A scenario analysis of Chinese Carbon Neutral based on STIRPAT & System Dynamic model

： With the statement of Chinese government on energy saving in 2020 at the United 12 Nations General Assembly, carbon neutral was widely spread as a new concept. As a big country, 13 China has the responsibility and obligation to make its own contribution to global climate change. 14 This paper aims to explore and find effective ways for China to achieve carbon neutrality by 2060. 15 We identify the main factors affecting carbon emissions by STIRPAT model, combined with the 16 scenarios analysis we divide the year 2020 to 2060 into three stages฀ year 2020-2030 is Carbon 17 Peak stage, year 2030-2050 is Rapid Emission Reduction stage, year 2050-2060 is Complete Carbon 18 Neutralization stage. At each stage, three development models, high, medium and low level, were 19 established. A total of 27 different scenarios in three stages. A system dynamics model was 20 established to simulate the effects of carbon emission factors and changes in carbon sinks in different 21 scenarios. Finally , 8 paths were found which in line with Chinese current goal of achieving carbon 22 neutrality with treating reach Carbon peak in 2030 as an additional filter condition. Comparing per 23 capita GDP levels in different scenarios, we eventually find that keep economic development at a 24 low level in the first stage, a high level in the second stage and a medium level in the finally stage, 25 the point where net carbon emissions are less than zero for the first time will appear between year 26 2056-2057.By then, the per capita GDP will reach 144,500 yuan (based on year 2000), nearly four 27 times 2000’s. In all, these findings are helpful for policymakers to implement reasonable policies to achieve carbon emission peaking & carbon neutral in China.


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Abstract：With the statement of Chinese government on energy saving in 2020 at the United 12 Nations General Assembly, carbon neutral was widely spread as a new concept. As a big country, 13 China has the responsibility and obligation to make its own contribution to global climate change. 14 This paper aims to explore and find effective ways for China to achieve carbon neutrality by 2060. 15 We identify the main factors affecting carbon emissions by STIRPAT  coefficients. This article has made some progress on the previous researches: 81 No.1 We innovatively put the two targets of carbon peak and carbon neutrality in the same 82 model, which can better reflect the overall trend. 83 No.2 Integrate natural carbon sequestration and human scientific and technological progress to 84 reduce emissions. 85 No.3 The simulation with a time span of 40 years is divided into 3 stages, and the goals and 86 tasks of each stage are proposed respectively. Based on this, all possibilities are exhausted to ensure 87 the rationality of the forecast.

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No.4 Based on the situational simulation of system dynamics, we have found a way to achieve 89 carbon peak and carbon neutrality. 90 In short, these results can give the Chinese government a certain reference value when formulating 91 future development policies. 92 The rest of the paper is organized as follows: Section 2 presents the current situation of CO2 93 emissions and carbon sink in China. Section 3 provides the data and methodology. Section 4 shows 94 the results and discussion. 95 96 2.The current situation of CO2 emissions and carbon sink in China.

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The changes in carbon sink from different projects, over the period 2000-2019 are presented 98 in Fig. 1.Note that the calculation method will be showed in next section. It can be found that 99 Chinese total carbon sinks grew slowly during the 13 years from 2000 to 2012, and changed slowly 100 after a small increase in carbon sinks in 2013. This is caused by the delay of Chinese statistics. On 101 the whole, Chinese overall natural carbon sink has remained stable over the past 20 years, with a 102 slight increase year by year, far from reaching the upper limit of natural carbon sink. In addition, it 103 is worth noting that although the forest system is the largest carbon sink storage system in the 104 terrestrial ecosystem, it is not the largest carbon sink growth point in the ecosystem. The huge carbon 105 sink storage of the forest comes from long-term reserves, but It grows slowly. Farm system has 106 become the largest contributor to the annual growth in the amount of carbon sinks, perhaps because 107 most of the crop growth cycle is less than one year, one year all generated carbon sinks are credited 108 for the growth in carbon sinks. The ranking of the contribution to the growth of carbon sinks in 109 descending order is: farmland (including crops), grassland, forest, wetland, orchard. 110 Taking the natural logarithm on both sides at the same time, the equation can be transformed 161 into: Among them, I, P, A, and T have the same meaning as mentioned before; a, b, and c represent 164 the elastic coefficients of I, P, A, and T respectively, and z represents the residual term. 165 Based on this, we propose a decomposition model of Chinese carbon emissions influencing 166 factors 167 = a + + + + + + (10) 168 In the formula, represents carbon dioxide emissions, P represents population, A represents 169 per capita GDP, IS represents industrial structure (the proportion of tertiary industry), ES represents 170 energy structure (the proportion of coal use in total energy), EI stands for energy intensity (the ratio 171 of total energy consumption to total GDP, indicating the level of technological development.).

