Forecasting China s per Capita Carbon Emissions under a New Three-step Economic Development Strategy

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1 Sept., 2015 Journal of Resources and Ecology Vol.6 No.5 J. Resour. Ecol (5) DOI: /j.issn x Article Forecasting China s per Capita Carbon Emissions under a New Three-step Economic Development Strategy DU Qiang 1 *, WANG Ning 1 and CHE Lei 2 1 Institute of Engineering Economics, Chang an University, Xi an , China; 2 School of Foreign Language, Chang an University, Xi an , China Abstract: Under the context of the New Three-step strategy of Chinese economic development, we use the reverse tracing method and petroleum, natural gas and coal as sources of carbon emissions. To eliminate the impact of regional and technological levels in the energy conversion process we improved the IPAT model and used it to predict and analyze China s per capita carbon emissions in three assumed scenarios up to We found that China s per capita carbon emissions peak will occur respectively in 2020 and 2030 in different assumed scenarios; national per capita carbon emissions will reach tons; China s per capita carbon emissions will grow continuously at an average rate of 2.25% 3.40% per year before the peak year and then will decline at a rate of 1.33% 1.78% per year. By the year of 2050, national per capita carbon emissions will be tons. Key words: Key words: New Three-step strategy; IPAT model; carbon emissions; forecasting 1 Introduction Since reform and opening up in the late 1970s, China has made great achievements regarding socioeconomic development and is now the second largest economy in the world. This has also resulted in large energy consumption and carbon emissions. With an average annual increase in carbon emissions of 10.05%, China surpassed the US and become the largest emitter of CO 2 in the world in In 2012, China s CO 2 emissions were 9.21 billion tons, accounting for 26.7% of total global carbon emissions (BP 2014; Zhang and Da 2015). Pressure from the international community to cut emissions and growing issues of unreasonable energy consumption structure domestically are now apparent. The Chinese government made a commitment at the Copenhagen Climate Change Conference in 2009 to reduce carbon emission intensity by 40% 45% by 2020 compared with levels in 2005 (Song et al. 2011; Paltsev et al. 2012; Saveyn et al. 2012; Yu et al. 2012). During the 12th Five-Year Plan period ( ), the government has promised a quantitative target to cut carbon emissions intensity by 17%. The government is facing dual challenges of solving worsening domestic environmental problems without impeding economic development, and everincreasing international pressure to curb CO 2 emissions. Using scientific methods to analyze and forecast China s future carbon emissions under different scenarios is significant for policy formulation and the government s response to climate change, demarcation of carbon emissions rights and CO 2 emissions trading. Research about carbon emissions has mainly focused on factor decomposition of carbon emissions and using the prediction model to project future carbon emissions. For example, the LMDI method was utilized to decompose changes in China s carbon emissions from from the perspectives of energy sources and indicated that economic growth appeared to be the main driver of carbon emissions (Zhang and Da 2015). The STIRPAT model was employed to analyze panel data of 99 countries from and argued that the relationship between urbanization, energy consumption and carbon emissions is influenced by economic conditions and affluence levels Received: Accepted: Foundation: National Natural Science Fund of China ( ); Scientific Research Foundation for the Returned Overseas Chinese Scholars, Ministry of Education of China ( ); Humanity and Social Science Foundation of Ministry of Education of China (15YJC790015); Shaanxi Research Fund (2013KW13-01, 13D231); Xi an Social Science Fund (15J24); and the Central Universities Fund (2014G , 2014G ). * Corresponding author: DU Qiang. qiang_du@126.com.

