China’s Non-CO2 Greenhouse Gas Emissions: Future Trajectories and Mitigation Options and Potential

Forecasts indicate that China’s non-carbon dioxide (CO2) greenhouse gas (GHG) emissions will increase rapidly from the 2014 baseline of 2 billion metric tons of CO2 equivalent (CO2e). Previous studies of the potential for mitigating non-CO2 GHG emissions in China have focused on timeframes through only 2030, or only on certain sectors or gases. This study uses a novel bottom-up end-use model to estimate mitigation of China’s non-CO2 GHGs under a Mitigation Scenario whereby today’s cost-effective and technologically feasible CO2 and non-CO2 mitigation measures are deployed through 2050. The study determines that future non-CO2 GHG emissions are driven largely by industrial and agricultural sources and that China could reduce those emissions by 47% by 2050 while enabling total GHG emissions to peak by 2023. Except for F-gas mitigation, few national or sectoral policies have focused on reducing non-CO2 GHGs. Policy, market, and other institutional support are needed to realize the cost-effective mitigation potentials identified in this study.

related to N 2 O and CH 4 emissions 15 . In addition, most previous studies have focused on one or two gases without attempting to quantify the total contribution or mitigation potential of all major non-CO 2 GHGs. Some studies have projected China's non-CO 2 GHGs either to only 2020 or 2030 3,6,16,17 or have focused only on certain gases, such as F-gases 18,19 . Other Chinese studies have evaluated the potential for deploying options for mitigating non-CO 2 emissions in certain sectors, such as coal mining, industrial processes, and waste and wastewater, without considering other key sectors such as oil and gas or agriculture and/or without evaluating the combined effects of deploying mitigation options in various sectors 16,20 .
Building on the existing body of research, this study seeks to use a comprehensive bottom-up end-use modeling approach to understand the activity and technology drivers of non-CO 2 GHG emissions in China through 2050 to produce a detailed outlook of possible future emission trajectories. It focuses on evaluating selected non-CO 2 GHG mitigation options that are already considered cost-effective today in terms of total deployment potential and emission reduction efficiency, in order to quantify the existing cost-effective potential for reducing China's future non-CO 2 GHG emissions in addition to CO 2 emissions.
This study employed a novel bottom-up end-use modeling approach to capture macroeconomic and physical drivers of both energy and non-energy demand in multiple sectors (e.g., agriculture, materials, waste generation) in China and the effects on CO 2 and non-CO 2 GHG emissions through 2050. Specifically, this analysis used the China 2050 Demand Resources and Energy Analysis Model (DREAM), which is based on an accounting framework for China's energy and economic structure using the Long-Range Energy Alternatives Planning (LEAP) software platform developed by the Stockholm Environmental Institute. LEAP is a medium-to long-term integrated modeling tool that can be used to track energy consumption, production, and resource extraction in all sectors of an economy and to conduct analysis of long-term scenarios. Bottom-up LEAP-based models have been used extensively to model energy scenarios and CO 2 emission trajectories for various regions, including the national and regional levels of China, as analyzed by Zhou [21][22][23][24][25][26] . However, LEAP-based models have been used only rarely to model non-CO 2 GHGs [21][22][23][24][25][26][27][28][29] . Lin et al. (2018) used LEAP to model both CO 2 and non-CO 2 emissions for the city of Xiamen, but projected the reduction in non-CO 2 emissions primarily based on reduced activities rather than through implementation of specific mitigation technologies or measures 30 .
