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The Effects of Coal Switching and Improvements in Electricity Production Efficiency and Consumption on CO_{2} Mitigation Goals in China

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## Abstract

**:**

_{2}emission for a person in China is only about 1/4 that of a person in the US, the government of China still made a commitment to ensure that CO

_{2}emissions will reach their peak in 2030 because of the ever-increasing pressure of global warming. In this work, we examined the effects of coal switching, efficiency improvements in thermal power generation and the electricity consumption of economic activities on realizing this goal. An improved STIRPAT model was developed to create the scenarios. In order to make the estimated elasticities more consistent with different variables selected to construct the formulation, a double-layer STIRPAT model was constructed, and by integrating the two equations obtained by regressing the series in each layer, we finally got the equation to describe the long-run relationship among CO

_{2}emissions (I

_{c}), the share of coal in overall energy consumption (FM

_{C}), coal intensity of thermal power generation (CI

_{p}) and electricity intensity of GDP (EI

_{elec}). The long term elasticities represented by the equation show that the growth of CO

_{2}emissions in China is quite sensitive to FM

_{C}, CI

_{p}and EI

_{elec}. After that, five scenarios were developed in order to examine the effects of China’s possible different CO

_{2}emission reduction policies, focusing on improving FM

_{C}, CI

_{p}and EI

_{elec}respectively. Through a rigorous analysis, we found that in order to realize the committed CO

_{2}emissions mitigating goal, China should obviously accelerate the pace in switching from coal to low carbon fuels, coupled with a consistent improvement in electricity efficiency of economic activities and a slightly slower improvement in the coal efficiency of thermal power generation.

## 1. Introduction

_{2}emissions worldwide, which are considered the major sources of global warming. This is especially true in China. As estimated by the IEA [1], the world emitted a total of 31,734 million tons of CO

_{2}through fuel combustion in 2012, while China emitted 8250.8 million tons, accounting for 26.00% of the world total. The statistics also show that China replaced the United States as the world’s largest CO

_{2}-emitting country in 2006. Because of this huge volume of CO

_{2}emissions, China faced great pressure to reduce CO

_{2}emissions both to slow down the worldwide warming trend and for domestic economic sustainability. Although the average CO

_{2}emission for a person in China is only about 1/4 that in the US, the Chinese government still promised that China will make its biggest effort to mitigate CO

_{2}emissions, and that they will peak in the year 2030.

_{2}emissions, with reducing the share of coal in overall energy consumption being well-recognized as one of the most necessary ways [2,3]. Because of its carbon-intensive attributes, coal has been abandoned by many developed countries as a main fuel source in order to reduce greenhouse gas (GHG) emissions; however, in China, coal has supported more than 70% of overall energy consumption and this situation does not seem to have improved very much during last 30 years or more. It is therefore very possible for China to reduce coal consumption and increase the use of other, low-carbon fuels like renewable energy, nuclear power, biomass energy and natural gas, thus slowing down the growth of CO

_{2}emissions. Besides coal switching, improvements in energy efficiency of electricity production and consumption are also very crucial for reducing China’s CO

_{2}emissions. That is mainly because power generation consumed more than 50% of China’s coal resources (Figure 1) and is also the largest CO

_{2}emitting sector in China, contributing to more than 30% of China’s overall CO

_{2}emissions (Figure 2). As a result of this China has made the improvement of coal efficiency of thermal power generation a national priority for reducing GHG emissions [4]. Moreover, improvement in efficiency of electricity consumption is also important for China to mitigate CO

_{2}emissions [5].

**Figure 2.**The contribution of power generation in overall CO

_{2}emissions in China from 1980 to 2012. Data sources: data on energy consumption was self-collected from China Energy Statistical Yearbook 1986, 1991, 1996, 1999, 2004–2013 [6,7,8,9,10,11], data on CO

_{2}was estimated by multiplying the CO

_{2}emission factor [12] and consumption of different kinds of energy.

