1 NCSC Working Paper Current Situation and Further Research Needs on China’s 2050 Low Carbon Transition FU Sha, CHAI Qimin National Center for Climate Change Strategy and International Cooperation 20th March, 2018 I. Introduction As the biggest developing country, China’s mitigation and development strategies have important implications for global efforts to hold warming to well below 2C or even 1.5C. China’s development and mitigation pathways through 2050 – including interactions among sectors, scales, and development goals as well as robust actions and conditions – are of great importance. Recent research has highlighted the importance of macroeconomic a n d s t r u c t u r a l assumptions for the understanding of Chinese emissions pathways Grubb et al., 2015; Qi, Stern, Wu, Lu, Spencer et al, 2016. This is particularly important in the light of recent Chinese policy announcements regarding the ambition to restructure the economy away from investment, industry and exports, and towards consumption, services, and innovation. Emerging signs of transition, with growth slowing and the share of industry in GDP declining in recent years is appearing, which all fed into significant transition in the energy sector, with coal use and emissions falling somewhat in recent years, primary energy growth moderating, and the share of non-fossil fuel energy increasing significantly. Thus, there is still a need, however, for increasing the understanding of the implications of new era development for the energy and climate trajectory towards 2030 and 2050. There have been some attempts at ad hoc quantitative analysis of such pathways in the literature Green Grubb et al., 2015; Qi et al., 2016; Spencer et al., 2016. However, the literature still lacks deep analysis on the implication of new economic and development 2 vision, implemented in a well-validated and detailed energy model. Energy models and integrated assessment models typically lack the requisite temporal and economic disaggregate to effectively explore the impacts of structural change on energy and emissions pathways. In the meanwhile, recent studies also highlight the importance to considering non-CO2 climate forcers and non-energy related CO2 emissions in achieving relative stringent long- term targets, especially the well below 2-degree or 1.5-degree goals Harmsen et al., 2017; Rogelj et al., 2014, 2015. In addition, assessing mitigation pathways in the context of SDGs or SD has also been more and more mainstreaming, an increasing number of modelling studies and literature show that sustainable development objectives and climate policy targets are interrelated, interact with each other and that synergies and trade-offs can be identified Jakob and Steckel 2016; von Stechow et al. 2016; Epstein et al. 2017; Wstemann et al. 2017. Therefore, this paper attempts to rethinking the research agenda and modelling work of China’s 2050 pathway study in a broader context, based on the review of current modelling studies. II. Reviewing current modelling studies on China’s future energy and emission trajectory 2.1 Reviewing the social economic trends Trends in socio-economic development, including population and urbanization and energy service demands, will all influence China’s emissions trajectory. 2.1.1 Review of trends in population and urbanization Population has significant implications for energy consumption. Figure 2.1 compares the population assumptions of a range of studies. As can be seen, there is broad consensus on China’s population trajectory with some minor differences. Across all scenarios, it is existing government policy will continue and that population growth will continue to increase gradually. The expected changes in population growth between 2005 and 2030 is 3 small, ranging from 5–15. This reflects a modest change over two decades largely due to the effectiveness of China’s population policies. Projections suggest that China’s population will peak by at approximately 1,450 million people by around 2030. As noted, China is undergoing rapid urbanisation. This urbanization requires extensive material and can consequently influence emissions levels. Based on historical experience internationally, the urbanisation process has three stages. First, there is the slow development stage which persists until an urbanisation level of approximately 30. This is follow by the accelerated development stage, and then the modern development stage. The urbanization rate of developed countries is generally more than 70. Some are higher, such as the US and the UK which are 81 and 90 respectively. Presently, China is in the accelerated development stage of urbanization. Table 2.1 sets out the assumptions on future urbanization across different modelling rcises. The models suggest that China’s urbanization level will reach 56–63 by 2020, 64–70 by 2030, and 76–79 by 2050. Figure 