2 DU Qiang, et al.: Forecasting China s per Capita Carbon Emissions under a New Three-step Economic Development Strategy 319 (Poumanyvong and Kaneko 2010). In addition, a structural decomposition analysis model was used to measure the effects of CO 2 emissions intensity reduction on four factors, energy efficiency, energy structure, industry structure, and economic growth mode (Li and Yang 2011). The bottomup LBNL China End-Use Energy Model was used by Zhou et al. (2013) to assess the role of energy efficiency policies in transitioning China to a lower emissions trajectory and meeting its 2020 intensity reduction goals. Xu et al. (2006) set up a decomposition model of carbon emissions per capita by adopting Logarithmic Mean Weight Divisia (LMD) based on the intensity of carbon emissions. Zhu et al. (2009) established a decomposition model based on the extended Kaya identity and examined impacts from economic scale, population size, industrial structure, energy mix and energy efficiency on carbon emissions. This body of work shows that the total economy, industrial structure, energy structure, population and level of technology are the main drivers of carbon emissions. With assistance from the decomposition approach, many models have projected future carbon emissions. The bottomup LBNL China End-Use Energy model has been used to assess the role of energy efficiency in China and shows that CO 2 could experience a plateau or peak around 2030 (Zhou et al. 2013). A framework for the analysis of lowcarbon scenario 2020 achieving national carbon emissions reduction targets was developed by Koo et al. (2014). Piecyk and McKinnon (2010) constructed three scenarios to assess CO 2 emissions levels from road freight transport up to Yue et al. (2010) predicted future carbon emissions for China up to 2050 based on a brief review of existing carbon emissions methods and models. Meng and Niu (2011) adopted logistic equations to simulate the S-shaped curve of CO 2 emissions from fossil fuel combustion. Köne and Büke (2010) employed trend analysis approaches in modeling to forecast energy-related CO 2 emissions. Qu and Guo (2010) used STIRPAT modeling to estimate China s future carbon emissions and found that China s peak will arrive between 2020 and Zhou et al. (2014) used the Longrange Energy Alternatives Planning System (LEAP) model to simulate energy demand and CO 2 emissions trends in China up to 2050 under Business as Usual (BAU), Carbon Reduction (CR) and Integrated Low Carbon Economy (ILCE) scenarios. Here, IPAT, which forms the basis for calculations in the Kaya equation (York et al. 2003), is used to predict CO 2 emissions. The main advantages of this approach are its simplicity and data availability. As indicated in the literature, common forecasting methods mainly focus on quantitative factors, but it is impossible to evaluate factors that cannot be quantified, e.g. economy growth rate using a scientific method. However, scenario analysis avoids the limitations of traditional analysis methods (Shrestha et al. 2007; Li et al. 2010; Chen et al. 2013; Gambhir et al. 2013; Zhou et al. 2014). Therefore, we employed the modified IPAT model based on data for to forecast per capita carbon emissions of China up to 2050 using three scenarios: Scenario1. High Economic Growth Case (HEG): Assume relatively optimistic economic development. Economic development is the main focus of China supported by high resource consumption. However, environmental protection and promotion of ecological civilization will also be used to achieve the objective of building a resource-conserving and environment-friendly society. Scenario 2. Reference Case (REF): On the basis of past and current laws and extending current development trends, China will implement stringent energy polices to adjust energy structure, reduce energy consumption and carbon intensity, and vigorously promote renewable energy and clean energy. Scenario 3. Low Economic Growth Case (LEG): Taking into account uncertainties in economic operation, the government will take comprehensive economic and energy policies and technology measures to move towards a lowcarbon economy. All scenarios differ in their economic models, which could result in changes in CO 2 emissions. By comparing these scenarios, the energy-saving and CO 2 abatement intervals can be acquired. 2 Methodology and data 2.1 Modified IPAT model The IPAT model was originally proposed by Kaya and is known as the Kaya identity; it has been widely used in the analysis of driving force of environmental degradation (Duro and Padilla 2006; Wang et al. 2011; Brizga et al. 2013). The basic equation of the IPAT model can be expressed as follows: G I I = P = P A T (1) P P where, I indicates environmental load; P indicates population; G indicates total GDP; A indicates GDP per capita, namely, richness degree; and T indicates environmental load per unit of GDP, namely, technology level. According to the purpose of our study, the identity decomposes carbon emissions into the product of several different factors, as shown in the following extended equation: G E C C = P( )( )( ) = pgec (2) P G E where, C is made to stand for I to indicate environmental impact due to energy consumption, namely, CO 2 emissions as the result of primary energy consumption; and P, G and E represent population, GDP and energy consumption, respectively. G=G/P, e=e/g, and c=c/e, respectively represent per capita GDP, GDP energy intensity and energy

