We obtain energy-related CO2 emissions from the IEA Emissions from Fuel Combustion database (IEA, 2018a), a time series from 1990 until 2015, the IEA World Energy Outlook (WEO) 2018 for 2016 (IEA, 2018c), and WEO 2019 for 2017 and 2018 values (IEA, 2019).
All other emissions are based on the Chinese inventory as submitted to the UNFCCC for the years 1994, 2005, 2010, 2012 and 2014 (Government of China, 2018). This includes non-CO2 emissions from fuel combustion, fugitive emissions from fuels, industrial process emissions, agricultural emissions, and waste emissions. As of September 2018, the CAT uses 100-year Global Warming Potentials (GWPs) from the IPCC Fourth Assessment Report (AR4). We convert the Chinese inventory to AR4 GWPs on a gas-by-gas basis using data collected from the UNFCCC website by Gieseke, Jeffery & Gütschow (Gütschow et al., 2019). Emissions are extrapolated to 1990 using sectoral growth rates from country-reported data in the PRIMAP-hist database (Gütschow et al., 2019). The historical emissions are extended to 2018 as described in the current policy section below.
NDC and other targets
For China’s 2020 pledge, we estimate emissions under the non-fossil target and the carbon intensity targets separately. For China’s NDC commitment, we quantify the non-fossil target, the peaking target, and the carbon intensity targets separately. China’s rating is based on the non-fossil (upper end of the NDC target) and peaking targets (lower end of the NDC target) in 2030.
The elements of China’s targets that we quantify apply to CO2 or energy-related CO2 only (excl. LULUCF). To calculate total GHG emissions (excl. LULUCF), we add process CO2 emissions and/or non-CO2 emissions based on our current policies scenario for 2020 or 2030 as described in “current policy projections” below.
China’s 2020 pledge aims for a 15% share of non-fossil fuels in its primary energy demand; the 2030 pledge increases this to 20%. We assume that this target excludes biofuels from primary energy demand.
To calculate energy-related CO2 emissions based on this target, we:
- Recalculate the total primary energy demand from the WEO 2019 Current Policies Scenario based on the Chinese methodology of applying the average efficiency of coal-fired power plants to calculate total primary energy demand from non-fossil sources (renewables and nuclear). We assume an efficiency of 310 grams coal equivalent (gce) per kWh (equals 9.08 MJ/kWh for Chinese coal-fired power plants), based on China’s target for 2020 from the 13th Five Year Plan.
- Modify the total primary energy demand from the WEO 2019 to meet China’s 10% gas target in 2020 and all future years by replacing coal with gas. In this scenario, the share of non-fossil fuels in primary energy demand slightly exceeds the targets, reaching 17% in 2020 and 23% in 2030.
- Further adjust the resulting total primary energy demand to precisely achieve the targets by replacing renewables with coal.
- Calculate emissions based on emissions factors calculated from emissions and total primary energy demand from WEO 2019.
Since the NDC contains the target of peaking CO2 emissions latest in 2030, the implications for what an “NDC scenario” constitutes can be interpreted in a variety of ways—for instance, the least ambitious way would be to assume emissions keep rising and simply peak in 2030, a more ambitious interpretation would be to assume that this peaking happens somewhat earlier. Given no national indications of an intent to peak well before 2030, we assume the former. We take the peak level of our minimum current policies scenario as the lower bound of CO2 emissions under the NDC scenario. We assume that the peaking target for CO2 emissions includes both energy and process-related CO2 emissions.
Carbon Intensity Targets:
For the calculation of the intensity targets for 2020 and 2030, we use historical emissions data from China’s most recent inventory submission to the UNFCCC, historical GDP data from China’s 2018 Statistical Yearbook (National Bureau of Statistics of China, 2018) and GDP projections from WEO 2019 and IMF (IMF, 2017). Our projection calculations are based on the GDP growth rate from the IEA WEO 2019 (5.2% annual growth) between 2018 and 2030, aside for years 2020 and 2021, which use other projections that account for COVID-19 impacts. In the lower bound we use the OECD single-hit scenario (-2.6% in 2020 and +6.8% in 2021) and for the upper bound we use CUHK projections (4.7% in 2020), which assumes further stimulus in 2020 (Lau, 2020; OECD, 2020). We no longer use the IMF 2017 as an alternative scenario. We assume that the carbon intensity target applies to all CO2 emissions. The emissions quantification for carbon intensity targets has dropped by roughly 1.5 GtCO2e since December 2019, due to the change in China’s GDP trajectory from COVID-19.
Note on global aggregation:
China’s NDC commitments pathway, as in some other countries, results in higher absolute emissions than its current policy pathways (they will overachieve its NDC with current policies). While China achieves all three elements of its NDC with its current policies in our analysis, the non-fossil target (incl. peaking target) results in the lowest commitments pathway emissions. Quantification of this target results in a range due to the uncertainty of future developments, such as the growth in energy consumption and developments in sectors unaffected by the target.
