Several representative studies on China’s carbon emission scenarios in 2050 are compared in scenario settings, methodologies, macro parameters, energy consumption and structure, carbon emissions, and carbon emission intensity. Under the baseline scenario of the present policy framework, the future energy structure will be optimized and carbon emission intensity will decrease continually. China’s carbon emissions up to 2050 show a significant increase reaching between 11.9 Gt and 16.2 Gt CO2 in 2050. By strengthening a low carbon policy, the optimization of energy structure and the decline in carbon emission intensity will become more obvious within the comparative scenarios, which show a significant decrease in carbon emission until 2050 reaching only between 4.3 Gt and 9.5 Gt CO2 by then.
carbon emission ; climate change ; emission scenario
Climate change is both an environmental issue and a developmental issue. At present, international negotiations on greenhouse gas (GHG) emission reduction are not only viewed from environmental but also from political, economic, and diplomatic perspectives. Studies on future carbon emission scenarios are not only an important basis for domestic policymaking, but also references to international negotiations. As a large developing country, China draws worldwide attention regarding its future carbon emission trend. There are several recent studies on China’s future carbon emission scenarios, respectively done by the Energy Research Institute (ERI) of China’s National Development and Reform Commission [ ERI , 2009 ; 2050CECERRT , 2009 ], the Tyndall Center for Climate Change Research (Tyndall) [ Wang and Watson, 2009 ; Wang and Watson, 2010 ], the Ernest Orlando Lawrence Berkeley National Laboratory (LBNL) [ Zhou et al. , 2010 ], the McKinsey [ McKinsey&Company , 2009 ], the International Energy Agency (IEA )  , and the United Nations Development Program (UNDP )  . These studies help China to understand its future carbon emission trend, but the significant disparity among the research findings is confusing. To better understand China’s future carbon emission, it is necessary to conduct a careful comparison on these different studies, in order to find their similarities and differences, and hence provide better ideas for policy-makers.
Most studies adopted a bottom-up methodology (IEA, LBNL, McKinsey, and UNDP), which has the merits of a clear structure and is easy to identify emission reduction contributions by each technology. The necessary basic data in the bottom-up methodology is measured by physical but not economic qualities, which implies that they do not integrate macroeconomic variables very well. Among these studies, LBNL, McKinsey, and UNDP focus only on China and do not set any global target, while IEA bases its analysis on a global model and sets the target of reducing global emission by 50% in 2050 relative to 2007 level in its blue map scenario.
Different from the other studies, the ERI study combines both bottom-up and top-down methodologies. For the top-down methodology, ERI uses a Computable General Equilibrium (CGE) model named IPAC-SGM and tries to link the amount of energyintensive products with the sectoral economic output. In its further break-down scenario, the ERI study also uses the bottom-up methodology, focusing on China only without setting a global target.
The Tyndall study is rather unique as it preliminarily sets certain emission budgets. This study is essentially different from the others, which aim to explore China’s 2050 emission possibilities under certain economic and social conditions, while the Tyndall study tries to explore the social and economic possibilities with a set carbon emission budget. As a result, it cannot be compared with the other studies.
In Table 1 , a comparison of methodologies of the afore-mentioned 6 studies is shown. All studies use 2005 as the baseline year, except IEA. In all but the McKinsey study, the target year is 2050. Except Tyndall and McKinsey, other studies employ models for their scenario analyses. IPAC-SGM, IPAC-AIM, and IPAC-Emission/Technology models were used in ERI’s study. UNDP uses the PECE model to analyze scenarios. The PECE model is a bottom-up, non-linear, technological optimization model that calculates the most cost-saving choice of technological option under a series of restrictions. IEA and Tyndall set clear global emissions reduction goals as the restrictive condition for China’s future carbon emissions. The difference between the two models is that IEA assumes a global target of “reducing global emissions by 50% in 2050 compared with the 2007 level” and allocates a carbon emissions target to China under the principle of minimum global abatement costs. The Tyndall model, on the other hand, assumes a global target of “limiting global CO2 concentration to 450×10–6 ” and allocates a carbon emissions target to China under the principle of the convergence of per capita carbon emissions or the convergence of carbon emissions per unit of GDP.
