Skip to main content

Analyzing the relationship between sustainable development indicators and renewable energy consumption


The transition to renewable energy sources remains a major challenge for developed and developing countries. Therefore, the study aims at investigating the relationship between sustainable development indicators and renewable energy consumption utilizing integrated data sets for 255 indicators expressing the sustainable development goals from 137 developed and developing countries. Principal component analysis then multiple linear regression tests are employed to conclude a mathematical model representing the numerical relationship between a set of sustainable development indicators and renewable energy consumption. The statistical analysis results include (i) an inverse correlation between Sustainable Development Index which expresses the dominant factor representing collected data and renewable energy consumption, (ii) a set of sustainable development indicators as the determinants of renewable energy consumption. The findings explain the rapid transformation of low Sustainable Development Index countries towards renewable energy technology by realizing the effective role of using renewable energy as a local solution. Moreover, the findings manifest the importance of the given sustainable development indicators in obtaining a more significant increase in renewable energy consumption. Using the concluded mathematical mode, planners and decision-makers can compromise the concluded indicators to attain a serious progressing step towards renewable energy transition aligned with achieving sustainable development.


Energy has a positive impact on health, education, transportation, business, and most crucial; how long people may survive [1]. There is an exponential growing energy demand to meet the global population growth and maintain higher living standards [2]. Primary energy sources are categorized based on long-term availability as renewable and conventional energy resources; thus, consuming energy resources have two critical options, using easily accessed, conventional but unhealthy environmental energy resources or adopting technology-oriented, non-conventional, and healthy environmental energy resources [3]. Nowadays, the world is heavily dependent on depletable energy sources tracking not sustainable pathways. Renewables are responsible for only 20% of global energy consumption, which is a small share compared to its benefits Fig. 1 [4, 5]. Therefore, the transition to renewable energy sources remains a major challenge for developed and developing countries. Renewables are a perfect key for increasing energy security since they are physically available, economically affordable, socially accessible, and publicly acceptable [6]. Geographic limitations are the main obstacle of renewable power technologies. Renewable-based energy generation is still not as cost-effective compared to other energy generation options; it has high initial costs besides the high cost of storing systems although the costs of renewables have been going down [7]. Also, land areas that are required for the installation of energy technology are large compared to plants powered by fossil fuel [8]. Despite some existing limitations and challenges that need to be overcome, clean sources make a significant contribution in providing energy within buildings, industry, and transport sectors. Accordingly, there is an imperative need for exploring the relationship between renewable energy use and sustainable development (SD) to ensure energy access, promote a healthier environment and achieve energy access equality among people.

Fig. 1
figure 1

(Global energy consumption growth by source 1965-2019 - [4, 5]

Importance of renewable energy and sustainable development nexus

United Nations’ Sustainable Development Goals (SDGs) are a blueprint that guides societies for achieving progress in all pressing challenges. The United Nations defined the SDGs as “a universal call to action to end poverty, protect the planet and ensure that all people enjoy peace and prosperity” [9]. Renewable energy expressed in Goal 7 “Ensure access to affordable, reliable, and modern energy for all” is considered the heart of SDGs [10]. Securing access to energy supply is a highly demanding concern, but it is more challenging to provide energy in a sustainable form. Governments worldwide have declared the 17 SDGs to be ‘integrated and indivisible’ [11]; meaning that SDG7 cannot be achieved in sectoral isolation apart from the achievement of SDGs. Renewable energy is strongly connected to all human activities, and it contributes to achieving urban and environmental sustainability [12]. Ensuring access to renewable energy sources contributes to the implementation of SDGs through enabling development processes and promoting progress path.

The analysis of the relationship between renewable energy and SDGs, which is the main aim of the current study, represents a step for mapping the links between energy systems and social well-being, economic activities, and the environment. Also, this interaction would affect future energy scenarios at national and local levels. The analysis of the relationship between renewable energy and SDGs at the targets level reveals a complex interaction including synergies and trade-offs [13] in which positive interactions between renewable energy and SDGs exceed the negative ones [14]. Evidence of synergies between 143 SDGs targets to achieve SDG7 is established, meaning that about 85% of SDGs targets support SDG7 [15].

