance of investing in renewable energy within a sustainable development framework. By diminishing reliance on fossil fuels, nations can alleviate the risks associated with global energy price volatility and adverse environmental effects. This study concluded that shifting to renewable energy in emerging nations could stabilize long-term economic growth, enhance energy security, and diminish carbon emissions.
Elevated energy consumption in a nation frequently indicates heightened industrial and economic activity. Nonetheless, efficient energy utilization is equally significant as elevated levels of consumption. Energy efficiency contributes to lowering manufacturing costs and mitigating adverse environmental effects from excessive energy consumption. Esen and Bayrak [26] assert that efficient energy usage is a crucial metric of sustainable economic development, particularly in nations encountering difficulties regarding energy supplies. Consequently, nations should attain an equilibrium between augmenting energy consumption and optimizing efficiency to guarantee sustainable economic expansion.
Moreover, energy is a fundamental and essential infrastructure for industrial and economic operations in every nation. Critical economic sectors, including transportation, manufacturing, and public services, rely significantly on energy for optimal functioning. Mahmoodi [27] asserted that the absence of robust energy infrastructure would disrupt industrial and economic operations, thereby impacting economic stability and growth. Consequently, investment in energy infrastructure and implementing technologies that enhance energy efficiency are crucial for fostering sustainable economic growth.
Sustainable development has emerged as a crucial framework for ensuring the long-term preservation and utilization of natural resources. Development that neglects sustainability may lead to the overexploitation of resources, ultimately constraining future economic progress. Moreover, environmental contamination constitutes a significant challenge encountered throughout industrialization, modernization, and urbanization. Azam et al. [28] assert that environmental contamination from industrial operations poses a challenge for developed and developing nations. The fast industrialization in numerous developing nations frequently results in heightened carbon emissions and environmental degradation, which can ultimately degrade community quality of life and impede long-term economic growth.
The shift to clean and renewable energy is increasingly vital to a sustainable development strategy. The excessive reliance on fossil fuels has demonstrated detrimental effects on the environment, prompting numerous governments to transition to more sustainable energy sources. This transformation is anticipated to mitigate environmental harm while enhancing energy efficiency and bolstering long-term energy security. Numerous studies indicate that using renewable energy mitigates carbon emissions and fosters economic growth [25]. Consequently, the sustainable utilization of energy resources is a crucial element in fostering future inclusive and enduring economic growth.
Concurrently with substantial progress, global energy consumption has escalated significantly. Without considerable attention from policymakers, global energy availability will emerge as a substantial concern in the future. Furthermore, with sustainable development objectives, a substantial focus must be directed towards minimizing energy consumption. Consequently, examining the determinants of energy consumption is essential for a comprehensive empirical understanding of its driving variables. Studying energy consumption is important because the pattern and amount of energy consumed impact many elements of human existence and the environment. As a result, energy consumption research is essential; by analyzing factors that influence energy consumption, areas for reducing excessive energy use can be identified and addressed. As a result, the findings of this study can provide insight into energy usage, which can then be used to ensure a stable and reliable energy supply.
Empirical study on energy use in various countries has primarily concentrated on technological and policy concerns, with less attention paid to which economic sectors contribute the most to increased energy use. Furthermore, most energy consumption research has been undertaken in industrialized countries, leaving a gap in knowledge about the specific conditions and issues that developing countries face. As such, there is a vacuum in our understanding of energy consumption trends. Thus, findings from this study contribute significantly to overcoming energy consumption difficulties and achieving sustainable development.
This study seeks to empirically examine the determinants of energy consumption, including sectoral economic development, exports, and inflation, which are crucial for developing a successful energy policy. The results of this study are anticipated to offer more thorough insights for policymakers in formulating more sustainable and efficient energy plans.
This paper consists of five sections. The first section is an introduction that summarizes the key results from the literature on energy consumption and the relevance of focusing on the energy sector. The second section discusses theoretical background and hypothesis development. The third section explains the econometric approach, analyzed data, and the development of the current analysis and model. The fourth section summarizes the study's primary findings based on the panel data model results for the factors influencing energy consumption. This section also explores the findings and compares them to other relevant studies. This paper concludes with the fifth section, which contains conclusions and recommendations.
Economic growth is the priority of a country's development. Solow’s classical theory holds that capital accumulation, increased labor productivity, and technical innovation drive economic growth. From this perspective, primary energy is critical in supporting manufacturing since its availability and efficiency can substantially impact a country's economic growth. Economic studies have extensively investigated the relationship between energy consumption and industrialization. Numerous studies, including those by Piccirilli et al. [10], Abboud and Betz [21], Yang et al. [25], Luo et al. [29], and Jilte et al. [30], show that rising energy consumption frequently correlates with increased manufacturing activity, which drives economic growth, particularly in developing countries.
Economic and demographic factors such as industrial activity, urbanization, and energy prices all impact energy demand. Kuznets’ [17] basic demand theory predicts that energy demand would rise with economic expansion and urbanization, as both drive increased energy consumption at household and industrial levels. Furthermore, Dahl's [23] price elasticity theory demonstrates that changes in energy prices can considerably impact energy demand, with higher energy prices typically resulting in reduced consumption.
Urbanization is characterized by an increase in the proportion of the population living in urban areas. According to Haughton and Hunter [31], urbanization hypothesis, population growth in cities leads to increasing demand for infrastructure, transportation, and energy services. This growth is due to the concentration of economic activity in urban regions, which require more energy than rural ones. As a result, high levels of urbanization tend to drive up a country's primary energy consumption.
In the context of globalization, international trade significantly impacts energy usage. According to Sadorsky [19], international economic theory, countries with high export levels have a more vital requirement for energy to support the production of exported goods and services. This is especially true for nations whose export businesses rely on energy-intensive sectors. As a result, one of the study's objectives is to investigate the relationship between energy consumption and exports.
Inflation, as measured by the Consumer Price Index (CPI), has the potential to influence energy consumption by affecting consumer purchasing power. Mohammed’s economic theory implies that excessive inflation can diminish purchasing power, reducing energy consumption, particularly in the family sector. Conversely, constant or low inflation can boost energy consumption by increasing consumer expenditure on energy-intensive goods and services.
2.1 Industrialization and energy consumption
Industrialisation is a key sign of modernisation and economic progress [29, 32-34]. Industrialization, characterized by manufacturing growth, is positively associated with the significant contribution of manufacturing to GDP and economic growth [35, 36]. In addition to other economic benefits, industrialization raises energy consumption because energy is a necessary input in both production and consumption [12, 37, 38]. Energy consumption is also linked to industrialization because energy is used in the manufacturing industry for production, lighting, and other commercial purposes. The production industry contributes more than half of world energy consumption and is expected to expand by 1.5% annually until 2035 [39]. Luo et al. [29] present an essential perspective in the context of OECD countries, finding that economic growth is significantly connected with higher energy consumption, particularly in highly industrialized countries. This study implies that the rapidly increasing industrial sectors in OECD countries demand much energy to power their expanding economic activity.
Li and Yuan [40] discovered a positive relationship between industrial expansion and energy consumption, particularly electricity in China, from 1995 to 2017. However, the correlation's strength is heavily influenced by the degree of industrial electricity usage and development. Their findings reveal that industrial expansion and energy consumption have a substantial link during the middle era of industrialization. Then, it steadily weakens, coinciding with consistent industrial growth and high electricity use. Meanwhile, Yang et al. [25] and Wang et al. [34] emphasize the relevance of investing in renewable energy in emerging countries to sustain long-term economic growth. They contend that by lowering reliance on traditional energy sources, developing nations can achieve more sustainable and ecologically friendly economic growth while avoiding the risks associated with global energy price volatility and fossil fuels' negative environmental repercussions.
2.2 Service sector and energy consumption
Like the manufacturing sector, the service industry has a favorable impact. It is a growth engine in many developed countries [36]. The service sector is considered a potential energy saver, particularly when compared with the manufacturing sector.
However, the service sector significantly impacts energy usage [41]. The service industry, a tertiary sector in private and public sectors, is the fastest-growing sector in consumption energy [42]. The service sector can boost energy consumption by growing service demand, using technology in manufacturing process sector services, developing infrastructure, and meeting varied transportation demands [3, 43].
Huseynli [41] investigated the relationship between the service industry and energy usage in Italy using data and panel analysis from 1997 to 2021. The study discovered that service exports were crucial in explaining the growth in energy consumption in a positive manner. The interaction between the factors that drive the service industry in Italy substantially impacts both the service sector and energy consumption.
Meanwhile, Yi [3] investigated the varied effects of economic sectors, including the service sector, on the demand for renewable energy in four major regions, Asia, America, Africa, and Europe, from 1998 to 2021. Yi discovered that the growth of the service sector increased long-term renewable energy usage in all regions but Africa. The study also discovered that the development of the service sector considerably influences short-term renewable energy usage in one or two locations.