|Study||Methodology||Model||Global emission reduction target||Baseline year||Target year|
|ERI||Both top-down and bottom-up||IPAC-SGM; IPAC-AIM; IPAC-Emission/Technology||None||2005||2050|
|IEA||Bottom-up||ETP model||Blue map scenario: reducing global emission by 50% in 2050 relative to 2007||2007||2050|
|Tyndall||Top-down||None||Setting emission budgets based on per capita emission and per unit GDP emission||2005||2050|
The setting of scenarios is the pre-condition of scenario analysis. Different scenario settings include important information on society, economy, technologies, and lifestyles etc., which impact energy consumption and carbon emission. There are generally two types of scenarios-baseline scenarios and comparative scenarios, which reflect carbon emission trajectories under the current policy framework and a new policy framework, respectively.
The Tyndall study has set an emission budget irrelevant of the current policy framework; as a result, all its four scenarios are seen as comparative scenarios. Except the Tyndall study, all other studies include one baseline scenario and several comparative scenarios. See Table 2 for the comparison of the scenario settings of the different studies.
|Study||Baseline scenario||Comparative scenario|
|ERIa||Energy-saving scenario: the current policies aiming at achieving energy-saving and emission reduction are fully considered||Low carbon scenario (LC): all feasible measures are adopted under China’s sustainable development, energy security and economy competitiveness are fully considered Enhanced low carbon scenario (ELC): more aggressive policy actions are adopted under global concerted efforts to address climate change|
|IEA||Baseline scenario: no new policies addressing climate change are adopted||Blue map scenario: lowest-cost technologies are used to achieve the target of reducing global emission by 50% in 2050 relative to the 2007 level|
|LBNL||Continued improvement scenario (CIS): unit GDP energy consumption decreases continuously, reaching the same level as that of developed countries||Accelerated improvement scenario (AIS): more progressive actions are adopted; each sector adopts the best available technologies in the near and mid-term, and uses them widely afterwardsCIS with CCS: wide adoption of carbon capture and storage among coal power plants, capturing 0.5 billion t CO2 by 2050|
|Tyndall||S1: with global target of 450×10–6 , assuming equal per capita emission by 2050 and 1990–2100 cumulative emission budget of 70 Gt carbon, referring to ERI curveb before 2020 and assuming emission peak in 2020 S2: with global target of 450×10–6 , assuming equal per unit GDP emission by 2050 and 1990–2100 cumulative emission budget of 111 Gt carbon, referring to ERI curve before 2030 and assuming emission peak in 2030 S3: with global target of 450×10–6 , assuming equal per capita emission by 2050 and referring to IEA curvec before 2020 and assuming peak in 2020. However 1990–2100 cumulative emission budget is set as 90 Gt carbon, because of excessive emission before 2020S4: with global target of 450×10–6 , assuming equal per unit GDP emission by 2050 and 1990–2100 cumulative emission budget of 111 Gt carbon, referring to IEA curve before 2030 and assuming emission peak in 2030|
|McKinseyd||Baseline scenario: a steady improvement in industrial product quality and energy efficiency, and a series of mature and proven technologies are considered||Abatement scenario: those technologies that are well studied and very possibly commercialized are considered|
|UNDP||Reference scenario: certain additional policies are adopted, but mandatory emission reduction measures are not considered||Emission control scenario (EC): a variety of policies are further adopted to achieve the upgrading of industrial structure and energy mix Emission abatement scenario (EA): an emission peak is set at 2030, and the highest emission reduction is to be achieved by 2050|
a. The ERI study sets 4 scenarios-baseline scenario, energy-saving scenario, low carbon scenario, and enhanced low carbon scenario. As China adopts stronger policies on energy-saving, the baseline scenario is no longer emphasized in later studies by ERI
d. The McKinsey study sets 3 scenarios: technology lock-in scenario, baseline scenario and abatement scenario, out of which the baseline scenario is the most comparable one with other studies
GDP is one of the key parameters in these studies since its total amount, growth rate and structure have significant implications for the total amount of energy use. See Table 3 for GDP growth rates of different studies. The Tyndall study has different GDP assumptions for its 4 scenarios, while others use the same GDP for the baseline scenario and comparative scenarios throughout their individual study. Different GDP growth rate assumptions have led to very large differences in China’s total GDP in 2050. For example, in the ERI scenario, China’s total amount of GDP by 2050 will be 10.3 times as much as its 2010 level, while in the Tyndall S4, it is only 5.1 times, less than half of what the ERI scenario predicts. ERI and UNDP used an industrial structure as an important variable for GDP growth, and adopted very similar values — the second industry accounts for 36%–38% of total GDP, while the primary industry accounts for 2%–4%, and the tertiary industry accounts for 58%–62% in 2050.