_{2}emissions in China, but it is very important for policy makers to know the extent to which improving these factors can help achieve the CO

_{2}emissions mitigation goal in 2030. The first important thing that needs to be known is the relationship between these factors and CO

_{2}emissions. There are many studies that deal with the relationship between CO

_{2}emissions and fuel switching. Özbuğday’s [13] research shows that substituting renewable energy for non-renewable energy reduces CO

_{2}emissions in the long run. Jorges [14] finds that an increase in the share of water heating and electric appliances has an effect on reducing household energy consumption and thus CO

_{2}emissions in Mexico’s residential sector. In Luciano’s [15] research, the diversification of the energy mix towards cleaner sources is found to be the main factor contributing to emission mitigation in Brazil. Similar research was also done in China. Geng [16] takes Liaoning Province in China as an example to investigate the different factors that contribute to the increase of CO

_{2}emissions, the findings show that improvement in energy intensity and fuel switching can partly offset the CO

_{2}emission increase caused by other factors such as growth in population and energy consumption per capita. In Yuan’s [17] research, it is found that fuel switching can reduce indirect emissions of CO

_{2}emissions in the residential sector of all regions in China, which is consistent with the researches of Ouyang [18], Wang [19] and Wang [20] with analyses on different sectors and regions in China. All of these studies confirm the positive role of diversification of the energy mix toward low carbon fuels in contributing to the reduction of CO

_{2}emission; however, the effects vary greatly depending on the countries, regions, sectors and economic developing levels researched. Also, because China’s energy consumption is dominated by coal and as pointed out by Lin [21], the share of coal in the total energy consumption is highly negatively related to the CO

_{2}performance in China, reducing the coal share of final energy consumption is among the first priorities in diversifying the energy mix toward low carbon fuels, like renewable energy, nuclear power, biomass fuels or natural gas. Regarding the roles of energy efficiency improvements in electricity production and consumption contributing to mitigating CO

_{2}emissions, there are also many studies. However, most of them just focus on the contribution of the electric power sector to CO

_{2}emissions, as found in [22,23,24,25,26] and also some specific research in China, like in [27,28]. Generally speaking, the results of this research show that improvements in energy efficiency in power generation, especially by integrating more renewable energy into the power sector, are important to reduce the CO

_{2}emissions of electricity production. Still other researchers study the improvement of power generation energy efficiency in contributing to reducing the overall CO

_{2}emissions for all sectors. These can be found in Sahbi [29] and Odenberger [30]. Although, as pointed out by Odenberger [30], integrating more renewable energy into power generation is crucial for the UK to achieve its goal of 60% reduction in CO

_{2}by 2050 compared to 1990, Sahbi [29]’s research shows that for fossil fuel-intensive power generation (like China), to improve the energy efficiency of fossil fuel power generation and electricity consumption are more important than to increase the share of renewable energies in mitigating all sectors’ CO

_{2}emissions.

_{2}emissions mitigation, there is limited evidence available with regard to putting them together to employ their aggregate effects on mitigating CO

_{2}emissions in China, and this is exactly the main contribution of this work. This work also contributes a double-layer STIRPAT model that was developed in order to make the estimation of long-term elasticities more consistent no matter what factors were selected, to reflect the technologies’ effects on CO

_{2}emission. Based on that contribution, the long-term elasticities of coal switching, energy efficiency improvement in thermal power generation and electricity efficiency of economic activities were estimated, which further guided the development of five scenarios used to trace the trend of CO

_{2}emissions. Through a rigorous scenario analysis, the reference paces for improving the coal efficiency of thermal power generation and electricity efficiency of economic activities as well as coal switching in order to achieve the commitment CO

_{2}mitigation goal in 2030 were obtained, which provided a clear guideline for future policy makers.

_{2}emissions trend in China during 2013–2040; Section 3 introduces the data processing methods; Section 4 develops the scenarios; Section 5 explains the results and provides the policy implications for China; and the final section concludes this work.