The role of renewable energy in achieving Sustainable Development Goals

This section discovers the connection between adopting renewable energy sources and the achievement of SDGs at goals level, using an analysis extracted from three studies, issued from global organizations scoped in SDGs interactions (1) accelerating the global energy transformation [16], (2) mapping the Renewable Energy Sector to the Sustainable Development Goals: An Atlas [17], and (3) a guide to SDG interactions: from science to implementation [18]. Following the results of the reviewed studies, a multi-perspective analyzed summary of the connection between renewable energy and SDGs is listed in Table 1.

Table 1 Multi-perspective summary of renewable energy-SDGs nexus

Literature review

The focus of the literature search was on the studies that examine the relationship between the use of renewable energy and one or more SD dimensions. The literature section is divided according to the examined SD dimensions, while environmental and economic dimensions have been most discussed among the majority of previous studies.

Renewable energy and environmental dimension

Most researchers use the amount of carbon dioxide (CO2) emissions to express global climate change and environmental quality. Empirical results, from a series of studies, indicate that CO2 emissions and REC are inversely correlated and there is a bidirectional causality running from CO2 to REC in developed and developing countries [1922]. Evidence, from 50 African countries across regions and income levels, confirm that REC contributes to mitigating CO2 emissions within ten years [23]. The result of examining a group of EU countries prove that the use of renewable energy options is a key solution to improve air quality by decreasing greenhouse gas emissions (GHG), where CO2 is the major component of GHG emissions [24]. A recent study confirms the role of REC in improving environmental sustainability characterizing environmental quality by ecological footprint [25]. Another study finds that global REC has a long-run significant positive impact on environmental sustainability by testing a global framework of developed and developing countries. The study recommends that the roles of renewable energy in increasing environmental quality should be considered by reforming the energy policies to encourage the use of renewable energy sources [26]. The empirical outcomes of a study analyzing the environmental degradation in Japan demonstrate proof for the existence of an interaction between renewable energy use and CO2 emissions. Hence, in the short and medium terms, renewable energy usage mitigates CO2. The study recommends that Japan should support renewable energy development [27]. Moreover, there is one-way causality from renewable energy consumption (REC) to CO2 emissions in Argentina; thus, renewables improves the environment [28]. Modelling the dynamic linkage between REC and environmental degradation, renewable energy use can predict CO2 emissions in South Korea [29]. A weak negative relationship is shown between renewable energy and CO2 emissions in China, the world’s biggest carbon emitter [30].

Renewable energy and economic dimension

A bidirectional causality is running between per capita Growth Domestic Product (GDP) and REC, addressing that developed countries are consuming more renewables sources, while lower GDP countries rely more on non-renewables sources [31, 32]. In most European countries, there is a positive relationship between REC and economic growth; REC has a positive impact on GDP [33, 34 ]. A panel of data for 102 countries with different income levels were analyzed and the results prove that for low-income countries, REC has a positive relationship with ‘industrial and service values added’ the industry/service contribution to overall GDP [35]. Testing data, from some Latin American countries, confirm that GDP per capita, technological innovation and trade have a statistically significant positive association with renewable energy use [36]. Evidence, from the association of southeast Asian nations countries, finds that the adopting of renewable sources in energy generation spurs economic growth and creates better export opportunities [37]. There is a positive nexus at the regional level in seven East African countries between the growth of renewable energy and economic growth [38]. In Rwanda, a low-income country, an asymmetric causality relationship running from REC positive shocks to economic growth is noted [39]. Contrary to popular belief, there is a bi-directional relationship between economic growth and the use of renewable energy in developing countries that are rapidly endorsing renewable energy to power the economic growth engine [40].

Renewable energy and other dimensions

A closer look at previous studies shows an average number of researches linking REC to a group of indicators that have not been frequently examined. In developed countries, income inequality is associated with REC, thus increasing REC plays a notable role in reducing income inequality [41]. An increase in the usage of renewable energy leads to a decrease in public health expenditure for the association of Southeast Asian nations countries [37]. Corruption control is positively linked to renewable energy participation; it increases the REC in developed and developing countries [42]. The education level has a significant impact on renewable energy deployment in developed and developing countries [43]. A study has agreed to use ‘adjusted net savings’ as a good SD variable and the results have confirmed that renewable energy has a statistically significant positive impact on SD for developed and developing countries [44]. Measuring the connection between the Human Development Index (HDI) and renewable energy, a study indicates that the deployment of renewable energy contributes to improving the SD level in 28 OECD countries [45].

