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Decomposition and Forecasting of CO 2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

: China faces signiﬁcant challenges related to global warming caused by CO 2 emissions, and the power industry is a large CO 2 emitter. The decomposition and accurate forecasting of CO 2 emissions in China’s power sector are thus crucial for low-carbon outcomes. This paper selects seven socio-economic and technological drivers related to the power sector, and decomposes CO 2 emissions based on two models: the extended stochastic impacts by regression on population, afﬂuence and technology (STIRPAT) model and the partial least square (PLS) model. Distinguished from previous research, our study ﬁrst compares the effects of eliminating the multicollinearity of the PLS model with stepwise regression and ridge regression, ﬁnding that PLS is superior. Further, the decomposition results show the factors’ absolute elasticity coefﬁcients are population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > the urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). Meanwhile, a novel hybrid PLS-Grey-Markov model is proposed, and is veriﬁed to have better precision for the CO 2 emissions of the power sector compared to the selected models, such as ridge regression-Grey-Markov, PLS-Grey-Markov, PLS-Grey and PLS-BP (Back propagation neutral network model). The forecast results suggest that CO 2 emissions of the power sector will increase to 5102.9 Mt by 2025. Consequently, policy recommendations are proposed to achieve low-carbon development in aspects of population, technology, and economy.


Introduction
The impacts of climate change have become increasingly severe, arousing extensive attention worldwide [1][2][3]. Global warming is mostly attributed to greenhouse gas (GHG) emissions, especially CO 2 originating from enhanced industrialization [4,5]. Substantial CO 2 emissions can bring about adverse effects on the natural environment. Moreover, the use of fossil fuel is the main origin of CO 2 emissions, which poses significant challenges to environmental protection in the context of securing worldwide energy supplies [6].
The power industry converts primary energy sources, such as coal, into electrical energy, which plays a natural monopoly role within the energy sector [7]. The electricity industry is not only the crucial energy sector in the process of economic growth, but also an industry fundamental to people's livelihoods [8]. The electricity industry is a substantial energy-related CO 2 emitter, and its global et al. [29] explored drivers of CO 2 emissions in China's power sector based on ridge regression and proposed related policy measures. Zhao et al. [30] applied ridge regression analysis and a neural network optimized by a Gauss prediction model to predict the CO 2 emissions of the power sector. Lindner et al. [31] estimated direct CO 2 emissions deriving from electrical exports and imports among China's provinces.
Reviewing previous research, most literature has mostly concentrated on the impacts of socio-economic progress on CO 2 emissions. However, the drivers affecting CO 2 emissions are various, and not only involve macro development, but also electricity productive activities. Moreover, previous studies have focused solely on the driving factor decomposition or prediction of CO 2 emissions in the power sector, and have not verified the quality of the driving factor decomposition model. Therefore, it is vital to control CO 2 emissions caused by the electric power industry and to investigate factors affecting CO 2 emissions compared with the common model. In addition, high-precision measurement of the CO 2 emissions of China's power industry is significant for accurate weather monitoring and control of air pollution. Therefore, uniformly combining factor decomposition with predictive analysis in the power sector is beneficial to the reduction of CO 2 emissions and the achievement of low-carbon development in China.
In summary, to contribute to filling the gaps in the existing literature, this study is conducted as follows. First, we use the STIRPAT model to probe into determinants of CO 2 emissions from China's power sector. We also use the PLS model to eliminate multicollinearity, compared with stepwise regression and ridge regression. Second, considering the relatively few data and good forecast results, a Grey-Markov model is established to accurately forecast influencing factors, compared with a GM (1, 1) model and a BP neural network. Third, we obtain high-precision estimates of the CO 2 emissions of China's power industry based on a PLS-Grey-Markov model. Finally, conclusions and policy recommendations to boost low-carbon development in China are presented based on model results. These are essential for controlling CO 2 emissions and deserve further study for other industries.
Hence, this paper distinguishes from the existing literature in the following aspects: (1) We select seven socio-economic and technology drivers related to the whole electricity sector to explore the mechanism driving CO 2 emissions by means of the extended STIRPAT model and PLS model. In particular, in contrast to previous studies that have solely regarded socio-economic factors, some specific technology drivers involved in the power production, such as line loss rate and electricity intensity, are included to investigate their effects on CO 2 emissions. (2) This is the first comparison of the effects of eliminating the multicollinearity of the PLS model, stepwise regression and ridge regression, in order to judge the superior quality of the PLS model. (3) The study is also the first to establish a novel hybrid PLS-Grey-Markov optimized approach, which is based on both the PLS and Grey-Markov models. The result appears to be highly beneficial and shows significant improvement for forecasting CO 2 emissions of China's power sector.

