Contribution of Renewable Energy Consumption to CO 2 Emission Mitigation: A Comparative Analysis from a Global Geographic Perspective

: Renewable energy consumption (REC) has an important significance in mitigating CO 2 emissions. However, currently, few scientists have analyzed the underlying impact of REC from a global geographic perspective. Thus, here, we divide the world into seven regions to study this impact during the period 1971–2016 using the logarithmic mean Divisia index (LMDI). These re ‐ gions were East Asia and the Pacific (EAP), Europe and Central Asia (ECA), Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), North America (NA), South Asia (SA), and Sub ‐ Saharan Africa (SSA). The results showed that ECA had the most obviously mitigating effect of − 10.13%, followed by NA and MENA ( − 3.91% and − 3.87%, respectively). Inversely, EAP had the largest driving effect of 4.12%, followed by SA (3.43%) and the others. Globally, REC had an overall mitigating contribution of − 11.04% to total CO 2 change. These results indicate that it is still important to exploit and utilize renewable energy, especially in presently developing or under ‐ developed countries. Moreover, for some countries at a certain stage, their REC effects were nega ‐ tive, but, concurrently, their energy intensity effects were positive. These results show that some developing countries recently reduced carbon emissions only by extensively using renewable en ‐ ergy, not by enhancing energy ‐ use efficiency. Finally, some policy implications for reducing CO 2 in different countries are recommended. stage), structure small annual Even the carbon still an overall mitigating impact (except for the second, third, and fourth stages), with the changing effects (average annual contributions) of − 92.79 ( 0.06%), (0.03%), (0.03%), (0.33%), and 1326.90 Mt ( − 1.21%). These results indicate that the REC effect of EAP had an overall mitigating impact on driving global CO 2 emis by deteriorating the structure of energy use or increasing the corresponding carbon with average annual contributions of 0.01% (=0.07% − 0.06%), 0.05% (=0.02% 0.11% (=0.08% + 0.03%), 0.61% (=0.28% (=0.11%