2.3 Urbanization and energy consumption
Urbanization can increase the consumption of energy [11, 43-45]. His high population and quick industrial development in metropolitan regions drive improvements in energy demand such as electricity, material burning, and source energy [46].
Zhang et al. [16] investigated the relationship between urbanization, economic growth, energy consumption, and the effects on carbon emissions in China. The study found that urbanization significantly impacts increasing energy consumption, particularly in areas such as transportation and housing. The findings show that as urban areas grow, so does energy demand, which directly correlates to increased carbon emissions, highlighting urbanization as an essential aspect of China's environmental dynamics.
Another study by Shazed et al. [18] provides further insight into the impact of urbanization on energy consumption in China, concluding that urbanization has a strong push to improve energy consumption. Interestingly, the influence of urbanization on energy consumption is more significant in large cities than in small cities. These findings demonstrate that more fabulous cities, with more complicated infrastructure and a denser population, have higher energy demands, exacerbating the country's energy challenges.
Klemeš et al. [47] and Sharma et al. [48] added another dimension by demonstrating that urbanization improves energy consumption in general and influences the structure of energy consumption. They discovered a shift in energy consumption patterns caused by urbanization, which encourages electricity use while decreasing the use of solid fuels. Such a shift reflects a shift in demands and preferences for energy in metropolitan areas, with a greater reliance on clean and efficient energy sources [48].
2.4 Exports and consumption energy
The export of goods and services can considerably impact energy usage [20, 41]. Exports need more output, which causes a rise in energy consumption. Furthermore, transport services consume significant energy after production operations [49, 50].
Abboud and Betz [21] conducted a study examining globalization's impact on energy consumption in the BRICS countries. They discovered that greater trade openness and foreign direct investment (FDI) dramatically increased energy consumption, particularly in export-oriented industries. This conclusion emphasizes the critical role of globalization in increasing economic growth through international trade, which raises energy demand to sustain production and exports.
In the context of European countries, Jilte et al. [30] discovered that economic globalization has a considerable impact on energy consumption, particularly in countries with high trade openness. This study emphasizes the need for energy policies that not only improve energy efficiency but also consider the effects of global economic integration. In a more open trading environment, energy consumption tends to rise with economic activity, highlighting the importance of a comprehensive approach to energy policy formulation.
Another study by La Monaca et al. [51] provides a broader viewpoint by demonstrating that international commerce and FDI lead to rising energy consumption in Sub-Saharan Africa. This study supports the idea that economic openness might boost energy demand, particularly in emerging countries attempting to integrate into the global economy. This conclusion emphasises that, while globalisation can spur economic progress, developing countries must address the environmental consequences and higher energy demands that come with economic openness.
2.5 Inflation and energy consumption
Various empirical research has discovered a complex relationship between inflation and energy consumption, with the overall finding that inflation tends to force people to utilize energy more carefully and efficiently [52]. Instead, increased energy consumption and global population induce inflation, increasing energy prices [52, 53]. In this study, the CPI is employed to measure inflation. The CPI estimates the average change in price paid by consumers for goods and services over time as an indicator of significant inflation [54].
Bekun et al. [24] investigated the relationship between economic conditions, including inflation, and their effects on energy consumption and health in OECD countries. The study discovered that inflation had a considerable impact on consumer purchasing power, reducing energy consumption. When inflation rises, so do the prices of products and services, including energy, making it more difficult for customers to purchase the same quantity of energy as previously. This effect is most noticeable in industries that use a lot of energy, such as transportation and home heating.
In his research in Asian countries, Liu [55] discovered that inflation had a detrimental influence on energy consumption, particularly in price-sensitive sectors such as transportation and housing. When energy prices rise due to inflation, customers in these sectors reduce consumption to make do with a tighter budget. This study emphasizes the need to control inflation to stabilize energy consumption, which is critical for long-term economic growth.
Shioji [56] provides another insight into a study conducted in developing nations, revealing that price stability is critical in sustaining consistent energy consumption. Sharp price swings, frequently caused by unchecked inflation, can substantially impact energy demand. In this context, Shioji's study emphasizes the importance of developing countries maintaining price stability to avoid a sharp drop in energy consumption, which could impact on total economic activity.
Based on the above discussion, the hypotheses statement below expresses the link between independent and dependent variables. The industrial sector's high activity in achieving economic expansion is expected to favor rising energy consumption (H1). The service sector increases energy consumption because numerous facilities and activities require a lot of energy (H2). Urbanization increases energy consumption in tandem with the number of urban residents (H3). Companies will increase production and energy consumption to increase the volume of goods and services exported. After production, goods and services are exported to other nations, increasing energy consumption during the distribution phase (H4). Unlike the preceding hypotheses, inflation is supposed to be negatively associated with energy consumption. If inflation causes energy prices to rise, consumers and businesses will restrict their energy use to manage expenditure. Inflation can also boost production costs when energy prices rise, which drives less energy consumption (H5).
3.1 Data and description of the variables
The current study employs an unbalanced panel data set spanning five years (2019–2023) and 38 countries. This research period was selected based on the availability of data for all variables evaluated in 38 countries. The dataset covers the consumption of primary energy as the dependent variable. The independent variables are (Table 1):
The selection of dependent and independent variables is based on the review objective and theoretical and empirical prior research. Energy consumption is the total quantity of primary energy consumed by a country, measured in Terawatt-hours (TWh). Primary energy refers to energy sources extracted directly from nature, such as oil, gas, coal, and renewable energy, before being processed into final energy used in various economic sectors. Furthermore, the Industry and Services variables are used to assess the contribution of the industrial and service sectors to a country's GDP. The industry variable is reported as a proportion of total GDP and includes the construction industry, indicating how much value it contributes to the economy. Meanwhile, the Services variable is a percentage of total GDP, indicating the importance of service industries such as banking, education, health, and tourism to the broader economy. The urban population variable depicts the proportion of a country's population that lives in cities. This information is used to determine the extent of urbanization and the distribution of the population between urban and rural areas, which is frequently linked to changes in consumption habits and energy demand.
Furthermore, exports of goods and services calculate the entire value of a country's exports of goods and services as a proportion of GDP. This variable represents a country's reliance on foreign trade and its competitiveness in the global market. Finally, the Consumer Price Index (CPI) measures inflation and changes in consumer purchasing power over time. Energy consumption data is sourced from Our in Data (https://ourworldindata.org/), with all other independent source variables sourced from World Bank Indicators (https://data.worldbank.org). Given data availability, this analysis used a database including a panel data set of 38 countries (n) over 5 years (t). The dataset is an unbalanced panel with 190 observations (n × t = 38 × 5). However, due to missing country data at various time points, the total number of observations evaluated is 178.
Table 1. Data and variables
|
Variables |
Definition |
Unit |
|
Energy consumption |
The total primary energy consumed by a country is measured in Terawatt-hours (TWh). Primary energy includes source energy extracted directly from nature, such as oil, gas, and coal, as well as renewable energy, before being transformed into energy used in various sectors. |
TWh |
|
Industry |
Industry, mainly building, contributes to a country's GDP. This metric is expressed as a percentage of total GDP, demonstrating how much the significant sectors of industry and construction contribute to the economy. |
% of GDP |
|
Services |
The contribution of the service sector to a country's Gross Domestic Product (GDP), expressed as a percentage of total GDP, illustrates how much the extensive sector services contribute to the entire. |
% of GDP |
|
Urban population |
A country's urban population is a percentage of its total population. This data is critical for assessing urbanization and the proportion of people living in cities versus rural areas. |
% of total population |
|
Exports of goods and services |
A country's total exports of goods and services are expressed as a proportion of its GDP. This metric indicates how much a country's economy relies on international trade. |
% of GDP |
|
Consumer price index |
A measure of the average price change in household goods and services, using 2010 as the base year (set to 100). The variable measures inflation and changes in consumer purchasing power over time. |
Index |
3.2 Model spesification and estimation strategy
The panel data model is used to analyze empirical data. Panel analysis considers both individual and temporal dimensions [57, 58]. Panel data is a collection of data gathered through observation of different units (cross-sectional) of time series [59, 60]. Panel data have several advantages, including more informative information, considerable variation, degrees of freedom, fewer collinearity between variables, and increased efficiency [61]. Furthermore, panel data analysis can control variables that do not change over time or between subjects because panel data has time dynamics with repeated cross-sectional data observations over time, allowing the effect of non-measurable variables to be controlled [62].