a. IEA sets GDP growth rate as 8.8% for 2007–2015, 4.4% for 2015–2030, and 3.8% for 2030–2050. To make it easy for comparison, these growth rates for 2010–2020 and 2020–2030 were re-calculated in this paper
b. Tyndall only listed GDP data in 2010, 2020, 2030, 2040, and 2050, and the rough GDP growth rates were estimated based on Tyndall data in this paper
Population is another key parameter since it also has significant implications for terminal energy consumption. See Table 4 for population assumptions in different studies. There is little difference in population assumptions among the studies.
Rural lifestyle and urban lifestyle have different carbon footprints. Moreover, the urbanization process requires extensive material input and has impacts on the demand and supply of such materials. As a result, the urbanization rate is also a key parameter for carbon emission scenario studies. Except the Tyndall study, all the other five studies have set urbanization rate assumptions.
Table 5 shows that these five studies assume rather different urbanization rates. LBNL and ERI assume similar rates — 79% by 2050, which is higher than the assumptions by IEA and UNDP. IEA assumes 73% by 2050, which equals to the ERI’s 2040 level, and UNDP assumes 70% by 2050, which equals to ERI’s 2030 level.
See Figure 1 for total primary energy consumption derived from each study. For comparison purposes, we categorize the baseline scenarios and comparative scenarios. In the baseline scenarios, although the studies are different, they all expect further increase in total primary energy consumption before 2050. In the baseline scenarios, China’s total primary energy consumption will double — increasing from 3.1–3.4 Gtce in 2010 to 5.4–7.4 Gtce in 2050. From 2010 to 2030, the UNDP reference scenario has the highest growth rate in energy consumption of nearly 1.9 times in 2030 relative to the 2010 level; the ERI energy-saving scenario and LBNL CIS have similar growth rates in energy consumption, which is about 1.6 times in 2030 relative to 2010 level. From 2030 to 2050, the IEA baseline scenario has the highest growth rate with energy consumption reaching about 1.4 times in 2050 relative to the 2030 level; the ERI energy-saving scenario and UNDP reference scenario are similar, with 1.2 times energy consumption; the LBNL CIS has the lowest growth rate with only 1.1 times energy consumption relative to the 2030 level.
Total primary energy consumption of different scenarios in the 5 studies
Figure 1 shows that except Tyndall, no other scenarios exhibit an energy consumption peak before 2050. From 2010 to 2050, China’s energy consumption will continually increase in spite of a decelerating growth rate. Before 2030, the ERI LC scenario, IEA blue map scenario, LBNL AIS, and the UNDP EC scenario all have very similar energy consumption results, ranging between 3.7–4.0 Gtce in 2020 and 4.4–4.5 Gtce in 2030. After 2030, these scenarios show rather significant differences. In 2050, the ERI LC scenario and IEA blue map scenario are rather similar, with energy consumption ranging between 5.2–5.3 Gtce. The LBNL AIS shows relatively low energy consumption of only about 4.6 Gtce, lower than that shown in the ERI ELC scenario. The UNDP EC scenario shows an energy consumption reaching nearly 5.7 Gtce, higher than the LBNL CIS. The comparative scenarios show much lower energy consumption in 2050 than the baseline scenarios, a reduction ranging from 1.0 to 2.0 Gtce.
