Research on New and Traditional Energy Sources in OECD Countries

To mitigate the problems associated with climate change, the low-carbon economy concept is now being championed around the world in an effort to reduce greenhouse gas emissions and ensure sustainable economic growth. Therefore, to reduce the dependence on traditional energy sources, the Organization for Economic Co-operation and Development (OECD) has been actively promoting the use of renewable energy. Past research has tended to neglect the influence of other pollutants such as fine particulate matter (PM2.5) and sulfur dioxide (SO2) and have mainly been based on static analyses. To make up for these research gaps, this study examined OECD country data from 2010–2014, with labor, fixed assets, new energy, and traditional energy as the inputs, and Gross Domestic Product (GDP), carbon dioxide (CO2), and PM2.5 as the outputs, from which it was found: (1) the overall efficiency of the individual countries varied significantly, with nine countries being found to have efficiencies of 1 for all five years, but many others having efficiencies below 0.2; (2) in countries where there was a need for improvements in traditional energy (which here refers to coal, petroleum and other fossil energy sources), there was also a significant need for improvement in new energy sources (which here refers to clean energy which will produce pollutant emissions and can be directly used for production and life, including resources like nuclear energy and “renewable energy”); (3) countries with poor traditional energy and new energy efficiencies also had poor CO2 and PM2.5 efficiencies; (4) many OECD countries have made progress towards sustainable new energy developments


Introduction
As the demand for energy continues to grow, the levels of carbon dioxide emitted into the atmosphere have also been increasing, which is adversely affecting efforts to combat climate change. The UN Intergovernmental Panel on Climate Change (IPCC) has clearly stated that climate change is a reality and is continuing to intensify around the world. One of the main climate change causes has been man-made carbon emissions from the use of fossil fuel derived energy; therefore, if countries do not implement CO 2 reduction targets, global carbon dioxide will continue to increase. As the pollution caused by traditional petrochemical energy has increased year on year, many countries are seeking energy generation alternatives, and have begun to invest in new energy developments with the aim of future sustainable development.
New energy research has focused on the impact of new energy on GDP or CO 2 levels , with most evaluations having been conducted using DEA methods such as CCR, BCC, SBM, Distance functions and two-stage DEA analysis. However, previous analyses in this area have tended to employ only static analyses that did not reveal the continuous development of new energy. Further,