The analysis of the above literature has revealed that existing studies have connected REC to limited SDGs dimensions which do not meet the SDGs wide-ranging concept. Previous researches offer an improved understanding of the REC-SDGs nexus. Therefore, the current study addresses the literature gap by examining the relationship between REC and an integrated panel of SDGs indicators.


The study aims at investigating the relationship between SD indicators and REC by concluding a mathematical model to represent the numerical relationship utilizing data of 255 SDGs indicators from 137 countries. Moreover, the study seeks to find out how effective is a high SD level in increasing REC by testing the adopted hypothesis which proposes that REC is associated with a group of SD indicators and its value can be calculated in terms of these indicators. This quantitative study is based on deductive and statistical analytical approaches to test the adopted hypothesis. The deductive approach extracts a key summary of the relation between renewable energy and SDGs through surfing theoretical background readings and previous studies guidelines. The statistical approach examines the influence of SD indicators on REC, and Fig. 2. illustrates research methodology scheme.

Fig. 2
figure 2

Research Methodology Scheme

Statistical indicators provide an accurate conception for analyzing and comparing data; therefore, it was crucial to interpret the nexus between renewable energy dependency and SDGs into measurable indicators. Renewable energy is frequently measured by: consumption, production, capacity, and energy supply. The current study agrees to measure the country dependency on renewable energy by consumption which specifies the actual energy need [46]. The arrangement of the SDGs is used as a format for collecting and allocating indicators that are gathered under each associated SDG. The research methods are designed within two parameters: (1) collecting and organizing data, and then (2) applying the statistical model.

Empirical study

Data collecting and organizing

Data source

The study depends on three sources of data: (1) SDGs indicators by World Bank [47], (2) Human Development Indices and Indicators by the United Nations Development Program [48, 49], and (3) SDGs indicators from the United Nations SDGs index and dashboards [50]. Each source endorses a group of indicators for measuring the achievement of SDGs and generates regularly updated data. The study collects each source of the endorsed indicators and compiles them into a preliminary list containing 517 indicators for 218 countries following the World Bank countries list order.

Data availability and update

Data within the years 2017, 2018, and 2019 are collected, and 2017 data is selected as it has the most available data. Indicators (variables) and countries are specified within the framework of data availability. An optimization process is applied to filter the collected data, including indicators and countries with complete data or less than 5% missing data, and other indicators are excluded.

Data adjusting

Due to the unavailability of some indicators, the study proposes a collection of supplementary indicators to represent demographic aspects, human development, and SDGs’ overall performance. The Series Mean method is employed to fill missing data [51] using Statistical Package for Social Sciences (SPSS) software. The matrix variables are is formed from using the adjusted indicators list which compiles 255 SDGs indicators for 137 countries. Figure 3 illustrates the data collection and organizing sequence, and Table 2 indicates additional indicators and variables distribution according to SDGs.

Fig. 3
figure 3

Data collecting and organizing scheme)

Table 2 Variables distribution according to SDGs indicators and Additional indicators

The study divided the statistical model into two tests: (1) principal component analysis (PCA) which is a method used for multivariate data analysis to reduce dimensionality [52]. PCA is a prerequisite for (2) multiple linear regression (MLR) analysis which is a conceptually analytic technique for understanding the interrelationships among variables [53]. Both PCA and MLR analyses are processed by the SPSS software.

Principal component analysis (PCA)

PCA is a widely used method for factor reduction. It reduces dataset dimensionality and preserves as much ‘variability’ as possible [54]. It applies to large data sets to minimize a large number of variables (indicators) into a small group of components. The study employs the PCA test to compute the dominant components that have the most variances in variables [55]. PCA test is applied by utilizing SPSS software to the 255 collected variables. A preliminary PCA run generates 40 components. The first component accounted for 34.2% of the variance of the variables explained. PCA analysis is based on which variables are most correlated with each component. Variables correlated with the first component with a saturation value of more than 0.5 whether positive or negative are obtained, while the rest variables are dropped.

To exclude the least influencing indicators a second PCA run is executed on obtained variables. Its results show that the first component of the 14 extracted components accounted for about 61.7% of variables variance which is a high eigenvalue as shown in Table 3. Therefore, the first component has the dominant forces to describe the change that occurred in variables. Variables expressed by the first component can explain 61.7% of a country’s SD level. One hundred twenty-six variables obtained from PCA are the input dependent variables in the next statistical analysis phase.

