Theories and empirical researches about the relationships between energy use, population growth, and GDP per capitaCO2 emissionsChapter 1: Literature review about the relationship between CO2 emissions and GDP per capita, population growth, energy use, forest area, industry and renewable energy consumption.Chapter 2: DataChapter 3: Statistics description of variables Chapter 4: Quantitative analysisChapter 5: Conclusion and policy implication
Chapter I: LITERATURE REVIEW .4 Theories 1.1 CO2 emissions (metric tons per capita) 1.2 Factors affect CO2 emissions Theories and emperical researches about the relationships between energy use, population growth, GDP per capita and CO2 emissions 2.1 Energy use and CO2 emissions 2.2 Population growth and CO2 emissions 2.3 GDP per capita and CO2 emissions 2.4 Forest area and CO2 emissions 2.5 Industry and CO2 emissions 2.6 Renewable energy consumption Chapter II: DATA 11 Methodology in collecting data 11 Methodology in processing data 11 Data overview 11 Data description 12 Chapter 3: STATISTICS DESCRIPTION OF VARIABLES .13 Methodology in researching 13 Constructing econometrics model 13 2.1 Specification of the model 13 2.2 Explanation of the variables 15 2.3 Correlation analysis 16 Chapter 4: QUANTITATIVE ANALYSIS .18 Estimated model 18 1.1 Estimation result 18 1.2 Sample regression model 18 Testing problems of the model (Dianosing the model problems) 19 2.1 Misspecification test 19 2.2 Multicollinearity 19 2.3 Heteroskedasticity 20 2.4 Testing normality 21 Hypothesis postulated 22 a Testing hypothesis about the regression parameter 22 b Testing the overall significance of the model 27 Chapter 5: CONCLUSION AND POLICY IMPLICATION 28 Conclusion 28 Policy implication 28 REFERENCES 30 APPENDIX .33 PREFACE The challenge of global warming and climate change has been the biggest ongoing problem for all societies in the last three decades Global climate change has altered the availability of water and weather patterns, altered the growing season for food crops, and endangered increased sea levels for coastal c ommunities In the report, we will apply what we learn in Econometrics course to investigate into this matter The report is divided into main parts: Chapter 1: Literature review about the relationship between CO emissions and GDP per capita, population growth, energy use, forest area, industry and renewable energy consumption Chapter 2: Data Chapter 3: Statistics description of variables Chapter 4: Quantitative analysis Chapter 5: Conclusion and policy implication Finally, we would like to express our sincere thanks to the dedicated guidance from Mrs Dinh Thi Thanh Binh Due to our limited knowledge, there are certainly some deficiencies in our report We look forward to receiving comments and suggestions from you to make our research more completely CHAPTER I: LITERATURE REVIEW Theories 1.1 CO2 emissions (metric tons per capita) a Definition and roles of CO2 (Carbon dioxide) A colorless gas with a density about 60 percent greater than that of dry air is carbon dioxide (chemical formula CO2) A carbon atom covalently double bonded to two oxygen atoms consists of carbon dioxide In the Earth's atmosphere, it occurs naturally as a trace gas CO2 is one of the most important gases on Earth because in a process called photosynthesis, plants use it to produce carbohydrates Since humans and animals rely on plants for food, photosynthesis is necessary for early life to survive CO2 may have harmful effects as well however It has a warming impact that could alter the earth's climate as CO2 builds up in our atmosphere CO2 levels indoors quickly increase above the recommended limit, which has negative effects b What is CO2 emission? The emissions of CO2 are those resulting from the combustion of fossil fuels and cement manufacturing They include carbon dioxide emitted by solid, liquid and gas fuels and gas flaring during consumption 1.2 Factors affect CO2 emissions There are a lot of factors that affect CO2 emissions However, in this research, we focus mainly on six significant ones, which are energy use, population growth, GDP per capita, forest area, industry, renewable energy consumption a Energy use (Energy consumption per capita) Energy use is the amount of energy or power used (kg of oil equivalent per capita) b Population growth The growth of the population is an increase in the number of people living in a nation, state, county, or area The following formula is used to determine if there has been population growth: (birth rate + immigration)-(death rate + emigration) This knowledge is used by corporations and public bodies to make determinations regardinginvesting in those populations or region c GDP per capita GDP per capita is a calculation of the economic performance of a country that accounts for the number of people it has It divides the gross domestic product of the country by its total population That makes it a good measurement of the standard of living of a country It tells you how prosperous a nation feels for each of its people d Forest area (sq Kilometre) Forests are home to a wide