Most studies make assumptions about supply capacities of different kinds of energy. Energy supply and demand shape the energy mix. Though the energy mix is a result of various scenario studies, it largely reflects the key assumptions in the scenario setting. In all scenarios, the share of coal in the energy mix decreases (Fig. 2 ) while the share of non-fossil energy increases (Fig. 3 )① .
The proportion of coal in primary energy consumption in the 5 studies
The proportion of non-fossil energy in primary energy consumption in the 5 studies
Coal is China’s primary energy source, accounting for nearly 71% of the primary energy consumption in 2005. Most scenarios show that although the share of coal in energy consumption will continually decrease before 2050, it is still China’s most important energy source. The UNDP reference scenario shows that the share of coal will still be 59% in 2050, while the IEA baseline scenario shows only 56%. In the comparative scenarios, the share of coal dramatically decreases to around 30%.
Non-fossil energy, including nuclear, hydro, wind, and solar energy, accounts for less than 7% of total energy consumption in 2005. By 2050, even the lowest estimation shows a share of about 14% (UNDP reference scenario, IEA baseline scenario). In Tyndall’s four scenarios, as they set a carbon emission trajectory, the share of non-fossil energy dramatically increases from 37% (S4) to 48% (S1) in 2050 in order to ensure economic growth. The IEA blue map scenario sets the target of global carbon emission reduction to half the current emission; hence the share of non-fossil energy reaches about 47% by 2050. The ERI LC scenario, LBNL AIS, and UNDP EA scenario show similar shares of non-fossil energy in 2050, ranging from 32% to 33%.
The scenarios show rather different carbon emission (Fig. 4 ). Except Tyndall, most studies show that China’s carbon emission range between 6.7–8.1 Gt CO2 in 2010, 7.0–14.0 Gt CO2 in 2030, and 4.3–16.2 Gt CO2 in 2050, with the highest emission amounts (UNDP reference scenario) being 3.7 times as much as the lowest emission amounts (IEA blue map scenario).
Comparison of carbon emissions in the 6 studies
For comparison purposes, we categorize the baseline scenarios and comparative scenarios. The baseline scenarios of different studies show different carbon emission trajectories. The ERI energy-saving scenario shows that carbon emissions keep growing during 2010–2040 and start to decrease during 2040–2050. The LBNL CIS indicates that China’s carbon emission will peak around 2030 and slowly decrease after that. The IEA baseline scenario and UNDP reference scenario show carbon emission maintaining a continuous growth without a peak until 2050. The baseline scenarios of the ERI, IEA, and LBNL studies show similar carbon emissions in 2030, but the emissions after that are rather different. The UNDP reference scenario shows emission reaching 16.2 Gt CO2 in 2050, slightly higher than 15.9 Gt shown in the IEA baseline scenario. The ERI energy-saving scenario and LBNL CIS show similar carbon emissions, at 12.7 Gt and 11.9 Gt CO2 , respectively.
The comparative scenarios show even larger differences. Every scenario shows an emission peak except the UNDP EC scenario and ERI LC scenario. An emission peak is mostly found in 2030, as shown in the ERI ELC scenario, LBNL AIS, CIS with CCS, Tyndall S2 and S4, and UNDP EA scenario, with corresponding peak emission of 8.2 Gt, 9.7 Gt, 11.7 Gt, 7.0 Gt, 9.3 Gt, and 8.8 Gt CO2 . The Tyndall S1 and S3, and IEA blue map scenario set strict global emission reduction targets, so their trajectories show an emission peak in 2020, being 6.3 Gt, 8.8 Gt, and 8.4 Gt CO2 , respectively. McKinsey’s baseline emission in 2030 is similar to the IEA baseline scenario and ERI energy-saving scenario. As the McKinsey abatement scenario captures maximum technical reduction potential, it derives much lower carbon emission than what other scenarios predict, except the Tyndall S1.