If we choose the maximum NDC pathway for the upper end of the commitments scenario for these countries, we would be assuming that they emit more than our analysis shows or that they would sell excess emission allowances (hot air) to other countries – both of which is unlikely. We therefore take the median of China’s NDC pathway range for the upper bound of the commitments scenario and the minimum of the current policy pathways for the lower bound. With the NDC commitment scenario between 13.7-14.5 GtCO2e and the current policies pathways being 12.9-14.7 GtCO2e, China may miss its target. However, there is a high degree of overlap in the uncertainty range between the scenarios and it is also possible that China can overachieve its NDC target.
Current policy projections
Energy-related CO2 emissions:
We create two scenarios for energy-related CO2 emissions, a minimum and a maximum scenario.
For the maximum scenario, we start with the WEO 2019 Current Policies Scenario and adjust it to meet China’s 10% gas in the primary energy supply target in 2020 and all following years by replacing coal with gas. The scenario also meets China’s 58% cap on coal in the primary energy supply in 2020 and exceeds the 15% non-fossil component of China’s 2020 pledge, but does not achieve China’s cap on total primary energy demand (TPED) of 5 billion tonnes coal equivalent in 2020. The latter is not quantified in this scenario as it is highly unlikely China achieves both this target and its 10% gas target. Under this scenario, energy-related CO2 emissions continue to increase, reaching 10.5 GtCO2 in 2030. For CO2 emissions from fuel combustion, the 2020 and 2030 values in the IEA WEO 2019 Current Policies Scenario were adjusted using the primary energy factors for renewable energy and nuclear power used in the Chinese accounting (different from IEA accounting).
For the minimum scenario, we also start with the WEO 2019 Current Policies Scenario but adjust electricity generation to reflect electricity generation from renewables projected in the Stated Policies Scenario from the China Renewable Energy Outlook 2017 (CREO 2017) (China National Renewable Energy Centre, 2017). As in the maximum scenario, we adjust the primary energy supply to meet the 10% gas target in 2020 and all following years by replacing coal with gas. In addition to meeting the targets mentioned in the maximum scenario description, this scenario exceeds China’s proposed target of 35% electricity generation from renewables in 2030, reaching 46% in 2030. This is comparable to other modelling scenarios where China reaches a 37% share in 2027 (BloombergNEF, 2019b). Under our minimum scenario, energy-related CO2 emissions remain roughly stable, reaching 9.5 GtCO2/year in 2030.
In our 2017 and early 2018 analyses, we assumed that coal consumption would decrease at the rates that it did between 2014 and 2016 until 2030. Under this optimistic scenario, energy-related CO2 emissions would have peaked around 2017. However, due to the increases in coal consumption in 2017 and 2018, we have adopted a more conservative approach. Under the minimum current policies scenario, coal consumption still decreases substantially – falling 35% below 2016 levels by 2030.
We project industrial-process CO2 emissions by applying growth rates from cement process emissions for the non-OECD region based on the IEA Energy Technology Perspectives 2016 report’s 6DS scenario to the 2014 value for industrial process emissions from the Chinese inventory (IEA, 2016a). This means that 2014–2018 values for these emissions are projections.
Other non-CO2 emissions:
For non-CO2 emissions from energy, fugitive emissions, agriculture, industrial processes, and waste, we apply sector-specific growth rates for non-CO2 emissions from (Lin et al., 2019) to the 2014 values from the Chinese inventory. This source considers recent policies implemented since 2015, leading to improved certainty on Chinese non-CO2 emissions in 2030, compared to previous assessments. As with industrial process emissions, 2014–2018 values for these emissions are projections. The methodological change significantly decreases non-CO2 emissions in 2030 by approximately 800 MtCO2e/year and has more accurate resolution regarding growth trends of sectors. In 2030, non-CO2 emissions make up 19-20% of projected total GHG emissions.
We applied a novel method to estimate the COVID-19 related dip in greenhouse gas emissions in 2020 and the deployment through to 2030. The uncertainty surrounding the severity and length of the pandemic creates a new level of uncertainty for current and future greenhouse gas emissions. We first update the current policy projections using most recent projections, usually prepared before the pandemic. We then distil the emission intensity (GHG emissions/GDP) from this pre-pandemic scenario and apply it to recent GDP projections that capture the upper and lower bounds of impact uncertainty from the pandemic, according to expert judgement. To capture the appropriate range, we employ an optimistic and pessimistic COVID-19 impact scenario on GDP projections.
For China’s upper bound, we use the GDP projections from the Chinese University of Hong Kong (Lau, 2020) for 2020 and 2021, assuming further national economic stimulus (which most experts expect). For the lower bound we use the OECD single-hit scenario GDP projections for 2020 and 2021 as a worst-case scenario, given that China is not expected to have negative GDP growth this year (OECD, 2020). As China dropped its annual GDP target this year for the first time since 1990 and GDP projections coming from Chinese banks and analysts are both rare and more optimistic than those coming from international sources, the CAT refrained from using post-pandemic GDP projections from Chinese domestic institutions. An overview of considered GDP projections at time of analysis is provided below.
Global Warming Potential values
The CAT uses Global Warming Potential (GWP) values from the IPCC's Fourth Assessment Report (AR4) for all its figures and time series. Assessments completed before December 2018 (COP24) used GWP values from the Second Assessment Report (SAR).