The panel regression employed corresponds to the work of Yessymkhanova et al. [63], Ogunsola and Tipoy [64], and Dokas et al. [65]. The following empirical model is specified:
$\begin{gathered} {ENERGY \,\,\, CONSUMPTION }_{i t}=\alpha+\beta_1 {INDUSTR } Y_{i t}+ \beta_2 { SERVICE }_{i t}+\beta_3 { URBANPOP}_{i t}+\beta_4 E X P O R T_{i t}+ \beta_5 { CPI }_{i t}+\mu_{i t}\end{gathered}$ (1)
where, i = 1, 2,..., N denotes the cross-sectional units, t = 1, 2, 3,..., t denotes the periods. Energy Consumption is the dependent variable indicating energy consumption, INDUSTRY refers to the contribution sector industry, including construction, against a country's Product Gross Domestic Product (GDP). SERVICE designates the contribution of the service sector to a country's GDP, URBANPOP represents the percentage of a country's population residing in urban areas, EXPORT indicates export, CPI symbolizes inflation, and μ is the stochastic error term. The independent variables forecast the response variable based on the explanatory variable available in relevant empirical studies. The panel approach begins with a static panel model approach and progresses to dynamic panels. The present study employs three static panel estimating methods: Pool, fixed effects (FE), and random effects (RE) [66, 67]. The analysis will subsequently proceed by employing dynamic panel data through the Generalized Method of Moments (GMM) estimator to enhance efficiency, minimizing the variants of the estimator [61, 68].
4.1 Descriptive results
Energy consumption averages 1,707,990 Terawatt-hours (TWh) with a standard deviation of 4,205.733, demonstrating significant diversity in energy consumption among nations. The minimum energy consumption value is 37.54582 TWh, and the most significant recorded is 26,578.49 TWh. This dataset comprises 188 observations. The average contribution of the industry sector to GDP is 24.15527%, with a standard deviation of 6.108604%. The minimum value of the contribution sector industry is 10.39672%, whereas the most outstanding value is 49.15819%. There are 180 observations documented for the variable. The services sector, which assesses its contribution to GDP, has an average value of 63.76291% and a standard deviation of 6.464811%. The minimum value documented is 41.74495%, and the maximum is 80.59983%. A total of 180 observations were included in the study. The urban population indicates that, on average, 78.81610% of the population resides in urban areas. Urban, with a standard deviation of 11.02505%. The smallest value is 53.729%, while the most outstanding value is 98.189%.
Table 2. Descriptive statistics
|
Variable |
Mean |
Std. Dev |
Min |
Max |
|
Energy Consumption |
1707.990 |
4205.733 |
37.54582 |
26578.49 |
|
Industry |
24.15527 |
6.108604 |
10.39672 |
49.15819 |
|
Services |
63.76291 |
6.464811 |
41.74495 |
80.59983 |
|
Urban Pop |
78.81610 |
11.02505 |
53.72900 |
98.18900 |
|
Export |
55.57759 |
36.23229 |
10.08356 |
213.2227 |
|
CPI |
133.9185 |
64.23336 |
98.82431 |
834.5931 |
The average value for the exports of goods and services variable is 55.57759% of GDP, with a standard deviation of 36.23229%. The least recorded export value is 10.08356%, while the most significant value attained is 213.2227%. A total of 187 observations were included in the study. The Consumer Price Index (CPI) variable averages 133.9185 and a standard deviation 64.23336. The minimum value of the CPI is 98.82431, whereas the most significant number is 834.5931. This dataset comprises 190 observations. Descriptive statistics (Table 2) provides a general description of the distribution and range of variables utilized in research, offering an initial knowledge of the behavior of each variable across observed samples.
4.2 Static panel regression
Empirical findings using Pool, FE, and RE models demonstrate substantial variance in the relationship between energy consumption and numerous economic variables such as industry, services, urbanization, exports, and consumer price indices. This discussion will not only review these results but also relate them to findings from previous research, as well as explore policy implications that can be drawn based on the results of this analysis.
Analysis of Pool Estimation
The estimates assume that variables across countries are homogeneous and do not account for effect still or randomness; the results demonstrate a robust association between energy consumption and significant economic sectors. Industry has a positive coefficient of 0.1863 (p = 0.0000), showing that increasing the industry sector's contribution to GDP is associated with increased energy consumption. The findings are consistent with the study of Luo et al. [29] which shows that the industry consumes a substantial amount of energy, particularly in rapidly industrializing countries. Industry requires significant energy for operations such as manufacturing, processing raw materials, and construction, all of which contribute to the country's overall energy consumption.
The Services variable likewise has a strong positive correlation with energy consumption, with a value of 0.1791 (p = 0.0000). It demonstrates that, while the service sector is generally seen as less energy-intensive than the industrial sector, significant growth in this sector significantly impacts energy consumption. Such a finding is also supported by Shazed et al. [18], who found that urbanization and the growth of the service sector might increase energy demand, particularly in supporting infrastructure such as transportation and hospitality.
Unexpectedly, the urban population has a positive coefficient but is insignificant at the 5% level (coefficient 0.0107, p = 0.1763). Although this shows a positive association between urbanization and energy consumption, the poor statistical significance suggests that the urbanization factor in the Pool model may need to be stronger. However, some studies, such as Zhang et al. [16], discovered that urbanization, particularly in developing nations, can significantly increase energy consumption in the housing and transportation industries. This difference could be attributed to the Pool model's homogenization methodology, which does not account for cross-country variances in urbanization rates and energy efficiency.
The exports of goods and services exhibit a negative coefficient of -0.0223 (p = 0.0000), showing that a rise in exports correlates with a reduction in energy usage. The results indicate that countries focused on exports are generally more efficient in energy utilization or employ more energy-efficient technologies in their industrial processes to maintain competitiveness in the global market. La Monaca et al. [51] previously discovered that international commerce and foreign direct investment (FDI) can elevate energy consumption; nevertheless, this negative outcome may indicate adopting more energy-efficient technology in these nations.
Finally, the CPI substantially correlates positively with energy consumption (coefficient 0.0035, p = 0.0043). These results highlight that inflation, as measured by the CPI, can impact energy consumption through higher energy prices that drive increased energy usage in the short term or as a response to economic uncertainty. In their study, Bekun et al. [24] stressed the impact of inflation on consumer purchasing power, which influences energy usage.
Fixed Effects Analysis
The FE estimations, which incorporate fixed effects for each country, yield more diverse results than the Pool model and, in certain instances, indicate an inverse relationship. The FE model's coefficients for Industry and Services are -0.0253 (p = 0.0002) and -0.0337 (p = 0.0000), respectively. The findings indicate that, after accounting for country-fixed effects, the rise in the contribution of these sectors to GDP may correlate with a reduction in energy consumption. This finding suggests that these sectors may have attained greater energy efficiency in certain countries or that other factors, such as a transition to greener technologies, are significant contributors. A study by Yang et al. [25] demonstrates that investment in renewable energy can alter energy consumption patterns, particularly in previously energy-intensive sectors.
The FE model indicates a significant negative correlation between urban population and energy consumption, with a coefficient of -0.0497 (p = 0.0001). This finding indicates that, when accounting for country-fixed effects, urbanization may contribute to a reduction in energy consumption. The observed decrease may result from enhanced energy efficiency in urban areas, which typically possess more modern and efficient infrastructure than rural regions. This finding challenges the prevailing notion that urbanization invariably leads to increased energy consumption. Zhang et al. [16] demonstrated that urbanization influences energy consumption in specific sectors, including transportation and housing.
The fixed effects model indicates a small yet statistically significant coefficient of 0.0023 for the Exports of Goods and Services variable (p = 0.0193). This finding suggests that, after controlling for country-fixed effects, the impact of exports on energy consumption is minimal. This result aligns with the perspective that while exports play a significant role in the economy, their effect on energy consumption may be less substantial than anticipated within the framework of the FE model, mainly if nations have implemented more energy-efficient production technologies.
In the FE model, the CPI exhibits a negligible and nearly insignificant coefficient (0.0001, p = 0.0709). This figure indicates that inflation minimally influences energy consumption within this context. This outcome diverges from the Pool model and may indicate the variability in the impact of inflation across different economies when fixed effects are considered.
Random Effects Analysis (RE)
The RE model posits that random effects are related to the independent variables, and the RE estimation results are comparable to the FE model, with a few notable exceptions. Industry and services have a negative connection with energy consumption, with coefficients of -0.022335 (p = 0.0007) and -0.030451 (p = 0.0000), respectively. These findings demonstrate that, in the context of a model that considers random variation among nations, the contribution of these sectors is associated with a decrease in energy consumption. These findings support the concept that energy efficiency improvements or technology breakthroughs in the industry and services sectors can help cut energy consumption despite economic growth.
In the RE model, the urban population has an insignificant negative coefficient of -0.0152 (p = 0.1142). The insignificant impact suggests that the influence of urbanization on energy consumption in the RE model needs to be more consistent and insignificant. This finding could imply that urbanization's impact on energy consumption dramatically depends on country-specific factors such as technical advancement and current energy policies.
Exports of goods and services have a very modest and insignificant coefficient of 0.0003 (p = 0.7212), confirming that in the RE model, exports have no meaningful impact on energy consumption. This study supports the concept that the impact of exports on energy consumption can be mitigated by efficiency improvements or a transition to more energy-efficient products.
CPI in the RE model has a relatively high but negligible coefficient (6.4134, p = 0.4741), which could indicate the inconsistent effect of inflation on energy consumption within this model framework. This fluctuation shows that the relationship between inflation and energy consumption may be more complex than a simple linear model, necessitating a more sophisticated analytical technique to comprehend it correctly.
Overall, the panel estimation study results reveal that different models provide varied perspectives on the link between energy consumption and the factors that influence it, as illustrated in Eq. (1). These differences highlight the necessity of selecting the appropriate model based on the features of the data and the study's aims. The Pool model results, which indicate a substantial positive association between most factors and energy consumption, support the hypothesis that rising economic activity, whether through industry, services, or urbanization, tends to increase energy consumption. However, the FE and RE results reveal that when country-specific or random effects are incorporated, this link can reverse direction or become negligible, indicating that other variables must be addressed in the analysis (Table 3). These findings add to the current literature by demonstrating the complexities of the relationship between economic conditions and energy usage and emphasizing the significance of a methodologically rigorous approach to panel data analysis. These findings further highlight the importance of a flexible energy policy that can account for the numerous factors that influence energy consumption across different economic contexts.
Table 3. Static regression: Pool, FE and RE estimation
|
Variable |
Pool |
FE |
RE |
|
Industry |
0.1863 |
-0.0253 |
-0.0223 |
|
(0.0000)*** |
(0.0002) *** |
(0.0007) *** |
|
|
Services |
0.1791 |
-0.0337 |
-0.0304 |
|
(0.0000) *** |
(0.0000) *** |
(0.0000) *** |
|
|
Urban population |
0.0107 |
-0.0497 |
-0.0152 |
|
(0.1763) |
(0.0001) *** |
(0.1142) |
|
|
Exports of goods and services |
-0.0223 |
0.0023 |
0.0003 |
|
(0.0000) *** |
(0.0193)** |
(0.7212) |
|
|
Consumer price index |
0.0035 |
0.0001 |
6.4134 |
|
(0.0043) *** |
(0.0709) * |
(0.4741) |
|
|
R-squared |
0.5184 |
0.9993 |
0.0931 |
|
Observations |
178 |
178 |
178 |
*Significance at 10%, **Significance at 5%, ***Significance at 1%.
4.3 Dynamic panel regression
Within the framework of economic variables, the previous period is also a significant determinant of the value of a variable in the following period. The dynamic panel estimation accounted for this possibility in this work with the Arellano and bond estimators [61, 69, 70]. Arellano and Bond estimators are employed in dynamic panel analysis to address the issues of unobserved individual effects and endogeneity in panel data models [53, 71]. The Arellano-Bond estimator corrects endogeneity using the dependent variable's lag value [61, 69, 70]. Table 4 shows the estimation results using the Arellano-Bond Estimator.
Table 4. Dynamic OLS and FE
|
|
Pool |
FE |
|
Industry (lag 1) |
0.1807 |
0.2024 |
|
(0.0000) *** |
(0.0000) *** |
|
|
Services (lag 1) |
0.1682 |
0.2227 |
|
(0.0001) *** |
(0.0000) *** |
|
|
Urban population (lag 1) |
0.0147 |
0.0097 |
|
(0.2961) |
(0.3304) |
|
|
Exports of goods and services (lag 1) |
-0.0223 |
-0.0254 |
|
(0.0000) *** |
(0.0000) *** |
|
|
Consumer price indeks (lag 1) |
0.0030 |
0.0054 |
|
(0.1667) |
(0.0023) |
|
|
Constant |
-9.4248 |
-12.9241 |
|
(0.0060) |
(0.0000) *** |
|
|
R-squared |
0.5262 |
0.5474 |
|
Observations |
178 |
178 |
*Significance at 10%, **Significance at 5%, ***Significance at 1%.
According to the Arellano-Bond estimator, the coefficient of the industrial sector variable at lag 1 is 0.1807 in the dynamic OLS (Pool) model and 0.2024 in the FE model, with a significant p-value of 0.0000 in both models. This research demonstrates that the contribution of the industrial sector, which is delayed by one period, has a considerable positive impact on energy usage. In other words, an increase in industrial activity in the preceding period will immediately contribute to an increase in energy consumption in the subsequent era.
The estimation results suggest that the service sector has a considerable positive effect on energy consumption, with coefficients of 0.168264 in the Pool model and 0.2227 in the FE model and a p-value of less than 0.0001. This result demonstrates that, while the service industry usually is less energy-intensive than the industrial sector, it nonetheless significantly impacts energy consumption. The substantial regression coefficient at lag 1 suggests that energy consumption in the service sector is continuous, with an increase in service sector activity in the previous period contributing to energy consumption in the subsequent period. This conclusion could be attributed to the ongoing demand for energy to power information technology infrastructure, servers, data centers, and telecommunications networks, all critical components of the modern service industry.
It is crucial to highlight that while the service sector is expanding and pushing energy consumption, global efforts are underway to lower its carbon footprint through green infrastructure development and renewable energy consumption. Klemeš et al. [47] underlined that there is a shift in the pattern of energy consumption in the service sector, with urbanization and technological improvements driving higher usage of electricity from clean energy sources rather than fossil fuels. The urban population variable at lag one yields insignificant results, with coefficients of 0.0147 in the Pool model and 0.0097 in the FE model and high p-values of 0.2961 and 0.3304, respectively. It reveals that the rise in urban population during the preceding period has no meaningful effect on future energy usage.
As stated by Shazed et al. [18], urbanization and the growth of the modern service sector, particularly in large cities, are driving up energy demand, particularly for public transit systems and communication networks. Rapid digitization in the service sector necessitates significant energy consumption and the development of information and communication technology (ICT). Piccirilli et al. [10] emphasized that rising energy consumption in the service sector contributes to economic growth in developing countries, mainly through digitization and economic globalization.
The first lag in urban population may explain a contradiction with the general notion of urbanization, which is frequently connected with higher energy consumption. However, there are various plausible causes. First, countries with a high urbanization rate may have achieved higher energy efficiency, as significant cities use more energy-efficient technology and infrastructure, such as electric-powered transportation and green buildings. Zhang et al. [16] discovered that industrialized countries with more energy-efficient technologies did not substantially increase energy consumption despite increased urbanization. Furthermore, cross-national heterogeneity is significant. Urbanization may have varying effects based on the country's economic development and energy policies. In industrialized countries, urbanization relates to better energy efficiency; however, in developing countries, urbanization can increase energy consumption due to the demand for basic infrastructure.
The estimation results demonstrate that exports of products and services have a substantial negative connection with energy consumption, with coefficients of -0.0223 in the Pool model and -0.0254 in the FE model. Such an association suggests increased exports in the previous period can lower energy consumption in the subsequent quarter. The negative regression results in the first lag show that export-oriented countries are more energy efficient, probably because they are motivated to boost competitiveness by lowering energy costs in the manufacturing process. According to Abboud and Betz [21], enterprises in the export sector frequently use energy-saving technologies to save manufacturing costs, which explains the negative link between exports and energy consumption. It is feasible that countries with large export industries will reduce domestic energy intensity while shifting production to countries with higher energy intensity. Such circumstances are referred to as "externalization of energy consumption," in which rich countries import items from developing countries that use more energy for production. According to Sadorsky [19], economic globalization can lead to complex dynamics between exports, energy consumption, and energy efficiency, with nations with high export levels being more efficient in domestic energy consumption.
The CPI coefficient at lag 1 of 0.0030 in the Pool model and 0.0054 in the FE model has a significant and positive influence on energy consumption, demonstrating that recent inflation can cause an increase in energy consumption in the following period. The first lag of CPI suggests that higher inflation in the prior period can raise energy consumption, particularly when consumers are obliged to spend more energy for fundamental needs. According to Bekun et al. [24], while high inflation might diminish purchasing power, it can also increase energy demand for primary requirements like electricity and heating.
5.1 Conclusion
This study focuses on the complex relationship between energy consumption and various economic indicators in the countries studied, including the industrial sector's contribution to GDP, the contribution of the service sector to GDP, urbanization, exports, and inflation. The research using the Pool, Fixed Effects (FE), and Random Effects (RE) models demonstrates significant diversity in the influence of these variables on energy consumption. The Pool model shows a positive association between energy consumption and the industrial and service sectors. An increase always follows an increase in the industrial sector's contribution to GDP in energy consumption. These findings emphasize the importance of the industrial sector as a significant energy user, especially in countries experiencing rapid industrialization. Implication of a positive association between energy consumption with the industrial sectors. The association reveals how energy consumption directly affects industrial production. As a result, when industries produce additional goods, their energy demand rises, implying that industrial growth promotes economic expansion.