Comparing baseline scenarios to comparative scenarios in different studies, the emission reduction differences get larger and larger as time goes on. The highest emission reduction in the comparative scenario relative to baseline scenario is 0.8 Gt CO2 in 2010, while such differences could reach 5.0 Gt in 2030 (nearly equivalent to China’s total emission in 2005) and 11.0 Gt in 2050. This strongly supports the argument that by adopting effective emission reduction policies, China could achieve large amount of emission reduction and contribute greatly to the world’s efforts in mitigating climate change.
Different studies use different units on GDP, therefore, to make them comparable, all GDP data are converted to 2005 RMB values. Figure 5 shows carbon emission per unit of GDP in different scenarios. Looking into the baseline scenario emission data in 2020 relative to 2005, carbon emission intensity reduces by about 30% in the UNDP reference scenario, 40% in the IEA baseline scenario, 43% in the LBNL CIS, 47% in the ERI energy-saving scenario, the latter 3 scenarios nearly meeting China’s carbon emission intensity reduction targets proposed as “2020 carbon emission intensity decrease by 40%–45% relative to 2005”. Carbon intensity in 2050 relative to 2005 shows a large decrease, with the smallest one reaching as high as 70% in the IEA baseline scenario, 73% in the UNDP reference scenario, 82% in the LBNL CIS, and 86% in the ERI energy-saving scenario.
Comparison of carbon emission intensities in the 5 studies
Looking into the comparative scenarios, from 2005 to 2020, carbon emission intensity decreases by about 48% in the IEA blue map scenario and LBNL AIS, and 50% in the UNDP EC scenario and UNDP EA scenario. Though such rates are higher than those in the baseline scenarios, it is still close to China’s official emission reduction target. In 2050, the comparative scenarios show much larger decreases relative to the baseline scenarios. The ERI LC scenario shows a carbon emission intensity of only 69% of that in its energy-saving scenario, while the carbon emission intensity in the ELC scenario reduces to 40% of that in the energy-saving scenario. Carbon emission intensity in the IEA blue map scenario is only 27% of that in its baseline scenario. Carbon emission intensity in the LBNL AIS is only 66% of that in its CIS. Carbon emission intensity in the UNDP EC scenario is 58% of that in its reference scenario, while in the EA scenario it is only 34% of that in its reference scenario.
Figure 6 shows electricity supply in 2050 under the LBNL, ERI and UNDP scenarios. All scenarios have similar amounts of hydro generation ranging between 1,300–1,600 TW h except for the LBNL AIS (930 TW h). Nuclear energy generation in most scenarios shows a significant increase, reaching 2,000–3,000 TW h, except the lowest one of 1,070 TW h in the UNDP reference scenario and the highest one of 4,000 TW h in the LBNL AIS. In comparison, China’s nuclear generation in 2005 was only about 60 TW h. Most scenarios show wind generation in 2050 somewhere between 800–1,300 TW h, even the lowest one (UNDP reference scenario) reaches nearly 400 TW h. In comparison, China’s wind generation in 2005 was only about 3 TW h. The amount of solar generation in 2050 is rather different among all scenarios. It is between 70–90 TW h in the LBNL scenarios, 100 TW h in the UNDP reference scenario, 330 TW h in the UNDP EC scenario, and nearly 600 TW h in the UNDP EA scenario. Biomass generation is 70–90 TW h in the LBNL scenarios, far less than in the UNDP scenarios (200–400 TW h). ERI did not give the amount of solar or biomass generation respectively, but overall, solar and other generation appear to be around 1,000 TW h in the ERI LC scenario and ELC scenario, more or less the same as the level of solar and biomass generation in the UNDP EA scenario.
Electricity generation in 2050 in different scenarios
Note: In ERI scenarios, “solar generation” include biomass and other generation besides solar
Industry contributes to a large share to the total carbon emission in China. As a result, it is also a key sector for different studies. Carbon emission from the industry sector mainly comes from the production of energy-intensive products, so the scenario research focuses on analyzing the output and energy intensity of energy-intensive products. Table 6 shows the output of energy-intensive products in different scenarios. It is notable that different studies have rather different estimations of the total output of energy-intensive products. For example, steel production in 2030 is 570 Mt in the ERI LC scenario and ERI ELC scenario, 776 Mt in the McKinsey scenarios, 960 Mt in the UNDP scenarios, and 1,180 Mt in the LBNL scenarios. In 2050, it is 360 Mt in the ERI scenarios, which is only 1/3 as that of the LBNL scenarios.