Table 3 Part of the first and second runs components of PCA

Multiple linear regression analysis (MLR)

MLR is a quantitative analytical tool to explain the behavior of some variables by another variable. The regression equation, which is the result of MLR, has the form of a mathematical function that quantifies the relationships between a set of independent variables and the dependent variable [56, 57]. The regression equation is used to estimate past values and predict future values of the dependent variable in terms of independent variables’ values [58]. MLR produces a regression equation that has the form of Eq. (1)

$$ \mathrm{Y}=\mathrm{a}+{\mathrm{b}}_1{\mathrm{X}}_1+{\mathrm{b}}_2{\mathrm{X}}_2+{\mathrm{b}}_3{\mathrm{X}}_3+\dots \dots +{\mathrm{b}}_{\mathrm{n}}{\mathrm{X}}_{\mathrm{n}} $$

Where Y is the response (dependent variable), Xi (i=1,2, 3…n) is the set of predictors (independent variables), bi (i=1,2, 3…n) is the line slope and a is the y-intercept. MLR analysis is applied within the 126 variables obtained from the first component produced from the PCA second run. The study employs MLR analysis utilizing SPSS software to set an equation with calculated values for constant and SDGs indicators coefficients. The regression equation summarizes the linear relationship between REC and SDGs indicators as shown in Eq. 2.


The response variable represents the REC, and the predictors (X1, X2, X3, …... Xn) represent SDGs indicators. The determined regression equation offers a calculated value for REC in terms of 111 SD Indicators given in Eq. (2).

Results and discussions

Sustainable Development Index

Along with PCA results, SPSS software generates factor score values for each extracted component. The factor score is a numerical value mapping the variables of each component into one composite value. The study proposes the factor score value of PCA dominant component to be an SD Index as it explains 60% of SD country level. The study classifies countries according to the proposed SD Index, Table 4. mentions four categories of SD Index high, medium-high, medium-low, and low referring to the share of REC. A closer look at SD Index and REC values for each category, it is evident that most countries with a high REC have a low SD Index and vice versa. Pearson correlation coefficient is calculated to measure the strength and the direction of the relationship between SD Index and RE. The obtained Pearson coefficient value (− 0.672) describes an inverted linear association as shown in Fig. 4. Contrary to most previous studies that demonstrate a positive association between REC and SD indicators [31, 32]. The obtained strong inverse relationship provides evidence that the SD level is not sufficient to explain the increase in REC. The transition to renewable energy use in countries with a high SD Index occurs slowly as these counties already have conventional power plants and a solid infrastructure network for energy generation and transmission, so generating energy from renewable sources is not an essential need for providing a normal life. In such countries, renewable energy is generated for saving natural resources, improving environmental conditions, and reducing global climate deterioration [38]. On the other side, countries with a low SD Index are rapidly turning to the new clean energy as they do not own suitable fossil resources enough to comply with their energy needs and the infrastructure network is not proper and sometimes does not exist [39, 40].

Table 4 Examples of countries classification according to SD Index
Fig. 4
figure 4

Correlation (− 0.672) between SD Index, factor score, and REC

Sustainable development indicators and REC relationship:

The determined regression equation Eq. (2) describes the mathematical relationship between each SDGs indicator (independent variables) and REC (the dependent variable) determining the perfect line to fit the relationship. Furthermore, the regression equation provides a calculated value for a country’s REC in terms of SDGs indicators values. Analyzing the 111 SDGs indicators given in the regression equation, further explanations to the relationship between REC and SDGs indicators can be provided in the following points:

  • The sign of the predictors is used to describe how an individual SDGs indicator change with REC; positive sign means direct relationship and negative sign means inverse relationship. The results indicate that 68 SDGs represent a direct relationship with REC, while 43 SDGs represent an inverse relationship with REC.

  • The value of the predictor coefficient is used to evaluate the importance of individual predictors. SDGs indicator coefficient, which has a more significant positive or negative value, makes a more remarkable change in REC value. Likewise, the SDGs indicator coefficient that has a smaller positive or negative value makes a smaller change in the value of REC.

  • The regression equation is used to adjust the individual predictors according to the sign and the value of the predictor coefficient to estimate the REC values in any year by changing the values of the predictors’ indicators given in the regression equation.