Literature Review
Past new energy research has taken three main directions; the impact of new energy on GDP or CO 2 , the sustainable development of the environment, or new energy efficiency and government policies. To analyze the impact of new energy on GDP and CO 2 , Bampatsou et al. [1] used a technical efficiency index from 15 EU countries from 1980 to 2008, and found that of the total energy input level, the nuclear energy input had a negative impact on technical efficiency, and when the consumption of fossil fuels was reduced and the renewable energy was better utilized, output significantly increased. Komiyama and Fujii [2] examined whether solar energy could be integrated into Japan's power system to its optimize power generation structure, finding that solar energy could improve Japan's economic performance. Pang et al. [3] examined economic output, energy conservation, emissions reduction, and the impact of clean energy use on total factor efficiency for 87 countries from 2004-2010, and found that clean energy consumption significantly increased the total-factor emissions reduction efficiency (TFCE), slightly improved total-factor economic output efficiency (TFYE), and significantly reduced total-factor energy efficiency (TFEE). Adewuyi and Awodumi [4] suggested that the pollution effects of the energy-growth nexus should be considered in sustainable development policies. Jebali et al. [5] used a double bootstrap DEA and bias correction efficiency truncated regression to study the energy efficiency of Mediterranean countries from 2009 to 2012. It is found that even though these countries had high energy efficiency levels, it had declined over time due to the effects of per capita GNI, population density, and renewable energy use. Atem and Hotaling [6] used System Generalized Method of Moments (GMM) data from 174 countries from 1980 and 2012 to analyze the impact of power generation on economic growth. They found that there was a significant relationship between renewable energy and non-renewable energy power generation and economic growth.
In sustainable development research, Hoang and Rao [7] used a non-radial DEA to analyze energy efficiency in 29 OECD countries, and found that agricultural production systems were generally sustainable, and Shiau and Jhang [8] used a Radial DEA to analyze the efficiency of Taiwan's transportation system, finding that when the three core service impact indicators, cost efficiency, and service reductions were excellent, the transportation system was sustainable. Camioto et al. [9] used an SBM DEA to analyze the overall efficiency of various industries in Brazil and they found that the most efficient industry in Brazil was the textile industry, and that the sustainable development of the metallurgical industry was poor. Wang [10] used an SBM DEA to analyze the energy efficiency of 109 countries, finding that high-income countries had more sustainable energy than low-and middle-income countries. Zhang and Xie [11] used a non-radial DDF to explore China's renewable energy and sustainable development from 1991 to 2005. It is found that China's environmental supervision costs needed to be increased to improve renewable energy efficiency. Raheli et al. [12] surveyed the Marinde region of Azerbaijan Province in eastern Iran to assess the sustainability and efficiency of tomato production using a fractional regression model (FRM) for analysis of the first stage, and farm-specific variables such as education level, farmer age, total land area, and manure in the second stage, and found that farmer age and education level as well as area had an impact on tomato production efficiency.
In energy efficiency and government policy analysis research, Chien and Ho [13] used a radial DEA to analyze the overall economic efficiency of 45 OECD countries. It is found that an increase in renewable energy could increase economic efficiency. Homma and Hu [14] studied the energy efficiency of 47 metropolitan areas in Japan from 1993 to 2003. They found that because of the high cost of renewable energy, the government needed to encourage these inefficient regions to change their industrial structures and reduce energy consumption to maintain the environment. Sueyoshi and Goto [15] used a non-radial DEA model to study the effect of the US Clean Air Act (CAA) on acid-induced gases (NO x ), and concluded that the CAA had been effective in controlling SO 2 and NO 2 emissions from US coal-fired power plants. Blokhuis et al. [16] used radial DEA to analyze the efficiency of new energy in The Netherlands, and it is found that wind energy was able to improve energy technology efficiency. Boubaker [17] used radial DEA to analyze the energy efficiency of Morocco, Algeria and Tunisia, and found that energy diversification was more efficient and better suited the common interests of the three countries. Fagiani et al. [18] found that renewable energy was able to effectively reduce CO 2 emissions from the power sector as well as reduce a country's dependence on imported oil. Menegak and Gurluk [19] compared renewable energy in Turkey and Greece, and found that Greece's poor performance was mainly due to its economic crisis, which had delayed the development of its renewable energy sector. Sueyoshi and Goto [20] used static DEA and dynamic Malmquist indices to assess the relationship between fuel mixtures, electricity and carbon dioxide in 10 industrial countries, finding that nuclear power generation in France and water and renewable energy in The Netherlands were important for the sustainable development of each society. Azamade et al. [21] used fuzzy-DEA to study Iranian wind power plants, and they found that they significantly contributed to energy consumption. Sueyoshi and Goto [22] evaluated the performance of 160 photovoltaic power plants in Germany and the United States, finding that the German power plants had more efficient operations. Hampf and Rodseth [23] proposed a new efficiency approach to analyze the impact of new energy policies on 160 asphalt-fired generating units in which the average generator set electric-to-carbon ratio was lower than the optimum ratio of 15.3%, which was because of non-autonomous reductions rather than management inefficiencies. Kim et al. [24] evaluated the efficiency of investing in three NRE technologies; wind power, photovoltaic power and fuel cells; finding that wind power was the most efficient.
Sueyoshi and Goto [25] assessed Japan's energy efficiency, finding that nuclear power generation efficiency ranged from 10.4% to 13.7%, and water and gas power generation efficiency ranged from 22.4% to 40.5%. Wu et al. [26] used two-stage DEA to explore the performance of China's production efficiency (first stage) and treatment efficiency (second stage), and found that the western and eastern regions needed to develop clean energy sources to achieve sustainable environmental development. Lin and Li [27] studied the relationship between coal-fired power generation and the coal consumption rate, and found that an increase in power generation hours would reduce the coal consumption rate. Ervured et al. [28] analyzed the renewable energy efficiency of the Turkish provinces, concluding that to improve overall technical efficiency in the future, investment decisions needed to be made for the inefficient regions. Guo et al. [29] found that energy savings and pollutant reduction required new technologies to ensure continued economic development in China. Steimberg et al. [31] suggested that the use of natural gas generator sets to replace coal-fired generating system would effectively reduce carbon dioxide emissions. Llamas et al. [32] analyzed the efficiency of a Mexican thermoelectric symbiosis plant and estimated its possible energy contribution by 2030. The above literature in the discussion of new energy is about the impact of the introduction of new energy on economic growth and CO 2 . The results show that the introduction of new energy will increase economic growth and reduce CO 2 environmental pollution. The analysis of new energy efficiency and new energy policies, the main literature uses static DEA (such as radial, non-radial, direction distance, two stage) to explore that whether the introduction of new energy efficiency and the adoption of new energy policy can improve economic, energy and operational efficiency. The results show that new energy input increases economic, energy and operational efficiency and reduces CO 2 , SO 2 and NO 2 pollutants. The government should actively promote new energy. The discussion of new energy mainly depends on the impact of new energy on economic growth and the reduction of CO 2 pollution or the introduction of new energy, and assesses its economy, new energy efficiency and energy consumption efficiency. The methods are based on static analysis and fail to consider new energy with traditional energy consumption. The air pollutants are CO 2 and SO 2 without PM 2.5 .Unfortunately, most of the above research was not dynamic analysis and did not consider other air pollution variables such as PM 2.5 and SO 2 . Therefore, to overcome these omissions, this paper considers a PM 2. environmental variable and employs a modified Dynamic DEA model to explore OECD new energy efficiencies.