Methodology
This section mainly introduces the PLS, GM (1, 1), and Grey-Markov models and error measures used in this paper.

PLS Model
The PLS method is identified by Wold et al. as a particularly useful multivariate statistical analysis method [32]. It surmounts the issue of multicollinearity because of the comprehensive means of extracting components and information and its screening methodology [33].
The main operation of the PLS model is to extract the integrated variables t 1 and u 1 from the data table of explanatory variables X and the explained variable Y, where t 1 and u 1 are the linear incorporation of x 1 , x 2 , . . . , x p and y 1 , y 2 , . . . , y p , respectively. Specifically, t 1 and u 1 should satisfy the following conditions: (1) t 1 and u 1 should contain as much of their original variation information as possible for representing data tables X and Y more adequately; (2) t 1 and u 1 should attain the maximum degree of correlation, which offers t 1 the greatest explanatory power to u 1 .
If the result of regression precision is satisfactory, the procedure will be terminated; if unsatisfactory, the second component t 1 should be extracted from the remaining information of X and the second regression analysis is conducted. The regression is repeatedly executed until the satisfactory precision is obtained and the latent variables t 1 , t 2 , . . . , t m are obtained.

GM (1, 1) Model
The grey system was firstly proposed by Deng [34]. The grey forecasting model is applied in cases with fewer data, and is used for prediction in a complex environment where policy-makers have limited historical data. The kernel of GM (1, 1) is shown as follows: Step 1: The non-negative raw time-series X (0) is defined as: Step 2: A new series X (1) can be obtained by taking accumulated generating operation (AGO) on X (0) : Step 3: The basic form of the GM (1, 1) model is obtained by: where a represents the developing coefficient and b denotes the grey input. Parameterâ is represented Step 4: Based on the estimated coefficients of a and b, the response equation and the prediction value can be achieved by:X

Grey-Markov Model
The forecasting accuracy of the GM (1, 1) prediction model is weak in data series with significant fluctuations. The Markov-chain prediction model can be applied to forecast a system with randomly dynamic data series. Hence, the Markov-chain model enhances the GM (1, 1) model if fluctuation in the dataset is relatively large [35]. The Grey-Markov model [36] is an particular form of stochastic process, which has been widely utilized to build stochastic models in diverse disciplines. Further, Markov chain is a prediction approach to forecast data from previous events. The simulation sequence can be obtained by means of Equation (5) as follows: The procedure of the Grey-Markov model can be summarized as follows: Step 1: Compute the relative error (RE) on the basis of the following equation: Based on the maximum and minimum of the RE, the residual series are appropriately separated into several intervals that can be considered as the state vector E = [E 1, E 2 , . . . , E m ] of the Markov chain: Step 2: Calculate the state transition probability P (k) ij , which represents the probability from E i to E j after k steps. The transition time is t 1 , t 2 , . . . , t n , so the k-step transition probability can be achieved by: where N i indicates the data number at which the stochastic process state is E i , and the n (k) ij denotes the transition number from E i to E j in k steps. The matrix of k-step transition probability can be expressed as follows: The future process of the system is predicted by deriving the state transition probability matrix P (k) . In general, it is essential and useful to survey the one-step transition matrix P (1) . Supposing that the target predicted is in state E q (1 ≤ q ≤ m), the row q in matrix P (1) should be considered. If max p ql (j = 1, 2, . . . , m, (1 ≤ q ≤ m)), then the transition from state E q to state E l will possibly occur to a large degree in the next moment.
Step 3: Calculate the modified forecasting values. After determining the future state transition of a system, the related residual error zone E = [E i−, , E i+ ] is attained, and the median of E = [E i−, , E i+ ] is chosen as the relative error. Thus, the modified prediction value of primal data sequence is gained with the help of the above explanation as: Energies 2018, 11, 2985 6 of 19