This study represents one of the first comprehensive modeling studies of China's total future non-CO 2 GHG emissions and potential for mitigating them using a bottom-up approach. In the China 2050 DREAM, new non-energy modules are used to represent the major emitting sectors of agriculture, waste and wastewater, and industrial processes. The China 2050 DREAM modeling framework for energy and non-energy sectors is included in the Methods section. The 2050 DREAM captures adoption of end-use technologies and macroeconomic and sector-specific drivers of energy demand 23,31 . For non-CO 2 GHGs, published statistics were used to calibrate historical activity levels to the latest year reported (i.e., 2016 or 2017). Table 1  For this study, two primary scenarios were developed to evaluate the potential impacts of non-CO 2 GHG mitigation options, focusing on a selected set of specific technologies, measures, and policies across key energy and non-energy sectors. In addition, deployment potential, costs, and efficacy were evaluated. The Reference Scenario represents a counterfactual baseline scenario that reflects only current policies including already announced energy and carbon intensity reduction and non-fossil power generation capacity targets, assuming that the only additional non-CO 2 mitigation measure will be the hydrochlorofluorocarbon (HCFC) phase-down schedule that already is underway under the Montreal Protocol. The CO 2 Plus Non-CO 2 GHG Mitigation Scenario includes key cost-effective and currently available technologies that could reduce non-CO 2 GHG emissions, in addition to full adoption of today's cost-effective efficiency and renewable technologies to reduce CO 2 in selected energy and non-energy sectors. Mitigation measures for CO 2 emissions include maximizing the adoption of cost-effective energy efficiency technologies and fully switching to cost-effective cleaner (e.g., natural gas) and/or renewable fuels by 2050. Previous analyses have focused on evaluating the cost-effective CO 2 mitigation potential for China 22,23 , whereas this study focuses specifically on cost-effective mitigation measures for non-CO 2 GHG emissions from various sectors that include the following:  2 , not the process-based CO 2 emissions reported in the national GHG inventory. Source: [5].
• Coal Mining: methane oxidation and methane recovery from ventilation air • Natural Gas: green completions; plunger lift systems; leak monitoring and repair; and low-bleed, no-bleed, or air pneumatic controllers • Landfill: methane collection, flaring, and recovery of landfill gas for energy use • Agriculture: humid and intermittent irrigation, improving livestock productivity, manure composting, and reducing nitrogen fertilization

Results
total GHG and non-co 2 GHG Results. This study shows that adopting today's cost-effective CO 2 and non-CO 2 GHG mitigation measures will enable China's total GHG emissions to peak earlier at a lesser amount. The CO 2 Plus Non-CO 2 GHG Mitigation Scenario peaks at 12.3 Bt CO 2 e in 2023, whereas in the Reference Scenario total GHG emissions peak at 17.0 Bt CO 2 e in 2036 (using a 100-year GWP) (Fig. 1a). (Unless stated otherwise, results for the 100-year GWP are based on the 2006 IPCC Guidelines for Global Warming Potentials, which is the most widely adopted GHG reporting convention in the United States). The GHG reductions achieved by applying non-CO 2 GHG mitigation measures are amplified using a 20-year GWP. Under the CO 2 Plus Non-CO 2 Mitigation Scenario, total GHG emissions peak at 14.4 BtCO 2 e in 2020, a 25% reduction compared to the peak of 19.4 BtCO 2 e in 2035 under the Reference Scenario (Fig. 1b).
Under the Reference Scenario, total non-CO 2 GHG emissions peak in 2030 with 2400 MtCO 2 e (at 100-year GWP) (Fig. 2a). In the absence of mitigation options, non-energy sectors (represented by the patterned bars in Fig. 2a) are expected to contribute the most to future increases in China's non-CO 2 GHG emissions; agriculture and non-energy industrial processes each contribute more than one-third of total non-CO 2 GHG emissions by 2050. Non-CO 2 GHG emissions from industrial processes increase rapidly, more than tripling between 2010 and 2050 because of continued demand for industrial products such as aluminum, air conditioners, and chemical feedstock for synthetic polymers. In contrast, total non-CO 2 GHG emissions from coal mining decrease over time, as coal use declines to meet China's climate change commitments and to address domestic environmental and air quality concerns.
The combined mitigation measures under the CO 2 Plus Non-CO 2 Mitigation Scenario reduce non-CO 2 GHGs from the 2020 peak level of 2.0 Bt CO 2 e to 1.2 Bt CO 2 e in 2050 (Fig. 2b). Compared to the Reference Scenario, the CO 2 Plus Non-CO 2 Mitigation Scenario would enable China to reduce future non-CO 2 GHG emissions by 47% in 2050.