Similarly, while the services sector uses less energy than the industrial sector, it substantially impacts energy consumption, coinciding with the rise of supporting infrastructure such as transportation, telecommunications, and information technology. From these findings, then from the policy side, there needs to be anticipation in efforts to diversify energy sources towards renewable energy because, in the future, the rapid growth of the service sector can significantly increase energy demand. From the energy supply side, efforts must increase energy efficiency in the service sector through energy-saving technology and sustainable practices.
However, the results differ dramatically when fixed factors between countries are examined using the FE model. The industrial and service sectors had positive coefficients in the Pool model but now have negative coefficients in the FE model. One possible reason for this finding is that in several nations assessed, the industrial and service sectors have made significant breakthroughs in energy efficiency. For example, developed countries with green technologies and renewable energy policies tend to reduce energy consumption in energy-intensive sectors despite increased economic activity.
Urbanization also produces varying results. The Pool model reveals that the association between urbanization and energy consumption is positive but insignificant. This insignificant association could be attributed to the model's homogenization method, which needs to account for cross-country variances in urbanization and energy efficiency. In general, urbanization increases energy consumption rather than decreasing it. However, there are nuances in urbanization that can dampen energy consumption in some conditions. The study's findings reveal that, throughout the 38 nations examined, there are characteristics in metropolitan settings that contribute to energy consumption efficiency. These findings highlight the need for authorities to increase the efficiency of various city energy supplies.
In contrast, the FE model demonstrates that urbanization negatively impacts energy consumption. These findings imply that in highly urbanized countries, increased energy efficiency and contemporary city infrastructure may help to reduce energy usage.
Interestingly, exports of products and services have a negative association with energy use in all models. These data suggest that export-oriented countries are more efficient in energy consumption or incorporate energy-saving technologies into their manufacturing processes. Such efficiencies could be attributed to global competitive pressures driving enterprises to use more energy-efficient production systems.
Finally, inflation, as measured by the CPI, has a positive association with energy consumption in the Pool model but is insignificant in the FE and RE models. These findings imply that the influence of inflation on energy consumption may be more significant in the near term or in nations whose economies are sensitive to changes in energy prices. These findings also indicate that, in the long run, the impact of inflation on energy consumption may be mitigated by energy policy changes and consumer consumption reductions. The dynamic panel method indicates that the industrial and service sectors substantially influence energy consumption. Exports exert a significant and adverse effect on energy consumption. The Consumer Price Index (CPI), an indicator of inflation, substantially and positively impacts energy consumption.
5.2 Recommendations
The manufacturing and service sectors contribute significantly to increased energy demand. As a policy implication, the government of a country must increase the quality of energy infrastructure to support industrial operations. Furthermore, from a policy standpoint, attempts to diversify energy sources toward renewable energy must be anticipated since the rapid growth of the service sector in the future has the potential to increase energy consumption dramatically. On the energy supply side, efforts must be made to improve energy efficiency in the service sector by implementing energy-saving technologies and sustainable practices. The government must also implement a sustainable energy strategy to decrease energy usage in the industrial and service sectors. The two sectors rely highly on energy for production activities. Therefore, regulations promoting enhanced energy efficiency are essential. The government can promote green technology innovation by offering incentives and subsidies to companies that invest in energy-reducing devices. Investment in renewable energy sources, including solar power, wind energy, and bioenergy, must be augmented to supplant fossil fuels, which pose more significant environmental risks. Policies aimed at energy efficiency and the transition to renewable energy will assist the industrial and service sectors establish a more sustainable economy.
Urbanization frequently increases energy consumption in cities, although this rise can be mitigated by measures that promote energy efficiency. To lower urban carbon footprints, city governments must invest in energy-efficient infrastructure such as electric vehicles and green buildings. The development of renewable energy-based public transportation, such as electric buses, green-powered trains, and energy-efficient structures, will contribute to lower urban energy consumption. This policy is also vital in promoting long-term city development.
Export-oriented countries should also promote energy efficiency in the industrial sector by using energy-saving technologies. The government can offer incentives, such as tax breaks, to businesses that successfully cut energy consumption in their operations. Furthermore, legislation promoting the use of renewable energy in the export sector must be improved to ensure that the products produced are both environmentally benign and competitive in the global market. Further research into the influence of international commerce, foreign investment, and the renewable energy transition on energy consumption is critically required to support future successful and evidence-based energy policy.
Unbalanced panel regression analyses
|
Country |
Period |
Year |
Industry |
Services |
Urban Population |
Exports of Goods and Services |
Consumer Price Index |
Energy Consumption |
|
Australia |
2019-01-01 |
2019 |
25.27350693 |
66.02339847 |
86.124 |
24.11178836 |
119.7970864 |
1679.8438 |
|
Australia |
2020-01-01 |
2020 |
25.38624951 |
66.26309207 |
86.241 |
23.96737274 |
120.8116545 |
1585.1263 |
|
Australia |
2021-01-01 |
2021 |
25.41757604 |
65.71675728 |
86.362 |
21.97681939 |
124.2715921 |
1589.0751 |
|
Australia |