Error Measures
To test and verify the rationality of data processing, we select mean absolute error (MAE), mean absolute percentage error (MAPE) and root-mean-square error (RMSE) to demonstrate the performance of the PLS-Grey-Markov model. The specific formulae are: whereŷ i , y i are forecast and value, respectively.

Data Illustration and Collection
This section details the model for selecting variables, the calculation of carbon dioxide emissions in the power industry, and the collection of data.

STIRPAT Model
To study drivers of the environment, Enrlich and Holdren first put forward the IPAT method [37]: where I denotes CO 2 emissions, P denotes total population, A represents affluence, and T refers to technology. In the hypothesized IPAT model, I is affected equally proportionally by the drivers, violating the reality condition [38]. Hence, Dietz and Rosa proposed a modified model named the STIRPAT model [39]: where I, P, A, and T are the identical independent variables in Equation (15), a denotes the whole model coefficient, b, c and d denote exponents of each explanatory variable respectively, and e refers to the error item [5]. The natural logarithm form of STIRPAT model is: When other variables are unchanging, 1% variation of P, A, and T, respectively, will cause b%, c%, and d% change of I. In addition, the STIRPAT model can be extended by adding extra variables for studying more probable factors pertaining to CO 2 emissions [40]. For the purpose of investigating the effects of the drivers of the CO 2 emissions of the power industry, considering the specific development situation and learning from relevant research experience, a few more factors have been included [41,42]. CO 2 emissions can be seen as a type of environmental influence due to their impact on climate change. To explore the drivers of CO 2 emissions from the power sector, taking the real-world situation and relevant literature into consideration, other factors have been added in this paper. Drivers influencing CO 2 emissions are classified under four headings. The first is population, represented by total population or urbanization level in earlier literature, which are found to be the contributory drivers of CO 2 emissions [27][28][29][30][41][42][43][44]. The second is affluence, usually reflected by gross domestic product (GDP) per capita. The third is structure: the percentage of secondary industry output to total GDP is used to indicate the industrial structure, and the generation structure is the share of thermal power generation within total electricity generation [29]. Both reflect the proportion of high-pollution industry or power generation. The fourth is technology, represented by electricity intensity and line loss rate. Electricity intensity can represent power consuming efficiency, and the line loss rate is the percentage of the electricity loss over the total power generation in the power grid. Hence, the STIRPAT model can be extended as follows: ln (CO 2 ) = ln (a) + β 1 ln (P) + β 2 ln (PGDP) + β 3 ln (GS) + β 4 ln (IS) + β 5 ln (EI) +β 6 ln (LLR) + β 7 ln (URB) + ln (e) (18) Descriptions of each variable are listed in Table 1.

Calculation of CO 2 Emissions from China's Power Sector
Currently, there are two major principles for accounting for CO 2 emissions: one is calculated from the perspective of terminal consumption and the other from the angle of life cycle assessment (LCA). This paper is designed to estimate CO 2 emissions in the power industry supposing that CO 2 emissions related to power generation are only from coal-fired power enterprises, and CO 2 emissions are accounted for from the perspective of power generation. Based on International Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories [45] and the types of fossil energy [46], CO 2 emissions are calculated as follows: where E denotes fuel consumption; i represents the fuel types coal, crude oil, gasoline, coke, fuel oil, natural gas, kerosene, and diesel oil; NCV denotes the mean low calorific value; CEF is the coefficient of carbon emissions per unit of heat, and various kinds of emission factors derived from the IPCC guidelines [45]; COF refers to the carbon oxidation factor; and the parameter 44/12 represents the proportion of the molecular weights of carbon dioxide and carbon.