Dynamic DEA
Farrell [33] first proposed a production frontier that was applicable only for a single input and a single output. Charnes et al. [34] then proposed a multiple input and output data envelope model (CCR model), after which Banker et al. [35] proposed the BCC model that replaced the constant return to scale (CRS) assumption with a variable return to scale (VRS). Tone [36] then developed non-radial and non-oriented estimation method, the slacks based measure model (SBM model), to resolve the inequality problems associated with the input and outputs In terms of dynamic DEA development, Kloop [37] developed window analysis, and Malmquist [38] and Fare et al. [39] designed the Malmquist index. However, these models did not consider intertemporal continuation activities and were not suitable for long-term efficiency measurements.

Modified Dynamic DEA Model
About DEA analysis on energy and environmental pollution efficiency, the main method is to compare static analysis. As this paper considers undesirable output in the dynamic SBM model, Tone and Tsutsui's [47] dynamic SBM model was modified to include undesirable output. Thus,·the Modified dynamic DEA model is as follows: Suppose the observation is a J (J = 1 . . . .n) dimension decision making unit (DMU) set in which the DMU under evaluation is represented by DMU o and is subject to DMU o ∈ J. The inputs and outputs used to compute the efficiency are labeled m inputs x ijt (i = 1 . . . m) and s outputs Y ljt , respectively. Let output Y be divided into (Y g , Y b ), where Y g is the desirable output, Y b is the undesirable output, and Z good is carried over from period t to period t + 1. The following is the non-oriented model: Equation (6) is the connection equation between t and t + 1.
The most efficient solution is:

New Energy, Energy Consumption, CO 2 , and PM 2.5 Efficiency Indices
Hu and Wang's [48] total-factor energy efficiency index was used to overcome any possible bias in the traditional energy efficiency indicators. For each evaluated country, the GDP, energy consumption, new energy, CO 2 and PM 2.5 efficiencies were calculated using Equations (9)-(13): NE= Target new energy input (i, t) Actual new energy input (i, t) (11) If the target E and NE input equaled the actual input and the CO 2 and PM 2.5 undesirable outputs equaled the actual undesirable outputs, then the E, NE, CO 2 , and PM 2.5 efficiencies equaled 1, which indicated overall efficiency. If the target E and NE input was less than the actual input and the CO 2 and PM 2.5 undesirable outputs were less than the actual undesirable outputs, then the E, NE, CO 2 , and PM 2.5 efficiencies were less than 1, which indicated overall inefficiency.
If the target GDP desirable output was equal to the actual GDP desirable output, then the GDP efficiency equaled 1, which indicated overall efficiency. If the actual GDP desirable output was less than the target GDP desirable output, then the GDP efficiency was less than 1, which indicated overall inefficiency.

Data Sources
This study researched the energy use performance of OECD member countries from 2010 to 2014; the data for which were extracted from the world development indicators of the world bank [49] and the climate analysis indicators tool of the world resource institute (WRI). Currently, there are 35 member OECD countries, include the United Arab Emirates, Argentina, Brazil, Botswana, China, Colombia, Costa Rica, Dominican Republic, Algeria, Indonesia, India, Iran, Kenya, Cambodia, Sri Lanka, Morocco, Malaysia, Nigeria, Nepal, Pakistan, Peru, Philippines, Romania, Russia, Singapore, Thailand, and South Africa. The goal was to compare the energy use efficiencies of the OECD members and identify the differences between the old and new energy consumption efficiencies.