variety of plants - the source of carbon dioxide through photosynthesis and also storage of carbon through organic synthesis, which are the earth's giant carbon pools The change in forest area means that the capacity of the carbon pools changes, so the amount of CO2 released into the atmosphere also changes e Industry Industry has the most impact on the environment in all remaining industries, especially for developing countries, which are in the process of Industrialization - Modernization of the country The higher the industrial value, the more developed the industry, which requires increased production Meanwhile, in many countries, the process of treating exhaust gas has not been strictly tested, leading to more and more emissions from factories and factories emitting into the environment, of which CO2 accounts for the majority Therefore, industry value has a great influence on the amount of CO2 in the atmosphere f Renewable energy consumption The emergence of energy from renewable sources is an alternative to reduce dependency and reliance on traditional sources as well as improving economic performance High consumption from RE improves the environmental quality by reducing CO2 emissions, and extensive use of energy from conventional sources increases the emissions Theories and emperical researches about the relationships between energy use, population growth, GDP per capita and CO2 emissions 2.1 Energy use and CO2 emissions Sustainable development (SD) implies the balancing of economic and social development with environmental protection: the so-called ‘Three Pillars’ model In the long term, Planet Earth will impose its own constraints on the use of its physical resources and on the absorption of contaminants, whilst the ‘laws’ of the natural sciences (such as those arising from thermodynamics) and human creativity will limit the potential for new technological developments SD is a method or path towards a 'sustainable goal When analyzing energy usage and related pollution, it is a central idea and has roots in architecture, economics, ecology, and social science Thao and Chon[1] noted that the use of energy has a positive influence on the economy, but not on the environment Energy use is widely recognised as the primary reason for the phenomenon of global warming and climate change, especially fossil energy consumption The environmental adverse effects of such used energy come not only from the use of energy, but also from the method of exploitation In the meantime, renewable energy consumption has a negative relationship with CO2 emissions, which implies profits Renewable energy usage, meanwhile, has a negative relationship with CO2 emissions, which means that increasing renewable energy consumption would minimize CO2 emissions Furthermore, Ito[2] found that consumption of fossil energy has a negative impact on economic growth in developing countries and that consumption of renewable energy has a positive influence on economic growth In this situation, emissions and environmental damage can be caused by the use of fossil fuels, since the residual burning of fossil energy is detrimental to the atmosphere, whereas the residue of renewable energy is considered more environmentally friendly In addition, Shafei and Ruhul[3], who conducted an OECD analysis on the Kuznets Curve Hypothesis (EKC) between urbanization and CO2 emissions, found that non-renewable energy consumption has a positive relationship to CO2 emissions, which suggests that an increase in non-renewable energy consumption would increase CO2 emissions On the other hand, renewable energy usage is negatively related to CO2 emissions and once again, supports the conclusion that an increase in the use of renewable energy would minimize CO2 emissions 2.2 Population growth and CO2 emissions Ehrlich (1968) and Holder and Ehrlich (1974) posited the effect of demographic change on environmental stress in the form of an equation relating to environmental impact on the development of population size, income, and environmental impact per unit of economic activity known as' IPAT.' IPAT is a useful tool for evaluating anthropogenic environmental change, in particular the effect on environmental change of population, income, and technology (CO2 emissions) The role of population pressure in environmental quality can be traced back to an early debate on the relationship between population and natural resources Malthus (1798 [1970]) was concerned with a rise in population growth, which placed pressure on a limited source of land Owing to a lower marginal labor product, the possible rise in food production may not be compatible with that of the population He projected that if the human race did not exercise protective controls, population growth would be reduced by welfare tests (poverty, disease, famine and war) Boserup (1981) held the opposite opinion, arguing that high population density was a requirement for technological advancement in four agriculture sectors Technological progress has made it possible to increase food yields and to distribute food more effectively It