Data Collection
This paper employs data covering the period 1995-2016, and uses R and MATLAB software to conduct the research. Statistics for GDP per capita, total population, the percentage of secondary industry and the proportion of urban population were collected from the China Statistic Yearbook 2017 [47]. Data for the ratio of thermal power generation over whole electricity generation, electricity generation per unit of GDP and the line loss rate were drawn from the China Electric Yearbook [48] and China Electricity Council (CEC) [49]. Further, this paper sets 1995 as the benchmark year, so GDP was converted to constant prices to avoid the impact of inflation.

Results
This section mainly presents qualitative analysis, driving factor decomposition analysis and forecasting analysis for CO 2 emissions in the power sector. Figure 1 shows the trend of CO 2 emissions from the power industry in China during the period 1995-2016. Clearly, CO 2 emissions were continually increasing, with emissions growth changing at various stages. The first stage was from 1995 to 2001, when CO 2 emissions experienced gentle growth, with an average annual growth rate of 3.85%. The second stage was from 2001 to 2011, during which CO 2 emissions increased rapidly, at an annual growth rate of 12.03%, despite a slight slowdown in 2008 owing to the financial crisis. The possible reason for this phenomenon was China's rapid economic growth during this period. At the same time, China's power industry witnessed rapid progress and power generation also grew rapidly. Since coal-fired electricity generation is the dominant means of power generation in China, a significant quantity of fossil fuels is consumed in power generation, which is accompanied by a large amount of CO 2 emissions. The third stage is from 2011 to 2016, which was a period of slowed growth in CO 2 emissions for China's power industry. This may be attributed to optimization of the power production structure and the reduction of power consumption intensity in the industrial sector.   Tables 2 and 3, the F test result describes the overall significant regression equation, while in terms of significant testing of regression coefficients, most do not pass the t-test. This implies the presence of multicollinearity in the model [50]. For further examination, the correlation analysis of each independent variable (see Table 4) demonstrates that several correlation coefficients between the variables are relatively high. To further check the degree of multicollinearity, the variance inflation factor (VIF) approach is explored to test the multicollinearity between variables. When VIF ≥ 10, it indicates that there exists serious multicollinearity between explanatory variables, unduly influencing the results of OLS estimation. Specifically, only the VIF values of ln(IS) and ln(GS) are less than 10; values for the remainder are far greater than 10. Therefore, there is a serious problem of

Multicollinearity Testing
We first apply ordinary least square (OLS) analysis to research the drivers of CO 2 emissions of China's power sector based on the extended STIRPAT model, and undertake regression analysis. From Tables 2 and 3, the F test result describes the overall significant regression equation, while in terms of significant testing of regression coefficients, most do not pass the t-test. This implies the presence of multicollinearity in the model [50]. For further examination, the correlation analysis of each independent variable (see Table 4) demonstrates that several correlation coefficients between the variables are relatively high. To further check the degree of multicollinearity, the variance inflation factor (VIF) approach is explored to test the multicollinearity between variables. When VIF ≥ 10, it indicates that there exists serious multicollinearity between explanatory variables, unduly influencing the results of OLS estimation. Specifically, only the VIF values of ln(IS) and ln(GS) are less than 10; values for the remainder are far greater than 10. Therefore, there is a serious problem of multicollinearity.  Table 4. The results of correlation analysis.

PLS Modeling
To overcome the multicollinearity phenomenon existing in the explanatory variables, the PLS estimation approach is employed to establish the STIRPAT model. Q 2 denotes the cross validation, Q 2 (cum) stands for the cumulative cross validation, and R 2 Y (cum) is the cumulative explanatory capacity of the principal components extracted from the original variables. From Table 5, when the third principal component is extracted, Q 2 is already negative, and the Q 2 of the first two components is high with a critical value of 0.0975. The first two components provide sufficient information, so two is the number of principal components extracted. R 2 Y (cum) is greater than 0.8, indicating that the regression is effective [51]. With the process of normalization and component extraction, the regression formula is: ln (CO 2 ) = −29.729 + 2.298 ln (P) + 0.669 ln (PGDP) + 0.522 ln (GS) + 0.060 ln (IS) +0.310 ln (EI) − 1.112 ln (LLR) + 0.512β 7 ln (URB) To judge the quality of the PLS model, we compare it with other classic models used to eliminate multicollinearity. According to the fitting results shown in Table 6, compared with stepwise regression and ridge regression, the mean absolute percentage error of the PLS model is the lowest, indicating the regression error of PLS is the smallest. In addition, the coefficients of the PLS model are more in line with real-world interpretations. Hence, by assessment of both the fitting accuracy and the model interpretation, the PLS method yields results that are more satisfactory than those of the other methods.

Forecasting the Drivers of CO 2 Emissions
The PLS-Grey-Markov method is employed to forecast CO 2 emissions in the electricity industry. The prediction results of CO 2 emissions could be achieved by bringing results through the Grey-Markov model to the regression equation with the STIRPAT model and PLS model. Thus, it is critical to predict factors that affect CO 2 emissions before forecasting CO 2 emissions. Based on the decomposition of the driving factors of CO 2 emissions above, the drivers can be predicted by the Grey-Markov model.

Forecasting CO 2 Emissions in the Power Generation Sector
After predicting driving factors, this study forecasts CO 2 emissions with the help of the PLS-Grey-Markov model from 1995 to 2016. The results are shown in Figure 9. By comparing predicted and actual values, it can be seen that CO 2 emissions can be well fitted by the PLS-Grey-Markov model. In summary, it is reasonable to conclude the PLS-Grey-Markov model is sufficiently accurate.

Forecasting CO2 Emissions in the Power Generation Sector
After predicting driving factors, this study forecasts CO2 emissions with the help of the PLS-Grey-Markov model from 1995 to 2016. The results are shown in Figure 9. By comparing predicted and actual values, it can be seen that CO2 emissions can be well fitted by the PLS-Grey-Markov model. In summary, it is reasonable to conclude the PLS-Grey-Markov model is sufficiently accurate.   We then appraise the forecasting accuracy of the PLS-Grey-Markov method by analyzing the prediction statistics of different models, including the PLS-Grey-Markov, ridge regression-Grey-Markov, PLS-Grey and PLS-BP models. According to the forecasting results, the performance comparison of prediction models measured by MAE, MAPE and RMSE is shown in Table 8. It reveals that MAE, MAPE and RMSE of the PLS-Grey-Markov model are less than for other models, indicating the superiority of the novel hybrid model. We can conclude that the novel PLS-Grey-Markov model can be superior to others in terms of effectiveness and availability pertaining to CO2 emissions forecasting.  We then appraise the forecasting accuracy of the PLS-Grey-Markov method by analyzing the prediction statistics of different models, including the PLS-Grey-Markov, ridge regression-Grey-Markov, PLS-Grey and PLS-BP models. According to the forecasting results, the performance comparison of prediction models measured by MAE, MAPE and RMSE is shown in Table 8. It reveals that MAE, MAPE and RMSE of the PLS-Grey-Markov model are less than for other models, indicating the superiority of the novel hybrid model. We can conclude that the novel PLS-Grey-Markov model can be superior to others in terms of effectiveness and availability pertaining to CO 2 emissions forecasting. CO 2 emissions from the power industry from 2017 to 2025 can be forecasted using the PLS-Grey-Markov model. All factors relating to CO 2 emissions, including population, GDP per capita, generation structure, industrial structure, electricity intensity, line loss rate and the urbanization level can be forecasted from 2017 to 2025 by the Grey-Markov model. The forecasting results are displayed in Table 9.