Variables
Labor, old energy consumption, and new energy consumption were the inputs, GDP, CO 2 , and PM 2.5 emissions were the outputs, and fixed assets was the carry-over. The variable details are shown in Table 1. Traditional energy consumption: fossil fuel consumption as a percentage of total energy consumption.
New energy consumption: renewable energy consumption as a percentage of total energy consumption.
Output: GDP (millions of US dollars): total market value of the final goods and services produced in a country in a certain period of time.
Carbon dioxide emissions (thousand tonnes): carbon dioxide emissions from the combustion of solid, liquid and gaseous fuels. PM 2.5 (micrograms per cubic meter): air particles with a particle size less than or equal to 2.5 microns.
Carry over: fixed asset (millions of US dollars): total capital formation (total domestic investment) including fixed asset expenditure and inventory changes.

Statistical Analysis
The following figures show the statistics for the input indicators (labor, capital, and new and old energy consumption) and the output indicators (gross domestic product). In Figure 1, the maximum labor force rose slowly. In 2012, there was a significant increase on the previous two years, after which the growth slowed. The average labor input in 2013 was slightly lower than in 2012, and the minimum labor force declined slightly in 2011 but then rose slowly from 2012 to 2014. Figure 2 shows that the maximum fixed assets rose substantially; however, the average value decreased in 2012 and then rose from 2013. Figure 3 shows that the maximum value for traditional energy consumption fluctuated and the average value declined. In 2012, the maximum traditional energy consumption value was at its highest, after which there was a slow decline and a slight rebound in 2014; however, the level was lower than in 2012. Policy interventions were responsible for traditional energy consumption. The minimum traditional energy consumption value was in 2012, after which it increased slightly.    Figure 3 shows that the maximum value for traditional energy consumption fluctuated and the average value declined. In 2012, the maximum traditional energy consumption value was at its highest, after which there was a slow decline and a slight rebound in 2014; however, the level was lower than in 2012. Policy interventions were responsible for traditional energy consumption. The minimum traditional energy consumption value was in 2012, after which it increased slightly.    Figure 3 shows that the maximum value for traditional energy consumption fluctuated and the average value declined. In 2012, the maximum traditional energy consumption value was at its highest, after which there was a slow decline and a slight rebound in 2014; however, the level was lower than in 2012. Policy interventions were responsible for traditional energy consumption. The minimum traditional energy consumption value was in 2012, after which it increased slightly.         Table 2 and Figure 6 show the overall efficiency and annual efficiency in each country. The countries that had efficiencies of in all five years were Australia, Denmark, France, Iceland, Luxembourg, Norway, Switzerland, United Kingdom, and the United States, countries with poor efficiencies at 0.2 or lower were Chile, Czech Republic, Estonia, Hungary, Latvia, Mexico, Poland, Slovak Republic, and Turkey with all other countries having efficiencies between 0.2 and 0.8.

Overall Efficiency and Efficiencies in Each Year
Germany, Italy, Sweden, and Australia's annual efficiencies all rose, with Sweden's attaining 1. In 2014, Germany had an increase in annual efficiency, in 2013 and 2014, Italy's efficiency fluctuated then rose to close to 0.7; however, Belgium, Canada, Ireland, the Netherlands, and Japan had some lower annual efficiencies, with the Netherlands having a significant decline in 2012 and 2014; however, its efficiency in all other years was 1. Japan's efficiency declined in 2014, but the efficiency score was attained 1 in all other years.   Table 2 and Figure 6 show the overall efficiency and annual efficiency in each country. The countries that had efficiencies of in all five years were Australia, Denmark, France, Iceland, Luxembourg, Norway, Switzerland, United Kingdom, and the United States, countries with poor efficiencies at 0.2 or lower were Chile, Czech Republic, Estonia, Hungary, Latvia, Mexico, Poland, Slovak Republic, and Turkey with all other countries having efficiencies between 0.2 and 0.8.