could then allow the natural environment to sustain a large population at the same level of welfare The effect of population growth on the quality of the environment is evident Every individual in a population demands energy for the necessities of life—food, water, clothes, shelter, and so on If all else is equal, the greater the number of individuals, the greater the energy demands Birdsall (1992) identified two mechanisms by which population growth could lead to greenhouse gas emissions First a larger population could lead to an increase in energy demand for power, industry and transport, resulting in an increase in fossil fuel emissions Second, population growth could lead to greenhouse gas emissions by influencing deforestation The loss of trees, changes in land use and burning of fuel wood could make a major contribution to greenhouse gas emissions Thus, two questions remain to be answered in full and empiric terms: (1) does population pressure have a net effect on carbon dioxide emissions that keeps affluence and technology constant? And (2) has demographic pressure had a greater effect in developing countries than in developed countries? 2.3 GDP per capita and CO2 emissions It was widely thought that rich countries destroyed the world at a faster rate than developing nations, the Environmental Kuznets Curve hypothesis Nevertheless the relationship between the protection of the ecosystem and the economy was reanalysed with the Environmental Kuznets Curve The theory is that the economy will worsen to a certain point where the nation hits a certain average income as economic development growth occurs Then money is invested back into the environment, and the ecosystem is restored Critics argue that economic growth doesn’t always lead to a better environment and sometimes the opposite may actually be true Three Completely Different Environmental/GDP Curves (2012)-In this paper, Bratt contrasts three different theories explaining the relation between environmental degradation and GDP The hypotheses discussed are the Environmental Kuznets Curve (ECC), the Brundtland Curve and the Daly Curve All three theories agree that the amount of GDP is having an effect on environmental degradation, but in different ways The ECC hypothesis argues that an increase in the amount of GDP will initially increase pollution until a certain level of GDP, at which the level of pollution starts to decrease In the case of the EKC, the relationship between environmental degradation and economic development is graphically displayed as an inverted U-shape The Brundtland curve theory offers another illustration, where the graphic form is the opposite, U-shaped, which means that the poorest and richest countries have the highest levels of pollution The Daly Curve Theory implies growing levels of emissions, with rising GDP still going on with no turning point at all Bratt points out that the three separate environmental/GDP curves deal with different forms of environmental degradation The EKC hypothesis may be used to calculate emissions or concentrations The Brundtland curve could be used to measure output and the Daly curve to measure consumption Bratt's final conclusion is that while either curve may be valid, the most likely scenario appears to be a positive, monotonous relationship between environmental degradation and GDP In summary, a variety of research studies have been performed in areas related to this thesis However, a large part of the study was performed on the relationship between CO2 and only one other factor, such as GDP per capita, population growth or energy usage There are not many current studies that explicitly analyze the impact of GDP per capital, energy use, population growth, all of them on CO2 emissions In this study, the current data and linear regression will be used to analyze the relationship between CO2 emissions and three other factors: per capita GDP, energy usage and population growth 2.4 Forest area and CO2 emissions Deforestation, and in particular the destruction of tropical forests, is also a significant cause of the increase in CO2 Scientists estimate that deforestation and other changes in land use account for about 23% of human-emitted CO2 emissions Plants and other plants absorb CO2 from the air Using energy from the sun, they turn the carbon obtained from the CO2 molecules into nutrients for their stems, branches and leaves This is part of the carbon cycle In other words, in the context of climate change, the most important thing for forests is not that they help reduce the amount of CO2 in the air, but that they are big carbon reservoirs If such a forest is burned or destroyed, much of that carbon will be released back into the atmosphere, increasing the amount of CO2 in the atmosphere Adedire (2002) reported that local and regional deforestation is associated with decline in rainfall, increasedsurface temperatures and the alteration of local hydrology It is implicated in the increasing atmospheric concentration of carbon-dioxide (CO 2) since trees required forsequestration have been drastically reduced or