The percentage of non-CO 2 emissions out of total GHGs will increase slightly from 17% in 2010 to 20% in 2050 after decreasing from the mid-2020s through 2040 (Fig. 3a). Under the CO 2 Plus Non-CO 2 Mitigation Scenario and using a 100-year GWP, methane and N 2 O dominate non-CO 2 GHG emissions, accounting for 55% and 34%, respectively, in 2050 (Fig. 3b). This result suggests that cost-effective measures for mitigating CO 2 are more readily available and can be more aggressively deployed than for non-CO 2 emissions, leading to a significant www.nature.com/scientificreports www.nature.com/scientificreports/ drop in both total CO 2 and total GHG emissions. Non-CO 2 measures, particularly cost-effective non-CO 2 measures, are less readily available and may be more difficult to deploy at a larger scale, such as in the agricultural sector. Agricultural policies and awareness programs are needed to change current farming and irrigation practices, which are based solely on maximizing agricultural yield.
Using a 20-year GWP that emphasizes the shorter lifetime of GHGs such as methane, the proportion of non-CO 2 out of total GHG emissions decreases from 30% in 2015 to 24% around 2035, but then increases to 31% in 2050. Compared with using a 100-year GWP, the total annual non-CO 2 GHG emissions almost double using a 20-year GWP (Fig. 3c). This result highlights the high GWP and relatively short lifetime of methane under the 20-year GWP accounting method. If the shorter lifetime of methane is considered through the use of a 20-year GWP, then by 2050 methane's contribution to non-CO 2 GHG emissions increases to 74%, whereas the proportion of N 2 O drops to 15% by 2050 (Fig. 3c). Strategies and policies that promote methane mitigation measures will have the greatest near-term effect on mitigating climate change effects. non-co 2 GHG mitigation potential. By 2050, China could reduce annual non-CO 2 GHG emissions by 870 Mt CO 2 e by utilizing a combination of CO 2 and non-CO 2 mitigation measures (Fig. 4). CO 2 mitigation measures such as improvements in energy efficiency, fuel switching, and lowered production activity provide only 8% of the reduction potential. The rest of the reduction potential can be achieved by implementing current cost-effective non-CO 2 GHG mitigation measures. The adoption of those mitigation measures can help offset the expected growth in non-CO 2 GHGs, particularly from the agricultural and industrial processes that in 2050 will produce more than 70% of non-CO 2 GHGs (Fig. 5a).
Industrial processes such as air conditioners, HCFC-22 production, and mobile air conditioners present the greatest opportunity for reducing non-CO 2 GHGs (Fig. 5b); activity drivers for F-gas emissions are expected to increase. By 2050, thermal oxidation during HCFC-22 production and replacing high-GWP refrigerants with low-GWP refrigerants in mobile and room air conditioners together can eliminate 510 Mt CO 2 e, or the equivalent of 90% of the total potential for reducing non-CO 2 GHG emissions from industrial processes. The HFC phase-down schedule under the Kigali Amendment to the Montreal Protocol and the financial incentives for low-GWP refrigerant air conditioners provide an effective policy framework for reducing HFCs. Specific policies and supporting measures targeting other GHGs and industrial processes are still needed, however.
The agriculture and coal mining sectors hold a relatively large potential for emissions reduction. The agricultural sector also holds potential for large absolute reductions of N 2 O and methane emissions, but the reduction potential is a small percentage of total agricultural non-CO 2 emissions (e.g., 23% in 2050), indicating a need for exploring other mitigation measures. Currently the greatest cost-effective reduction potential in the agricultural sector is related to livestock management, including improved manure management and enteric fermentation through improving livestock productivity, use of appropriate feed, and the conversion of manure to compost. But, unlike for other sectors, there are limited technological measures or direct strategies for reducing non-CO 2 GHG emissions from agriculture, making it difficult to reduce agricultural non-CO 2 emissions.
Mitigating emissions from coal mining contributes to the relatively large (70%) reduction in methane emitted by 2050. The absolute reduction in coal mining methane is greater in earlier years, because future coal production will decline to meet current energy and climate policy targets. www.nature.com/scientificreports www.nature.com/scientificreports/ The waste and wastewater sectors offer additional potential for reducing methane and N 2 O emissions: by 2050 total methane emissions from those sectors can be reduced by 20%. The model accounts for mitigation measures only for landfill waste, because there currently are few cost-effective mitigation measures for the wastewater sector. The need to reduce non-CO 2 emissions from municipal wastewater disposal and solid waste treatment is now recognized in policies such as the 13 th Five Year Plan, but more specific policy support is needed.