Tables 5 and 6 show the SDGs indicator given in Eq. (2) that have a high positive and negative relationship with REC. Comparing indicators, that have appeared in the regression equation to previous literature results, has found that the environmental dimension characterized by CO2 emissions has a negative relationship with REC. Hence, the use of renewable energy contributes to improving the environment as stated in most previous studies [1930]. Regarding the economic dimension, most previous studies have mentioned the positive relationship between GDP and REC [3238], while the current study demonstrates that GDP has no relationship with REC meantime the results indicate a positive relationship between Income Index and REC. A positive correlation between service and industrial value-added and REC is indicated in both the current results and a former study [35]. Previous studies signify a positive correlation between the education level [43], income inequality [41], HDI [45], public health expenditure [37], adjusted net savings [44], and REC. On the other hand, the results indicate a negative correlation between the education level characterized by Education Index, income inequality represented by Inequality-Adjusted Income Index, HDI, and REC, and no relationship appears between adjusted net savings, public health expenditure, and REC.

Table 5 SDGs indicators that have a high positive relationship with REC
Table 6 SDGs indicators that have a high negative relationship with REC

The extracted indicators make a significant change in REC value when their value change, meaning that to increase the REC, it is helpful for planners and decision-makers to consider these indicators.

Estimated renewable energy consumption

The study uses the determined regression equation Eq. (2) to calculate the estimated value of REC for the 137 tested countries in terms of SDGs indicators values. Pearson correlation coefficient is calculated to investigate the connection between estimated REC and the concluded SD Index (Factor Score), The values of the estimated REC have a positive weak relationship (+ 0.25) with the SD Index. The range of the estimated REC values indicates that an increase in most countries REC should be considered except in the low SD Index category that the REC real value is greater than the estimated value as shown in Table 7.

Table 7 Examples of estimated REC according to SD Index


Most existing studies have examined the relationship between SD and REC using economic and environmental indicators but only a few studies have included some social indicators. However, this study extends the literature on investigating the relationship between SD indicators and REC. A quantitative dedicative approach is adopted for setting a statistical model to test the proposed hypothesis, which suggests that REC is associated with a group of SD indicators and its value can be calculated in terms of these indicators. The statistical model, which consists of PCA and MLR that tests and utilizes data of 255 SDGs indicators from 137 countries, is employed to examine the REC-SD nexus.

The results, from the statistical tests, declare a further explanation for the relationship between REC and SDGs indicators. PCA results are (1) reducing data and extracting the dominant SDGs indicators, (2) concluding the SD Index, (3) classifying countries according to SD Index, and (4) determining the correlation between REC and SD Index. On the other hand, MLR results are (1) determining the relationship between SDGs indicators and REC, (2) evaluating the importance of each SGDs indicator, and (3) estimating REC value in a certain year by adjusting the SDGs indicators values.

The inverse correlation between REC and SD Index, which expresses the dominant factor representing collected data, explain the rapid transformation of low SD Index countries towards renewable energy technology. In low SD Index countries, many factors drive people to depend on renewable resources but the most forcing factor is the lack of a source of energy and the absence of transmission infrastructure due to many economic, political or natural obstacles.

The results. also. provide perceptible evidence of the relationship between REC and a set of SDGs indicators. In contrast, the importance of the individual SDGs indicators is varied according to the change they make in REC value. This variation provides planners and decision-makers with SDGs indicators that have the greatest importance (coefficients values) to obtain a more significant increase in REC value. For planners and decision-makers, the concluded regression equation, which represents the relationship between REC and a set of an integrated panel of SDGs indicators, is an effective optimization tool to increase the opportunities of providing societies with clean, modern and affordable sources of energy at the same time accelerating the development wheel.

Availability of data and materials

The datasets used are available from the World Bank database [47], Human Development Data Center [49], and Sustainable Development Report 2021 [50]. The combined dataset is available to the authors.



Carbon dioxide


Principal component analysis


Growth domestic product


Renewable energy consumption


Greenhouse gas emissions


Statistical Package for Social Sciences


Human Development Index


Sustainable development


Multiple linear regression


Sustainable Development Goals


  1. Lloyd PJ (2017) The Role of Energy in Development. J Energy Southern Africa 28(1):54–62

    Google Scholar 

  2. Avtar R, Tripathi S, Aggarwal AK, Kumar P (2019) Population–Urbanization–Energy Nexus: A Review. Resources 8(3):136

    Google Scholar 

  3. Kumar M (2020) Social, Economic, and Environmental Impacts of Renewable Energy Resources. In: Okedu KE, Tahour A, Aissaoui AG (eds) Chapter in Wind Solar Hybrid Renewable Energy System. BoD – Books on Demand, London, UK, pp 227–234. Available from: (Accessed 22/10/2020)