completely lost Increase in CO2 content of the atmosphere modifies the climate and causes general warming of the earth (Sorensen, 1994; Okojie, 1991) The consequenceof global warming brought about through the green house effects are massive flooding of coastal regions ofthe world, changes in food chains and general disruption in agricultural production 2.5 Industry and CO2 emissions In the process of Industrialization - Modernization along with the socio -economic development, more and more factories and concentrated industrial zones were built and put into operation and discharged into the environment a very large amount a large amount of industrial waste gas, especially industrial zones, still use outdated production technologies and have not yet invested in a system to treat exhaust gas before being discharged into the environment This has had a huge impact on the air environment that in particular increases the concentration of CO2 in the air The amount of CO2 produced by humans is much smaller than natural gas emissions, but they unbalanced the carbon cycle that existed before the Industrial Revolution Since the beginning of the industrial revolution, artificial CO2 emissions have been increasing The use of fossil fuels as well as deforestation are the main causes of the increase in carbon dioxide concentrations in the atmosphere 87% of CO2 emissions are generated by humans from the burning of fossil fuels such as coal, natural gas and oil The remainder is the result of deforestation and land use activities (9%), as well as some industrial processes (4%) 2.6 Renewable energy consumption the empirical studies by Bölük and Mert (2014), Dogan and Seker (2016a, 2016b) Irandoust (2016), Jebli et al (2016), Liu et al (2017) and Sebri and Ben-Salha (2014) incorporated RE consumption as an additional variable to explore the linkages between nonRE consumption, economic growth and environmental quality However, only Bölük and Mert (2014), Dogan and Seker (2016a, 2016b), Jebli et al (2016) and Liu et al.(2017) investigated the presence of the EKC hypothesis On the contrary, the empirical findings by Bölük and Mert (2014), Jebliet al (2016) and Liu et al (2017) found that the EKC hypothesis is invalidated in 16 European Union, 25 OECD and ASEAN-4 countries, respectively Meanwhile, Dogan and Seker (2016a, 2016b) found that the EKC hypothesis is validated in 15 EU and 40 top RE countries According to Dogan and Seker (2016a,2016b), Jebli et al (2016) and Liu et al (2017), high consumption from RE improves the environmental quality by reducing CO2 emissions, and extensive use of energy from conventional sources increases the emissions Meanwhile, Bölük and Mert (2014) found that energy used from renewable sources contributes to a 50% reduction in emissions compared to energy from conventional source The empirical studies by Azlina et al (2018), Bölük and Mert (2015) and Sugiawan and Managi (2016) provides evidence that the inverted U-shape of the EKC hypothesis is validated and energy production from renewable sources can mitigate pollution by reducing CO2 emissions in the long-run for the case of Malaysia, Turkey and Indonesia In contrast, previous study by Azlina et al (2014) found that the inverted U-shape of the EKC hypothesis is invalid, but energy consumption from renewable sources still provides a positive impact on environmental quality in Malaysia Meanwhile, Aung et al (2017) found that EKC hypothesis is failed to validate for CO2 emissions but the existence of inverted U-shaped can be observed for methane (CH4) and nitrous oxide (N2O) in Myanmar CHAPTER II: DATA Methodology in collecting data The data collected are secondary data, mixed data, which include information on main factors related to the amount of CO2 emissions (metric tons per capita): forest area, GDP per capita, % industry (including construction) of GDP, population in total, % renewable energy consumption of total final energy consumption, and energy consumption per capita Secondary data were collected from a prestigious and reliable source of information-World Bank Methodology in processing data Using Gretl in order to process data cursorily then calculate the correlation matrix among variables Data overview - The dataset was collected from the official website of World Bank, including 199 observations of 199 countries in 2015 - Data source: https://data.worldbank.org/ Variable Sources Forest area https://data.worldbank.org/indicator/AG.LND.FRST.K2 GDP per capita https://data.worldbank.org/indicator/NY.GDP.PCAP.CD Industry proportion https://data.worldbank.org/indicator/NV.IND.MANF.ZS Population https://data.worldbank.org/indicator/SP.POP.TOTL Renewable energy consumption Energy consumption per capita https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE The structure of Economic data: cross-sectional data Data description Running DES function Run the command “des lnco2 fa gpdppercpt ind lnpop rec lnecpercp” to interpret the dataset Another way to test normality is using Jacque – Bera test sktest u Skewness/Kurtosis tests for Normality joint -Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -+ u | 191 0.0000 0.0000 0.0000 According to the result, p-value (skewness) = 0.0000 < 0.05 => reject null hypothesis Conclusion: data does not have normal distribution Hypothesis postulated a Testing hypothesis about the regression parameter State the hypothesis: Forest area (fa): Critical Value Method Confidence Interval Method p - value Method Conclusion Forest area (fa) doesn’t have statistically significant effect on CO2 emissions Gross Domestic Productions per Capita (gdppercpt) Critical Value Method Confidence Interval Method p - value Method Conclusion Gross Domestic Productions per Capita has no statistically significant effect on CO2 emissions as Industry (ind) Critical Value Method Confidence Interval Method p - value Method Conclusion Industry has no statistically significant effect on CO2 emissions as Population (lnpop) Critical Value Method Confidence Interval Method p - value Method Conclusion Industry has no statistically significant effect on CO2 emissions as Renewable energy consumption (rec): Critical Value Method Confidence Interval Method p - value Method Conclusion - Renewable energy consumption has statistically significant effect on CO2 emissions The higher the renewable enery consumption is, the higher CO2 emissions are - In particular, with the sample we have, the estimated result shows that an increase in renewable enerygy consumption will decrease CO2 emissions by 0.013% on average, holding other factors fixed Energy consumtion per capita (ecpercpt) Critical Value Method Confidence Interval Method p - value Method Conclusion - Energy consumption per capita has statistically significant effect on CO2 emissions The higher the enery consumption is, the higher CO2 emissions are - In particular, with the sample we have, the estimated result shows that an increase in enerygy consumption per capita will raise CO2 emissions by 0.693% on average, holding other factors fixed b Testing the overall significance of the model State the hypotheses: , where: n: numbers of observations or sample size, n = 199 k: the numbers of variables, k = The overall model is statistically significant at a significant level of 5% CHAPTER 5: CONCLUSION AND POLICY IMPLICATION Conclusion Forest area (fa) doesn’t have statistically significant effect on CO2 emissions As Gross Domestic Productions per Capita has no statistically significant effect on CO2 emissions as α=5% Industry has no statistically significant effect on CO2 emissions as Population has no statistically significant effect on CO2 emissions as Renewable energy consumption has statistically significant effect on CO2 emissions The higher the renewable energy consumption is, the lower CO2 emissions are In particular, with the sample we have, the estimated result shows that an 1% increase in renewable energy consumption will decrease CO2 emissions by 0.013% on average, holding other factors fixed Energy consumption per capita has statistically significant effect on CO2 emissions The higher the energy consumption is, the higher CO2 emissions are In particular, with the sample we have, the estimated result shows that an 1% increase in energy consumption per capita will raise CO2 emissions by 0.693% on average, ceteris paribus Policy implication The rate of increase in carbon dioxide (CO2) emissions into the atmosphere reached a record level in 2015, showing the urgency of solutions to reduce emissions of these harmful greenhouse gases - the cause of the to global warming, rapid ice melting and rising sea levels In a report on atmospheric CO2 levels published March 10 2016, the National Oceanic and Atmospheric Administration (NOAA) said that in 2015, the annual growth rate of CO2 concentrations in the gas rights are 3.05 parts per million (ppm), the largest annual increase in the past 56 years Commenting on this problem, NOAA scientists warn that CO2 concentrations are increasing faster than thousands of years ago According to them, two main causes of the skyrocketing CO2 emissions in the atmosphere are extreme weather phenomena and burning fossil fuels There are factors that affect the amount of CO2 emissions which are Forest area (fa) Gross Domestic Productions Per capita (gdppercpt), Industry (ind), Population (lnpop), Renewable energy consumption (rec), Energy consumption per capita (ecpercpt) In 2015, meanwhile Renewable energy consumption Energy consumption per capita have statistically significant effect on the amount of CO2 emissions, Industry, Population, Forest area and Gross Domestic Productions Per capita have no statistically significant effect on the amount of CO2 emissions There are also many ways to reduce the amount of CO2 emissions to protect our environment Limiting the use of fossil fuels and looking for the alternative energy sources Fossil fuels (coal, oil ) are essential causal of the greenhouse effect People have been searching for environmentally friendly alternative energy sources such as wind, solar, tidal, and geothermal energy In the United States, most states have laws that require engineered vehicles to