2022-01-01 |
2022 |
27.48299454 |
63.30351904 |
86.488 |
25.42712413 |
132.4661811 |
1661.016 |
|
Australia |
2023-01-01 |
2023 |
27.36265573 |
64.22493429 |
86.617 |
26.72198648 |
139.880333 |
1672.4558 |
|
Austria |
2019-01-01 |
2019 |
25.20452933 |
63.10854422 |
58.515 |
55.76078481 |
118.0579801 |
427.9604 |
|
Austria |
2020-01-01 |
2020 |
25.48873232 |
63.19179111 |
58.748 |
51.58494694 |
119.6894358 |
400.14407 |
|
Austria |
2021-01-01 |
2021 |
26.07048658 |
62.15068292 |
58.995 |
55.9518373 |
123.0008436 |
406.03314 |
|
Austria |
2022-01-01 |
2022 |
26.10688389 |
62.13923717 |
59.256 |
62.08317498 |
133.5135657 |
379.76904 |
|
Austria |
2023-01-01 |
2023 |
26.26128397 |
62.29232556 |
59.53 |
59.47718589 |
143.9464948 |
384.8108 |
|
Belgium |
2019-01-01 |
2019 |
19.25936573 |
69.34731662 |
98.041 |
82.39718206 |
117.1104572 |
738.434 |
|
Belgium |
2020-01-01 |
2020 |
19.39065678 |
69.75468976 |
98.079 |
78.66790638 |
117.9780019 |
664.01184 |
|
Belgium |
2021-01-01 |
2021 |
19.32979728 |
69.23520237 |
98.117 |
87.90139849 |
120.8569583 |
738.3453 |
|
Belgium |
2022-01-01 |
2022 |
20.04596783 |
69.03784587 |
98.153 |
95.70223544 |
132.456219 |
694.4863 |
|
Belgium |
2023-01-01 |
2023 |
18.44217596 |
70.70729909 |
98.189 |
86.68123029 |
137.8193856 |
642.0631 |
|
Canada |
2019-01-01 |
2019 |
24.07740788 |
67.67116348 |
81.482 |
32.35269582 |
116.7572982 |
4067.8503 |
|
Canada |
2020-01-01 |
2020 |
22.45161732 |
69.55977998 |
81.562 |
29.47364723 |
117.5944476 |
3821.6829 |
|
Canada |
2021-01-01 |
2021 |
|
|
81.653 |
31.21635296 |
121.5870063 |
3866.976 |
|
Canada |
2022-01-01 |
2022 |
|
|
81.752 |
33.84504756 |
129.8583286 |
3971.9736 |
|
Canada |
2023-01-01 |
2023 |
|
|
81.862 |
33.53640807 |
134.8955352 |
3874.8867 |
|
Chile |
2019-01-01 |
2019 |
27.11844224 |
58.88317546 |
87.643 |
27.87466776 |
131.913567 |
473.9787 |
|
Chile |
2020-01-01 |
2020 |
29.8295676 |
56.34461458 |
87.727 |
31.33039024 |
135.9309826 |
442.47766 |
|
Chile |
2021-01-01 |
2021 |
30.88386558 |
54.9828043 |
87.817 |
31.9735835 |
142.0812728 |
476.857 |
|
Chile |
2022-01-01 |
2022 |
31.25733429 |
54.94103922 |
87.912 |
35.51510707 |
158.6250269 |
503.5053 |
|
Chile |
2023-01-01 |
2023 |
29.66516641 |
56.9110073 |
88.012 |
31.13673653 |
170.6514728 |
504.06754 |
|
Colombia |
2019-01-01 |
2019 |
25.99342684 |
58.13645917 |
81.104 |
15.86803865 |
140.9478497 |
563.1924 |
|
Colombia |
2020-01-01 |
2020 |
23.4392386 |
60.12182627 |
81.425 |
13.52227556 |
144.5090874 |
512.58594 |
|
Colombia |
2021-01-01 |
2021 |
24.55656974 |
58.19262238 |
81.74 |
16.19700595 |
149.5597632 |
574.74524 |
|
Colombia |
2022-01-01 |
2022 |
26.1475271 |
54.87249548 |
82.05 |
20.23240039 |
164.7808063 |
610.0674 |
|
Colombia |
2023-01-01 |
2023 |
24.54839616 |
56.85544993 |
82.354 |
17.76035462 |
184.1193244 |
626.48535 |
|
Costa Rica |
2019-01-01 |
2019 |
19.23365693 |
69.20499402 |
80.076 |
34.32532156 |
128.8457433 |
66.162155 |
|
Costa Rica |
2020-01-01 |
2020 |
20.36785113 |
68.23240729 |
80.771 |
31.90649766 |
129.7797609 |
58.84567 |
|
Costa Rica |
2021-01-01 |
2021 |
20.60579236 |
67.07941614 |
81.425 |
36.17635703 |
132.0203794 |
61.628967 |
|
Costa Rica |
2022-01-01 |
2022 |
20.64643811 |
67.28309024 |
82.042 |
40.56655498 |
142.9447686 |
|
|
Costa Rica |
2023-01-01 |
2023 |
20.45925384 |
67.99307844 |
82.622 |
37.2888642 |
143.6955052 |
|
|
Czechia |
2019-01-01 |
2019 |
31.53023622 |
56.96358697 |
73.921 |
73.87978033 |
116.475542 |
477.90494 |
|
Czechia |
2020-01-01 |
2020 |
30.68692941 |
58.36769204 |
74.061 |
69.94877154 |
120.1576778 |
442.5676 |
|
Czechia |
2021-01-01 |
2021 |
30.26568427 |
58.75660961 |
74.214 |
72.7282341 |
124.7715463 |
466.40598 |
|
Czechia |
2022-01-01 |
2022 |
29.61139528 |
59.1783657 |
74.377 |
76.45232425 |
143.6122559 |
455.73114 |
|
Czechia |
2023-01-01 |
2023 |
30.15961367 |
59.75078226 |
74.552 |
72.01070037 |
158.9231321 |
423.37268 |
|
Denmark |
2019-01-01 |
2019 |
20.70893809 |
65.07011648 |
87.994 |
58.64718222 |
110.3472904 |
196.04588 |
|
Denmark |
2020-01-01 |
2020 |
19.72066483 |
65.75775012 |
88.116 |
55.11980167 |
110.8115347 |
176.48528 |
|
Denmark |
2021-01-01 |
2021 |
19.58833796 |
66.40170039 |
88.24 |
58.70294413 |
112.8649228 |
189.43509 |
|
Denmark |
2022-01-01 |
2022 |
19.65629709 |
67.26537248 |
88.367 |
70.01238944 |
121.5516472 |
197.27802 |
|
Denmark |
2023-01-01 |
2023 |
21.48597245 |
66.07089109 |
88.495 |
69.00899989 |
125.5691456 |
195.52307 |
|
Estonia |
2019-01-01 |
2019 |
23.03356811 |
61.61932415 |
69.051 |
73.35955444 |
122.1423128 |
62.760654 |
|
Estonia |
2020-01-01 |
2020 |
22.70556274 |
63.08379046 |
69.229 |
69.21103086 |
121.5993523 |
58.73331 |
|
Estonia |
2021-01-01 |
2021 |
23.40177085 |
62.28384349 |
69.415 |
80.26909523 |
127.2575729 |
61.401203 |
|
Estonia |
2022-01-01 |
2022 |
23.9599725 |
61.84817981 |
69.609 |
85.78174381 |
151.9433321 |
63.392017 |
|
Estonia |
2023-01-01 |
2023 |
22.3084445 |
63.79366595 |
69.81 |
78.37933194 |
165.8601638 |
55.5125 |
|
Finland |
2019-01-01 |
2019 |
23.87829466 |
60.19478191 |
85.446 |
39.8806794 |
112.3317121 |
326.9818 |
|
Finland |
2020-01-01 |
2020 |
24.03649837 |
60.1185525 |
85.517 |
35.78756333 |
112.658097 |
313.94992 |
|
Finland |
2021-01-01 |
2021 |
23.99068075 |
60.22524176 |
85.596 |
39.47754763 |
115.1304627 |
320.63977 |
|
Finland |
2022-01-01 |
2022 |
25.13794095 |
59.43321865 |
85.681 |
45.41647521 |
123.3317901 |
320.72968 |
|
Finland |
2023-01-01 |
2023 |
24.532733 |
60.67969383 |
85.773 |
40.96461054 |
131.0408203 |
332.15573 |
|
France |
2019-01-01 |
2019 |
17.42467597 |
70.04358733 |
80.709 |
31.59205541 |
110.0485668 |
2729.5625 |
|
France |
2020-01-01 |
2020 |
16.78585851 |
70.89236839 |
80.975 |
27.32773557 |
110.5729469 |
2441.1663 |
|
France |
2021-01-01 |
2021 |
16.39562962 |
70.62424714 |
81.242 |
30.03499435 |
112.3889211 |
2593.6248 |
|
France |
2022-01-01 |
2022 |
16.82211154 |
70.73364627 |
81.509 |
34.68639972 |
118.2582836 |
2298.5596 |
|
France |
2023-01-01 |
2023 |
18.65085084 |
69.24009133 |
81.777 |
32.67942635 |
124.0273452 |
2406.6328 |
|
Germany |
2019-01-01 |
2019 |
26.99111427 |
62.32937357 |
77.376 |
47.12758088 |
112.8548771 |
3694.8396 |
|
Germany |
2020-01-01 |
2020 |
26.70737691 |
63.22998593 |
77.453 |
43.47697967 |
113.0183789 |
3443.882 |
|
Germany |
2021-01-01 |
2021 |
27.03409861 |
62.82541569 |
77.544 |
47.2795201 |
116.4842759 |
3544.581 |
|
Germany |
2022-01-01 |
2022 |
26.90219536 |
62.70640037 |
77.648 |
50.92392973 |
124.4897444 |
3413.0002 |
|
Germany |
2023-01-01 |
2023 |
28.08185074 |
62.572334 |
77.765 |
47.13524833 |
131.8924482 |
3169.5498 |
|
Greece |
2019-01-01 |
2019 |
13.35468493 |
69.50215683 |
79.388 |
40.11076393 |
101.9494141 |
316.8873 |
|
Greece |
2020-01-01 |