Introduction
With the growth of world population and economic development, global energy consumption has increased sharply since the industrial revolution [1,2]. The consequent pollutant emissions, especially carbon dioxide (CO2), have also grown rapidly, which has caused some extreme environmental problems, such as climate warming, and attracted more and more attention from the public, governments, and so on [3][4][5]. Thus, it is important and urgent to study the mechanism of CO2 emission mitigation and the transformation of energy consumption structure or mode [6][7][8]. For example, the fact that the consumption growth of renewable or nonfossil energy (e.g., hydro, wind, solar energy, geothermal energy, biomass energy) can achieve the goal of reducing carbon emissions, to some extent, has been noticed by the academic community [9][10][11].
Focusing on the nexus between renewable or nonfossil energy consumption (REC or NFEC) and CO2 emission, some core literature can be retrieved from the WOS (web of science) database (Table 1). It can be easily seen that most studies have concluded the increasing REC/NFEC can reduce regional carbon emissions or improve air quality by controlling/decreasing carbon emissions [12][13][14]. However, there have also been some conclusions that the nexus between REC growth and CO2 emission is not obvious [15][16][17]. There were even a few studies that found that the REC's growth could also cause the increase of CO2 emission, and vice versa [18][19][20]. Moreover, except for certain studies [21,22], almost all researchers chose one/some specific countries or regions as cases to study this nexus between REC and CO2 emissions (Table 1); for example, researchers have chosen Asian countries, 31 developed countries, Turkey, and European Union as cases to study this nexus [18,[23][24][25]. However, studies on this issue from a global scale are quite rare. Next, the studied periods were short, except for the four articles written before 1970 that were relatively long [13,[25][26][27]. Last, the methods used were mainly some econometric models. The classical theories of the environmental Kuznets curve (EKC) and the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model were often used. Other models or methods, such as the generalized method of moment (GMM) and three-stage least squares (3SLS), were also introduced ( Table 1).
Why do the results of the studies on this nexus between REC and CO2 emissions have different, even opposite, findings? This question may arise from a number of reasons, such as the differences in economic development patterns, geographical characteristics, and habits of energy use [28][29][30]. An interesting reason may be that the threshold point where renewable energy supply (use) starts to mitigate CO2 emissions is 8.39% [6,31]. That is to say, REC has to account for 8.39% of total energy consumption before it starts to make an obvious impact on mitigating CO2 emissions. Inversely, when REC does not reach 8.39% of total energy consumption, REC will have no Granger causality with mitigating CO2 emissions. Even so, in line with most studies [32][33][34], we believe that the use of renewable energy is bound to contribute positively to global carbon reduction in the long run [35][36][37]. Thus, the hypothesis of this paper is that, on a global scale, REC growth can reduce carbon emissions, while other factors such as the total amount and efficiency of energy use remain constant.
However, previous studies centering in this nexus on a global scale are still inadequate (Table 1), and a better decomposition method called the logarithmic mean Divisia index (LMDI) model has been ignored [38,39]. Among these studies, only one article focused on the differences in the income levels and inferred that this difference might cause the varied findings on the nexus between REC and CO2 emissions [22]. Nevertheless, the studied period of this article was short, only for 1995-2014, and the number of countries was also not sufficient (only 120, about two-thirds of the total number of countries worldwide). Thus, on the global scale, it is necessary to include all countries or areas worldwide and to conduct longer time-series analysis to draw more accurate and detailed conclusions that can help all relevant countries, organizations, and institutions to make more scientific and reasonable development strategy decisions related to energy conservation and emission reduction. Therefore, we, for the first time, try to do this new work, containing all the countries or areas for the period 1971-2016 and applying the LMDI method, to analyze this nexus between REC and CO2 emissions; this is the innovation of this paper (Table 1). The rest of the contents of this paper are arranged as follows: data sources and methodology are explained in Section 2; specific results and the related discussion and analysis are listed in Section 3; conclusions and some policy implications are summarized or proposed in Section 4.

Data Explanation
Three data sources (the Energy Information Administration of United States (EIA), British Petroleum (BP), and the International Energy Agency (IEA)) [59-61] are compared with the World Bank (WB) database [62], but all the three were rejected because of the integrity of the data's terms and periods. Ultimately, using the standard LMDI index decomposition method, only the WB database was chosen to analyze this nexus between global REC and CO2 emissions in the period 1971-2016.
Annual data on the population, gross domestic product (GDP) in constant 2010 dollar of United States (USD, or $), the total of all kinds of energy use and their respective percentages in total energy consumption, carbon emissions arising from the different energy categories and their corresponding percentages in total energy-related CO2 emissions, and the carbon intensity of energy use over the period 1971-2016, can be acquired or simply calculated from the World Development Indicator (WDI) datasets of the WB. According to the World Bank and the respective geographical locations, the world is divided into seven regions (  Corresponding datasets of the countries in EAP, ECA, LAC, MENA, NA, SA, and SSA can also be directly acquired from the WB database. The specific countries or areas contained in the seven regions are shown in Table A1. The descriptive statistics of the main variables for the whole world are in Table A2.

Methodology
In the decomposition analysis, the additive LMDI is employed, which is considered a preferred method [38,63,64]. The total CO2 emissions of the studied regions are decomposed into the following Equation (1) or Equation (2) underlying factors: Next, the CO2 emissions' changes in energy consumption from time period 0 ( ) to period T ( ) can be divided into the following contributions of different factors: where L is the logarithmic mean given by Hence, some variables were selected, and their respective abbreviations and units are shown in Table 2. These decomposed factors can be called the population effect (∆ ), the economic output effect (∆ ), the energy intensity effect (∆ ), and the integrated carbon coefficient effect of energy-mix use (∆ ). It should be noted that the impact of mitigating CO2 emissions from REC is mainly manifested by the ∆ index. Thus, this ∆ index can also be simply called the REC effect. Furthermore, this REC effect can be divided into the following two effects of the carbon emission coefficient of energy use (∆ ) and energy structure optimization (∆ ). The reasons are as follows. First, the CO2 emissions produced by REC are less than that of the equivalent fossil fuels. Thus, with the rise of the REC amount, the carbon coefficient of the energy mix should be smaller, and the corresponding effect of mitigating carbon emissions should be more obvious. Second, the higher the REC ratio in total energy consumption, the larger the mitigation effect of carbon emissions from energy structure optimization.

Decomposition Results of the Growth of Global CO2 Emission in Total
In 1971, the total world population was 3.76 billion, and it grew stably up to 7. Similarly, GDP, energy consumption, and REC's percentage in total energy use (REC ratio) were 20.05 × 10 3 billion$, 5.79 billion tonnes of oil equivalent (Btoe), and 15.55% in 1971, respectively, and they increased to 77.94 × 10 3 billion $, 13.79 Btoe, and 19.22% in 2016, with an annual average increase (rate) of 1.286 × 10 3 billion $ (3.06%), 177.8 Mt (1.95%), and 0.08% (0.47%). However, energy intensity had a decreasing trend due to the faster increasing rate of GDP than the rate of energy use. It was 0.289 B toe/10 3 $ in 1971, and it decreased to 0.177 B toe/10 3 $ in 2016, with an annual average change of −0.0025 B toe/10 3 $ and a rate of −1.08%.   (Table 3). In particular, the change in CO2 emissions, driven by the four factors, was 15.95, 15.89, −11.50, and −2.02 Bt, with the contributions of 87.05%, 86.73%, −62.74%, and −11.04%, respectively, to total CO2 change (Table 3). In Model 2, ∆ was decomposed into five factors: ∆ , ∆ , ∆ , energy structure effect (∆ ), and carbon coefficient effect ( ). The change in CO2 emissions, driven by the five factors, was 15.95, 15.89, −11.50, −0.77, and −1.25 Bt, with the contributions of 87.05%, 86.73%, −62.74%, −4.22%, and −6.82%. These results indicated that the population effect was the most important driver of carbon emission growth, followed by the economic output effect. Inversely, the energy intensity effect was the most obvious inhibitor, followed by the REC effect (or energy structure effect and carbon coefficient effect), which was consistent with many other studies [17,25,48]. Table 3. Decomposition results of the growth of global CO2 emissions in the period 1971-2016.  It should be noteworthy that no matter whether CO2 emission growth was decomposed into four or five factors, the numerical values and percentages of driving contribution from the same factors (∆ , ∆ , and ∆ ) were unchanged. Moreover, the REC effect was −2.02 Bt, and its contribution to total CO2 change was −11.04%. The effects of the two subfactors (∆ and ) of REC were −0.77 and −1.25 Bt, and the sum of these effects was −2.02 Bt, which was just equivalent to the REC effect (Table 3). Similarly, the contributions of the two subfactors were −4.22 and −6.82%, and the sum of these contributions was −11.04%, also just equivalent to the REC contribution. These results showed that the REC always had an overall inhibiting or mitigating impact of −11.04% to CO2 emission growth from the global perspective, which contained the two effects of energy structure Similarly, the REC ratios of ECA and NA were 6.13% and 5.30% in 1971, and they grew up to 20.85% and 17.71% in 2016, with an annual average increase (rate) of 0.33% (2.76%) and 0.28% (2.72%). Moreover, the REC ratios of ECA had a sharp fall due to world political changes during the period 1989-1990. However, the REC ratios of EAP and SA were 27.03% and 66.13% in 1971, and they obviously decreased to 13.42% and 29.97% in 2016, with an annual average change (rate) of −0.30% (−1.54%) and −0.80% (−1.74%). In addition, the REC ratios of LAC, MENA, and SSA were 32.03%, 4.92%, and 63.78% in 1971, and they had almost no rules; there was a variable and, overall, slight decrease to 25.51%, 2.26%, and 60.87% in 2016 ( Figure 2).

Model 1 ∆
Hence, it could be concluded that SSA, MENA, and LAC had small populations and produced less carbon emissions. Moreover, SA had a moderate-sized population and also produced less carbon emissions. However, NA and ECA had moderate-sized populations but always produced much higher carbon emissions. Nevertheless, EAP always had the largest population, but, at first, produced much less carbon emissions. The growth rate of carbon emissions produced by EAP was more than many other regions, so, in the end, it produced the highest carbon emissions. In addition, the REC ratios of the seven geographical regions had some obviously different trends. Therefore, it is necessary and meaningful to study, in depth, the different reasons or mechanisms of carbon emission mitigation for the seven geographical regions due to their heterogeneity.

Comparisons of the Decomposition Results of the Seven Regions by Their Respective Geographical Locations
The decomposition results of EAP, ECA, LAC, MENA, NA, SA, and SSA from the period 1971-2016 are listed for convenience of explanation in Figure 3. The corresponding contributions and average annual contributions are in Table 4. It can be easily seen that the change amounts of EAP, ECA, LAC, MENA, NA, SA, and SSA from 1971 to 2016 were 11.83, −1.18, 1.31, 2.26, 1.02, 2.49, and 0.60 Bt. Thus, the contributions of the seven regions to the total growth of carbon emissions were 64.53%, −6.41%, 7.12%, 12.33%, 5.55%, 13.59%, and 3.29%. The corresponding annual average contributions were 1.43%, −0.14%, 0.16%, 0.27%, 0.12%, 0.30%, and 0.07% (Table 4). From the bold figures in the table, it is clear that attention should be paid to the EAP, followed by SA, MENA, LAC, NA, and SSA. ECA should be the last to be given attention.
In addition, for LAC, the change in CO2 emissions, driven by the five factors (∆ , ∆ , ∆ , ∆ , and ), was 0.80, 0.66, −0.22, 0.09, and −0.02 Bt, and the corresponding contributions (average annual contributions) were 4.38% (0.10%), 3.62% (0.08%), −1.22% (−0.03%), 0.47% (0.01%), and −0.12% (−0.00%). The contribution of the REC effect of LAC was 0.35% (=0.47% − 0.12%), and its annual average contribution was 0.01% (=0.01% − 0.00%). These results showed that the population effect, the economic output effect and the energy structure effect were the drivers of carbon emission growth. Inversely, the energy intensity effect and the carbon coefficient effect were the inhibitors. Similar results can also be found for SSA. In this region, the change in CO2 emissions, driven by the five factors, were 0.58, 0.07, −0.06, 0.02, and −0.01 Bt, and the corresponding contributions (average annual contributions) were 3.15% (0.07%), 0.39% (0.01%), −0.30% (−0.01%), 0.12% (0.00%), and −0.07% (−0.00%). The contribution of the REC effect of SSA was 0.05% (=0.12% − 0.07%), and its annual average contribution was 0.00% (=0.00% − 0.00%). Hence, it can be concluded that ECA had the most obvious mitigating REC effect of −10.13%, followed by NA and MENA (−3.91 and −3.87%, respectively). Inversely, EAP had the largest driving REC effect of 4.12%, followed by SA, LAC, and SSA (3.43, 0.35, and 0.05%). In addition, the population increase effect and economic output effect were always the two most important drivers of CO2 emission growth, but the energy intensity effect was often the inhibitor (except for MENA), which is consistent with many other studies [17,25,48]. The reasons were easily understandable. The MENA countries have abundant fossil energy resources, and, for many years, their economic development has mainly relied on the excessive exploitation and utilization of these resources. With the growth in population and economic development, energy consumption and corresponding CO2 emissions undoubtedly grew. However, people in the MENA countries have not paid enough attention to improving the level of science and technology and the efficiency of energy use; the opposite has happened in the other regions of the world.

Regions
Variables Why were the REC effects of EAP, SA, LAC, and SSA the drivers for the growth of global CO2 emissions? Some obvious reasons are as follows. For example, it can be easily seen that there was a stable decrease in REC ratios in regions such as EAP and SA ( Figure  2). Then, as mentioned above, the CO2 emissions produced by REC would be less than that of the equivalent fossil fuels. Therefore, the use of fossil fuels in these regions became higher and higher and emitted more and more CO2, which gave rise to the driving (not mitigating) impact on the growth of global carbon emissions.

Overall Decomposition Results for Five Different Periods
The total CO2 changes, decomposed into five driving factors for five diferent periods (1971-1980, 1980-1990, 1990-2000, 2000-2010, and 2010-2016), were analyzed to provide an understanding of potential mechanisms. Table 5 shows the overall decomposition results for the five different periods; the corresponding change percentages of CO2 growth and the contributions of the drivers' effects to total CO2 change are presented in Figure 4.
It can be easily seen that the world's CO2 emissions increased by 4.36 Bt, with a change of 23.81% to total CO2 growth, in the first stage of 1971-1980 ( Figure 4 and Table  5) and then slightly increased by 2.57 (1.50) Bt, with the change of 14.00% (8.171%) in the second (third) stage of 1980-1990 (1990-2000). In the fourth stage of 2000-2010, this CO2 sharply increased by 8.01 Bt, with a change of 43.70% in total CO2 growth, and grew slightly again by 1.89 Bt, with a change of 10.32% in the fifth stage of 2010-2016.  Overall, total CO2 emissions exhibited a sequentially increasing trend, with total growth of 18.83 (=4.36 + 2.57 + 1.50 + 8.01 + 1.89) Bt (Table 5). The population always had a positive or driving impact on CO2 emission growth, with increasing effects of 1.82 2.31, 2.27, 2.45, and 1.67 Bt, and contributions of 9.91%, 12.61%, 12.36%, 13.37%, and 9.12% to total CO2 growth from Stage 1 to 5, respectively ( Figure 4 and Table 5).
Similarly, economic output also always had a driving impact on CO2 emission growth, with the increasing effects (contributions)  07%). In addition, the trends of the REC effect (containing the energy structure effect and the carbon coefficient effect) were not stable and often had only a small influence on CO2 emissions (Figure 4 and Table 5).
Thus, for analyzing REC's impact on mitigating global CO2 emissions more deeply, we divided the world into seven different regions by their different geographical locations for five different periods, respectively, to further study these mechanisms.

Decomposition Results of Seven Different Regions by Their Geographical Locations for Five Different Periods
The CO2 emission change and the effects of the decomposed drivers from seven different regions by their geographical locations for five different periods are shown in Table  6. The corresponding percentage change of CO2 emission growth and the contribution of the decomposed drivers' effects are depicted in Figure 5. The average annual change percentage of CO2 emission and the contribution rates of drivers for the five different periods are shown in Table 7.
EAP: The CO2 emissions of EAP increased by 1023.86 Mt, with a change of 5.59% to total CO2 growth and an average annual change rate of 0.62% in the first stage (Figure 5a, Tables 6 and 7). Then, it decreased by 1533.85 Mt, with a change of 8.37% and an average annual change rate of 0.84% in the second stage. In the third stage, CO2 increased again by 1851.81 Mt, with a change of 10.10% and an average annual change rate of 1.01%. Then, it sharply increased by 6011.41 Mt, with a change of 32.80% and an average annual change rate of 3.28% in the fourth stage. In the fifth stage, CO2 increased again by 1405.60 Mt, with a change of 7.67% and an average annual change rate of 1.28%. Overall, the CO2 emission of EAP exhibited a sequentially increasing trend, with a total growth amount of 11,826.53 Mt ( Table 6).
Similarly, the population of ECA always had a driving impact on CO2 emission growth, with the increasing effects (average annual contributions) of 551.73 (0.33%), 563. 43

Regions
Periods   Table 7. The average annual percentage change of CO2 emissions and the contribution rates of drivers from seven different regions for the five different periods.

Regions
Periods   Tables 6 and 7). It increased by 259.62 Mt, with a change percentage of 1.42% and an average annual change rate of 0.14% in the second stage. In the third stage, CO2 increased again by 593.47 Mt, with a change of 3.24% and an average annual change rate of 0.32%. Then, it increased again by 795.80 Mt, with a change of 4.34% and an average annual change rate of 0.43% in the fourth stage. In the fifth stage, CO2 increased again by 339.67 Mt, with a change of 1.85% and an average annual change rate of 0.31%. Overall, the CO2 emission of MENA exhibited a sequentially increasing trend, with a total growth amount of 2260.39 Mt ( Table  6).
The It can be easily seen that the CO2 emission growth and the corresponding drivers' contributions were exhibited mainly in EAP, ECA, and NA ( Figure 5). Furthermore, the population and economic output almost always had driving effects, the latter often more than the first, especially in the fourth stage of EAP. These results could arise from the fact that some developing countries, such as China (in EAP, Table A1), had high economic output and, concurrently, rapid economic development. Moreover, energy intensity almost always had a mitigating effect (except in MENA), especially in the second stage of ECA. These results could arise from the fact that many developed countries, such as Germany and Sweden (in ECA, Table A1), have, for a long time, paid much more attention to enhancing the level of science and technology to increase energy use efficiency and reduce energy intensity and carbon emissions.
It should be noteworthy that CO2 emission growth and the corresponding drivers' contributions of LAC, MENA, SA, and SSA were extremely small and can almost be neglected ( Figure 5, Tables 6 and 7). However, an interesting result was that the annual average contribution rate of the energy intensity effect of LAC was positive (0.02%) in the second stage and, concurrently, the annual average contribution rates of the energy structure effect and the carbon coefficient effect were not more than zero (0.00 and −0.02%, respectively). The annual average contribution rate of the REC effect was negative (−0.02% =0.00% − 0.02%). These results indicate that many developing countries (i.e., Panama, Mexico, and Haiti) of LAC have recently developed their own economy and reduced carbon emissions only by using more and more renewable energy to replace the utilization of fossil energy such as coal, oil, and natural gas. These countries worry that they have not paid attention to improving their level of science and technology and production efficiency for saving energy and reducing energy intensity. Hence, with the fast development of their economy and rapid growth of REC, energy intensity exhibited a driving impact, although the REC effect brought out an obvious mitigating impact on CO2 emission growth (Table 7). Similar situations can also be seen in the first stage of ECA, the first, second, and fourth stages of MENA, and the second stage of SSA.

Discussion
There is no doubt that mitigating global warming is a worldwide systematic project. The successful completion of this project requires the joint efforts and cooperation of all mankind. This paper only traces the quantitative contribution of REC growth to the reduction of carbon dioxide emissions from a macro perspective. However, from the micro perspective, it is of more scientific importance and significance to promote the application of engineering technologies to reduce carbon emissions and increase carbon sink. These engineering technologies have the following aspects: reducing waste pollutants [65], reducing carbon emissions in agriculture [66], construction [67][68][69], the paper industry [70], and other industries, and increasing soil carbon absorption [66]. We cannot study these microscopic problems in this paper. Therefore, this is also the deficiency of this paper, and we will continue to research the directions mentioned above in the future. If similar studies are conducted based on other criteria (i.e., income, not geography), some different and distinct results can be obtained. This is also a study direction in the future.
In addition, the reliability and stability of the results in the paper are indisputable. However, these results may also have a few errors. The main sources of errors are as follows. First, the data source itself might produce the error. Some data of WDIs on the WB website are generated and acquired using the reasonable estimating method. Hence, inevitable, although slight, errors exist in their own database. These errors have an extremely slight impact on the results of this study. Furthermore, the original statistical data of renewable energy should have many categories, such as hydro, wind, solar energy, geothermal energy, and biomass energy. However, complete and detailed data containing each category of renewable energy are almost impossible to find. Thus, the subtraction of total energy consumption and total fossil energy consumption was used to replace total renewable energy consumption. The approximate approach might bring certain errors, but the impact on the final results of this study is still small. Finally, some small errors (although they can be ignored) might be produced by our computations, e.g., using the rounded integer arithmetic method, in the whole study process.