3 320 Journal of Resources and Ecology Vol.6 No.5, 2015 carbon emissions intensity. Because of its simple structure and easy operation, the IPAT model has been widely used in the field of energy and environmental economics. The main drawback of this model is the limited number of variables which means results are limited to the macro quantizing relations between carbon emissions and energy, economic scale and population. In recent years, a number of studies have shown that changes in carbon emissions have close relations with industrial structure and science and technology levels (Gao and Wang 2007). Industry return on capital and per capita workers compensation are the direct causes of industrial structural evolvement, while industrial technological progress is the direct cause of changes in the industry return on capital and industry per capita labor remuneration, and is the indirect motivation of changes in industrial structure. There exists a stable and strong correlation between industrial technology progress and change in per capita labor remuneration, where the coefficient of correlation is up to 0.91(Lü 2010). If workers compensation rate is expressed in f, we could use 0.91f to represent the influence of technological progress, industrial restructuring and energy utilization efficiency improvement on carbon emissions. In using the IPAT model for the prediction of carbon emissions, we have to first predict population changes in the future, which will seriously affect the accuracy of forecasts. Therefore, the IPAT model could be improved. This paper removes the population factor on the basis of the original IPAT model and introduces the variable workers compensation rate representing technological progress, industrial restructuring and energy utilization efficiency improvements. This improved IPAT model eliminates the influence of population differences and its forecast value is per capita carbon emissions in the future. The improved IPAT model is written as follows: G E C C = ( )( )( ) ( f ) = gec k (3) P G E where, the value c of energy carbon emissions is obtained by weighing sums of the proportion of coal, oil, natural gas and three electric s carbon emissions factor in China s energy consumption structure based on the cumulative sum of energy consumption from 2005 to 2012, the final value is 0.637; and k represents the technological progress influences coefficient, k=1 0.91f. These four factors have a significant influence on China s CO 2 emissions and are selected to structure the extended model in scenario analysis. Table 1 Assumptions of GDP growth rate (%). Scenario HEG REF LEG Data sources This paper sets 2005 as the base year. The population data used in this study was from the China Statistical Year Book (National Bureau of Statistics of the People s Republic of China 2013). Energy consumption measured by standard coal and GDP statistics over the year came from China s economic and social development statistics database. Data for carbon emissions was obtained from the Oak Ridge National Laboratory (Marland et al. 2012). 3 Scenario parameter s design China s growth rate of GDP was gleaned from the literature (Jiang et al. 2009; Ding et al. 2013), as shown in Table 1. Based on 2005 constant CNY, this paper converts each year s GDP values into energy intensity from 2005 to It also calculates the per capita labor wages of urban workers in , and then takes the logarithm of per capita labor wages of that year; in the end we obtained the labor wage per capita growth curve fit as shown in Fig.1 and the coefficient of determination (correlation coefficient) is The future of the globe will not lie on a technological revolution similar to the scientific and technological revolution in the next 50 years and this study uses the slope of the constant price wage growth function fitted in Fig. 2 to express technology rates of change. From Fig. 2 the coefficient of variation of the rate of workers compensation f is 0.045, so the corresponding value of technological progress is It usually takes five years or so to turn technological innovation into application. Supposing the value of technological progress is 4.2% (every five years), the influence coefficient of science and technology in terms of carbon emissions k (1 0.91f) is 95.9%. Based on this assumption, we calculated that the influence value of annual technical factors is 99.17%. Unchanged wage increase (log10) y = x R 2 = Year Fig. 1 Flash point chart of per capital labor wage and trend for