pass certification that they pass periodic testing for vehicle exhaust emissions On the other hand, some of the electricity comes from burning fossil fuels, which generates large amounts of CO2 Using natural light, energy-saving bulbs, turn off all electrical appliances when leaving the room Restrict using of personal vehicles like motorbike, using public transport, going to school by bike to protect your budget and the environment Fewer personal vehicles mean less emissions The use of public transport also contributes significantly to reducing the release of greenhouse gases into the atmosphere In general, it can be said that the higher awareness about environmental pollution, the less amount of CO2 released into the environment Our research examined the statistically dependent relationship of CO2 emissions at forest area and industry, GDP per capita, population, renewable energy consumption and energy consumption per capita The results obtained from this research are consistent with the economic theories and some previous published researches Specifically: There are positive impacts of total energy consumption and industrial proportion on CO2 emissions If total energy consumption and industrial proportion increase, average CO2 emissions would increase followingly In contrast, renewable energy consumption has negative relationship as reduce the amount of CO2 to protect environment The report was completed by the whole group’s effort and the knowledge that we have studied at class Despite our lack of knowledge and collecting data, we have tried our best to gain more understanding about the basic process of running the econometrics model to bring out analyze the relationships between variables and solve problems in environmental quality development We would like to express our sincere appreciation to the guidance and devotion of Mrs Dinh Thi Thanh Binh who helped us finish the report in the right direction We are willing to revise our research problems based on your comments and advices to improve both theory and applications aspects of our report REFERENCES Per capita energy consumption in 2015 by Our World in Data, 2016 Available at: https://ourworldindata.org/grapher/per-capita-energy-use? tab=table&time=2015®ion=World [Retrieved in 10th Dec 2020] Naomi Klein (2014) This Changes Everything: Capitalism vs The Climate (Hardcover), (shelved 525 times as environment), 65-101 Elizabeth Kolbert (2014) The Sixth Extinction: An Unnatural History (Hardcover), (shelved 498 times as environment) 34-97 Thomas L.Friedman (2008) Hot, Flat, and Crowded: Why We Need a Green Revolution 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2020 Available at: https://research.noaa.gov/article/ArtMID/587/ArticleID/2636/Rise-of-carbon-dioxideunabated [Retrieved at 12th Dec 2020] GDP per capita (current US$) in 2015 by World Bank national accounts data, and OECD National Accounts data files Available at: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD [Retrieved 10th Dec 2020] 10 Greenhouse gas emissions, the World Factbook, 2015 Available at: https://www.cia.gov/library/publications/resources/the-worldfactbook/docs/notesanddefs.html#279 [Retrieved at 10th 2020] 11 Global carbon emissions from fossil fuel in 2015, June 2016 Available at: https://www.co2.earth/global-co2-emissions [Retrieved at 10th Dec 2020] 12 CO2 Emissions from Fuel Combustion: Overview, July 2016 Available at: https://www.iea.org/data-and-statistics/data-tables [Retrieved 10th Dec 2020] 13 Tropical rainforests: Disappearing Opportunities Carbon Dioxide Emissions Charts, 2015 Available at: https://rainforests.mongabay.com/09-carbon_emissions.htm 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41-259 29 Peter Kennedy (February 2008) A Guide to Econometrics, The classical Linear Regression Model, 6th edition, 51-82 30 12 ways you can protect the environment by Green Mountain Energy, 2020 Available at: https://www.greenmountainenergy.com/why-renewable-energy/protect-theenvironment/ [Retrieved at 12th Dec 2020] 31 Forest area (sq km) by Food and Agriculture Organization, electronic files and web site on data world bank in 2015, 2016 Available at: th https://data.worldbank.org/indicator/AG.LND.FRST.K2 [Retrieved in 10 Dec 2020] 32 World population in 2015 by World Population Data, 2017 Available at: https://www.prb.org/2015-world-population-data-sheet/ [Retrieved at 10th Dec 2020] APPENDIX The dataset of global CO2 emissions (kilo tons per capita) in 2015 Country name CO2 emissions (tons per Forest area (sq km) GDP per capita (current Industry (includin g Population in total Renewable energy consumption Energy consumptio n per capita 0.26 1.60 3.85 1.24 13500 7715 19560 578560 578.47 3,952.80 4,177.89 4,166.98 construct ion), value added (% of GDP) 22.12 21.76 35.73 41.93 5.84 4.66 1.64 8.61 15.34 7.09 3.90 5.39 23.98 0.52 4.40 6.14 8.81 1.61 0.57 8.54 1.47 1.85 98 271120 3320 1247510 38690 11394 5150 14290 63 86335 6834 13663 43110 10 27549 547640 14,286.09 13,789.06 3,607.30 27,980.88 56,755.72 44,178.05 5,500.31 31,405.96 22,688.94 1,248.45 16,525.07 5,949.11 40,991.81 