2020 |
14.66704864 |
68.9288963 |
79.715 |
32.06408533 |
100.6771022 |
276.95612 |
|
Greece |
2021-01-01 |
2021 |
15.07815965 |
68.38526442 |
80.038 |
40.94179961 |
101.9092137 |
302.5362 |
|
Greece |
2022-01-01 |
2022 |
16.82286357 |
67.35457004 |
80.357 |
49.13682964 |
111.7386222 |
314.33743 |
|
Greece |
2023-01-01 |
2023 |
15.65550462 |
67.61912833 |
80.673 |
44.86849659 |
115.6101434 |
305.181 |
|
Hungary |
2019-01-01 |
2019 |
24.71178166 |
56.41881423 |
71.644 |
81.52974061 |
121.6420474 |
274.8925 |
|
Hungary |
2020-01-01 |
2020 |
24.38025026 |
56.62920398 |
71.942 |
78.67507861 |
125.6887667 |
270.3273 |
|
Hungary |
2021-01-01 |
2021 |
24.1759579 |
56.99194658 |
72.245 |
79.94113828 |
132.1126761 |
283.95834 |
|
Hungary |
2022-01-01 |
2022 |
24.64836488 |
57.23795091 |
72.552 |
90.41536975 |
151.4118859 |
267.01694 |
|
Hungary |
2023-01-01 |
2023 |
24.32018356 |
57.5565435 |
72.864 |
81.20417972 |
177.3411199 |
252.93295 |
|
Iceland |
2019-01-01 |
2019 |
19.53163316 |
65.96650908 |
93.855 |
43.71324844 |
129.0033312 |
63.328125 |
|
Iceland |
2020-01-01 |
2020 |
20.1810958 |
65.1821176 |
93.898 |
33.30964505 |
132.6772481 |
58.08151 |
|
Iceland |
2021-01-01 |
2021 |
20.55813055 |
64.70049056 |
93.944 |
37.25462798 |
138.573743 |
60.644577 |
|
Iceland |
2022-01-01 |
2022 |
21.4071174 |
64.17278589 |
93.992 |
45.77997406 |
150.087496 |
63.73455 |
|
Iceland |
2023-01-01 |
2023 |
21.04203392 |
64.63683122 |
94.042 |
43.36806641 |
163.1995944 |
62.83699 |
|
Ireland |
2019-01-01 |
2019 |
35.3148022 |
57.51717856 |
63.405 |
128.0042429 |
106.5620392 |
187.35852 |
|
Ireland |
2020-01-01 |
2020 |
37.63855549 |
56.3579063 |
63.653 |
132.9391552 |
106.2144512 |
176.07672 |
|
Ireland |
2021-01-01 |
2021 |
38.46564517 |
54.63156911 |
63.912 |
133.7393556 |
108.7002317 |
179.446 |
|
Ireland |
2022-01-01 |
2022 |
41.48556677 |
52.61022544 |
64.183 |
137.0877814 |
117.21087 |
185.96701 |
|
Ireland |
2023-01-01 |
2023 |
37.59181399 |
56.62345644 |
64.466 |
134.1361029 |
124.5944807 |
180.76672 |
|
Israel |
2019-01-01 |
2019 |
18.63429673 |
70.91960901 |
92.501 |
29.04935295 |
108.1853575 |
301.50592 |
|
Israel |
2020-01-01 |
2020 |
18.14044581 |
71.81574649 |
92.587 |
27.51471658 |
107.5206533 |
285.13525 |
|
Israel |
2021-01-01 |
2021 |
17.17212049 |
72.44853741 |
92.674 |
29.39727111 |
109.1444307 |
292.04428 |
|
Israel |
2022-01-01 |
2022 |
|
|
92.763 |
31.72837991 |
113.9397968 |
308.64883 |
|
Israel |
2023-01-01 |
2023 |
|
|
92.854 |
30.86278317 |
118.7541544 |
309.45895 |
|
Italy |
2019-01-01 |
2019 |
21.52700987 |
66.2531597 |
70.736 |
31.60241973 |
110.6235956 |
1819.5557 |
|
Italy |
2020-01-01 |
2020 |
21.55892846 |
66.8991015 |
71.039 |
29.43227703 |
110.4712586 |
1651.9623 |
|
Italy |
2021-01-01 |
2021 |
23.1961729 |
64.75076548 |
71.346 |
32.11291448 |
112.5412505 |
1768.6172 |
|
Italy |
2022-01-01 |
2022 |
23.54411131 |
64.43335997 |
71.657 |
36.50740165 |
121.7710848 |
1718.2512 |
|
Italy |
2023-01-01 |
2023 |
23.12581484 |
64.92288967 |
71.973 |
35.05454365 |
128.6172919 |
1651.4344 |
|
Japan |
2019-01-01 |
2019 |
28.80408481 |
69.62767166 |
91.698 |
17.46352643 |
105.4842679 |
5205.5366 |
|
Japan |
2020-01-01 |
2020 |
29.07340052 |
69.46161618 |
91.782 |
15.5285155 |
105.4579012 |
4825.2485 |
|
Japan |
2021-01-01 |
2021 |
29.26286087 |
69.12976314 |
91.867 |
18.12547664 |
105.2118123 |
5037.4814 |
|
Japan |
2022-01-01 |
2022 |
26.91643406 |
71.38850987 |
91.955 |
21.54172312 |
107.8396906 |
5004.881 |
|
Japan |
2023-01-01 |
2023 |
|
|
92.043 |
|
111.3640359 |
4834.3735 |
|
Korea, Rep. |
2019-01-01 |
2019 |
32.67914892 |
57.24215056 |
81.43 |
39.27586107 |
115.1586343 |
3462.6758 |
|
Korea, Rep. |
2020-01-01 |
2020 |
32.5381602 |
57.00752121 |
81.414 |
36.35959055 |
115.7773678 |
3346.965 |
|
Korea, Rep. |
2021-01-01 |
2021 |
32.4320155 |
56.82189945 |
81.414 |
41.8772295 |
118.6698724 |
3506.508 |
|
Korea, Rep. |
2022-01-01 |
2022 |
31.72630126 |
58.03401549 |
81.427 |
48.27176422 |
124.7095918 |
3542.344 |
|
Korea, Rep. |
2023-01-01 |
2023 |
31.59416497 |
58.42226999 |
81.456 |
43.99566987 |
129.1901759 |
3453.8945 |
|
Latvia |
2019-01-01 |
2019 |
18.9192156 |
64.38253472 |
68.222 |
60.02102714 |
116.8569846 |
44.83716 |
|
Latvia |
2020-01-01 |
2020 |
19.64637229 |
63.63650735 |
68.315 |
60.7512914 |
117.1129773 |
40.858128 |
|
Latvia |
2021-01-01 |
2021 |
20.35116147 |
62.9128603 |
68.421 |
64.59124088 |
120.9493986 |
42.6252 |
|
Latvia |
2022-01-01 |
2022 |
21.45003618 |
61.67974694 |
68.54 |
72.89352548 |
141.8860818 |
38.474934 |
|
Latvia |
2023-01-01 |
2023 |
20.93407592 |
63.06478321 |
68.671 |
64.05959465 |
154.5679254 |
40.232597 |
|
Lithuania |
2019-01-01 |
2019 |
25.32789166 |
61.4320691 |
67.855 |
77.2441153 |
118.3820983 |
70.59712 |
|
Lithuania |
2020-01-01 |
2020 |
24.91142533 |
61.21988992 |
68.046 |
73.09807766 |
119.8025585 |
70.29448 |
|
Lithuania |
2021-01-01 |
2021 |
24.88210343 |
61.18293944 |
68.249 |
80.06601567 |
125.4135643 |
70.14949 |
|
Lithuania |
2022-01-01 |
2022 |
25.72457569 |
61.20804216 |
68.465 |
86.7990152 |
150.1263651 |
65.06625 |
|
Lithuania |
2023-01-01 |
2023 |
24.07650919 |
63.11620596 |
68.694 |
78.49444266 |
163.8138656 |
66.32199 |
|
Luxembourg |
2019-01-01 |
2019 |
11.59889581 |
79.11892083 |
91.223 |
206.4116498 |
115.0878151 |
47.288246 |
|
Luxembourg |
2020-01-01 |
2020 |
11.31189448 |
79.75605209 |
91.453 |
203.1202633 |
116.031486 |
40.540794 |
|
Luxembourg |
2021-01-01 |
2021 |
10.72099889 |
80.11875619 |
91.672 |
213.2226787 |
118.963509 |
43.215237 |
|
Luxembourg |
2022-01-01 |
2022 |
10.39672252 |
80.38341008 |
91.881 |
211.2782056 |
126.5010465 |
38.763626 |
|
Luxembourg |
2023-01-01 |
2023 |
10.47195005 |
80.59982558 |
92.078 |
212.5306183 |
131.2339612 |
37.545822 |
|
Mexico |
2019-01-01 |
2019 |
31.7765006 |
59.22502268 |
80.444 |
38.4970333 |
141.542523 |
2241.414 |
|
Mexico |
2020-01-01 |
2020 |
30.95199504 |
59.23560145 |
80.731 |
39.24343244 |
146.3504877 |
2041.9174 |
|
Mexico |
2021-01-01 |
2021 |
32.07246049 |
58.19772293 |
81.016 |
40.67732036 |
154.6766721 |
2175.8943 |
|
Mexico |
2022-01-01 |
2022 |
33.27033991 |
58.00985834 |
81.3 |
42.76103074 |
166.8903693 |
2273.0837 |
|
Mexico |
2023-01-01 |
2023 |
31.7515055 |
58.31887734 |
81.582 |
36.20345662 |
176.1160036 |
2348.0437 |
|
Netherlands |
2019-01-01 |
2019 |
17.56228053 |
69.96685341 |
91.876 |
82.5377127 |
115.9079949 |
1022.32947 |
|
Netherlands |
2020-01-01 |
2020 |
17.856829 |
69.63403764 |
92.236 |
78.2654765 |
117.3828783 |
990.14575 |
|
Netherlands |
2021-01-01 |
2021 |
18.16165415 |
69.28405777 |
92.572 |
84.10520718 |
120.5237155 |
1014.6149 |
|
Netherlands |
2022-01-01 |
2022 |
19.50927913 |
68.68412569 |
92.886 |
93.75222341 |
132.5775428 |
955.1913 |
|
Netherlands |