Overall Efficiency and Efficiencies in Each Year
Germany, Italy, Sweden, and Australia's annual efficiencies all rose, with Sweden's attaining 1. In 2014, Germany had an increase in annual efficiency, in 2013 and 2014, Italy's efficiency fluctuated then rose to close to 0.7; however, Belgium, Canada, Ireland, the Netherlands, and Japan had some lower annual efficiencies, with the Netherlands having a significant decline in 2012 and 2014; however, its efficiency in all other years was 1. Japan's efficiency declined in 2014, but the efficiency score was attained 1 in all other years.    Tables 3 and 4 show the efficiencies for labor and fixed assets from 2010 to 2014, from which it can be seen the gap between the countries for fixed-assets was smaller than for labor. Australia,  Tables 3 and 4 show the efficiencies for labor and fixed assets from 2010 to 2014, from which it can be seen the gap between the countries for fixed-assets was smaller than for labor. Australia, Austria, Belgium, Demark, Finland, France, Iceland, Iceland, Ireland, Luxemburg, Netherlands, Switzerland all had labor efficiencies of 1, Canada, Germany, Israel, Italy, New Zealand and Sweden had efficiencies slightly less than 1, but above 0.9, and Chile, the Czech Republic, Estonia, Hungry, Korea Republic, Latvia, Mexico, Poland, Slovak Republic, Spain, Turkey had efficiencies below 0.7 for all years, with the countries needing the most improvement being Mexico, followed by Poland, Turkey and Latvia. As shown in the Table 4, Canada, Chile, the Czech Republic, Estonia, Hungary, Korea Republic, Latvia, New Zealand, and Slovak republic all had fixed asset efficiencies below 0.8, Turkey had an efficiency of 1 in 2011 but below 0.7 in all other years, Spain's efficiency in 2010 and 2011 was still below 0.7, but rose to above 0.8 in 2012, and nearly reached 0.9 in 2014, and Australia, Canada, Greece, Korea Republic, Mexico, Poland, Slovak Republic, Slovenia has rising fixed asset efficiencies.  There were three more countries; Australia, Greece, and Sweden; that had new energy consumption efficiencies of 1 than countries with traditional energy consumption efficiencies of 1, all of which had low traditional energy consumption efficiencies for one year. While Greece's traditional energy consumption efficiency score in 2010 was less than 0.2, its new energy consumption efficiency was 1. Similarly, Sweden's traditional energy consumption efficiency in 2010 was below 0.7, but its new energy consumption efficiency was 1. Japan's new energy consumption efficiency was 0.95 in 2014 and 1 in other years, Italy's was 0.6 in 2010, fell to nearly 0.2 in 2011, and began to rise slightly to around 0.4 in 2012, the Netherlands' fell to 0.6 in 2012, was less than 0.5 in 2014, but was 1 in all other years, Portugal's was 1 in 2013, and below 0.2 in 2010, 2011, and 2014, and Spain's was only slightly higher than 0.2 in 2013, but in the other years, was lower than 0.2, and in 2011, was less than 0.1.

Input and Output Efficiency
Austria, Canada, Chile, Czech Republic, Latvia, Estonia, Finland, Germany, Hungary, Israel, Italy, New Zealand, Slovak, Slovenia, and Turkey had both low traditional energy consumption efficiencies and low new energy consumption efficiencies. However, the new energy efficiency in some countries was better than their traditional energy efficiency; for example, Belgium had a new energy efficiency higher than 0.3 for five years, Korea's efficiency was nearly 0.5 in 2010, but began to decline to be only slightly higher than 0.2 in 2014, which was still higher than its traditional energy efficiency, and the Netherlands had new energy efficiencies of 1 in 2011 and 2013, 0.6 in 2012, and slightly higher than 0.4 in 2014.  Tables 7 and 8 show that the CO 2 efficiencies in these five years were significantly higher than the PM 2.5 efficiencies in almost all countries.
Belgium, Canada, Denmark, France, Germany, Iceland, Italy, Japan, Luxemburg, Netherlands, New Zealand, Spain, Sweden, Switzerland, United Kingdom, United States all had CO 2 efficiencies of Denmark, France, Iceland, Luxembourg, Norway, Switzerland, United Kingdom, and United States had PM 2.5 efficiencies of 1 in all 5 years, Australia's efficiency was only slightly higher than 0.4 in 2010 but was 1 in the other 4 years, Germany's PM 2.5 efficiency was slightly above 0.6 in 2010, fell to 0.6 in 2011, rose to above 0.7 in 2012 and then rose to above 0.8, Italy's PM 2.5 efficiency was between 0.5 and 0.6, and was slightly below 0.6 in 2014, Japan's efficiency in the last year fell from 1 in the previous years to around 0.6, The Netherlands' PM 2.  To further understand the relationships between traditional energy and new energy and CO 2 and PM 2.5 in OECD countries, Table 9 was developed, from which it can be seen that the United States, United Kingdom, Switzerland, Denmark, France, Iceland, Luxembourg and Norway had traditional energy, new energy, CO 2 and PM 2.5 , efficiencies of 1 for all 5 years, and that Australia, Germany, Greece, Japan, Ireland and Sweden Netherlands had poor performance for 1~2 years, but were efficient in most years. In Austria, Chile, the Czech Republic, Estonia, Finland, Hungary, Latvia, the Slovak Republic, Slovenia and Turkey, however, all indicators required improvement. Although the CO 2 efficiencies in Belgium, Canada, Israel, Korea, Mexico, New Zealand and Poland were good, there was a need for significant improvements in traditional energy, new energy, and PM 2.5 .
In countries with both poor traditional energy and new energy efficiencies, the CO 2 and PM 2.5 efficiencies were also poor whereas the countries that had good new energy efficiencies, and especially the United States, Japan, and Western European countries, had moved toward the sustainable development of new energy.