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Based on the above analysis, the carbon dioxide emission prediction model can be written as 173 = ( a+ 3.2 Data processing: The collection of data is a huge task. We have checked and collected the total 175 energy consumption, the total number of livestock in the livestock industry, the area of rice fields, 176 and the specific data of natural carbon sink factors in Chinese statistical yearbooks from 2000 to 177 2019. We use the stock volume method (Shi et al.,2014) to calculate the annual total forest carbon 178 sink of the forest, compare the carbon sink amount of the next year, and use the difference as the 179 forest carbon sink of the year. 180 Where represents the total carbon sink; represents the area of type forest in area 183 ; represents the carbon density of type forest biomass in area ; represents stock volume  Where represents the total carbon sink represents the area of project i represents the 202 carbon fixation coefficient of project i. 203

3.3system dynamics (SD) 225
System Dynamics (SD) was originally derived from the work of Jay W. Forrester and his 226 colleagues at the MIT Sloan School of Management in the 1960s. The original system dynamics 227 thought was formed when they applied the concept of feedback control theory to study industrial 228 systems. Forrester completed the book "Industrial Dynamics" and used Dynamo to implement the In order to make the setting of the situation more real and reliable, this article uses the situation 242 analysis method to combine with historical data, and sets the parameters of each situation according 243 to the latest government guidance documents. 244 This article divides the period from 2020 to 2060 into three phases. From 2020 to 2030 is the 245 carbon peak phase, from 2030 to 2050 is the rapid emission reduction phase, and from 2050 to 2060 246 is the carbon neutral phase. 247 According to the characteristics of each stage, three development modes of high-speed, 248 medium-speed and low-speed are set up. The growth rate of each indicator of each mode is shown 249 in Table 7.A total of 27 different development models, as shown in Fig. 8. 250 Judging from historical data, Chinese GDP has been maintaining rapid development, but in 251 recent years, with the increase in volume and changes in economic structure, the growth rate of GDP 252 has slowed down. 253 According to the "China in 2030" published by the World Bank and the Development Research 254 Center of the State Council, the prediction of China's future economy, we set the GDP growth rate 255 for the period from 2020 to 2030 between 5% and 5.9%, and the high and low scenarios fluctuate 256 0.5% on the basis of 5%. The growth rates of the tertiary industries are determined according to the 257 proportion of the total tertiary industries,the total population will increase further under the 258 conditions of the opening of the two-child policy, and the cost of clean energy will gradually 259 decrease, leading to an increase in the use of clean energy. However, due to the insignificant decline 260 in the use of coal at the technical level, the dependence on oil is still significant. The excessive use 261 of natural gas as a clean energy product will rise sharply, the area of forests, grasslands, wetlands, 262 etc. will maintain a small increase, and the area of farmland will not change significantly. During 263 the period from 2030 to 2050, due to the huge economic aggregate, the growth rate of the first, 264 second and third industries will decline significantly, and the country will enter the threshold of a 265 moderately developed country. According to the economic growth data of moderately developed 266 countries, we set the economic growth rate to 3%, and determine the growth rates of the tertiary 267 industries according to the total proportion of the tertiary industries. With the demographic dividend 268 period of the 1960s and 1970s, people born in the demographic dividend period gradually entered 269 the 60-year-old threshold, the aging of the population further aggravates the population will decline 270 slightly, the cost of clean energy has been further reduced due to the development of technology. 271 The use of clean energy continues to maintain rapid growth, and the dependence on energy such as 272 coal and petroleum has dropped significantly. The use of natural gas continues to grow, but due to 273 the huge volume, the growth rate has decreased significantly. The changes in natural carbon sink 274 factors at this stage are still increasing slowly. During the carbon neutral period from 2050 to 2060, 275 Chinese economy is huge, and the focus of development will continue to shift from economic 276 construction to environmental protection, and economic growth will further slowdown. The aging 277 of the population has eased, the number of people has rebounded slightly, clean energy has become 278 the main force in energy use, the use of energy has continued to grow, the use of coal, oil and other 279 energy continues to decline, the use of natural gas has flattened, and the natural carbon sink has 280 increased significantly. The growth rate corresponding to each stage is shown in Table 5. 281

Results and discussions 284
In this paper, total carbon emissions are used as dependent variables, and population (POP), 285 GDP per capita (A), industrial structure (IS) (the proportion of tertiary industry), energy structure 286 (ES) (the proportion of coal use), and energy intensity (EI) as the independent variable. In fact, the 287 respective variables are not independent of each other but there is serious multicollinearity. The 288 correlation of all variables is shown in the Table 6. 289 According to the system dynamics simulation, the net carbon emission diagram of each 307 situation is shown in Fig. 6. 308 According to the simulation of the 27 development models one by one, we have obtained the 316 future development of the net carbon emissions of the 27 different models. In the case of the 317 established 27 models, the net carbon emissions of all models will show varying degrees of growth 318 from 2020 to 2030, and an extreme point will appear around 2030, which means that all models can 319 achieve carbon emissions around 2030. In the 20 years from 2030 to 2050, the net carbon emissions 320 in all scenarios will decline rapidly as planned. In the period from 2050 to 2060, the decline in net 321 carbon emissions in some scenarios tends to be flat. In the 20 years from 2030 to 2050, the net 322 carbon emissions in all scenarios will decline rapidly as planned. In the period from 2050 to 2060, 323 the decline in net carbon emissions in some scenarios tends to be flat. The total carbon emissions 324 chart for all scenarios are shown in Fig. 7 This means that these eight scenarios will not only peak carbon emissions in 2030, but also control 333 the total amount within 15 billion tons, and achieve carbon neutrality by 2060. Among them, the per 334 capita GDP corresponding to scenario 132 will reach 144,500 yuan by 2060 (based on 2000), which 335 is nearly four times that of 38,000 in 2020, which is the highest among all scenarios that meet the 336 above three conditions at the same time. The schematic results of GDP per capita in all scenarios 337 are shown in Fig. 8. 338 339 340