Note: The ERI energy saving scenario assumes different products form its other two scenarios. In this table the ERI data come from its LC scenario and ELC scenario. Products are assumed to be the same among the different scenarios of LBNL, McKinsey and UNDP studies
Energy intensity is different in the investigated studies (Table 7 ). In 2030 energy intensity of steel is 564 kgce per ton in the ERI LC scenario and ELC scenario, 484 kgce per ton in the LBNL CIS, 401 kgce per ton in the LBNL AIS; in 2050, it is 525 kgce per ton in the ERI studies, 406 kgce per ton in the LBNL CIS, and 327 kgce per ton in the LBNL AIS. Energy intensity of aluminum is even more diverse. In 2050, it is 12,000 kW h per ton in the ERI LC scenario and ELC scenario, 7,005 kW h per ton in the LBNL CIS and only 6,313 kW h per ton in AIS which equals to only half of that of ERI.
|LBNL CIS||LBNL AIS||ERI LC||LBNL CIS||LBNL AIS||ERI LC||LBNL CIS||LBNL AIS||ERI LC|
|Steel(kgce per ton)||712||712||760||484||401||564||406||327||525|
|Cement(kgce per ton)||125||125||132||99||89||86||90||75||81|
|Synthetic ammonia (kgce per ton)||1,670||1,670||1,645||1,402||901||1,189||1,189||787||1,170|
|Ethylene(kgce per ton)||700||700||1,092||559||478||713||478||478||705|
|Aluminum(kW h per ton)||10,798||10,798||14,320||7,847||7,692||12,170||7,005||6,313||12,000|
Note: The ERI energy saving scenario assumes different products form its other two scenarios. In this table the ERI data come from its LC scenario and ELC scenario
The IEA study shows that by 2050 steel accounts for 35% emission reduction potential out of all industrial products, and “other non-categorized industry” accounts for 21%. Chemical industry and cement industry account for the next two largest shares. The LBNL and ERI studies do not specify emission reduction potential, but rank the industries by their energy-saving potential. The LBNL study shows the steel industry has the highest potential for energy-saving, because it accounts for about half of total energy-saving potential. After steel, the cement industry, aluminum industry, and synthetic ammonia industry account for most of the rest. The ERI study shows “other non-categorized industry” has the largest potential for energy-saving, next to non-metal minerals (including cement, glass, bricks and tiles, and lime) industry, steel industry, chemical industry, and non-ferrous metal industry. Though different studies generate different results, it can be concluded that steel, cement, and chemical industry are the three priority sectors for achieving emission reduction.
The building sector is categorized into residential buildings and commercial buildings, while residential buildings are often sub-categorized into rural buildings and urban buildings. The IEA study shows that, in 2050, relative to the baseline scenario, the blue map scenario shows that residential buildings have an energy-saving potential of 290 Mtce, with heating contributing to over 55% of total energy saving, while commercial buildings have a potential of 119 Mtce, with heating contributing to 1/3 of the total. The LBNL study shows that for 2050 the AIS, relative to the CIS, sees the residential buildings with an energy-saving potential of 184 Mtce, with heating contributing to 42% of the total savings, which is less than what the IEA study indicates. The LBNL study indicates that commercial buildings have an energy-saving potential of 357 Mtce, with lighting contributing to 82% of the total saving, this is nearly three times more than in the IEA study. Relative to the energy-saving scenario, the ERI LC scenario estimates an energy-saving potential in residential building sector being 213 Mtce (184 Mtce by urban buildings and 29 Mtce by rural buildings) in 2050, which is higher than the LBNL scenarios and lower than the IEA scenarios. The ERI study shows that commercial buildings have a potential of 88 Mtce, which is lower than the IEA study, and is only 1/4 of the LBNL study.