Discussion
The decomposition of the main drivers of CO2 emissions coming from the power sector is undertaken using the extended STIRPAT and PLS models. The results demonstrate that whole population, GDP per capita, industrial and generation structure, electricity intensity, and the urbanization level have positive effects on CO2 emissions, while the line loss rate has a negative impact on CO2 emissions. Based on absolute values of coefficients, the order of drivers from largest to smallest is: total population, line loss rate, GDP per capita, generation structure, the urbanization level, electricity intensity and industrial structure.
The total population has the strongest positive effect on CO2 emissions. On one hand, population

Discussion
The decomposition of the main drivers of CO 2 emissions coming from the power sector is undertaken using the extended STIRPAT and PLS models. The results demonstrate that whole population, GDP per capita, industrial and generation structure, electricity intensity, and the urbanization level have positive effects on CO 2 emissions, while the line loss rate has a negative impact on CO 2 emissions. Based on absolute values of coefficients, the order of drivers from largest to smallest is: total population, line loss rate, GDP per capita, generation structure, the urbanization level, electricity intensity and industrial structure.
The total population has the strongest positive effect on CO 2 emissions. On one hand, population growth greatly contributes to the increase in residential electricity consumption; on the other hand, variations of population scale and enhancement of living standards have augmented the demand for products and services, which has also led to the growth of electricity consumption demand.
Line loss rate is found to be a critical factor negatively influencing the growth of CO 2 emissions of the electricity sector, and such a conclusion is consistent with [42,52]. This indicates the loss rate of electricity during the transport process has been decreasing, leading to the amount of electricity reaching the terminal to rise. Hence, CO 2 emissions in the power industry caused by line losses have been decreasing. The line loss rate declined from 8.77% in 1995 to 6.47% in 2016; however electricity production and transmission are extremely large, and losses in transmission and distribution are inevitable and cannot be ignored. For example, in 2012, aggregate electricity wastage in transmission and distribution was 289.62 TWh, greater than two year's power use in Shanghai in terms of the current year [42]. In addition, although China has increased its technological innovation in power grid improvement, many cities have less investment in electricity-producing equipment, resulting in increased age of power transmission lines and outdated devices, and, hence, large transmission and distribution losses.
Growth in the economy is also an important contributor to CO 2 emissions of the electricity sector, accounting for a 0.669% increase in CO 2 emissions from a 1% rise in GDP per capita. GDP per capita increased from CNY 61,339.9 in 1995 to CNY 115,683.2 in 2016, the boost in which would have promoted significant development and utilization of electricity. In particular, flourishing of the economy and growth of residents' wealth leads to increasing requirements for consumption of household appliances, which in turn leads to substantial growth in electricity consumption. The urbanization level is a relatively strong explanatory driver of CO 2 emissions. The reason for this trend is that cities are the main sources of electricity consumption in China. Along with the advancement of urbanization, the demand for electricity in urban buildings, transportation and residential buildings is also increasing. In China, the promotion of the urbanization process mainly depends on the development of secondary industry, which has intensified electricity consumption accordingly.
The indicators "generation structure" and "industrial structure", representing the proportion of thermal power on the production side and the proportion of secondary industry on the use side, respectively, are relatively less important contributors to growth of CO 2 emissions of the electricity sector. Model results indicate a 0.522% decrease in CO 2 emissions from a 1% decrease in generation structure and a 0.060% decrease in CO 2 emissions due to a 1% decrease in industrial structure. These results are partly because economic growth in China is mostly due to construction of real estate, whose development increases the requirement for thermal power, thereby generating prominent growth in CO 2 emissions. In addition, the upgrade of secondary industry is strengthening the momentum of development; secondary industry plays a dominant role in driving growth in the short term.
Meanwhile, electricity intensity can reflect the efficiency of power consumption, but it is not one of the principal drivers of CO 2 emissions, mainly because electricity consumption per unit of GDP output was merely 16.415 kWh/CNY 10 4 in 1995, rising to 52.080 kWh/CNY 10 4 in 2016. Recently, China's electricity consumption productivity has sustained an improvement because of the impact of R&D investment in energy-saving projects. Growth in the productivity of power consumption can propel electricity savings and CO 2 emission mitigation.
The PLS-Grey-Markov model can be established to perfectly fit the CO 2 emissions, compared with the other analyzed models, from 1995 to 2016. According to the forecasting model, the MAE, MAPE and RMSE values of the PLS-Grey-Markov model are lower than for other models, which shows the PLS-Grey-Markov model can effectively improve forecasting accuracy and indicates that this is a promising method for prediction of CO 2 emissions of the power sector. The hybrid model proposed offers a foundation for establishing an effectual CO 2 emission forecasting mechanism. Because of the complexity of the drivers of CO 2 emissions of the power industry, this model is mostly applicable to predicting CO 2 emissions. The feasibility of the novel forecasting method is verified above.
The PLS-Grey-Markov model has been found to perfectly predict the CO 2 emissions of the power sector. According to the forecasting model, it is expected that CO 2 emissions of the power sector in 2025 will reach 5102.925 Mt. As a result, the government and relevant departments can analyze future emission reduction measures in the power industry, to effectively adopt reasonable policies and realize low-carbon development.