2.1 Trends in population among different scenarios 11Note Population changes are indd to 2005. The 2005 population was 1,300–1,450 million, and an 4 Table 2.1 Urbanization ratio assumptions from different scenarios 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 China MARKAL 43 49 52 56 60 64 68 71 75 78 PECE 43 50 56 62 65 68 71 73 75 76 IEA 43 49 56 61 65 69 71 74 IPAC 43 49 56 63 67 70 72 74 77 79 Medium 43 49 56 62 65 69 71 74 75 78 2.1.2 Review of trends in economic growth Thus, China’s economic structure, as well as the rate of economic growth, is one of the key variables determining its future emissions pathways. Industry has been the major driver of emissions growth over the period 2000–14. During the period of almost 40 years since re and opening-up, China’s GDP increased by around 9.4 on an average annual basis, and a rapid growth rate was maintained. Structural changes and growth in the Chinese economy will significantly influence energy demand and emissions. Table 2.2 and figure 2.2 contrasts the assumptions on the GDP growth rate across various studies. The assumptions in most studies are 6.9–8.8 for 2010–20; 4.9–5.8 for 2020–30; 3.1–4.5 for 2030–40; and 2.1–3.3 for 2040–50. It is generally assumed that the relatively high rate of growth will continue and gradually drop due to restructuring of the economy towards the new normal, as well as demographic changes. However, if considering the latest two-stage target set out in the 19th national congress of CPC report, the annual growth rate of GDP till 2050 may need to be further increased. estimated 1,308 million from the NBS. 5 Table 2.2 GDP growth rate assumptions among different scenarios 2010–20 2020–30 2030–40 2040–50 AIM-Enduse 9.2 5.8 3.1 2.1 GCAM 6.9 5.2 4.1 3.3 IMAGE 8.8 4.8 3.9 3.0 MESSAGE 6.9 5.1 3.5 2.9 REMIND 9.2 5.8 3.1 2.1 TIAM-ECN 6.1 4.4 3.2 3.2 WITCH 8.8 5.6 3.0 2.1 IPAC-ERI 8.4 7.1 5.0 3.6 IEA-WEO 7.2 5.3 3.2 IEA-ETP 8.1 4.9 2.9 2.9 China MARKAL 7.4 6.0 4.5 3.0 PECE 7.4 5.5 4.5 3.4 NCSC DDPP 7.5 5.5 3.5 2.5 Among which 20th percentile 6.9 4.9 3.1 2.1 Median 7.5 5.5 3.5 3.0 80th percentile 8.8 5.8 4.5 3.3 6 Figure 2.2 Trends in GDP growth among different scenarios 22.1.3 Review of trends in energy service demand The future demand for energy services will be a key driver in overall energy demand and CO2 emissions. The demand for energy services includes demand for high-energy- consuming products, transportation, and building space and construction. Tables 2.3, 2.4 and 2.5 compare the assumptions on the future energy service demand across the various scenarios reviewed. Several conclusions can be drawn from the tables. First, there is a wide range of projected drivers for energy service demand in the residential and transport sectors in China to 2050. Few models explicitly assess this parameter and those that do use a different base-year data. Second, activity levels for the analyzed sectors are projected to grow, by around a factor of 5 on average for passenger/freight kilometers, and 1.6 on 2Note GDP/ per capita changes are indd to 2010. 2010 GDP per capita levels ranged from US2,300 to 3,400 per capita 2005 price. The official NBS estimate was 2,900. 7 average for residential and commercial floor space. This is consistent with a transition from industrial to transport and residential energy demand. Controlling these emissions may be a major challenge for China in the future, and should be subject to more intensive scenario assessment. Table 2.3 Comparison of energy service demand for residential and commercial floor space billion m 2 /year Model Scenario 2005 2010 2015 2020 2025 2030 2040 2050 China MARKAL ROSE 38.6 46.7 56.6 62.9 68.3 73.7 84.0 93.2 GCAM LIMITS- StrPol 53.1 56.2 59.6 62.9 65.9 68.7 73.6 77.0 POLES AMPERE 15.4 18.4 23.2 28.0 32.3 36.7 41.7 44.5 PECE AME 38.6 46.7 52.2 58.8 65.0 70.0 74.1 76.3 IEA WEO 34.2 40.4 45.9 50.6 53.8 57.0 60.2 Medium 38.6 46.7 52.2 58.8 65.0 68.7 73.6 76.7 Table 2.4 Comparison of energy service demand for freight transportation billion tonne- km/year Model Scenario 2005 2010 2020 2030 2040 2050 AIM- Enduse EMF27- Base- FullTech 2,338.7 2,878.5 4,117.2 5,644.3 7,530.9 9,842.8 POLES 2,941.9 4,265.1 8,280.5 12,460.3 15,458.9 1,7885.7 GCAM 6,802.5 8,232.5 11,021.2 13,664.9 16,235.8 18,759.6 GCAM AMPERE 6,802.5 8,322.4 12,331.7 15,721.6 17,873.2 19,639.0 8 POLES 2-Base- FullTech 2,819.4 4,936.4 13,837.1 20,903.8 22,438.8 23,399.7 PECE AME 9,394.0 14,454.0 27,686.0 42,337.0 61,398.0 75,660.0 China MARKAL ROSE 1z3,964.1 23,003.1 38,347.6 56,253.3 75,691.7 Table 2.5 Comparison of energy service demand for passenger transportation billion passenger-km/year Model Scenario 2005 2010 2020 2030 2040 2050 GCAM AMPERE 1,504.0 2,148.3 4,069.4 5,862.6 6,994.0 7,787.5 GCAM EMF27 1,504.0 2,128.7 3,521.5 4,977.4 6,435.4 7,798.7 GCAM ROSE 1,504.0 2,131.2 4,006.1 5,836.7 7,111.5 8,064.1 DNE21 AMPERE 2,733.7 