    Chapter  Google Scholar 

  4. Dudley B (2018) BP Statistical Review of World Energy. Published online at, London, UK, British Petroleum. Retrieved from: Accessed 15 July 2020

  5. Ritchie H, Roser M (2020) Renewable Energy. Published online at, England Retrieved from: (Accessed 15/7/2020)

    Google Scholar 

  6. Paravantis JA, Kontoulis N. “Energy Security and Renewable Energy: A Geopolitical Perspective”, Chapter at Renewable Energy - Resources, Challenges and Applications, Edited by Mansour Al Qubeissi, Ahmad El-kharouf and Hakan Serhad Soyhan, London, IntechOpen, 2020, Doi: Available from: (Accessed 9/6/2021)

  7. Bogdanov D, Ram M, Aghahosseini A, Gulagi A, Oyewo AS, Child M et al (2021) Low-cost renewable electricity as the key driver of the global energy transition towards sustainability. Energy 227:120467

    Google Scholar 

  8. Halkos GE, Gkampoura EC (2020) Reviewing usage, potentials, and limitations of renewable energy sources. Energies 13(11):2906

    Google Scholar 

  9. UNDP (2020) Sustainable Development Goals. Published online at, New York, USA Retrieved from: (Accessed 19/4/2021)

    Google Scholar 

  10. IEA (2018) Energy is at the heart of the sustainable development agenda to 2030. Published online at, Paris Retrieved from: (Accessed 16/4/2021)

    Google Scholar 

  11. UNDP (2015) Transforming our world: the 2030 Agenda for Sustainable Development. Published online at, New York, USA Retrieved from: (Accessed 26/5/2021)

    Google Scholar 

  12. Barmelgy MMEL, Shalaby AM, Kamal RM (2020) A Framework for Developing Sustainable New Cities in Egypt. J Eng Appl Sci 67(3):585–604

    Google Scholar 

  13. Santika WG, Anisuzzaman M, Bahri PA, Shafiullah GM, Rupf GV, Urmee T (2019) From goals to joules: A quantitative approach of interlinkages between energy and the Sustainable Development Goals. Energy Res Soc Sci 50:201–214

    Google Scholar 

  14. McCollum DL, Echeverri LG, Busch S, Pachauri S, Parkinson S, Rogelj J et al (2018) Connecting the sustainable development goals by their energy inter-linkages. Environ Res Lett 13(3):033006

    Google Scholar 

  15. Nerini FF, Tomei J, To LS, Bisaga I, Parikh P, Black M et al (2018) Mapping synergies and trade-offs between energy and the Sustainable Development Goals. Nat Energy 3(1):10–15

    Google Scholar 

  16. IRENA (2017) Rethinking Energy 2017: Accelerating the Global Energy Transformation. Published online at, Abu Dhabi, UAE Retrieved from: (Accessed 16/4/2021)

    Google Scholar 

  17. SDSN (2019) Mapping the Renewable Energy Sector to the Sustainable Development Goals: An Atlas. Published online at, New York, USA Retrieved from: (Accessed 5/2/2020)

    Google Scholar 

  18. Griggs DJ, Nilsson M, Stevance A, McCollum D (2017) A Guide to SDG Interactions: From Science to Implementation. Published online at, Paris, France Retrieved from: (Accessed 2/2/2021)

    Google Scholar 

  19. Kahia M, Jebli MB, Belloumi M (2019) Analysis of the Impact of Renewable Energy Consumption and Economic Growth on Carbon Dioxide Emissions in 12 MENA Countries. Clean Technol Environ Policy 21(4):871–885

    Google Scholar 

  20. Bekun FV, Alola AA, Sarkodie SA (2019) Toward a Sustainable Environment: Nexus between CO2 Emissions, Resource Rent, Renewable and Nonrenewable Energy in 16-EU Countries. Sci Total Environ 657:1023–1029

    Google Scholar 

  21. Hanif I (2018) Impact of Economic Growth, Nonrenewable and Renewable Energy Consumption, and Urbanization on Carbon Emissions in Sub-Saharan Africa. Environ Sci Pollut Res 25(15):15057–15067