At present, transportation only accounts for 10%–20% of total energy consumption in China. As vehicle usage increases, transportation is expected to play a more important role in energy consumption in the future. The IEA baseline scenario assumes that automobile vehicle ownership per 1,000 people will increase from the current 25 to 300 in China in 2050, in which year transportation energy consumption will reach 1,280 Mtce. The blue map scenario assumes that with energy mix upgrade, transportation pattern change and efficiency improvement, the energy consumption of the transportation sector will reduce to 850 Mtce in 2050, a reduction of 430 Mtce relative to the baseline scenario.
The LBNL assumes that in 2050 the automobile vehicle ownership per 1,000 people will increase to 260, which is lower than IEA’s assumption. The LBNL CIS shows that the energy consumption of the transportation sector will reach 1,000 Mtce in 2050, while in its AIS it reaches 900 Mtce. The ERI energy-saving scenario assumes that China’s automobile vehicle ownership per 1,000 households in 2050 will increase to 341(rural)-375(urban), which is higher than LBNL’s assumption. In the ERI LC scenario, the assumption is 291(urban)-322(rural). Energy consumption of the transportation sector has an energy-saving potential of 540 Mtce in the ERI LC scenario, which is lower than what the LBNL study and IEA study show.
Different baseline scenarios show different carbon emission trajectories. The carbon emission peak will happen around 2040 in the ERI energy-saving scenario, around 2030 in the LBNL CIS, or will not happen at all before 2050 in the IEA and UNDP baseline scenarios. In terms of the comparative scenarios, all scenarios show carbon emission peaks mostly around 2030 except the UNDP EC scenario and ERI LC scenario. The Tyndall study has two scenarios showing carbon emission peaks as early as 2020 since it has set a total emission amount target.
Through adopting emission reduction policies, China could achieve a large scale decrease in carbon emission. The maximum reduction shown within all scenarios is 0.8 Gt CO2 in 2010, 5.0 Gt CO2 in 2030, and 11.0 Gt CO2 in 2050.
All scenarios in different studies demonstrate a continuous decrease in carbon emissions intensity. Tyndall S3 scenario resulted in the greatest decrease, followed by Tyndall S1 and ERI ELC scenarios. IEA baseline scenario resulted in the least decrease.
All scenarios show an upgrade in energy mix. In 2050, the share of non-fossil energy in the energy mix increases from 22% in the ERI energy-saving scenario to 32% in the ERI LC scenario, or to 39% in the ERI ELC scenario. In the LBNL study, it increases from 17% in the CIS to 32% in the AIS. In the UNDP study it increases from 14% in the reference scenario to 25% in the EC scenario, or to 33% in the EA scenario. The IEA blue map scenario is the most progressive one, where the shares of non-fossil energy in energy mix in 2050 increases from 14% in the baseline scenario to 47%.
Different studies have different scenario settings. Hence, these scenarios use different parameters. The Tyndall study has set a total carbon emission control for China, as a result it tries to answer what China’s economy and energy could look like. The IEA study has set the target of reducing global carbon emission by half in 2050 and tries to find out what changes China needs to make to meet this target. The McKinsey study tries to find out how much emission reduction potential China could achieve through applying technical measures. The ERI, LBNL, and UNDP studies have similar goals of finding out what will possibly happen to China’s carbon emission in the future if different policies are adopted. However, with different understanding of the policies, the latter three studies also use different parameters. In fact, these studies have rather different assumptions about every parameter (except population), such as GDP, urbanization rate, industrial production, industrial product energy consumption, transportation pattern, and the energy mix. Such differences in parameters lead to different results by each study. The choice of parameters in the scenario analysis is actually a judgment from researchers; as a result, the results of these studies are very uncertain.
This paper is supported by the “Low Carbon Economy Academy Special Programs, Tsinghua University Independent Research Plan”.
Received: 4 September 2011
①. LBNL energy consumption is calculated via electricity equivalent, while other studies calculated from generation heat rate. LBNL study shows a higher proportion of coal and a lower proportion of non-fossil energy than other studies