4,775.96 1,076.80 85,853.55 2,752.66 3,035.97 16.46 23.15 25.71 14.24 23.64 25.17 44.89 11.80 40.30 26.83 12.56 32.69 19.67 14.16 16.39 4.15 42.49 25.20 93566 43131966 2925553 104341 23815995 8642699 9649341 374206 1371851 156256276 285324 9489616 11274196 360933 10575952 65237 727876 10869730 0.00 10.04 15.79 6.73 9.18 34.39 2.31 1.21 0.00 34.75 2.79 6.77 9.20 35.02 50.86 2.36 86.90 17.54 32,408 23,124 13,842 52,174 67,750 44,370 17,770 31,633 148,832 2,355 24,006 28,499 60,020 8,670 2,079 38,944 33,456 7,685 5.41 2.56 2.47 21850 108400 4935380 4,727.28 6,799.88 8,814.00 22.54 29.98 19.36 3429361 2120716 204471769 40.75 28.88 43.79 23,000 9,979 16,619 7.30 36 42,300.00 8.61 29152 1.23 25,748 17.05 6.23 0.18 0.04 0.94 0.56 0.34 15.39 9.03 0.07 4.63 3800 38230 53500 2760 899 94570 188160 3470690 127 48750 177350 31,164.56 7,053.60 653.33 305.55 3,043.01 1,162.90 1,327.50 43,585.51 76,280.49 776.02 13,574.17 61.36 23.68 24.35 11.76 18.12 27.68 25.18 24.42 7.01 13.65 29.78 0.01 17.65 74.17 95.68 26.58 64.92 76.54 22.03 0.00 89.36 24.88 116,332 30,872 794 149 7,019 2,465 1,997 107,895 41,696 99 23,115 7.40 1.98 0.22 2083213 585017 370 8,066.94 6,175.88 1,242.60 40.84 28.59 10.41 414907 7177991 18110624 10160030 524743 15521436 23298368 35702908 61724 14110975 17969353 137122000 47520667 777424 12.41 23.56 45.33 24,755 10,007 976 0.04 0.67 1.55 0.47 4.12 2.58 5.29 9.47 5.42 0.69 2.47 1525780 223340 27560 104010 19220 32000 1727 26670 6122 56 433 497.32 1,762.03 11,299.14 1,972.55 11,782.90 7,694.01 23,333.71 17,715.62 53,254.86 2,658.98 7,596.44 41.70 54.67 19.42 19.53 21.36 22.35 10.17 33.99 19.99 11.37 12.53 76244544 4856095 4847804 23226143 4203604 11324781 1160985 10546059 5683483 913993 71183 95.82 62.40 38.73 64.53 33.13 19.28 9.94 14.83 33.17 15.38 7.83 500 6,028 12,552 7,162 21,182 11,509 25,059 44,065 34,133 4,204 11,667 capita) Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil British Virgin Islands Brunei Darussalam Bulgaria Burkina Faso Burundi Cabo Verde Cambodia Cameroon Canada Cayman Islands Chad Chile China Colombia Comoros Congo, Dem Rep Congo, Rep Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica US$) (% total final energy consumption (kWh) 34413603 2880703 39728025 27884381 18.42 38.62 0.06 49.57 957 11,652 15,543 4,063 Dominican Republic Ecuador Egypt, Arab Rep El Salvador Equatorial Guinea Estonia Eswatini Ethiopia Faroe Islands Fiji Finland France French Polynesia Gabon Gambia, The Georgia Germany Ghana Greece Greenland Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hong Kong SAR, China Hungary Iceland India Indonesia Iran, Islamic Rep Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Rep Kuwait Kyrgyz Republic Lao PDR Latvia Lebanon Lesotho Liberia Libya Liechtenstein 2.35 2.67 19830 125479 6,921.52 6,124.49 28.50 31.87 10281680 16212020 16.48 13.82 9,509 11,438 2.43 1.08 730 2650 3,598.97 3,705.58 36.63 25.29 92442547 6325124 5.71 24.40 10,675 6,138 5.16 12.44 0.89 0.14 12.67 2.48 7.78 4.89 15680 22320 5860 124990 10172 222180 169890 11,283.47 17,522.23 3,689.52 640.54 52,404.66 5,390.75 42,784.70 36,638.18 59.10 23.69 35.63 16.30 16.57 15.17 23.31 17.68 1168568 1315407 1104044 100835458 48051 868627 5479531 66548272 7.82 27.48 66.10 92.16 7.51 31.26 43.24 13.50 13,935 53,293 4,262 747 71,655 12,780 58,375 42,768 2.81 2.70 0.25 2.63 8.90 0.59 6.01 9.08 2.38 1.02 0.24 0.16 2.63 0.27 1.10 1550 230000 4880 28224 114190 93370 40540 170 35400 63640 19720 165260 970 45920 17,000.00 7,384.72 649.51 4,014.19 41,139.54 1,743.85 18,167.77 44,536.40 9,096.87 3,994.64 769.26 603.16 4,166.13 815.73 2,302.20 18.34 48.18 17.14 19.15 27.06 31.68 14.19 17.05 12.32 22.74 26.32 12.25 30.11 61.60 25.65 273124 1947686 2085860 3725276 81686611 27849205 10820883 56114 109599 15567419 11432088 1737202 767432 10695542 9112916 9.83 82.01 51.51 28.66 14.21 41.41 17.17 15.53 10.92 63.65 76.27 86.85 25.26 76.07 51.54 17,564 10,593 1,114 18,187 45,511 2,907 29,348 64,775 10,803 4,976 1,072 948 11,014 1,160 5,380 5.85 4.51 6.01 20690 492 42,431.89 12,651.57 52,564.43 7.06 26.45 20.12 0.85 15.56 77.03 45,445 26,010 177,833 1.78 1.98 706820 910100 1,605.61 3,331.70 27.35 40.05 7291300 9843028 330815 131015240 258383256 36.02 36.88 6,100 7,636 8.28 4.70 7.64 7.90 5.42 2.62 9.15 2.83 13.80 0.36 0.56 11.71 24.24 106920 8250 7540 1650 92970 3352 249580 975 33090 44130 121 61840 63 4,904.33 4,989.80 61,995.42 35,776.80 30,230.23 4,907.50 34,524.47 4,105.45 10,510.77 1,336.88 1,542.58 28,732.23 29,869.53 32.97 41.96 38.17 20.17 20.86 19.26 29.02 28.24 30.85 17.30 15.22 34.15 55.94 78492215 35572261 4701957 8380100 60730582 2891021 127141000 9266575 17542806 47878336 110930 51014947 3835591 0.91 0.80 9.08 3.71 16.52 16.77 6.30 3.23 1.56 72.66 4.25 2.71 0.00 36,164 13,108 37,179 35,544 29,203 12,569 41,168 11,680 42,076 1,877 2,385 10,497 117,466 1.75 1.34 3.54 3.75 1.14 0.28 8.88 1.37 6370 187614 33560 1373 490 41790 2170 69 1,121.08 2,134.71 13,698.94 7,644.55 1,152.14 710.38 4,337.92 167,290.9 25.08 27.69 19.44 15.73 33.56 12.54 63.84 37.26 5956900 6741164 1977527 6532678 2059021 4472230 6418315 37470 23.31 59.32 38.10 