    Google Scholar 

  22. Sarkodie SA, Adams S (2018) Renewable Energy, Nuclear Energy, and Environmental Pollution: Accounting for Political Institutional Quality in South Africa. Sci Total Environ 643:1590–1601

    Google Scholar 

  23. Namahoro JP, Wu Q, Zhou N, Xue S (2021) “Impact of energy intensity, renewable energy, and economic growth on CO2 emissions: Evidence from Africa across regions and income levels” Renewable and Sustainable Energy Reviews. Vol. 147:111233

    Google Scholar 

  24. Vasylieva T, Lyulyov O, Bilan Y, Streimikiene D (2019) Sustainable Economic Development and Greenhouse Gas Emissions: The Dynamic Impact of Renewable Energy Consumption, GDP, and Corruption. Energies 12(17):3289

    Google Scholar 

  25. Alola AA, Bekun FV, Sarkodie SA (2019) Dynamic Impact of Trade Policy, Economic Growth, Fertility Rate, Renewable and Non-renewable Energy Consumption on Cological Footprint in Europe. Sci Total Environ 685:702–709

    Google Scholar 

  26. Kirikkaleli D, Adebayo TS (2021) Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence. Sustain Dev 29(4):583–594

    Google Scholar 

  27. Adebayo TS, Kirikkaleli D (2021) Impact of renewable energy consumption, globalization, and technological innovation on environmental degradation in Japan: application of wavelet tools. Environ Dev Sustain 1:26

    Google Scholar 

  28. Adebayo TS, Akinsola GD, Bekun FV, Osemeahon OS, Sarkodie SA (2021) Mitigating human-induced emissions in Argentina: role of renewables, income, globalization, and financial development. Environ Sci Pollut Res 1:15

    Google Scholar 

  29. Adebayo TS, Coelho MF, Onbaşıoğlu DÇ, Rjoub H, Mata MN, Carvalho PV et al (2021) Modeling the dynamic linkage between renewable energy consumption, globalization, and environmental degradation in South Korea: does technological innovation matter? Energies 14(14):4265

    Google Scholar 

  30. Soylu ÖB, Adebayo TS, Kirikkaleli D (2021) The imperativeness of environmental quality in China amidst renewable energy consumption and trade openness. Sustainability 13(9):5054

    Google Scholar 

  31. Aydin M (2019) Renewable and Non-renewable Electricity Consumption–economic Growth Nexus: evidence from OECD countries. Renew Energy 136:599–606

    Google Scholar 

  32. Marinaș MC, Dinu M, Socol AG, Socol C (2018) Renewable Energy Consumption and Economic Growth. Causality Relationship in Central and Eastern European Countries. PLoS One 13(10):e0202951

    Google Scholar 

  33. Ntanos S, Skordoulis M, Kyriakopoulos G, Arabatzis G, Chalikias M, Galatsidas S et al (2018) Renewable Energy and Economic Growth: Evidence from European Countries. Sustainability 10(8):2626

    Google Scholar 

  34. Simionescu M, Strielkowski W, Tvaronavičienė M (2020) Renewable Energy in Final energy Consumption and Income in the EU-28 countries. Energies 13(9):2280

    Google Scholar 

  35. Jebli MB, Farhani S, Guesmi K (2020) Renewable Energy, CO2 Emissions and Value Added: Empirical Evidence from Countries with Different Income Levels. Structural Change Econ Dynamics 53:402–410

    Google Scholar 

  36. Vural G (2021) Analyzing the impacts of economic growth, pollution, technological innovation and trade on renewable energy production in selected Latin American countries. Renew Energy 171:210–216

    Google Scholar 

  37. Khan SAR, Zhang Y, Kumar A, Zavadskas E, Streimikiene D (2020) Measuring the Impact of Renewable Energy, Public Health Expenditure, Logistics, and Environmental Perform Sustainable Economic Growth. Sustain Dev 28(4):833–843

    Google Scholar 

  38. Namahoro JP, Wu Q, Xiao H, Zhou N (2021) The Impact of Renewable Energy, Economic and Population Growth on CO2 Emissions in the East African Region: Evidence from Common Correlated Effect Means Group and Asymmetric Analysis. Energies 14(2):312

    Google Scholar 

  39. Namahoro JP, Wu Q, Xiao H, Zhou N (2021) The asymmetric nexus of renewable energy consumption and economic growth: New evidence from Rwanda. Renew Energy 174:336–346