4 DU Qiang, et al.: Forecasting China s per Capita Carbon Emissions under a New Three-step Economic Development Strategy 321 Table 2 Carbon emissions projections calculated. Year GDP per capita (10 4 CNY) Carbon emissions Energy intensity Technical Per capita carbon emissions (tons) (tons 10-4 CNY -1 intensity LEG REF HEG ) (tons tons -1 factors ) LEG REF HEG

5 322 Journal of Resources and Ecology Vol.6 No.5, 2015 The national economy has developed with remarkable speed at the cost of environmental destruction. The Chinese government has decided to control growth and energy intensity. The National 12th Five-Year Plan targets decreasing energy intensity by 16% in 2015 compared to China is now in a stage of industrial modernization. During the next 20 years, energy consumption will continue to grow. Accordingly, using energy intensity and per capita GDP in 2012 as the base, it is assumed that energy intensity decreases by 16% every five years, equivalent to an annual 3.4% reduction in energy intensity; energy intensity reduces by 20% every five years, equivalent to reduced energy intensity of 4.4% annually; and energy intensity of each five-year experiences a 25% reduction in equivalent, converted to an annual reduction in energy intensity of 5.6%. 4 Forecast results and discussion Apply the Formula (3) to forecast carbon emissions and verify the calculation of carbon emissions from 2005 to 2012, as shown in Table 2. Fig. 2 is national carbon emissions and trend curve. Through the improved IPAT model, the national per capita carbon emissions are , , , , , , , tons respectively (Table 2). According to energy consumption statistics and carbon conversion factors, the national per capita carbon emissions are , , , , , , , tons, respectively. Taking into account statistical errors that exist in the process of energy consumption statistics and administrative intervention, the forecasted result is slightly higher than the statistical calculation, which is in line with the actual situation. The errors between statistics and predicted values from 2005 to 2012 were 2.90%, 1.95%, 1.27%, 1.54%, 0.80%, 1.42%, 0.06%, 1.03%, with an average error of 1.37%. The errors above were all within 5%. Predictably, based on the sample data converted by the actual energy consumption, the improved IPAT model is of high accuracy in predicting carbon emissions. China s per capita carbon emissions peak will appear in 2020 in the scenario of LEG and 2030 in the scenario of HEG and REF, and per capita carbon emissions will be tons. China s per capita carbon emissions will continue to increase at a rate of 2.25% 3.40% per year before the peak year and then decrease at an annual rate of %. National per capita carbon emissions in 2050 will be tons. 5 Conclusions From , with approximate linear growth in China s energy consumption, high carbon emissions factor energy such as coal and oil accounted for 85.4% to 90.6% of total energy consumption in energy consumption structure. Chinese energy consumption of coal, oil and Per capita carbon emmissions (tons) Fig. 2 National per capita carbon emissions and trend curve for other fossil fuel-based energy structure results in a large amount of carbon emissions, and is the main reason for the continuous growth in carbon emissions. China s per capita carbon emissions will peak respectively in 2020 and 2030; national per capita carbon emissions will reach tons; 2050 per capita carbon emissions will be reduced to tons; and national per capita cumulative carbon emissions will be tons. This result demonstrates large carbon emissions for China in the future, and also reminds us of the need to accelerate the development of a low-carbon economy and efforts to explore carbon reduction. Changes in industrial structure and technological progress are expressed by a constant rate of scientific and technological progress in this research. As a matter of fact, the rate will become much faster, which may cause the predicted outcome of the model to not match the time distribution. It will further improve the accuracy of the forecast if, combined with future economic development forecasts and climate change mitigation policies, we can use more accurate factors reflecting different periods of scientific and technological progress to better predict China s future per capita carbon emissions. References Year 2030 HEG Scenario REF Scenario LEG Scenario BP Statistical review of world energy. bp.com/statisticalreview Brizga J, Feng K, K Hubacek Drivers of CO 2 emissions in the former Soviet Union: A country level IPAT analysis from 1990 to Energy, 59: Chen L, Yang Z, Chen B, Scenario analysis and path selection of lowcarbon transformation in China based on a modified IPAT model. PLoS ONE, 8(10): e Ding Y, Shen W, Yang S, et al Car dieselization: A solution to China s energy security? Energy Policy, 62: Duro J A, E Padilla International inequalities in per capita CO 2 emissions: A decomposition methodology by Kaya factors. Energy