Conclusions and Policy Suggestions
This paper analyzed the efficiency of new and traditional energy sources and CO 2 and PM 2.45 emissions, from which the following conclusions were made.
(1) There was a significant difference in the overall efficiencies in the OECD countries. Only Australia, Denmark, France, Iceland, Luxembourg, Norway, Switzerland, United Kingdom, and United States had overall efficiencies of 1 for the five years. (2) However, Chile, the Czech Republic, Estonia, Hungary, Latvia, Mexico, Poland, Slovak Republic, and Turkey all had efficiencies around or below 0.2 for the five years, and therefore, had a significant need for improvement. As the efficiencies in the other 16 countries ranged from 0.2 to 0.8, there was some room for improvement. (3) Germany, Italy, Sweden, and Australia had rising efficiencies and Belgium, Canada, Ireland, the Netherlands, and Japan had falling efficiencies. The Netherlands had a significant decline in 2012 and 2014, and efficiencies of 1 in all other years. Japan's efficiency dropped in 2014, but was 1 in all other years. (4) The differences in traditional energy efficiencies between the countries was large. Australia, Demark, France, Iceland, Luxembourg, Norway, UK and USA all had traditional energy efficiencies 1; however, Austria, Belgium, Canada, Chile, Czech Republic, France, Hungary, Israel, Korea Republic, Latvia, Mexico, New Zealand, Poland, Slovak Republic, Slovenia, and Turkey only had efficiencies below 0.2 for the five consecutive years. Obviously, there is a significant need for traditional energy efficiency improvements in many countries. Eleven countries have new energy efficiencies of 1, which was more than the countries with traditional energy efficiencies of 1. However, Austria, Canada, Chile, Czech Republic, Latvia, Estonia, Finland, Germany, Hungary, Israel, Italy, New Zealand, Slovak, Slovenia, and Turkey all had new energy efficiencies of less than 0.2 and had a significant need for improvements. Most Eastern European countries (Cezch, Hungary, Latvia, Poland, Lovenia etc..) had new energy efficiency scores below 0.10, and there was still much room for improvement. (5) Belgium, Canada, Denmark, France, Germany, Iceland, Italy, Japan, Luxemburg, Netherlands, New Zealand, Spain, Sweden, Switzerland, United Kingdom, United States all had CO 2 efficiencies of 1, and as the efficiencies in the other 19 countries were lower than 1, there was some need for improvement. Countries with relatively low carbon emission efficiency score were mainly Eastern European countries, Estonia only had an efficiency of 0.2 for the five years, while most other countries had efficiencies above 0.5. (6) The PM 2.5 efficiencies were generally much lower than the CO 2 efficiencies, with only eight countries; Denmark, France, Iceland, Luxembourg, Norway, Switzerland, United Kingdom, and the United States; having efficiencies of 1 for the five years compared to 16 countries with CO 2 efficiencies of 1. Austria, Belgium, Canada, Chile, Czech Republic, Hungary, Israel, Korea Republic, Latvia, Mexico, New Zealand, Poland, Slovenia, and Turkey all had PM 2.5 efficiencies below 0.2, with most being lower that 0.1; therefore, there was a significant need for improvement. It could also be seen that the efficiency score for most Eastern European countries was also below 0.1. There was a lot of room for improvement. (7) Countries that had poor traditional energy and new energy performances also had poor CO 2 and PM 2.5 performances. (8) Although the new energy performance in many countries still needs to be strengthened, new energy has been performing better with many of the OECD countries moving towards the sustainable development of new energy.
Based on the above conclusions, most developed economies such as Australia, Denmark, France, Iceland, Luxembourg, Norway, Switzerland, United Kingdom, and the United States all had efficiencies of 1 for each indicator, and therefore there was no need for further improvements. While the Netherlands