There have been few national or sectoral policies focused on reducing non-CO 2 GHG emissions other than F-gas emissions. Policy, market, and other institutional support will be needed to realize the cost-effective mitigation potential identified in this study.

Discussion and policy implications
Although the national 13 th Five Year Plan (FYP) and 13 th Five Year GHG Emission Control Workplan for 2016 to 2020 stated that China will control non-CO 2 GHG emissions, the plans offered no specific targets or timelines beyond a commitment under the Kigali Amendment of the Montreal Protocol 32,33 . This study identified a potential for cost-effectively mitigating 47% of non-CO 2 GHGs by 2050 if cost-effective non-CO 2 GHG mitigation measures are adopted in addition to CO 2 mitigation measures. In this case, total GHG emissions peak in 2023, two years earlier than when adopting only CO 2 mitigation measures. China has an opportunity to continue to demonstrate its global climate leadership by establishing concrete policy targets and mitigating measures for non-CO 2 GHGs in its 14 th FYP and for enhanced NDCs by 2020 under the Paris Agreement framework.
The industrial sector offers the greatest potential for reducing non-CO 2 GHGs, mostly through the cost-effective reduction of F-gas emissions in industrial processes. This reduction is partly due to the success of the Montreal Protocol, including the recent adoption of the Kigali Amendment, which sets a specific phase-down schedule for the production and consumption of key HFCs. China has also been working bilaterally and multilaterally with the United States and the European Union to phase down HFCs, and from 2014 to 2019 provided financial incentives for mitigating HFC 23 emissions. These efforts suggest that a well-established policy framework having specific reduction targets and pathways is instrumental in achieving emissions reduction potentials. However, non-CO 2 GHG mitigation measures for other industrial processes have been promoted only generally through national policies such as the Circular Economy Promotion Law. Sector-specific policies and measures are still lacking.
Beyond sector-specific and gas-specific policies, co-control or complementary policies, such as reducing coal consumption and improving energy efficiency, can be even more effective in reducing GHG emissions. Shah et al. found that increasing the energy efficiency of room air conditioners by 30% through the refrigerant transition can double potential GHG reduction compared with simply switching to low-GWP refrigerants 34 .
Policies to promote reduction of methane emissions in the coal mining and waste sectors, which involve participation from diverse stakeholders and large numbers of companies, often evolve from voluntary practices to regulated standards. In the United States, for example, mitigation of methane emissions in the oil and gas sector started with public-private partnerships whereby companies voluntarily adopted methane reduction technologies in four oil and gas subsectors. That partnership then developed into a specific program, the Natural Gas STAR Methane Challenge Program, which provides flexible performance-based mechanisms beyond technology-only recommendations for companies to meet emission reduction targets. Building on the lessons learned from that voluntary program, national and state-level methane reduction targets, along with performance and technology standards, were developed to further reduce methane emissions in the oil and gas sector 35 .
China has used local pilot studies to perform policy experimentation on many issues, most recently including emissions trading schemes, and could adopt similar approaches to establish national or subnational targets for reducing non-CO 2 GHG emissions. Such an experimental approach can help target the application of mitigation technologies in selected sectors or regions and identify best practices that can then be scaled up.
Mitigation of methane and N 2 O emissions in the agricultural sector may be the greatest challenge, because many mitigation options require the participation of millions of farmers in a highly decentralized sector. The choice of options also needs to account for impacts on yields and material inputs. As a result, robust institutional mechanisms are needed to disseminate cost-effective mitigation options while promoting behavioral changes to traditional farming practices. There is little international experience with mitigating non-CO 2 GHG emissions in the agricultural sector. More research is needed to fully capture the mitigation potential in the agricultural sector. This study describes one possible pathway for China to reduce its total GHG emissions, which is by expanding the adoption of both CO 2 and non-CO 2 mitigation measures that are cost-effective now. This study does not attempt to project what will happen in China, given the long time frame (to 2050) and inherent uncertainties surrounding emissions factors, costs trends, pace of technological improvements, and rates of market adoption. Rather, this study is meant to be a first, comprehensive bottom-up assessment of the potential for cost-effectively reducing non-CO 2 GHG emissions in China under a possible scenario. The study incorporates the presumption that deployment of commercially available mitigation measures can be increased via policy, market, and other institutional supports.
This study utilizes average emissions factors for many sources of methane and N 2 O emissions, although a wide range of emissions factors are presented in recent inventory guidelines and reports. In addition, simplifying assumptions are made about leakage rates for HFC-no attempt was made to quantify emissions from the various stages in the life-cycle of air conditioning equipment. Compared to other sectoral reports, relatively conservative assumptions were made about the adoption of non-CO 2 mitigation measures in several subsectors, especially as regards to replacing high-GWP refrigerants with low-GWP alternatives for mobile and room air conditioners. For other sectors, however, such as the landfill sector, predictions of the market penetration of mitigation measures are higher in this study than in many other studies. All of these variables introduce uncertainties in the estimates of future non-CO 2 emissions, but the uncertainties do not significantly change the observed trends and the key findings of this analysis.
To better understand how much China can reduce its non-CO 2 GHG emissions in the short and mid-terms, more in-depth assessments of the cost-effectiveness must be conducted using China-specific costs and market conditions. Doing so will further the applicability of mitigation measures specifically to China. In addition, the scope of non-CO 2 mitigation measures must be expanded to other subsectors, including non-conventional gas extraction. Interactions between energy systems and non-energy systems, as well as behavioral changes in meat consumption and waste management, could also be considered further in the model, along with more detailed sensitivity analysis of scenarios. To achieve the mitigation potential that this analysis has identified, concrete policy recommendations and strategies for sector-specific measures must be developed in collaboration with Chinese stakeholders.

Modeling framework. This analysis utilized the China 2050 Demand Resources and Energy Analysis Model
(DREAM), which is based on an accounting framework of China's energy and economic structure using the Long-Range Energy Alternatives Planning (LEAP) software platform developed by the Stockholm Environmental Institute. Previously published reports and papers 21,23,29,36 have presented in-depth discussion of the overall modeling methodology, key sectoral drivers, and modeling parameters, as well as the assumptions and basis for future projections of energy demand drivers. The validity of the chosen model and modeling applications has been examined by others, including Li and Qi 37 and Bellevrat 38  The China 2050 DREAM model includes a demand module consisting of five energy demand subsectors (residential buildings, commercial buildings, industry, transport, and agriculture) and energy transformation modules consisting of energy production, transmission, and distribution subsectors. The modules represent energy extraction and transformation sectors, such as the energy required to extract fossil fuels, and a power sector based on specialized generation dispatch algorithms. Using LEAP, the model captures adoption of end-use technologies as well as macroeconomic and sector-specific drivers of energy demand. The model enables detailed consideration of technological development-industrial production, equipment efficiency, residential appliance usage, vehicle ownership, power sector efficiency, lighting, and heating usage-as a way to evaluate China's path toward reducing energy and emissions at each subsector's end-use level. Published energy sector statistics were used to prepare a time-series database representing primary energy use that accounts for supply-side losses. After the model was built from the bottom up, sector-and fuel-specific consumption data were calibrated by comparing the calculated results with the end-use energy data reported for the most recent year. Figure 6 shows the China 2050 DREAM modeling framework applied to energy and non-energy sectors. Key drivers of future energy use in the model include activity drivers (total population growth, urbanization, building and vehicle stock, commodity production); economic drivers (total GDP, value-added GDP, income); trends in energy intensity (for energy-using equipment and appliances); and trends in carbon intensity. These factors are, in turn, driven by changes in consumer preferences, settlement/population and infrastructure patterns, technical changes, and overall economic conditions. Key macroeconomic parameters such as economic growth, population, and urbanization are aligned with international sources (e.g., the United Nations World Population Prospects 40 ) as well as Chinese sources (e.g., China Energy Research Institute reports 41 ).
More specifically, urbanization plus growth in household income drive energy consumption in residential building, because urban households generally consume more commercially supplied energy than do rural households and because rising household incomes correspond to increased housing unit size (and thus increased heating, cooling, and lighting loads), along with appliance ownership. Energy demand for commercial buildings is driven by two similar factors: building area (floor space) and end-use intensity related to heating, cooling, and lighting (megajoules per m 2 ). For the industrial sector, the model includes 12 energy-intensive industries that are driven by key physical drivers related to expanding demand for the built environment (e.g., for housing, agricultural products, and plastics), and 18 light manufacturing industries that deliver value-added products. Transportation demand is driven by trends in freight and passenger transport. Freight transport is calculated www.nature.com/scientificreports www.nature.com/scientificreports/ as a function of economic activity (measured by GDP); passenger transport is based on average number of vehicle-kilometers traveled by each mode of transportation (bus, train, or car).
The energy transformation module in the China 2050 DREAM accounts for the energy required to extract primary fossil fuels and to operate processing and conversion plants that produce derived products such as electricity, coke, and petroleum products (Fig. 6). The energy extraction and processing subsectors in the model include coal mining, oil extraction, natural gas extraction, coking, oil refining, and coal liquefaction. Technological improvements, resource quality, and resource limits are considered when accounting for the energy required to extract, process, and convert these sources of energy.
To capture the non-CO 2 GHG emissions from key non-energy-consuming activities, a non-energy module was added to the China 2050 DREAM to represent the major emitting sectors of agriculture, waste and wastewater, and industrial processes (Fig. 6). Non-energy-consuming activities in these sectors, such as rice cultivation, livestock management, wastewater treatment, and industrial processes, are documented sources of non-CO 2 GHG emissions, with increased activity expected in some sectors as China continues its economic transition. The drivers of these non-energy sectoral activities and the related non-CO 2 GHG emissions are discussed in greater detail in the section below, titled Scenario Analysis. Future projections of these drivers including key data points are detailed in the online Supplementary Information. For the energy sector, published statistics were used to calibrate historical activity levels, and total emissions were calibrated against the latest published data (collected in 2014 5 ). The model captures cross-sectoral linkages between energy activities (e.g., electricity demand) and non-energy activities (e.g., power generation, system production).

Scenario Analysis
Two primary scenarios were developed for this analysis: a reference or baseline scenario and a Non-CO 2 GHG Mitigation Scenario. The scenarios evaluate potential trajectories for China's GHG emissions from 2015 through 2050. A scenario focusing only on CO 2 mitigation, developed in a previous study, was also referenced to compare the accompanying benefits to reducing non-CO 2 GHG emissions from CO 2 mitigation measures such as efficiency and fuel switching 23,42 . The three scenarios are described below.
1. Reference Scenario: assumes all energy-related policies currently in place, along with autonomous technological improvements, will continue to affect all energy demand, supply, and transformation. The reference, or baseline, scenario assumes that no new policies will be developed. This scenario also assumes that no non-CO 2 mitigation measures will be implemented before 2050, exception for efforts made to reach the Montreal Protocol targets for HFCs from HCFC-22 production. 2. CO 2 Mitigation Scenario: assumes that the greatest efficiency and renewable achievements that currently are cost effective or nearly cost effective are adopted fully in all energy demand sectors and the power sector by 2050 to reduce CO 2 emissions. 3. CO 2 Plus Non-CO 2 GHG Mitigation Scenario: includes key commercially available technologies that currently are cost effective or nearly cost effective. This scenario includes the annualized costs of CO 2 e reductions of less than 50 RMB/tCO 2 e, with the exception of the slightly higher costs for replacing hydrofluoro-olefin (HFO)-1234yf in new light-duty vehicles 3,16 , which potentially could reduce non-CO 2 GHG emissions in the energy and non-energy sectors. This scenario also includes mitigation measures that are beginning to be adopted in the Chinese market without reference to specific policies, assuming that those mitigation measures started being adopted only after 2010. www.nature.com/scientificreports www.nature.com/scientificreports/

Data and Assumptions
For historical data, the China 2050 DREAM inputs were calibrated with the latest reported national statistics for both energy and non-energy activity variables such as population, households, industrial production, agricultural activity, and waste generation. Energy consumption data by fuel and by sector also were calibrated to the latest published national energy balances. Calculations of emissions data used China-specific fuel energy content and emissions factors for CO 2 and a mix of emissions intensities from the 2010 Chinese Provincial Guidelines for GHG Inventories, the 2006 IPCC Guidelines for GHG Inventories and other sector-specific Chinese studies for non-CO 2 GHGs 14,43 . Detailed data inputs and assumptions for future activity drivers, activity levels and emission factors can be found in Supplementary Tables S1, S2, and S3.

Data availability
The data that support the plots within this paper and other findings of this study are available from the corresponding author upon reasonable request.