## 2. Methodology

#### 2.1. Description

_{2}emission. For several years’ development, there are many kinds of scenario analysis methods that can be used to trace the long-term trend of CO

_{2}emissions, which can be divided into 2 categories: (1) the ones based on econometric modelling [22,31,32]; (2) and the ones based on long-run systematic energy analysis tools, such as LEAP [5,24,33], system dynamic [34] and so on. Obviously, these two kinds of methods are quite different; the econometric scenario models are useful to identify the key factors that influence the trend of CO

_{2}emissions and also can clearly show the path that each factor’s effect exerts in every stage, while the long-run systematic energy analysis tools are more suitable for simulating a CO

_{2}emissions system with complex internal influences among all parts of the system. However the difficulties in accurately carving out the internal relationships may result in large simulating errors. In this regard, through systematic tests and examination of historical data, the econometric scenario analysis can minimize the simulating errors and thus build more accurate equations that describe the long-term relationship among all the factors selected.

_{2}emissions. One is based on the Kuznets curve theory [31,35], and the other one is based on IPAT (Impact of Population, Affluence and Technology) theory. In Kuznets curve theory, it is assumed that CO

_{2}emissions are mainly caused by affluence, while in IPAT theory, the effects of technologies and population are also included. Moreover, because technologies can be decomposed into different factors, scenario analysis based on IPAT theory is superior to Kuznets curve theory in scaling the effects of various factors in contributing to the development of CO

_{2}emissions.

_{2}emissions can be explained by different factors like energy intensity, fuel mix and industrial structure, the STIRPAT model is widely used to express the relationship among different factors that contribute to change in CO

_{2}emissions [38,39,40,41].

#### 2.2. A double-Layers STIRPAT Model

_{2}emissions is that the coefficients obtained by the econometric model depend greatly on what factors are chosen to reflect the technologies, even for the same regions. For example, in both Meng’s [42] and Li’s [43] research, the driving factors of China’s CO

_{2}emissions are researched, but because of the different factors selected to reflect the technologies, with Meng [42] selecting CO

_{2}intensity of GDP and Li [43] selecting energy intensity, the elasticities of both population and affluence are quite different in these two studies. The results of Meng [42] show that a 1% increase in population and affluence will result in a 1.81% and 1.91% increase in CO

_{2}emissions, respectively, while in Li’s [43] research, these two figures are 1.12% and 1.31%. This is quite confusing, and in order to conquer this obstacle in using the STIRPAT model, this work developed a double-layer STIRPAT model, the basic framework of which is illustrated by Figure 3. This framework illustrates the thought that CO

_{2}emissions in China are basically caused by the activities of the population, affluence and energy consumption technologies. The change in energy consumption technologies can be further reflected by the change of fuel mix, industrial structure and some other unknown factors. Thus, the basic STIRPAT model can be further developed into a double-layer model which is formulated as

_{1}, ET

_{2}, …, ET

_{n}represent the factors that influence the level of energy consumption technologies. Equations (4) and (5) show that, from a mathematical view, because there exists an equation that can explain the relationship among I and ET, and at the same time an equation describing the relationship among EI and ET

_{1}, ET

_{2}, …, ET

_{n}, substituting ET in Equation (4) into Equation (5) can provide the equation that represents the relationship among I and ET

_{1}, ET

_{2}, …, ET

_{n}.

_{2}emissions [44,45]. In China and in this work, because we want to examine the effects of coal switching, energy efficiency improvement of electricity production and consumption on mitigating CO

_{2}emissions, we chose coal proportion in aggregate energy consumption, electricity intensity of GDP, coal intensity of thermal power generation and tertiary industry proportion in overall economy to regress the change of energy intensity in China.

_{c}represents the amount of CO

_{2}emissions, P represents the size of population, GDP/P represents the gross domestic products per capita and is used to reflect people’ affluence in China, EI represents the energy intensity of GDP, FM

_{c}represents the coal proportion in aggregate energy consumption, EI

_{elec}represents the electricity intensity of GDP and is used to reflect the electricity consumption efficiency, CI

_{p}represents the coal intensity of thermal power generation and is used reflect the energy efficiency of electricity production, IS represents tertiary industries’ share in aggregate GDP, a and f are the model constants, e and k are the residual errors, and b, c, d, g, h, i, j are the coefficients that need to be forecasted.

## 3. Data Processing and Scenario Development

#### 3.1. Data Collection

_{2}emissions were estimated by multiplying the CO

_{2}emission factor [12] and consumption of different kinds of fuels. The data on real GDP were processed by GDP value in 2012 multiplying its growth rate each year, and the energy intensity was calculated by real GDP dividing aggregate energy consumption in equivalent tons of coal, and the data on aggregate energy consumption were collected from China Statistical Yearbook 2013 [46], the same as the data on population, electricity consumption, proportion of coal in aggregate energy consumption, and share of the tertiary industry in aggregate GDP. The electricity intensity of GDP is calculated by dividing the aggregate electricity consumption by aggregate real GDP.

#### 3.2. Data Processing

#### 3.2.1. Unit Root Tests

#### 3.2.2. Cointegration

_{c}, lnP, lnGDP/P and lnEI, the test results refuted the assumptions that there is no cointegration equation and at most 1 cointegration equation, but agreed with the assumptions that there are at most 2 cointegration equations among them. Thus, it can be inferred that lnI

_{c}, lnP, lnGDP/P and lnEI are cointegrated. Similarly, for the variables lnEI, lnFM

_{c}, ln EI

_{elec}, lnCI

_{p}, and lnIS, the testing results imply that there is one cointegration equation existing among them, so it can also be concluded that lnEI, lnFM

_{c}, ln EI

_{elec}, lnCI

_{p}, and lnIS, are cointegrated.

Variables | ADF | DF-GLS | PP | |||
---|---|---|---|---|---|---|

Level | 1st Difference | Level | 1st Difference | Level | 1st Difference | |

ln
I_{c} | 0.081 | −2.801 * | 0.030 | −2.385 ** | 0.686 | −2.835 * |

ln P | −2.245 | −2.834 * | 0.859 | −2.665 ** | −7.760 *** | - |

ln GDP/P | 1.495 | −3.453 ** | 0.203 | −1.968 ** | 0.606 | −3.362 ** |

ln EI | −1.039 | −3.103 ** | 0.212 | −2.955 *** | −1.335 | −1.612 * |

ln
FM_{c} | −0.193 | −4.404 *** | −1.257 | −2.218 ** | −0.799 | −4.520 *** |

ln
EI_{elec} | −1.263 | −3.598 ** | −0.486 | −3.203 *** | −2.417 | −2.445 ** |

ln
CI_{p} | 4.187 | 0.570 | 0.123 | −2.563 ** | 3.959 | −3.712 ** |

ln IS | −2.129 | −3.819 *** | −0.015 | −3.881 *** | −2.462 | −3.753 *** |

Group of Variables | Hypothesized No. of CE(s) | T-Statistic | Prob. |
---|---|---|---|

ln
I_{c}, lnP, lnGDP/P, lnEI | None * | 53.864 | 0.0123 |

At most 1 * | 32.183 | 0.0261 | |

At most 2 | 12.788 | 0.1228 | |

At most 3 * | 4.575 | 0.0324 | |

ln
EI, lnFM_{c}, ln EI_{elec}, lnCI_{p}, lnIS | None * | 74.990 | 0.0182 |

At most 1 | 40.066 | 0.2202 | |

At most 2 | 19.188 | 0.4795 | |

At most 3 | 15.495 | 0.4183 | |

At most 4 | 0.047 | 0.8287 |

#### 3.2.3. Estimation of the Long-Term Relationship

_{c}, lnP, lnGDP/P and lnEI was firstly obtained as

_{c}= 0.548lnp + 1.143lnGDP/P + 1.212lnEI

Adjusted R

^{2}= 0.998, DW = 0.563

^{2}shows that the actual curve is greatly fitted by the estimating curve, the value of DW shows that the residual error series is positively auto-correlated; it is necessary to correct the autocorrelation of residual error series in order to get an accurate estimation of the long-term relationship, so we added the first lag of residual error into the model and the simulation result is

_{c}= 0.542lnp + 1.158lnGDP/P + 1.246lnEI

Adjusted R

^{2}= 0.999, DW = 2.021

_{c}, lnP, lnGDP/P and lnEI.

_{c}, ln EI

_{elec}, lnCI

_{p}and lnIS was obtained after adding both the first and second lag of residual errors and can be expressed as

_{c}+ 1.051lnEI

_{elec}+ 1.549lnCI

_{p}− 0.691lnIS

Adjusted R

^{2}= 0.995, DW = 1.989

_{c}, lnP, lnGDP/P, lnFM

_{c}, ln EI

_{elec}, lnCI

_{p}and lnIS should be

## 4. Scenario Development

#### 4.1. Elasticities

_{c}, EI

_{elec}, CI

_{p}and IS to CO

_{2}emissions in China are 0.542, 1.158, 1.705, 1.309, 1.930, −0.861, which means that among all the factors, increase in population, GDP per capita, coal proportion in aggregate energy consumption, electricity intensity of GDP and coal intensity of thermal power generation will result in positive growth of CO

_{2}emissions, while the increase in share of tertiary industry in aggregate GDP will reduce CO

_{2}emissions. The elasticities also show that the growth of CO

_{2}emissions in China is more sensitive to the growth of GDP per capita, proportion of coal in aggregate energy consumption, electricity intensity of GDP and coal intensity of thermal power generation, in which the role of coal intensity of thermal power generation ranks as the top one, followed by coal proportion in aggregate energy consumption, then the electricity intensity of GDP and finally the GDP per capita. The growth in population will also result in positive growth of CO

_{2}emissions, but its effect is quite low compared with other factors. The increase in the share of tertiary industry in aggregate GDP implies reduction in CO

_{2}emissions, but the effect is rather low compared with improving the energy efficiency of electricity production and consumption and also reducing coal’s proportion in aggregate energy consumption. Therefore, in order to effectively mitigate CO

_{2}emissions and reach the carbon reduction commitment for 2030, China should make more efforts to improve energy efficiency of thermal power generation and electricity consumption as well as encouraging coal switching.

#### 4.2. Features of the Scenarios

_{c}, EI

_{elec}, CI

_{p}and IS to CO

_{2}emissions, five different scenarios were developed to track the CO

_{2}emissions trend in China in 2013–2040, which can be described as

_{2}emissions due to compressed coal combination. At the same time, the efforts put into substituting coal consumption with other low-carbon fuels and the improvement in electricity consumption efficiency will not change. As a result, we assumed that in this scenario, the electricity intensity of GDP and coal proportion in aggregate energy consumption will develop at the same rate as in 1980–2012, which decreased by 1.00% and 0.215% annually, while the coal intensity of thermal power generation will drop by 2 times the historical average level, which was 2.07% per year before the level of 275 gce/KWh achieved, and after that it will decrease by 0.2% every year.

_{2}mitigation goal, so the reduction rate for coal intensity of thermal power generation and coal proportion in aggregate energy consumption will be the same as in 1980–2012, which was 0.215% and 1.38% annually, while the reduction pace of electricity intensity of GDP will be accelerated to 2 times the historical average level, 2.00% per year.

**Table 3.**Assumption for growth of population, GDP per capita and tertiary industrial proportion in GDP.

Variables | Periods | ||
---|---|---|---|

2013–2020 | 2020–2030 | 2030–2040 | |

P | 0.4% | 0.329% | 0.244% |

GDP/P | 6.9% | 5.3% | 3.2% |

IS | 0.87% | 1.21% | 1.13% |

## 5. Results Analysis

#### 5.1. Simulation Results Analysis

_{2}emissions are shown in Figure 5.

Scenario | Variables | Growth Rate (%) | Value | ||
---|---|---|---|---|---|

2020 | 2030 | 2040 | |||

C | I_{c}(Billion tons) | 1.49 | 12.152 | 13.989 | 14.314 |

FM_{c}(%) | 0.215 | 69.405 | 67.928 | 66.480 | |

EI_{elec}(KWh/Yuan) | 1.00 | 0.088 | 0.080 | 0.072 | |

CI_{p}(gce/KWh) | 1.38 | 291 | 272 | 266 | |

S1 | I_{c}(Billion tons) | 1.13 | 11.799 | 13.092 | 12.914 |

FM_{c}(%) | 0.43 | 68.217 | 65.340 | 62.584 | |

EI_{elec}(KWh/Yuan) | 1.00 | 0.088 | 0.080 | 0.072 | |

CI_{p}(gce/KWh) | 1.38 | 291 | 272 | 266 | |

S2 | I_{c}(Billion tons) | 1.50 | 10.806 | 14.121 | 15.314 |

FM_{c}(%) | 0.215 | 69.405 | 67.928 | 66.480 | |

EI_{elec}(KWh/Yuan) | 1.00 | 0.088 | 0.080 | 0.072 | |

CI_{p}(gce/KWh) | 2.76 | 291 | 274 | 263 | |

S3 | I_{c}(Billion tons) | 0.20 | 10.926 | 11.404 | 10.829 |

FM_{c}(%) | 0.215 | 69.405 | 67.928 | 66.480 | |

EI_{elec}(KWh/Yuan) | 2.00 | 0.082 | 0.067 | 0.055 | |

CI_{p}(gce/KWh) | 1.38 | 291 | 272 | 266 | |

S4 | I_{c}(Billion tons) | −0.37 | 10.731 | 10.300 | 9.218 |

FM_{c}(%) | 1.19 | 64.161 | 56.922 | 50.500 | |

EI_{elec}(KWh/Yuan) | 1.19 | 0.087 | 0.077 | 0.069 | |

CI_{p}(gce/KWh) | 1.19 | 295 | 273 | 267 |

_{2}emissions of all the other scenarios will increase compared to the level in 2012, and the growth of C is the fastest, which will increase to nearly 16 billion tons in 2040, an increase of 48.27%. The CO

_{2}emissions peak level of scenario C is 15.753 billion tons, which will be reached in 2038 (Figure 5), and this implies that if China does not change its current carbon mitigation policies, the CO

_{2}mitigation commitment goal for 2030 cannot be achieved.

_{2}emissions will reach over 14 billion tons in S1 by the end of 2040, which is higher than the level in 2012 by 33.76%. The peak CO

_{2}emissions will be reached in 2037 (Figure 5), a total of about 14.338 billion tons, about 9% lower than the peak level in scenario C. The results of S1 imply that, although encouraging extensive coal switching cannot cause peak CO

_{2}emissions to come earlier, it is very helpful in reducing the overall amount of CO

_{2}emissions.

_{2}emissions of S2 will reach over 15 billion tons in 2040, an increase of 44.54% comparing with the level in 2012. The peak level will be reached in 2038 (Figure 5), which is the same as in scenario C and later than scenario S1. The peak level is 15.257 billion tons, slightly lower than the peak level in scenario C and much higher than scenario S1.

_{2}emissions level of 11.512 billion in 2032, which is much earlier than in scenarios C, S1 and S2, but still cannot meet the commitment requirement. The peak CO

_{2}emissions level in S3 is 26.92%, 19.71% and 24.55% less than the peak levels in scenarios C, S1 and S2, respectively. The simulation results of S3 show that intensively reducing electricity intensity of GDP can greatly speed up the coming year of peak CO

_{2}emissions and can also reduce the total amount of CO

_{2}emissions in China.

_{2}emissions substantially in 2013–2040, a bit more than 9.0 billion tons in 2040, reduced by 41.32%, 34.95%, 39.81% and 14.88% compared with the levels in C, S1, S2 and S3 in 2040, respectively. The peak level of CO

_{2}emissions in S4 is 10.73 million tons, which is far less than the peak levels in C, S1, S2 and S3. The peak time is 2020, which is much earlier than in scenarios C, S1, S2 and S3. The results in S3 and S4 show that by cooperatively improving the electricity efficiency of economic activities and substituting coal consumption can effectively help China to achieve the CO

_{2}emissions mitigate commitment.

_{2}emissions always means a loss of economic growth. We found that after the decreasing rates of all the three variables drop to less than 1% per year, the peak year will suddenly jump from 2023 to 2032, so we kept on trying by slowing down the decrease rates of two of the three variables, and keeping the decrease rate of the other at 1%. As a result, we found four cases with different values of the three variables in which the peak CO

_{2}emissions years were very close to the committed peak year of 2030 (Table 5). As Table 5 shows, although the peak level years of Case 1, Case 2 and Case 4 were very similar, the peak CO

_{2}emissions levels were very different. Case 1 performed better than the other 3 cases in regard of reducing carbon emissions, so the decreased rates in Case 1 were chosen as the reference steps in improving the coal efficiency of thermal power generation, electricity efficiency of economic activities and coal switching, which are necessary in order to reach the CO

_{2}emissions mitigation goal committed to for 2030.

Cases | Peak CO_{2} Emissions Level (Billion Tons) | Peak Level Year | Growth (%) | ||
---|---|---|---|---|---|

FM_{c} | EI_{elec} | CI_{p} | |||

Case 1 | 12.96 | 2029 | −0.8 | −1 | −0.8 |

Case 2 | 13.36 | 2029 | −0.7 | −0.7 | −1 |

Case 3 | 13.51 | 2029 | −1 | −0.7 | −0.7 |

#### 5.2. Policy Implications

_{2}emissions mitigation goal in 2030. Although China’s electricity efficiency of economic activities greatly improved in 1980–2012, decreasing from 0.121 KWh/Yuan in 1980 to 0.095 KWh/Yuan 2012, by 20.74%, when compared with the world average level, or the OECD countries’ average level or even the non-OECD countries’ average level, the level of China’s electricity intensity of GDP is much higher than other countries (Figure 7). This means the electricity efficiency of China’s economic activities is very low, so the government should exert even more efforts to encourage improvements in electricity consumption efficiency.

**Figure 7.**World electricity intensity of GDP in 2011 [11].

## 6. Conclusions

_{2}emissions producer in the world, China has the responsibility to mitigate CO

_{2}emissions to slow down global warming. This work deals with the problem of what China can do to achieve the CO

_{2}emissions mitigation commitment by 2030, and the research results indicate that the change in China’s CO

_{2}emissions greatly depends on how much coal accounts for overall energy consumption, how efficient the coal used for thermal power generation is, and how efficiently electricity is used for economic activities. Therefore, in the future, China should at least do the following three kinds of works in order to achieve the CO

_{2}emissions mitigation goal in 2030: intensively substitute coal fuels with low-carbon fuels, like renewable energy, nuclear power, biomass fuels or natural gas; continue improving the coal efficiency of thermal power generation at a slower pace; and increase the electricity efficiency of economic activities as usual.

_{2}emissions, which may be due to their very small size, we cannot simulate their future effects on CO

_{2}emissions in our model. Consequently, if we want to precisely identify the effects of developing nuclear energy and renewable energy in contributing to CO

_{2}emissions mitigation, we need to use other research methods to achieve greater insight; these are the main research areas for our future work. Furthermore, for the coal intensity of thermal power generation, in this work we only wanted to examine its overall and long-term performance on the environmental side, so we did not add too much technical analysis, which is another main research point in our future work.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Li, L.; Wang, J.
The Effects of Coal Switching and Improvements in Electricity Production Efficiency and Consumption on CO_{2} Mitigation Goals in China. *Sustainability* **2015**, *7*, 9540-9559.
https://doi.org/10.3390/su7079540

**AMA Style**

Li L, Wang J.
The Effects of Coal Switching and Improvements in Electricity Production Efficiency and Consumption on CO_{2} Mitigation Goals in China. *Sustainability*. 2015; 7(7):9540-9559.
https://doi.org/10.3390/su7079540

**Chicago/Turabian Style**

Li, Li, and Jianjun Wang.
2015. "The Effects of Coal Switching and Improvements in Electricity Production Efficiency and Consumption on CO_{2} Mitigation Goals in China" *Sustainability* 7, no. 7: 9540-9559.
https://doi.org/10.3390/su7079540