3,518.3 5,034.0 6,911.7 8,525.4 10,031.9 AIM- Enduse EMF27 1,872.2 2,507.6 4,013.1 6,071.8 9,024.5 13,231.3 POLES AMPERE 2,941.9 4,265.1 8,280.5 12,460.3 15,458.9 17,885.7 POLES EMF27 2,819.4 4,936.4 13,684.0 20,617.0 22,198.8 23,096.5 PECE AME 3,446.0 5,163.0 10,056.0 16,085.0 20,849.0 26,019.0 China MARKAL ROSE 3,545.6 10,314.4 16,229.6 21,601.9 28,424.6 9 2.2 Reviewing the energy and CO2 emission trends 2.2.1 Review of trends in energy-related CO2 emissions Figure 2.3 shows projections of total CO2 emissions from energy related fossil fuel use in China ie excluding land use or industrial process emissions, from 2005 to 2050, according to the results of the 89 separate scenarios produced within the various different modelling plats reviewed for this paper. The scenarios have been grouped into three categories as detailed in the figure 2.3a, b, c. Reference scenarios are shown in figure 2.3 a – these scenarios project emissions on the basis of current climate policies or no new additional climate policies from a single year. Enhanced Policy scenarios, shown in figure 2.3 b, project emissions on the basis of some additional climate related policies being implemented. Realistic 450 or 500 ppm scenarios, shown in figure 2.3c, represent projections on the basis of strong climate policies consistent with a global effort that would achieve stabilisation of atmospheric CO2 at 450-500 ppm in 2100, consistent with a roughly 50 or greater chance of keeping global temperature rise to within about 2 degrees centigrade above pre-industrial levels. Figures shows, the range of projections is large Chinese emissions in 2030 span 7-18 GtCO2, and in 2050 span 8.7-23.5 Gt CO2 depending on the scenario considered. 10 （a） Reference scenarios （b） Enhanced policy scenarios 0 5000 10000 15000 20000 25000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 CO2 Emission from Fossil Fuels and Industry, Mt CO2 0 5000 10000 15000 20000 25000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 CO2 Emission from Fossil Fuels and Industry, Mt CO2 11 （c）Realistic 2degree scenarios 450/500 ppm Figure 2.3 Total energy related CO2 emissions in all reviewed scenarios, 2005-2050 32.2.2 Peaking year and level According to figure 2.4, most reference scenarios imply that China will peak between 2030-2050, mostly will not peak before 2050. Under enhanced policy scenarios, with additional policy and measures, China will peak around 2030. For realistic 2-degree scenarios, China need to peak relatively earlier, between 2020-2030. 3Note Red line NDC Scenario by PECE model with data adjustment; Yellow line NDC Scenario by PECE model without data adjustment; Light purple shadow full range; Dark blue shadow 20th-80th percentile. 0 5000 10000 15000 20000 25000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 CO2 Emission from Fossil Fuels and Industry, Mt CO2 12 Figure 2.4 Peaking year and level 2.2.3 PE share of non-fossil fuel Unlike China official energy data, the international modelling forum adopted direct equivalent while transs primary electricity to primary energy. Same as other indicators, the results of different scenarios vary and results in wide range. The base year data varies because of different data sources, with or without traditional biomass, which will affect the outputs of scenarios in the future. According to China’s INDC target on non-fossil fuel, the share of non-fossil fuel shall reach 11 in 2030 with direct equivalent which within the range of Enhanced policy scenarios and 450-500 ppm scenarios. （a） Reference scenarios （c）Realistic 2degree scenarios 450/500 ppm Figure 2.5 PE share of non-fossil fuel in all reviewed scenarios, 2005-2050 2025 2030 2035 2040 2045 2050 2055 5000 10000 15000 20000 25000 Peakingyear PeakingLevel, MtCO2 2015 2020 2025 2030 2035 2040 2045 5000 10000 15000 20000 25000 Peakingyear PeakingLevel,MtCO2 2018 2020 2022 2024 2026 2028 2030 2032 5000 10000 15000 20000 25000 Peakingyear PeakingLevel,MtCO2 REF Peaking year 2040-2050Enhance Policy LC Peaking year 2030Realistic 500450 Ambitious policy Peaking year 2020-20300.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 2010 2015 2020 2025 2030 2035 2040 2045 2050 P E C E s h are o f non‐ fossil fuel, , d irect equavilent 13 2.3 lessons learned from multi‐modelling comparison study Modelling analysis has proven to be an invaluable to policymaking surrounding low emissions development. Modelling analysis has supported not only domestic decision making in China and around the world, it has also been a critical to both multi-lateral and bilateral discussions and negotiations. The comparison study in this section is very preliminary and far from calling multi- modelling analysis. Though, several observations could be drawn from the reviewing 1 Multi-modeling analysis is of great value. Comparison above show huge divergence in both s and outputs of modeling st