3.65 52.14 83.85 1.97 63.13 11,385 10,811 20,468 14,397 2,184 1,088 30,934 10,533 Lithuania Luxembourg Macao SAR, China Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Moldova Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Caledonia New Zealand Nicaragua Niger Nigeria North America North Macedonia Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Romania Russian Rwanda Samoa Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovak Republic Slovenia 4.50 21800 16.24 867 14,249.11 101,376.5 3.47 0.15 0.07 7.76 2.86 0.18 3.64 2.49 0.72 3.34 3.96 1.74 7.77 3.60 1.77 0.24 0.42 1.72 3.82 0.23 10.18 17.45 7.59 0.86 0.10 0.64 15.93 124730 31470 221950 10 47150 126 2245 386 660400 4090 125528 8270 56320 379400 290410 69190 36360 3760 8390 101520 31140 11420 69930 6571650 3.41 9.10 15.32 0.95 2.80 26.65 2904910 28.96 21,618 11.04 569604 9.03 75,678 75,340.99 467.24 380.60 9,955.24 9,033.39 751.48 24,002.52 3,213.84 1,524.07 9,260.45 9,605.95 2,732.46 3,918.58 6,517.16 2,875.26 589.86 1,287.43 4,869.38 6,956.25 792.55 45,175.23 14,184.58 38,616.00 2,049.85 483.34 2,730.43 55,502.69 7.67 12.59 14.81 38.45 10.68 17.60 12.36 12.33 22.75 19.24 30.01 22.69 31.04 14.35 26.09 18.10 33.91 27.61 6.33 13.72 18.19 26.90 21.24 25.73 21.59 20.16 19.06 602085 24234088 16745303 30270962 454915 17438778 445053 57439 4046301 1262605 121858258 2834530 2998439 622159 34663603 27042002 52680726 2314904 12475 27015031 16939923 272400 4595700 6223240 20001663 181137448 356403308 7.05 70.17 83.65 5.19 1.01 61.53 5.36 11.16 32.16 11.54 9.22 14.27 3.43 43.00 11.32 86.40 61.53 26.47 0.08 85.26 5.89 4.76 30.79 48.20 78.94 86.64 10.18 7,927 685 399 36,670 14,535 900 64,896 11,316 2,837 18,550 17,526 9,413 18,821 20,044 6,329 3,169 215 8,675 28,610 962 57,672 71,450 53,524 4,523 469 2,343 88,563 9980 121120 20 14720 46170 4,840.27 74,355.52 16,028.75 1,356.67 13,630.31 23.92 31.01 54.24 19.09 27.56 2079328 5188607 4267348 199426964 3968487 24.22 57.77 0.00 46.48 21.23 14,017 101,181 78,465 4,071 27,456 0.92 0.94 1.73 1.15 7.52 4.76 0.21 41.64 3.62 11.79 0.08 1.21 335590 153230 739730 80400 94350 31820 4960 68610 8149305 4800 1710 2,679.35 5,406.70 6,229.10 3,001.04 12,572.43 19,242.37 29,763.49 63,039.02 8,977.44 9,313.01 754.91 4,073.67 34.81 34.63 30.34 30.48 30.25 19.46 50.94 57.44 29.95 29.79 17.64 17.51 8107775 6688746 30470734 102113212 37986412 10358076 3473232 2565710 19815616 144096870 11369071 193513 52.50 61.68 25.50 27.45 11.91 27.16 1.84 0.00 23.70 3.30 86.66 34.32 3,310 27,396 9,312 4,321 29,037 27,562 27,171 221,634 18,938 53,914 432 7,808 0.57 20.40 0.73 6.19 5.26 0.15 11.10 5.77 6.07 536 9770 82730 27200 407 30440 164 19400 12480 1,595.86 20,627.93 1,219.25 5,585.12 14,745.34 588.23 55,646.62 16,309.07 20,881.77 15.04 45.27 23.59 25.72 11.82 4.56 24.29 30.51 28.03 199432 31717667 14578459 7095383 93419 7171914 5535002 5423801 2063531 41.06 0.01 42.71 21.17 1.35 77.66 0.71 13.41 20.88 3,218 94,883 2,257 20,929 47,745 624 166,492 33,172 35,577 Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka St Kitts and Nevis St Lucia St Vincent and the Grenadines Sudan Suriname Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Uzbekistan Vanuatu Venezuela, RB Vietnam Virgin Islands (U.S.) West Bank and Gaza Yemen, Rep Zambia Zimbabwe 0.32 0.05 8.38 0.18 5.39 0.96 21850 63630 92410 71570 184179 20700 1,914.47 483.30 5,734.63 1,119.65 25,732.02 3,843.78 6.46 7.48 26.03 33.09 20.09 27.17 603118 13797201 55386367 10715658 46444832 20970000 63.31 94.29 17.15 39.07 16.25 52.88 1,576 248 25,306 411 33,405 3,865 4.58 2.29 110 203 18,029.33 10,093.90 23.58 10.94 51203 179126 1.64 2.13 21,017 10,298 2.02 0.34 3.08 3.90 4.31 270 192099 153320 280730 12540 6,920.88 1,909.74 8,561.97 51,545.48 82,081.60 15.41 14.12 25.26 22.16 25.11 109148 38902950 559143 9799186 8282396 5.81 61.60 24.91 53.25 25.29 9,373 2,294 23,339 61,902 39,452 1.63 0.59 0.25 4.14 0.42 0.36 1.20 4910 4120 460600 163990 6860 1880 90 2,532.62 929.10 947.93 5,840.05 1,334.66 570.91 4,320.64 19.51 24.39 24.49 36.18 18.29 15.59 16.92 17997408 8454028 51482633 68714511 1196302 7323158 100781 0.52 44.66 85.71 22.86 18.22 71.26 1.88 7,384 7,940 1,183 21,206 1,787 1,345 5,555 33.76 2.71 4.46 12.70 2345 10410 117150 41270 18,289.70 3,861.69 10,948.72 6,432.68 38.97 24.98 27.90 56.97 1370328 11179949 78529409 5565287 0.28 12.56 13.37 0.04 162,264 10,020 20,245 59,806 5.71 0.14 4.35 344 20770 96570 24,832.60 840.40 2,124.66 9.10 26.52 21.73 35981 38225453 45154036 0.57 89.06 4.14 23,723 689 21,956 21.03 3226 38,663.38 43.89 9262900 0.14 134,494 6.22 15.99 1.96 3.29 0.49 5.71 2.03 31440 3100950 18450 32199 4400 466830 147730 44,974.83 56,822.52 15,613.76 2,615.03 2,801.94 16,035.90 2,085.10 18.14 18.52 25.44 23.72 11.06 40.41 33.25 65116219 320635163 3412009 31298900 271130 30081829 92677076 8.71 8.72 58.02 2.97 36.11 12.84 35.00 34,208 79,772 19,710 16,975 2,465 30,422 8,679 14.68 176 34,797.14 20.31 107710 3.88 115,387 0.70 0.50 0.29 0.89 92 5490 486350 140620 2,967.85 1,395.44 1,337.80 1,445.07 18.89 41.75 33.66 22.36 4270092 26497889 15879361 13814629 10.47 2.28 87.99 81.80 254 2,778 3,275 4,028