Conclusions and Policy Implications
This paper explores drivers of CO 2 emissions of the power industry in China and obtains high-precision estimates of CO 2 emissions based on PLS and Grey-Markov models. The decomposition results indicate that the PLS model can effectually eliminate multicollinearity and illustrates that the order of emissions driving factors by absolute coefficients is: population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). We find that GDP per capita and total population make the strongest contributions to CO 2 emissions within the electricity sector, and that the line loss rate has a restrained impact on CO 2 emissions. The PLS-Grey-Markov model can be established to perfectly predict the CO 2 emissions of the power sector, compared with the other analyzed models, from 1995 to 2016. The forecast results indicate that CO 2 emissions of the power sector will have an increasing trend from 2017 to 2025.
These conclusions are instructive for decision-makers. Some concrete suggestions are proposed in aspects of population, technology, and the economy in order to carry out CO 2 emissions reduction and to practically implement the low-carbon development concept: (1) Population. Population growth can greatly contribute to an increase in residential electricity consumption. It is believed that human activities will induce CO 2 emissions growth. Further, people are essential to the production process. Hence, it is crucial to achieve a balance between population growth and emissions reduction. Moreover, greater attention should be paid to improving the overall quality of population and providing motivation to implement the low-carbon concept, maintain green productivity and reduce their carbon footprints in their daily activities. Further, the urbanization rate should be rationally controlled in the urbanization process. In addition, local governments should understand the development stages and characteristics of a city, designating the future direction of urbanization. New forms of urbanization should be promoted to guarantee green electricity consumption.
(2) Technology. Environmental technology should be given top priority, playing a critical role in technological progress in the reduction of CO 2 emissions and development of a low-carbon economy. Electricity consumption productivity can be augmented by incentive policies through adopting advanced techniques, renewing power production devices and phasing out small coal-fired power enterprises. Furthermore, the investment in R&D and updating manufacturing technology should be increased in terms of fuel preparation, power plant electricity rates, and equipment amelioration. Furthermore, the implementation of ultra-low emissions and energy-saving transformations for coal-fired units and clean coal power generation technology will improve the overall quality of the power industry.
In addition, the relevant departments should pay attention to building a smart grid. Smart grid construction is a win-win technology by offering a place for both consumers and power plants to interact with the grid, improving cross-level resource scheduling. It is beneficial to enhance demand-side administration to diminish electricity consumption intensity and CO 2 emissions, and to promote consumers to adopt a low-carbon lifestyle.
(3) Economy. Relevant sectors should continue to pay attention to hastening the transformation of economic advancement. The conventional mode of large electricity usage and low efficiency production should be transformed to knowledge-intensive and technology-intensive development for attaining a more efficacious development pattern. Changing the regional economic mode mainly by raising the level of electricity delivery offers rewards, and adjustment of the industrial structure is also critical [28]. Relevant departments should not only back low-carbon enterprises, but also facilitate technology reformation to propel cleaner production and optimize the industrial structure, transforming electricity-intensive industrial sectors into more efficient ones.
At the same time, it is also critical to aspire to the coordination of development between the economy and the environment to ensure a reduction in CO 2 emissions while the economy sustains low-carbon growth. China has a strong resource commitment to renewable energy, and the acceptance of clean energy generation should also be enhanced by power grid construction. This will help the development of the local economy and also contribute to the reduction of electricity CO 2 emissions. In addition, a nation-wide carbon trading market should be established for selling certified emission reductions, to provide returns to offset the expenditure associated with low-carbon power production.