2023-01-01 |
2023 |
19.36047872 |
69.31512466 |
93.179 |
84.96391983 |
137.6663907 |
954.5236 |
|
New Zealand |
2019-01-01 |
2019 |
20.27454925 |
65.58291418 |
86.615 |
27.35030566 |
114.2409118 |
263.68475 |
|
New Zealand |
2020-01-01 |
2020 |
19.7803605 |
66.46015756 |
86.699 |
21.72857875 |
116.1996427 |
239.25703 |
|
New Zealand |
2021-01-01 |
2021 |
18.96949822 |
67.10921858 |
86.789 |
22.33729873 |
120.7792107 |
235.5494 |
|
New Zealand |
2022-01-01 |
2022 |
|
|
86.884 |
24.39482557 |
129.4417672 |
232.21115 |
|
New Zealand |
2023-01-01 |
2023 |
|
|
86.985 |
|
136.8628745 |
239.13474 |
|
Norway |
2019-01-01 |
2019 |
29.82445898 |
57.31028928 |
82.616 |
36.64092532 |
120.2696589 |
516.76984 |
|
Norway |
2020-01-01 |
2020 |
26.88316734 |
59.60411085 |
82.974 |
32.21033778 |
121.8170301 |
557.34674 |
|
Norway |
2021-01-01 |
2021 |
37.90192304 |
50.56725003 |
83.323 |
43.03630192 |
126.06099 |
568.4789 |
|
Norway |
2022-01-01 |
2022 |
49.158192 |
41.74494892 |
83.664 |
55.46006703 |
133.3273007 |
532.7134 |
|
Norway |
2023-01-01 |
2023 |
38.97509651 |
49.99343881 |
83.995 |
47.17844247 |
140.684101 |
553.08405 |
|
Poland |
2019-01-01 |
2019 |
28.63654319 |
56.88400921 |
60.037 |
53.19533562 |
114.1117794 |
1187.2677 |
|
Poland |
2020-01-01 |
2020 |
28.34833116 |
57.16430705 |
60.043 |
52.9909243 |
117.9624468 |
1139.2343 |
|
Poland |
2021-01-01 |
2021 |
28.10471774 |
56.670462 |
60.075 |
57.69934428 |
123.9254804 |
1224.7583 |
|
Poland |
2022-01-01 |
2022 |
29.05566479 |
57.2601192 |
60.134 |
62.87918751 |
141.8072466 |
1186.8682 |
|
Poland |
2023-01-01 |
2023 |
28.67694172 |
58.752273 |
60.218 |
57.81424617 |
158.1560804 |
1143.4487 |
|
Portugal |
2019-01-01 |
2019 |
18.83272376 |
65.62641557 |
65.764 |
43.50842185 |
110.6243586 |
293.05664 |
|
Portugal |
2020-01-01 |
2020 |
19.400455 |
65.58349407 |
66.31 |
37.04677424 |
110.6105988 |
265.24768 |
|
Portugal |
2021-01-01 |
2021 |
19.24268109 |
65.1921444 |
66.849 |
41.40171507 |
112.0105498 |
266.48996 |
|
Portugal |
2022-01-01 |
2022 |
18.62083279 |
66.0928537 |
67.381 |
49.59896849 |
120.7839903 |
262.67505 |
|
Portugal |
2023-01-01 |
2023 |
18.01878415 |
67.00009623 |
67.906 |
47.44143467 |
125.9913286 |
263.4581 |
|
Slovak Rep. |
2019-01-01 |
2019 |
29.60431404 |
58.07612357 |
53.729 |
91.90868525 |
115.3389877 |
184.84892 |
|
Slovak Rep. |
2020-01-01 |
2020 |
28.3577579 |
59.57656873 |
53.76 |
85.05091836 |
117.5730362 |
181.1254 |
|
Slovak Rep. |
2021-01-01 |
2021 |
28.59852317 |
58.848401 |
53.82 |
92.06064246 |
121.276124 |
195.22316 |
|
Slovak Rep. |
2022-01-01 |
2022 |
26.33248051 |
60.971041 |
53.909 |
99.29740359 |
136.7681135 |
179.89285 |
|
Slovak Rep. |
2023-01-01 |
2023 |
32.72022512 |
56.53508904 |
54.027 |
91.43088959 |
151.1724597 |
186.63306 |
|
Slovenia |
2019-01-01 |
2019 |
28.86490755 |
56.34164923 |
54.822 |
83.61469331 |
111.0510749 |
80.00229 |
|
Slovenia |
2020-01-01 |
2020 |
29.21464935 |
57.04246677 |
55.118 |
77.7607326 |
110.9901558 |
77.29536 |
|
Slovenia |
2021-01-01 |
2021 |
28.42644864 |
57.68334538 |
55.427 |
83.56290137 |
113.1179093 |
76.58192 |
|
Slovenia |
2022-01-01 |
2022 |
28.13757419 |
58.03596439 |
55.751 |
94.14607036 |
123.1104048 |
70.60175 |
|
Slovenia |
2023-01-01 |
2023 |
29.12646047 |
57.81823267 |
56.088 |
83.999064 |
132.2782854 |
72.429436 |
|
Spain |
2019-01-01 |
2019 |
20.0416214 |
68.18451514 |
80.565 |
34.90690181 |
110.9614436 |
1583.871 |
|
Spain |
2020-01-01 |
2020 |
20.136728 |
68.30081947 |
80.81 |
30.77926024 |
110.6033122 |
1422.96 |
|
Spain |
2021-01-01 |
2021 |
20.32406385 |
67.41902494 |
81.056 |
34.17642294 |
114.0244221 |
1530.2695 |
|
Spain |
2022-01-01 |
2022 |
20.79462142 |
67.89888716 |
81.304 |
40.87406425 |
123.5917283 |
1591.0308 |
|
Spain |
2023-01-01 |
2023 |
20.24093485 |
68.52927958 |
81.552 |
38.95596725 |
127.9574347 |
1572.863 |
|
Sweden |
2019-01-01 |
2019 |
21.90371194 |
65.51512104 |
87.708 |
47.80921095 |
110.5092198 |
625.81903 |
|
Sweden |
2020-01-01 |
2020 |
21.59068762 |
65.84370307 |
87.977 |
43.84730253 |
111.0588566 |
600.27936 |
|
Sweden |
2021-01-01 |
2021 |
22.69256973 |
64.83447364 |
88.238 |
46.4968565 |
113.4612788 |
633.67957 |
|
Sweden |
2022-01-01 |
2022 |
23.95072905 |
63.60839922 |
88.492 |
52.86155975 |
122.9571834 |
621.6937 |
|
Sweden |
2023-01-01 |
2023 |
22.77915937 |
65.27817362 |
88.738 |
53.9821811 |
133.4683318 |
597.5023 |
|
Switzerland |
2019-01-01 |
2019 |
24.72115913 |
71.86188459 |
73.849 |
66.96539368 |
99.54689637 |
335.947 |
|
Switzerland |
2020-01-01 |
2020 |
24.82791568 |
71.75040688 |
73.915 |
64.09555716 |
98.82431041 |
308.59106 |
|
Switzerland |
2021-01-01 |
2021 |
25.57885649 |
70.98408554 |
73.996 |
71.28755618 |
99.39928425 |
298.0824 |
|
Switzerland |
2022-01-01 |
2022 |
24.87466743 |
71.78913327 |
74.092 |
76.93561539 |
102.2172818 |
290.10965 |
|
Switzerland |
2023-01-01 |
2023 |
24.92771976 |
71.8940438 |
74.202 |
75.32686523 |
104.4000305 |
314.64438 |
|
Turkiye |
2019-01-01 |
2019 |
27.33170171 |
56.3110349 |
75.63 |
33.07421471 |
234.4371263 |
1849.1666 |
|
Turkiye |
2020-01-01 |
2020 |
28.02576622 |
54.20113467 |
76.105 |
29.12078986 |
263.2235613 |
1821.7764 |
|
Turkiye |
2021-01-01 |
2021 |
31.1327996 |
52.7548604 |
76.569 |
35.74369481 |
314.8061472 |
1951.1511 |
|
Turkiye |
2022-01-01 |
2022 |
31.29376741 |
51.73761542 |
77.022 |
38.58420213 |
542.4388079 |
1972.118 |
|
Turkiye |
2023-01-01 |
2023 |
28.26128491 |
54.04781494 |
77.463 |
32.26905871 |
834.5931428 |
1945.299 |
|
United Kingdom |
2019-01-01 |
2019 |
17.75881958 |
70.97301113 |
83.652 |
31.63478028 |
119.6227113 |
2199.0686 |
|
United Kingdom |
2020-01-01 |
2020 |
17.38801913 |
72.14663582 |
83.903 |
29.69199083 |
120.8063621 |
1971.1879 |
|
United Kingdom |
2021-01-01 |
2021 |
16.6124289 |
72.28243857 |
84.152 |
29.59731253 |
123.8487146 |
1990.9581 |
|
United Kingdom |
2022-01-01 |
2022 |
16.65619173 |
72.1714171 |
84.398 |
33.43046097 |
133.6600703 |
2018.8452 |
|
United Kingdom |
2023-01-01 |
2023 |
16.93422041 |
72.83570992 |
84.642 |
32.17265199 |
142.7408914 |
1930.5928 |
|
United States |
2019-01-01 |
2019 |
18.16115523 |
76.67753687 |
82.459 |
11.79500678 |
117.2441955 |
26578.494 |
|
United States |
2020-01-01 |
2020 |
17.29290722 |
77.1756474 |
82.664 |
10.08355786 |
118.6905016 |
24622.646 |
|
United States |
2021-01-01 |
2021 |
17.67320446 |
76.68176073 |
82.873 |
10.80797936 |
124.2664138 |
25956.828 |
|
United States |
2022-01-01 |
2022 |
|
|
83.084 |
11.63390862 |
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United States |
2023-01-01 |
2023 |
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83.298 |
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139.7357936 |
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