6 DU Qiang, et al.: Forecasting China s per Capita Carbon Emissions under a New Three-step Economic Development Strategy 323 Economics, 28(2): Gambhir A, N Schulz, T Napp, et al A hybrid modelling approach to develop scenarios for China s carbon dioxide emissions to Energy Policy, 59: Gao Z Y, Wang Y The decomposition analysis of change of energy consumption for production in China. Statistical Research, 24(3): (in Chinese) Jiang K J, Hu X L, Zhuang X, et al China s low-carbon scenarios and roadmap for Sino-Global Energy, 06: 1-7. (in Chinese) Köne A Ç, T Büke Forecasting of CO 2 emissions from fuel combustion using trend analysis. Renewable and Sustainable Energy Reviews, 14(9): Koo C, Kim H, Hong T Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon emissions reduction target: Focused on educational facilities. Energy Policy, 73: Li L, Chen C, Xie S, et al Energy demand and carbon emissions under different development scenarios for Shanghai, China. Energy Policy, 38(9): Li Y M, Yang T, Structural decomposition analysis of decline in CO 2 emissions intensity in China: input-output analysis based on the 1997 to Resources Science, 04: (in Chinese) Lü W The causes and mechanism of industrial structure evolution of United States. Economic Perspectives, 08: (in Chinese) Marland G, T A Boden, R J Andres Global regionaland national fossil-fuel CO 2 emissions. Oak Ridge, Tenn. USA: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy. Meng M, Diu N Modeling CO 2 emissions from fossil fuel combustion using the logistic equation. Energy, 36(5): National Bureau of Statistics of the People s Republic of China China statistical year book Beijing: China Statistical Press. (in Chinese) Paltsev S, J Morris, Cai Y, et al The role of China in mitigating climate change. Energy Economics, 34: S444-S450. Piecyk M I, A C McKinnon Forecasting the carbon footprint of road freight transport in International Journal of Production Economics, 128(1): Poumanyvong P, S Kaneko Does urbanization lead to less energy use and lower CO 2 emissions? A cross-country analysis. Ecological Economics, 70(2): Qu S N, Guo C Forecast of China s carbon emissions based on STIRPAT model. China Population, Resources and Environment, 20(12): (in Chinese) Saveyn B, L Paroussos, J Ciscar Economic analysis of a low carbon path to 2050: A case for China, India and Japan. Energy Economics, 34: S451-S458. Shrestha R M, S Malla, M H Liyanage Scenario-based analyses of energy system development and its environmental implications in Thailand. Energy Policy, 35(6): Song M, Wang S, Yu H, et al To reduce energy consumption and to maintain rapid economic growth: Analysis of the condition in China based on expended IPAT model. Renewable and Sustainable Energy Reviews, 15(9): Wang D, Nie R, Shi H Y Scenario analysis of China s primary energy demand and CO 2 emissions based on IPAT model. Energy Procedia, 5: Xu G, Liu Z, Jiang Z Decomposition model and empirical study of carbon emissions for China, China Population, Resources and Environment, 16(6): (in Chinese) York R, E A Rosa, T Dietz STIRPAT, IPAT and ImPACT: Analytic tools for unpacking the driving forces of environmental impacts. Ecological Economics, 46(3): Yu S, Wei Y, Fan J, et al Exploring the regional characteristics of inter-provincial CO 2 emissions in China: An improved fuzzy clustering analysis based on particle swarm optimization. Applied Energy, 92: Yue C, Wang S, Zhu J, et al carbon emissions projection for China (Carbon Emissions and Social Development, IV). Acta Scientiarum Naturalium Universitatis Pekinensis, 04: (in Chinese) Zhang Y, Da Y The decomposition of energy-related carbon emissions and its decoupling with economic growth in China. Renewable and Sustainable Energy Reviews, 41: Zhou N, D Fridley, N Z Khanna, et al China s energy and emissions outlook to 2050: Perspectives from bottom-up energy end-use model. Energy Policy, 53: Zhou Y, Hao F, Meng W, et al Scenario analysis of energy-based lowcarbon development in China. Journal of Environmental Sciences, 26(8): Zhu Q, Peng X, Lu Z, et al Factors decomposition and empirical analysis of variations in energy emissions in China. Resources Science, 31(12): (in Chinese) 中国经济 新三步走 战略下人均碳排放预测 杜强 1, 王宁 1 2, 车雷 1 长安大学工程经济研究所, 西安 ; 2 长安大学外国语学院, 西安 摘要 : 在中国经济发展 新三步走 战略背景下, 本文采用逆向追踪法, 以石油 煤炭 天然气三种主要一次能源消耗为研究对象, 消除在能源转化过程中因地区科技水平不同造成的影响, 通过改进 IPAT 模型, 对 2050 年前中国人均碳排放在三种经济发展情境假设下进行分析预测 结果表明 : 中国人均碳排放峰值随经济发展情境不同将分别在 2020 和 2030 年出现, 人均碳排放量将达到 t; 峰值出现前人均碳排放量将以每年 2.25%-3.40% 的速度增长, 之后会以每年 1.33%-1.78% 的速度降低 至 2050 年, 人均碳排放量将减少为 t 本研究为中国未来碳排放政策的制定以及国际碳排放权限的划定提供了方法与数据支持 关键词 : 新三步走 战略 ;IPAT 模型 ; 碳排放 ; 预测

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