Conclusions and Policy Implications
As the hypothesis states, on a global scale, REC has had an overall mitigating effect of −11.04% on total CO2 change, with REC growth. The REC mitigating effect contained the two effects of energy structure optimization (−4.22%) and the carbon emission coefficient (−6.82%). ECA had the most obvious mitigating REC effect of −10.13%, followed by NA and MENA (−3.91 and −3.87, respectively). Inversely, EAP had the largest driving REC effect of 4.12%, followed by SA, LAC, and SSA (3.43%, 0.35%, and 0.05%). These results indicated that it is still important to exploit and utilize renewable energy, especially in developing or underdeveloped countries, as renewable energy use is extremely insufficient and even decreasing in these countries. Furthermore, the population and economic output almost always had driving effects, the latter often more than the first, especially in the fourth stage of EAP. These results could arise from the fact that some developing countries, such as China, had high economic output and, concurrently, rapid economic development. Moreover, energy intensity often had a mitigating effect, especially in the second stage of ECA. The reason could be that many developed countries, such as Germany and Sweden, have, for a long time, paid much more attention to enhancing the level of science and technology to increase energy use efficiency to reduce energy intensity and carbon emissions. In addition, for regions at a certain stage, the annual average contribution rate of the REC effect could be negative. However, concurrently, their annual average contribution rate of the energy intensity effect could be positive. The result shows that some developing countries have recently reduced carbon emissions only by extensively using renewable energy, not by enhancing their energy use efficiency. Thus, some policy implications for reducing CO2 in different countries are listed.
First, globally, it should become a long-term development strategy to exploit and utilize renewable energy in order to replace the use of traditional fossil fuels. This is because, presently, only the REC of some developed countries (i.e., Sweden in ECA) have had an obvious mitigating effect on CO2 emissions. The RECs of many other countries still have a positive or driving impact on CO2 emission growth. The exploitation and utilization of renewable energy in these countries are relatively insufficient.
Moreover, for some developed countries (especially in ECA), their population should continue to strengthen the development of renewable energy. Meanwhile, their population should also continue to improve the related technology and energy-use efficiency. Thereby, to the greatest extent, it is possible to achieve rapid economic development and, concurrently, generate the least CO2. Moreover, the advanced technologies of renewableenergy exploitation and utilization should, as far as possible, be transferred to other developing or underdeveloped countries in an appropriate way.
Next, for some developing countries such as China in EAP, their economic development is overly dependent on the consumption of a large amount of fossil fuels. More attention should be given to exploit renewable energy and, concurrently, improve the related level of science and technology and the efficiency of energy use. These two aspects have the same importance. Particularly, the measures contain the introduction of advanced technology for renewable energy exploitation (i.e., photovoltaic generation) and energy efficiency improvement.
Lastly, the other countries (especially in LAC, MENA, and SSA) have developed their own economy and reduced carbon emissions only by using more and more renewable energy. They have not paid enough attention to improving their level of science and technology for saving energy. Therefore, in these countries, their population should give importance to improving their energy-use efficiency in order to save energy and reduce emissions. All technologies that are helpful to improving energy-use efficiencies should be given the same attention and be introduced.