    Google Scholar 

  40. Fu Q, Álvarez-Otero S, Sial MS, Comite U, Zheng P, Samad S, Oláh J (2021) Impact of Renewable Energy on Economic Growth and CO2 Emissions—Evidence from BRICS Countries. Processes 9(8):1281

    Google Scholar 

  41. Topcu M, Tugcu CT (2020) The Impact of Renewable Energy Consumption on Income Inequality: Evidence from Developed Countries. Renew Energy 151:1134–1140

    Google Scholar 

  42. Uzar U (2020) Is Income Inequality a Driver for Renewable Energy Consumption? J Clean Prod 255:120287

    Google Scholar 

  43. Özçiçek Ö, Ağpak F (2017) The Role of Education on Renewable Energy Use: Evidence from Poisson Pseudo Maximum Likelihood Estimations. J Bus Econ Polic 4(4):49–61

    Google Scholar 

  44. Güney T (2019) Renewable Energy, Non-renewable Energy and Sustainable Development. Int J Sustain Dev World Ecol 26(5):389–397

    Google Scholar 

  45. Soukiazis E, Proenca S, Cerqueira PA, ‘The interconnections between Renewable Energy, Economic Development and Environmental Pollution. A simultaneous equation system approach,” Centre for Business and Economics Research (CeBER), CeBER Working Papers 2017-10, Coimbra, University of Coimbra, 2017.

  46. IEA (2018) Understanding and using the Energy Balance. Published online at, Paris Retrieved from: (Accessed 5/9/2020)

    Google Scholar 

  47. World Bank (2017) World Development Indicators: Sustainable Development Goal. Published online at, Washington, DC, USA Retrieved from: (Accessed 2/10/2020)

    Google Scholar 

  48. UNDP (2018) 2018 Statistical Update: Human Development Indices and Indicators. Published online at, New York, USA Retrieved from: (Accessed 15/10/2020)

    Google Scholar 

  49. UNDP (2018) Human Development Data Center. Published online at, New York, USA Retrieved from: (Accessed 20/9/2020)

    Google Scholar 

  50. Sachs J, Kroll C, Lafortune G, Fuller G, Woelm F (2021) The Decade of Action for the Sustainable Development Goals: Sustainable Development Report 2021. Published online at, Cambridge, UK Retrieved from: (Accessed 5/11/2020)

    Google Scholar 

  51. IBM (2016) Estimation Methods for Replacing Missing Values. Published online at, New York, USA Retrieved from: (Accessed 20/21/2020)

    Google Scholar 

  52. Lever J, Krzywinski M, Altman N (2017) Points of significance: Principal component analysis. Nat Methods 14(7):641–643

    Google Scholar 

  53. Mukhopadhyay P (2014) Learning Regression Analysis by Simulation by Kunio Takezawa. Int Stat Rev 82(2):325–325

    Google Scholar 

  54. IBM (2016) Categorical Principal Components Analysis. Published online at, New York, USA Retrieved from: (Accessed 15/21/2020)

    Google Scholar 

  55. Meng Y, Qasem S, Shokri M (2020) Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis. Mathematics 8(8):1233

    Google Scholar 

  56. Bolshakova L (2021) Correlation and Regression Analysis of Economic Problems. Revista Gestão Inovação e Tecnologias 11(3):2077–2088

    Google Scholar 

  57. Weisberg S (2014) Applied linear regression, 4th edn. Wiley, Hoboken, New Jersy

    MATH  Google Scholar 

  58. Gogtay NJ, Deshpande SP, Thatte UM (2017) Principles of regression analysis. J Assoc Physic India 65(48):48–52

    Google Scholar 

Download references


Not applicable.


The authors declare that they did not receive any funding sources.

Author information

Authors and Affiliations



Each author has made substantial contributions to the conception and design of the work. R.H. has prepared the original draft, conceptualization and methodology, has performed the data curation formal analysis and interpretation of data, has utilized the software, and has attained manuscript review and editing. T.A. has substantively revised the manuscript, has verified all data and materials, and has approved the submitted version. All authors have read and approved the final manuscript to be personally accountable for the authors’ contributions.

Corresponding author

Correspondence to Rania Hamed Rashed.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aboul-Atta, T.AL., Rashed, R.H. Analyzing the relationship between sustainable development indicators and renewable energy consumption. J. Eng. Appl. Sci. 68, 45 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: